Study on Identification of Inductive-Motors Load Partition Based on Coherence

Abstract

A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (PSO-FCM) algorithm is proposed to identify coherent motors base on its physical essence of fuzziness. The merits of PSO algorithm are independent to initial value and convergent to optimum value rapidly, and the validity function is constructed to assess clustering validity. The test on IEEE 39-Bus System is presented to evaluate the effectiveness of the new algorithm, the membership matrix definite not only coherence group of motors but also correlation value of coherence between motors. The algorithm can be used to partite motor load based on coherency in dynamic equivalence with power system operating on different modes.