Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A course in fuzzy systems and control
A course in fuzzy systems and control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A systematic study of fuzzy PID controllers-function-based evaluation approach
IEEE Transactions on Fuzzy Systems
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
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In this paper a variable structure fuzzy PI-genetic control scheme is proposed for a Voltage Source Inverter based STATCOM in damping the electro-mechanical oscillations of a power system. The function based Takagi-Sugeno-Kang (TSK) fuzzy controller uses two rules and generates the proportional action which, by one-to-two mapping, gives a variable gain PI controller. This single-input, function based fuzzy controller, dispenses the gain dependency of the proportional or integral gains and generates independent controls. To demonstrate the application of the proposed controller, a case study is done with a single and multimachine power system operating with STATCOM. Further, Genetic Algorithm (GA) is used to tune the parameters of the analytical fuzzy PI controller for optimal damping performance. Computer simulation results clearly reveal the superior performance of the proposed controller. A comparison is also made with a two-input fuzzy controller using four rules instead of just two as in the single input case.