Content uploaded by Heba Moussa
Author content
All content in this area was uploaded by Heba Moussa on Jan 08, 2015
Content may be subject to copyright.
1 23
Energy Systems
Optimization, Modeling, Simulation,
and Economic Aspects
ISSN 1868-3967
Energy Syst
DOI 10.1007/s12667-011-0047-4
Wide area measurement system for smart
grid applications involving hybrid energy
sources
Mahmoud M.Amin, Heba B.Moussa &
Osama A.Mohammed
1 23
Your article is protected by copyright and
all rights are held exclusively by Springer-
Verlag. This e-offprint is for personal use only
and shall not be self-archived in electronic
repositories. If you wish to self-archive your
work, please use the accepted author’s
version for posting to your own website or
your institution’s repository. You may further
deposit the accepted author’s version on a
funder’s repository at a funder’s request,
provided it is not made publicly available until
12 months after publication.
Energy Syst
DOI 10.1007/s12667-011-0047-4
ORIGINAL PAPER
Wide area measurement system for smart grid
applications involving hybrid energy sources
Mahmoud M. Amin ·Heba B. Moussa ·
Osama A. Mohammed
Received: 31 August 2011 / Accepted: 29 December 2011
© Springer-Verlag 2012
Abstract This paper presents a model and experimental verification for a complete
scenario of a proposed wide area measurement system (WAMS) based on synchro-
nized phasor measurement units (PMUs). The proposed system is feasible for hybrid
smart ac/dc power networks; such as grid-connected PV-power plants. The purpose
is to increase the overall system reliability for all power stages via significant de-
pendence on WAMS as distributed intelligence agents with improved monitoring,
protection, and control capabilities of the power networks. The developed system
is simulated in the Matlab/Simulink environment. The system was tested under two
different cases; normal operation and fault state. Furthermore, the proposed WAMS
was experimentally validated with results obtained from a reduced scale setup which
built and tested in the laboratory based on the Hardware-in-the-loop concept. It was
verified that the power system status can be easily monitored and controlled in real
time by using the measured bus data in real time. This improves the overall sys-
tem reliability and avoids cascaded blackout during fault occurrence. The simulation
and experimental results confirm the validity of the proposed WAMS technology for
smart grid applications.
Keywords Hybrid energy networks ·Real time monitoring ·Synchrophasors ·
Smart grid ·Wide area measurement system
1 Introduction
WAMS became one of the most recent technologies that are quite popular for upgrad-
ing the traditional electric grid. This upgrade has become a necessity to modernize
M.M. Amin ·H.B. Moussa ·O.A. Mohammed ()
Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida
International University, Miami, FL 33174, USA
e-mail: mohammed@fiu.edu
Author's personal copy
M.M. Amin et al.
the electricity delivery system following the occurrence of major blackouts in power
systems around the world. Although many algorithms were developed in the past
for online monitoring of transmission systems and distribution systems including the
estimation of operating frequency, the required level of details for real-time online
assessment is yet to be achieved [1–3]. In the early 1980s, synchronized phasor mea-
surement units (PMUs) were first introduced and since have become the ultimate data
acquisition technology, which will be used in wide area measurement systems with
many applications currently under development around the world [4].
Synchronized phasor measurements, or synchrophasors, provide a method for
comparing the phase and sequence values from anywhere on a power system which
can be integrated with phasor data concentrators (PDCs) at substations in a hierarchi-
cal structure [5,6].
The precise and accurate data that can be acquired from PMUs in a WAMS built
on the power system confirms the need for a robust, reliable communication network
with secure and high speed capabilities for online data access. As smart grid applica-
tions, utility power grid analysts can benefit from WAMS in the validation of system
models and components which has been one of the first uses of synchrophasors. This
validation occurs through the use of inter-area communication or simultaneous data
collection of conditions at a single point in time [7].
In addition, real-time system monitoring (RTSM) for stability assessment and state
measurement is another application where phasor measurements at nodes help the
system operators to gain a dynamic view of the power system and initiate the nec-
essary measures at the proper time. This is done in accordance with the latest IEEE
standard (C37.118-2005) developed to standardize data transmission format and sam-
pling rates of PMUs. This can significantly be supported by the stability assessment
algorithms, which are designed to take advantage of the phasor measurement infor-
mation [8].
In the past, post-event analysis was an application of synchrophasors (PMUs)
without wide-area communication where data was archived locally. However, it was
not a useful tool for online (dynamic) control. Recently, real-time control (RTC) of
WAMS became a powerful control and analysis tool which provides a new view of
power systems [9]. This is achieved by improving the communication network ca-
pabilities while maintaining PMUs as a main component in the network. The use of
PMUs for RTC will increase the control accuracy since the data are measured online.
Also, it will enhance the power system stability and delivery automation capabili-
ties after challenges of new data communication requirements across the system are
firstly resolved [10–12]. The depth of observability is another advantage for PMUs.
It means that the ability of measuring the bus voltage phasor directly or calculating
it using the PMU voltage and line current of the nearest connected bus. This is the
cost effective part since it reduces the number of data acquisition instruments and
tools needed across the network as the measuring line currents can extend the voltage
measurements to buses where no PMU is installed. In Fig. 1, a simple generalization
of the PMU block diagram is shown. This serves as the basis of simulating such unit
[13,14].
In this paper, the proposed WAMS network was studied and discussed to utilize
this type of data collection to check the health state of hybrid power system networks.
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 1 The block diagram of
PMU
Fig. 2 Single-line diagram of the proposed hybrid ac/dc network
This is achieved through building WAMS infrastructure communication network. The
performance of the overall proposed system is investigated through a Matlab simu-
lation of the PMUs in a small system scenario of a WAMS network based on a 6-
bus power utility network along with the associated communication network. Such
a system is shown in Fig. 2. Moreover, the proposed WAMS power network was
experimentally built in the laboratory in order to dynamically interact (online) with
the PMUs readings. The PMU functions were programmed in the Matlab/Simulink
environment based on the Hardware-in-The loop (HIL) concept.
2 System description
The principle of a WAMS network based on synchrophasors data with the aid of a
broadband communication network is described in this section. The system consists
mainly of two layers as shown in Fig. 3. First, the electrical power system layer,
which consists of line-line 208 V generating station with 50-kW output rated power,
a PV-power renewable source of 24-kW rated power, 3-power transformers (T1, T2,
and T3) linking the different parts of the electrical system, 2-short transmission lines
(T.L.1 and T.L.2), 6-buses (B1-B6), 4-circuit breakers (CB1, CB2, CB3, and CB4)
Author's personal copy
M.M. Amin et al.
Fig. 3 Schematic diagram of
the proposed WAMS involving
the PV sustainable power plant
and 2-loads each of 30-kW. Secondly, the WAMS layer which consists of 3-PMUs, lo-
cated at generation and load buses, and one phasor data concentrator (1-PDC) which
collects the data received from the remote PMUs. The PDC performs protocol con-
version from IEEE C37.118 to a number of common power system protocols suitable
for analysis and control actions in the control center.
3 System modeling
A small size WAMS platform was designed and built on a 208-V, 60-Hz test-bed net-
work that was modeled as shown in Fig. 4. This proposed communication network
was implemented in the lab by locating one PMU at each generation or load bus.
All PMUs will send their measured voltage and current measurements to the PDC to
monitor the system status and take the proper control action if required. Furthermore,
the depth of observability can be utilized here to significantly reduce the system costs
through the reduction of the number of PMUs. This is, since one PMU can read the
voltage and current measurements at its bus location with other bus measurements,
located in same area, can be calculated. However, this algorithm has less accuracy
than installing one PMU at each bus. A simulation of the PMU units was done with
using the sampling clock pulses to achieve synchronization between the synchropha-
sors which are phase locked to the signal provided by the global positioning system
(GPS) receiver built inside or outside the PMU. The GPS module is simulated as a
clock enabling pulses sent to all PMUs at the same time so that all of them will have
the same time tags. Accordingly, the same reference wave can be used at all different
PMU locations through the WAMS.
3.1 PMU network analysis
The PMU must separate the fundamental frequency component from other harmonics
and find its phasor representation. The Discrete Fourier transform (DFT) method is
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 4 Simulink model for a scenario of the proposed PMUs communication network layer on a hybrid ac/dc power system smart grid
Author's personal copy
M.M. Amin et al.
then applied on the sampled input signal to compute its phasor. Also, it should com-
pensate for the phase delay introduced to the signal by the antialiasing filters present
in the input to the PMU. For xk{k=0,1,...,N}where Nis the number of samples
taken over one period, the phasor representation is given by:
X=√2
N
N−1
K=0
xk−jk2π
N(1)
Since the components for the real input signals at a given frequency appears in DFT
and are complex conjugates of each other, they can be combined giving the factor of
2 in front of the summation in (1). The rms value of the fundamental frequency is
obtained by dividing the peak value by √2. In steady state, all generators have the
same frequency (fss Hz). Accordingly, the voltage at all points in the power system
will have the same frequency fss, which is measured by the PMU through to the
following equation:
ei(t) =Eicos(2πfsst+δi)(2)
In case of frequency disturbance, the power system generators will run at different
frequencies and each generator may be considered as a voltage source with different
values of Ei,fss and δias slow time varying functions. It can be assumed that for
a small time interval (t=ncycles)the Ei,fss and δiare constants. As a result,
the power system can be represented as a circuit with several voltage sources of dif-
ferent frequencies. The actual voltage at any bus iusing superposition becomes as
follows [15]:
vact
i=vi,1(t) +···+vi,NG(t ) =
NG
j=1
vi,j (t) =
NG
j=1
Vi,j cos2πfNGt+θi,NG(3)
Where Vi,j represents the voltage at bus idue to generator j. This indicates that this
bus will have a multi frequency voltage that is close to 60-Hz. In dynamic power
system studies, this can be estimated as:
vest
i(t) =Vest
icos(2πf est
it+θest
i)(4)
In (4), the frequency fest
irepresents the frequency of the system at this location. It
equals the frequency measured by the PMU at that bus. This is done by assuming that
vest
i(t) =vact
iand having access to the sampled data of vact
i;sofest
ican be easily
evaluated [16].
3.2 Communication channel analysis
The IEEE PC37.118 16 protocol format is usually used in PMUs communication.
This standard format includes the frequency and the rate of change of frequency in
each message. Once the frequency and size of the messages are known, the following
equation can be used to determine the bit-per-second (bps) rate at which the data can
be sent [17]:
bps =1.2(nn ·L·f) (5)
Author's personal copy
Wide area measurement system for smart grid applications
Where nn is the message size (bytes), Lis the frame length (1 start bit, 8 data bits, 2
stop bits, 1 parity =12), fis the messages frequency, 1.2 is a factor to account for
system delays (based on typical experience). The PMU data can be sent at various
rates, depending on the application requirements. Most PMUs have Ethernet cards
that use the IEEE PC37.118 16 protocol for data exchange on the physical layer.
The physical layer of the Ethernet can be unshielded twisted pair (UTP) or fiber
optical network that support data speeds up to 100 Mbit/s in each data stream. The
communications link connecting the substations could be a fiber-optic multiplexer.
The relays communicate with the multiplexer via EIA-232 asynchronous interface.
3.3 The communication system constraints
Communication networks suitable for smart grid applications—even in a loose
sense—need to provide distinct qualities and services which are closely related to
application requirements and distinguish them from other networks [18]:
(1) High reliability and availability are standard requirements for nearly every com-
munication system. Nodes should be reachable under all circumstances. While
this is normally not a problem in a wired network, it may be challenging for wire-
less or power line infrastructures because communication channels can change
during operation. In the particular case of power line systems, such a change may
be introduced by distribution network management which balances the power
consumption load on the power grid, particularly on the medium-voltage (MV)
level. Switching actions are initiated via various supervisory control and data ac-
quisition (SCADA) and controlling systems (or even manually) using specific
communication protocols that may not be modified.
(2) High coverage and distances. Evidently, the nodes to be connected by the com-
munication network are distributed in a wide area. Network concepts based on
telecommunication systems or power lines have the potential to fulfill this re-
quirement.
(3) Large number of communication nodes. If we assume that only one energy meter
per customer is connected, a primary station can supply up to tens of thousands of
nodes, particularly in areas of large apartment block concentration. Even though
the commands and data packets are usually short, total data volume to be trans-
ferred in the network is substantial, and communication overheads can become
an issue.
(4) Appropriate communication delay and system responsiveness. The Quality-of-
Service (QoS) needs to take care of different data classes such as metering,
control, or alarm data. Even if the predominant communication relationship is
client/server (i.e., an application server polls the meter data or issues control
commands), it may be necessary to foresee something like a fast event channel
to transmit.
(5) Communication security. Data related to smart grid applications are considered
critical, in particular, when they are relevant for billing purposes or grid control.
Secure communication is therefore important. Surveys among utilities showed
that integrity (no malicious modification) and authenticity (origin and access
Author's personal copy
M.M. Amin et al.
Tab l e 1 Simulation parameters Parameter Specification
Rated voltage and frequency 208 Vrms, 60 Hz
Generation station rated power 50 kW
PV-plant rated power 24 kW
Total load rated power 60 kW
No. of loads 2 (at B2, B3)
No. of transformers (Y/Y) 3 (208 V/11 kV)
No. of transmission lines 2 (10 km each)
No. of buses 6
PMU message report rate 60 msg/sec
Tab l e 2 Experimental
parameters Parameter Specification
Grid voltage and frequency 208 Vrms, 60 Hz
Programmable power supply 6 kW (4:1)
Total load rated power 6 kW (10:1)
Inverter power rating 10 kW
Inverter L-filter 1 mH
Inverter C-filter 40 µF
Switching frequency 20 kHz
rights are guaranteed) are the most important security goals for energy trans-
mission and distribution networks, whereas the confidentiality aspect is not con-
sidered to be an issue.
(6) Ease of deployment and maintenance. For any distributed communication sys-
tem, mechanisms must be foreseen which facilitate not only the initial installa-
tion but particularly the maintenance of the infrastructure during the operation.
Features like error mode analysis and error localization, easy update of firm- and
software and remote configuration are essential.
4 Simulation and experimental results
A Matlab Simulink model was constructed to investigate the performance of the pro-
posed WAMS for smart grid applications. The model was carried out according to
the operation described in Sect. 2. The simulation parameters are shown in Table 1.
Furthermore, a reduced scale experimental setup of 6-kW (4:1 scale) programmable
power supply was utilized as a PV-characteristics emulator connected to AC-grid
network. The setup was designed and implemented in the laboratory to verify the
obtained simulation results. The experimental verification is based on HIL concept
utilizing real-time DSP controller. The dSPACE1104 R&D TMS320F240 DSP con-
troller board was used for interfacing the simulated PMUs with the hardware circuit
to achieve fast real time response during the transient and steady state operations. An
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 5 The HIL WAMS hardware implementation: (a) The schematic diagram, and (b) The experimental setup
Author's personal copy
M.M. Amin et al.
Fig. 5 (Continued)
Fig. 6 PMU1 readings under
normal operation condition
(simulation)
LEM (LA 25-NP) current and voltage (LV 25-P) transducers were used for measuring
the actual power network bus-signals. The required measuring and interface circuits
were designed and built. All the measurements from across the scaled model are time
tagged using GPS synchronization clock. These measurements are then transmitted
to the simulated PMUs that communicate with the setup. The measurements and the
signals received from the power network communicating with the scaled model are
transmitted to a local host through an Ethernet network. The interface software and
the simulation algorithms are located on the host computer. This information is then
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 7 PMU1 readings under normal operation condition (experimental): (a) The line voltage (100 V/div,
30 ms), and (b) The voltage amplitude (100 V/div, 30 ms) and phase difference (180 degree/div, 30 ms)
Fig. 8 PMU2 readings under
normal operation condition
(simulation)
used as inputs to the state estimator and estimates the state of the system considering
all the imbalances, asymmetries, faults, and instrumentation errors. The results can
then be compared with the actual measurements from the system.
In this section, the simulation and the experimental results of the proposed WAMS
are given. The experimental ratings and parameters are listed in Table 2. Figure 5
shows the descriptive schematic diagram and the overall experimental setup for the
proposed reduced scale HIL WAMS network. To estimate the PMUs characteristics,
two types of tests were carried out. The first is a normal operation test without any
fault or unbalanced conditions in the network. The second test is a fault test which
was used as an extreme case to show the behavior of the network under this condition.
Author's personal copy
M.M. Amin et al.
Fig. 9 PMU2 readings under normal operation condition (experimental): (a) The line voltage (100 V/div,
30 ms), and (b) The voltage amplitude (100 V/div, 30 ms) and phase difference (180 degree/div, 30 ms)
Fig. 10 PMU3 readings under
normal operation condition
(simulation)
4.1 Normal operation test
In this test, the system was observed under normal operation condition. The 30-kW
load on bus 2 was supplied locally from the PV-power plant and the other 30-kW
load on bus 3 was supplied by the generating station sharing the PV-energy. In this
case, all the PMUs show stable readings within the references. From Figs. 6–11,the
three PMUs read accurate information about line voltage vab, the voltage amplitude
of about 296-V starting from 0 sec for buses 1 and 2. At bus 3, zero voltage amplitude
for the first 0.1 sec; since load bus was not connected to the network. After 0.1 sec,
breaker 4(CB4) will connect load bus 3 to the network, the same average voltage
amplitude level appears at other buses with a phase difference of 2.65 degrees under
stable operation for all readings. The exported data by the simulated PMUs to the
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 11 PMU3 readings under normal operating condition (experimental): (a) The line voltage (100 V/div,
30 ms), and (b) The voltage amplitude (100 V/div, 30 ms) and phase difference (180 degree/div, 30 ms)
Fig. 12 Hybrid ac/dc power network during fault occurrence located at bus 3
Fig. 13 Hybrid ac/dc power network during fault occurrence located at bus 2
control center show that the developed WAMS succeeded to accurately reflect the
system status in real-time (online). For a complete verification of its performance,
another test with a fault occurrence is required.
Author's personal copy
M.M. Amin et al.
Fig. 14 PMUs readings during fault occurrence located at bus 3 (simulation)
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 15 PMUs readings during fault occurrence located at bus 3 (experimental): (a)PMU1,(b)PMU2,
and (c)PMU3
4.2 Fault operation test
In this test, a three phase to ground short circuit fault occurred at bus 3 then was
repeated for bus 2. Figures 12 and 13 show the single-line diagram for the hybrid
ac/dc power network during fault occurrence at B3 and B2, respectively. Figures 14
and 15 show the readings for all PMUs at the 3-buses. The whole system shows
normal operation for 0.2 sec while bus 3 was loaded after 0.1 sec. The fault has
occurred after 0.2 sec and it is cleared after 0.05 sec later. PMUs 1 and 2 read larger
phase differences (10.8 and 11 degrees, respectively) than in the normal mode (2.16
and 2.18 degrees, respectively). Accordingly, the voltage amplitude dropped by 40 V
which means that the fault is not located on those buses area. On the other hand,
PMU 3 has extremely large phase difference change (54 degrees) associated with a
large drop in the voltage amplitude as a result of the fault that occurrence in this area.
Consequently, the control center must send a control signal to the relay to release the
Author's personal copy
M.M. Amin et al.
Fig. 16 PMUs readings during fault occurrence located at bus 2 (simulation)
Author's personal copy
Wide area measurement system for smart grid applications
Fig. 17 PMUs readings during fault occurrence located at bus 2 (experimental): (a)PMU1,(b)PMU2,
and (c)PMU3
circuit breaker at that bus upon receiving these data in real time from the PMUs to
protect the other generation stations which are the most valuable part in the power
network. Protecting against such damage prevents cascaded turnoff of stations which
may result in major blackouts in the power system [19]. Furthermore, it helps analysts
to determine the type of fault that has occurred using the data transmitted from PMUs.
Additionally, the fault test is repeated for bus 2 (PV-plant area) to confirm the
validity of PMU readings in showing the behavior for the system health status. Fig-
ures 16 and 17 show the system response while the fault occurred at bus 2. We can
notice that PMU2 observed the fault status at B2 while PMU1 and PMU3 indicate the
fact that the fault is located inside the network but neither at B1 nor B3. This test can
be utilized for studying the depth of observability for each PMU which will optimize
the number of PMUs inside the WAMS network. Also, it leads to better economic
operation and higher system reliability [20].
Author's personal copy
M.M. Amin et al.
5 Conclusion
A performance analysis for a PMU based WAMS network was presented. The devel-
oped system was tested under two different possible conditions. The simulated PMUs
show the real values of a maximum phase difference of 2.18 degrees and normal av-
erage amplitude reading showing the system stability. In this case, no action is to be
taken from the control center during dynamic system monitoring.
During fault state, the PMUs data shows that the system has an unstable part with
about 55 degrees phase difference. Additionally, a large voltage drop was observed
in the area of the fault occurrence. This area was isolated via dynamic control sig-
nals before spreading to other parts resulting in catastrophic failure in some parts of
the power system or blackouts. Furthermore, the fault test was repeated at different
locations to study the behavior of each PMU. The Depth of observability was iden-
tified through different fault locations; one PMU can give the status indications for
each area. PMU2 was able to observe B2 locally and give indication for fault located
at B3.
Furthermore, a reduced scale HIL-based experimental verification system was test
as an experimental verification in this paper. The real-time code for the PMU func-
tion was automatically generated using embedded target in dSpace and real time
workshop facility (RTW) in the Matlab/simulink. All results obtained confirm the
effectiveness of the developed WAMS network for smart grid applications.
References
1. Girgis, A., Ham, F.M.: A new FFT-based digital frequency relay for load shedding. IEEE Trans. Power
Appar. Syst. PAS-101(2), 433–439 (1982)
2. Sachdev, M.S., Giray, M.M.: A least error squares technique for determining power system frequency.
IEEE Trans. Power Appar. Syst. PAS-104(2), 437–443 (1985)
3. Paul, S., Basu, S.K., Hazra, U.S.: Novel frequency factor method for power system frequency mea-
surement and estimation of load shedding. IEE Proc., Gener. Transm. Distrib. 134(6), 389–395
(1987)
4. Hrasnica, H., Haidine, A., Lehnert, R.: Broadband Powerline Communications Networks: Network
Design. Wiley, New York (2004)
5. Metke, A.R., Ekl, R.L.: Security technology for smart grid networks. IEEE Trans. Smart Grid 1(1),
99–107 (2010)
6. Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: de-
sign and implementation. In: IEEE PES 2009 Power Systems Conference and Exposition (PSCE’09),
Seattle, USA (2009)
7. Zhong, Z., Xu, C., Billian, B., Zhang, L., Tsai, S., Conners, R., Centeno, V., Liu, Y.: Power system
frequency monitoring network (FNET) implementation. IEEE Trans. Power Syst. 20(4), 1914–1921
(2005)
8. Liu, X., Liu, G., Sherwood, M., Venkatasubramanian, V.: Wide-area monitoring and control algo-
rithms for large power systems using synchrophasors. In: Bulk Power System Dynamics and Control,
(iREP)—VIII (iREP) Symposium, Aug. 2010, pp. 1–5 (2010)
9. De La Ree, J., Centeno, V., Thorp, J.S., Phadke, A.G.: Synchronized phasor measurement applications
in power systems. IEEE Trans. Smart Grid 1(1), 20–27 (2010)
10. Bose, A.: Smart transmission grid applications and their supporting infrastructure. IEEE Trans. Smart
Grid 1(1), 11–19 (2010)
11. Zhang, Y., Bose, A.: Design of wide area damping controllers for interarea oscillations. IEEE Trans.
Power Syst. 23(3), 1136–1143 (2008)
Author's personal copy
Wide area measurement system for smart grid applications
12. Phadke, A.G., Novosel, D., Horowitz, S.H.: Wide area measurement applications in functionally in-
tegrated power systems. Presented at the CIGRE B-5 Colloq., Madrid, Spain (2007)
13. Nuqui, R.F., Phadke, A.G.: Phasor measurement placement techniques for complete and incomplete
observability. IEEE Trans. Power Deliv. 20(4), 2381–2388 (2005)
14. Xu, B., Yoon, Y.J., Abur, A.: Optimal placement and utilization of phasor measurements for state
estimation. Presented at Power Sys. Comp. Conf., Belgium (2005)
15. Ning, J., Gao, W., Radman, G., Liu, J.: The application of the Groebner basis technique in power flow
study. Presented in North American Power Symposium (NAPS), pp. 1–7 (2009)
16. Malpani, R., Abbas, Z., Swarup, K.S.: High precision frequency estimation using Internet-based pha-
sor measurement unit. IEEE Trans. Power Syst. 25(2), 607–614 (2009)
17. Sood, V.K., Fischer, D., Eklund, M., Brown, T.: Developing a communication infrastructure for the
smart grid. Presented in Elect. Power & Energy Conf. (EPEC), Montreal, pp. 1–7 (2009)
18. Sauter, T., Lobashov, M.: End-to-end communication architecture for smart grids. IEEE Trans. Ind.
Electron. 58(4), 1218–1228 (2011)
19. Horowitz, S.H., Phadke, A.G.: Boosting immunity to blackouts. IEEE Power Energy Mag. 1(5), 47–
53 (2003)
20. Amin, M.M., Moussa, H.B., Mohammed, O.A.: Development of a wide area measurement system for
smart grid applications. This paper has been presented in the 18th World Congress of the International
Federation of Automatic Control (IFAC) Invited Session, Milano, Italy, Aug. 28–Sep. 2, 2011
Author's personal copy
