Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models

Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The proposed methodology establishes a predictive baseline model of the system’s healthy behavior under normal operating conditions, enabling real-time detection of deviations between expected and actual performance. Faults such as string disconnections, module short-circuits, and shading effects have been identified using two key indicators: current error (Ec) and voltage error (Ev). By focusing on power losses as a fault indicator, this method provides high-accuracy fault detection without requiring extensive labeled data, a significant advantage for large-scale PV systems where data acquisition can be challenging. Additionally, a key contribution of this work is the identification and correction of faulty sensors, specifically pyranometer misalignment, which leads to inaccurate irradiation measurements and disrupts fault diagnosis. The approach ensures reliable input data for the predictive models, where RF achieved an R 2 of 0.99657 for current prediction and 0.99459 for power prediction, while KNN reached an R 2 of 0.99674 for voltage estimation, improving both the accuracy of fault detection and the system’s overall performance. The outlined approach was experimentally validated using real-world data from a 500 kWp grid-connected PV system in Ain El Melh, Algeria. The results demonstrate that this innovative method offers an efficient, scalable solution for real-time fault detection, enhancing the reliability of large PV systems while reducing maintenance costs.