【摘要】 非侵入式负荷识别是高级电力量测系统的首要环节，对智能电网的建设具有重要意义。为解决传统非侵入式负荷识别算法识别速度慢、准确率低的问题，提出一种基于麻雀搜索算法（SSA）优化极限学习机（ELM）的非侵入式负荷识别方法。该方法通过SSA获取ELM隐含层的最优输入权值和阈值，构建出SSA-ELM非侵入式负荷识别模型。在实际采集的6种常用家庭负荷数据集上对该模型进行负荷识别实验，结果表明，基于SSA-ELM的非侵入式负荷识别算法的识别准确率为96.1%，优于传统的ELM(86.3%)和BP神经网络算法（91.8%）。基于SSA-ELM的非侵入式负荷识别算法能有效应用于家庭用电负荷的识别中。更多还原

【Abstract】 Non-intrusive load identification is the first step of advanced power measurement system,which is of great significance to the construction of smart grid. In order to solve the problems of slow speed and low accuracy of traditional non-invasive load identification algorithm,a non-invasive load identification method based on sparrow search algorithm(SSA)to optimize extreme learning machine(ELM)is proposed. The SSA is used to obtain the optimal input weights and thresholds of the hidden layer of extreme learning machine,and the SSA-ELM non-invasive load identification model is constructed. The model is put on six commonly used family load data sets to carry out load identification experiments. The results show that the recognition accuracy of non-invasive load identification algorithm based on SSA-ELM is 96.1%,which is better than that of traditional ELM and BP neural network algorithms(86.3% and91.8%). The non-invasive load identification algorithm based on SSA-ELM can be effectively applied to the non-invasive load identification of household electricity load.更多还原