Qikai Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Qikai Zhao has a total of 6 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
2 shared publications - Mingshen Wang
Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
2 shared publications - Ying Wang
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
2 shared publications - Jiamin Lv
College of Information Engineering, China Jiliang University, Hangzhou 310018, China
1 shared publication - Jiamin Lv
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
Latest Publications
- Open Access
Single-Ended Fault Location Method for DC Distribution Network Based on Bi-LSTM
When a line short-circuit fault occurs in a DC distribution network, the fault current rises quickly and affects a wide range, jeopardizing the safe operation of the system. In order to locate the fault quickly and accurately, this study proposes a fault localization method based on the Variational Mode Decomposition (VMD) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. First, the nonlinear relationship between the intrinsic principal frequency and fault distance is analyzed; then, the intrinsic principal frequency of the faulty traveling wave is extracted by using VMD, and the nonlinear relationship between the spectral energy of the principal frequency of the intrinsic frequency and the fault distance is fitted by training the Bi-LSTM network incorporating the attention mechanism. Finally, in response to the issue that a small amount of fault data in practical engineering is difficult to support the amount of data required for deep learning, a transfer learning method is used to locate the fault in the target domain. A small sample test of the target domain is carried out using the migration learning method. The experimental results show that the proposed method has high localization accuracy and good resistance to over-resistance and noise; compared with the traditional network training, the localization error based on migration learning is smaller, and the network convergence effect is better.
Open AccessSingle-Ended Fault Location Method for DC Distribution Network Based on Bi-LSTM
When a line short-circuit fault occurs in a DC distribution network, the fault current rises quickly and affects a wide range, jeopardizing the safe operation of the system. In order to locate the fault quickly and accurately, this study proposes a fault localization method based on the Variational Mode Decomposition (VMD) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. First, the nonlinear relationship between the intrinsic principal frequency and fault distance is analyzed; then, the intrinsic principal frequency of the faulty traveling wave is extracted by using VMD, and the nonlinear relationship between the spectral energy of the principal frequency of the intrinsic frequency and the fault distance is fitted by training the Bi-LSTM network incorporating the attention mechanism. Finally, in response to the issue that a small amount of fault data in practical engineering is difficult to support the amount of data required for deep learning, a transfer learning method is used to locate the fault in the target domain. A small sample test of the target domain is carried out using the migration learning method. The experimental results show that the proposed method has high localization accuracy and good resistance to over-resistance and noise; compared with the traditional network training, the localization error based on migration learning is smaller, and the network convergence effect is better.
- 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.