Performance Evaluation of IPFC by Using Fuzzy Logic Based Controller for Damping of

Power System Oscillations Ms. S.M. BELWANSHI, Dr. V.K. CHANDRAKAR, Prof. S. N. DHURVEY Department of Electrical Engg, Department of Electrical Engg. Department of Electrical Engg.

G. H. Raisoni College of Engg, G.H. Raisoni Institute of Engg. & Tech. for Women’s G. H. Raisoni College of Engg.

Nagpur Nagpur Nagpur

supriya_belwanshi@yahoo.co.in , vc_vkc@yahoo.co.in, sonal_nd2000@yahoo.co.in.

Abstract—In this paper, Fuzzy logic based supplementary controller is installed with Interline Power Flow Controller [IPFC] to damp low frequency oscillations. IPFC is a new concept of the Flexible AC Transmission system controller for series compensation with the unique capability of power flow of multiple transmission lines. For the analysis Modified linearized Philips–Heffron model of Single Machine Infinite Bus system is established with a IPFC. The simulation results are presented to show the effectiveness and robustness of the proposed control schemes like Power Oscillation Damping [POD] controller,Power System Stabilizer [PSS] controller and Fuzzy logic controller by selecting effective control signals. Investigations reveal that coordinated tuning of IPFC with Fuzzy logic controller provide the robust dynamic performance. Eigen value analysis validates the performance of various controllers.

Keywords- POD, PSS IPFC, FACTS, Fuzzy logic controller

I. INTRODUCTION

Today power systems are much more loaded than before because of growth of the power demand. Thus occurrence of the low frequency oscillations has increased due to interconnected power system. The oscillation frequencies are low, are in the range of 0.2 to 0.5 Hz. If not well damped, these oscillations may keep growing in magnitude until loss of synchronism results. Also three parameters that affect on real and reactive power flows on line are voltage magnitudes at terminals of the line, line impedance and power angle.In the last decade, the flexible ac transmission systems (FACTS) devices have been progressively developed to deal with the above control objectives [3]. A stream of voltage source converter (VSC) based FACTS devices[5] such as Static Compensator (STATCOM), Static Synchronous Series Compensators (SSSC), static VAR compensators (SVC), thyristor controlled series capacitors (TCSC) and Unified Power Flow Controller (UPFC) have been successfully applied in damping power system oscillations [6-12]. Interline Power Flow Controller (IPFC), an advanced voltage sourced converter (VSC) based FACTS controller, consists of two voltage-sourced converters (VSCs) inserted in series in transmission lines, whose DC capacitors are linked such that

active power can be transferred between the two VSCs. Each VSC can provide series compensation for the selected line of the transmission system (master or slave line) and is capable of exchanging reactive power with its own transmission system. Basic function is to control power flow among transmission lines and damping of oscillations. Kazemi et al [10] proved the effective damping control function of an IPFC installed in a power system by using POD controller. However, they have not implemented PSS controller. Alivelu M. Parimi [5] has proved that IPFC control signal m2 is the most effective. M.R.Banaei et al [4] has proved that signals m1, m2 based controllers have more effect on damping of oscillation and signal 1 , 2 based controllers have less effect on damping of oscillation. However, they have not presented an approach fir obtaining the simultaneous coordination of IPFC with each control signal and Fuzzy Logic Controller. In view of this, the main objective of this paper is to study effectiveness of various control signals of IPFC for damping of power system oscillations. To design and the dynamic performance of IPFC along with- 1) Power Oscillation Damping controller (POD) 2) Power System Stabilizer controller (PSS)3) Fuzzy logic controller

II. SYSTEM MODEL

Fig. 1 is a single-machine infinite-bus power system installed with a IPFC which consists of an excitation transformer (ET), a boosting transformer (BT), two three-phase GTO based voltage source converters (VSC’s), a DC link capacitor and also the amplitude modulation ratio and phase angle of the control signal of each VSC, which are the input control signals to the IPFC.

Fig. 1 SMIB system installed with an IPFC

VSC1

VSC2

CONTROL

GENERATOR

Vt

Vse1

Vse2

DC LINKCdc

line 1

line 2

I1

I2

2011 Fourth International Conference on Emerging Trends in Engineering & Technology

978-0-7695-4561-5/11 $26.00 © 2011 IEEE

DOI 10.1109/ICETET.2011.43

168

A. Power system Non Linear Dynamic Model Phillips-Heffron linear model of a single-machine infinite-bus system with IPFC is derived from the nonlinear differential equations. Neglecting the resistances of all the components of the system like generators, transformers, transmission lines, and series converter transformers, a nonlinear dynamic model of the system is derived as follows:

= ω0 (ω-1) (1)

= (Pm –Pe –PD)/ M (2)

q= (-Eq+Efd)/ T’do (3)

fd= [-Efd+KA(Vref-Vs)]/TA (4) where,

Pe=Vsd(I1d+I 2d)+Vsq(I1q+I2q) (5)

Pe=P1+P2 (6)

Eq=E’q+(Xd-X’d)(I1d+I2d) (7)

Vs=Vsd+jVsq (8)

=XqIq+j[E’q-X’d(I1d+I2d)] (9)

The voltage equations of the IPFC converters in d-qcoordinates will be

(10)

(11)

where, VqK=VpqK (12)

(13)

From fig.1 ,we have: (14)

+j =j [( + )+j(I1q+I2q)] (15)

+jXL(I2d+jI2q)+VP2+jVq2+ Vb +jVb

(16)

Vsq=E’q-(Xd-X’d)(I1d+I2d) (17)

(18)

where,

Xqs=Xq+Xs; (20)

XTL=Xt+XL (21)

Xds=Xd’+Xs (22)

XTL (23)

XTL (24)

and XL is the series reactance of each transmission line.

B. Power system Linear Model The damping controller in the power system must be designed at a nonlinear dynamic model of power system, but because of difficulty of this process, generally the linear dynamic model of system at an operating point is put and analysis to design the controller and an obtained controller is investigated in the nonlinear dynamic model for its accuracy and desirable operation at damping of oscillations as shown in fig 2. The linear dynamic model of power system in state space form is obtained as follow:

(25)

Where X is state vector and U is control vector. (26)

+ (27)

Fig. 2 Philips–Heffron model of power system installed with IPFC

1sM+D

w0s

K2

K6

K4 K5

1K3+sT'do

KA1+sTA

K8 Kqu Kqd Kvu Kvd Kpd

1s+K9

Kcu

Kpu

-

-

-

-

K1

delta Vdcdelta u

IPFC

delta udelta u

delta Efddelta E'q

169

III. COORDINATED TUNING OF POD AND IPFCA power-flow controller as shown in Fig.3 is provided to improve the damping of power system oscillations andregulates the power flow on this line where Kpp and Kpi are the proportional and integral gain settings of the power-flow controller. This damping controller consists of three cascade connected blocks. First block compares the output power of generator with reference power (Pe(ref)).Then the error is applied to a PI controller to create proper amplitude modulation ratio for the second converter. Pe(ref) is the output power of the generator in the operating condition [4].

Fig. 3 Structure of power flow controller

In Fig 4. POD controller is shown which is provided to improve the damping of power system oscillations. The damping controller may be considered as comprising two cascade-connected blocks. Block I is provided to derive a speed-deviation signal from the electrical power, Pe. The total electrical power is measured at the IPFC location. It is then compared with the set point (mechanical power). The error is integrated and multiplied by l/M to derive a speed-deviation signal. It may be noted that the speed-deviation signal derived is used instead of the speed-deviation signal which has been measured, since the speed-deviation signal, in general, may not be available at the IPFC location. The second block comprises a lead-lag compensator. An electrical torque in phase with the speed deviation is to be produced in order to improve the damping of the system oscillations.

The POD controller may be considered as comprising gain KDC, wash out block and lag-lead compensator. The washout circuit is provided to eliminate steady-state bias in the output of POD Controller. The Tw must be chosen in the range of 10 to 20. The IPFC controllable signals can be modulated in order to produce a damping torque. Controllability indices for the different Interline Power Flow Controller controllable parameters. The parameters of the lead-lag compensator are chosen so as to compensate for the phase shift between the control signal and the resulting electrical power deviation. The gain setting of the damping controller is chosen so as to achieve the desired damping ratio of the electromechanical mode.

Fig.4 Transfer function block diagram of the IPFC based damping controller

IV. POWER SYSTEM STABILIZER (PSS) Power system stabilizers (PSS) [1] have been used in the

last few decades to serve the purpose of enhancing power system damping to low frequency oscillations. The main objective of designing PSS is to provide additional damping torque without affecting the synchronizing torque at critical oscillation frequency. The PSS are designed to stabilize local and inter area modes. The transfer function block diagram of PSS as shown in Fig. 5 which comprises of a gain block, signal washout and phase compensator. The dynamic compensator is made up of lead-lag stages having the following transfer function:

T(s) = KPSS(1+sT1)(1+sT3) (1+sT2)(1+sT4) (28)

The output of PSS must be limited to prevent the PSS acting to counter the action of Automatic Voltage Regulator (AVR). The gain setting of the damping controller is chosen such that, the desired damping of the electomechanical mode of concern is obtained, without affecting the damping of the other modes.

U WASH OUT DYNAMICCOMPENSATOR TORSIONAL

FILTER

LIMITER

U

VS

Fig. 5 Transfer function block diagram of PSS

V. FUZZY LOGIC CONTROLLER In order to damp low frequency oscillations effectively, fuzzy logic controller is designed. In the proposed method, Mamdani’s fuzzy inference method is used, because it is the most commonly employed fuzzy methodology. Here the proposed fuzzy supplementary controller block diagram is shown in fig.6 The fuzzy controller consists of four main components:

Fuzzification Knowledge base Inference Engine Defuzzification

Fuzzy rule base

Inference engine DefuzzifierFuzzifierPe y

Fig. 6 Fuzzy supplementary controller

For the current application, speed deviation of the synchronous machine are chosen as the input to the fuzzy logic controller and the output of the controller controls one of the input signal, m2 , of the IPFC. The numerical value of membership functions of the input and output signals are shown in Table 1 and 2 respectively.

Kpp+Kpis

Pe

Pe (ref)

+-

u

KDCsTw

1+sTw

1+sT1

1+sT2

U1Ms+D

Set Point

Pe

Pm

Block 1 Block 2

Wo

170

TABLE 1: MEMBERSHIP FUNCTION FOR INPUT PARAMETERS SR . NO.

Membership function

For Input parameters

1 High -0.2211 -0.04092 0.1582

2 Medium 0.0578 0.409 0.763

3 Low 0.4039 0.804 1.204

TABLE 2: MEMBERSHIP FUNCTION FOR OUTPUT PARAMETERS SR . No.

Membership function

For Output parameters

1 Good -0.4127 0.009283 0.3873

2 Better -0.106 0.294 0.694

3 Best 0.3038 0.7038 1.109

0.5

1

0-1 0-0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.8 1

high medium low

Fig 7. Membership function for the inputs of m2 Fuzzy logic controller

0.5

1

0-1 0-0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.8

good better best

Fig. 8. Membership function for the outputs of m2 Fuzzy logic controller

For instance, the membership functions for the inputs and output of m2 fuzzy controller is shown in Fig 7 and 8. Membership functions for all signals are of triangular shape having different range.

VI. SIMULATION RESULTS The performance of the designed fuzzy logic controller is shown through time domain simulations. The SMIB power system is simulated in Matlab simulink, Performance of IPFC without damping controller is shown in fig.9 it is clear that system is unstable.

Fig.9 Dynamic response without damping controller

A. Dynamic performance of the system with control signal m1

Result shown in Fig 10 indicates that with control signal m1 , Speed deviation response with PSS ,POD and Fuzzy controller is represented with coordinated action of IPFC. Result indicates that the coordinated action of controller and IPFC reduces first peak of speed deviation , settling time and steady state error has been significantly improved and Table 3 shown numerical values for the different controller. From above result it concluded that fuzzy controller gives the best result. Table 4 refer for eigen value analysis of system which shows that all eigen values lies on negative part of real axis which proves that system is stable.

Fig.10 Speed deviation response of linearized SMIB system for signal m1

Table 3 : Comparison with POD , PSS damping controller and fuzzy logiccontroller for control signal m1

S. N.

Parameters IPFC+PI IPFC + POD

IPFC+ PSS

Fuzzy logic

1 No. of oscillations

4 4 4 3.5

2 Peak 0.015 0.006 0.007 0.0035

3 Settling time

0.4 sec 0.4 sec 0.4 sec 0.33 sec

Table 4 : Eigen value analysis of linearized SMIB system with signal m1

Control signal m1

IPFC with POD

IPFC with PSS

Fuzzy logic

-11.1671 ±64.8836i

-11.4712 ±64.9366i

-11.1784 ±64.8856i

-11.1917 ±64.8880i

-0.0002 -19.4666-3.4642 ± 5.4093i

-3.4642 ±5.4093i

-0.0000-3.4642 ± 5.4093i -4.5455 -0.0001

-3.4642 ±5.4093i -0.1009 0

-11.1784 ±64.8856i

-11.1784 ±64.8856i 0.0000

-11.1917 ±64.8880i

-3.4642 ±5.4093i

B . Dynamic performance of the system with control signal m2

From Fig 11 it is clear that with control signal m2, POD Controller can suppress the oscillation with settling time 0.25 sec. With fuzzy logic controller it is observed that it gives better result and also transient response is significantly improved as shown in Table 5.Table 6 eigen value analysis of m2 which shows that all eigen values lies on negative part of real axis which proves that system is stable.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.03

-0.02

-0.01

0

0.01

0.02

0.03

Time(sec)

delta

w

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-0.01

-0.005

0

0.005

0.01

0.015

0.02

Time(sec)

spee

d de

viat

ion

(rad

/sec

)

%<change in speed >

a PI

b PODc PSS

d Fuzzy

a

c

b

d

171

Fig.11 Speed deviation response of linearized SMIB system for control signal m2

Table 5: comparison with POD , PSS damping controller and fuzzy logic controller for control signal m2

S. N.

Parameters IPFC+PI IPFC + POD

IPFC+ PSS

Fuzzy logic

1 No. of oscillations

3.5 3 3.5 2.9

2 Peak 6.2*10^-3

4*10^-3 4.4*10^-3 1.7*10^-3

3 Settling time

0.35sec 0.29 sec 0.35 sec 0.25 sec

Table 6 : Eigen value analysis of linearized SMIB system with control signal m2

Control signal m2

IPFC with POD IPFC with PSS

Fuzzy logic controller

-11.0682 ±64.8664i

-9.5784 ±64.5889i

-11.1240 ±64.8762i

-11.1892 ±64.8875i

-0.0002 -19.4476-3.4642 ± 5.4093i 0

0-3.4642

±5.4093i -4.5455 -0.0001-3.4642 ± 5.4093i -0.1003 0

-3.4642 ±5.4093i

-11.1240 ±64.8762i -0.0002 -0.1

-11.0682 ±64.8664i

-3.4642 ± 5.4093i 0

-11.0682 ±64.8664i -0.0002

-4.5455 ± 0.0000i

-11.0682 ±64.8664i -0.0002 0

C. Dynamic performance of the system with control signal 1

Result shown in Fig. 12 indicates that with control signal 1,simulated with coordinated tuning of IPFC and POD, PSS & Fuzzy logic controller. Table 7 shows comparison with POD, PSS damping controller and Fuzzy logic controller for control signal 2 in terms of the number of oscillation, peak value and settling time. Table 8 eigen value analysis of 1 which shows that all eigen values lies on negative part of real axis which proves that system is stable.

Fig. 12 Speed deviation response of linearized SMIB system for control signal 1

Table 7 : comparison with POD , PSS and fuzzy logic controller for signal 1

S. N.

Parameters IPFC+PI IPFC + POD IPFC+ PSS Fuzzy logic

1 No. of oscillations

4 3 4 3

2 Peak 13*10^-3 11*10^-3 12*10^-3 6.5*10^-3

3 Settling time 0.4sec 0.25sec 0.4 sec 0.3 sec

Table 8: Eigen value analysis of linearized SMIB system with control signal 1

Contol signal 1

IPFC with POD

IPFC with PSS

Fuzzy logic controller

-11.1833 ±64.8864i

-11.1833 ±64.8864i

-11.1833 ±64.8864i

-11.1833 ±64.8864i

-3.4642 ± 5.4093i

-3.4642 ±5.4093i

-3.4642 ± 5.4093i

-3.4642 ± 5.4093i

-4.5455 ±0.0000i -4.5455

-4.5455 ±0.0000i

-4.5455 ±0.0000i

-0.0001 -0.0001 -0.0001 0-0.1 -0.1 -0.1 -0.1

-19.1606 ±65.9038i

-19.1606 ±65.9038i

-19.1606 ±65.9038i

-19.1606 ±65.9038i

-19.1462 -19.1462 -19.1462

D. Dynamic performance of the system with control signal 2

Fig. 13 shows the dynamic performance of the system with control signal 2. POD controller ensures better performance with less settling time compared with PSS. By using Fuzzy logic based controller gives best result with transient response is significantly improved is as shown in Table 9. For eigen value analysis of 2 ref Table 10 which shows that all eigen values lies on negative part of real axis which proves that system is stable. Simulation is done for each control signal m1, m2, 1 and 2resp. The designed fuzzy –based IPFC controller adjusts four IPFC inputs by appropriately processing of the input error signal, and provides an efficient damping. The result of the simulation show that the IPFC with fuzzy –based controllers is more effective in damping low frequency oscillation compared to IPFC with control signal m2 as shown in Fig. 9.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-4

-2

0

2

4

6

8x 10

-3

Time(sec)

spee

d de

viat

ion(

rad/

sec)

%<change in speed >

a PI

b PODc PSS

d Fuzzy

a

b

c

d

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

-5

0

5

10

15x 10

-3

Time(sec)

spee

d de

viat

ion

(rad

/sec

)

%<change in speed >

a PI

b PODc PSS

d Fuzzy

a

b

c

d

172

Fig 13:- Speed deviation response of linearized SMIB system for control signal 2

Table 9 : Comparison with POD , PSS damping controller and fuzzy logic controller for control signal 2

S. N.

Parameters IPFC+PI IPFC + POD

IPFC+ PSS

Fuzzy logic

1 No. of oscillations

4.5 3.5 4.5 3

2 Peak 6.5*10^-3 5*10^-3 6*10^-3 3.1*10^-3

3 Settling time

0.44sec 0.35 sec 0.44 sec 0.29 sec

Table 10: Eigen value analysis of linearized SMIB system with signal 2

Contol signal 2 IPFC with POD IPFC with PSS Fuzzy logic

controller -11.1833 ±64.8864i

-11.1833 ±64.8864i

-11.1833 ±64.8864i

-11.1833 ±64.8864i

-3.4642 ± 5.4093i -3.4642 ±5.4093i -3.4642 ±5.4093i-3.4642 ± 5.4093i

-4.5455 ±0.0000i -4.5455 -4.5455-4.5455 ±0.0000i

-0.0001 -0.0001 -0.0001 0-0.1 -0.1 -0.1 -0.1

-13.5466±65.1498i -13.5466±65.1498i

-13.5466±65.1498i

-19.1606 ±65.9038i

-19.1462 -19.1462 -19.1462 -19.1462

VII. CONCLUTION In this paper, the relative effectiveness of Interline Power Flow Controller (IPFC) control signals (m1,m2, 1, 2 ) in damping low frequency oscillations has been examined. The linearized power system model of Single Machine Infinite Bus system for analyzing the performance comparison of IPFC in coordination with Power Oscillation Damping (POD) Controller , Power System Stabilizer (PSS) and fuzzy logic controller has been considered. These control signals gives the significant improvement in performance of system for damping of power system oscillations. Simulations results of control signals m2 and m1 in coordination with Fuzzy logic controller shows that oscillations are effectively damped. The proposed controller fulfils the main objective of this paper. Time domain analysis and eigen value analysis results validated the performance of various Interline Power Flow Controller . This work can be continued with Artificial

Intelligence (AI) application for improved dynamic performance.

ACKNOWLEDGMENT

The authors would like to thank for providing useful inputs to this and also for fully acknowledges the support of G. H. Raisoni College of Engineering, Nagpur for this project.

REFERENCES

[1] P. Kundur, Power System Stability and Control. Mc Graw-Hill, New York, 1994, ch. 12.

[2] N.G. Hingorami, L.Gyugyi, “Understanding FACTS: Concepts and Technology of Flexible AC Transmission system,” IEEE Press

[3] Y.H. Song and A.T. Johns, Flexible AC Transmission systems,IEE Power and Energy series 30, 1999.

[4] M.R.Banaei, A. Kami, “IPFC based damping controller forDamping low frequency oscillations”, Telecommunications Energy Conference , INTELEC 2009,IEEE,pp.1-6.

[5] Alivelu M. Parimi , “IPFC Bas ed damping controllers for damping low frequency oscillations in a power system” , ICSET 2008, PP 334-339.

[6] P.K.Dash, S.MishraA, G.Panda, “Damping multimodal power system oscillation using a hybrid fuzzy controller for series connected FACTS devices,” IEEE Trans. Power Systems, vol. 15, no. 4, pp.1360-1366, November 2000.

[7] N.Tambey and M.L.Kothari, “Damping of power system oscillation with unified power flow controller,” IEE Proc. Gener. Trans. Distib. vol. 150, no. 2, March 2003, pp. 129-140.

[8] L.Gyugyi, K.K.Sen, C.D.Schauder, “The interline power flowcontroller concept: A new approach to power flow management in transmission Systems,” IEEE Trans. Power Delivery, vol. 14, no. 3, pp. 1115-1123, July 1999.

[9] Jun Zhang, and Akihiko Yokoyama, “Optimal power flow control for congestion management by interline power flow controller (IPFC),” International Conference on Power System Technology,2006.

[10] Kazemi, A.; Karimi, E, “The effect of interline power flow controller(IPFC) on damping inter-area oscillations in the interconnected power systems,” Industrial Electronics, 2006 IEEE Internationa Symposium, volume 3, July 2006, pp. 1911 – 1915.

[11] J. Chen, T.T. Lie, D.M. Vilathgamuwa, “Design of interline powerflow controller,” 14th PSCC, Sevilla, June 2002.

[12] X.-P. Zhang, “Modelling of the interline power flow controller and the generalised unified power flow controller in Newton power flow,” Generation, Transmission and Distribution, IEE Proceedingsvol.150, no. 3, May 2003, pp. 268 – 274.

APPENDIX The parameters of the single machine infinite bus power system are as follows (in pu except where indicated): Generator : M=2H=0.1787 D 4.0, T’d0 5.044s., xd 1.0, xq 0.6, x’d 0.3, Reactances: xt 0.01, xt1 0.015, xt2 =0.015,xL1 0.5, xL2 0.5, AVR: KA 50, TA 0.05s., Operating condition: Pe 0.8, Vb 1.0, Vt 1.0. The parameters of the POD (m2) are KDC= -12 T1s=0.05137 T2s= 0.05142 The parameters of the PSS are:K pss 21.31, T1 T3 0.31, T2 T4 0.22

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-4

-2

0

2

4

6

8x 10

-3

Time(sec)

spee

d de

viat

ion(

rad/

sec)

%<change in speed >

a PI

b PODc PSS

d Fuzzy

a

b

c

d

173