基于改进PSO-PFCM聚类算法的电力大数据异常检测方法

针对传统电力大数据异常检测方法检测精度低、复杂度高等问题,提出了一种将可能性模糊C均值算法和改进的粒子群优化算法相结合的电力大数据异常检测方法.使用改进的粒子群优化算法和重新定义的聚类有效函数来优化可能性模糊C均值算法的初始中心和数目.通过仿真将该算法与改进前算法进行对比分析,验证该算法的优越性.实验结果表明,该算法能...

Power big data anomaly detection method based on an improved PSO-PFCM clustering algorithm

There are problems of low detection accuracy and high complexity of traditional power big data anomaly detection methods. Thus a power big data anomaly detection method is proposed, one which combines the possibility fuzzy C-means algorithm with the improved Particle Swarm Optimization (PSO) algorithm. The improved PSO algorithm and the redefined clustering effective function are used to optimize the initial centers and number of the possibilistic fuzzy C-means algorithm. Through the simulation, the proposed algorithm is compared with the algorithm before improvement. The superiority of the proposed algorithm is verified. The experimental results show that the algorithm can accurately realize power big data outlier detection, and the error detection rate is reduced from 0.36% to 0.05%.This work is supported by the China Southern Power Grid Guangdong Provincial Science and Technology Project (No. 0002200000041529).