In this paper, a new approach is recommended for damping out power system oscillations. In this approach, a new fuzzy neural proportional–integral (PI) controller (FNPIC) based static synchronous series compensator (SSSC) and a fuzzy-power system stabilizer (fuzzy-PSS) with a new structure are simultaneously employed. Adaptive learning rates based on the Lyapunov stability theory to enhance the convergence speed of the proposed controller are obtained. In the structure of fuzzy-PSS, a pattern search algorithm is used to adjust four gains. A two-area four-machine power system and a three-area, three-machine power system are employed to investigate the efficiency of the proposed method. Simulations results confirm the capabilities of proposed controller in suppressing the power system oscillations.
Abd-ElazimSAliE (2016) Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system. International Journal of Electrical Power & Energy Systems76: 136–146.
2.
AchaEAgelidisVGAnaya-LaraO. (2002) Power Electronic Control in Electrical Systems. Oxford: Newnes.
3.
Al-OthmanAEl-NaggerK (2007) Application of pattern search method to power system security constrained economic dispatch. Transactions on Engineering, Computing and Technology19(1): 28–33.
4.
AlizadehMGanjefarSAlizadehM (2013) Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement. Engineering Applications of Artificial Intelligence26(9): 2227–2242.
5.
AlizadehMTofighiM (2013) Full-adaptive THEN-part equipped fuzzy wavelet neural controller design of FACTS devices to suppress inter-area oscillations. Neurocomputing118: 157–170.
6.
ÅströmKJHangCCPerssonP. (1992) Towards intelligent PID control. Automatica28(1): 1–9.
7.
BadarRKhanL (2012) Online adaptive neuro-fuzzy wavelet based SSSC control for damping power system oscillations. In: International Conference on Emerging Technologies, IEEE.
8.
DeyCMudiRK (2009) An improved auto-tuning scheme for PID controllers. ISA Transactions48(4): 396–409.
9.
ElazimSAAliE (2016) Optimal SSSC design for damping power systems oscillations via gravitational search algorithm. International Journal of Electrical Power & Energy Systems82: 161–168.
10.
FarahAGuesmiTAbdallahHH. (2016) A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem. International Journal of Electrical Power & Energy Systems77: 197–209.
11.
FarahaniM (2012) Damping of subsynchronous oscillations in power system using static synchronous series compensator. IET, Generation, Transmission & Distribution6(6): 539–544.
12.
FarahaniM (2013) Intelligent control of SVC using wavelet neural network to enhance transient stability. Engineering Applications of Artificial Intelligence26(1): 273–280.
13.
FarahaniMGanjefarS (2012) Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations. International Journal of Control, Automation and Systems10(4): 744–752.
14.
FarahaniMGanjefarS (2013) Intelligent control of a static synchronous series compensator via a self-tuning proportional-integral-derivative controller based on the Lyapunov method to mitigate inter-area oscillations. Electric Power Components and Systems41(7): 716–728.
15.
GanjefarSFarahaniM (2012) Damping of subsynchronous resonance using self-tuning PID and wavelet neural network. COMPEL –The International Journal for Computation and Mathematics in Electrical and Electronic Engineering31(4): 1259–1276.
16.
HingoraniNGGyugyiL (2000) Understanding Facts. New York: IEEE Press.
17.
KazemiALadjevardiMMasoumMAS (2005) Optimal selection of SSSC based damping controller parameters for improving power system dynamic stability using genetic algorithm. Iranian Journal of Science & Technology, Transaction B, Engineering29(B1): 1–9.
18.
KhodabakhshianAEsmailiMRBornapourM (2016) Optimal coordinated design of UPFC and PSS for improving power system performance by using multi-objective water cycle algorithm. International Journal of Electrical Power & Energy Systems83: 124–133.
19.
KundurPBaluNJLaubyMG (1994) Power System Stability and Control. New York: McGraw-Hill.
20.
PandaS (2009) Multi-objective evolutionary algorithm for SSSC-based controller design. Electric Power Systems Research79(6): 937–944.
21.
PandaSPadhyNPatelRN (2008) Power-system stability improvement by PSO optimized SSSC-based damping controller. Electric Power Components and Systems36(5): 468–490.
22.
RadaidehSNejdawiIMushtahaMH (2012) Design of power system stabilizers using two level fuzzy and adaptive neuro-fuzzy inference systems. International Journal of Electrical Power & Energy Systems35(1): 47–56.
23.
ReddyMJMohantaDK (2008) Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings. IET Generation, Transmission & Distribution2(2): 235–244.
24.
SadeghzadehSEhsanMSaidNH. (1998) Improvement of transient stability limit in power system transmission lines using fuzzy control of FACTS devices. IEEE Transactions on Power Systems13(3): 917–922.
25.
SadeghzadehSMAnsarianM (2006) Neuro-fuzzy control of SSSC and SMES for transient stability improvement of power system transmission lines. In: International Joint Conference on Neural Networks, IEEE.
26.
TalaatHEAbdennourAAl-SulaimanAA (2010) Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer. International Journal of Electrical Power & Energy Systems32(7): 751–759.
27.
VegaPPradaCAleixandreV (1991) Self-tuning predictive PID controller. In: IEE Proceedings Control Theory and Applications, IET.
28.
XuJ-XHangC-CChenLiu (2000) Parallel structure and tuning of a fuzzy PID controller. Automatica36(5): 673–684.
