基于VMD和PSO-SVM的非侵入式负荷识别方法

杨锐; 邹晓松; 熊炜; 袁旭峰; 郑华俊; 刘斌

杨锐,邹晓松,熊炜,袁旭峰,郑华俊,刘斌.基于VMD和PSO-SVM的非侵入式负荷识别方法[J].电测与仪表,2025,62(5):111-119.
Yang Rui,Zou Xiaosong,Xiong Wei,Yuan Xufeng,Zheng Huajun,Liu Bin.Non-intrusive load identification method based on VMD and PSO-SVM[J].Electrical Measurement & Instrumentation,2025,62(5):111-119.

基于VMD和PSO-SVM的非侵入式负荷识别方法

Non-intrusive load identification method based on VMD and PSO-SVM

DOI：10.19753/j.issn1001-1390.2025.05.014

中文关键词: 非侵入式负荷监测单通道盲源分解变分模态分解能量熵粒子群算法优化支持向量机

英文关键词:non-intrusive load monitoring, single-channel blind source decomposition, variational modal decomposition, energy entropy, particle swarm optimization-support vector machine

基金项目:国家自然科学基金项目（52067004）；贵州省科学技术基金项目（[2019]1058,[2019]1128）,国家自然科学基金资助项目（项目编号）

作者中文名	作者英文名	单位
杨锐	Yang Rui	贵州大学电气工程学院
邹晓松	Zou Xiaosong	贵州大学电气工程学院
熊炜	Xiong Wei	贵州大学电气工程学院
袁旭峰	Yuan Xufeng	贵州大学电气工程学院
郑华俊	Zheng Huajun	贵州大学电气工程学院
刘斌	Liu Bin	贵州电网有限公司电力科学研究院

摘要点击次数: 116

中文摘要:

非侵入式负荷监测是智能用电的未来发展趋势，其中负荷的分解与辨识是实现该技术的重要环节。鉴于变分模态分解（variational mode decomposition,VMD）在信号处理方面的优势，提出一种基于VMD-FastICA（variational mode decomposition and fast independent component analysis)和VMD-Entropy-PSOSVM(variational mode decamposition-entropy-particle swanm optimization fo optimizing support vector machines)的负荷识别算法。该方法利用VMD对总负荷功率信号进行分解得到多个模态分量（intrinsic mode functions,IMF），再依据峭度准则和奇异值分解对分解后的模态分量重构，将单通道盲源分离虚拟成多通道盲源分离，输入快速独立分量分析（fast independent component analysis,FastICA）进行负荷信号分离，求取分解负荷波形模态分量的能量与能量熵。构建多维特征矩阵输入建立粒子群算法优化支持向量机（particle swarm optimization for optimizing support vector machines,PSO-SVM），进行负荷的分类辨识。采用开源数据集(reduced electricity dataset, REDD)对实验算法进行仿真，与其他算法相比，验证算法在分解和识别上都具有较好的效果。

英文摘要:

Non-intrusive load monitoring is one of the important technologies of intelligent power consumption, among which load decomposition and identification is an important link to realize the technology. In view of the advantage of variational mode decomposition (VMD) in signal processing, a load identification algorithm based on variational mode decomposition and fast independent component analysis (VMD-FastICA) and variational mode decamposition-entropy-particle swanm optimization fo optimizing support vector machines (VMD-Entropy-PSOSVM) is proposed. The total load power signal is decomposed using VMD to obtain multiple intrinsic mode functions (IMF), and then, the IMF is reconstructed based on the cliff criterion and singular value decomposition to virtualize single-channel blind source separation into multi-channel blind source separation into fast independent component analysis （FastICA） for load signal separation. Then, the energy and energy entropy of the modal components of the decomposed load waveform are obtained, and the multi-dimensional feature matrix input is constructed to establish a particle swarm optimization-support vector machine particle swarm optimization for optimizing support vector machines (PSO-SVM) for classification and identification of the load. The experimental algorithm is simulated using the reduced electricity dataset (REDD), and it is verified that the algorithm has better results in both decomposition and recognition compared to other algorithms.

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