A Novel Strategy for Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

作     者：Lu, Shiue-Der Liu, Hwa-Dong Wang, Meng-Hui Wu, Chia-Chun 

作者机构：Department of Electrical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Failure analysis 

摘      要：This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Ultimately, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we were able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. These innovative monitoring and fault diagnosis techniques can be effectively applied to the daily monitoring and fault diagnosis of solar power systems, significantly improving the system’s operational stability and efficiency. © 2024, The Authors. All rights reserved.