基于时间分区和粒子群优化的非侵入式负荷分解研究OA北大核心CSTPCD
Research of non-intrusive load decomposition based on time partition and V-shaped particle swarm optimization
非侵入式负荷分解技术是智能电网技术体系的重要组成部分,针对现有分解技术对功率相近或小功率负荷辨识精度较低的问题,提出基于时间分区和V型粒子群优化的非侵入式负荷分解算法.文章通过具有噪声的基于密度的聚类算法对负荷的功率特征进行聚类分析,得到负荷的功率特征模板,并求解负荷典型工作时间区间,得到负荷的时间特征模板;综合考虑功率及时间两种特征,构建V型粒子群算法的目标函数,实现负荷分解;在AMPds2公开数据集上实现仿真,并与隐马尔可夫模型对比,验证了文章方法的有效性.
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.
杨海英;孙伟;史梦阳
中国矿业大学信息与控制工程学院,江苏徐州 221116
动力与电气工程
负荷分解V型粒子群算法聚类算法特征提取
load decompossthonV-shaped particle swarm optimizationclustering algorithmfeature extraction
《电测与仪表》 2024 (005)
52-59 / 8
国家自然科学基金资助项目(61973306)
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