电源学报

基于CNN-LSTM的直流微电网稳定性判别方法

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张晓棋 ¹ , 覃默 ² , 熊波 ³ , 蔡慧 ¹ , 李灵至 ¹

电源学报 | 电力电子化电力系统 2026,24(8): 192-199

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电源学报 |电力电子化电力系统 2026 , 24 (8) : 192 -199

基于CNN-LSTM的直流微电网稳定性判别方法

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张晓棋¹, 覃默², 熊波³, 蔡慧¹, 李灵至¹

作者信息

^1.中国计量大学机电工程学院，杭州　310018

^2.杭州海康威视数字技术股份有限公司，杭州　310051

^3.中国电建集团华东勘测设计研究院有限公司，杭州　310061

通讯作者:

蔡慧（1980—　），男，通信作者，博士，教授。研究方向：电力电子技术与电力传动，空调系统优化控制，新能源发电及其控制。E-mail：caihui@cjlu.edu.cn。

作者简介:

张晓棋（2000—　），女，硕士研究生。研究方向：直流微电网失稳监测。E-mail：zhangxiaoqi@cjlu.edu.cn。

覃默（1987—　），女，本科，高级工程师。研究方向：开关电源，充电桩。E-mail：qinmo@hikvision.com。

熊波（1984—　），男，本科，高级工程师。研究方向：水利水电工程，光伏电站，储能电站。E-mail：xiong_b@hdec.com。

李灵至（1993—　），女，博士，讲师。研究方向：电力设备故障诊断，建筑室内系统控制。E-mail：20a0102180@cjlu.edu.cn。

Stability Discrimination Method for DC Microgrid Based on CNN-LSTM

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

出版时间: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.192

文章导航

摘要

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针对直流微电网系统中阻抗不匹配的问题，提出了一种基于卷积神经网络CNN（convolutional neural network）和长短期记忆LSTM（long short-term memory）网络的直流微电网稳定性判别方法。首先，构建了CNN-LSTM复合网络模型，阐述了所提方法的基本结构和原理；其次，在MATLAB/Simulink中搭建了直流微电网系统的仿真模型，并进行了算例分析，详细描述了所提算法的实现过程；最后，将所提方法与人工神经网络ANN（artificial neural network）模型、CNN模型和LSTM模型进行了性能对比，并在半实物仿真测试平台上进行了实验验证。实验结果表明，所提方法能够自动提取特征，实现快速、准确的直流微电网系统的稳定性判别。

关键词

直流微电网 / 稳定性判别 / 卷积神经网络 / 长短期记忆网络

Abstract

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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.

Key words

DC microgrid / stability discrimination / convolutional neural network (CNN) / long short-term memory (LSTM) network

引用本文

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

基金

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国家自然科学基金资助项目(12404539)

参考文献

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2026年第24卷第8期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.8.192

接收时间：2024-11-14
首发时间：2026-10-29
出版时间：2026-08-30

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收稿日期：2024-11-14
修回日期：2024-12-29
录用日期：2025-02-05

基金

National Natural Science Foundation of China(12404539)

国家自然科学基金资助项目(12404539)

作者信息

^1.中国计量大学机电工程学院，杭州　310018

^2.杭州海康威视数字技术股份有限公司，杭州　310051

^3.中国电建集团华东勘测设计研究院有限公司，杭州　310061

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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