1
A Wide Area Synchrophasor Based
ANN Transient Stability Predictor for the
Egyptian Power System
Fahd Hashiesh, Member, IEEE, Hossam E. Mostafa, Member, IEEE, Ibrahim Helal, Member, IEEE
and Mohamed M. Mansour, Senior Member, IEEE
Abstract--This paper proposes an Artificial Neural Networks
(ANN) based technique for transient stability prediction. The
ANN makes use of the advent of Phasor Measurements Units
(PMU) for real-time prediction. Rate of change of bus voltages
and angles is used to train a two layers ANN. Potential of the
proposed approach is tested using the Egyptian Power System
(EPS) as a study system.
common reference for the phasor calculations at all different
locations.
Index Terms-- Artificial Neural Networks, Egyptian Power
System, Phasor Measurements Units, Synchrophasor, Transient
Stability Prediction, Wide Area Applications.
R
I. INTRODUCTION
eal time data can be highly valuable information for
proper protection and control actions, which could be
taken to ensure the reliability of power system. Also the
availability of real time system information will enable
advanced smart grid applications that were not possible
before. The primary benefits of a real-time wide area
monitoring system could be to [1]:
• Provide early warning of unstable system conditions, so
operators can take fast corrective actions. Early warning
or indication of grid problems include (abnormal angle
difference; inter-area oscillations; voltage stability);
• limit the cascading effect of disturbances (by providing
wide-area system visibility); and
• Improve transmission reliability planning and allow for
immediate post-disturbance analysis and visualization
through the use of archived monitoring system data.
Now Phasor technology is considered to be one of the most
important measurement technologies in power systems due to
its unique ability to sample voltage and current waveforms
data in synchronism with a GPS-clock and compute the
corresponding 50/60 Hz phasor component from widely
dispersed locations as shown in Fig. 1. This synchronized
sampling process of the different waveforms provides a
Fahd Hashiesh is with ABB Limited, Staffordshire, United Kingdom
(e-mail: [email protected]).
Hossam E. Mostafa is with Electrical Department, Faculty of Industrial
Education Suez Canal University, Egypt (e-mail: [email protected])
Ibrahim Helal is with Department of Electrical Power and Machines
Engineering, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Mohamed M. Mansour is with Department of Electrical Engineering, King
Fahd University of
Petroleum
&
Mineral,
KSA
(email:
[email protected])
Fig.1 Phasor Measurements at Remote Locations
After predicting instability, the power systems should be
ideally separated to maintain a balance between load and
generation in each of the separated areas. To accomplish this,
out-of-step tripping should be used at the desired points of
separation and out-of -step blocking used elsewhere to prevent
separating the system in an indiscriminative manner.
Where a load-generation balance can’t be achieved in a
separated area and there is excess load as compared to
generation, some means of shedding for non-essential loads
have to be done in order to avoid a complete shutdown of the
area [2].
Planning studies of power system dynamics have resulted in
number of techniques used to detect power system
instabilities. In the past decade two techniques have become
popular for this application. They are; the Lyapunov or energy
function approach and the extended equal area criterion. It
should be noted that this techniques were not developed with
real time applications in mind [3].
Liancheng Wang and Adly A. Girgis in [4] proposed a
method for power system transient instability detection. The
method is based on generator angle, angular velocity and their
rate of changes. According to the proposed method, system
instability or out-of step condition is detected by identifying
the characteristics-concave or convex of a surface on which
the post-fault system trajectory lies. The system is unstable
when the surface is convex.
2
The authors in reference [5] present a development of an
adaptive out-of-step relay. The adaptive relay seeks to make
adjustments to its characteristics as system conditions change,
thereby making it more attuned to the prevailing power system
conditions. The reference also describes the theory of such a
relay and its hardware configuration. Also, the development of
these relay with the use of PMUs is given in [6, 7].
On the other hand, one of the primary objects of the Power
System Engineering Research Center (PSERC) projects [8] is
to develop an application that dealt with on-line transient
stability assessment using measurements from PMUs. This is
done through the development of a software tool that uses
artificial intelligence (decision trees). The proposed tool is
developed by training a set of trees based on simulations
conducted offline. Another concept of using decision trees is
presented in [9].
Development of different techniques and approaches to
determine the system instabilities are also the aim of many
other researchers. In reference [10], an approach based on
observation of the difference between substations phase angles
is presented. Another approach using equal area criteria is
given in [11]. In [12] a concept of using autoregressive model,
a kind of time-series analysis, is also demonstrated.
A method for predicting transient stability using fuzzy
hyper rectangular composite neural network (FHRCNN) is
proposed in [13]. It computes the velocities and acceleration
for generator angles on a time window of eight cycles, with a
total of six FHRCNN inputs per generator. The criterion for
instability used is whether the difference between any two
generator angles exceeds π radian in the first second after
clearing time or not.
Reference [13] indicates that the selection of π radian is just
for illustration of the used scheme and it must be calculated as
it heavily depends on the characteristics of power system
under study. Some other new methods using the advance of
real time monitoring and/or ANN methods to predict the
transient instability are presented in [14 - 16].
In this paper an on-line ANN predictor is proposed to
predict transient instabilities in the power system. The
proposed ANN predictor makes use of the advent of PMU for
real-time monitoring. The determination of angle of instability
is carried out with only two inputs for each generator and is
processed within three cycles prediction window.
Implementation of the proposed approach is applied on the
Egyptian Power System (EPS) as a case study. Good
prediction is achieved through the tested conditions.
II. ON-LINE DETERMINATION OF ANGLE OF INSTABILITY
In this study, the used algorithm for on-line determination
of angle of instability given in [17] is selected to be the base of
the proposed predictor. The algorithm detects the fast
separation of phase angle among the critical areas using the
PMUs. In general center of angles can be defined as:
N
δ
COA
∑
δ
j=1
N
=
∑
'
j
H
H
j
(1)
j
j=1
Where,
N is the number of generators
δj’ is the internal rotor angle of generator j
Hj inertia time constant of generator j
Although the internal machine rotor angle cannot be
directly measured, researchers in [18] built an ANN platform
to estimate rotor angles and speeds from measurements of
PMUs installed at generator buses.
In this work, as per assumption given in [17], the internal
angle will be substituted with the phase angle of the high side
bus voltage, which is normally monitored by PMUs. Similarly,
the inertia time constant H is substituted by the high side
active power injections for the generator as machine inertia is
typically proportional to the real power output.
Assuming the availability of installed PMUs at all
generators buses, the on-line phase angle measurement for
each generator bus δj can be measured. Where, j=1, 2, 3, ..n
and n is the number of generator bus. Therefore equation (1)
can be re-written as:
∑
=
∑
n
δc
j =1
n
δ j Pj
P
j =1 j
(2)
Where,
δc is the center of angles of the system
δj is the phase angle of bus voltage for generator bus j
n
is the number of generator buses in the network
Pj is the current MW generation schedule at generator bus j
Another term Δδj can defined as:
Δδ j = δ j − δ c
(3)
Two accumulated integral terms are then computed, Ωa and
Ωd, respectively to denote the speeding up or slowing down of
generator j with respect to the center of angles calculated in
the previous step. Defining Ωa as the integral for Δδj,
whenever Δδj continuously stays above a threshold, say Δδ*a.
When Ωa grows above a pre-specified value, say Ω*a, a
remedial action is initiated. This remedial action can be either
generator tripping or activating load shedding. In this case as
generator j is speeding away from the rest of the system,
accordingly tripping generation is the right choice. This
process can be clarified using Fig 2.
From figure, it is clear that generators G1 & G2 are speeding
away from reset of the system, as angle of generator G1
reaches Δδ*a, we start calculating the area under the curve as
this area reaches the value of Ω*a, a tripping action should be
3
initiated at time t. For G2 the area under the curve doesn’t
reach Ω*a, so, there is no action tacking.
Fig. 2 A Graph Represent The Implementation of The On-line Determination
of Angle of Instability Technique
The computation of the Ωd is similar to accumulate the
integral of Δδj below threshold, denoted Δδ*d. When Ωd grows
above a pre-specified value, say Ω*d, load shedding, as a
remedial action, would be initiated to mitigate the disturbance
event.
The threshold Δδ*a is set to be 60 degrees, and Δδ*d is set to
be -70 degrees, where Ω*a and Ω*d are set to be 5 and -5
respectively.
III. DEVELOPMENT OF ANN FOR ON-LINE STABILITY
PREDICTION
With the advent of PMU and availability of on-line
measurements, it is desirable to predict system instabilities
before out of step occurs and taking appropriate remedial
action, such as system splitting, to avoid unnecessary
cascading tripping. By using the mentioned technique, an
ANN can be trained to predict system transient instabilities. In
next sections a two-layer ANN for on-line transient stability
predication is proposed.
A. Artificial Neural Networks
Neural networks are composed of simple elements
operating in parallel. These elements are inspired by biological
nervous systems. As in nature, the network function is
determined largely by the connections between elements.
Neural networks can be trained to perform a particular
function by adjusting the values of the connections (weights)
between elements.
Commonly neural networks are adjusted, or trained, so that
a particular input leads to a specific target output. Therefore,
the network is adjusted, based on a comparison of the output
and the target, until the network output matches the target.
Typically many such input/target pairs are needed to train a
network.
B. Stages needed to build an ANN predictor
Stage 1
Input selection; this is the first step in any pattern
recognition problem; it has a direct effect on the
performance and size of the ANN.
Stage 2
Selection of training data.
Stage 3
Selection of ANN Size (hidden neurons &
layers)
Stage 4
Train ANN.
Stage 5
Tests and compare results.
Input Selection
The input data is based on number of phasor measurements
cycles window, which begins one cycle after the fault clearing.
The actual number of cycles will be determined in throughout
the implementation on the study system (section IV) as it is
strongly depends on system parameters. The rate of change for
both generators bus voltages and angles are found to give
more accurate instability detection status, for a total of two
inputs per generator. The input vector X can be written as:
X = {x1 , x2 ,..., xi ,..xn }
(4)
Where,
xi = (
dvi dδ i
,
)
dt dt
(5)
and,
v is the generator bus voltage magnitude
δ is generator bus voltage angle
t is the time window
n is the number of generators buses
ANN Selection
The ANN proposed is a two layer feed-forward type, with
two hidden layers as shown in Fig. 3.
ANN Output
The output of the ANN consists of one neuron representing
the system status. Its values are as follows
0 Æ System is stable after fault clearing
1 Æ System is unstable after fault clearing
Fig. 3 Proposed Artificial Neural Networks
4
Table 1
Simulated Components in PSAF for EPS
IV. IMPLEMENTATION ON THE EGYPTIAN POWER SYSTEM
(EPS)
The total installed generation capacities of the EPS in 2008
are about 23,530 MW with a peak load of 21,250 MW during
summer [19]. The generation types of the EPS vary between
thermal, which represents 89.8% of total generation, hydro
9.3% & wind 0.9%. The Egyptian grid’s transmission system
is covering Egypt and linked by interconnector lines to
neighborhood countries as Jordan from east & Libya from
West. The total length of transmission lines within Egypt is
more than 430,000 km, covering different high voltage levels
of 500 kV, 400 kV, 220 kV, 132 kV & 66 kV. Figure (4)
shows EPS transmission lines where voltage levels are
presented by different line styles, also main generation stations
are indicated.
Total number of
Buses
Transmission Lines
Power Transformers
Generation Stations
Shunt Capacitors
Shunt Reactors
Load Buses
System Frequency
Total Generation
Total Demand
Fig. 4 Egyptian Power System 500/220/132 kV transmission lines
The Power Systems Analysis Framework (PSAF) software
[20] is chosen for simulating the dynamic response of the EPS.
Fig 5 shows a single line diagram for the simulated system
including buses (stations), generating stations and
transmission lines. The total numbers of simulated
components using PSAF are summarized in Table 1.
218
400
154
31
6
11
134
50 Hz
19.5 GW
19.0 GW
Fig. 5 EPS 500/220/132/kV Single Line Diagram
The test steps for implementing & evaluating the proposed
ANN predictor are as follows:
Step 1
Step 2
Applying line faults at different locations in
the system to test the stability of the EPS after
fault clearing.
Building the on-line ANN predictor. (Using
the five stages in the last section)
5
A. Step 1- Applying line faults at different system locations
A number of 52 fault cases are applied at different system
locations to cover all the grid zones. All faults are applied at
0.1 second (5 cycles) and cleared by removing the fault and
disconnect the faulted line or double lines with fault duration
of 10 cycles. A MATLAB [21] m-file is created using the
algorithm early discussed in section II to detect the system
stability after clearing the fault. As a result, 27 cases appeared
to be unstable after fault clearing.
The results for this step can be summarized Table (2). The
table also indicates the time (in cycles) required to initiate a
remedial action. Using that time the size of predication
window can be determined.
that A.SOLT generation station is the first to lose its
synchronism from rest of system (move away from center of
angles). Also from Table 2 the maximum recommended time
to initiate remedial action is 20 cycles (5 cycles after tripping
the fault).
TABLE 2
THE 27 UNSTABLE CASES & RAS ACTIONS
Case #
Faulted
Bus
Lines Removed
Time to
initiate
remedial
action
(Cycles)
20
1
A.SOLT.
A.SOLT - SUEZ 220
2
A.SOLT.
A.SOLT. - MANAYEF
20
3
ABU_KIR
ABU_KIR - I. BAROD
20
4
C.SOUTH
C.SOUTH - W. HOUF
41
5
DAM.GAS
DAM.GAS - DAMANH
19
6
DAM.GAS
DAM.GAS - MAHMOUDI
19
7
GN.CN2
GN.CN2 - HELOPLS
45
8
GN.CN2
GN.CN2 - BASOUS 2
48
9
HIGH-DAM
HIGH.DAM - D1 HD 5
19
10
D1 HD 5
D1 HD5 - N. HAMADY
19
11
ISNA P
ISNA P - ISNA
20
12
ISNA
ISNA - LUXOR
21
13
K.DWAR
K.DWAR - ABIS
21
14
K.DWAR
K.DWAR - DAMANH
21
15
AMERIYA
AMERIYA - K.DWAR
26
16
AMERIYA
AMERIYA - DEKHALA
26
17
DEKHALA
DEKHALA – EZZ DEK.
27
18
EZZ-DEK.
DEKHALA – EZZ DEK.
27
19
KURIMAT
KURIMAT - FAYOUM
19
20
KURIMAT
KURIMAT - BENI SUEF E.
19
21
KRIMA500
KRIMA500 - TABEN
22
22
MAHMOUDI
MAHMOUDI - DAM.GAS
20
23
A.MOUSA
A.MOUSA - TABA
22
24
A.MOUSA
A.MOUSA - SUEZ 500
22
25
SUEZ 500
A.MOUSA - SUEZ 501
22
26
N.ASSP
N.ASSP - MALAWI
48
27
N.H.P.S
N.H.P.S - NH 220
24
To evaluate the results given in Table 2, case#2 and case #7
will be demonstrated in more details. Starting with case#2, Fig
6 shows the generators bus angle after fault clearing. It is clear
Fig. 6 Bus Angles for Case # 2
For case #7, Fig 7 shows that the generation station CN2 is
the first to lose synchronism. Again and from Table 2 the
required time to initiate a remedial action is 30 cycles after
clearing the fault.
Fig. 7 Bus Angles for Case # 7
Table 3 highlights the final conclusion of the first step of
the implementation test. It can be noted from this step that the
minimum time required to initiate a remedial action is 19
cycles (i.e. 4 cycles after clearing the fault).
TABLE 3
RESULTS OF APPLYING FAULTS AT DIFFERENT SYSTEM LOCATIONS
Total number of applied faults
System stable cases after clearing faults
System unstable cases after clearing faults
Min. time to initiate the remedial action
Max. time to initiate the remedial action
52
25
27
19 cycles
48 cycles
6
B. Step (2) - On-line transient stability predictor for EPS
The Neural Network toolbox in MATLAB software is used
to design the online ANN predictor. After many trial & errors
for different structures of ANN networks with different inputs
pattern, the rate of change of bus voltages & angles obtained
from PMUs within a specific window of time, found to give
the most accurate results.
A proposed ANN with two hidden layers Feed–Forward
Back Propagation is chosen to predict system instabilities. The
ANN is built using 3 cycle prediction window, total number of
62 inputs (2 input for each generation bus) and one output (0
for stable system or 1 for unstable systems). The training &
transfer functions of the ANN predictor are TRAINLM and
TANSIG respectively
38 % of the study cases (from last step) are used to train the
ANN, the rest 62% of the cases (Which the ANN never seen
before) are used for the testing purpose. The proposed ANN
predictor succeeded in estimating the system stability status in
91 % of the tested cases (Fig. 8 & 9).
Egyptian Power System about 38 % of the simulated cases on
the Egyptian Grid are chosen for training purpose, resulting in
right estimation of 91 % of the tested cases.
VI. REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Training
38%
[9]
Testing
62%
[10]
Fig. 8 A Chart Showing the Percentage of the Training & Testing Sets.
[11]
[12]
Failed
9%
[13]
[14]
Succeeded
91%
[15]
Fig. 9 A Chart Showing the Percentage of Success of the Proposed ANN
Predictor
[16]
V. CONCLUSION
A two layer on-line ANN transient stability predictor is
proposed. This predictor makes use of PMU readings to
estimate the stability status after system disturbance. Rate of
change of bus voltage and angles, resulted from PMU, are
used to train and test the proposed ANN. Dynamic behavior
under different disturbances of the Egyptian power system is
considered for different case studies. The proposed predictor
uses only 3 cycles prediction window, giving enough time for
activating remedial action in case of instabilities. With the
implementation of the proposed On-line ANN Predictor in the
[17]
[18]
[19]
[20]
[21]
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Systems Conference and Exposition, PES 2004, Vol. 1, pp. 307-312,
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C-W. Liu et al., "Neuro-Fuzzy Approach to Real-Time Transient
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Ministry of Electricity & Energy (Egypt) - http://www.moee.gov.eg
PSAF version 3.00, CYME International T & D Inc.
Matlab Software, MathWorks, version 7.1
7
VII. BIOGRAPHY
Fahd Hashiesh (M'09): was born in Cairo, Egypt,
on June 7, 1973. He received his B.Sc., M.Sc. &
PhD. from Faculty of Engineering, Ain Shams
University, Egypt in 1995, 2003 &2009 respectively.
He worked for ABB High Voltage, Egypt as a
projects manager for turnkey HV substations
projects from 1996 to 2001. Also he worked for
SABA Electric, Egypt from 2002 to 2009. Currently
he is substations design team leader with ABB
Limited in the UK. His current research interests are
power system control, operation and protection, power line communication,
smart
grid
implementation
&
applications.
His
email
is:
[email protected]
Hossam E. Mostafa (M'09): was born in Cairo,
Egypt. He received his B. Sc, M. Sc & Ph. D. From
Ain Shams University, Cairo, Egypt in 1987, 1994
and 1999.
From 1991 to 2001, he was working in Egypt Air
as second engineer. Since 2001, he has been a
faculty member with the Electrical Department at
the Faculty of Industrial Education (FIE), Suez
Canal Univ., Egypt. He is currently an Associate
Professor and Vice Dean for Student Affairs. His
research interests are applying AI techniques in power system control,
protection & operation. His email is: [email protected]
Ibrahim Helal: was born in Kalubia, Egypt,
received his B.Sc. and M.Sc. in Electrical Eng. in
1982 & 1988 from Ain Shams University, Egypt. He
received his Ph.D. in 1995 from University of New
Brunswick, Canada.
Currently, he is Professor in the of Electrical
Power and Machines, Ain Shams Univ,. He is
registered as Professional Engineer in Egyptian
Syndicate, and acted as independent consultant for
several electrical installation and power networks
planning projects. He is a member in IEEE, IEC
regional committee and is the secretary of energy saving committee in the
Scientific Research Academy of Egypt.
Prof. Mohamed M. Mansour: (M’81, SM’08) B.sc
& M. Sc. From Ain Shams University, Cairo, Egypt
in 1975 and 1980 respectively. His PhD was from
University of Manitoba, Canada, in 1983.
He has been a Prof. of power system in Dept of
Electrical Engineering in Ain Shams University
(since 1995). He is currently on leave as a Chair
Professor in King Fahd University of Petroleum &
Minerals (KFUPM) in Suadi Arabia. He had been a
visiting Prof in many universities in Canada, Egypt,
Kuwait and Suadi Arabia. He has more than 100 published papers in journals
and conferences. He supervised about 35 M. Sc. and PhD thesis (granted),
mainly in protection and control on power system and/ or machine. His major
research interest is in power system protection, measurement and control. His
email is: [email protected]