当前位置: X-MOL 学术Integr. Ferroelectr. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Research on Non-Intrusive Load Monitoring Method Based on Feature Difference Enhancement
Integrated Ferroelectrics ( IF 0.7 ) Pub Date : 2020-08-05 , DOI: 10.1080/10584587.2020.1728854
Wang Min 1 , Zhou Yidi 2 , Zhang Shuang 3 , Zheng Yaopeng 2 , Liu Zihan 4 , Feng Hui 2
Affiliation  

Abstract Non-intrusive load monitoring technology is the key technology to realize smart grid construction. The identification and classification of similar electrical appliances and low-power small-current electrical appliances in non-intrusive load monitoring has always been the focus of research and the difficulty of breakthrough. This paper uses current harmonic amplitude as classification data and based on the composition characteristics of harmonic amplitude data, and the current situation of fuzzy classification of similar electrical appliances, pointing out a method for enhancing the characteristic difference of harmonic amplitude data. This method can improve the current situation that the low-power electric load is difficult to identify when the low-power electric load and the high-power electric load work together. The status quo of identification has a good effect on the classification of low power and similar electrical load. In this paper, a non-intrusive load monitoring system is built, the bus current data is collected, and a feature database is built, and the network is used to classify the feature data and finally obtain the classification result. The experimental results show that the proposed method has a significant effect on the accuracy of classification and recognition of similar electrical appliances with similar eigenvalues.

中文翻译:

基于特征差异增强的非侵入式负荷监测方法研究

摘要 非侵入式负荷监测技术是实现智能电网建设的关键技术。非侵入式负载监测中同类电器和小功率小电流电器的识别和分类一直是研究的重点和突破的难点。本文以电流谐波幅值为分类数据,根据谐波幅值数据的组成特征,以及同类电器模糊分类的现状,提出了一种增强谐波幅值数据特征差异的方法。该方法可以改善小功率电负载与大功率电负载协同工作时小功率电负载难以识别的现状。识别现状对小功率和类似用电负载的分类有很好的效果。本文搭建了一个非侵入式负荷监测系统,采集母线电流数据,建立特征数据库,利用网络对特征数据进行分类,最终得到分类结果。实验结果表明,该方法对特征值相似的同类电器的分类识别精度有显着影响。
更新日期:2020-08-05
down
wechat
bug