基于改进DPC-IGWO-Elman的负荷分解方法  

Load Decomposition Method Based on Improved Dpc-Igwo-Elman

作　　者：胡胜 袁功进 刘聪 HU Sheng;YUAN Gongjin;LIU Cong(School of Electrical and Electronic Engin.,Hubei Univ.of Tech.,Wuhan 430068,China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068

出　　处：《湖北工业大学学报》2024年第5期1-7,共7页Journal of Hubei University of Technology

基　　金：青年科学基金项目(61901165)。

摘　　要：针对现有负荷分解方法负荷特征单一、分解精度低的问题,提出一种结合改进密度峰值聚类算法与改进灰狼算法优化Elman神经网络的非侵入式负荷分解方法。首先针对密度峰值聚类算法(DPC)在处理复杂数据集时缺乏自适应能力的问题对局部密度的计算方法进行改进,再将改进DPC算法应用于用电器负荷数据的聚类分析,从而得到用电器的工作状态标签并进行编码;之后运用Elman神经网络构建分解模型同时引入改进灰狼优化算法(IGWO)对网络参数进行寻优,最后根据网络输出编码获取用电器工作状态标签并根据对应负荷特征信息进行有功功率拟合,完成负荷分解。经公开数据集测试和实验对比,IGWO-Elman模型的识别准确率以及有功功率拟合效果均优于其他模型。Aiming at the problems of single load characteristics and low decomposition accuracy in existing decomposition methods,a non-intrusive load decomposition method combining the improved density peak clustering algorithm and Elman neural network optimized by the improved gray wolf optimization algorithm was proposed.Firstly,the calculation method of local density was improved for the lack of adaptive ability of Clustering by fast search and find of density peaks(DPC)when dealing with complex data sets,and the improved DPC was applied to the clustering analysis of electrical load data,then the working status labels of electrical appliances were obtained and coded.Subsequently,Elman neural network was used to construct the decomposition model and improved grey wolf optimizer(IGWO)was applied to optimize the network parameters.Finally,according to the network output code,the working state labels of the electrical appliance were obtained,and the active power was fitted according to the corresponding load characteristic information,then the load decomposition was completed.The test and experimental comparison on public data sets proved that the load identification accuracy and active power fitting effect of IGWO Elman model were better than other models.

关 键 词：非侵入式负荷分解 密度峰值聚类算法 灰狼优化算法 ELMAN神经网络 

分 类 号：TM714[电气工程—电力系统及自动化]