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<p>Central inverters are widely used in large photovoltaic systems, but struggle with inefficiency and large energy losses with direct current (DC). To solve these problems, Artificial Intelligence (AI) and the Internet of Things (IoT) offer alternative solutions. In this study, a low-cost open-source IoT system is proposed for the 5&#xa0;MW Thap-Sakae photovoltaic plant in Thailand, where the DC power parameters are collected in an AI-based time-series fault classification mode. The fault diagnosis data includes five entries per set for no fault, open circuit fault and shading fault. The system achieves 94% accuracy in the diagnosis of non-linear faults and time series parameters. Compared to other solutions, the long-range private wide area network provides cost-effective communication and supports data transmission of up to 180&#xa0;m. In this study, conducted in a photovoltaic plant with unregulated conditions, a low-cost AI-powered IoT solution has been shown to be effective in real-time fault classification on the DC side. The proposed solution is sustainable and easy to manage over time, as it can handle non-linear problems caused by the volatility of the system or the fluctuations of the sensors without significant changes.</p>

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Artificial intelligence-based fault classification on photovoltaic plants using a low-cost open-source IoT system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Wattanasak Srisiri,
  • Ngoc Thien Le,
  • Muhammad Asim Saleem,
  • Pasu Kaewplung,
  • Surachai Chaitusaney,
  • Watit Benjapolakul

摘要

Central inverters are widely used in large photovoltaic systems, but struggle with inefficiency and large energy losses with direct current (DC). To solve these problems, Artificial Intelligence (AI) and the Internet of Things (IoT) offer alternative solutions. In this study, a low-cost open-source IoT system is proposed for the 5 MW Thap-Sakae photovoltaic plant in Thailand, where the DC power parameters are collected in an AI-based time-series fault classification mode. The fault diagnosis data includes five entries per set for no fault, open circuit fault and shading fault. The system achieves 94% accuracy in the diagnosis of non-linear faults and time series parameters. Compared to other solutions, the long-range private wide area network provides cost-effective communication and supports data transmission of up to 180 m. In this study, conducted in a photovoltaic plant with unregulated conditions, a low-cost AI-powered IoT solution has been shown to be effective in real-time fault classification on the DC side. The proposed solution is sustainable and easy to manage over time, as it can handle non-linear problems caused by the volatility of the system or the fluctuations of the sensors without significant changes.