一种基于粒子群算法的配电网低电压诊断模型研究

A low voltage diagnosis model for distribution network based on particle swarm optimization algorithm

随着智能电网建设的发展,传统的基于检测技术的配电低电压原因诊断已变成基于数据挖掘的电力大数据分类技术,而着眼于低电压故障原因的数据分类研究在国内尚处于起步阶段,为此该文提出一种采用改进聚类算法和支持向量机分类算法的配电网低电压诊断模型。该模型首先采用Canopy-Kmeans的聚类算法基于配电网历史运行数据进行低电压原因的聚类分析并得出可能存在的低电压原因,然后采用经粒子群算法对支持向量机数据分类算法进行参数优化,最后使用结果参数优化的支持向量机算法对智能电表所采集的配电网实时运行数据进行低电压原因分类并最终输出低压故障原因的诊断结果。实验表明,采样基于粒子群优化的支持向量机诊断模型能够实现90%的低电压原因诊断准确度。

With the development of smart grid construction,traditional low-voltage cause diagnosis based on detection technology has become a data big data classification technology based on data mining,and data classification research focusing on the cause of low voltage failure is still in its infancy in China.For this reason,a low-voltage diagnostic model of distribution network using improved clustering algorithm and support vector machine classification algorithm is proposed.The model first uses Canopy-Kmeans clustering algorithm to analyze the low voltage causes based on the historical operation data of the distribution network and obtain the possible low voltage causes.Then the particle swarm optimization algorithm is used to support the SVM data classification algorithm.Parameter optimization,finally using the support vector machine algorithm of the result parameter optimization to classify the real-time operation data of the distribution network collected by the smart meter for low-voltage causes and finally output the diagnosis result of the lowvoltage fault.Experiments show that sampling based on particle swarm optimization based support vector machine diagnostic model can achieve 90%low-voltage cause diagnostic accuracy.