电源学报

基于改进人工神经网络的太阳能光伏阵列故障诊断研究

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孙鸿哲 , 丁强 , 曾启星 , 张梦鑫

电源学报 | 故障诊断与可靠性 2026,24(7): 326-334

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电源学报 |故障诊断与可靠性 2026 , 24 (7) : 326 -334

基于改进人工神经网络的太阳能光伏阵列故障诊断研究

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孙鸿哲（2000—　），男，硕士研究生。研究方向：光伏阵列故障诊断。E-mail：sun2333@hdu.edu.cn。

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张梦鑫（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：222060360@hdu.edu.cn

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张梦鑫（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：222060360@hdu.edu.cn

孙鸿哲, 丁强, 曾启星, 张梦鑫

作者信息

杭州电子科技大学自动化学院，杭州　310018

通讯作者:

丁强（1972—　），男，通信作者，硕士，研究员。研究方向：能量利用系统检测与控制。E-mail：dingqiang@hdu.edu.cn。

作者简介:

孙鸿哲（2000—　），男，硕士研究生。研究方向：光伏阵列故障诊断。E-mail：sun2333@hdu.edu.cn。

曾启星（2000—　），男，硕士研究生。研究方向：机器学习故障诊断。E-mail：19548174158@163.com

张梦鑫（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：222060360@hdu.edu.cn

Research on Fault Diagnosis of Solar Photovoltaic Array Based on Improved ANN

Hongzhe SUN, Qiang DING, Qixing ZENG, Mengxin ZHANG

Affiliations

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

出版时间: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.326

文章导航

摘要

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为提高光伏阵列故障诊断的准确率，提出一种改进的人工神经网络ANN（artificial neural network）的光伏阵列故障检测方法。在ANN中，增加门控卷积层、扁平化层和全连接层替代隐藏层，解决现有ANN容易产生梯度消失的问题，并利用自适应Adaptive Softmax作为模型的输出层激活函数，减少由于数据集规模较大带来的计算耗时。利用MATLAB建立了光伏阵列仿真模型，获得不同状态的模拟运行数据，同时利用从文献中获得的光伏阵列实际不平衡运行数据集，对改进后的ANN与文献所提6种机器学习方法进行诊断性能对比分析，结果证明了所提方法的诊断性能优势。

关键词

光伏阵列 / 故障诊断 / 人工神经网络ANN / 门控卷积

Abstract

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In order to improve the accuracy of photovoltaic array fault diagnosis, an improved artificial neural network (ANN) method for photovoltaic array fault detection is proposed. In ANN, a gated convolutional layer, a flat layer and a fully connected layer are added to replace hidden layer, which solves the problem of gradient disappearance easily in existing ANN. Adaptive Softmax is used as the activation function of the output layer of the model to reduce the computational time incurred due to the large data set size. The simulation model of photovoltaic array was established by using MATLAB to obtain the simulation operation data of different states. Meanwhile, the actual unbalanced operation data set of photovoltaic array was obtained from the literature, and the diagnostic performance of the improved ANN was compared with the six machine learning methods mentioned in the literature, showing the diagnostic performance advantages of the proposed method.

Key words

Photovoltaic array / fault diagnosis / artificial neural network (ANN) / gated convolution

引用本文

孙鸿哲, 丁强, 曾启星, 张梦鑫. 基于改进人工神经网络的太阳能光伏阵列故障诊断研究. 电源学报, 2026 , 24 (7) : 326 -334 . DOI: 10.13234/j.issn.2095-2805.2026.7.326

Hongzhe SUN, Qiang DING, Qixing ZENG, Mengxin ZHANG. Research on Fault Diagnosis of Solar Photovoltaic Array Based on Improved ANN[J]. Journal of Power Supply, 2026 , 24 (7) : 326 -334 . DOI: 10.13234/j.issn.2095-2805.2026.7.326

基金

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浙江省自然科学基金资助项目(LY21E060013)

参考文献

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文献

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参考文献引证文献

排序方式：

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2026年第24卷第7期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.7.326

接收时间：2024-08-02
首发时间：2026-10-29
出版时间：2026-07-30

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出版历史

收稿日期：2024-08-02
修回日期：2024-10-03
录用日期：2024-11-03

基金

Natural Science Foundation of Zhejiang Province(LY21E060013)

浙江省自然科学基金资助项目(LY21E060013)

作者信息

杭州电子科技大学自动化学院，杭州　310018

通讯作者:

丁强（1972—　），男，通信作者，硕士，研究员。研究方向：能量利用系统检测与控制。E-mail：dingqiang@hdu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.7.326

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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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