电力系统保护与控制2016，Vol.44Issue(7)：55-61,7.DOI:10.7667/PSPC151825
RPROP神经网络在非侵入式负荷分解中的应用
Application of RPROP neural network in nonintrusive load decomposition
摘要
Abstract
In order to identify the common-used household appliances, this paper proposes a kind of neural network which is effective to enhance the identification ability. First of all, based on load signature, aiming at harmonic characteristics of steady-state current in each electrical equipment, the feature tag is thereby established. Then, the RPROP neural network is adopted, which makes the input data feature nonlinearly map to output layer, and guides the neural network to converge to global optimal point rapidly. When training the neural network, the combined features are used to decompose the characteristics of electrical equipment. Finally, the experimental results of five common electrical appliances demonstrate that the proposed algorithm can effectively identify combined working states of household appliances, and it also can decompose the working states of electric appliances with similar power and little different harmonics.关键词
非侵入式/负荷分解/神经网络/RPROP算法/系统架构Key words
nonintrusive/load decomposition/neural network/RPROP algorithm/system architecture引用本文复制引用
李如意,王晓换,胡美璇,周洪,胡文山..RPROP神经网络在非侵入式负荷分解中的应用[J].电力系统保护与控制,2016,44(7):55-61,7.基金项目
国家科技支撑计划项目(2013BAA01B01) （2013BAA01B01）
