Jiamin Lv
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jiamin Lv has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Manqian Yu
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication - Mingshen Wang
Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
1 shared publication - Qikai Zhao
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication - Yi Ru
College of Information Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication - Ying Wang
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication
Latest Publications
- Open Access
Risk Assessment of Distribution Network Operation Based on Generalized Load
With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation.
Open AccessRisk Assessment of Distribution Network Operation Based on Generalized Load
With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation.