coordinated design of pss and statcom based power oscillation damping controller using mol algorithm

8/20/2019 Coordinated Design of PSS and STATCOM based Power Oscillation Damping Controller using MOL Algorithm

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International Journal of Application or Innovation in Engineering& Management (IJAIEM)Web Site: www.ijaiem.org Email: [email protected]

Volume 4, Issue 12, December 2015 ISSN 2319 - 4847

Volume 4, Issue 12, December 2015 Page 9

ABSTRACT

In this paper power-system stability enhancement by simultaneous tuning of Power System Stabilizer (PSS) and Static

Compensator (STATCOM) based damping controllers is thoroughly investigated. The power system stabilizer (PSS) input

signal can be either speed deviation or active power Pa are considered for the proposed analysis. The design problem of

the proposed controller is formulated as an optimization problem, and MOL algorithm is employed to search for the optimal

controller parameters. The performance of the proposed coordinated control of based PSS with based STATCOM is

compared with Pa based PSS with based STATCOM controller under different disturbances and loading conditions

for SMIB and multi-machine power system. It is verified that coordinated based PSS with based STATCOM

controller better than coordinated control of Pa based PSS with based STATCOM controller of the proposed power

system in term of power system stability improvement.

Keywords- MOL Algorithm, STATCOM, Power System Stabilizer, Multi Machine Power System

1.INTRODUCTION

Power system stability and security are important factor for power system operation [1, 2]. The low frequencyoscillations in the range of 0.1-2 Hz observed in large power systems and their connection, which has poor damping ina power system. The Power System Stabilizers (PSS) has been widely used for damping oscillations and increasing thestability of power system. However, PSS may not be able to provide the required damping in modern complex powersystems. Generally, it is important to recognize that machine power parameters changes with loading, making themachine behavior quite different at different operating conditions. Hence, PSS should provide some degree ofrobustness to the variation in system parameters, loading condition and configurations. H∞ Optimization techniqueshave been applied to robust PSS design problem [3]. However, the order of the H∞ based stabilizer is as high as that ofthe plant. This gives rise to complex structure of such stabilizers which reduces their applicability. A comprehensiveanalysis of the effects of the different conventional PSS parameters on the dynamic performance of the power systemwas presented in [4]. It is shown that the conventional PSS provide satisfactory damping over a wide range of systemloading conditions [5]. Although PSS provide supplementary feedback stabilizing signals, they suffer a drawback of being liable to cause great variations in the voltage profile. The recent advances in power electronic technologies havemade the application of FACTS devices very popular in power systems. Most FACTS devices are installed ontransmission lines far away from any generator and their purposes are mainly for reasons other than increasing thedamping of low frequency oscillations. A supplementary controller may be designed for each FACTS device to increasethe damping of certain electromechanical oscillatory modes (inter-area modes), while meeting the primary goal of thedevice. Since electronic devices are not directly involved with electromechanical oscillations and the generator signalsare not available locally, the damping controller design is not as straightforward as those of the PSS.The interaction among PSS and FACTS based controllers may enhance or degrade the damping of certain modes ofrotor’s oscillating modes. To improve overall system performance, many researches were made on the coordination between PSS sand FACTS power oscillation damping controllers [6-12].Also, the controllers should provide some

degree of robustness to the variations loading conditions, and configurations as the machine parameters change withoperating conditions. A set of controller parameters which stabilize the system under ascertain operating condition mayno longer yield satisfactory results when there is a drastic change in power system operating conditions andconfigurations [13].The problem of PSS and FACTS controllers parameter tuning is a complex exercise as

Coordinated Design of PSS and STATCOM

based Power Oscillation Damping Controller

using MOL Algorithm

Sangeeta Nayak1, Sangram Keshori Mohapatra

2

1Department of Electrical Engineering C.V. Raman College of EngineeringBhubaneswar, Odisha, India

2Department of Electrical Engineering C.V. Raman College of Engineering

Bhubaneswar, Odisha, India






uncoordinated local control of FACTS devices and PSS may cause destabilizing interactions. In this Paper, thecoordinated design of PSS and STATCOM controller is presented. The large numbers of conventional techniques have been reported in the literature pertaining to design problems of lead-lag (LL) controller structure namely the eigenvalueassignment, mathematical programming, gradient procedure for optimization, and also the modern control theory.Unfortunately, the conventional techniques are time consuming as they are iterative and require heavy computation

burden and slow convergence. In addition, the search process is susceptible to be trapped in local minima, and thesolution obtained may not be optimal. Many optimizing liaisons (MOL) algorithm is the simplified form of particleswarm optimization (PSO) algorithm. PSO algorithm was first developed in 1995 by Kennedy and Eberhart [14].In this paper MOL algorithm used to find out the optimal controller parameter.

2.POWER SYSTEM UNDER STUDY

A.Single-Machine infinite-bus power system with PSS and STATCOM

The Single-Machine Infinite-Bus (SMIB) power system with PSS and STATCOM shown in single line diagram asshown in Fig.1 is considered at the first instance in this study. The simulation model of SMIB power system with PSSand STATCOM controller are considered by taking all the relevant parameters are taken as reference [8, 9].

Load

STATCOM V

T V BV

Tr. lineT 1

STATCOM

Generator Bus-1 Bus-2 Bus-3

Shunt

FACTS

Devices

T 2

I P LP L1

PSS

Figure1.Single-machine infinite-bus power systems with PSS and STATCOM

3.THE PROPOSED APPROACH

A.Structure of STATCOM based damping controller

The structure of STATCOM based damping controller is shown in Fig.2. The STATCOM uses a lead-lag structure andacts as a controller to regulate the voltage signals VSTATCOM_ref . Each structure consists of a gain block, a signal washout block and two-stage phase compensation block and a sensor delay block. The phase characteristic to be compensated

changes with the system conditions, therefore a characteristic acceptable for a range of frequencies (normally 0.1 to 2.0Hz) is sought. This may result in less than optimum damping at any one frequency. The required phase lead can be

obtained by choosing appropriate values of time constants S T 1 , S T 2 , S T 3 , S T 4 .The stabilizing gain S K determines the

amount of damping introduced and, ideally, it should be set to a value corresponding to maximum damping. Timedelays can make the less damping features. Recently there is a growing interest in designing the controllers in the presence of uncertain time delays[17].

S K W

W

sT

sT

1 S

S

sT

sT

2

1

1

1

S

S

sT

sT

4

3

1

1

Input

signal GainBlock

WashoutBlock

Two stagelead-lag Block

Output

ref STATCOM V _

+

+

max

_ ref STATCOM V

D

Delay

STATCOM V

min

__ ref STATCOM V

Figure2. Structure of STATCOM based controller






B.Structure of the power system stabilizer

The PSS includes an amplification block, a signal washout block, a lead-lag block. The lead-lag block provides a proper phase-lead characteristic to compensate for the phase lag between the generator electrical torque and the exciter input.

The PSS input signal can be either speed deviation or active power Pa .The output signal of the PSS is the signalV S which is used as an additional input to the excitation system block. The structure of the PSS controller is presented

in Fig.4.

Gain

block Washout

block

Two-stage

lead-lag block

Input PS K

WP

WP

sT 1

sT

P2

P1

sT 1

sT 1

P4

P3

sT 1

sT 1

Output

SV

maxS

V

minS

V

Figure3. Structure of power system stabilizer

C . Objective function

The washout function the value of washout time constant is not very critical and may be in the range 1 to 20 s [14].In

the present analysis, wash out time constant TW = TWP =10s are used. The gains (PS K and S K ) and the time constants

( S T 1 , S T 2 , S T 3 , S T 4 , PT 1 , PT 2 , PT 3 , PT 4 ) are to be determined in lead-lag controllers and the time constants( PT 1 ,

PT 2 , PT 3 , PT 4 ) are to be determined . It is worth mentioning that the PSS and STATCOM-based controllers are

designed to damp the power system oscillations after a disturbance. In the present study, an integral time absolute errorof the speed deviation is taken as the objective function. The objective function is expressed as:

simt t

t

dt t J 0

|| (1)

where, is the speed deviation and simt is the time range of the simulation.For objective function calculation, the

time-domain simulation of the power system model is carried out for the simulation period. It is aimed to minimize thisobjective function in order to improve the system response in terms of the settling time and overshoots. The problemconstraints are the STATCOM controller parameter bounds. Therefore, the design problem can be formulated asoptimization problem

Minimize J (2)Subject to

maxmin

iiiK K K ,

maxmin

iiiT T T

wheremini

K andmaxi

K are the lower and upper bounds of all the controllers (STATCOM and PSS) andminiT and

maxi

T are the lower and upper bounds of the time constants of all the controllers.

4.OVERVIEW OF MANY OPTIMIZING LIAISONS (MOL) ALGORITHM

Many optimizing liaisons (MOL) algorithm is the simplified form of particle swarm optimization (PSO) algorithm.

PSO algorithm was first developed in 1995 by Kennedy and Eberhart [14]. Initially the PSO algorithm was introducedfor simulating the behaviour of bird flock. Latter the PSO algorithm was simplified and applied to the individual particles (bird) which were actually involved in performing the optimization. In PSO algorithm, all the particles are placed at random position and are supposed to move randomly in a defined direction in the search space. Each particle’s direction is then changed gradually to insist to move along the direction of its best previous positions of andits peers, searching in their locality to discover even a new better position with respect to some fitness

measures .: n f

Letn X

be the position of a particle and

V be its velocity. Both the initial velocity and position of the particleare chosen randomly and updated iteratively. The formula for updating the velocity of the particle is given by [15].

)()(

X G R X P RV wV GGPP (3)

In the above formula w is a user defined behavioural parameter termed as inertia weight which controls the

number of repetition in the velocity of particle.

P and

G are the best positions of particle and swarm respectively.






P and

G are weighted by the stochastic variables )1,0(~, U R R GP . P , G are the user defined behavioural

parameters. Velocity is added with the current position of the particle to move to another new position in the searchspace.

V X X (4)After updating a particle's position, limitations are imposed on the distance covered by the particle in a single step sothat the particle can move from one search space to another in a single step. The steps involved in PSO algorithm areas follows [13]:

a. Initialize randomly the positions and velocities of each particle. b. Update the position and velocity of each particle.c. Update the personal and global best.d. Find the velocity of a new particle using equation (3).e. Using equation (4) move the particle to a new position.f. Enforce search-space boundaries.

g. Update the particle’s best position, if)()(

P f X f

h.

The above steps are repeated for the swarm’s best position )(

G .

The MOL algorithm is similar to PSO algorithm but the difference is that in MOL algorithm the particle is updatedrandomly where as in PSO algorithm the particle is updated iteratively over the entire swarm. This simplified version

of PSO is also known as Social Only PSO. In the MOL algorithm the swarm’s best position

P is eliminated by

setting P =0 and the velocity update formula becomes:

)(

X G RV wV GG (5)

Where w is inertia weight and )1,0(~U RG is a stochastic variable weighted by the user defined behavioral

parameter G . The particles current position is denoted by

X and updated using equation (4) as before.

G represents

entire swarm's best known position.

5.RESULT AND DISCUSSION

Here the fitness function can be obtained from time-domain simulation of power system model. Using each set ofcontroller’s parameters, the time-domain simulation is performed and the fitness value is determined. The optimizationwas repeated 20 times and the best final solution among the 20 runs is chosen as proposed controller parameters. The best final solutions obtained in the 20 runs are given in Table I & Table II.

TABLE I. Controller Parameters for SMIB power system with based PSS

Signal/ parameters

S K /

PS K

S T 1 /

PT 1

S T 2 /

PT 2

S T 3 /

PT 3

S T 4 /

PT 4

-based

PSS 71.2714 1.1820 1.7718 2.3952 1.2649

basedSTATCOM

15.2534 1.9747 0.5917 0.5865 1.1623

Pa -basedPSS

0.7451 2.2308 0.6073 0.3249 0.5634

based

STATCOM 17.5013 0.7184 2.3188 0.1292 1.4821






0 1 2 3 4 5-5

0

5x 10

-3

Time (sec)

( p u )

no control

case-1

case-2

Figure 6 .Speed deviation responses for light loading in case-b

0 1 2 3 4 510

15

20

25

30

35

40

45

Time (sec)

( d e g

r e e )

no control

case-1

case-2

Figure 7. Power angle responses for light loading in case-b

Case c: Heavy loadingTo test the robustness of the controller to operating condition and fault clearing sequence, the generator loading ischanged to heavy loading condition and a 5-cycle, 3-phase fault is applied at Bus2. The fault is cleared by opening boththe lines. The lines are reclosed after 5-cycles and original system is restored. The system response for the above severedisturbance is shown in Figs. 8 & 9. It can be clearly seen from Figs. 8 &9 that, for the given operating condition and

contingency, the system is unstable without control. Stability of the system is maintained and power system oscillationsare effectively damped out with the application of case-1. The proposed coordinated controller case-2 provides the best performance and outperforms by minimizing the transient errors and quickly stabilizes the system.

0 1 2 3 4 5-3

-2

-1

0

1

2

3

4x 10

-3

Time (sec)

( p u )

no control case-1 case-2

Figure 8. Speed deviation responses for light loading in case-c

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 550

55

60

65

70

75

Time (sec)

( d e g r e e )

no control case-1 case-2

Figure 9. Power angle responses for light loading in case-c






B.Extension to Multi-Machine power system with STATCOM with PSS

From the above analysis comparison of STATCOM with Pa based PSS and based PSS in SMIB for power

system stability analysis it is clear that coordinated control of based PSS with based STATCOM controller

better than coordinated control of Pa based PSS with based STATCOM controller. Now for the verification

and effectiveness of proposed analysis can be extended to multi machine power system consisting of 3 generators with 5 bus systems is considered. It is similar to the power system used [17, 18]. The proposed multi machine power systemare divided into two subsystem connected by intertie. The improvement of power system stability the line issectionalized and a STATCOM is shunted at bus5. The Fig.10 shows the single line diagram of the proposed testsystem [9].For remote input signal speed deviation of generator G1 and G3 is chosen as the control input of STATCOM

based damping controller. Speed deviations ( ) and active power ( Pa ) the individual generators are chosen asthe input signals for all three PSSs.

G2

G3

G1

STATCOM

T2

T3

BUS2

BUS1

BUS3

BUS4

BUS5

LOAD1

LOAD2

LOAD3

T1

L2

L3

L1

L1

L1

L1

LOAD4

Figure 10. Three machine power system PSS with STATCOM

TABLE III. Optimized controller Parameters for multi machine power system

Signal/

Parameters

S K /

PS K

S T 1 /

PT 1

S T 2 /

PT 2

S T 3 /

PT 3

S T 4 /

PT 4 -basedSTATCOM

98.7935 0.4269 0.6452 0.9926 0.1859

-basedPSS1

34.2051 1.0066 2.4571 1.0061 1.5521

-basedPSS2

7.7193 0.9540 0.4037 1.8955 2.1779

-basedPSS3

17.5395 1.7142 0.7361 1.3270 2.0812

-basedSTATCOM

81.4726 2.2646 0.3183 2.2835 1.5813

P -basedPSS1

4.8779 0.6970 1.3677 2.3938 2.4123

P -basedPSS2

7.8815 2.4265 2.3930 1.2140 2.0009

P -basedPSS3

7.0952 1.0550 2.2894 1.9807 2.3988

The objective functions J is defined as

simt t

t

I L dt t J 0

)||||(

(6)

Where Δω

I and Δω

L are the speed deviations of inter-area and local modes of oscillations respectively and t sim is thetime range of the simulation. The same approach as explained for SMIB case is followed to optimize the STATCOM

Pa based PSS with based PSS damping controller parameters for three-machine case. The best among the 20runs for both the input signals are shown in Table III.






Simulations results

Case-1: Three phase self clearing fault:Here a 3-phase fault is applied near bus 1 at t = 1 sec and it continues for 5 cycles. In these Figs. 11 &12, the responsewithout control is shown with dotted line with legend no control; and responses with the signals for Δω based

STATCOM with Pa based PSS is shown with dashed line with legend case-1 and the same for Δω based PSS with

Δω based STATCOM is shown with solid line with legend case-2 respectively. It is clear from Fig. 11 &12 that inter-area and local modes of oscillations are highly oscillatory in the absence of STATCOM-based damping controller andPSS. But the proposed controller significantly improves the power system stability by damping these oscillations with both case-1 and case-2. However, case-2 based coordinated controller to be a better than case-1 based coordinatedcontroller as the power system oscillations are quickly damped out with case-1 based coordinated controller.

0 2 4 6 8 10 12-4

-2

0

2

4x 10

-3

Time (sec)

2 -

3 ( p u )

no control

case-1case-2

Figure 11.local mode of oscillation for three phase fault disturbance

0 2 4 6 8 10 12-2

-1

0

1

2

3x 10

-3

Time (sec)

1 -

2

( p u )

no control

case-1

case-2

Figure12. Inter area mode of oscillation for self clearing three phase fault disturbance

Case-2- Line outage disturbanceTo show the robustness of the proposed approach, another disturbance is considered. The transmission line between bus5 and bus 1 is tripped at t=1.0 sec and reclosed after 5 cycles. The system response is shown in Figs.13 & 14 fromwhich it is clear that case-2 coordinated control to be a better choice than case-1 coordinated control for stability

improvement.

0 2 4 6 8 10 12-4

-2

0

2

4x 10

-3

Time (sec)

2 - 3 ( p u )

no control

case-1

case-2

Figure 13.Local mode of oscillation for line outage disturbance






0 2 4 6 8 10 12-3

-2

-1

0

1

2x 10

-3

Time (sec)

1 -

3

( p u )

no control

case-1

case-2

Figure14.Inter area mode of oscillation for line outage disturbance

Case-3-Small disturbanceFor completeness, the load at bus 4 is disconnected for 100 ms and the system response is shown in Figs. 15 &16. It isclear from these Figs. that the proposed controllers are robust and damps power system oscillations even under smalldisturbance conditions. Further, the performance with case-2 coordinated controller to be a better choice than case-1

coordinated controller.

0 2 4 6 8 10 12-4

-2

0

2

4x 10

-3

Time (sec)

2 -

3 ( p u )

no control

case-1

case-2

Figure 15.Local mode of oscillation for small disturbance

0 2 4 6 8 10 12-2

-1

0

1

2x 10

-3

Time (sec)

1 -

2 ( p u )

no control

case-1

case-2

Figure 16. Inter area mode of oscillation for small disturbance6.CONCLUSION

In this analysis, the proposed MOL optimization technique has been employed for the coordinated design of PSS with

STATCOM based controllers. Two input signal based

PSS and Pa based PSS are considered. Coordinated design of based PSS controller with based

STATCOM controller is compared with coordinated design of Pa based PSS and based STATCOM controllers

for different loading condition and disturbance. It is observed that based PSS with based STATCOM

controller gives better system response than Pa based PSS with based STATCOM controllers from powersystem stability point of view for both SMIB and multi machine power system .

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coordinated design of pss and statcom based power oscillation damping controller using mol algorithm

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