OPTIMAL TUNING OF FACTS CONTROLLERS FOR POWER SYSTEM ...pep.ijieee.org.in/journal_pdf/11-270-14690836961-7.pdf · Optimal Tuning of Facts Controllers For Power System ... Optimal

International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982 Volume-4, Issue-6, Jun.-2016

Optimal Tuning of Facts Controllers For Power System Stability Enhancement

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OPTIMAL TUNING OF FACTS CONTROLLERS FOR POWER SYSTEM STABILITY ENHANCEMENT

1E.KIRANKUMAR, 2V.C.VEERA REDDY, 3P.HEMA CHANDU

1,3Research scholar, EEE,SV University, Tirupathi 2Rtd.Professor, EEE, SV University, Tirupathi

E-mail: [email protected], [email protected], [email protected],

Abstract— Stability exploration has drawn more attention in contemporary research for huge interconnected power system. It is a complex frame to describe the behaviour of system, hence it can create an overhead for modern computer to analyse the power system stability. The preliminary design and optimization can be achieved by low order liner model. This paper presents the stability analysis of single machine infinite bus system using different compensator namely: Generic power system stability (GPSS), proportional-integral-derivative, multiband power system stabilizer, Thyristor controlled series compensators and static series compensators. Keywords— GPSS, PIDMB-PSS, TCSC, STATCOM. I. INTRODUCTION The complicated structure of power system model, which may incorporate many synchronous generators, controllers’ transmission lines and varying loads. To overcome the power system instability or electromechanical oscillation, the role power system stabilizer exploration is become essential. The ability of extend the restoring force equal or greater then disturbing force to achieve equilibrium is called stability of system. Power efficiency and low price are the major concern of power industries. Electromechanical oscillation of generators (power swing) and varying load caused disturbance in power system. F.P. Demello and C. Concordia have shown the effect of synchronous machine stability by excitation control, in the case of single machine connected to infinite bus system. Further Philips and Heffron developed a K-control method to explain the small signal ability. II. SINGLE MACHINE INFINITE BUS SYSTEM (SMIB) Algorithmic simplicity can be achieved by focusing on one machine. Therefore, the single machine infinite bus (SMIB) system came into existence instead of multi-machine power system. As shown in Figure 1, a single machine is connected to infinite bus system through a transmission line containing inductance and resistance .

Figure 1: Single machine infinite bus system

The generator is demonstrated using transient model, as indicated by the accompanying equations. Stator Winding Equations:

(1) (2)

Where is the d-axis transient voltage. is the q-axis transient voltage is the q-axis transient resistance is the d-axis transient resistance

is the stator winding resistance Rotor Winding Equations:

(3)

(4) Where, is the d-axis open circuit transient time constant.

is the q-axis open circuit transient time constant is the field voltage. Torque Equation:

(5) Rotor Equation:

(6) Then

(7) Where,

is the electrical torque. is the damping torque and

is the damping coefficient. represents mechanical torque, which is constant

in this model.



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Figure 2: Heffron-Phillips model – SMIB

For the study of single machine infinite bus system a Heffron-Phillips model can be obtained by linearizing the system equations around an operating condition. The obtained Heffron model is as in figure 2 and essential mathematical equations related with SMIB framework are:

(8)

(9)

(10)

(11)

(12)

(13)

Where, =Rotor angle.

= Slip speed. and = Mechanical and

Electrical torques respectively. = Damping coefficient. = Transient EMF due to field flux linkage. = d-axis component of stator current. =q-axis component of stator current.

d-axis open circuit time constant. - d-axis reactance. = q-axis reactance.

= Field voltage. : Exciter gain and time constant.

Voltage measured at the generator terminal. = Reference voltage.

Linearized equations are:

(14) (15)

(16)

(17)

(18)

(19)

Where, Heffron-Phillip's constants are explained as:

Where

, and represents the values at the initial operating condition. Figure 3 showing the SIMULINK Implementation of Phillip-Heffron model stated above.

Figure 3: Simulink Implementation of SMIB

Figure 4: Simulink model for proposed SMIB

III. LITERATURE REVIEW 1. Power System Stability Power system stability is a property that enable it to operate in its equilibrium state under normal operating condition and regain its normal state of equilibrium when disturbance occurs. This paper concentrates electromechanical disturbance, which furthers causes for power fluctuations between electrical networks and generating units. In addition the electromechanical will also cause the instability of rotating part of power system [20]. Security of the power system relies on its ability to survive any disturbances which may occur without any disturbance in the services.A



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large synchronous generator of a typical excitation control system is shown in Figure5.

Figure 5: Basic block diagram of a synchronous generator

excitation control system [5]

Power system stabilizers (PSS) are utilized on a synchronous generator to expand the damping of oscillations of the rotor/turbine shaft. The traditional PSS was initially proposed in the 1960s and traditional control hypothesis, characterized in transfer functions, was utilized for its structure. Later the progressive work of DeMello and Concordia [1] in 1969, control engineers, and additionally power system engineers, have shown incredible knowledge and made huge assistance with PSS outline and applications for both single and multi-machine power systems. Optimal control hypothesis for stabilizing out SMIB power systems was created by Anderson [2] and also by Yu [3]. These controllers had linear property. Adaptive control methodologies have likewise been proposed for SMIB, the vast majority of which include linearization or model estimation. Klein et al. [4, 5] demonstrated that the PSS area and the voltage characteristics of the system loads are huge component in the capacity of a PSS to expand the damping of inter-area oscillations. Currently, the traditional lead-lag power system stabilizer is broadly utilized by power system usages [6]. Additional types of PSS, for example, proportional-integral power system stabilizer (PI-PSS) and proportional-integral-derivative power system stabilizer (PID-PSS) have additionally been developed [7-8]. Certain methodologies have been connected to PSS design issue. These incorporate pole placement, , adaptive control, optimal control, variable structure control, and various artificial intelligence and optimization methodologies [9]. The linearized equations of GPSS are:

(20)

(21)

(22)

Figure 6 and Figure 7 show the Simulink model for Generic-PSS and SMIB system connected with GPSS respectively.

Figure 6: Simulink model for Generic-PSS

Figure 7: Simulink model for SMIB with GPSS

2. Multiband Power System Stabilizer (MB-PSS) Electromechanical oscillations of the electrical generators produces disturbances in a power system. Such oscillations, additionally called power swings, must be viably checked to keep up the constancy of system. The requirement for successful damping of these electromechanical oscillations spurred the idea of the multiband power system stabilizer (MB-PSS). The structure of MB-PSS depends on different working bands. A Multi-Band Power System Stabilizer (MB-PSS) is suggested in [10] to get a modest change in phase at all available frequencies for request to make up for the intrinsic lag between the field excitation and the induced electrical torque to guarantee robust damping in oscillation. The primary thought of the MB-PSS is that three separate bands are utilized, individually committed to three frequency modes of oscillations; low, moderate, and high frequency. The low band is regularly connected with the power system worldwide mode, the moderate band with the inter-area modes, and the high band with the neighbourhood modes as shown in Figure 8.

Figure 8: Multi-band power system stabilizer (MB-PSS)

Figure 9 and Figure 10show the Simulink model for MB-PSS and SMIB system connected with MB-PSS respectively

Regulator Exciter Generator

Power System Stabilizer

Output Ref.



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Figure 9: Simulink model for MB-PSS

Figure 10: Simulink model for SMIB with MB-PSS

3. Thyristor Controlled Series Compensation Flexible AC transmission systems(FACTS) [11] devices are playing important role in power system performance improvement. The thyristor controlled series compensation (TCSC) is the most preferred device over other FACTS family members for its simplicity and easy operation for improving system performance, under normal as well as abnormal conditions, such as fault. TCSC can have different parts in the operation and control of power systems, for example, planning power stream, diminishing unsymmetrical segments, lessening net loss, giving voltage support, constraining short-circuit currents, alleviating sub-synchronous resonance (SSR), damping the power oscillation, and upgrading transient constancy. Figure 11 demonstrates the basic outline of TCSC.

Figure11: Simple diagram of TCSC

Mattavelly et al. [12] created straightforward yet precise continuous-time, large-signal element models for a thyristor-controlled arrangement capacitor (TCSC). The models depend on the representation of currents and voltages as time-varying Fourier series, and focus on the elements of the short-term Fourier coefficients.M. A. Abido [13] created a Genetic-based damping controller for a Thyristor-Controlled Series Capacitor (GCSC). The outline issue of TCSC based stabilizer is planned as an optimization issue where the genetic algorithm (GA) is connected to

hunt down the ideal setting of the stabilizer parameter. This technique enhances incredibly the voltage profile of the framework under severe disturbances. Chang and Chow [14] established a time optimal control strategy for the TCSC where the execution time of operation was minimized. A fuzzy logic controller for a TCSC was proposed in [15].In [16], Rosso et al. presented a detailed analysis of TCSC control performance system stability improvement with diverse input signals for a classified TCSC control structure. In [17] Sidhartha et al. developed an approach of power system stability improvement using multi-objective Genetic Algorithm (GA) method which is used for tuning of TCSC parameters. In [21] Jalilzadeh et al. proposed a multi-objective designing of multi-machine Thyristor Controlled Series Compensator(TCSC) using Strength Pareto Evolutionary Algorithm (SPEA).The TCSC parameters designing problem is changed over to an optimization issue with the multi-objective function including the anticipated damping factor and the desired damping ratio of the power system modes, which is resolved by a SPEA algorithm. The mathematical model of the SMIB connected with TCSC framework can be achieved using following equations:

(20)

(21)

(22)

(23)

Where,

(24)

Here, equation (24) is utilized to get the Phillips-Heffron model of SMIB with TCSC. Figure 12 and Figure 13show the Simulink model for TCSC and SMIB system connected with TCSC respectively.

Figure 12: Simulink model for TCSC

Reactor

Thyristor



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Figure 13: Simulink model for SMIB with TCSC

4. Static Synchronous Compensators (STATCOM) Considering various FACTS devices, a static synchronous compensators (STATCOM) assumes substantially more imperative part in reactive power compensation and voltage support in light of its alluring steady state execution and working attributes. The key standard of a STATCOM introduced in a power system is the generation of ac voltage source by a voltage source inverter (VSI) associated with a dc capacitor. The active and reactive power transfer between the power system and the STATCOM is brought about by the voltage difference over the reactance. The STATCOM can likewise expand transmission limit, damping low frequency oscillation, and enhancing transient stability. The STATCOM is spoken to by a voltage source, which is associated with the framework through a coupling transformer. The voltage of the source is in stage with the alternating current framework voltage at the connection point, along with the controllable voltage output. The current from the source is constrained to a most extreme value by changing the voltage. Authors of [18]-[19] presented the mathematical model for staticcompensator (STATCOM). Figure 14 shows abasic model of a STATCOM.

Figure14: Functional model of STATCOM

The relationship between fundamental component of the converter ac output voltage and voltage across dc capacitor is given as

Where k is coefficient which depends upon on the converter configuration, number of switching pulses and the converter controls. The linearized model for SMIB-STATCOM is given by:

(25)

(26)

(27)

(28)

(29)

Where,

Figure 15 and Figure 16show the Simulink model for STATCOM and SMIB system connected with STATCOM respectively.

Figure 15: Simulink model for STATCOM

Figure 16: Simulink model for SMIB with STATCOM

5. Proportional-Integral-Derivative (PID) PID controllers give satisfactory performance for many of the control processes. Due to their simplicity and usefulness, PID controller has become a powerful solution to the control of a large number of industrial processes. The control systems performance is complicated by the numerator dynamics (presence of a zero) of the process. Several processes exhibit second order plus time delay system with a zero transfer function model. PID controller contains Proportional Action, Integral Action and Derivative Action which is generally known as Ziegler-Nichols PID tuning parameters. PID controller’s algorithm is generally utilized as a part of feedback loops. PID controllers can be realised in numerous structures. It can be realised as a stand-alone controller or as a component of Direct



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Digital Control (DDC) bundle or even Distributed Control System (DCS). It is fascinating to note that more than half of the industrial controllers being used today use PID based control techniques. Figure 17 shows a basic block diagram of the PID controller which is known as non-interacting form or parallel form.

Figure 17: Schematic of the PID Controller- Non Interacting

form

Mathematical expression for the output of PID controller is given by:

Figure 18 and Figure 19 shows the Simulink model for PID and SMIB system connected with PID respectively.

Figure 18: Simulink model for PID

Figure 19: Simulink model for SMIB with PID

IV. SIMULATION RESULTS The performance of proposed algorithms has been studied by means of MATLAB simulation.

0 5 10 15 20 25 30 35 40 45 50-1.5

-1

-0.5

0

0.5

1

1.5x 10-3 Deviations for fault at t = 10 Sec

Rot

ar A

ngle

Figure 20: Rotor angle deviations for fault at t=10 sec

0 5 10 15 20 25 30 35 40 45 50-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Pha

se A

ngle

Time (Sec) Figure 21: Phase angle deviations for fault at t=10 sec

0 5 10 15 20 25 30 35 40 45 50-2

-1

0

1

2x 10

-4 Deviations for fault at t = 10 Sec

Rot

ar A

ngle

0 5 10 15 20 25 30 35 40 45 50-0.015

-0.01

-0.005

0

0.005

0.01

Pha

se A

ngle

Time (Sec) Figure 22: Rotor and Phase angle deviations of GPSS for fault

at t=10 sec

0 5 10 15 20 25 30 35 40 45 50-2

-1

0

1

2x 10-3 Deviations for fault at t = 10 Sec

Rot

ar A

ngle

0 5 10 15 20 25 30 35 40 45 50-0.1

-0.05

0

0.05

0.1

Pha

se A

ngle

Time (Sec) Figure 23: Rotor and Phase angle deviations of MBPSS for

fault at t=10 sec

0 5 10 15 20 25 30 35 40 45 50-4

-2

0

2


Rota

r Ang

le

0 5 10 15 20 25 30 35 40 45 50-0.02

-0.01

0

0.01

0.02

Pha

se A

ngle

Time (Sec) Figure 24: Rotor and Phase angle deviations of SMIB for fault

at t=10 sec

Feedback

Output

P

I

D

Plant

Input Error



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0 5 10 15 20 25 30 35 40 45 50-2

-1

0

1


Rot

ar A

ngle

0 5 10 15 20 25 30 35 40 45 50-0.01

-0.005

0

0.005

0.01

Pha

se A

ngle

Time (Sec) Figure 25: Rotor and Phase angle deviations of PID for fault at

t=10 sec

0 5 10 15 20 25 30 35 40 45 50-2

-1

0

1


Rot

ar A

ngle

0 5 10 15 20 25 30 35 40 45 50-0.01

-0.005

0

0.005

0.01

Pha

se A

ngle

Time (Sec) Figure 26: Rotor and Phase angle deviations of TCSCfor fault

at t=10 sec

CONCLUSION This paper developed MATLAB based SIMULINK model for single machine infinite bus system using various compensators; Generic power system stability (GPSS), proportional-integral-derivative, multiband power system stabilizer, Thyristor controlled series compensators and static series compensators. Performance evaluation parameters are the power angle and speed deviation in SMIB. It was found that the TCSC based SMIB outperforms than the other techniques. REFERENCE

[1] F. P. Demello and C. Concordia, “Concepts of Synchronous Machine Stability as Affected by Excitation Control,” IEEE Trans.on Power Apparatusand Systems, vol. PAS-88, no. 4, April 1969.

[2] Angeline J. H, “The control of a synchronous machine using optimal control theory,” Proceedings of the IEEE, vol. 1, pp. 25–35, 1971.

[3] Yu YN and Moussa HAM, “Optimal stabilization of a multi-machine system,” IEEE Transactions on Power Apparatus and Systems, vol. 91,no. 3, pp. 1174–1182,1972.

[4] Klein, M.; Rogers, G.J.; Kundur, P., “A fundamental study of inter-area oscillations in power systems,” IEEE Transactions on Power Systems, Volume: 6 Issue: 3, Aug. 1991, Page(s): 914 -921.

[5] Klein, M., Rogers G.J., Moorty S., and Kundur, P., “Analytical investigation of factors influencing power system stabilizers performance”, IEEE Transactions on Energy Conversion, Volume: 7 Issue: 3, Sept. 1992, Page(s): 382 -390.

[6] G. T. Tse and S. K. Tso, "Refinement of Conventional PSS Design in Multimachine System by Modal Analysis," IEEE Trans. PWRS, Vol. 8, No. 2, 1993, pp. 598-605.

[7] Y.Y. Hsu and C.Y. Hsu, “Design of a Proportional-Integral Power System Stabilizer,” IEEE Trans. PWRS, Vol. 1, No. 2, pp. 46-53, 1986.

[8] Y.Y. Hsu and K.L. Liou, “Design of Self-Tuning PID Power System Stabilizers for Synchronous Generators,” IEEE Trans. on Energy Conversion, Vol. 2, No. 3, pp. 343-348, 1987.

[9] V. Samarasinghe and N. Pahalawaththa, “Damping of Multimodal Oscillations in Power Systems Using Variable Structure Control Techniques”, IEEE Proc. Genet.Transm. Distrib. Vol. 144, No. 3, Jan. 1997,pp. 323-331.

[10] Youssef A. Mobarak, “A Simulink Multi-Band Power System Stabilizer”, Electric Engineering Department, High Institute of Energy,South Valley University,Aswan, Egypt.

[11] Yong-Hua Song, Allan T. Johns, “Flexible Alternating CurrentTransmission Systems (FACTS)” IET, 1999.

[12] P. Mattavelli, G. C. Verghese, A. Stankovic, “Phasor dynamics of thyristor-controlled series capacitor systems”, IEEE Transactions onPower Systems, Volume:12, Issue: 3, pp. 1259-1267, Aug. 1997.

[13] M. A. Abido, “Genetic-Based TCSC Damping Controller Design for Power System Stability Enhancement”, IEEE Power Tech'99 Conference, Budapest,Hungary, Aug 29 - Sep 2, 1999.

[14] J. Chang and J. Chow, “Time optimal series capacitor control for damping inter-area modes in interconnected power systems”, IEEE Trans. PWRS, Vol. 12, No. 1, pp. 215-221, 1997.

[15] T. Lie, G. Shrestha, A.Ghosh, “Design and application of fuzzy logic control scheme for transient stability enhancement in power systems”, Electric Power Systems Research, pp. 17-23, 1995.

[16] Alberto D. Del Rosso, Claudio A. Cañizares, Victor M. Doña, “A Study of TCSC Controller Design forPower System Stability Improvement”, IEEE Transactions on Power Systems, Volume: 18,Issue: 4, pp. 1487 – 1496, Nov. 2003.

[17] Sidhartha Panda, R.N.Patel, N.P.Padhy, “Power System Stability Improvement by TCSC Controller Employing a Multi-ObjectiveGenetic Algorithm Approach”,International Journal of Electrical and Computer Engineering, 2006.

[18] C.Schaduer and H.Mehta, “Vector analysis and control of advanced static var compensator,” IEEE Proceedings on generation, transmission and distribution, Vol 140, No 4, pp: 299-306, July 1993.

[19] Laszlo Gyugi, “Dynamic compensation of ac transmission line by solid state synchronous voltage sources,” IEEE transaction on power delivery, Vol 9, No 2, pp: 904-911, April 1994.

[20] J. Machowski, J. W. Bialek, and J. R. Bumby, Power system dynamics: stability andcontrol. Chichester:Wiley, 2008.

[21] S. Jalilzadeh, M. Darabian, M. Azari, “Power System Stability Improvement via TCSC Controller Employing a Multi-objective Strength Pareto Evolutionary Algorithm Approach”, Journal of Operation and Automation in Power Engineering, Vol. 1, No. 1, March 2013.

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OPTIMAL TUNING OF FACTS CONTROLLERS FOR POWER SYSTEM ...pep.ijieee.org.in/journal_pdf/11-270-14690836961-7.pdf · Optimal Tuning of Facts Controllers For Power System ... Optimal