Application of Synchronized Phasor Measurements Units in Power Systems

The International Journal Of Engineering And Science (IJES)
|| Volume || 6 || Issue || 3 || Pages || PP 25-39 || 2017 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
DOI : 10.9790/1813-0603012539 www.theijes.com Page 25
Application of Synchronized Phasor Measurements Units in
Power Systems
Nikolaos. P. Theodorakatos
School of Electrical and Computer Engineering, National Technical University of Athens
Athens, 15780, Greece
--------------------------------------------------------ABSTRACT-----------------------------------------------------------
The last decades, electric power industry is undergoing multiple changes due to the process of deregulation,
providing efficient power generation, technological innovations, and eventually lower retail prices. In this
environment, dynamic phenomena in power systems have made ever more urgent the development of reliable
tools for their monitoring and control. An effective tool for the close monitoring of their operation conditions is
the state estimator. The traditional estimators are based on real time measurements obtained through SCADA
(Supervisory Control and Data Acquisition) system. These measurements are commonly provided by the remote
terminal units (RTUs) installed at the high voltage substations. The phase angle of bus voltages can not be
easily measured due to technical difficulties associated with the synchronization of measurements at RTUs.
Global Positioning System (GPS) alleviated these difficulties and led to the development of Phasor
Measurement Units (PMUs). This weakness was eliminated with the arrival of GPS, which led to the
development of Phasor Measurement Units. A PMU unit, equipped with a GPS receiver, provides high accuracy
voltage and current phasor measurements with respect to a common reference phase angle. In the first part of
the paper, an overview of the PMU technology and a review about the optimal allocation of PMUs in power
network are presented. The most important issues regarding design and operation of PMUs are discussed and
an analysis of their commercial penetration in the electric energy markets is made. The second part of the paper
presents a wide range of applications related with the choice of the strategic PMU placement as well as an
algorithm for finding the optimal number of PMUs needed for full observability.
Index Terms: Bibliographic Analysis, Optimal PMU Placement, Phasor Measurement Unit, synchronized
measurements, Global Positioning System, Smart Grid, software applications, monitoring, system, architecture
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Date of Submission: 16 February 2017 Date of Accepted: 05 March 2017
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I. INTRODUCTION
State estimation (SE) is a critical power system management application. The function of SE is needed to obtain
the best estimate of the current state of an electric system, based on available redundant measurements, such as
voltage magnitudes and real and reactive power flows and injections [1]. For an power system, these
measurements are collected from devices well distributed over the system, digitized and, finally, transmitted to
the central computer, whereas, for power system, the digital data are transmitted using fiber optics due to the
high data rate, noise immunity, light weight, and electrical isolation. The state estimator accuracy, in power
systems, is significantly improved by the inclusion of the more accurate and time time-tagged measurements
provided by the phasor measurement units (PMUs). A PMU, when placed at a bus, can measure the voltage
phasor of the installed bus and the current phasors of some or all the branches incident to the bus, assuming that
the PMU has sufficient number of channels [2]-[5]. Nowadays, utilization of PMUs in the monitoring,
protection and control of power systems has become increasingly important. The objective of the optimal PMU
placement (OPP) problem is the determination of the minimum number and locations of PMUs ensuring the
power system observability. Several methods have been proposed for the OPP problem. These methods are
classified into two major categories: deterministic and heuristic algorithms. The first category includes
optimization-based deterministic methods. The basic idea is to use the deterministic algorithm but starts with
different initial points. Deterministic algorithms follow a rigorous procedure and its path and values of both
design variables and the functions are repeatable. The basic idea is to use the deterministic algorithm, but start
with different initial points. Deterministic algorithms including discrete and continuous programming
techniques follow a rigorous procedure. On the other hand the stochastic algorithms always have some
randomness solving a design which is an iterative process [6]. Both algorithms have been proposed and
implemented for the solution of the optimal allocation of PMUs in a power network [7]. An integer
programming formulation of OPP problem considering or not the existence of conventional measurements and
ensuring network observability is proposed in [8], [9]. In [10], a comparative study is implemented between a

Application of Synchronized Phasor Measurements Units in Power Systems
deterministic technique and a heuristic method. The binary integer programming algorithm and a genetic
algorithm has been used in order to solve the OPP problem as proposed in [9]. A multistage PMU placement
procedure based on integer linear programming (ILP) and modeling the zero injections as linear constraints is
suggested in [11]. Two indices, the bus observability index (BOI) and the system observability redundancy
index (SORI), are also introduced to further rank the multiple solutions. For OPP problematic, the nonlinear
programming and a sequential quadratic programming method are formulated using binary integer
programming (BIP) model in [12]. Based on NLP the feasibility is investigated for obtaining optimal solution.
The System Observability Redundancy Index as proposed in [11] is measured for ranking the multiple solutions.
An integer linear programming placement algorithm, considering the full network observability with or without
loss of a single transmission line or PMU as well as conventional measurements existence, is suggested in [13].
Sequential quadratic programming algorithm is adopted to solve the optimal allocation of PMUs in power
networks in [14]-[15]. A wide range of metaheuristic optimization techniques have also been proposed [6]. A
binary particle swarm optimization (BPSO) technique considering contingency conditions, i.e., PMU loss or
branch outage, and ensuring network observability is suggested in [16]. Also, others non-deterministic algorithm
such as Binary Search Algorithm (BSA) [17], Binary Particle Swarm Optimization (BPSO) [18], Tabu Search
Algorithm (TSA) [19], and Recursive Tabu Search [19], have been used in solving the OPP problem. This paper
is organized as follows : Section II discusses the fundamentals of PMUs. Section III discusses the PMU
technology. In section IV, it is presented the synchrophasor standard IEEE C37.118. Section V presents the
manufacturing companies of PMUs and the installation of these devices in power network for better and reliable
monitoring in different countries around the world. The PMU placement optimization is discussed in section VI,
whereas section VII presents a case study of the optimization of optimal allocation of PMUs applied to real
power systems. In this section, a branch and bound algorithm is presented for the solution of the discrete
optimization problem. In section VIII, it is presented the application of PMUs in modern power systems. In the
existing power measurements, the available measurements are received by SCADA system, so the contribution
of PMUs in the state estimator is presented. Section X presents existing synchrophasor installations in power
networks in countries around the world. In section XI a phasor measurement application study is presented.
Finally, Section XII concludes this paper.
II. FUNDAMENDALS OF PMUS
PMUs technology provides phasor information (both magnitude and phase angle) in real time [5]. The
advantage of referring phase angle to a global reference time is helpful in capturing the wide area snap shot of
the power system. Effective utilization of this technology is very useful in mitigating blackouts and learning the
real time behaviour of the power system. With the advancement in technology, the micro processor based
instrumentation such as protection Relays and Disturbance Fault Recorders (DFRs) incorporate the PMU
module along with other existing functionalities as an extended feature [5]. A pure sinusoidal waveform can be
represented by a unique complex number known as a phasor. Consider a sinusoidal signal [5] :
   ˆ co s   y t y t (1)
The phasor representation of this sinusoidal is given by [5] :
 
ˆ ˆ( ) R e R e
       
    
j t j t j
y t ye e ye (2)
   
ˆ ˆ ˆ
cos sin
2 2 2

       
jy y y
y t Y e j (3)
The magnitude of the phasor is the rms value of the sinusoid
ˆ
2
y
and its phase is  . The sinusoidal signal and
its phasor representation given by (1) and (3) are illustrated in Fig. 1
φ
y^
Y
t=0
φ
Y
Im
Re
Fig. 1. Phasor representation of a sinusoidal signal. (a) Sinusoidal signal. (b) Phasor representation [5]

III. PMU TECHNOLOGY
The first phasor measurement units (PMUs) built at the Power Systems Research Laboratory at Virginia Tech.
The GPS receiver clock was external to the PMU, and with the small number of GPS satellites deployed at that
time, the clock had to be equipped with a precision internal oscillator which maintained accurate time in the
absence of visible satellites [5].
III.1 PMU CONFIGURATION
A PMU block diagram is shown in Figure 2.
GPS receiver
Phase Locked
Oscillator
Anti-Aliasing Filters A/D Converter
Phase
Micro -Processor
Modem
Analog Inputs
Figure 2. PMU configuration [5], [21]
Each block is detailed below [5]. A voltage or current level matched with the requirements of the converters are
obtained by using appropriate measurement transformers. The analog AC waveforms are digitized by an
Analog-to-Digital converter. The role of the anti-aliasing filter placed before the signal sampler is to remove the
components of the signal whose frequency is equal or greater than one-half the Nyquiste (sample) rate. The
sampling is done using the Phasor Measurement Algorithm based on the Discrete Fourier Transform (DFT). Let
us assume that the sampling frequency is S
F N f where f is the signal frequency, so this means that there are
N sam ples per cycle of the signal and the sampling of the signal over the window is denoted by the set . The
variable N is also called the window-length, equal to the number of samples per cycle. The sampling period is
therefore
1
T
f
 . The process of computing this phasor estimate requires sampling the waveform over some
interval of time, which is the measurement window. The time at which the phasor is estimated is the phasor
time-tag. DFT-based phasor calculation at a particular instant of time is
1
0
2
N
j T k
k
k
X x e
N




  . Estimating the
phasor requires sampling the waveform over some interval of time [23]. An important point discussed in the
standard is the choice of the time-tag within the window as shown in Figure 3 whereas Fig 4 shows a PMU
devices with GPS receiver.
Fig. 3 Signal sampling using DFT

Figure 4 . PMU with GPS receiver [5]
The analog inputs are current and voltage phasors obtained from the secondary windings of the current and
voltage transformers (Fig. 5).
PMU
Current
Transformer
Potential
Transformer
Bus i Bus j
Vj
δj
δi
I ij
θij
Vi
φ
ij
Figure 5. GPS referenced PMU at bus i .
III.2 PHASOR MEASUREMENT UNIT
It is assumed that each PMU has enough channels to measure the bus voltage phasor at its associated bus and
the current phasors along all lines connected to that bus (Fig 6) [8].
PMU
I phasor
V phasor
i
i-j
i-k
i-l
i-n
Figure 6. Phasor measurements provided by a PMU installed at a network bus

The PMUs are installed in power system substations [5]. The selection of substations where these installations
take place depends upon the use of the measurements they provide. The optimal placement of PMUs will be
consider in the following section. An architecture involving PMUs, communication links, and data
concentration must exist in order to realize the full benefit of the Phasor measurement system. A generally
accepted architecture of such system is shown in Fig.7 [5]. The devices at next level of the hierarchy are
commonly known as ―phasor data concentrators‖ (PDCs). Typical function of the PDCs is to gather data from
several PMUs, reject bad data, align the time–stamps, and create a coherent record of simultaneously recorded
data from a wider part of the power system. There are local storage facilities in the PDCs, as well as application
functions which need the PMU data available at the PDC. This can be made available by the PDCs to the local
applications in real time [5]. So, the real time data, provided by PMUs located in different regions, are
transmitted over fast communication links and gathered to higher level devices, commonly known as phasor
data concentrators (PDCs). Data concentrators ensure that phasor measurements are used effectively, and is a
system that will retrieve files recorded at the measurement site and correlate files from different sites by the time
stamp associated with each measurement. As mentioned before, a simple diagram of the phasor measurement
systems hierarchy is illustrated in Fig. 7 [5]. In Fig. 7, the PMUs are suited in power system substations and
provide measurements of time-stamped positive sequence voltages and currents of all monitored buses and
feeders (as well as frequency and rate of frequency).
PMUs located in substations
Figure 7. Hierarchy of the phasor measurement systems and levels of phasors data concentrators.
The available data are processed in control centres providing an estimate of the bus voltage phasors, the tap
positions, the circuit breaker status, and other metered and unmetered electrical quantities and parameters of the
power system (Fig 8).
Control Centre Computer
PDC
Fig 8. Phasor Data Concentrator [5]

IV. SYNCHROPHASOR STANDARD IEEE C37.118
Synchronized phasor measurements or synchrophasors refer to the use of a common time reference for all
measured phasors. A detailed definition of a time-synchronized phasor has been codified by the IEEE C37.118
standard synchrophasor for all measured phasors. The standard defines the synchronized phasor measurements
used in power systems, the methods to verify compliance with the standard and the message formats for
communication with a phasor measurement unit (PMU). The phasors are tagged with a Universal Time
Coordinate (UTC) time corresponding to the time of measurement [22].
V. PMU MANUFACTURERS
Technology for PMUs has existed for some time now and the main manufacturers are Schweitzer Engineering
Laboratories (SEL), ABB, Siemens and ALSTOM GRID. Below the products are listed in the following Table I
[25].
TABLE I: MODEL OF PMUS
VI. PMU PLACEMENT OPTIMIZATION
Due to the high costs generated by the purchase and installation of a PMU, it is not feasible to locate and install
the PMU at all the buses of the electric power system [7]. Therefore the objective function posed in this
optimization problem focuses on minimizing the number of PMU and finding its optimal location, subject to the
restriction of achieving complete observability of the network. The PMU installs at substations [5]. The
dominant programming algorithm for minimizing the required PMUs is the integer linear program with binary-
valued variables. The programming model is formulated as follows :
 
m in ( ) , ( 1, 1 .. )
. . 1
0,1
T
i
ij i
i
n
J x c x c i n
s t a x
x
  


 (4)
where :
1,
0
i
if a P M U in sta lled a t b u s i
x
o th erw ise

 

(5)
1,
;
0
ij ij
if i j
A a a
o th erw ise

    

(6)
The specific representation of the algorithm used to solve the optimal placement problem is detailed in the
following Algorithm. This code solves the discrete optimization problem and uses as optimizer function the
bintprog routine [26].
MANUFACTURING
PLANT
MODEL OF PMU
RES 521
P847B&C
MODEL 1133A
TESLA 4000
D 60
macrodyne, inc.
MODEL 1690
SEL-421
SIMEAS R-PMU
PMU2002

TABLE II
Algorithm : Optimal Placement of PMU
Algorithm : Optimal Placement of PMU
1. :C The set of implementation cost
2. :n Set of buses (nodes)
3. :l Set of branches
4. Define set of variables  1 2
, , ...,
T
n
x x x x  0 ,1
n
i
x 
5. Define binary connectivity matrix
6. ( )
T
J x c x ;objective function considering costs
7. /* solving the problem of binary integer
8. programming*/
9. int ( , , )x b prog f A b   ; b :unity vector;  0 ,1
n
i
x 
10. X optim al solution for P M U s
11. Solution X
The bintprog implements a branch and bound algorithm in order to solve the discrete optimization problem [6],
[26]. The Branch and Bound (BB or B&B) algorithm is first proposed by A. H. Land and A. G. Doig in 1960 for
discrete programming. It is a general algorithm for finding optimal solutions of various optimization problems,
especially in discrete and combinatorial optimization. A branch and bound algorithm consists of a systematic
enumeration of all candidate solutions, where large subsets of fruitless candidates are fathomed, by using upper
and lower estimated bounds of the quantity being optimized [6]. The exact algorithm procedure is as below [6] :
TABLE III
IMPLEMENTATION OF BRANCH AND BOUND Algorithm
Algorithm : Branch and Bound
 In itia liza tio n
:
:
L B Inf
U B Inf
 

Store root node in waiting node list
while waiting node list is not empty do
{Node selection}
Choose a node from the waiting node list
Remove it from the waiting node list
Solve subproblem
if infeasible then
Node is fathomed
else if optimal then
if integer solution then
if obj L B then
{Better integer solution found}
:L B obj
Remove nodes j from list with j
U B L B
end if
else
{Variable selection}
Find variable k
x with fractional value u
Create node new
j with bound k
x u   
:jnew
U B obj
Store node new
j in waiting node list
Create node new
j with bound k
x u   
_ :new
U B j obj

Store node new
j in waiting node list
end if
else
Stop: problem in solving subproblem
end if
m ax j j
U B U B
end while
The flowchart of the branch and bound, the implementation of its succeed by bintprog is illustrated in Fig. 8.
Original Discrete problem
Equivalent relaxed problem
Choose a node from the waiting node list
and solve LP solver
If optimal Fathom this node
If all 0 1iy or
yes
no
Create two new nodes with and
add to the waiting node list
0 1i iy and y 
If
Bobj L
yes
no
Removes nodes with
BL obj
i
i BUB L
If waiting node list is empty
max
pt
B B i Bi
O
L U U
end

 
yes
yes
no
Fig. 8. Flowchart of branch and bound

VII. CASE STUDY
Let examine the algorithm for finding strategically PMUs at realistic power systems. Northern Regional Power
Grid (NRPG) of Power Grid Corporation of India Limited (PGCIL), is the largest among all five regional
power grids in India, comprising of the 9 states. It covers around 30% geographical area and 28% population of
India. The reduced NRPG system (220 kV and 400 kV only) network only consists of 246 buses, 376 branches
(lines/transformers), 42 generating units and 40 shunt reactors [28]. According with the above ''Algorithm :
Optimal Placement of PMU'', the bintprog routine determines 70 PMUs in order to be the power system
completely observable. Another realistic power system is a 129-bus test system that models the Italian HV
transmission network (129-bus Italian 400 kV transmission system (most of this information is publicly
available at the GRTN web site www.grtn.it)) [29]. For the 129-bus Italian 400 kV transmission system, the
bintprog determines 40 PMUs. Another realistic power system is the Mexican power system consisting of 190
buses and 46 generators [30]. In order to be fully observable, the branch and bound through bintprog finds 59
PMUs devices. An American power system (WECC 240- bus) has been presented in [31], [32]. It is a
240-bus model as a realistic test system for the California and WECC market [31]. The numbering of the buses
in this power network is not successive. The buses of each power system must be re-numbered from 1 up to the
total number of buses before the simulation run of the optimization model in MATLAB. The total number of
PMUs needed for full observability is 74 PMUs. The PMU locations for each realistic power system is
presented in table IV.
TABLE IV: REQUIRED PMUS ENSURING COMPLETE OBSERVABILITY
Test System PMU location (Bus #)
246 Indian
bus
6, 21, 23, 24, 29, 34, 40, 45, 48, 54, 55, 57,
60, 61, 62, 63, 65, 69, 73, 74, 75, 78, 80, 88,
93, 95, 98, 100, 101, 102, 103, 106, 109, 116,
117, 121, 122, 125, 126, 129, 132 134, 140,
141, 142, 147, 157, 158, 160, 163, 168, 173,
181, 183, 185, 187, 190, 191 194, 199, 201,
202, 203, 215, 216, 219, 234, 235, 243, 245
Italian 129
bus
5, 28, 30, 33, 35, 37, 39, 43, 46, 47, 49, 50,
51, 52, 54,55, 57, 59, 61, 64, 66, 69, 72, 73,
74, 79, 80, 83, 92, 101,103, 106, 107, 109,
110, 115 116, 117, 121, 124
Mexico 190
bus
48, 49, 50, 53, 55, 56, 59, 60, 64, 77, 80, 81,
83, 84, 86, 89, 90, 91, 96, 97, 99, 105, 106,
107, 108, 109, 110, 111, 112, 117, 120, 122,
128, 129, 131, 133, 136, 137 , 138, 143, 145
146, 151, 153, 154, 158, 162, 164, 165 ,168,
170, 172, 174, 175, 176, 180, 185, 186, 189
WECC 240-
bus
2, 4, 7, 11, 13, 15, 18, 22, 24, 28, 30, 31, 37,
38, 41, 46, 48, 53, 54, 58, 60, 61, 62, 65, 71,
78, 80, 81, 83, 89, 94, 99, 100, 105, 107, 109,
111, 113, 115, 116, 127, 128, 133 146, 147,
148, 149, 150, 156, 157, 160, 164, 165, 172,
174, 177, 178, 180, 183, 184 189, 190, 196,
200, 204, 209, 211, 216, 220, 221, 230, 231,
236, 237
VIII. APPLICATION OF PMUS
In this section, applications using synchrophasors are described. These applications can be grouped into three
categories [5] :
1. Wide-area visualization and increased state awareness
2. Improved system planning and analysis
3. Response-based control applications that use real-time wide area information
4. State estimator
A very useful application for synchrophasors is Wide-Area Measurement Systems (WAMS). The WAMS refers
to the possibility of observing a wide area of the electrical grid with synchrophasors. A useful application is to
make a surface plot of angle measurements among PMUs located in widely dispersed location around a power
system, which represents the general direction of power flows as well as the areas of sources and sinks [24].

IX. STATOR ESTIMATOR
For observability analysis of systems including SCADA as well as PMU measurements, an efficient integer
numerical procedure is proposed in [27] . For observability purposes, an linearized measurement vector equation
is considered. z Hx e  (7) where ( 1)z m  is the measurement vector composed of SCADA and PMU
measurements, ( 1)n  is the vector of all bus phase angles, ( )H m n is the measurement Jacobian matrix,
( 1)e m  is the measurement error vector, m is the number of measurements and n the number of buses. If no
PMUs exist in the measurement system, then an artificial zero phase angle measurement is introduced at an
arbitrarily selected reference bus, without excluding that bus from the problem formulation. It should be noted
that the system observability is independent of the branch parameters as well as the operating state of the
system. Therefore, all system branches can be assumed to have an impedance of 1.0 .j p u . and all bus voltages
can be set equal to 1.0 .p u . for the purpose of observability analysis. The SCADA measurements are modeled as
follows: Active power flow measurement at bus i of branch -i j : active power flow at bus i in branch i j
can be approximated as ij i jP    (8) where i
 , j
 are the phase angles of the voltages at bus i and j . An
active power or zero injection at bus i can be approximated as follows :
( ) ( )
( )i ij i j
j a i j a i
P P  
 
    (9)
where ( )a i is the set of buses connected with bus i . Note that 0iP  for a zero injection at bus i . A PMU
located at bus i can measure the phase angle i and the real part ,rijI of the current phasor ijI of some or all
incident branches i j , which can be approximated as ,rij i jI    (10). The linearized Jacobian matrix is
as [27] :
   

 
 
 
 
  
 
   
 
 
 
   
       
      
      
    
    
,
1
1 1
1 1
1 1 1
i j k l
i
ij r
ij
i i
I
H
P
N P
(11)
where i is the number of incident lines at bus i and , ,j k  are the buses connected with bus i . The gain
matrix can be formed as :  
T
G H H (12). Therefore, the power network is considered to be completely
observable if and only if : the Jacobian matrix H or the gain matrix G has full rank, i.e,

  ( ) ( )ra nk H H ra nk H n (13) [27], where the measurement error covariance matrix is assumed to be the
identity matrix without loss of generality [27]
X. EXISTING SYNCHROPHASOR INSTALLATIONS
It can be said now that finally the technology of synchronized phasor measurements has come of age, and most
modern power systems around the world are in the process of installing wide-area measurement systems
consisting of the phasor measurement units [5]. As the initiator PMU technology, use of phasor measurement
units is becoming common in electrical grids across the US. This is shown by the ambitious project NASPI and
the numerous PMUs already installed in the grid. The geographical distribution of these units, in descending
order, is shown in Table V .
TABLE V: Geographical area of PMUs
A/A Geographical area /
Country
Number of
PMUs
1. North Αmerica, China 1000
2. Μexico 272
3. India 130
4. Βrazil 100
5.
Eastern Europe, Central
Αsia, Sibiria
26

6. Finland 20
7. Ιtaly 20
8. Spain 13
9. Αustralia 12
10. Νew Zeland 10
11. SWEDEN, ΙCELAND 8
12. Colombia 6
13.
France, Slovenia, Croatia,
Switzerland
5
14.
Denmark, Norway,
Poland, Hungary
4
15. German, Slovakia 3
16. Austria, Romania 2
17. Βelgium, Bulgaria 1
18. Greece 2
Figure 9 shows the Union for the Co-ordination of Transmission of Electricity [25]
XI. PHASOR MEASUREMENT APPLICATION STUDY
A detailed analysis has distinguished key applications and distinguished those that have a major improvement
impact like angle/frequency monitoring, visualization, post-mortem analysis, model benchmarking, outage
prevention, state measurement and real-time control. More advanced applications are voltage stability monitoring
and state estimation using PMUs. The priorities were found to be those shown in the Figure 9.

Fig. 9 PMU applications and industry needs
The CIEE study has also written a guidebook to provide general guidance for building a business case for PMU
technology [25].
Fig 10. Business Case Analysis Process
In Figure 10, five general phases for a business case analysis for PMU technology deployment are detailed. Phase
I tries to understand performance gaps with existing technologies and practices and understanding what the added
value of the PMU would be. Phase II analyses the issues that can be addressed by PMU technology. Phase III
identifies the stakeholders and the benefits for them of the PMUs. Phase IV establishes an expected plan for PMU
deployment. Finally, phase V compares projected benefits over a time horizon. The applications are separated in
new-term (1-3 years), medium-term (3-5 years) and long-term (more than 5 years). The traditional approach to
evaluate the project is to calculate the NPV (Net Present Value), where all future benefits, expenses and needed
investment for a number of years is included in the calculation (TABLE VI)
TABLE VI: NPV estimations for different uses of PMUs
Case NPV (1.000€)
1. Full deployment 43.666 έως 317.759
2. Post-mortem analysis only έως 78
3. Outage Prevention and Post-
mortem analysis only
19.023 έως 314.932
4. Congestion management only 1.258
As shown by Table VI, full deployment of all applications is most beneficial since the same capital investment
can be used by different applications. The study concluded that there are large potential benefits of synchronized
phasor measurement technology, such as improvement of safety, reliability and efficiency of the grid as well as
large reliability and financial benefits for all grid users. The challenge is to move from the research and

development of PMUs and their applications to commercial operation. As the study has shown, phasor
measurement capability is advanced enough for commercial implementation to be possible. The difficulty is that
business processes and models for the implementation of PMUs have not been developed in most companies so
that the move to PMU technology and its applications is restricted. Due to the significant costs in infrastructure
and technology for PMU projects, identification of quantifiable benefits can facilitate acceptance and funding of
the projects. These benefits fall in the tangible and intangible categories. Tangible benefits are the better
utilization of system capacity, more efficient use of manpower and improved reliability of operations. However,
most benefits are not so tangible. These include avoided costs associated with outage investigation, blackout
restoration costs, costs of regulation following major system events [25].
XII. FINALLY REMARKS
Synchrophasor technology is an innovative measurement technique which is very useful for power system
operation and control. In many countries, synchrophasor technology is still in the research and development
phase. As has been shown in this paper, an international standard has been presented for synchrophasors
measurements and many applications have already been presented for these PMUs. Synchrophasor technology
has already been adopted by many countries around the world, and this has been described extensively in this
paper. Various companies have worked on Phasor Measurement Units and also have manufactured Phasor Data
Concentrators, which allow the processing of many phasor measurements for power system analysis and
control. Due to the high cost of a PMU device, it is crucial an optimal allocation of PMUs in power networks. A
branch and bound algorithm has been presented in order to solve this combinatorial optimization problem.
Therefore, PMU technology is being implemented in grids around the world for mainly analyzing wide-area
phenomena and better and reliable monitoring of power system.
ACKNOWLEDGMENT
The author is grateful to his professor Nicholas G. Maratos at the School of Electrical and Computer
engineering of NTUA for his support to the author's studies at the university and his valuable advices about
optimization techniques. This work was supported by Greeks national funds under a scholarship provided by
ELKE -NTUA (http://edeil.ntua.gr/) to the author for postgraduate studies and research at the university.
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AUTHOR’S BIOGRAPHY
Nikolaos P Theodorakatos received his B.S. and his Diploma degrees in electrical
engineering from Technological Educational Institute of Athens and National Technical
University of Athens, Athens, Greece, in 2000 and 2012, respectively. Currently, he is
pursuing his Ph.D. at the School of Electrical Engineering of National Technical
University of Athens, Athens, Greece. His research interests include PMU technology
and optimization techniques, mathematical programming and Control of Power Systems

Application of Synchronized Phasor Measurements Units in Power Systems

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