Showing papers by "Lin Jiang published in 2003"
TL;DR: An investigation of a methodology using intelligent learning techniques based on system measurements to construct power system load models alongside with distribution network reduction and a population diversity-based genetic algorithm is developed to obtain the structure and parameters of the load model.
Abstract: This paper is concerned with an investigation of a methodology using intelligent learning techniques based on system measurements to construct power system load models alongside with distribution network reduction. A comprehensive load model is proposed to represent the loads in an area of a power system. A population diversity-based genetic algorithm (GA) is developed to obtain the structure and parameters of the load model. Simulation results on a five-bus power system and an IEEE 30-bus power system are given to show the potential of this new methodology of power system modeling.
56 citations
TL;DR: A pseudo-gradient is proposed to improve the performance of evolutionary algorithms and its performance is compared with the standard evolutionary programming and standard genetic algorithm.
Abstract: A pseudo-gradient is proposed to improve the performance of evolutionary algorithms. A pseudo-gradient based evolutionary programming is derived and its performance is compared with the standard evolutionary programming and standard genetic algorithm.
25 citations