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• W4387536151 startingPage "109881" @default.
• W4387536151 abstract "A reliable fault ride-through mechanism for PV systems assures sustainable, cost-effective, and uninterrupted power output. According to the US-based National Renewable Energy Lab (NREL), solar energy losses due to faults were 3.5 % in 2004, which increased to 17.5 % in 2018. This article proposes a sub-data set modeling approach based on Bayesian interactions in linear regression to forecast PV faults precisely. Fault prediction for ultra-short times is crucial for large-scale PV systems. Existing fault detection strategies rely on examining the system variable's status to identify whether the variable is faulty. The proposed fault forecasting algorithm uses the rate of change of solar cell parameters to forecast fault occurrence. The proposed procedure is validated using MATLAB on fault data set of different severity levels. This research will help the PV industry introduce reliable designs for small PV systems and solar parks. The proposed fault forecasting algorithm requires short circuit current, open circuit voltage, and power at MPPT from the physical system to detect faults before they occur." @default.
• W4387536151 created "2023-10-12" @default.
• W4387536151 creator A5013917654 @default.
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• W4387536151 date "2024-01-01" @default.
• W4387536151 modified "2023-10-12" @default.
• W4387536151 title "A novel procedure for photovoltaic fault forecasting" @default.
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• W4387536151 doi "https://doi.org/10.1016/j.epsr.2023.109881" @default.
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