基于集群智能的智能电能表异常检测技术研究

Research on Anomaly Detection Technology of Intelligent Electricity MeterBased on Cluster Intelligence

提出了一种基于集群智能的智能电能表异常检测方法,旨在提升检测准确性和效率。通过模拟自然界群体行为,该方法利用自组织、分布式协作的集群智能优势,应对智能电能表运行中的异常现象。构建了随机种群形成策略,采用矢量距离、置信度和基于Kullback-Leibler散度的3种算法进行异常识别。实验基于NYISO数据集,结果显示,集群智能算法能有效识别异常,其中,置信度导向方法在检测效果上表现突出,而基于Kullback-Leibler散度的方法收敛速度最快。本研究为智能电网的稳定运行和异常管理提供了新的技术路径。

This paper proposes a method detection method based on cluster intelligence,aiming to improve the accuracy and efficiency of detection.By simulating the natural group behavior,this method takes advantage of the cluster intelligence of self-organization and distributed collaboration to deal with the abnormal phenomena in the operation of intelligent electricity meters.A random population formation strategy was constructed,and three algorithms including vector distance,confidence,and Kullback-Leibler divergence-based were used for anomaly recognition.The experiment is based on NYISO data set,and the results show that the cluster intelligent algorithm can effectively identify anomalies,among which the confidence oriented method is outstanding in the detection effect,while the Kullback-Leibler divergence-based method has the fastest convergence speed.This study provides a new technical path for the stable operation and exception management of smart grid.