CNN-based photovoltaic fault diagnosis using normalized I–V curves with Explainability analysis

IF 6 2区 工程技术 Q2 ENERGY & FUELS
Woogyun Shin, Jin Seok Lee, Young Chul Ju, Hye Mi Hwang, Sukwhan Ko
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引用次数: 0

Abstract

Countries worldwide are expanding the adoption of renewable energy to achieve carbon neutrality by 2050. Among the renewable sources, solar energy has experienced the fastest growth and largest deployment. As the number of photovoltaic (PV) plants increases, the operation and maintenance market expands, along with fault-diagnosis technologies that integrate traditional methods with artificial intelligence. This study proposes a fault-diagnosis technique that utilizes normalized current–voltage (I–V) curves of PV strings and a convolutional neural network (CNN). Measured I–V curves were normalized using a simulation model considering irradiance, module temperature, and degradation rate. The normalized curves were labeled as normal or as one of six fault types based on patterns and electrical parameters. A CNN trained with these data achieved training and validation accuracies of 99.34% and 99.39%, respectively. Layer-wise and occlusion sensitivity analyses were performed to interpret the classification process of CNN. Additionally, in a PV string where normal and faulty conditions were simulated, the trained CNN classified measured I–V curves with an average accuracy of 98.3%. When evaluated at an operational PV plant, the PVDF model prioritized the dominant I–V curve pattern for fault classification and successfully classified faults with subtle patterns.
基于cnn的可解释性归一化I-V曲线的光伏故障诊断
世界各国正在扩大对可再生能源的采用,以在2050年前实现碳中和。在可再生能源中,太阳能发展最快,部署规模最大。随着光伏电站数量的增加,运维市场不断扩大,将传统方法与人工智能相结合的故障诊断技术也在不断发展。本研究提出了一种利用PV串的归一化电流-电压(I-V)曲线和卷积神经网络(CNN)的故障诊断技术。测量的I-V曲线使用考虑辐照度、模块温度和降解率的模拟模型进行归一化。根据模式和电气参数,归一化曲线被标记为正常或六种故障类型之一。用这些数据训练的CNN的训练和验证准确率分别达到99.34%和99.39%。进行分层和闭塞敏感性分析来解释CNN的分类过程。此外,在模拟正常和故障工况的PV管柱中,训练后的CNN对测量的I-V曲线进行分类,平均准确率为98.3%。在运行中的光伏电站进行评估时,PVDF模型优先考虑了主要的I-V曲线模式进行故障分类,并成功地对具有微妙模式的故障进行了分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Solar Energy
Solar Energy 工程技术-能源与燃料
CiteScore
13.90
自引率
9.00%
发文量
0
审稿时长
47 days
期刊介绍: Solar Energy welcomes manuscripts presenting information not previously published in journals on any aspect of solar energy research, development, application, measurement or policy. The term "solar energy" in this context includes the indirect uses such as wind energy and biomass
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