M. a. Abido and Y. L. Abdel-Magid

8/14/2019 M. a. Abido and Y. L. Abdel-Magid

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M. A. Abido and Y. L. Abdel-Magid

April 2007 The Arabian Journal for Science and Engineering, Volume 32, Number 1B 85

DYNAMIC STABILITY ENHANCEMENT OF EAST-CENTRAL

SYSTEM IN SAUDI ARABIA VIA PSS TUNING

M. A. Abido *

Electrical Engineering Department

King Fahd University of Petroleum & Minerals

and Y. L. Abdel-Magid

Electrical Engineering ProgramThe Petroleum Institute

Abu Dhabi, United Arab Emirates

:

. . .

..

.

* Address for corespondence

P. O. Box 183

King Fahd University of Petroleum & Minerals

Dhahran 31261, Saudi Arabia

Paper Received 1 January 2005; Revised 7 February 2006; Accepted 17 May 2006.


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The Arabian Journal for Science and Engineering, Volume 32, Number 1B. April 200786

ABSTRACT

This paper presents a practical case study on the dynamic stability of the Saudi

Electricity Company (SEC) power system and its effect on increasing the powertransfer limit of the interconnection between Eastern Operating Area (SECEOA)

and Central Operating Area (SECCOA). The problem of optimal tuning of the

power system stabilizer parameters was converted into an optimization problem witheigenvalue-based objective functions, which was then solved by genetic algorithms.

In this regard, two eigenvaluebased objective functions were considered and the

problem is solved using real-coded genetic algorithms (RCGA). The effectiveness ofthe suggested technique to enhance the power system dynamic stability and to extend

the power transfer capability limit of the SEC-EOA and the SECEOA power system

was verified through a comprehensive eigenvalue analysis and time-domain nonlinearsimulation. The results also indicated that the proposed tuning schemes of the

existing stabilizers in the system have led to an improvement in the system damping

compared with the existing stabilizer settings. In addition to the generally improvedresults realized using the suggested technique, an important issue has emerged which

is the availability of a systematic procedure for tuning the power system stabilizers.

In the future, when new plants are added to the system or new machines are equipped

with stabilizers, the procedure will prove invaluable.

Key words: Power system stabilizer, PSS, low frequency oscillations, dynamic

stability, genetic algorithms.


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DYNAMIC STABILITY ENHANCEMENT OF EAST-CENTRAL SYSTEM IN SAUDI

ARABIA VIA PSS TUNING

1. INTRODUCTION

Inadequate or negative damping of low frequency oscillations has, in many cases, imposed undesired limitations on

the operation of power systems. In some cases, these oscillations continue to grow, causing system separation if thesystem is exposed to a severe disturbance. In the past two decades, the utilization of supplementary excitation control

signals for improving the dynamic stability of power systems has received much attention [124]. DeMello andConcordia in 1969 [1] presented the concept of synchronous machine stability as affected by excitation control. They

established an understanding of the stabilizing requirements for static excitation systems. Nowadays, the conventional

leadlag power system stabilizer (CPSS) is widely used by power system utilities [2,3]. Other types of PSS such as proportionalintegral power system stabilizer (PI PSS) and proportionalintegralderivative power system stabilizer

(PID PSS) have also been proposed [4,5]. In recent years, several approaches based on modern control theory have been

applied to PSS design problem. These include optimal control, adaptive control, variable structure control, and intelligentcontrol [611].

Despite the potential of modern control techniques with different structures, power system utilities still prefer aconventional leadlag power system stabilizer (CPSS) structure [12,13]. The reasons behind that choice might be the

decentralized nature and the ease of on-line tuning of CPSS and the lack of assurance of the stability related to adaptive

or variable structure techniques.Kunduret al. [12] have presented a comprehensive analysis of the effects of the different CPSS parameters on the

overall dynamic performance of the power system. It is shown that the appropriate selection of CPSS parameters results

in satisfactory performance during system upsets. In addition, Gibbard [13] demonstrated that the CPSS provides

satisfactory damping performance over a wide range of system loading conditions. The robustness nature of the CPSS isdue to the fact that the torque-reference voltage transfer function remains more or less invariant over a wide range of

operating conditions.

Many different techniques have been reported in the literature pertaining to the optimum location and coordinated

design problems of CPSSs. Generally, most of these techniques are based on phase compensation and eigenvalue

assignment [1424]. Different techniques for sequential design of PSSs are presented [1417] to damp out one of theelectromechanical modes at a time. Generally, the dynamic interaction effects among various modes of the machines are

found to have a significant influence on the stabilizer settings. Therefore, considering the application of the stabilizer to

one machine at a time may not finally lead to an overall optimal choice of PSS parameters. Moreover, stabilizers

designed to damp one mode can produce adverse effects in other modes. In addition, the optimal sequence of design is avery involved question. The sequential design of PSSs is avoided in [1822] where various methods for simultaneous

tuning of PSSs in multimachine power systems are proposed. Unfortunately, the proposed techniques are iterative and

require a heavy computation burden due to the system reduction procedure. This gives rise to time-consuming computercodes. In addition, the initialization step of these algorithms is crucial and affects the final dynamic response of the

controlled system. Hence, different designs assigning the same set of eigenvalues were simply obtained by using

different initializations. Therefore, a final selection criterion is required to avoid long runs of validation tests on the

nonlinear model. Several heuristic search and artificial intelligent techniques have been successfully proposed and

implemented to enhance power systems dynamic stability [23,24].

Generally speaking, PSSs can extend the power system stability limit by enhancing the system damping. The

encouraging success achieved in this study, will open the door to further scrutiny of ways and means to further

improving the dynamic stability of the SECEOA and SECEOA power systems.

2. THE SYSTEM CONSIDERED

2.1. Reduced SECEOA/SECEOA Power System

In this work, the single-line diagram and the pertinent data of the SECEOA and the tie-line to SECEOA power

system for the year 2003 were considered. It was observed that the size of the network is very large and that it comprises

several voltage levels (380, 230, 115, 69, and 34.5 kV). It was decided, for the purpose of dynamic stability studies, toreduce the network by retaining only the 380 and the 230 kV levels. A reduction of the SECEOA network and the

relevant part of the SECEOA network including the tie-lines was made using the PSS/E software.

The overall network is schematically represented by the schematic diagram shown in Figure 1, which consists of the

SECEOA 230 kV DAMMAM AREA network, the SEC-EOA 230 kV SOUTH AREA network, the SEC-EOA 230 kV

NORTH AREA network, the 380 kV SECEOA network, and the SECEOA / SECEOA interconnection.


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Figure 1. Schematic diagram of the reduced network

2.2. Features of the Reduced System

In the reduced network, the available generating units have been grouped into equivalent 17 generators, of which 13generators are in the SECEOA network, and the remaining 4 generators are in the relevant SECEOA network. The

reduced network contains a total of 167 lines, 87 buses, 33 transformers, and 4 static VAR compensators. Load flow

analysis of the reduced network validated the approach taken in reducing the overall SECEOA / SECEOA system. The

main results of the load flow analysis are included in Table 1. The results obtained agree with the load flow resultsperformed on the overall system. It is worth mentioning that the maximum deviation observed in voltage magnitude

between the reduced and the overall systems for the buses retained is 0.0002 pu.

Table 1. Summary of Load Flow Study on Reduced System

Total MW Transfer from SEC-EOA to SECEOA 1230

Total MW Generation in SEC-EOA 10102

Total MW Load in SEC-EOA 8751

Total MVAR Generation in SEC-EOA 3161

Total MVAR Load in SEC-EOA 3626

Total MW Generation in SEC-COA 2197

Total MW Load in SEC-COA 3410

Total MVAR Generation in SEC-COA 292

Total MVAR Load in SEC-COA 1565

Total MW Generation 12299

Total MVAR Generation 3454

Total MW Load 12161

Total MVAR Load 5191

Total MW Loss 138

Total MVAR Loss -1737


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2.3. Generator Model

In this work, the two-axis model of the machine is suitable for low frequency oscillation study [14]. The i-th

machine model is given as follows.

)1( =

ibi (1)

iiieimii MDTT /))1(( =

(2)

'''' /))(( doiqidididifdiiq TEixxEE =

(3)

''''/))(( qoidiqiqiqiid TEixxE =

(4)

qididiqiqiqididiei iixxiEiET )('''' +=

,(5)

where dand q refer to the direct and quadrature axes respectively; is the rotor angle; is rotor speed; Te is the electric

torque; M is the inertia constant; D is the damping coefficient; dx and'

dx are d-axis reactance and d-axis transient

reactance of the generator respectively; d and'

dx are q-axis reactance and q-axis transient reactance of the generator

respectively; idand iqare the d-axis and q-axis components of the armature current respectively;'

qE and'

dE are the

internal voltages behind 'qx and 'dx respectively;Efd is the equivalent excitation voltage; 'doT and 'qoT are open-circuit

time constants of excitation circuit.

2.4. Exciters and Power System Stabilizers

Each generator in the overall SEC-EOA / SECEOA is equipped with a static exciter. Table 2 lists the models of theexisting exciters in the system. It is worth mentioning that a FORTRAN source code has been developed to simulate

these exciters.

Table 2. Excitation System Models

ESST4B IEEE Type ST4B Potential or Compounded Source-Controlled Rectifier Exciter

EXAC1 IEEE Type AC1 Excitation System

EXPIC1 Proportional / Integral Excitation System

EXST1 IEEE Type ST1 Excitation System

EXST2 IEEE Type ST2 Excitation System

The generator connected to bus 737 in the SECEOA network is equipped with a power-based power system

stabilizer of Type IEE2ST. The generators connected to busses 188 in the SECEOA network and 1305 and 1307 in the

relevant SECEOA network are equipped with Type PSS2A power system stabilizers. The PSS on bus 188 is power-

based stabilizer, while the PSSs on busses 1305 and 1307 are speed-based stabilizers. The power system stabilizermodels are shown in Figures 2 and 3. The models of exciters and power system stabilizers have been integrated with the

generator models to complete the dynamic stability model used in this study.

In this work, the problem of PSS design is formulated as an optimization problem using some eigenvalue-based

objective functions. A genetic algorithm is then employed to solve this problem. The PSS designed in this manner willbe optimal and the stability of the system guaranteed.

w4

w3

sT1

sT

+

5

6

1 sT

1 sT

+

+ sT81

sT1 7

+

+

1

1

sT1

K

+

2

2

sT1

K

+

10

9

sT1

sT1

+

+

Figure 2. PSS model: IEE2ST (on G1 at bus # 737)


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N

M9

8

)sT(1

sT1

+

+

2

1

sT1

sT1

+

+S1K

4

3

sT1

sT1

+

+

S3K

w3

w3

sT1

sT

+

w1

w1

sT1

sT

+

w4

w4

sT1

sT

+ 7

S2

sT1

K

+

w2

w2

sT1

sT

+ 6sT1

1

+

Figure 3. PSS model: PSS2A (on G3, G9, and G10 at buses # 188, 1305, and 1307 respectively)

3. GENETIC ALGORITHMS

3.1. GA Overview

Genetic algorithms (GA) are search algorithms based on the mechanics of natural selection and survival-of-the-fittest

[2526]. One of the most important features of the GA as a method of control system design is the fact that minimalknowledge of the plant under investigation is required. Since the GA optimize a performance index based on

input/output relationships only, far less information than other design techniques is needed. Further, because the GA

search is directed towards increasing a specified performance, the net result is a controller which ultimately meets theperformance criteria. In addition, because derivative information is not needed in the execution of the algorithm, many

pitfalls that gradient search methods suffer can be overcome. Finally, because the GA do not need an explicit

mathematical relationship between the performance of the system and the search update, the GA offer a more general

optimization methodology than conventional analytical techniques.

GA are distinguished from other optimization techniques by the use of these principles to guide the search. Unlike

other optimization techniques, GA work with a population of individuals represented by bit strings and modify thepopulation with random search and competition. The advantages of GA over other traditional optimization techniques

can be summarized as follows.

GA work on a coding of the parameters to be optimized, rather than the parameters themselves.

GA search the problem space using a population of trials representing possible solutions to the problem, not a singlepoint, i.e., GA have implicit parallelism. This property ensures GA to be less susceptible to getting trapped on local

minima.

GA use an objective function assessment to guide the search in the problem space.

GA use probabilistic rules to make decisions.

The basic concepts of GA are given in the following section.

3.2. GA Implementation

Due to difficulties in binary representation when dealing with continuous search space, the real-coded genetic

algorithm (RCGA) [27] has been implemented in this study. A decision variablexi is represented by a real number within

its lower limit ai and upper limit bi, i.e.xi [ai,bi]. The RCGA crossover and mutation operators are described as follows.

3.2.1. Crossover

A blend crossover operator (BLX-) has been employed in this study. This operator starts by choosing randomly a

number from the interval )](),([ iiiiii xyyxyx + , wherexi and yi are the i

th

parameter values of the parentsolutions and xi < yi. To ensure the balance between exploitation and exploration of the search space, = 0.5is selected.

This operator is depicted in Figure 4.

iaibi

x iy

)( iii xyx )( iii xyy +

exploitation

exploration

Figure 4. Blend crossover operator (BLX-)


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3.2.2. Mutation

The non-uniform mutation operator has been employed in this study. In this operator, the new value 'ix of the

parameterxi after mutation at generation tis given as:

=

=+=

1if),(

0if),('

iii

iii

iaxtx

xbtxx , (6)

max

(1 )

( , ) (1 )

t

gt y y r

= , (7)

where is a binary random number, ris a random numberr [0,1],gmax is the maximum number of generations, and

is a positive constant chosen arbitrarily. In this study, = 5 was selected. This operator gives a value'

ix [ai,bi] such

that the probability of returning a value close toxi increases as the algorithm advances. This makes uniform search in the

initial stages where tis small and very locally at the later stages.

4. PROBLEM FORMULATION

4.1. Power System Model

A power system can be modeled by a set of nonlinear differential equations as:

),( UXfX =

, (8)

whereXis the vector of the state variables and Uis the vector of input variables.In the design of PSSs, the linearized incremental models around an equilibrium point are usually employed.

Therefore, the state equation of a power system can be written as:

UBXAX +=

(9)

whereA is the open-loop system matrix and equals / X whileB is control matrix and equals / U . BothA andB

are evaluated at a certain operating point.Xis the state vector while Uis theinput vector.

4.2 PSS Optimized Parameters

In this study, there are 16 optimized parameters specified as follows. The power-based IEE2ST power system

stabilizer of generator connected to bus 737 in the SECEOA network has 5 optimized parameters. These parameters areK1,T5, T6, T7, and T8 as shown in Figure 2. The power-based PSS2A power system stabilizer of generator connected to

bus 188 in the SEC-EOA network has 5 optimized parameters. These parameters are KS1,T1, T2, T3, and T4 as shown in

Figure 3. The speed-based PSS2A of generators connected to buses 1305 and 1307 in the relevant SECEOA network

has 3 optimized parameters for each since the existing stabilizers utilize only one lead-lag block. These parameters areKS1,T1, and T2 as shown in Figure 3. This gives rise to 16 optimized parameters.

Table 3 gives the existing settings of the power system stabilizers. The tuned parameters are highlighted in the table.

Table 3. Existing PSS Parameters

PSS on Generator

SEC-EOA SEC-COAParameter

737 188 1305 1307

PSS Type IEE2ST PSS2A PSS2A PSS2A

Input Signal # 1 Power Disabled Speed Speed

Input Signal # 2 Disabled Power Disabled Disabled

T1 0.0 0.150 0.15 0.15

T2

0.00.016 0.02 0.02

T3 1.5 0.150 0.0 0.0

T4 1.5 0.033 0.0 0.0

T5 0.20 -------- ------- -------

T6 0.05 0.01 0.0 0.0

T7 0.20 99.0 0.0 0.0

T8 0.05 0.15 0.5 0.5

K1 0.367 -------- -------- --------

KS1 -------- 1.0 15.0 15.0


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4.3. Objective Functions

To tune the parameters of the PSSs and enhance the system damping, two eigenvalue-based objective functions

defined below were considered.

( )

0

2

1 0

i

iJ

= (10)

( )0

22 0

i

iJ

= (11)

where i and i are the real part and the damping ratio of the ith eigenvalue respectively. Also, 0 and 0 are chosen

thresholds. It is aimed to improve the damping ratio and the settling time by minimizingJ1 andJ2 respectively. The value

of0 represents the desirable level of system damping. This level can be achieved by shifting the dominant eigenvalues

to the left of thes=0 line in thes-plane. This insures some degree of relative stability. Also, the value of 0 represents

the desirable damping ratio which can be achieved by shifting the dominant eigenvalues to the left of 0 = line in the

s-plane. This insures a good time-domain response in terms of overshoots and settling time. The conditions i 0 and

0i are imposed to consider only the unstable or poorly damped modes which mainly belong to theelectromechanical ones. The problem constraints are the PSS parameter bounds. In general, the design problem can beformulated as the following optimization problem.

Minimize J (12)

Subject to

Kimin Ki Kimax (13)

T1imin T1i T1imax (14)




It is worth mentioning that the PSS parameter indices in Equations (13)-(17) are adapted according to the PSS

structures shown in Figures. 2 and 3. The PSS parameter indices are also highlighted in Table 3.

4.4. Settings of GA to PSS Tuning Problem

The proposed approach employs GA to solve this optimization problem and search for optimal or near optimal set of

PSS tuned parameters. The computational flow of the solution methodology can be shown in Figure 5.

The following GA parameters were used in the search:

Population size = 200

Maximum number of GA generations = 500

Crossover probability = 0.9

Mutation probability = 0.01

Number of parameters = 16.

5. RESULTS AND DISCUSSION

To tune the parameters of the power system stabilizers available on the equivalent SECEOA and SECEOA system,the approach described in Section 4 was used. Because the objective functions suggested are based on eigenvalue

analysis, the equivalent SECEOA and SECEOA system was first linearized. The reduced system is a 17-machine 87-

bus, system. In the linearized system, each machine is represented by a fourth-order model. As indicated earlier, only

four machines are equipped with power system stabilizers. These are machines # 1, 3, 9, and 10 connected to busses 188,737, 1305, and 1307. The existing situation is such that the PSSs installed on the SECEOA machines are power-based

(machines 1 and 3), while the PSSs installed on the SECEOA machines are speed-based (machines 9, and 10).


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Electromechanical Mode Identification

Objective Function Evaluation

Initialization

Linearization and Eigenvalue

GA Operators

Generation =

Generation + 1

Converged?

Generation


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5.2. Tuning of Existing PSSs withJ1

In this part of the work, the objective function J1 is considered. The condition 0i is imposed to consider

unstable or poorly damped modes. A value of 0 0.1 = was adopted to improve the damping ratio of all the

electromechanical modes.

The optimum values of the PSSs parameters [highlighted for clearness], when the objective function isJ1, are givenin Table 5 for the four PSSs used.

The system response for Case 1-1 and Case 1-2 are shown in Figure 6 and Figure 7 respectively. The nonlinear time-

domain simulations have been carried out using the PSS/E software. For comparison purposes, the responses withexisting PSS settings are also included.

Table 5. PSS Parameters [withJ1]

PSS on Generator

SECEOA SECCOAParameter

737 188 1305 1307


Input Power Power Speed Speed

T1 0.0 0.30200 0.90200 0.46370

T2 0.0 0.11970 0.01020 0.01040

T3 1.5 0.36180 0.0 0.0

T4 1.5 0.26070 0.0 0.0

T5 0.33980 -------- ------- -------

T6 0.20320 0.01 0.0 0.0

T7 0.38820 99.0 0.0 0.0

T8 0.18750 0.15 0.5 0.5

K1 0.7381 -------- -------- --------

KS1 -------- 3.7230 33.214 14.185

(a) (b)

Figure 6. Electrical power response of Case 1-1. (a) G9 [bus 1305]; (b) G10 [bus 1307]

1 3 5 7 90 2 4 6 8 10

Time (s)

6

10

4

8

12

P9

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

2.50

3.50

4.50

2.00

3.00

4.00

5.00

P

10

(pu)

Existing PSSs

Proposed PSSs


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(a) (b)

Figure 7. Electrical power response of Case 1-2. (a) G9 [bus 1305] (b) G10 [bus 1307]

It is clear that the results with the tuned PSSs show a noticeable improvement in the damping of the system

oscillations. Both the amplitude and the settling time of the oscillations have decreased.

It can be seen that the electrical power responses of the machines are greatly improved in terms of overshoots and

settling time. Generally speaking, the results have indicated that the improvement was more pronounced in the SECEOA system.

5.3. Tuning of Existing PSSs withJ2

In this part of the work, the objective function J2 is considered. The condition 0i is imposed to consider

unstable or poorly damped modes. A value of 0 1.0 = was adopted to improve the settling time of the

electromechanical modes.

The optimum values of the PSSs parameters [highlighted for clearness], when the objective function isJ2, are given

in Table 6 for the four PSSs considered in this study.

Table 6. PSS Parameters [withJ2]

PSS on Generator

SECEOA SECCOAParameter

737 188 1305 1307


Input Power Power Speed Speed

T1 0.0 0.48210 0.95270 0.99790

T2 0.0 0.24900 0.01100 0.01260

T3 1.5 0.28050 0.0 0.0

T4 1.5 0.27100 0.0 0.0

T5 0.15670 -------- ------- -------

T6 0.20120 0.01 0.0 0.0

T7 0.43200 99.0 0.0 0.0

T8 0.11480 0.15 0.5 0.5

K1 0.9028 -------- -------- --------

KS1 -------- 5.2483 49.9185 45.590

To demonstrate the effectiveness of the suggested technique, the nonlinear model of the equivalent SECEOA and

SECEOA system was simulated using the PSS/E software. For comparison purposes, the responses with existing PSS

settings are also included. The system response for Case 2-1 and Case 2-2 are shown in Figure 8 and Figure 9

respectively.

1 3 5 7 90 2 4 6 8 10

Time (s)

6

10

14

8

12

P

9

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

3.5

5.5

2.5

4.5

P10

(pu)

Existing PSSs

Proposed PSSs


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(a) (b)

(c) (d)Figure 8. Electrical power response of Case 2-1.

(a) G9 [bus 1305]; (b) G10 [bus 1307]; (c) G11 [bus 1363]; (d) G12 [bus 1364]

(a) (b)

(c) (d)

1 3 5 7 90 2 4 6 8 10

Time (s)

6

10

4

8

12

P9

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

2.5

3.5

4.5

2.0

3.0

4.0

5.0

P10

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

3

5

7

4

6

8

P11

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

0.5

1.5

2.5

1.0

2.0

P12(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

9

13

7

11

P9

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

3.5

5.5

2.5

4.5

P10

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

5

7

4

6

8

P11

(pu)

Existing PSSs

Proposed PSSs

1 3 5 7 90 2 4 6 8 10

Time (s)

1.5

2.5

1.0

2.0

3.0

P12

(pu)

Existing PSSs

Proposed PSSs

Figure 9. Electrical power response of Case 2-2 .

(a) G9 [bus 1305]; (b) G10 [bus 1307]; (c) G11 [bus 1363]; (d) G12 [bus 1364]


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It can be concluded that the results with the tuned PSSs exhibit better damping characteristics of the system

oscillations. The results also indicated that the improvement was more pronounced in the SECCOA.

It was also observed that generally speaking the use of the objective function J2 resulted in slightly betterperformance than the objective function J1 in terms of the settling time.

5. 4. Eigenvalue Analysis

For completeness, the eigenvalues and damping ratios of the system electromechanical modes are listed in Table 8in ascending order of the real part of the eigenvalues. It is clear that the damping characteristics are greatly improved

with the tuned stabilizers. It can be also concluded that the damping profile is significantly improved with the proposed

stabilizers.

6. CONCLUSIONS

In this work, a practical case study of the dynamic stability of the SECEOA and SECEOA power system has beenanalyzed. In particular, the existing power system stabilizers installed in both SECEOA and SECEOA systems were

investigated. For this purpose, the SECEOA and SECEOA power system was reduced to retain only the 380 and the

230 kV voltage levels. Load flow analysis of the reduced network validated the approach taken in reducing the overallSECEOA / SECEOA system.

The effectiveness of the suggested technique in enhancing the power system dynamic stability and extend the power

transfer capability limit of the SEC-EOA and the SEC-CER power system was verified through a comprehensive

eigenvalue analysis and time-domain nonlinear simulation.

It was observed that in all cases considered, the eigenvalue analysis performed clearly demonstrated a noticeable

improvement in the dynamic stability when the relevant tuned power system stabilizers parameters were used.Moreover, the nonlinear simulation results, with the PSSs tuned using the GA and under severe fault conditions, show a

noticeable improvement in the damping of the system oscillations. Both the amplitude and the settling time of the

oscillations have decreased. The results also indicated that the improvement was more pronounced in the SECEOAsystem.

In addition to the generally improved results realized using the suggested technique, an important issue has emerged

which is the availability of a systematic procedure for tuning the power system stabilizers. In the future, when new plants

are added to the system or new machines are equipped with stabilizers, the procedure will prove invaluable.

The encouraging success achieved in this study, will open the door to further scrutiny of ways and means to further

improving the dynamic stability of the SECEOA and SECEOA power system. Efforts may be directed toward issueslike considering the installation of PSSs on more machines in either SECEOA, SECCOA, or both, simultaneous and

coordinated used of various supplementary signals, installation of FACTS based stabilizers, and the robust tuning ofPSSs.

Table 8. Eigenvalues and Damping Ratios of Electromechanical Modes


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7. ACKNOWLEDGMENTS

The authors acknowledge the support of King Fahd University of Petroleum & Minerals, Saudi Arabia.

The second author also acknowledges the support of the Petroleum Institute, Abu Dhabi, UAE.

8. REFERENCES

[1] F. deMello and C. Concordia, Concepts of Synchronous Machine Stability as Affected by Excitation Control, IEEETrans. PAS, 88 (1969), pp. 316329.

[2] E. Larsen and D. Swann, Applying Power System Stabilizers,IEEE Trans. PAS, 100(6) (1981), pp. 30173046.

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M. a. Abido and Y. L. Abdel-Magid

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