A real-parameter genetic algorithm application in parameters identification for synchronous generator

This paper presents a searching method for parameters identification of three phase synchronous generator by using a real-parameter genetic algorithm (GA). It is well known that GA method is an optimal or near optimal search technique borrowing the concepts from biological evolutionary theory. The ordinary form of GA used for solving a given optimization problem is a binary encoding during operating procedures. However, in the real applications a real-valued encoding is usually used and is easy to directly implement the programming operations. Thus, in this paper we develop a multi-crossover real-coded GA and utilize it to identification the parameters of three phase synchronous generator, even though those are not linear in the parameters. The effectiveness of the proposed algorithms is compared with binary-coded GA. Simulation results of two kinds of process systems will be illustrated to show that the more accurate identification can be achieved by using our proposed method.

[1]  Zheng Niu,et al.  Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification , 2004, Future Gener. Comput. Syst..

[2]  I. Dumitrache,et al.  Genetic learning of fuzzy controllers , 1999 .

[3]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[4]  David E. Goldberg,et al.  Search space boundary extension method in real-coded genetic algorithms , 2001, Inf. Sci..

[5]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[6]  Zhengming Zhao,et al.  A dynamic on-line parameter identification and full-scale system experimental verification for large synchronous machines , 1995 .

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Rong-Fong Fung,et al.  Optimization of an impact drive mechanism based on real-coded genetic algorithm , 2005 .

[9]  Armando Blanco,et al.  A real-coded genetic algorithm for training recurrent neural networks , 2001, Neural Networks.

[10]  K. Deb,et al.  Design of truss-structures for minimum weight using genetic algorithms , 2001 .

[11]  M. Senthil Arumugam,et al.  New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems , 2005, Appl. Soft Comput..

[12]  Wei-Der Chang,et al.  Nonlinear system identification and control using a real-coded genetic algorithm , 2007 .

[13]  Michele Zamparelli,et al.  Genetically Trained Cellular Neural Networks , 1997, Neural Networks.

[14]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[15]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..

[16]  Jiang Bo Parameter Estimation of Nonlinear System Based on Genetic Algorithms , 2000 .

[17]  J. A. Chen,et al.  A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm , 2004, Expert Syst. Appl..