Title :
The induction motor parameter estimation through an adaptive genetic algorithm
Author :
Xiaoyao Zhou ; Haozhong Cheng
Author_Institution :
Dept. of Electr. Eng., Shanghai Jiao Tong Univ., China
Abstract :
This paper presents a new adaptive genetic algorithm for third-order induction motor model parameter estimation. The crossover and mutation probability of adaptive genetic algorithm change according to the fitness statistics of the population at each generation. The proposed algorithm can enhance the convergence performance of GA and prevent premature problems. This algorithm is successfully applied to the third-order induction motor model parameter estimation.
Keywords :
adaptive estimation; convergence of numerical methods; genetic algorithms; induction motors; load (electric); parameter estimation; power system analysis computing; probability; GA; adaptive genetic algorithm; convergence performance; crossover; fitness statistics; induction motor; mutation probability; third-order model parameter estimation; Biological cells; Genetic algorithms; Induction motors; Parameter estimation; Power system modeling; Power system transients; Rotors; Stators; Testing; Voltage;
Conference_Titel :
Universities Power Engineering Conference, 2004. UPEC 2004. 39th International
Print_ISBN :
1-86043-365-0
