Load classification based on improved FCM algorithm with adaptive fuzziness parameter selection

This paper proposes an adaptive fuzziness parameter selection method of fuzzy c-means (FCM) algorithm based on the establishment of five-stage load classification process model. The evaluation index of adaptive fuzziness parameter selection is the ratio of the sum of within-class distances and the sum of between-class distances. At the same time, simulated annealing algorithm and genetic algorithm are utilized to optimize the global search capability of FCM algorithm. Experimental results show that the widely used fuzziness parameter of FCM algorithm in load classification m=2 is not optimal, and we give the optimum range that is [2.6, 3.2]. The modified algorithm enhances the global search capability of traditional FCM algorithm, thus enhancing the accuracy and effectiveness of load classification.