Monitor Energy Flow in Smart Grids

abtechnosolutions

Abstract— This paper aims to describe the role of advanced
sensing systems in the electric grid of the future. In detail the
project, development and experimental validation of a smart
power meter are described in the following. The authors provide
an outline of the potentialities of the sensing systems and IoT to
monitor efficiently the energy flow among nodes of electric
network. The described power meter uses the metrics proposed
in the IEEE Standard 1459-2010 to analyse and process voltage
and current signals. Information concerning the power
consumption and power quality could allow the power grid to
route efficiently the energy by means of more suitable decision
criteria. The new scenario has changed the way to exchange
energy in the grid. Now energy flow must be able to change its
direction according to needs. Energy cannot be now routed by
considering just only the criterion based on the simple shortening
of transmission path. So, even energy coming from a far node
should be preferred if it has higher quality standards. In this
view, the proposed smart power meter intends to support the
smart power grid to monitor electricity among different nodes in
an efficient and effective way.
Index Terms— Electric grid, smart grid, sensing systems,
smart power meter, IoT.
I. INTRODUCTION
N the power grid of the future, sensors and transducers will
have a significant role to monitor energy in real time
according to demand. Smart sensing systems can provide new
opportunities for automatic power measurement and data
processing so to take decisions in real time.
The electric network is a complex and interconnected
system commonly called grid. Growing electricity demand
needs more sustainable energy generation by renewable
sources. Today, we are observing a radical transformation of
the public electric system. For example, energy flow becomes
bidirectional due to the presence of distributed generation
plants. Electricity is shared among the several nodes of the
R. Morello, C. De Capua and G. Fulco are with the Department of
Information Engineering, Infrastructure and Sustainable Energy (DIIES),
University Mediterranea of Reggio Calabria, Italy (e-mail:
rosario.morello@unirc.it decapua@unirc.it gaetano.fulco@unirc.it)
S.C. Mukhopadhyay is with the Department of Engineering, Macquarie
University, NSW 2109, Australia (e-mail: Subhas.Mukhopadhyay@mq.edu.au)
power grid, named microgrids, based on local demand. So
energy flow has to change dynamically even its direction. As a
general rule, energy must be routed from microgrids with a
large energy amount to microgrids having an energy lack.
Nevertheless, several factors affect this general criterion such
as the intermittent production of energy from renewable
sources. In addition, the quality of the voltage and current
signals provides further constraints to energy routing.
Consequently, the management of energy flow becomes
today a really complex task, [1]. Currently, these aspects are
not paid with sufficient attention in the grid. As a
consequence, the final user sometimes has to tolerate energy
having low quality. The major consequences are paid by the
domestic users. Therefore today, uninterrupted energy supply
and high quality energy are two basic and fundamental
requirements to be guaranteed in the transmission and
distribution of electricity.
This new concept entails new and important challenges for
researchers dealing with this field. Several issues and
problems must be faced such as the development of new
efficient and smart sensing systems. Contextually, electric
network needs a radical renovation to be able to change
dynamically its configuration. In fact, the current architecture
was projected to manage only mono-directional energy flow
from the central generation plant to the final users.
Such a new scenario requires new systems which allow the
power grid to be really smart by managing the bi-directional
and changing flow of energy, [1]. In addition these systems
must assure interoperability between new and old equipment.
Figure 1 shows the current scenario, where different
distributed generation plants supply their energy to users and
provide the surplus to the electric network. However, energy
production from renewable sources suffers from supply
discontinuity. Thus, the risk of blackouts and service
inefficiency increase. The smart power grid should be able to
prevent promptly a supply discontinuity [2]. These features
require the use of advanced and innovative sensing systems.
So sensors must make measurements and process results in
real time to get a clear overview of power grid state in each
node. For instance, power meters are sensing systems which
are able to measure power features. Depending on its purpose,
measures can include just power consumption or additional
information concerning the power quality [3]-[5].
A Smart Power Meter to Monitor Energy
Flow in Smart Grids:
The Role of Advanced Sensing and IoT in the Electric Grid of the Future
R. Morello, Member, IEEE, C. De Capua, Member, IEEE, G. Fulco, S.C. Mukhopadhyay, Fellow, IEEE
I
1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
Sensors Journal
Fig. 1. The current scenario of the Power Grid [1].
The general architecture of a power meter consists of a
voltage transducer, a current transducer, an A/D converter and
a processor for processing data. At the present time, most of
the commercial power meters perform measurement of active
power. This parameter is commonly used for computing the
user power consumption. Few power meters provide
information on the power quality. Anyway, such information
is not used by power grid for managing the energy delivery or
billing the consumptions. In addition, in literature several
definitions of reactive power exist, as a consequence the
metrics or power computing algorithms are not worldwide
univocally defined. Therefore the concept of metric is still
object of studies and research activities.
In this paper, the authors propose their idea of the future
smart power grid. In detail, power meters will be integrated in
the electric grid to provide information concerning local
energy consumption and the quality of power in the several
nodes of the electric network. Such augmented information,
supported by new decision criteria, will allow the power grid
to manage fault events or rapid changes in energy
requirements. The electric grid can be compared with the
internet network. Therefore Internet of Things (IoT) can
provide new opportunities to developers during the project of
smart power meters. IoT can provide new criteria for sharing
data and information into the whole grid, [6], such as multihop
communication, where each sensor communicates through
several successive nodes. In this sight, IoT can allow sensors
to share information by using internet and web service
architectures so to improve the grid management, [7]. In
addition, sensing systems must cooperate and satisfy several
features such as to be flexible to changing conditions, be able
to monitor and predict electrical energy consumption, and to
control the grid security. So ISO/IEC/IEEE 21451 Standards
can allow smart grid to improve its efficiency by making easy
the interoperability among several sensing systems due to the
protocol standardization. In such a scenario, the modernization
of the electric network will be possible by means of power
meters geographically distributed, which can cooperate to
monitor the grid by performing a distributed data processing,
[8]-[14].
The project, development and experimental validation of a
smart power meter able to monitor the power in real-time are
described in the following Sections. The next Section
describes the proposed smart power meter. Section III reports
the validation and experimental results. Section IV provides a
brief description of the application to the future power grid
based on a IoT vision. The conclusions have been drawn in
Section V.
II. THE SMART POWER METER
The above described future vision of smart grid needs the
project and development of innovative sensing systems with
specific features. The solution proposed in the present paper is
based on a smart power meter with improved characteristics:
 remotely programmable and controllable;
 interoperability among several power meters;
 embedded data processing and decision making
algorithms;
 power quality analysis;
 decision-based management of energy flow routing
according to the power quality requirements defined by the
final user.
The hardware architecture and the soft computing
algorithms are described in the following sub-Sections. A
remote control station has been developed in order to manage
information coming from different power meters so to
simulate a central management station for controlling and
performing in real-time the configuration of the power
network. A further sub-Section describes in brief the
potentialities offered by ISO/IEC/IEEE 21451 Standards.
A. Hardware Architecture
The smart power meter architecture is based on a National
Instruments Single-Board RIO 9626. Two transducers allow to
acquire the voltage and current signals, which are successively
1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
Sensors Journal
digitally converted for data processing. In detail, the power
meter mounts on board two additional modules: NI-9225 and
NI 9246. A 400 MHz processor with 512 MB non-volatile
storage and 256 MB DRAM performs the real time data
processing. The processor is supported by a reconfigurable
Xilinx Spartan-6 LX45 FPGA for custom timing, inline
processing, and control tasks, see Figure 2 for reference.
Fig. 2. The Smart Power Meter.
Table I reports the main electrical and technical
specifications of the power meter.
TABLE I
SMART POWER METER SPECIFICATIONS
Quantity Value
Voltage Range 0-300 Vrms
Current Range 0-20 Arms
Peak Current 30 A
Maximum Sampling Frequency 50 kS/s
Resolution 24 bit
Temperature Operating Range -40 – +70 °C
Dimension 15.4×10.3×5 cm
Mass 350 g
Supply Voltage 9-30 V
The power meter is compliant with the specifications
reported in the guidelines of IEC 61000-4 Standards family
[15]-[17], and of IEC 62052-11 and IEC 62053-21 Standards
[18], [19]. The technical specifications in Table I and the data
processing algorithms allow the performed measurements to
meet the specifications for the range of uncertainty defined for
metering instruments of class A [17].
The meter performs in the following order these operations:abtechnosolutions

Abstract— This paper aims to describe the role of advanced
sensing systems in the electric grid of the future. In detail the
project, development and experimental validation of a smart
power meter are described in the following. The authors provide
an outline of the potentialities of the sensing systems and IoT to
monitor efficiently the energy flow among nodes of electric
network. The described power meter uses the metrics proposed
in the IEEE Standard 1459-2010 to analyse and process voltage
and current signals. Information concerning the power
consumption and power quality could allow the power grid to
route efficiently the energy by means of more suitable decision
criteria. The new scenario has changed the way to exchange
energy in the grid. Now energy flow must be able to change its
direction according to needs. Energy cannot be now routed by
considering just only the criterion based on the simple shortening
of transmission path. So, even energy coming from a far node
should be preferred if it has higher quality standards. In this
view, the proposed smart power meter intends to support the
smart power grid to monitor electricity among different nodes in
an efficient and effective way.
Index Terms— Electric grid, smart grid, sensing systems,
smart power meter, IoT.
I. INTRODUCTION
N the power grid of the future, sensors and transducers will
have a significant role to monitor energy in real time
according to demand. Smart sensing systems can provide new
opportunities for automatic power measurement and data
processing so to take decisions in real time.
The electric network is a complex and interconnected
system commonly called grid. Growing electricity demand
needs more sustainable energy generation by renewable
sources. Today, we are observing a radical transformation of
the public electric system. For example, energy flow becomes
bidirectional due to the presence of distributed generation
plants. Electricity is shared among the several nodes of the
R. Morello, C. De Capua and G. Fulco are with the Department of
Information Engineering, Infrastructure and Sustainable Energy (DIIES),
University Mediterranea of Reggio Calabria, Italy (e-mail:
rosario.morello@unirc.it decapua@unirc.it gaetano.fulco@unirc.it)
S.C. Mukhopadhyay is with the Department of Engineering, Macquarie
University, NSW 2109, Australia (e-mail: Subhas.Mukhopadhyay@mq.edu.au)
power grid, named microgrids, based on local demand. So
energy flow has to change dynamically even its direction. As a
general rule, energy must be routed from microgrids with a
large energy amount to microgrids having an energy lack.
Nevertheless, several factors affect this general criterion such
as the intermittent production of energy from renewable
sources. In addition, the quality of the voltage and current
signals provides further constraints to energy routing.
Consequently, the management of energy flow becomes
today a really complex task, [1]. Currently, these aspects are
not paid with sufficient attention in the grid. As a
consequence, the final user sometimes has to tolerate energy
having low quality. The major consequences are paid by the
domestic users. Therefore today, uninterrupted energy supply
and high quality energy are two basic and fundamental
requirements to be guaranteed in the transmission and
distribution of electricity.
This new concept entails new and important challenges for
researchers dealing with this field. Several issues and
problems must be faced such as the development of new
efficient and smart sensing systems. Contextually, electric
network needs a radical renovation to be able to change
dynamically its configuration. In fact, the current architecture
was projected to manage only mono-directional energy flow
from the central generation plant to the final users.
Such a new scenario requires new systems which allow the
power grid to be really smart by managing the bi-directional
and changing flow of energy, [1]. In addition these systems
must assure interoperability between new and old equipment.
Figure 1 shows the current scenario, where different
distributed generation plants supply their energy to users and
provide the surplus to the electric network. However, energy
production from renewable sources suffers from supply
discontinuity. Thus, the risk of blackouts and service
inefficiency increase. The smart power grid should be able to
prevent promptly a supply discontinuity [2]. These features
require the use of advanced and innovative sensing systems.
So sensors must make measurements and process results in
real time to get a clear overview of power grid state in each
node. For instance, power meters are sensing systems which
are able to measure power features. Depending on its purpose,
measures can include just power consumption or additional
information concerning the power quality [3]-[5].
A Smart Power Meter to Monitor Energy
Flow in Smart Grids:
The Role of Advanced Sensing and IoT in the Electric Grid of the Future
R. Morello, Member, IEEE, C. De Capua, Member, IEEE, G. Fulco, S.C. Mukhopadhyay, Fellow, IEEE
I
1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
Sensors Journal
Fig. 1. The current scenario of the Power Grid [1].
The general architecture of a power meter consists of a
voltage transducer, a current transducer, an A/D converter and
a processor for processing data. At the present time, most of
the commercial power meters perform measurement of active
power. This parameter is commonly used for computing the
user power consumption. Few power meters provide
information on the power quality. Anyway, such information
is not used by power grid for managing the energy delivery or
billing the consumptions. In addition, in literature several
definitions of reactive power exist, as a consequence the
metrics or power computing algorithms are not worldwide
univocally defined. Therefore the concept of metric is still
object of studies and research activities.
In this paper, the authors propose their idea of the future
smart power grid. In detail, power meters will be integrated in
the electric grid to provide information concerning local
energy consumption and the quality of power in the several
nodes of the electric network. Such augmented information,
supported by new decision criteria, will allow the power grid
to manage fault events or rapid changes in energy
requirements. The electric grid can be compared with the
internet network. Therefore Internet of Things (IoT) can
provide new opportunities to developers during the project of
smart power meters. IoT can provide new criteria for sharing
data and information into the whole grid, [6], such as multihop
communication, where each sensor communicates through
several successive nodes. In this sight, IoT can allow sensors
to share information by using internet and web service
architectures so to improve the grid management, [7]. In
addition, sensing systems must cooperate and satisfy several
features such as to be flexible to changing conditions, be able
to monitor and predict electrical energy consumption, and to
control the grid security. So ISO/IEC/IEEE 21451 Standards
can allow smart grid to improve its efficiency by making easy
the interoperability among several sensing systems due to the
protocol standardization. In such a scenario, the modernization
of the electric network will be possible by means of power
meters geographically distributed, which can cooperate to
monitor the grid by performing a distributed data processing,
[8]-[14].
The project, development and experimental validation of a
smart power meter able to monitor the power in real-time are
described in the following Sections. The next Section
describes the proposed smart power meter. Section III reports
the validation and experimental results. Section IV provides a
brief description of the application to the future power grid
based on a IoT vision. The conclusions have been drawn in
Section V.
II. THE SMART POWER METER
The above described future vision of smart grid needs the
project and development of innovative sensing systems with
specific features. The solution proposed in the present paper is
based on a smart power meter with improved characteristics:
 remotely programmable and controllable;
 interoperability among several power meters;
 embedded data processing and decision making
algorithms;
 power quality analysis;
 decision-based management of energy flow routing
according to the power quality requirements defined by the
final user.
The hardware architecture and the soft computing
algorithms are described in the following sub-Sections. A
remote control station has been developed in order to manage
information coming from different power meters so to
simulate a central management station for controlling and
performing in real-time the configuration of the power
network. A further sub-Section describes in brief the
potentialities offered by ISO/IEC/IEEE 21451 Standards.
A. Hardware Architecture
The smart power meter architecture is based on a National
Instruments Single-Board RIO 9626. Two transducers allow to
acquire the voltage and current signals, which are successively
1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
Sensors Journal
digitally converted for data processing. In detail, the power
meter mounts on board two additional modules: NI-9225 and
NI 9246. A 400 MHz processor with 512 MB non-volatile
storage and 256 MB DRAM performs the real time data
processing. The processor is supported by a reconfigurable
Xilinx Spartan-6 LX45 FPGA for custom timing, inline
processing, and control tasks, see Figure 2 for reference.
Fig. 2. The Smart Power Meter.
Table I reports the main electrical and technical
specifications of the power meter.
TABLE I
SMART POWER METER SPECIFICATIONS
Quantity Value
Voltage Range 0-300 Vrms
Current Range 0-20 Arms
Peak Current 30 A
Maximum Sampling Frequency 50 kS/s
Resolution 24 bit
Temperature Operating Range -40 – +70 °C
Dimension 15.4×10.3×5 cm
Mass 350 g
Supply Voltage 9-30 V
The power meter is compliant with the specifications
reported in the guidelines of IEC 61000-4 Standards family
[15]-[17], and of IEC 62052-11 and IEC 62053-21 Standards
[18], [19]. The technical specifications in Table I and the data
processing algorithms allow the performed measurements to
meet the specifications for the range of uncertainty defined for
metering instruments of class A [17].
The meter performs in the following order these operations:

1. synchronous acquisition of voltage and current
   waveforms with a sampling rate of 5 kS/s;
2. FFT calculation of voltage and current waveforms (see
   Section II.B);
3. evaluation of several power and electrical parameters
   according to embedded metrics in compliance with the
   IEEE Std.1459-2010 [20] (see Section II.B);
4. characterization of power quality disturbance events.
   The detected events are stored and can be used by the
   remote control station for managing and configuring the
   power grid on needs (see Section II.C).
   B. Metrics and Signal Processing Algorithms
   The NI Single-Board RIO 9626 has been programmed by
   using the National Instruments LabVIEW software
   environment. It is a graphical programming language; the
   source code has been developed with the LabVIEW Real Time
   Tool. In this way, the projected smart power meter is a
   standalone system able to perform the previous four
   operations in real-time both on-line and off-line. The code
   section concerning the data processing has been entirely
   developed by using all metrics suggested in the IEEE
   Std.1459-2010, [21],[22]. Lastly, a Fast Fourier Transform
   (FFT) algorithm allows to evaluate the harmonic content of
   the voltage and current signals. All computed parameters are
   reported with more detail in Table II.
   TABLE II
   COMPUTED PARAMETERS
   Parameter Description
   Measurement
   Unit
   PC Active Power Consumption per hour kW/h
   QC Reactive Power Consumption per hour var/h
   P1 Fundamental Active Power W
   PH Harmonic Active Power W
   P Total Active Power W
   Q1 Fundamental Reactive Power var
   S Apparent Power VA
   S1 Fundamental Apparent Power VA
   SH Harmonic Apparent Power VA
   DI Current Distortion Power var
   DV Voltage Distortion Power var
   SN non-Fundamental Apparent Power VA
   N non-Active Power var
   PF Power Factor –
   HP Harmonic Pollution –
   PF1 Fundamental Power Factor –
   THDV Voltage Total Harmonic Distortion –
   THDI Current Total Harmonic Distortion –
   k Crest Factor –
   f Frequency Hz
   Vrms root mean square Voltage V
   Vpk peak Voltage V
   V1 Fundamental Voltage V
   VH Harmonic Voltage V
   Vrms,i root mean square Voltage of i-th
   harmonic with 2<i<40
   V
   Irms root mean square Current A
   Ipk peak Current A
   I1 Fundamental Current A
   IH Harmonic Current A
   Irms,i root mean square Current of i-th
   harmonic with 2<i<40
   A
   The previous parameters allow the meter to provide a
   complete overview about the energy flowing in a specific node
   of the electric grid. Figure 3 shows, as an example, a section
   of the developed code.
   Data concerning voltage and current signals, frequency,
   power consumption and power quality is stored and made
   accessible to a remote control station for decision making
   purpose.
   1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
   This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
   Sensors Journal
   Fig. 3. A detail of the Source Code.
   Each record includes date and time of the event, type of
   disturbance (sag, swell or interruption), maximum value over
   the threshold, duration of the event. In detail, the power meter
   compares the measurement results with user-defined
   thresholds in order to characterize specific stationary and
   transient events or supply discontinuities (voltage swell,
   voltage dip, overvoltage, undervoltage, voltage sags, microoutages,
   voltage fluctuations, short and long breaks, impulsive
   overvoltage, over-current, blackouts, etc…) so to send a
   warning or an alert message to the remote control station if
   necessary.
   In addition, the embedded metrics allow the smart meter to
   characterize the bi-directional power flow through the node. In
   detail, by considering the current sign, the meter is able to
   distinguish if the power is supplied by the node to the other
   ones (power production) or if it is consumed by the node
   (power consumption). Such information provides important
   evidence about the power flow in the grid from microgrids
   with a large energy amount to microgrids having an energy
   lack.
   C. Remote Control Station
   The power meter has been projected in order to permit the
   interoperability among several meters geographically
   distributed in the grid. For this reason, a software-based
   control panel has been developed to make possible the
   communication with each meter. The program runs on a server
   which could simulate the control center of a smart power grid.
   By internet network or the same electric network, the
   control station can get access simultaneously to several meters
   of the grid acquiring the computed data. The control station
   can even reconfigure or reprogram the single meter if
   required. Figure 4 shows a screenshot of the control panel.
   By means of the control panel, the remote control station
   gets a clear overview of the grid state in each node.
   Information concerning the voltage and current waveforms,
   power quality, stationary and transient events or supply
   discontinuities provide to the control center an instantaneous
   snapshot of the grid so to manage in real time the energy
   routing along specific and suitable paths. In this way,
   information collected by the smart meter is used to configure
   the grid, so to manage the power flow in a bi-directional way
   from or toward a specific node depending on needs.
   Fig. 4. Screenshot of the Remote Control Panel.
   The Control Station is configured to communicate with
   each smart meter of the grid every hour to reduce the network
   congestion. It is the standard time interval used to evaluate the
   power consumption. However, depending on needs, this time
   interval can be decreased or increased. To guarantee the
   interoperability among the meters, embedded decision criteria
   allow each meter to characterize the occurrence of specific
   faults or inefficiency conditions of the power grid. In detail,
   the meter puts constantly in comparison the measurement
   results of each parameter in Table II with user-defined
   reference values. When a threshold is overcome, the meter
   alerts the Control Station. That can occur for example when
   quality standards go down fixed tolerable limits, or when a
   blackout occurs, or when power consumption of the node
   overcomes the power supply. Successively, the meters in the
   neighbouring nodes are demanded to synchronize their
   measurements. Results are sent to the Remote Control Station
   for processing data. Information on power consumption and
   power quality allows the grid control center to manage
   efficiently the energy routing by acting on actuators located in
   the nodes so to configure the electric network according to
   needs. For an instance, microgrids which supply energy with
   poor quality can be isolate, or nodes with a large power
   amount are connected with nodes having a power lack. All
   that can happen dynamically when network faults,
   malfunctions or disruptions occur.
   To improve the interoperability features, the projected smart
   meter is even able to communicate directly with the other
   neighbouring meters so to demand power measurements or to
   synchronize them. These features can be configured according
   to the power grid requirements. Since the specific application
   case refers to a small-sized power grid, the control and
   communication rights have been exclusively assigned to the
   Remote Control Station. So the single smart meter is
   configured to communicate only with the control center.
   However, when the power grid size increases, it could be
   preferable to transfer specific communication and control
   rights to peripheral meters so to decongest the network and to
   decentralize the grid management task.
   1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
   This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
   Sensors Journal
   D. ISO/IEC/IEEE 21451-x Standards
   The projected power meter is compliant with the guidelines
   of the IEEE 21451 Standards family so to provide a networkindependent
   communication interface. This aspect becomes
   basic when we consider that several smart transducers and
   sensors will be dislocated along the power grid. Therefore, to
   guarantee the interoperability among the several sensing
   systems, the project and development of any device need
   standardization. The growing demand and interest in smart
   sensing systems has induced Working Groups of experts to
   revise the family of ISO/IEC/IEEE 21451-x Standards with
   the joint effort of ISO/IEC/JTC1. The aim of ISO/IEC/IEEE
   21451-x Standards family is to provide a guideline for
   projecting smart transducer interfaces and smart sensor
   networks, [23]-[27]. The Standards allow users and designers
   to project smart sensing systems by using different protocols,
   such as eXtensible Messaging and Presence Protocol (XMPP),
   TCP/IP, HTTP, and Web services so to make easy
   communication among sensors and/or actuators distributed in
   a wide sensing network. Transducer Electronic Data Sheets
   (TEDS) are used for sensor identification and configuration
   purpose. Additional Standards of ISO/IEC/IEEE 21451-x
   family deal with the signal treatment. In such a scenario, the
   project of sensing systems for smart grid needs specific
   attention. For this reason, the remote control panel in the
   Section II.C has been developed to implement a Network
   Capable Application Processor (NCAP). The NCAP performs
   the following functions:
    Transducer Interface Module (TIM) Discovery;
    Transducer Electronic Data Sheet (TEDS) Reading;
    Transducer Data Reading.
   The TIM Discovery function allows individuating the
   available TIMs and automatically adding them to the list of
   the installed power meters [28]. In this way, it is possible to
   expand the meter network according to needs. In the TEDS of
   each smart power meter are stored data concerning its
   identification, its geographical location, technical
   specifications, last calibration, next calibration interval.
   III. VALIDATION AND EXPERIMENTAL RESULTS
   In this Section, the tests performed to validate the above
   described smart power meter are reported. Two test bench
   configurations have been developed to execute three different
   test sets. In detail, a preliminary test set has allowed us to
   validate the measurement results provided by the voltage and
   current transducers (hardware testing, see Section III.A). A
   second test set has been performed to check the FFT algorithm
   so to evaluate the meter capacity to discriminate the several
   harmonic contributions of the voltage and current signals
   (software testing, see Section III.B). And then finally, a further
   experimentation has been performed on a real application case
   to check the precision of the embedded metrics and the
   measurement accuracy of the power meter (experimental
   validation, see Section III.C).
   A. Voltage and current transducers testing
   To check the accuracy of the two transducers, the test bench
   configuration in Figure 5 has been used. In detail, a Calibrator
   FLUKE 5500A has been configured to test the calibration
   curve of each transducer. The environment temperature has
   been controlled and kept constant to 25 °C for the whole test.
   Several sinusoidal voltage and current waveforms have been
   generated with a frequency of 50 Hz and with steps of 10 V
   and 1 A of rms amplitude, respectively, in compliance with
   the respective measurement ranges, see Table I.
   Fig. 5. Overview of the first test bench configuration.
   Results are reported in Tables III and IV.
   TABLE III
   VOLTAGE TRANSDUCER CALIBRATION CURVE (50 HZ)
   Reference Value
   [V]
   Measured Value
   [V]
   Percentage Deviation
   %
   10 9.9978 0.0220
   20 19.9957 0.0215
   30 29.9942 0.0193
   40 39.9856 0.0360
   50 49.9895 0.0210
   60 59.9842 0.0263
   70 69.9826 0.0248
   80 79.9865 0.0168
   90 89.9810 0.0211
   100 99.9811 0.0189
   110 109.976 0.0218
   120 119.976 0.0200
   130 129.974 0.0200
   140 139.980 0.0142
   150 149.971 0.0193
   160 159.970 0.0187
   170 169.981 0.0111
   180 179.973 0.0150
   190 189.963 0.0194
   200 199.973 0.0135
   210 209.973 0.0128
   220 219.966 0.0154
   230 229.969 0.0134
   240 239.964 0.0150
   250 249.963 0.0148
   260 259.957 0.0165
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   Sensors Journal
   270 269.963 0.0137
   280 279.955 0.0160
   290 289.989 0.0037
   300 299.955 0.0150
   TABLE IV
   CURRENT TRANSDUCER CALIBRATION CURVE (50 HZ)
   Reference Value
   [A]
   Measured Value
   [A]
   Percentage Deviation
   %
   1 1.0001 0.0100
   2 2.0002 0.0100
   3 2.9992 0.0266
   4 3.9988 0.0300
   5 4.9984 0.0320
   6 5.9983 0.0283
   7 6.9982 0.0257
   8 7.9962 0.0475
   9 8.9943 0.0633
   10 9.9970 0.0300
   11 10.9939 0.0554
   The results show a maximum percentage deviation equal to
   0.036% for the voltage calibration curve and 0.0633% for the
   current calibration curve. The estimated voltage and current
   offset values are 0.001 V and 0.0014 A, respectively. Such
   results are compliant with the IEC requirements concerning
   the electricity metering equipment so confirming the class A
   for the projected power meter [17]-[19].
   B. FFT and Harmonics Detection testing
   The test bench in Figure 5 has been furthermore used to
   check the accuracy of the harmonics detection performed by
   the FFT algorithm. The Calibrator has been programmed to
   generate six sinusoidal voltage and current waveforms with
   different frequency values. For each waveform, the voltage
   amplitude has been set equal to 230 Vrms with a current
   amplitude of 5 Arms. The harmonics until the seventh order
   have been considered for this test, since they are the
   harmonics which occur frequently in the real case. The results
   are showed in Tables V and VI for the voltage and current
   waveforms, respectively. The maximum percentage deviation
   obtained for the voltage waveform is equal to 0.0282% and
   equal to 0.096% for the current waveform. The values show a
   good accuracy of the harmonics detection algorithm to discern
   the harmonic content for each waveform.
   TABLE V
   FFT ALGORITHM TESTING (VOLTAGE WAVEFORM)
   Harmonic
   Order
   Frequency
   [Hz]
   Reference
   Value
   [V]
   Measured
   Value
   [V]
   Percentage
   Deviation
   %
   2 100 230 229.945 0.0239
   3 150 230 229.959 0.0178
   4 200 230 229.956 0.0191
   5 250 230 229.951 0.0213
   6 300 230 229.951 0.0213
   7 350 230 229.950 0.0217
   8 400 230 229.944 0.0243
   9 450 230 229.946 0.0234
   10 500 230 229.935 0.0282
   TABLE VI
   FFT ALGORITHM TESTING (CURRENT WAVEFORM)
   Harmonic
   Order
   Frequency
   [Hz]
   Reference
   Value
   [A]
   Measured
   Value
   [A]
   Percentage
   Deviation
   %
   2 100 5 4.9985 0.030
   3 150 5 4.9983 0.034
   4 200 5 4.9952 0.096
   5 250 5 4.9986 0.028
   6 300 5 4.9985 0.030
   7 350 5 4.9986 0.028
   8 400 5 4.9982 0.036
   9 450 5 4.9981 0.038
   10 500 5 4.9979 0.042
   C. Experimental Results
   An additional experimental analysis has been performed by
   considering a specific application case. The used test bench
   configuration is showed in Figure 6.
   Fig. 6. Overview of the second test bench configuration.
   The test equipment consists of an AC Power Source Pacific
   360-AMX with programmable controller, a Precision Power
   Analyzer Yokogawa WT1800, an electric motor used as load, a
   Hysteresis Dynamometer Magtrol HD-715-8NA with a
   Dynamometer Controller Magtrol DSP6001.
   The load has been supplied by applying a sinusoidal voltage
   of 225 Vrms amplitude and a frequency of 50 Hz generated by
   the power source. Voltage harmonic components until the
   seventh order have been added to the voltage signal in order to
   simulate non-sinusoidal operating conditions. Each harmonic
   has been generated with an amplitude equal to 30% of
   fundamental component amplitude. The voltage and current
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   waveforms are shown in the Figures 7 and 8, respectively.
   Fig. 7. Voltage waveform.
   Fig. 8. Current Waveform.
   The harmonic components of the voltage and current
   waveforms are depicted in Figure 9.
   Fig. 9. Harmonics components of the voltage and current signals.
   By analysing the previous figure, it is possible to observe
   the presence of harmonic components beyond the seventh
   order. That is the result of the distortion introduced by the
   load. The control panel displayed by the remote control station
   is reported in Figure 10. The panel shows all parameters
   measured by the smart power meter as reported in Table II.
   Each measured value has been put in comparison with the
   value provided by the power analyser, which has been
   considered as a reference for this experimentation.
   Fig. 10. Control Panel of the Remote Control Station.
   Table VII reports the results of the experimental
   comparison.
   TABLE VII
   EXPERIMENTAL VALIDATION RESULTS OF THE SMART POWER METER
   Parameter Reference Value Measured Value
   Percentage
   Deviation
   %
   PC [kW/h] – n.a. –
   QC [var/h] – n.a. –
   P1 [W] 530.600 527.682 0.5499
   PH [W] 214.800 200.293 6.7537
   P [W] 745.400 727.975 2.3376
   Q1 [var] 860.600* 555.182* 35.4889*
   S [VA] 1139.700 1104.911 3.0524
   S1 [VA] 778.4200 765.947 1.6023
   SH [VA] 347.8185 338.423 2.7012
   DI [var] 507.8204 492.139 3.0879
   DV [var] 533.1599 526.710 1.2097
   SN [VA] 832.4532 796.338 4.3384
   N [var] 860.600 831.193 3.4170
   PF 0.6556 0.659 0.5186
   HP 1.06 1.040 1.8867
   PF1 0.6816 0.6890 1.0856
   THDV 56.661 % 68.766 % 21.3639
   THDI 56.456 % 64.252 % 13.8089
   k – 1.2045 –
   f [Hz] 50.0020 50.0014 0.0011
   Vrms [V] 223.0200 222.331 0.3089
   Vpk [V] 417.6800 415.8700 0.4333
   V1 [V] 183.7200 183.1960 0.2852
   VH [V] 125.8343 125.9770 0.1134
   Vrms,i [V]
   with 1<i<8
   1) 183.72
   2) 52.78
   3) 50.77
   4) 50.78
   5) 50.42
   6) 51.36
   1) 183.1960
   2) 52.8025
   3) 50.6235
   4) 50.6606
   5) 50.3228
   6) 51.1990
   1) 0.2852
   2) 0.0426
   3) 0.2885
   4) 0.2351
   5) 0.1927
   6) 0.3134
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   Sensors Journal
   7) 52.08
   8) 2.34
   7) 51.9397
   8) 2.32733
   7) 0.2693
   8) 0.5414
   Irms [A] 5.110 4.970 2.7397
   Ipk [A] 6.235 6.025 3.3680
   I1 [A] 4.237 4.1810 1.3216
   IH [A] 2.7641 2.686 2.8255
   Irms,i [A]
   with 1<i<8
   1) 4.237
   2) 1.615
   3) 1.078
   4) 1.176
   5) 0.943
   6) 0.941
   7) 0.844
   8) 0.495
   1) 4.1810
   2) 1.5485
   3) 1.0136
   4) 1.1019
   5) 0.8724
   6) 0.8688
   7) 0.7722
   8) 0.4260
   1) 1.3216
   2) 4.1176
   3) 5.9740
   4) 6.3010
   5) 7.4867
   6) 7.6726
   7) 8.5071
   8) 13.9393

• value not reported
• value obtained by using a different parameter definition
  The analysis of the results does not allow us to make a
  complete comparison between the two measurement systems,
  since the projected smart power meter integrates a major
  number of metrics. In addition the two measurement systems
  use a different definition of the reactive power, as a
  consequence the parameter Q1 is not comparable. Anyway, by
  considering the only parameters in common, a significant
  percentage deviation has been obtained for the Harmonic
  Active Power parameter. It is due to an expected systematic
  error caused by the voltamperometric connection of the two
  instruments. This is cause of error on the measurement of the
  harmonic current components. It is useful to observe that the
  results of the FFT algorithm testing reported in the Section
  III.B have shown a maximum percentage deviation equal to
  0.096%. This test has been performed by using a different test
  bench configuration, which has avoided the above described
  error. Consequently, the percentage deviations of the harmonic
  currents in Table VII are attributable to the voltamperometric
  method error.
  IV. TOWARDS THE GRID OF THE FUTURE: THE IOT VISION
  It is expected that the electric grid of future will be a
  complex flow of energy and information shared among several
  nodes. New sensing systems and services will be necessary to
  manage a so complex distributed system, [29]. Even protocols
  and standards will have an important role. The power grid can
  be compared to the internet network. Each node of the grid
  can be equipped with a power meter. The resulting sensing
  grid can be considered as a complex system geographically
  distributed. The power meter network will have the task to
  monitor in real time the energy flow of a large number of
  nodes. Such amount of information is used to make timely
  decisions when critical events occur. By gaining experience
  with the evolution of the internet network in the last years,
  such described scenario offers a large number of research
  topics. The internet protocol is universal and has been widely
  validated in the years. As a consequence, the real time control
  of the power grid could take advantage of internet so to use
  power meter data for taking timely decisions and configuring
  itself based on needs. Internet of Things (IoT) concept can
  help to share information and data in the grid so to improve
  efficiency, reliability and security of the electric system [6].
  IoT aims to add value by connecting objects to internet
  network. So IoT implies that power meters can be able to
  utilize internet to communicate data about their condition,
  position, or other measurement parameters. Therefore smart
  power meters will take advantage of this by using the internet
  network so making available data and information on the
  power grid with a new approach in respect to the past. Power
  meters can even monitor constantly the power line
  temperature so to estimate the carrying capacity of the line.
  Such information can be used to manage dynamically the
  power flow amount by using suitable dynamic line rating
  algorithms. Therefore, IoT can allow the smart power grid to
  increase its features and services. In this new scenario, IoT
  promises to turn a power meter into an object which provides
  information about the grid and its environment. This will
  create in the next future a new way to differentiate the services
  of the electric network and a new source of value. This IoT
  vision will improve the efficiency of the power grid and will
  provide new opportunities. The user will be able to define and
  change his/her power requirements, and the smart power grid
  will configure itself to assure the required power quality
  specifications.
  The main issue to be faced in the next future concerns the
  large number of power meters and sensors to be managed and
  maintained. Typically, an electric grid consists of 10,000 or
  even 100,000 nodes. Consequently, the scalability issue
  should be resolved before considering a power grid to be
  smart. The collaborative signal processing among nodes is
  another important aspect. Even the querying ability is relevant
  to the electric grid of the future. A node or a power meter must
  be able to query an individual node or group of nodes for
  getting information concerning a specific microgrid. All these
  aspects have been formerly faced by the internet network. So
  the main features and characteristics today requested to the
  power grid are the same ones owned by the present internet
  network. Therefore, by a IoT vision, the power grid will be
  able to perform its tasks: demand management, disturbances
  detection, energy flow amount management, isolation of
  specific microgrids, management of the energy storage,
  transport of energy from any node it is produced to nodes
  where it is lacking by using innovative routing algorithms. In
  the view of the future power grid, smart power meters could
  resolve all the current difficulties concerning the sensing and
  measurement issues.
  The projected smart power meter takes advantage of IoT
  concept. In fact, the current power meters are able to share
  information only with the control centre of the electric
  company just to bill the user power consumption. With a
  different approach, the proposed power meter shares
  information on consumption and power quality with the
  internet network to improve the management of the power
  grid. The power meter cannot be anymore considered as a
  simple instrument billing consumption. By such a IoT vision,
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  information on each node of the grid is shared with the whole
  grid to increase its efficiency. Measurement data is used to bill
  consumption but at the same time to configure the power
  network based on power demands and on the power quality
  requirements defined by the user. In this view, the described
  power meter implements the IoT concept to improve the
  features and the services offered by the future power grid.
  However, several further issues remain still unsolved. In
  detail, the new challenges include:
   the standardization of communication protocols;
   the improvement of the security standards;
   integration of the sensing systems into existing systems
  to assure interoperability;
   harmonisation of equipment standards to allow plug-andplay
  and interface;
   new power flow routing algorithms and innovative
  routing criteria;
   management of big data coming from thousands of
  sensing systems distributed through the grid;
   redefinition of the metrics used for billing consumptions;
   modernisation of current electric network architecture.
  V. ACKNOWLEDGMENT
  The described research activity is part of the Project
  “Laboratorio RENEW-MEL” which has been funded, through
  the PON Program, by the Italian Ministry of Education,
  Universities and Research (MIUR) and by the European
  Commission (contract no. PON03PE_000122 &
  PON03PE_00012).
  VI. CONCLUSIONS
  The paper proposes an innovative smart power meter to
  monitor the energy flow in smart power grids. Expecting the
  power grid of the future, the authors take stock of the situation
  concerning the state of the current power grid. Weaknesses
  and strengths are discussed so to highlight the role of the
  advanced sensing systems in the power grid management.
  Other issues remain still unsolved before that the power grid
  can be considered really smart. IoT offers new interesting
  answers in order to face and resolve such issues. So this paper
  intends to solicit the debate on the role of IoT in the
  development of the power grid of the next future. The current
  power network needs to be updated in order to adapt itself to
  the new requirements of the current power demand. As a
  consequence, there is still much to be done especially in the
  matter of defining the most suitable architecture of the electric
  network. By means of the proposed IoT vision, the projected
  smart power meter uses the internet network to improve the
  efficiency and features of the power grid.
  The paper aims to propose a possible solution to the issues
  concerning the sensing and measurement aspects by
  discussing the potentialities of the developed smart power
  meter. The project and development of the proposed power
  meter have been described in the paper. Hardware architecture
  and software algorithms have been outlined. In detail, the
  embedded metrics offer additional information on the energy
  flowing in the grid nodes in respect to the other commercial
  power meters. The main strengths concern:
   the possibility to control and program remotely the
  meter;
   data are processed in real time to support the decision
  making tasks;
   the remote control station is able to manage
  simultaneously several smart meters;
   the embedded metrics offer new potential criteria to
  manage efficiently the energy routing and sharing among
  several nodes by considering even the power quality
  features.
  Tests and experimentation on an application case have
  allowed to validate the developed prototype. Two test benches
  have been used for testing the hardware and software
  operations. The additional experimental results have proved
  the compliance of the power meter with the IEC requirements.
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  Rosario Morello (M’03) was born in Reggio Calabria,
  Italy, in 1978. He received the M.Sc. Degree (cum
  laude) in Electronic Engineering and the Ph.D. Degree
  in Electrical and Automation Engineering from the
  University “Mediterranea” of Reggio Calabria, Italy, in
  2002 and 2006, respectively. Since 2005, he has been
  Postdoctoral Researcher of Electrical and Electronic
  Measurements at the Department of Information
  Engineering, Infrastructure and Sustainable Energy of
  the same University. At the present he is an Assistant Professor. His main
  research interests include the design and characterization of distributed and
  intelligent measurement systems, wireless sensor network, environmental
  monitoring, decision-making problems and measurement uncertainty, process
  quality assurance, instrumentation reliability and calibration, energy, smart
  grids, battery testing, biomedical applications and statistical signal processing,
  non-invasive systems, biotechnologies and measurement, instrumentation and
  methodologies related to Healthcare. Dr. Morello is a member of the Italian
  Group of Electrical, Electronic Measurements (GMEE) and IEEE.
  Claudio De Capua (M’99) received the M.S. and the
  Ph.D. degrees in Electrical Engineering from the
  University of Naples “Federico II”, Naples, Italy. Since
  2012, he is Full Professor of Electrical and Electronic
  Measurements at the Department of Information
  Engineering, Infrastructure and Sustainable Energy,
  University “Mediterranea” of Reggio Calabria. His
  current research includes the design, realization and
  metrological performance improvement of the automatic
  measurement systems; web sensors and sensor data
  fusion; biomedical instrumentation; techniques for remote didactic laboratory;
  measurement uncertainty analysis; problems of electromagnetic compatibility
  in measurements.
  Prof. De Capua is member of the Italian Group of Electrical and Electronic
  Measurements (GMEE).
  Gaetano Fulco was born in Reggio Calabria, Italy, in

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Sensors Journal

Abstract— This paper aims to describe the role of advanced
sensing systems in the electric grid of the future. In detail the
project, development and experimental validation of a smart
power meter are described in the following. The authors provide
an outline of the potentialities of the sensing systems and IoT to
monitor efficiently the energy flow among nodes of electric
network. The described power meter uses the metrics proposed
in the IEEE Standard 1459-2010 to analyse and process voltage
and current signals. Information concerning the power
consumption and power quality could allow the power grid to
route efficiently the energy by means of more suitable decision
criteria. The new scenario has changed the way to exchange
energy in the grid. Now energy flow must be able to change its
direction according to needs. Energy cannot be now routed by
considering just only the criterion based on the simple shortening
of transmission path. So, even energy coming from a far node
should be preferred if it has higher quality standards. In this
view, the proposed smart power meter intends to support the
smart power grid to monitor electricity among different nodes in
an efficient and effective way.
Index Terms— Electric grid, smart grid, sensing systems,
smart power meter, IoT.
I. INTRODUCTION
N the power grid of the future, sensors and transducers will
have a significant role to monitor energy in real time
according to demand. Smart sensing systems can provide new
opportunities for automatic power measurement and data
processing so to take decisions in real time.
The electric network is a complex and interconnected
system commonly called grid. Growing electricity demand
needs more sustainable energy generation by renewable
sources. Today, we are observing a radical transformation of
the public electric system. For example, energy flow becomes
bidirectional due to the presence of distributed generation
plants. Electricity is shared among the several nodes of the
R. Morello, C. De Capua and G. Fulco are with the Department of
Information Engineering, Infrastructure and Sustainable Energy (DIIES),
University Mediterranea of Reggio Calabria, Italy (e-mail:
rosario.morello@unirc.it decapua@unirc.it gaetano.fulco@unirc.it)
S.C. Mukhopadhyay is with the Department of Engineering, Macquarie
University, NSW 2109, Australia (e-mail: Subhas.Mukhopadhyay@mq.edu.au)
power grid, named microgrids, based on local demand. So
energy flow has to change dynamically even its direction. As a
general rule, energy must be routed from microgrids with a
large energy amount to microgrids having an energy lack.
Nevertheless, several factors affect this general criterion such
as the intermittent production of energy from renewable
sources. In addition, the quality of the voltage and current
signals provides further constraints to energy routing.
Consequently, the management of energy flow becomes
today a really complex task, [1]. Currently, these aspects are
not paid with sufficient attention in the grid. As a
consequence, the final user sometimes has to tolerate energy
having low quality. The major consequences are paid by the
domestic users. Therefore today, uninterrupted energy supply
and high quality energy are two basic and fundamental
requirements to be guaranteed in the transmission and
distribution of electricity.
This new concept entails new and important challenges for
researchers dealing with this field. Several issues and
problems must be faced such as the development of new
efficient and smart sensing systems. Contextually, electric
network needs a radical renovation to be able to change
dynamically its configuration. In fact, the current architecture
was projected to manage only mono-directional energy flow
from the central generation plant to the final users.
Such a new scenario requires new systems which allow the
power grid to be really smart by managing the bi-directional
and changing flow of energy, [1]. In addition these systems
must assure interoperability between new and old equipment.
Figure 1 shows the current scenario, where different
distributed generation plants supply their energy to users and
provide the surplus to the electric network. However, energy
production from renewable sources suffers from supply
discontinuity. Thus, the risk of blackouts and service
inefficiency increase. The smart power grid should be able to
prevent promptly a supply discontinuity [2]. These features
require the use of advanced and innovative sensing systems.
So sensors must make measurements and process results in
real time to get a clear overview of power grid state in each
node. For instance, power meters are sensing systems which
are able to measure power features. Depending on its purpose,
measures can include just power consumption or additional
information concerning the power quality [3]-[5].
A Smart Power Meter to Monitor Energy
Flow in Smart Grids:
The Role of Advanced Sensing and IoT in the Electric Grid of the Future
R. Morello, Member, IEEE, C. De Capua, Member, IEEE, G. Fulco, S.C. Mukhopadhyay, Fellow, IEEE
I
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Fig. 1. The current scenario of the Power Grid [1].
The general architecture of a power meter consists of a
voltage transducer, a current transducer, an A/D converter and
a processor for processing data. At the present time, most of
the commercial power meters perform measurement of active
power. This parameter is commonly used for computing the
user power consumption. Few power meters provide
information on the power quality. Anyway, such information
is not used by power grid for managing the energy delivery or
billing the consumptions. In addition, in literature several
definitions of reactive power exist, as a consequence the
metrics or power computing algorithms are not worldwide
univocally defined. Therefore the concept of metric is still
object of studies and research activities.
In this paper, the authors propose their idea of the future
smart power grid. In detail, power meters will be integrated in
the electric grid to provide information concerning local
energy consumption and the quality of power in the several
nodes of the electric network. Such augmented information,
supported by new decision criteria, will allow the power grid
to manage fault events or rapid changes in energy
requirements. The electric grid can be compared with the
internet network. Therefore Internet of Things (IoT) can
provide new opportunities to developers during the project of
smart power meters. IoT can provide new criteria for sharing
data and information into the whole grid, [6], such as multihop
communication, where each sensor communicates through
several successive nodes. In this sight, IoT can allow sensors
to share information by using internet and web service
architectures so to improve the grid management, [7]. In
addition, sensing systems must cooperate and satisfy several
features such as to be flexible to changing conditions, be able
to monitor and predict electrical energy consumption, and to
control the grid security. So ISO/IEC/IEEE 21451 Standards
can allow smart grid to improve its efficiency by making easy
the interoperability among several sensing systems due to the
protocol standardization. In such a scenario, the modernization
of the electric network will be possible by means of power
meters geographically distributed, which can cooperate to
monitor the grid by performing a distributed data processing,
[8]-[14].
The project, development and experimental validation of a
smart power meter able to monitor the power in real-time are
described in the following Sections. The next Section
describes the proposed smart power meter. Section III reports
the validation and experimental results. Section IV provides a
brief description of the application to the future power grid
based on a IoT vision. The conclusions have been drawn in
Section V.
II. THE SMART POWER METER
The above described future vision of smart grid needs the
project and development of innovative sensing systems with
specific features. The solution proposed in the present paper is
based on a smart power meter with improved characteristics:
 remotely programmable and controllable;
 interoperability among several power meters;
 embedded data processing and decision making
algorithms;
 power quality analysis;
 decision-based management of energy flow routing
according to the power quality requirements defined by the
final user.
The hardware architecture and the soft computing
algorithms are described in the following sub-Sections. A
remote control station has been developed in order to manage
information coming from different power meters so to
simulate a central management station for controlling and
performing in real-time the configuration of the power
network. A further sub-Section describes in brief the
potentialities offered by ISO/IEC/IEEE 21451 Standards.
A. Hardware Architecture
The smart power meter architecture is based on a National
Instruments Single-Board RIO 9626. Two transducers allow to
acquire the voltage and current signals, which are successively
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digitally converted for data processing. In detail, the power
meter mounts on board two additional modules: NI-9225 and
NI 9246. A 400 MHz processor with 512 MB non-volatile
storage and 256 MB DRAM performs the real time data
processing. The processor is supported by a reconfigurable
Xilinx Spartan-6 LX45 FPGA for custom timing, inline
processing, and control tasks, see Figure 2 for reference.
Fig. 2. The Smart Power Meter.
Table I reports the main electrical and technical
specifications of the power meter.
TABLE I
SMART POWER METER SPECIFICATIONS
Quantity Value
Voltage Range 0-300 Vrms
Current Range 0-20 Arms
Peak Current 30 A
Maximum Sampling Frequency 50 kS/s
Resolution 24 bit
Temperature Operating Range -40 – +70 °C
Dimension 15.4×10.3×5 cm
Mass 350 g
Supply Voltage 9-30 V
The power meter is compliant with the specifications
reported in the guidelines of IEC 61000-4 Standards family
[15]-[17], and of IEC 62052-11 and IEC 62053-21 Standards
[18], [19]. The technical specifications in Table I and the data
processing algorithms allow the performed measurements to
meet the specifications for the range of uncertainty defined for
metering instruments of class A [17].
The meter performs in the following order these operations:

1. synchronous acquisition of voltage and current
   waveforms with a sampling rate of 5 kS/s;
2. FFT calculation of voltage and current waveforms (see
   Section II.B);
3. evaluation of several power and electrical parameters
   according to embedded metrics in compliance with the
   IEEE Std.1459-2010 [20] (see Section II.B);
4. characterization of power quality disturbance events.
   The detected events are stored and can be used by the
   remote control station for managing and configuring the
   power grid on needs (see Section II.C).
   B. Metrics and Signal Processing Algorithms
   The NI Single-Board RIO 9626 has been programmed by
   using the National Instruments LabVIEW software
   environment. It is a graphical programming language; the
   source code has been developed with the LabVIEW Real Time
   Tool. In this way, the projected smart power meter is a
   standalone system able to perform the previous four
   operations in real-time both on-line and off-line. The code
   section concerning the data processing has been entirely
   developed by using all metrics suggested in the IEEE
   Std.1459-2010, [21],[22]. Lastly, a Fast Fourier Transform
   (FFT) algorithm allows to evaluate the harmonic content of
   the voltage and current signals. All computed parameters are
   reported with more detail in Table II.
   TABLE II
   COMPUTED PARAMETERS
   Parameter Description
   Measurement
   Unit
   PC Active Power Consumption per hour kW/h
   QC Reactive Power Consumption per hour var/h
   P1 Fundamental Active Power W
   PH Harmonic Active Power W
   P Total Active Power W
   Q1 Fundamental Reactive Power var
   S Apparent Power VA
   S1 Fundamental Apparent Power VA
   SH Harmonic Apparent Power VA
   DI Current Distortion Power var
   DV Voltage Distortion Power var
   SN non-Fundamental Apparent Power VA
   N non-Active Power var
   PF Power Factor –
   HP Harmonic Pollution –
   PF1 Fundamental Power Factor –
   THDV Voltage Total Harmonic Distortion –
   THDI Current Total Harmonic Distortion –
   k Crest Factor –
   f Frequency Hz
   Vrms root mean square Voltage V
   Vpk peak Voltage V
   V1 Fundamental Voltage V
   VH Harmonic Voltage V
   Vrms,i root mean square Voltage of i-th
   harmonic with 2<i<40
   V
   Irms root mean square Current A
   Ipk peak Current A
   I1 Fundamental Current A
   IH Harmonic Current A
   Irms,i root mean square Current of i-th
   harmonic with 2<i<40
   A
   The previous parameters allow the meter to provide a
   complete overview about the energy flowing in a specific node
   of the electric grid. Figure 3 shows, as an example, a section
   of the developed code.
   Data concerning voltage and current signals, frequency,
   power consumption and power quality is stored and made
   accessible to a remote control station for decision making
   purpose.
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   Sensors Journal
   Fig. 3. A detail of the Source Code.
   Each record includes date and time of the event, type of
   disturbance (sag, swell or interruption), maximum value over
   the threshold, duration of the event. In detail, the power meter
   compares the measurement results with user-defined
   thresholds in order to characterize specific stationary and
   transient events or supply discontinuities (voltage swell,
   voltage dip, overvoltage, undervoltage, voltage sags, microoutages,
   voltage fluctuations, short and long breaks, impulsive
   overvoltage, over-current, blackouts, etc…) so to send a
   warning or an alert message to the remote control station if
   necessary.
   In addition, the embedded metrics allow the smart meter to
   characterize the bi-directional power flow through the node. In
   detail, by considering the current sign, the meter is able to
   distinguish if the power is supplied by the node to the other
   ones (power production) or if it is consumed by the node
   (power consumption). Such information provides important
   evidence about the power flow in the grid from microgrids
   with a large energy amount to microgrids having an energy
   lack.
   C. Remote Control Station
   The power meter has been projected in order to permit the
   interoperability among several meters geographically
   distributed in the grid. For this reason, a software-based
   control panel has been developed to make possible the
   communication with each meter. The program runs on a server
   which could simulate the control center of a smart power grid.
   By internet network or the same electric network, the
   control station can get access simultaneously to several meters
   of the grid acquiring the computed data. The control station
   can even reconfigure or reprogram the single meter if
   required. Figure 4 shows a screenshot of the control panel.
   By means of the control panel, the remote control station
   gets a clear overview of the grid state in each node.
   Information concerning the voltage and current waveforms,
   power quality, stationary and transient events or supply
   discontinuities provide to the control center an instantaneous
   snapshot of the grid so to manage in real time the energy
   routing along specific and suitable paths. In this way,
   information collected by the smart meter is used to configure
   the grid, so to manage the power flow in a bi-directional way
   from or toward a specific node depending on needs.
   Fig. 4. Screenshot of the Remote Control Panel.
   The Control Station is configured to communicate with
   each smart meter of the grid every hour to reduce the network
   congestion. It is the standard time interval used to evaluate the
   power consumption. However, depending on needs, this time
   interval can be decreased or increased. To guarantee the
   interoperability among the meters, embedded decision criteria
   allow each meter to characterize the occurrence of specific
   faults or inefficiency conditions of the power grid. In detail,
   the meter puts constantly in comparison the measurement
   results of each parameter in Table II with user-defined
   reference values. When a threshold is overcome, the meter
   alerts the Control Station. That can occur for example when
   quality standards go down fixed tolerable limits, or when a
   blackout occurs, or when power consumption of the node
   overcomes the power supply. Successively, the meters in the
   neighbouring nodes are demanded to synchronize their
   measurements. Results are sent to the Remote Control Station
   for processing data. Information on power consumption and
   power quality allows the grid control center to manage
   efficiently the energy routing by acting on actuators located in
   the nodes so to configure the electric network according to
   needs. For an instance, microgrids which supply energy with
   poor quality can be isolate, or nodes with a large power
   amount are connected with nodes having a power lack. All
   that can happen dynamically when network faults,
   malfunctions or disruptions occur.
   To improve the interoperability features, the projected smart
   meter is even able to communicate directly with the other
   neighbouring meters so to demand power measurements or to
   synchronize them. These features can be configured according
   to the power grid requirements. Since the specific application
   case refers to a small-sized power grid, the control and
   communication rights have been exclusively assigned to the
   Remote Control Station. So the single smart meter is
   configured to communicate only with the control center.
   However, when the power grid size increases, it could be
   preferable to transfer specific communication and control
   rights to peripheral meters so to decongest the network and to
   decentralize the grid management task.
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   D. ISO/IEC/IEEE 21451-x Standards
   The projected power meter is compliant with the guidelines
   of the IEEE 21451 Standards family so to provide a networkindependent
   communication interface. This aspect becomes
   basic when we consider that several smart transducers and
   sensors will be dislocated along the power grid. Therefore, to
   guarantee the interoperability among the several sensing
   systems, the project and development of any device need
   standardization. The growing demand and interest in smart
   sensing systems has induced Working Groups of experts to
   revise the family of ISO/IEC/IEEE 21451-x Standards with
   the joint effort of ISO/IEC/JTC1. The aim of ISO/IEC/IEEE
   21451-x Standards family is to provide a guideline for
   projecting smart transducer interfaces and smart sensor
   networks, [23]-[27]. The Standards allow users and designers
   to project smart sensing systems by using different protocols,
   such as eXtensible Messaging and Presence Protocol (XMPP),
   TCP/IP, HTTP, and Web services so to make easy
   communication among sensors and/or actuators distributed in
   a wide sensing network. Transducer Electronic Data Sheets
   (TEDS) are used for sensor identification and configuration
   purpose. Additional Standards of ISO/IEC/IEEE 21451-x
   family deal with the signal treatment. In such a scenario, the
   project of sensing systems for smart grid needs specific
   attention. For this reason, the remote control panel in the
   Section II.C has been developed to implement a Network
   Capable Application Processor (NCAP). The NCAP performs
   the following functions:
    Transducer Interface Module (TIM) Discovery;
    Transducer Electronic Data Sheet (TEDS) Reading;
    Transducer Data Reading.
   The TIM Discovery function allows individuating the
   available TIMs and automatically adding them to the list of
   the installed power meters [28]. In this way, it is possible to
   expand the meter network according to needs. In the TEDS of
   each smart power meter are stored data concerning its
   identification, its geographical location, technical
   specifications, last calibration, next calibration interval.
   III. VALIDATION AND EXPERIMENTAL RESULTS
   In this Section, the tests performed to validate the above
   described smart power meter are reported. Two test bench
   configurations have been developed to execute three different
   test sets. In detail, a preliminary test set has allowed us to
   validate the measurement results provided by the voltage and
   current transducers (hardware testing, see Section III.A). A
   second test set has been performed to check the FFT algorithm
   so to evaluate the meter capacity to discriminate the several
   harmonic contributions of the voltage and current signals
   (software testing, see Section III.B). And then finally, a further
   experimentation has been performed on a real application case
   to check the precision of the embedded metrics and the
   measurement accuracy of the power meter (experimental
   validation, see Section III.C).
   A. Voltage and current transducers testing
   To check the accuracy of the two transducers, the test bench
   configuration in Figure 5 has been used. In detail, a Calibrator
   FLUKE 5500A has been configured to test the calibration
   curve of each transducer. The environment temperature has
   been controlled and kept constant to 25 °C for the whole test.
   Several sinusoidal voltage and current waveforms have been
   generated with a frequency of 50 Hz and with steps of 10 V
   and 1 A of rms amplitude, respectively, in compliance with
   the respective measurement ranges, see Table I.
   Fig. 5. Overview of the first test bench configuration.
   Results are reported in Tables III and IV.
   TABLE III
   VOLTAGE TRANSDUCER CALIBRATION CURVE (50 HZ)
   Reference Value
   [V]
   Measured Value
   [V]
   Percentage Deviation
   %
   10 9.9978 0.0220
   20 19.9957 0.0215
   30 29.9942 0.0193
   40 39.9856 0.0360
   50 49.9895 0.0210
   60 59.9842 0.0263
   70 69.9826 0.0248
   80 79.9865 0.0168
   90 89.9810 0.0211
   100 99.9811 0.0189
   110 109.976 0.0218
   120 119.976 0.0200
   130 129.974 0.0200
   140 139.980 0.0142
   150 149.971 0.0193
   160 159.970 0.0187
   170 169.981 0.0111
   180 179.973 0.0150
   190 189.963 0.0194
   200 199.973 0.0135
   210 209.973 0.0128
   220 219.966 0.0154
   230 229.969 0.0134
   240 239.964 0.0150
   250 249.963 0.0148
   260 259.957 0.0165
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   270 269.963 0.0137
   280 279.955 0.0160
   290 289.989 0.0037
   300 299.955 0.0150
   TABLE IV
   CURRENT TRANSDUCER CALIBRATION CURVE (50 HZ)
   Reference Value
   [A]
   Measured Value
   [A]
   Percentage Deviation
   %
   1 1.0001 0.0100
   2 2.0002 0.0100
   3 2.9992 0.0266
   4 3.9988 0.0300
   5 4.9984 0.0320
   6 5.9983 0.0283
   7 6.9982 0.0257
   8 7.9962 0.0475
   9 8.9943 0.0633
   10 9.9970 0.0300
   11 10.9939 0.0554
   The results show a maximum percentage deviation equal to
   0.036% for the voltage calibration curve and 0.0633% for the
   current calibration curve. The estimated voltage and current
   offset values are 0.001 V and 0.0014 A, respectively. Such
   results are compliant with the IEC requirements concerning
   the electricity metering equipment so confirming the class A
   for the projected power meter [17]-[19].
   B. FFT and Harmonics Detection testing
   The test bench in Figure 5 has been furthermore used to
   check the accuracy of the harmonics detection performed by
   the FFT algorithm. The Calibrator has been programmed to
   generate six sinusoidal voltage and current waveforms with
   different frequency values. For each waveform, the voltage
   amplitude has been set equal to 230 Vrms with a current
   amplitude of 5 Arms. The harmonics until the seventh order
   have been considered for this test, since they are the
   harmonics which occur frequently in the real case. The results
   are showed in Tables V and VI for the voltage and current
   waveforms, respectively. The maximum percentage deviation
   obtained for the voltage waveform is equal to 0.0282% and
   equal to 0.096% for the current waveform. The values show a
   good accuracy of the harmonics detection algorithm to discern
   the harmonic content for each waveform.
   TABLE V
   FFT ALGORITHM TESTING (VOLTAGE WAVEFORM)
   Harmonic
   Order
   Frequency
   [Hz]
   Reference
   Value
   [V]
   Measured
   Value
   [V]
   Percentage
   Deviation
   %
   2 100 230 229.945 0.0239
   3 150 230 229.959 0.0178
   4 200 230 229.956 0.0191
   5 250 230 229.951 0.0213
   6 300 230 229.951 0.0213
   7 350 230 229.950 0.0217
   8 400 230 229.944 0.0243
   9 450 230 229.946 0.0234
   10 500 230 229.935 0.0282
   TABLE VI
   FFT ALGORITHM TESTING (CURRENT WAVEFORM)
   Harmonic
   Order
   Frequency
   [Hz]
   Reference
   Value
   [A]
   Measured
   Value
   [A]
   Percentage
   Deviation
   %
   2 100 5 4.9985 0.030
   3 150 5 4.9983 0.034
   4 200 5 4.9952 0.096
   5 250 5 4.9986 0.028
   6 300 5 4.9985 0.030
   7 350 5 4.9986 0.028
   8 400 5 4.9982 0.036
   9 450 5 4.9981 0.038
   10 500 5 4.9979 0.042
   C. Experimental Results
   An additional experimental analysis has been performed by
   considering a specific application case. The used test bench
   configuration is showed in Figure 6.
   Fig. 6. Overview of the second test bench configuration.
   The test equipment consists of an AC Power Source Pacific
   360-AMX with programmable controller, a Precision Power
   Analyzer Yokogawa WT1800, an electric motor used as load, a
   Hysteresis Dynamometer Magtrol HD-715-8NA with a
   Dynamometer Controller Magtrol DSP6001.
   The load has been supplied by applying a sinusoidal voltage
   of 225 Vrms amplitude and a frequency of 50 Hz generated by
   the power source. Voltage harmonic components until the
   seventh order have been added to the voltage signal in order to
   simulate non-sinusoidal operating conditions. Each harmonic
   has been generated with an amplitude equal to 30% of
   fundamental component amplitude. The voltage and current
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   waveforms are shown in the Figures 7 and 8, respectively.
   Fig. 7. Voltage waveform.
   Fig. 8. Current Waveform.
   The harmonic components of the voltage and current
   waveforms are depicted in Figure 9.
   Fig. 9. Harmonics components of the voltage and current signals.
   By analysing the previous figure, it is possible to observe
   the presence of harmonic components beyond the seventh
   order. That is the result of the distortion introduced by the
   load. The control panel displayed by the remote control station
   is reported in Figure 10. The panel shows all parameters
   measured by the smart power meter as reported in Table II.
   Each measured value has been put in comparison with the
   value provided by the power analyser, which has been
   considered as a reference for this experimentation.
   Fig. 10. Control Panel of the Remote Control Station.
   Table VII reports the results of the experimental
   comparison.
   TABLE VII
   EXPERIMENTAL VALIDATION RESULTS OF THE SMART POWER METER
   Parameter Reference Value Measured Value
   Percentage
   Deviation
   %
   PC [kW/h] – n.a. –
   QC [var/h] – n.a. –
   P1 [W] 530.600 527.682 0.5499
   PH [W] 214.800 200.293 6.7537
   P [W] 745.400 727.975 2.3376
   Q1 [var] 860.600* 555.182* 35.4889*
   S [VA] 1139.700 1104.911 3.0524
   S1 [VA] 778.4200 765.947 1.6023
   SH [VA] 347.8185 338.423 2.7012
   DI [var] 507.8204 492.139 3.0879
   DV [var] 533.1599 526.710 1.2097
   SN [VA] 832.4532 796.338 4.3384
   N [var] 860.600 831.193 3.4170
   PF 0.6556 0.659 0.5186
   HP 1.06 1.040 1.8867
   PF1 0.6816 0.6890 1.0856
   THDV 56.661 % 68.766 % 21.3639
   THDI 56.456 % 64.252 % 13.8089
   k – 1.2045 –
   f [Hz] 50.0020 50.0014 0.0011
   Vrms [V] 223.0200 222.331 0.3089
   Vpk [V] 417.6800 415.8700 0.4333
   V1 [V] 183.7200 183.1960 0.2852
   VH [V] 125.8343 125.9770 0.1134
   Vrms,i [V]
   with 1<i<8
   1) 183.72
   2) 52.78
   3) 50.77
   4) 50.78
   5) 50.42
   6) 51.36
   1) 183.1960
   2) 52.8025
   3) 50.6235
   4) 50.6606
   5) 50.3228
   6) 51.1990
   1) 0.2852
   2) 0.0426
   3) 0.2885
   4) 0.2351
   5) 0.1927
   6) 0.3134
   1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
   This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
   Sensors Journal
   7) 52.08
   8) 2.34
   7) 51.9397
   8) 2.32733
   7) 0.2693
   8) 0.5414
   Irms [A] 5.110 4.970 2.7397
   Ipk [A] 6.235 6.025 3.3680
   I1 [A] 4.237 4.1810 1.3216
   IH [A] 2.7641 2.686 2.8255
   Irms,i [A]
   with 1<i<8
   1) 4.237
   2) 1.615
   3) 1.078
   4) 1.176
   5) 0.943
   6) 0.941
   7) 0.844
   8) 0.495
   1) 4.1810
   2) 1.5485
   3) 1.0136
   4) 1.1019
   5) 0.8724
   6) 0.8688
   7) 0.7722
   8) 0.4260
   1) 1.3216
   2) 4.1176
   3) 5.9740
   4) 6.3010
   5) 7.4867
   6) 7.6726
   7) 8.5071
   8) 13.9393

• value not reported
• value obtained by using a different parameter definition
  The analysis of the results does not allow us to make a
  complete comparison between the two measurement systems,
  since the projected smart power meter integrates a major
  number of metrics. In addition the two measurement systems
  use a different definition of the reactive power, as a
  consequence the parameter Q1 is not comparable. Anyway, by
  considering the only parameters in common, a significant
  percentage deviation has been obtained for the Harmonic
  Active Power parameter. It is due to an expected systematic
  error caused by the voltamperometric connection of the two
  instruments. This is cause of error on the measurement of the
  harmonic current components. It is useful to observe that the
  results of the FFT algorithm testing reported in the Section
  III.B have shown a maximum percentage deviation equal to
  0.096%. This test has been performed by using a different test
  bench configuration, which has avoided the above described
  error. Consequently, the percentage deviations of the harmonic
  currents in Table VII are attributable to the voltamperometric
  method error.
  IV. TOWARDS THE GRID OF THE FUTURE: THE IOT VISION
  It is expected that the electric grid of future will be a
  complex flow of energy and information shared among several
  nodes. New sensing systems and services will be necessary to
  manage a so complex distributed system, [29]. Even protocols
  and standards will have an important role. The power grid can
  be compared to the internet network. Each node of the grid
  can be equipped with a power meter. The resulting sensing
  grid can be considered as a complex system geographically
  distributed. The power meter network will have the task to
  monitor in real time the energy flow of a large number of
  nodes. Such amount of information is used to make timely
  decisions when critical events occur. By gaining experience
  with the evolution of the internet network in the last years,
  such described scenario offers a large number of research
  topics. The internet protocol is universal and has been widely
  validated in the years. As a consequence, the real time control
  of the power grid could take advantage of internet so to use
  power meter data for taking timely decisions and configuring
  itself based on needs. Internet of Things (IoT) concept can
  help to share information and data in the grid so to improve
  efficiency, reliability and security of the electric system [6].
  IoT aims to add value by connecting objects to internet
  network. So IoT implies that power meters can be able to
  utilize internet to communicate data about their condition,
  position, or other measurement parameters. Therefore smart
  power meters will take advantage of this by using the internet
  network so making available data and information on the
  power grid with a new approach in respect to the past. Power
  meters can even monitor constantly the power line
  temperature so to estimate the carrying capacity of the line.
  Such information can be used to manage dynamically the
  power flow amount by using suitable dynamic line rating
  algorithms. Therefore, IoT can allow the smart power grid to
  increase its features and services. In this new scenario, IoT
  promises to turn a power meter into an object which provides
  information about the grid and its environment. This will
  create in the next future a new way to differentiate the services
  of the electric network and a new source of value. This IoT
  vision will improve the efficiency of the power grid and will
  provide new opportunities. The user will be able to define and
  change his/her power requirements, and the smart power grid
  will configure itself to assure the required power quality
  specifications.
  The main issue to be faced in the next future concerns the
  large number of power meters and sensors to be managed and
  maintained. Typically, an electric grid consists of 10,000 or
  even 100,000 nodes. Consequently, the scalability issue
  should be resolved before considering a power grid to be
  smart. The collaborative signal processing among nodes is
  another important aspect. Even the querying ability is relevant
  to the electric grid of the future. A node or a power meter must
  be able to query an individual node or group of nodes for
  getting information concerning a specific microgrid. All these
  aspects have been formerly faced by the internet network. So
  the main features and characteristics today requested to the
  power grid are the same ones owned by the present internet
  network. Therefore, by a IoT vision, the power grid will be
  able to perform its tasks: demand management, disturbances
  detection, energy flow amount management, isolation of
  specific microgrids, management of the energy storage,
  transport of energy from any node it is produced to nodes
  where it is lacking by using innovative routing algorithms. In
  the view of the future power grid, smart power meters could
  resolve all the current difficulties concerning the sensing and
  measurement issues.
  The projected smart power meter takes advantage of IoT
  concept. In fact, the current power meters are able to share
  information only with the control centre of the electric
  company just to bill the user power consumption. With a
  different approach, the proposed power meter shares
  information on consumption and power quality with the
  internet network to improve the management of the power
  grid. The power meter cannot be anymore considered as a
  simple instrument billing consumption. By such a IoT vision,
  1558-1748 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
  Sensors Journal
  information on each node of the grid is shared with the whole
  grid to increase its efficiency. Measurement data is used to bill
  consumption but at the same time to configure the power
  network based on power demands and on the power quality
  requirements defined by the user. In this view, the described
  power meter implements the IoT concept to improve the
  features and the services offered by the future power grid.
  However, several further issues remain still unsolved. In
  detail, the new challenges include:
   the standardization of communication protocols;
   the improvement of the security standards;
   integration of the sensing systems into existing systems
  to assure interoperability;
   harmonisation of equipment standards to allow plug-andplay
  and interface;
   new power flow routing algorithms and innovative
  routing criteria;
   management of big data coming from thousands of
  sensing systems distributed through the grid;
   redefinition of the metrics used for billing consumptions;
   modernisation of current electric network architecture.
  V. ACKNOWLEDGMENT
  The described research activity is part of the Project
  “Laboratorio RENEW-MEL” which has been funded, through
  the PON Program, by the Italian Ministry of Education,
  Universities and Research (MIUR) and by the European
  Commission (contract no. PON03PE_000122 &
  PON03PE_00012).
  VI. CONCLUSIONS
  The paper proposes an innovative smart power meter to
  monitor the energy flow in smart power grids. Expecting the
  power grid of the future, the authors take stock of the situation
  concerning the state of the current power grid. Weaknesses
  and strengths are discussed so to highlight the role of the
  advanced sensing systems in the power grid management.
  Other issues remain still unsolved before that the power grid
  can be considered really smart. IoT offers new interesting
  answers in order to face and resolve such issues. So this paper
  intends to solicit the debate on the role of IoT in the
  development of the power grid of the next future. The current
  power network needs to be updated in order to adapt itself to
  the new requirements of the current power demand. As a
  consequence, there is still much to be done especially in the
  matter of defining the most suitable architecture of the electric
  network. By means of the proposed IoT vision, the projected
  smart power meter uses the internet network to improve the
  efficiency and features of the power grid.
  The paper aims to propose a possible solution to the issues
  concerning the sensing and measurement aspects by
  discussing the potentialities of the developed smart power
  meter. The project and development of the proposed power
  meter have been described in the paper. Hardware architecture
  and software algorithms have been outlined. In detail, the
  embedded metrics offer additional information on the energy
  flowing in the grid nodes in respect to the other commercial
  power meters. The main strengths concern:
   the possibility to control and program remotely the
  meter;
   data are processed in real time to support the decision
  making tasks;
   the remote control station is able to manage
  simultaneously several smart meters;
   the embedded metrics offer new potential criteria to
  manage efficiently the energy routing and sharing among
  several nodes by considering even the power quality
  features.
  Tests and experimentation on an application case have
  allowed to validate the developed prototype. Two test benches
  have been used for testing the hardware and software
  operations. The additional experimental results have proved
  the compliance of the power meter with the IEC requirements.
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  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2017.2760014, IEEE
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  Rosario Morello (M’03) was born in Reggio Calabria,
  Italy, in 1978. He received the M.Sc. Degree (cum
  laude) in Electronic Engineering and the Ph.D. Degree
  in Electrical and Automation Engineering from the
  University “Mediterranea” of Reggio Calabria, Italy, in
  2002 and 2006, respectively. Since 2005, he has been
  Postdoctoral Researcher of Electrical and Electronic
  Measurements at the Department of Information
  Engineering, Infrastructure and Sustainable Energy of
  the same University. At the present he is an Assistant Professor. His main
  research interests include the design and characterization of distributed and
  intelligent measurement systems, wireless sensor network, environmental
  monitoring, decision-making problems and measurement uncertainty, process
  quality assurance, instrumentation reliability and calibration, energy, smart
  grids, battery testing, biomedical applications and statistical signal processing,
  non-invasive systems, biotechnologies and measurement, instrumentation and
  methodologies related to Healthcare. Dr. Morello is a member of the Italian
  Group of Electrical, Electronic Measurements (GMEE) and IEEE.
  Claudio De Capua (M’99) received the M.S. and the
  Ph.D. degrees in Electrical Engineering from the
  University of Naples “Federico II”, Naples, Italy. Since
  2012, he is Full Professor of Electrical and Electronic
  Measurements at the Department of Information
  Engineering, Infrastructure and Sustainable Energy,
  University “Mediterranea” of Reggio Calabria. His
  current research includes the design, realization and
  metrological performance improvement of the automatic
  measurement systems; web sensors and sensor data
  fusion; biomedical instrumentation; techniques for remote didactic laboratory;
  measurement uncertainty analysis; problems of electromagnetic compatibility
  in measurements.
  Prof. De Capua is member of the Italian Group of Electrical and Electronic
  Measurements (GMEE).
  Gaetano Fulco was born in Reggio Calabria, Italy, in

1. He received from the University “Mediterranea”
   of Reggio Calabria, Italy, a bachelor’s degree in
   Electronic Engineering, in 2013, and the M.Sc. Degree
   (cum laude) in Electronic Engineering, in 2015. At the
   present he is Ph.D. Student of Information Engineering
   Course at the University “Mediterranea” of Reggio
   Calabria, Italy. His main research interests are
   development, realization and test of smart meter, power
   quality, energy, smart grids.
   Subhas Mukhopadhyay (M’97, SM’02, F’11) holds a
   B.E.E. (gold medalist), M.E.E., Ph.D. (India) and
   Doctor of Engineering (Japan). He has over 26 years of
   teaching, industrial and research experience.
   Currently he is working as a Professor of
   Mechanical/Electronics Engineering, Macquarie
   University, Australia and is Discipline Leader of the
   Mechatronics Engineering Degree Programme. Before
   joining Macquarie he worked as Professor of Sensing
   Technology, Massey University, New Zealand. His
   fields of interest include Smart Sensors and sensing technology,
   instrumentation techniques, wireless sensors and network, numerical field
   calculation, electromagnetics etc. He has supervised over 40 postgraduate
   students and over 100 Honours students. He has examined over 50
   postgraduate theses.
   He has published over 400 papers in different international journals and
   conference proceedings, written six books and thirty book chapters and edited
   fifteen conference proceedings. He has also edited twenty-eight books with
   Springer-Verlag and seveneen journal special issues. He has organized over
   20 international conferences as either General Chairs/co-chairs or Technical
   Programme Chair. He has delivered 298 presentations including keynote,
   invited, tutorial and special lectures.
   He is a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of
   IEEE Sensors journal, and an associate editor of IEEE Transactions on
   Instrumentation and Measurements. He is a Distinguished Lecturer of the
   IEEE Sensors Council from 2017 to 2019. He chairs the IEEE IMS Technical
   Committee 18 on Environmental Measurements.
   More details can be available at
   http://web.science.mq.edu.au/directory/listing/person.htm?id=smukhopa

1. He received from the University “Mediterranea”
   of Reggio Calabria, Italy, a bachelor’s degree in
   Electronic Engineering, in 2013, and the M.Sc. Degree
   (cum laude) in Electronic Engineering, in 2015. At the
   present he is Ph.D. Student of Information Engineering
   Course at the University “Mediterranea” of Reggio
   Calabria, Italy. His main research interests are
   development, realization and test of smart meter, power
   quality, energy, smart grids.
   Subhas Mukhopadhyay (M’97, SM’02, F’11) holds a
   B.E.E. (gold medalist), M.E.E., Ph.D. (India) and
   Doctor of Engineering (Japan). He has over 26 years of
   teaching, industrial and research experience.
   Currently he is working as a Professor of
   Mechanical/Electronics Engineering, Macquarie
   University, Australia and is Discipline Leader of the
   Mechatronics Engineering Degree Programme. Before
   joining Macquarie he worked as Professor of Sensing
   Technology, Massey University, New Zealand. His
   fields of interest include Smart Sensors and sensing technology,
   instrumentation techniques, wireless sensors and network, numerical field
   calculation, electromagnetics etc. He has supervised over 40 postgraduate
   students and over 100 Honours students. He has examined over 50
   postgraduate theses.
   He has published over 400 papers in different international journals and
   conference proceedings, written six books and thirty book chapters and edited
   fifteen conference proceedings. He has also edited twenty-eight books with
   Springer-Verlag and seveneen journal special issues. He has organized over
   20 international conferences as either General Chairs/co-chairs or Technical
   Programme Chair. He has delivered 298 presentations including keynote,
   invited, tutorial and special lectures.
   He is a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of
   IEEE Sensors journal, and an associate editor of IEEE Transactions on
   Instrumentation and Measurements. He is a Distinguished Lecturer of the
   IEEE Sensors Council from 2017 to 2019. He chairs the IEEE IMS Technical
   Committee 18 on Environmental Measurements.
   More details can be available at
   http://web.science.mq.edu.au/directory/listing/person.htm?id=smukhopa