工程科学与技术2024，Vol.56Issue(4)：316-324,9.DOI:10.12454/j.jsuese.202201264
基于在线自组织增量学习的非侵入式负荷识别方法
Non-intrusive Load Identification Method Based on the Online Self-organizing Incremental Neural Network
摘要
Abstract
With the development of intelligent electronic technology,the accurate identification of electrical load usage will have extensive user demands in the field of smart electricity.In order to achieve the online real-time accurate monitoring of the electrical equipment,this paper pro-posed a non-intrusive load identification method based on the online self-organizing incremental neural network(SOINN).This method included two steps,which are the load feature extraction and the load feature classification with the equipment identification.In the process of load feature extraction,a 12-dimensional feature extraction scheme was proposed,which includes the odd harmonics,the mean value,the variance value,the third-order moment,the fourth-order moment,the root mean square current,the peak value of power spectrum,and the trough value of power spectrum.In the second step,a method combining SVM and SOINN for the load feature classification and the electrical equipment identification was proposed to overcome the limitation of the traditional SOINN algorithm in appliance type recognition.The functional algorithms in the pro-posed method are implemented as executable functional modules for the microprocessor system using C++programming language.The function-al modules were then ported and deployed on the HPS side of the SoC FPGA,achieving collaborative high-speed data communication between FPGA and HPS.Eight types of conventional household appliances were selected as the load identification objects.A hardware experimental plat-form based on SoC FPGA was built to select the optimal load characteristics.The proposed method was validated for identifying online loads of both single and multiple appliances.Experimental results showed that the above 12-dimensional features were selected as the optimal feature combination for the method proposed in this paper.The recognition rates of both single and multiple appliances using the proposed method were above 95％.The proposed load identification method can effectively and accurately identify both single and multiple electrical appliances.The system has strong implementability,high flexibility,the advantages of online learning,and practical feasibility for practical applications.关键词
增量学习/负荷识别/12维样本特征/FPGAKey words
incremental learning/load identification/12-dimensional sample characteristics/FPGA分类
信息技术与安全科学引用本文复制引用
胡正伟,王志红,畅瑞鑫,谢志远,曹旺斌..基于在线自组织增量学习的非侵入式负荷识别方法[J].工程科学与技术,2024,56(4):316-324,9.基金项目
国家自然科学基金面上项目(52177083) （52177083）
国家自然科学基金青年科学基金项目(62001166) （62001166）
