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Stability Discrimination Method for DC Microgrid Based on CNN-LSTM
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Xiaoqi ZHANG1, Mo QIN2, Bo XIONG3, Hui CAI1, Lingzhi LI1
Journal of Power Supply | 2026, 24(8) : 192 - 199
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Journal of Power Supply | 2026, 24(8): 192-199
• Power Electronic Dominated Power Systems •
Stability Discrimination Method for DC Microgrid Based on CNN-LSTM
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Xiaoqi ZHANG1, Mo QIN2, Bo XIONG3, Hui CAI1, Lingzhi LI1
Affiliations
  • 1.College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
  • 2.Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China
  • 3.PowerChina Huadong Engineering Co., Ltd., Hangzhou 310061, China
Published: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.192
Outline
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To solve the problem of impedance mismatch in a DC microgrid system, a stability discrimination method for DC microgrid based on convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed. First, a CNN-LSTM composite network model is constructed, and the basic structure and principle of the proposed method are expounded. Second, a simulation model of the DC microgrid system is built in MATLAB/Simulink, and a case study is carried out to describe the implementation process of the proposed algorithm in detail. Finally, the performance of the proposed method was compared with those of the artificial neural network (ANN), CNN and LSTM models, and the experimental verification was carried out on a hardware-in-the-loop simulation test platform. Experimental results show that the proposed method can automatically extract features and achieve fast and accurate stability discrimination of the DC microgrid system.

DC microgrid  /  stability discrimination  /  convolutional neural network (CNN)  /  long short-term memory (LSTM) network
张晓棋, 覃默, 熊波, 蔡慧, 李灵至. 基于CNN-LSTM的直流微电网稳定性判别方法. 电源学报, 2026 , 24 (8) : 192 -199 . DOI: 10.13234/j.issn.2095-2805.2026.8.192
Xiaoqi ZHANG, Mo QIN, Bo XIONG, Hui CAI, Lingzhi LI. Stability Discrimination Method for DC Microgrid Based on CNN-LSTM[J]. Journal of Power Supply, 2026 , 24 (8) : 192 -199 . DOI: 10.13234/j.issn.2095-2805.2026.8.192
  • National Natural Science Foundation of China(12404539)
Year 2026 volume 24 Issue 8
45
5
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.8.192
  • Receive Date:2024-11-14
  • Online Date:2026-10-29
  • Published:2026-08-30
Article Data
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History
  • Received:2024-11-14
  • Revised:2024-12-29
  • Accepted:2025-02-05
Funding
National Natural Science Foundation of China(12404539)
Affiliations
    1.College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
    2.Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China
    3.PowerChina Huadong Engineering Co., Ltd., Hangzhou 310061, China
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