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<p>As photovoltaic (PV) systems become used widely, reliability and efficiency improvements are essential. Faults caused in the PV systems could produce energy losses up to 20% yearly. Fault detection is essential to diminish these losses and maintain the system efficiently. This work focuses on machine learning for reliable and accurate detection of faults in PV systems. The work proposes Gradient Boosted Decision Tree (GBDT) algorithms with ensemble method to identify arc faults, line to line faults and open circuit faults using real time data collected from voltage, current and irradiance sensors. GBDT integrates with ensemble methods to improve predictive performance and reliability. Both training and testing of this model were performed with the dataset. After trained, the framework is deployed on a web server for instantaneous fault condition monitoring. Also, the data for the algorithm was generated using MATLAB/Simulink simulations under different operating conditions to validate the datasets. Experimental results shown that the design achieving 100% accuracy during training and 98.54% accuracy during testing on randomly split datasets. If any fault occurs, the notification mail or Message will be sent to the user, provided the fault details through the server. These findings exhibit the framework potential to considerably improve the reliability and efficiency of PV systems in real world developments.</p>

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Improving operational efficiency in photovoltaic systems with advanced fault monitoring solutions using machine learning algorithms

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • N Vijayasarathi,
  • Vijay Ravindran

摘要

As photovoltaic (PV) systems become used widely, reliability and efficiency improvements are essential. Faults caused in the PV systems could produce energy losses up to 20% yearly. Fault detection is essential to diminish these losses and maintain the system efficiently. This work focuses on machine learning for reliable and accurate detection of faults in PV systems. The work proposes Gradient Boosted Decision Tree (GBDT) algorithms with ensemble method to identify arc faults, line to line faults and open circuit faults using real time data collected from voltage, current and irradiance sensors. GBDT integrates with ensemble methods to improve predictive performance and reliability. Both training and testing of this model were performed with the dataset. After trained, the framework is deployed on a web server for instantaneous fault condition monitoring. Also, the data for the algorithm was generated using MATLAB/Simulink simulations under different operating conditions to validate the datasets. Experimental results shown that the design achieving 100% accuracy during training and 98.54% accuracy during testing on randomly split datasets. If any fault occurs, the notification mail or Message will be sent to the user, provided the fault details through the server. These findings exhibit the framework potential to considerably improve the reliability and efficiency of PV systems in real world developments.