Frequency Domain Parameter Estimation of a Synchronous Generator Using Bi-objective Genetic Algorithms P. PAO-LA-OR * , T. KULWORAWANICHPONG, and A. OONSIVILAI Power and Energy Research Unit, School of Electrical Engineering Institute of Engineering, Suranaree University of Technology 111 University Avenue, Muang District, Nakhon Ratchasima, 30000 THAILAND * corresponding author: padej@sut.ac.th Abstract: - This paper presents a way to obtain parameters of a direct-axis equivalent circuit of a synchronous generator from frequency response data using bi-objective genetic algorithms. The genetic algorithms is capable of finding a global minimum within a given search interval. The sum square error of magnitude and phase of the d-axis equivalent circuit transfer function to formulate a bi-objective optimization problem is minimized to best fit the measured data extracted from the frequency response test of the machine. As a result, exploitation of the bi-objective optimization based on Genetic Algorithms gives very good results than those of using either the magnitude or the phase as a single objective. Key-Words: - Parameter Estimation, Genetic Algorithms, Bi-objective Optimization, Synchronous Generator, Frequency Response 1 Introduction To date under deregulated power market environment electric utility has become increasingly much more complex that the past. Apart from economic view point, stability problems are equally important to operate electric power system in real time. To handle any stability-related problems accurate parameter estimation of a synchronous generator is concerned in both direct and quadrature models. Several kinds of tests are used to determine the direct-axis equivalent circuit parameters. These include on-line tests [1], standstill frequency response (SSFR) [2,3,4] and time domain [5] testing. From literature, the frequency response test has become one of the most popular approaches to obtain the synchronous transfer function parameters. With this method, the problem is reduced to find location of suitable poles and zeros of the machine transfer function. To complete this task, an efficient intelligent search method can be employed. Genetic algorithm (GA) is a searching method based on two natural processes: selections and genetics. It is considered as an evolutionary computation which has been proved to be a very powerful optimization method in an artificial intelligence area of interest. There have been various researches and applications of GA covering in most fields of studies. Therefore, it would be good for solving this problem based on the Genetic Algorithms. This paper illustrates the way to apply the Genetic Algorithms to solve a bi-objective optimization problem in order to estimate a d-axis transfer function of a synchronous generator, which is explained in detail in section 2. Section 3 gives a brief of the step-by-step intelligent parameter estimation based on the Genetic Algorithms. Section 4 shows test results and discussion. The last section is the conclusion. 2 Direct-Axis Model Structure of a Synchronous Machine The direct-axis of a synchronous machine includes two terminal ports. These correspond to the direct- axis equivalent armature winding and the field winding. The complete direct-axis equivalent circuit which second order model referred to the stator is shown in Fig.1 [6] Fig. 1. Direct-axis equivalent …, where l L = armature leakage inductance ad L = stator to rotor mutual inductance Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, Beijing, China, September 15-17, 2007 429