Yongsheng Shu

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Jinyang Jiang

Nanjing University of Posts and Telecommunications

Yingying Cheng

Hefei University of Technology

Shuo Liang

Aerospace Center Hospital

Huaichun Zhou

China University of Mining and Technology

Shiliang Chang

State Grid Corporation of China (China)

Jie Du

Tianjin University

Feng Zhou

Beijing University of Posts and Telecommunications

Jiaming Zhang

Chengdu University of Traditional Chinese Medicine

Jie Du

Shenzhen University Health Science Center

Xiao Ji

State Grid Corporation of China (China)

Cooperative Institutions

State Grid Corporation of China (China)

Electric Power Research Institute

Chongqing University

Tianjin University

Peking University

Nanjing University of Aeronautics and Astronautics

Shijiazhuang University

Beijing University of Posts and Telecommunications

Nanjing University of Posts and Telecommunications

Karlsruhe Institute of Technology

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Research on Power Load Classification Based on Feature Weighted Fuzzy Clustering

Jiaming Zhang Yingying Cheng Jie Du Yongsheng Shu Feng Zhou

The daily load curve of the substation in the power system can be used for the classification and synthesis of load characteristics. The typical daily load curve data obtained and processed by the SCADA system is the basis for the study of load classification. In order to solve the problem of original curve mode aliasing, a signal processing method based on variational mode decomposition (VMD) is proposed. At the same time, in view of the unique problem of the feature weight of the Euclidean distance in the fuzzy C-means (FCM) clustering algorithm, a feature weighted fuzzy clustering method is used to propose a feature weighted VMD-FCM clustering algorithm. Examples show that VMD algorithm can effectively decompose the intrinsic mode of signals, improve the convergence speed and clustering accuracy of FCM function; Based on the Euclidean distance, the weight coefficient improves the practicability and accuracy of FCM algorithm. The results show that the improved method can effectively distinguish different load types, which has a guiding role in load forecasting, pricing standards and even marketing strategies.

Feature (linguistics)

10.1109/icisce50968.2020.00244

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Research on Abnormal Diagnosis Method of Electric Energy Metering Device Based on BSO-BPNN

Jiaming Zhang Yingying Cheng Jie Du Yongsheng Shu Jinyang Jiang

The traditional electric energy metering device anomaly detection mainly relies on manual detection, the electric network staff need to analyze the data of metering device collected by the electric information system periodically. Aiming at the problems of missing report, false report and low accuracy in manual judgment. A back propagation neural network algorithm (BPNN) based on beetle swarm optimization algorithm (BSO) is proposed to construct an anomaly diagnosis model for power metering devices. BSO is based on particle swarm optimization algorithm (PSO), combined with the merits of beetle antennae search algorithm (BAS). And it improved the PSO algorithm easy to fall into the local minimum problem. Both PSO and BSO improve the performance of BPNN by optimizing the weight and threshold of BPNN. The experimental results show that BSO-BPNN has higher accuracy than BPNN and PSO-BPNN in the abnormal diagnosis of metering device.

Metering mode

10.1109/icisce50968.2020.00480

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Citations (5)