Derivation of load model parameters using improved Genetic Algorithm

TLDR: In this article, the authors proposed an improved GA to derive the parameters of load models using the measured disturbance data, which is termed as the measurement-based approach, and compared with the Levenberg-Marquardt method using a 23-bus test system.

Abstract: Load components have strong effects on the power system's behavior and should be modeled accurately in system studies. With more and more disturbance measurement equipments have been installed in transmission systems, it opens up an opportunity to use the measurement data to derive load model parameters. This method is termed as the measurement- based approach. The theoretical foundation of the measurement- based approach is system identification. In this paper, we propose to apply an improved Genetic Algorithm (GA) to derive the parameters of load models using the measured disturbance data. The improved genetic algorithm is based on following aspects: (i) the strategy of keeping the best individual (ii) the adaptive rates of mutation and crossover (iii) the strategy of immigration (iv) the optimal search direction. The improved GA method is compared with the Levenberg-Marquardt method using a 23-bus test system.