Intelligent Fault Management for Photovoltaic System using Cross-Correlation and Machine Learning

Authors

  • C. U. Nwamuo Renewable and New Energy System, African Center of Excellence for Sustainable Power and Energy Development, University of Nigeria Nsukka, ACESPED UNN.
  • O. Ojike Bio Resources Engineering Department, University of Nigeria Nsukka, UNN.
  • S. O. Enibe Mechanical Engineering Department, University of Nigeria Nsukka, UNN.

DOI:

https://doi.org/10.63746/njtd.v22i4.3085

Keywords:

Photovoltaic, Bayesian, Adam, hybrid optimization, fault, cross correlation.

Abstract

This paper presents a fault management model for the protection of off-grid PV system. Data was collected from an off-grid 4.2 kW PV system installed at Edugen technologies Ltd, located at Nsukka, Enugu State, Nigeria. An adaptive feature extractor using a cross-correlation approach was used to identify and extract intricate and high dimensional features of PV characteristics. This data was then used to train a neural network. For the training of the network, Bayesian optimization (Bayes) and adaptive movement estimation (Adam), were used individually and as hybrid. The models generated were evaluated experimentally, considering Mean Square Error (MSE), Coefficient of determination (), and Root Mean Square Error (RMSE). Comparative analysis of the three training algorithms showed that the hybrid optimization algorithm recorded better  of 0.9824, MSE of 9.5673e-9, and RMSE of 9.781e-5 when compared to Bayes and Adam. In conclusion, the hybrid optimization-based PV fault detection model was the most suitable for PV protection and is therefore recommended for the protection of off-grid standalone PV systems to ensure power sustainability.

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Published

2025-09-29

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