article · Scientific Reports
Monitoring the health of photovoltaic systems is vital for maximising energy efficiency, system reliability, and operational lifespan. When faults occur, solar panels exhibit notable changes in their current-voltage characteristics. To address this, an artificial intelligence diagnostic framework was developed using real-time electrical data. The system employs a direct current to direct current buck-boost converter to extract and present live measurement data from solar panels. These experimental measurements are analysed using a hybrid technique that pairs the Harris Hawks Optimisation algorithm for feature selection with an advanced XGBoost machine learning classifier. The resulting model accurately identifies and categorises system faults, attaining high diagnostic precision. When evaluated against other established classification methods, the approach demonstrated superior reliability and robustness in detecting diverse solar panel operating conditions.
Solar power installations must operate reliably to deliver clean energy efficiently. Undetected hardware faults reduce power output and can shorten equipment lifespan. By pairing automated electronic hardware with artificial intelligence, operators can quickly and accurately diagnose panel problems from live electrical signals, reducing downtime and supporting more cost-effective solar plant maintenance.
This methodology applies directly to solar power plant health monitoring, condition-based maintenance, and automated fault diagnostics. Solar plant operators, maintenance service providers, and monitoring hardware manufacturers are the prospective end users. Because the methodology was experimentally implemented and tested using a physical converter alongside an analytical model, the technology sits at an applied and tested stage, though integration into commercial monitoring products would require further development.
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Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions. This paper introduces a diagnostic methodology for photovoltaic panels using I-V curves, enhanced by new techniques combining optimization and classification-based artificial intelligence. The research is organized into two key sections. The first section outlines the implementation of a DC/DC buck-boost converter, which is designed to extract and display real-time data from the PV system based on actual (I-V) measurements. The second section focuses on the comprehensive processing of the experimental dataset, where the Harris Hawks Optimization (HHO) algorithm is combined with machine learning methods to identify the most critical features. The HHO algorithm is combined with an advanced machine learning model, XGBoost, to accurately detect faults within the PV system. The proposed HHO-XGBoost algorithm achieves an impressive accuracy of 99.49%, outperforming other classification-based artificial intelligence methods in fault detection. In validation and comparison with previous approaches, the HHO-XGBoost model consistently outperforms established methods such as GADF-ANN, PCA-SVM, PNN, and Fuzzy Logic, achieving an overall accuracy of 98.48%. This outstanding performance confirms the model's effectiveness in accurately diagnosing PV system conditions, further validating its robustness and reliability in fault detection and classification.
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DOI: 10.1038/s41598-024-84365-5
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