电力系统自动化2017，Vol.41Issue(22)：66-73,8.DOI:10.7500/AEPS20170516002
基于多特征序列融合的负荷辨识方法
Load Identification Method Based on Multi-feature Sequence Fusion
摘要
Abstract
Aiming at the problem of low recognition accuracy for non-intrusive load identification method at the low sampling rate,a load identification method based on multi feature sequence fusion is proposed.First,an integer programming model is developed to solve the possibility of load existence,thus reducing the dimensions of load identification process.According to the sliding window method,the combined power sequence and the original power sequence are obtained,from which the statistical characteristic value and the contour singular value are extracted.Then the probabilistic neural network (PNN) is used to obtain the observed sequence of the hidden Markov model (HMM).Meanwhile,the information of load sequence is fused by HMM,and the similarity between the observed sequence and the combined power sequence is calculated.Thus the load identification at a low sampling rate is completed and the power consumption of each household load is obtained.Finally,through the simulation experiments of single load identification,multi-load identification,load identification with different sampling rates and load identification of each user,the average results of the load accuracy and the identification accuracy are more than 85 ％,which has verified that the rationality and immediacy of the proposed method can meet the requirement of load identification at the low sampling rate.关键词
负荷辨识/整数规划/概率神经网络/隐马尔可夫模型Key words
load identification/integer programming/probabilistic neural network (PNN)/hidden Markov model (HMM)引用本文复制引用
杨东升,孔亮,胡博,苑婷..基于多特征序列融合的负荷辨识方法[J].电力系统自动化,2017,41(22):66-73,8.基金项目
This work is National Natural Science Foundation of China (No.61273029),Fundamental Research Funds for the Central Universities (No.N160402003) and State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (No.LAPS17013).国家自然科学基金资助项目(61273029) （No.61273029）
中央高校基本科研业务费专项资金资助项目(N160402003) （N160402003）
新能源电力系统国家重点实验室立项资助项目(LAPS17013). （LAPS17013）
