Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference system (ANFIS). In this paper determined optimal parameters of PID controller with SNR-PSO approach for some events and use these optimal parameters of PID controller for design the intelligent PID controller for AVR system with ANFIS.  Trial and error method can be used to find a suitable design of anfis based an intelligent controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimization algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the SNRPSO approach to design an intelligent controller for AVR. SNR-PSO is a method that combines the features of PSO and SNR in order to improve the optimize operation. In order to emphasize the advantages of the proposed SNR-PSO PID controller, we also compared with the CRPSO PID controller. The proposed method was indeed more efficient and robust in improving the step response of an AVR system and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on SNRPSO algorithm. DOI: http://dx.doi.org/10.11591/ijece.v4i5.6521

[1]  Lixiang Li,et al.  Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm , 2012, Expert Syst. Appl..

[2]  Dong Hwa Kim.  Hybrid GA-BF based intelligent PID controller tuning for AVR system , 2011, Appl. Soft Comput..

[3]  Antonio Visioli,et al.  Tuning rules for optimal PID and fractional-order PID controllers , 2011 .

[4]  Leandro dos Santos Coelho,et al.  Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach , 2009 .

[5]  J. Faiz,et al.  Analysis and Simulation of the AVR System and Parameters Variation Effects , 2007, 2007 International Conference on Power Engineering, Energy and Electrical Drives.

[6]  Saptarshi Das,et al.  Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system , 2012, ArXiv.

[7]  D. Devaraj,et al.  Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral - derivative controller in automatic voltage regulator system , 2009 .

[8]  Sidhartha Panda,et al.  Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization , 2012, J. Frankl. Inst..

[9]  Nasser Sadati,et al.  Design of a fractional order PID controller for an AVR using particle swarm optimization , 2009 .

[10]  KimDong Hwa.  Hybrid GA-BF based intelligent PID controller tuning for AVR system , 2011 .

[11]  Zwe-Lee Gaing.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[12]  Tao Xu,et al.  Electrical Power and Energy Systems , 2015 .

[13]  M. Widyan.  On the effect of AVR gain on bifurcations of subsynchronous resonance in power systems , 2010 .

[14]  S. P. Ghoshal.  Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control , 2004 .

[15]  Christian N. Madu,et al.  Design optimization using signal-to-noise ratio , 1999, Simul. Pract. Theory.

[16]  Yu Guo,et al.  CAS algorithm-based optimum design of PID controller in AVR system , 2009 .

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Madan M. Gupta,et al.  Neuro-Control Systems: Theory and Applications , 1993 .

[19]  John Yen,et al.  Industrial Applications of Fuzzy Logic and Intelligent Systems , 1995 .

[20]  Sakti Prasad Ghoshal,et al.  Comparison of intelligent fuzzy based AGC coordinated PID controlled and PSS controlled AVR system , 2007 .

[21]  Hany M. Hasanien,et al.  Design Optimization of PID Controller in Automatic Voltage Regulator System Using Taguchi Combined Genetic Algorithm Method , 2013, IEEE Systems Journal.

[22]  Shantanu Das,et al.  A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices , 2012, Eng. Appl. Artif. Intell..

[23]  Sakti Prasad Ghoshal,et al.  INTELLIGENT PARTICLE SWARM OPTIMIZED FUZZY PID CONTROLLER FOR AVR SYSTEM , 2007 .