TCPS Controller Design Using Fuzzy Logic Controller for Power ...

2010 IEEE International Conference on Power and Energy (PECon2010)., Nov 29 - Dec 1, 2010, Kuala Lumpur, Malaysia

TCPS Controller Design Using Fuzzy Logic Controller for Power System Stability Enhancement M.Reza Safari Tirtashi

Kazem Mazlumi

Ahmad Rohani

Electrical Engineering Department, Zanjan University, Iran [email protected], [email protected], [email protected] Abstract-In this paper, a Fuzzy Logic Controller (FLC) is proposed for Thyristor Controlled Phase Shifter (TCPS) to improve power system dynamic performance. The FLC has been designed for the TCPS which is located at the terminal of the generator in order to damp the Low Frequency Oscillations (LFO). From the first, for normal loading condition, the system with proposed controller has been simulated while the input power of generator has been changed suddenly, then the dynamic responses of generator have been presented with and without controller. Thereafter, for two states viz normal loading condition and heavy loading condition, to show the robustness of proposed controller, the system with previous disturbance has been simulated, then the dynamic responses of generator have been shown. The effectiveness of the proposed controller is demonstrated by simulation results.

I.

disturbance in either of the areas, as well. Phase shifters also provide series compensation to augment stability. The highspeed responses of phase shifters make them attractive for use in improving stability [5]. Due to the characteristics of power transmission systems, the FACTS Compensator control algorithm must be designed resorting to control methods capable to deal with system non-linearities and unknown disturbances [6]. In this paper a Fuzzy Logic Controller (FLC) has designed for TCPS to damp power system oscillations when the input power of generator has been changed suddenly. The remainder of the paper is organized as follows. Section II describes the modeling of proposed system and the TCPS connected in the distribution system. Power system linearized model is presented in Section III. The design of the proposed FLC is detailed in Section IV. The computer simulation results are presented and discussed in Section V. Finally Section VI concludes this paper.

INTRODUCTION

For economic and ecological reasons, the building of new transmission lines and expansion of existing transmission systems are becoming more and more difficult. In this new situation, it is necessary to utilize the existing power transmission system at its maximum capacity to meet increasing demand of electrical energy [1]. Flexible AC Transmission System (FACTS) controllers are potent tools to achieve this Goal. In the steady state, FACTS controllers help in controlling and increasing the power flow through a line. However, the other important aspect of these controllers is their use during large disturbances like faults because of their capability to improve the transient stability condition of a power system [2], also FACTS controllers are capable of controlling the network condition in a very fast manner and this feature of FACTS can be exploited to improve the stability of a power system [3]. Some of the FACTS devices are Static Synchronous Compensator (STATCOM), Static Var Compensator (SVC), Unified Power Flow Controller (UPFC), Inter-phase Power Flow Controller (IPFC), Static Synchronous Series Controller (SSSC), Convertible Series Compensator (CSC), Thyristor Controlled Series Compensator (TCSC), Thyristor Controlled Phase Shifter (TCPS), Super conducting Magnetic Energy Storage (SMES).The devices may be connected so as to provide either series compensation or shunt compensation depending upon their compensating strategies [4]. TCPS is a device that allows dispatchers to change the relative phase angle between two system voltages, thereby helping them to control real power transfers between the two interconnected power systems. It attenuates the frequency of oscillations of power flow following a load

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II.

MODEL OF PROPOSED SYSTEM

A synchronous machine with an IEEE type 1 excitation System connected to an infinite bus through a transmission Line has been selected to demonstrate the derivation of simplified linear models of power system for dynamic stability analysis. Fig. 1 shows the model consists of a generator supplying bulk power to an infinite bus through a transmission line, with a TCPS located at its terminal. The equations that describe the generator and excitation system have been represented in following equations:

δ = ωD (ω − 1) ω =

Ρm − Ρe − D (ω − 1) Μ

E q′ =

E fd − ( Χ d − X d′ )id − Eq′ ′ τ do

Fig. 1: Single machine-infinite bus system model with TCPS

195

(1) (2)

(3)

−Κ Ε 1 E fd = VR Ε fd + ΤΕ ΤΕ

(4)

[

1 VR = Κ ΑVref − Κ ΑVt − Κ ΑVΕ − VR ΤΑ ⎛ − Κ ΕΚ F VΕ = ⎜⎜ ⎝ ΤΕ ΤF

⎛ ΚF ⎞ ⎟⎟Ε fd + ⎜⎜ ⎝ ΤΕ ΤF ⎠

]

⎛ −1 ⎞ ⎞ ⎟⎟V E ⎟⎟V R + ⎜⎜ ⎝ ΤF ⎠ ⎠

(6)

Ρe = Vtd Ι d + Vtq Ι q

(7)

Vt = Vtd + jVtq

(8)

Vtd = Χ q Ι q

(9)

Vtq = Ε ′q − Χ ′d Ι d

(10)

C1Ι d + C 2 Ι q = Vb sin(δ ) + C3 Ε′q

(11)

C 4 Ι d + C 5 Ι q = Vb cos(δ ) − C 6 Ε ′q

(12)

Solving (11) and (12) simultaneously, Id and Iq expressions can be obtained. C1…C6 are constant. δ is the rotor angle, Vb the infinite bus voltage, ω the rotor speed, Pm the mechanical input power, Pe active power, E'q the internal voltage, Efd the excitation voltage, and Vref is the reference voltage. The constant values of these equations have been represented in Table I. POWER SYSTEM LINEARIZED MODEL

A linear dynamic model is obtained by linearizing the nonlinear model round an operating condition (Pe = 0.9, Qe = -0.17). The linearized model of power system as shown in Fig. 1 is given as follows:

Δδ = ωD Δω ΔΡm − ΔΡe Μ ΔΕ fd − ( Χ d − Χ ′d )Δi d − ΔΕ' q ΔE q′ = ′ τ do Δω =

−Κ Ε 1 )ΔΕ fd + ( )ΔV R ΤΕ ΤΕ 1 ΔVR = ( )[Κ Α ΔVref − Κ Α ΔVt − Κ Α ΔV E − ΔV R ] ΤΑ

ΔE fd = (

Synchronous Machine

H = 2.37 s; T'do = 5.9 s; D = 0; Xd = 1.7; Xq = 1.64; X'd = 0.245;

Excitation System

KA = 400; TA = 0.05; KF = 0.025; TF = 1; KE = -0.17; TE = 0.95;

Transmission Lines

Xe = 0.4; Re = 0.02;

TCPS Parameters

Kc = 1; Tc = 0.15;

Operating Condition

Vt = 1; Vb = 1; Pe = 0.9; Qe = 0.17;

(5)

Where

III.

TABLE I SMIB AND TCPS PARAMETER VALUES

(13) (14) (15) (16) (17)

Κ −Κ Ε Κ F −1 )ΔΕ fd + ( F )ΔVR + ( )ΔVΕ ΔVE = ( ΤΕ ΤF ΤF Τ ΕΤF

(18)

ΔΡe = Κ 1 Δδ + Κ 2 ΔΕ ′q + Κ 3 ΔΦ

(19)

ΔVt = Κ 4 Δδ + Κ 5 ΔΕ ′q + Κ 6 ΔΦ

(20)

K1, K2, …, K6 are linearization constants.

196

IV.

FUZZY LOGIC CONTROLLER

In 1965, Zadeh proposed Fuzzy logic; it has been effectively utilized in many field of knowledge to solve such control and optimization problems [7]. FLC is a good mean to control the parameters when there isn't any direct and exact relation between input and output of the system, and we only have some linguistic relations in the If-Then form [8]. The use of fuzzy logic has received increased attention in recent years because of its usefulness in reducing the need for complex mathematical models in problem solving [9]. In power system area, it has been used to stability studies, load frequency control, unit commitment, and to reactive compensation in distribution network and other areas. Fuzzy control system is made from different blocks such as numeral quantity converter to fuzzy quantities (fuzzifier interface) block, the fuzzy logical decision maker section, knowledge base section, and defuzzier interface block. In the inference engine, the fuzzy AND operation is implemented by the “min” operator, fuzzy OR operation is implemented by the “max” operator. The centroid defuzzification scheme has been used here for obtaining the output (Uc), which is a supplementary Signal of the TCPS control scheme. By controlling the Uc, φ is controlled (Fig. 2). Consequently the relative phase angle between generator and infinite bus is controlled. Then the active power flow between generator and infinite bus is controlled. The following steps are involved in designing the fuzzy TCPS controller [10]: 1) Choose the inputs to the FLC. As shown in Fig. 2, only two inputs, the generator speed deviation (Δω) and generator speed derivative deviation (Δŵ), have been employed in this study. The symbol Uc has been synonymously used to represent the output or decision variable of FLC. 2) Choose membership functions to represent the inputs in fuzzy set notation. Triangular functions are chosen in this work. Fuzzy representations of generator speed change, acceleration, and output variable have been illustrated in Fig. 3. Similar membership functions for the other inputs and the fuzzy TCPS output are also defined. The proper range for each membership functions and the number of them can be defined based on designer experiments and the system configuration. 3) A set of decision rules relating the inputs to the output are compiled and stored in the memory in the form of a “decision surface”. The decision surface is provided in Fig. 4.

V. Kc 1 + sT c

The performance of the TCPS controller (with FLC strategies) for stabilization of synchronous generator is evaluated by computer simulation studies. The SMIB system was tested with the FLC for various operating conditions. The system data is given in the table I. The transient performance of the rotor speed variation, rotor angle variation, terminal voltage variation, and internal voltage variation are compared in Figs. 5 and 6, for the nominal operating point following 5% disturbance on mechanical generator power input for a sixcycles fault duration. It is clearly seen that with the inclusion of FLC on TCPS controller, electromechanical damping characteristics of the system is improved.

(a) Degree of membership

Fig. 2: Model of TCPS controller with FLC

1 1

4

5

0.8 0.6 0.4

The robustness of the controller was tested by applying the fuzzy strategy to a number of operating conditions. Fig. 7 shows the system dynamic response for a six-cycle fault disturbance for rotor speed variation, rotor angle variation, terminal voltage variation, and internal voltage variation for the following 2 loading conditions;

0.2 0 -5

(b) Degree of membership

3

2

-2.5 0 2.5 Generator speed deviation

1 1

2

3

4

5

5 -4

x 10 6

7

a) Nominal loading: Pe = 0.9 p.u. and Qe = 0.17 p.u.;

0.8

b) Heavy loading: Pe = 1.1 p.u. and Qe = 0.25 p.u.;

0.6

It can be observed the fuzzy control scheme gives very good damping profile over a range of operating conditions even for severe fault conditions.

0.4 0.2 0 -0.01 -0.005 0 0.005 0.01 Generator speed derivative deviation

1 1

2

3

4

5

6

0.015

1.5

(a) rotor speed variation

-0.015 (c) Degree of membership

SIMULATION RESULTS

7

0.8 0.6 0.4 0.2 0 -0.04

-0.02

0 0.02 0.04 0.06 Fuzzy logic controller output Fig. 3: Membership functions of inputs and output

x 10

-3

FLC None

1 0.5 0 -0.5 -1

0.08 -1.5

0

1

2

3

4

5

Time (s)

(b) rotor angle variation

Fuzzy controller output

0.04

0.06 0.04 0.02 0 -0.02 Spe 0.01 ed d er iv a

-5

0.03 0.02 0.01 0 -0.01 -0.02 -0.03

5 0 t ive dev -0.01 iatio n

FLC None

-0.04

0 vi ati on -4 x 10 d de e e p S

0

3 4 5 Time (s) Fig. 5: system dynamic response for a six cycle fault disturbance. (a) rotor speed variation, and (b) rotor angle variation.

Fig. 4: Decision surface of proposed FLC

197

1

2

x 10

-3

x 10

FLC None

4

(a) rotor speed variation

(c) terminal voltage variation

5

3 2 1 0 -1 -2

-4

Heavy Normal

4 2 0 -2 -4

-3 0

-4

0

1

2

3

4

5

2 0 -2

-3

10 5 0

0

1

2

3

4

5

Time (s)

1

2

(c) terminal voltage variation

0

5

CONCLUSION

In this paper, a fuzzy logic controller has been proposed for TCPS to improve the power system damping. Analysis was carried for TCPS equipped with FLC controller. The proposed controller has been applied and tested on SMIB power system under 5% disturbance in input power of generator and different loading conditions (normal, heavy). It has been observed that the damping of a SMIB power system can be significantly enhanced with proposed controller and Effective damping control for wide ranges of operation demonstrates the robustness of recommended controller.

x 10

-3

Heavy Normal

4 3 2 1 0 -1 -2 -3 -4

0

1

2

3

4

5

Time (s)

(d) internal voltage variation

2

REFERENCES

[4]

5

15

-5

-4

VI.

[3]

4

Heavy Normal

3 4 5 Time (s) Fig. 6: system dynamic response for a six cycle fault disturbance. (c) terminal voltage variation, and (d) internal voltage variation

[2]

3

-3

FLC None

-6

[1]

x 10

20

(b) rotor angle variation

(d) internal voltage variation

4

2 Time (s)

Time (s)

x 10

1

S. Hameed, B. Das and V. Pant, “A self-tuning fuzzy PI controller for TCSC to improve power system stability,” International journal of Electric Power Systems Research, pp. 1726-1735, 2008. D. Chatterjee, and A. Ghosh, “TCSC control design for transient stability improvement of a multi-machine power system using trajectory sensitivity,” International journal of Electric Power Systems Research, pp. 470-483, 2007. S. Panda, and N. Padhy, “Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design,” International journal of Applied Soft Computing, pp. 1418-1427, 2008. R. Jayabarathi, M.R Sindhu, N. Devarajan , and T.N.P. Nambiar, “Development of a Laboratory Model of Hybrid Static Var Compensator,” IEEE conference, 2006.

x 10

-3

Heavy Normal

1 0 -1 -2 -3 -4 -5 -6

0

1

2

3

4

5

Time (s)

Fig. 7: system dynamic response for a six cycle fault disturbance with normal, light and heavy loading. (a) rotor speed variation, (b) rotor angle variation, (c) terminal voltage variation, and (d) internal voltage variation

198

[5] [6]

[7]

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