A Novel Improved Approach for Fast and Accurate Load Clustering in Power System

The load in power system has the two characters, one is random time-variety and the other one is geographical dispersion. Both have negative effects on the accuracy of load model. In this paper, an improved fuzzy C-means (FCM) algorithm based on density function is proposed to minimize this influence. Firstly, the disadvantage of traditional FCM algorithm is analyzed to show its subjectivity, which is hard to obtain the available global optimal solution. Then, a FCM algorithm based on density function theory is applied to initialize the clustering centers. It can determine the number of load category and optimize the clustering result of power system daily load profile. The simulation result of a real power system load model verifies the accuracy and practicability of the proposed method. It can obtain the objective load category number and effectively solve the non-convergence problem in traditional FCM algorithm.