Wensong Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wensong Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bin Li
Changsha University of Science and Technology Changsha China
1 shared publication - Huafeng Su
Dongguan Electric Power Corporation, Guangdong Power Grid Company Ltd., Dongguan, China
1 shared publication - Peijun Zhong
State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science and Technology, Changsha, China
1 shared publication - Sheng Su
Changsha University of Science and Technology, Changsha, CHINA
1 shared publication - Xianzhong Duan
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China
1 shared publication - Xiaofei Feng
Changsha University of Science and Technology
1 shared publication
Latest Publications
- 16 April 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 74 (00189456) , 1-12
A Noninvasive Data and Physics-Driven Approach to Identify Wiring Fault User in LVDS
Low-voltage distribution systems (LVDS) are prone to leakage current faults, which pose significant electrical safety risks. The wiring complexity, diverse potential causes, and scarce data make identifying and troubleshooting leakage faults in LVDS a persistent challenge in operation and maintenance. A common issue encountered onsite is the incorrect connection of the neutral wire and protective earth (PE) wires within the user’s panel box. Initially, the impact and hazards associated with such wiring faults are analyzed, taking into account the graded residual current protection device and the grounding protection configuration in LVDS. Subsequently, the differential impact on the residual current of the LVDS is examined when wiring faults occur in either a single user or multiple users. A ridge regression-based identification method for detecting users with wiring faults is then proposed, utilizing the time series data of the residual current of the LVDS and the load current of users. Further, considering the impact of complex fault scenarios and the number of users in LVDS, the sliding window technique combined with the Mean-shift cluster analysis algorithm is employed to enhance the accuracy of identifying users with wiring faults. Finally, laboratory data from an LVDS model is used to conduct numerical simulations of wiring fault leakage, verifying the effectiveness and superiority of the proposed method.
- 16 April 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 74 (00189456) , 1-12
A Noninvasive Data and Physics-Driven Approach to Identify Wiring Fault User in LVDS
Low-voltage distribution systems (LVDS) are prone to leakage current faults, which pose significant electrical safety risks. The wiring complexity, diverse potential causes, and scarce data make identifying and troubleshooting leakage faults in LVDS a persistent challenge in operation and maintenance. A common issue encountered onsite is the incorrect connection of the neutral wire and protective earth (PE) wires within the user’s panel box. Initially, the impact and hazards associated with such wiring faults are analyzed, taking into account the graded residual current protection device and the grounding protection configuration in LVDS. Subsequently, the differential impact on the residual current of the LVDS is examined when wiring faults occur in either a single user or multiple users. A ridge regression-based identification method for detecting users with wiring faults is then proposed, utilizing the time series data of the residual current of the LVDS and the load current of users. Further, considering the impact of complex fault scenarios and the number of users in LVDS, the sliding window technique combined with the Mean-shift cluster analysis algorithm is employed to enhance the accuracy of identifying users with wiring faults. Finally, laboratory data from an LVDS model is used to conduct numerical simulations of wiring fault leakage, verifying the effectiveness and superiority of the proposed method.