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.