Smart Grid and Renewable Energy, 2013, 4, 1-7 Published Online September 2013 (
Copyright © 2013 SciRes. SGRE
Vulnerability Analysis of Wide Area Measurement System
in the Smart G ri d
Mohd Rihan1, Mukhtar Ahmad1, M. Salim Beg2
1Electrical Engineering Department, AMU, Aligarh, India; 2Electronics Engineering Department, AMU, Aligarh, India.
Received February 23rd, 2013; revised March 23rd, 2013; accepted April 8th, 2013
Copyright © 2013 Mohd Rihan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The smart grid will be a power grid more awareof its operating state and having the ability to s elf-heal. These f eatures
may be incorporated into the grid by implementing a phasor measurement units based wide area measurement system.
Such a system will help in better real time monitoring and control of the grid. However, the wide area measurement
system is subject to ch allenges with respect to its security. In this paper, a comprehensive analysis of security issues with a
wide area measurement system is presented and the research efforts required to be taken are identified. Moreover, the
effect of communication failure on a PMU installed system has been presented using integer linear programmin g.
Keywords: Wide Area Measurement Security; PMU; Smart Grid; Sm art Grid Security
1. Introduction
Interconnected power systems span large geographical
areas and virtually work as a single machine. The inter-
connected nature of the power system makes economical
use of the power generated and improves the overall re-
liability. However, this increase in reliability comes at a
price of increased risk associated with the possibility of a
minor disturbance propagating and results in complete
shutdown of the whole system [1]. Analysis has shown
that most of the large scale blackouts shared a common
thread and could have been prevented or at least these
effects could have been mitigated. Leaving aside the
natural causes of blackouts in a number of cases, the
blackout condition was precipitated because of some
controllable events. Some of these events are: lack of
reactive power support, ageing equipment, lack of coor-
dination in preventive measures, inadequate monitoring
and communication equipments, and human error in-
volved because of lacking automation [2]. With large
interconnected power systems, blackouts are inevitable.
The need is to develop a mechanism which should pre-
vent the conditions leading to blackouts and even if one
occurs the system should be able to recover very quickly.
It requires a power grid which is more aware of its oper-
ating state and should be able to “self-heal” in case of a
failure. Such a power grid was coined as “smart Grid”.
Smart Grid will be characterized by a two-way flow of
electricity and information to create an automated,
widely distributed energy delivery network. It incorpo-
rates into the grid, the benefits of distributed computing
and communications, to deliver real time information to
balance power supply and demand [3]. Smart grid is the
integration of power infrastructure with an information
infrastructure, combining the maturity of the electric grid
with the efficiency, connectivity, and cost gains brought
about by Information Technology. Although there is no
formal definition of a smart grid but based on its features
proposed in the literature, the smart grid may be consid-
ered as a power grid in which modern sensors, commu-
nication links, and computational power are used to im-
prove efficiency, stability, and flexibility of the system
Fast and accurate real time monitoring throughout the
grid is a fundamental requirement of the smart grid.
Phasor measurement units may be deployed in the power
grid to achieve this feature. Phasor Measurement Unit
(PMU) is a device which measures both magnitude and
angle of voltage and current. Moreover, these measure-
ments are synchronized via the Global Positioning Sys-
tem (GPS). These measurements are highly accurate and
sampled at a high rate sufficient to monitor the dynamic
performance of the power grid in a much improved way.
In fact, widely deployed PMUs in the power grid will
Vulnerability Analysis of Wide Area Measurement System in the Smart Grid
Copyright © 2013 SciRes. SGRE
completely change the way that state estimation has been
performed over the years. Due to all these factors, utili-
ties are working towards installing PMU based Wide
Area Measurement Systems (WAMS) in their networks.
Such a network will facilitate accurate measurement of
Synchrophasors at the network buses and their transmis-
sion to the control centre for contingency analysis and
initiation of control actions. However, one of the key is-
sues in the successful implementation and operation of
such a system is the availab ility of a reliable communica-
tion system. In addition, there are other security issues
with a WAMS like reliance on GPS and cyber security.
In this paper, a comprehensive analysis of security issues
with a WAMS is presented and the research efforts re-
quired to be taken are identified. Moreover, the effect of
communication failure on a PMU installed system has
been presented using integer linear programming. The
structure and benefits of a WAMS are described in Sec-
tion 2. Section 3 pr esents the secu rity issues w ith WA MS
and resear c h directions to be pursued. Sect ion 4 describes
the optimal PMU placement problem while in Section 5
the effect of communication failure on optimal PMU
placement has been analyzed. Section 6 presents the con-
clusions drawn from the present work.
2. Wide Area Measurement System
The smart grid influence all parts of a power system:
generation, transmission and distribution. However the
transmission system in a smart grid is set to be com-
pletely revolutionized with the help of synchrophasor
measurements using Phasor Measurement Units (PMUs).
The PMUs measurements are synchronized through
Global Positioning system (GPS). With PMUs, advanc ed
communications and computing techniques it will be
possible to precisely measure the state of a power grid. I t
will be useful in preventing cascading blackouts. Today’s
power system operators take action in the multi-second
to multi-minute time frame, but PMU based system can
make and execute decisions in the100 millisecond time
frame. A number of widely distributed PMUs in the
power system may be utilized to implement a Wide Area
Measurement System (WAMS) [5]. The architecture of a
typical WAMS is shown in Figure 1. It consists of
widely distributed PMUs in the power grid. These PMUs
send the measurements to Phasor Data Concentrator
(PDC). In general one PDC caters to a fixed number of
PMUs. The PDC collects and sort the data based on the
time tags. It keeps the data required for local applications
and transmits the data for advanced applications to a su-
per PDC through a dedicated communication network.
These three layers of a WAMS may be categorized as:
data acquisition, data management and applications lay-
ers [6].
The PMU based WAMS is one of the most important
technologies expected to play a key role in the smart
Figure 1. Wide area measurement system.
grid. The WAM technology may be utilized for the fol-
lowing [7,8].
2.1. Preventing Blackouts
PMU data provide information about the system at a
common instant of time that can be used for real-time
dynamic analysis. The real time information will be ex-
tremely useful in continuous monitoring and early detec-
tion of abnormalities. Timely initiation of corrective ac-
tion will help in restricting the disturbance to a smaller
2.2. Improved State Estimation
The set of complex voltage phasors across its buses
completely specifies the system; it is known as the sys-
tem state. State estimator utilizes telemetered measure-
ments from Remote Terminal Units (RTUs) to generate
an optimal estimate of the system state. However these
measurements do not contain the phase angles due to the
difficulty associated with the synchronization of meas-
urements. Consequently the phase angle has to be esti-
mated with the slack bus as reference. However with the
advent of Phasor Measurement Units (PMUs) this diffi-
culty can be removed as the PMU measures voltage and
current phasors synchronized through GPS. Due to tech-
nical and economical constraints it may not be fe asible to
install PMUs at every bus of the system. Therefore the
existing SE can be improved by using data from a few
PMUs installed at critical locations [9].
2.3. Transmission Line Congestion
The traditional approach to real-time line congestion
management is based on the Nominal Transfer Capability,
computed off-line using conservative hypothesis con-
cerning thermal, voltage or stability limitation s. However
Vulnerability Analysis of Wide Area Measurement System in the Smart Grid
Copyright © 2013 SciRes. SGRE
the WAMS will allow computing the Real-time Transfer
Capability for the actual operating conditions. Therefore
it will result in better utilization of transmission line ca-
pacity. It is also expected that in future the PMU data
will beintegrated with smart sensors measuring line tem-
perature and sagetcthen the operational limit may further
be increased.
2.5. Calibration of Instrumen t Transformers
2.4. The monitoring and control of a power grid depends
heavily on the measurement of current and voltage sig-
nals derived from the secondary circuits of instrument
transformers. The synchronized data available from
WAMS may also be used to obtain accurate calibration
of current and voltage transformers in a network.
2.6. Model Validation
For proper decision making during operation and control
it is extremely important to have accurate models of
synchronous machines of the system. PMUs can provide
reliable information for validation of these models when
located near or at the power plant.
3. Security Ch alle n ge s to the WAMS
An essential feature of smart grid is the two way com-
munication in order to monitor the ‘health’ of the system
in a better way. However this two way communication
feature presents new security challenges to protect data
security and customer privacy. The smart grid as envi-
sioned by the EPRI, should be resilient to the cyber/
physical attacks. As the WAM system is going to form
an integral part of the smart grid, it is crucial to en-
sure the availability and integrity of the data it carries
and the communication and computation infrastructure
involved. As monitoring and control applications in the
grid may rely on those data. The WAM systems are ex-
pected to operate over large geographical areas, which
make the security aspect more complex. Some of the
security vulnerability issues of WAMS are presented
below [10,11].
3.1. Time Delay
The information from PMUs is time sensitiv e and it must
reach the point of use within about two seconds. Late
arriving data is either discarded or passed on to data store.
Therefore any security measure adopted should not in-
troduce a time delay.
3.2. Reliance on GPS
PMUs utilize the Global Positioning System for syn-
chronization of the measurement. However the GPS sig-
nals may be jammed or spoofed by a hacker easily. If this
happens then serious errors may be deliberately intro-
duced in the time tags of the data. An invalid time stamp
may result in a loss of data and visibility into the gr id.
3.3. Configuration Management and Data
The crucial measurement data from the sensors should
not be shared with anyone other than authorized data
sharing partners. Not all PMUs deployed today support
authentication for configuration. Moreover apart from the
identity authentication, it is mandatory to preserve the
integrity of data being shared between two authenticated
3.4. Cyber Security
With the increased use of information and computation
tools in the WAMS, its vulnerability to a cyber attack
will increase. The ability of a phasor data concentrator or
PMU to protect itself and recover from a cyber attack is
not fully established and this area needs to be pursued
3.5. Communication Infrastructure
To ensure availability of reliable data from PMUs it is
necessary to ensure reliability of the communication in-
frastructure. The communication infrastructure being
utilized at present for PMU communication contains
vulnerabilities that may be exploited to interrupt com-
munication or compromise integrity of data.
Based on the recommendations of various studies the
requirements of security security measures for a WAMS
are [11,12]:
The security measures adopted should not in any way
hamper the primary objective of the WAMS.
The access to every PMU of a utility should be
through an authentication procedure.
The system should accept only authenticated and au-
thorized changes in the configuration of the network.
There should be proper mechanism to validate the
integrity of data exchanged.
The system should continue to perform essential
functions in case of loss of synchronized measure-
The security mechanism should be able to minimize
the impact of abnormalities on the performance of
Considering the crucial role of synchronized meas-
urements in order to achieve a smart grid, various
groups/organizations are working on developing security
Vulnerability Analysis of Wide Area Measurement System in the Smart Grid
Copyright © 2013 SciRes. SGRE
Table 1. Research initiatives on security challenges to
Initiative Research Direction
IEC 62351[13] Describes recommended security profiles
for various communications media and
002-009[14] Deals with cyber-security standards
1686-2007[15] describes security measures from the
perspective of an IED
IEEE C37.118[15] the communications protocol for PMU
NISTIR[16] guidelines for smart grid security
standards and recommendations for WAMS. Table 1
provides a summary of these initiatives.
4. Optimal Placeme n t of PMU s
Theapplication of state estimation (SE) for online power
flow analysis was first proposed in [17] during the late
1960s. The estimation is done based on the measurement
of real power injections and flows (P), reactive power
injections and flows (Q) and voltage magnitudes V. The
conventional SEs use the Intercontrol Center Communi-
cations Protocol (ICCP) for gathering the asynchronous
data with a sampling rate of 1 sample per 4 - 10 seconds.
The measurement model is given as:
( )
Z hxe=+ (1)
Z: measurement data;
x: state of the system comprising of V and phase angle δ;
except the phase angle at slack bus;
h: non linear power flow equations;
e: measurement noise.
The Weighted Least Squares Method (WLS) is most
commonly used to generate an optimal estimate of the
system state. The WLS minimizes the error between the
measurements and the estimation of these measurements
when using the state variables. The performance measure
is given as:
( )( )
( )
( )
( )
Jxz hxRz hx
= −
This may be solved in terms of x to give:
( )
The jacobian matrix H may be calculated by deter-
mining the derivatives of each measurement with respect
to the state variables.
The relationship between measurements and state vari-
ables is non linear and it has to be linearised and then the
solution to WLS problem is obtained by using iterative
technique. However if a PMU is placed at every bus of
the system the relation between measurements and state
variables is linear and a non-iterative least square solu-
tion may be used to determine the system state [18 ]. The
PMU provides a synchronized measurement of the volt-
age phasor at a bus and the current phasors through the
branches associated incident on it. The measurements are
synchronized via the GPS signal with a frequency of
about 30 samples per second. Due to technical and eco-
nomical constraints it may not be feasible to install
PMUs at every bus of the system. Moreover a system can
still be made observable by placing PMUs only on few
selected buses. Therefore the existing SE can be im-
proved by using data from a few PMUs installed at criti-
cal locations. The data from these units may be utilized
as pseudo measurement in the conventional SE. The re-
sult of such installations have reported benefits like in-
creased accuracy, increased stability of estimator, less
computation time, and increased redundancy etc. [19]. The
observability of a system can be assessed in two ways;
numerical and topological. Numerical observability is the
ability of the system model to be solved for the state es-
timation. If the measurement jacobianmatrixis of full
rank, then the system is considered to be numerically
observable. Topological observability is defined as the
existence of at least one spanning measurement tree of
full rank in the network. A number of methods/algo-
rithms have been reported for determining the optimal
locations for PMUs in a system. An extensive review of
these methods is given in [20].
Consider the IEEE 14 bus system shown in Figure 2.
The objective is to find an optimal location set for PMUs
which makes the system observable.
The optimal PMU placement problem can be defined
Figure 2. IEEE 14 bus system.
Vulnerability Analysis of Wide Area Measurement System in the Smart Grid
Copyright © 2013 SciRes. SGRE
n is the number of buses in the system;
wj is the cost of installation of a PMU on bus i;
xi is a vector of dimension n and has binary values de-
fined as:
Subjected to the following constraints:
1xxxxx+++ ≥+
32 4
1xx x++≥
42 593 7
1xxxx xx++ ++≥+
(9 )
412 5
1xxxx+ ++≥
1621113 1x x
+++ ≥ (11)
847 9
1xxxx+ ++≥
1x x+≥
47941101xxxx x++++ ≥ (14)
10 1191xx x
10 1161xx x
12 136
1xx x++≥
1213164 1xx xx
91314 1xx x
The constraint equations (6)-(19) will ensure that the
system is observable. These constraints may also be ex-
pressed as
[ ]
AX b
A is called the bus to bus connectivity matrix defined as;
1, if bus is incident to bus
0, otherwise
b is a vector having all its elements equal to 1.
Solving the optimal placement problem using Binary
Integer Linear Programming (BILP), the buses for opti-
mal location of PMUs for the IEEE 14 bus system are 2,
6, 7 and 9.
Here it may be noted that the number of PMUs re-
quired for complete observability of the system may re-
duce in the presence of conventional measurements and
zero injection buses. However since in the present work
the main objective is to show the effect of communica-
tion failure on optimal PMU placement, these measur e-
ments and zero injection buses have not been considered.
5. Placement of PMUs against
Communication Failure
Availability of communicatio n is an integral requirement
for a PMU based measurement system. In the present
work the effect of communication failure has been shown
on the requirement of number of PMUs to maintain ob-
servability of the system. It is obvious that incorporation
of this constraint in the PMU placement scheme will re-
sult into a greater number of PMUs than required under
normal condition. Moreover this constraint may even
limit the attainment of complete observability.
For studying the effect of communication failure on
the observability of IEEE 14 bus system, it is assumed
that there is failure of communication at buses 1 and 2.
Under this condition, the constraints represented by (6) -
(19) will be modified as follows:
(2 2 )
(23 )
53 794
1xxx xx+ ++≥+
(25 )
1xx+ ≥
(2 6 )
1621113 1x x
+++ ≥
(2 7 )
847 9
1xxxx+ ++≥
(28 )
1x x+≥
(2 9 )
1xxxx x++++ ≥
10 1191xx x
10 1161xx x
12 136
1xx x++≥
12 13 164
1xx xx+++≥
91314 1xx x++≥
Now again solving the optimal placement problem us-
ing BILP subjected to the constraints (22)-(35), it is
found that the PMUs are required to be placed at five
buses; 4, 5, 8, 11, 13. Which means a higher number of
PMUs is required to maintain observability of the net-
work in case of failure of communication at two buses of
the system. Similarly failure of communication at larger
number of buses was considered and the minimum num-
ber of PMUs required for each case was determined. The
results are given in Table 2.
The results indicate that the number of PMUs required
for maintaining observability of the network is increasing
with the increase in the locations of communication fail
Vulnerability Analysis of Wide Area Measurement System in the Smart Grid
Copyright © 2013 SciRes. SGRE
Table 2. PMUs required for observability of 14 bus system
against communication failure.
eruliaF Number of PMUs
Required PMU Location
enoN 4 2, 6, 7, 9
1, 2 5 4, 5, 8, 11, 13
1, 2, 4 5 3, 5, 6, 7, 9
1, 2, 4, 6 5 3, 5, 7 , 10, 13
1, 2, 4, 6, 13 6 3, 5, 7, 9, 11, 12
1, 2, 4, 6, 9, 10, 13 6 3, 5, 7, 11, 12 , 14
ure. Although the system under consideration is a small
system of 14 buses only but the increase in number of
PMUs is significant. It may be inferred that for a large
practical system, the difference in number of PMUs re-
quired under normal operating conditions and those re-
quired under communication failure will be considerably
These results are impor tant as the utilities are p lanning
to implement a PMU only linear state estimator in future.
For this the network has to be observable with PMUs.
However any such placement scheme should take into
account the possibility of communication failure at the
buses because otherwise the observability of the system
may be lost and it will create serious problems in the
monitoring of dynamic performance of the power net-
6. Conclusion
Wide area measurement systems based on phasor meas-
urement units will form an integral part of the smart grid.
However, there are various concerns related to the secu-
rity of these systems which have been identified in the
present work. Moreover, reliable communication is a
fundamental requirement of a phasor measurement sys-
tem. The effect of communication failure on a network
installed with PMUs has been analyzed. It has been
shown that the number of PMUs that are required to
maintain observability increases s ignifican tly as the num -
ber of locations with communication failure increases
and it may even restrict complete observability. There-
fore availability of a robust communication infrastru cture
at the system buses should be an integral consideration of
a PMU placement methodology.
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PMU Phasor Measurement Unit
GPS Global Positioning System
OPP Optimal PMU Placement
SMAW Wide Area Management System
PDC Phasor Data Concentrator
UTR Remote Terminal Unit
IRPE Electric Power Research Institute
SLW Weighted Least Squares Method