Multiverse optimized fuzzy-PID controller with a derivative lter for load frequency control of multisource hydrothermal power system

In this paper, a multiverse optimized (MVO) fuzzy PID controller with a derivative lter (fuzzy-PIDF) is proposed for the load frequency control (LFC) of a two-area multisource hydrothermal power system. The superiority of the MVO algorithm is demonstrated by comparing the system LFC performance with integral and fuzzy-PIDF controllers, both optimized using MVO, as well as some of the recent heuristic optimization techniques such as the ant lion optimizer, gray wolf optimizer, differential evolution, bacterial foraging optimization algorithm, and particle swarm optimization. To the best of the knowledge of the authors, the use of the MVO technique has not yet been reported for LFC studies. Among many of the controllers implemented here for comparison, the proposed MVO fuzzy-PIDF controller exhibits the best performance under different operating conditions in terms of settling times, maximum overshoot, and values of cost function, i.e. integral time absolute error. Furthermore, the robustness of the proposed control scheme is also investigated against variation of system parameters within 10%, along with random step load disturbances. The proposed control scheme is not very sensitive to parametric variations and therefore keeps providing effective performance even under 10% variations in system parameters. System modeling and simulations are carried out using MATLAB/Simulink.

PDF

___

[1] Ibraheem N, Kumar P, Kothari DP. Recent philosophies of automatic generation control strategies in power systems. IEEE T Power Syst 2005; 20: 346-357.
[2] Pandey SK, Mohanty SR, Kishor N. A literature survey on load-frequency control for conventional and distribution generation power systems. Renew Sust Energ Rev 2013; 25: 318-334.
[3] Parmar KPS, Majhi S, Kothari DP. Load frequency control of a realistic power system with multi-source power generation. Int J Elec Power 2012; 42: 426-433.
[4] Kundur P. Power System Stability and Control. 8th ed. New York, NY, USA: McGraw-Hill Education, 2009.
[5] Hassan B. Takashi H. Intelligent Automatic Generation Control. 1st ed. Boca Raton, FL, USA: CRC Press, 2011.
[6] Hassan B. Robust Power System Frequency Control. 1st ed. New York, NY USA: Springer, 2009.
[7] Sonmez S, Ayasun S. Stability region in the parameter space of pi controller for a single-area load frequency control system with time delay. IEEE T Power Syst 2016; 31: 829-830.
[8] Nanda J, Mangla A, Suri S. Some new ndings on automatic generation control of an interconnected hydrothermal system with conventional controls. IEEE T Energy Conver 2006; 21: 187-194.
[9] Visioli A. Tuning of PID controllers with fuzzy logic. IEE Proc-D 2001; 148: 1-8.
[10] Daneshfar F, Bevrani H. Multiobjective design of load frequency control using genetic algorithms. Int J Electr Power 2012; 42: 257-263.
[11] Jadhav AM, Vadirajacharya K, Toppo ET. Application of particle swarm optimization in load frequency control of interconnected thermal-hydro power systems. International Journal of Swarm Intelligence 2013; 1: 91-113.
[12] Ali E, Abd-Elazim S. BFOA based design of PID controller for two area load frequency control with nonlinearities. Int J Elec Power 2013; 51: 224-231.
[13] Storn R, Price KV. Differential evolution{a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 1997; 11: 341-359.
[14] Sahu RK, Panda S, Padhan S. A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system. Appl Soft Comput 2015; 29: 310-327.
[15] Pradhan PC, Sahu RK, Panda S. Fire y algorithm optimized fuzzy PID controller for AGC of multi-area multi- source power systems with UPFC and SMES. Engineering Science and Technology 2016; 16: 338-354.
[16] Sahu BK, Pati S, Mohanty PK, Panda S. Teaching{learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system. Appl Soft Comput 2015; 27: 240-249.
[17] Sahu BK, Pati TK, Nayak JR, Panda S, Kar SK. A novel hybrid LUS{TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Int J Elec Power 2016; 74: 58-69.
[18] Guha D, Roy PK, Banerjee S. Load frequency control of interconnected power system using grey wolf optimization. Swarm and Evolutionary Computation 2016; 27: 97-115.
[19] Omar M, Soliman M, Abdel Ghany A, Bendary F. Optimal tuning of PID controllers for hydrothermal load frequency control using ant colony optimization. International Journal on Electrical Engineering and Informatics 2013; 5: 348- 360.
[20] Mirjalili S, Mirjalili SM, Hatamlou A. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 2016; 27: 495-513.
[21] Mirjalili S. Dragon y algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 2016; 27: 1053-1073.
[22] Mudi KR, Pal RN. A robust self-tuning scheme for PI-and PD-type fuzzy controller. IEEE T Fuzzy Syst 1999; 7: 2-16.
[23] Sahu RK, Panda S, Padhan S. Optimal gravitational search algorithm for automatic generation control of intercon- nected power systems. Ain Shams Engineering Journal 2014; 5: 721-733.
[24] Woo ZW, Chung HY, Lin JJ. A PID type fuzzy controller with self-tuning scaling factors. Fuzzy Set Syst 2000; 115: 321-326.
[25] LI HX, Gatland HB. Conventional fuzzy control and its enhancement. IEEE T Syst Man Cy B 1996; 26: 791-796.
[26] Das S, Pan I, Gupta A. A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices. Eng Appl Artif Intel 2012; 25: 430-442.
[27] Zhao ZY, Tomizuka M, Isaka S. Fuzzy gain scheduling of PID controllers. IEEE T Syst Man Cyb 1993; 23: 1392- 1398.
[28] Palm R, Driankov D, Hellendoorn H. Model Based Fuzzy Control. Berlin, Germany: Springer-Verlag, 1997.
[29] Wu ZQ, Mizumoto M. PID type fuzzy controller and parameters adaptive method. Fuzzy Set Syst 1996; 78: 23-36.