Power Meter to Monitor Energy 2020 2019

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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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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
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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
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 15.4x10.3x5 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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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.
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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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Sensors Journal
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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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,
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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
Sensors Journal
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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
1990. 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