In this paper, a new controller based on combination of Fuzzy Power System Stabilizer (FPSS) and Flexible Ac Transmission Systems (FACTS) proposed in multi machine power system environment. In this model, the power system oscillation damping has been analyzed by considering the wide area control method. A fuzzy controller is a nonlinear controller and it is not so sensitive to system topology, parameter and operating condition changes as the conventional ones. According to wide area power system complexity and variation of the loads and network conditions, FPSS has been considered in this paper. Where, this structure is based on fewer fuzzy rules and less computational burden. Furthermore, the proposed FACTS controller has been improved by Vector Evaluated Honey Bee Mating Optimization (VEHBMO) as an optimization problem. Effectiveness of the proposed method has been applied over two case studies of single-machine infinite-bus (SMIB) and ten machine 39 bus New England power system. Obtained results demonstrate the superiority of proposed strategy.
KamwaI., GrondinR. and HebertY., Wide-area measurement based stabilizing control of large power systems-a decentralized/hierarchical approach, IEEE Trans Power Syst16(1) (2001), 136–153.
2.
MajumderR., PalB.C., DufourC. and KorbaP., Design and realtime implementation of robust FACTS controller for damping interarea oscillation, IEEE Trans Power Syst21(2) (2006), 809–816.
3.
ChaudhuriN.R., RayS., MajumderR. and ChaudhuriB., A new approach to continuous latency compensation with adaptive phasor power oscillation damping controller (POD), IEEE Trans Power Syst25(2) (2010), 939–946.
4.
FarsangiM.M., Nezamabadi-pourH., SongY.H. and LeeK.Y., Placement of SVCs and selection of stabilizing signals in power systems, IEEE Trans Power Syst22(3) (2007), 1061–1071.
5.
MaoX.M., ZhangY., GuanL. and WuX.C., Coordinated control of inter-area oscillation in the China Southern power grid, IEEE Trans Power Syst21(2) (2006), 845–852.
6.
PadhyB.P., SrivastavaS.C. and VermaN.K., Robust wide-area TS fuzzy output feedback controller for enhancement of stability in multimachine power system, IEEE Systems Journal6(3) (2012), 426–435.
7.
TanakaK. and WangH.O., Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach, Wiley, New York, 2001.
8.
FengG., A survey on analysis and design of model-based fuzzy control systems, IEEE Trans Fuzzy Syst14(5) (2006), 676–697.
9.
DelmotteF., GuerraT.M. and KsantiniM., Continuous Takagi–Sugeno’s models: Reduction of the number of LMI conditions in various fuzzy control design technics, IEEE Trans Fuzzy Syst15(3) (2007), 426–438.
10.
TeixeiraM.C.M. and ZakS.H., Stabilizing controller design for uncertain nonlinear systems using fuzzy models, IEEE Trans Fuzzy Syst7(2) (1999), 133–142.
11.
SuX., WuL., ShiP. and SongY.D., A novel approach to output feedback control of fuzzy stochastic systems, Automatica50(12) (2014), 3268–3275.
12.
KazachekN., LokhinV., MankoS. and RomanovM., Research of periodic oscillations in control systems with fuzzy controllers, International Journal of Innovative Computing, Information and Control11(3) (2015), 985–997.
13.
SuX., ShiP., WuL. and SongY.D., A novel control design on discrete-time takagi–sugeno fuzzy systems with time-varying delays, IEEE Trans on Fuzzy Systems20(6) (2013), 655–671.
14.
SinghB., SharmaN.K., TiwariA.N., VermaK.S. and SinghS.N., Applications of phasor measurement units (PMUs) in electric power system networks incorporated with FACTS controllers, International Journal of Engineering, Science and Technology3(3) (2011), 64–82.
15.
“IEEE Standard for Synchrophasors for Power Systems”, IEEE C37.118-2005
16.
YoneyamaJ., NishikawaM., KatayamaH. and IchikawaA., Design of output feedback controllers for Takagi-Sugeno fuzzy systems, Fuzzy Sets Systems121(1) (2001), 127–148.
17.
FengG., A survey on analysis and design of model-based fuzzy control systems, IEEE Trans Fuzzy Syst14(5) (2006), 676–697.
18.
ZhangY. and BoseA., Design of wide-area damping controllers for interarea oscillations, IEEE Trans Power Syst23(3) (2008), 1136–1143.
19.
DongJ., WangY. and YangG.-Hong, Control synthesis of continuous-time T-S fuzzy systems with local nonlinear models, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics39(5) (2009), 1245–1258.
20.
ZhaoQ. and JiangJ., Robust SVC controller design for improving power system damping, IEEE Trans Power Syst10(4) (1995), 1927–1932.
21.
RayS. and VenayagamoorthyG.K., Wide-area signal-based optimal neuro controller for a UPFC, IEEE Trans Power Del23(3) (2008), 1597–1605.
22.
FirouziB., Sha SadeghiM., and NiknamTaher, A new hybrid algorithm based on PSO, SA, and K - means for cluster analysis, International Journal of Innovative Computing, Information and Control6(7) (2010), 3177–3192.
23.
GhadimiN., A new hybrid algorithm based on optimal fuzzy controller in multimachine power system, Complexity (2014). doi: 10.1002/cplx.21544
24.
NiknamTaher, An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multiobjective distribution feeder reconfiguration, Energy Conversion and Management50(8) (2009), 2074–2082.
25.
MohammadiM. and GhadimiN., Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization, Complexity (2014). doi: 10.1002/cplx.21560
26.
DaiX-D, HeD., FanL-L, LiN-H and ChenH., Improved ANN alpha;th-order inverse TCSC controller for enhancing power system transient stability, Proc, IET Gen Transm Distrib146(6) (1999), 550–556.
27.
GhadimiNoradin, Ali Afkousi - Paqaleh and Ali Emamhosseini, A PSO based fuzzy long term multi objective optimization approach for placement and parameter setting of UPFC, Arabian Journal for Science and Engineering39(4) (2014), 2953–2963.
28.
KundurP., Power System Stability and Control, 2nd ed. New York, NY, USA: McGraw-Hill, 2008.
29.
GhadimiNoradin, MDE with Considered Different Load Scenarios for Solving Optimal Location and Sizing of Shunt Capac itors, National Academy Science Letters37(5) (2014), 447–450.
