电测与仪表2024，Vol.61Issue(5)：52-59,8.DOI:10.19753/j.issn1001-1390.2024.05.008
基于时间分区和粒子群优化的非侵入式负荷分解研究
Research of non-intrusive load decomposition based on time partition and V-shaped particle swarm optimization
摘要
Abstract
Non-intrusive load decomposition technology is an important part of the smart grid technology system.As the existing decomposition methods perform low identification accuracy for similar power or low power load,this pa-per proposes a non-intrusive load decomposition algorithm based on time partition and V-shaped particle swarm opti-mization.Firstly,the clustering analysis of load power characteristics is conducted through the density-based spatial clustering of applications with noise to obtain the power feature template of the load,and then,the typical working time of load is solved to obtain the time characteristic template of the load.Moreover,considering power and time characteristics,the objective function of the V-shaped particle swarm optimization algorithm is constructed to a-chieve load decomposition.Finally,the simulation is implemented on the AMPds2 public data set and compared with the hidden Markov model to verify the effectiveness of the proposed method in this paper.关键词
负荷分解/V型粒子群算法/聚类算法/特征提取Key words
load decompossthon/V-shaped particle swarm optimization/clustering algorithm/feature extraction分类
信息技术与安全科学引用本文复制引用
杨海英,孙伟,史梦阳..基于时间分区和粒子群优化的非侵入式负荷分解研究[J].电测与仪表,2024,61(5):52-59,8.基金项目
国家自然科学基金资助项目(61973306) （61973306）
