Fault diagnosis system with machine learning and cloud processing for a photovoltaic solar system1

Development and implementation of an artificial intelligence-based decision support system for fault detection and diagnosis in photovoltaic systems

Empirical findings demonstrate that the proposed DSS infrastructure significantly enhances predictive maintenance reliability in domestic SPP projects while substantially reducing operational downtime and technical maintenance costs.

Intelligent fault diagnosis and simulation-based modelling of grid-connected photovoltaic systems under desert dust conditions

Photovoltaic (PV) power plants operating in desert environments experience continuous efficiency losses because dust accumulation gradually reduces solar radiation reaching the module surface, leading to lower energy production even under favourable weather conditions. Accurate prediction of normal operating behaviour therefore provides a reference for distinguishing genuine faults from natural fluctuations in plant performance. This study proposes a data-driven framework for PV power prediction and residual-based fault diagnosis at the Aoulef PV power plant in southern Algeria. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were developed from measured solar irradiance and ambient temperature to estimate the healthy-state PV power output. Model performance was assessed through regression analysis and statistical error indices, whereas fault detection relied on residuals computed from the difference between measured and predicted power. Both models achieved excellent prediction accuracy, with coefficients of determination approaching 0.99. The ANN produced lower prediction errors than the ANFIS, with a root mean square error of 0.009 and an mean absolute error of 0.004, which improved the sensitivity of the residual-based diagnosis. Dust accumulation, identified as fault F13, generated clear residual deviations that enabled automatic fault detection without interrupting plant operation. The findings indicate that the ANN framework combines high predictive accuracy with low computational demand, offering a practical and reliable solution for intelligent monitoring and maintenance of PV systems operating under harsh desert conditions.

Solar photovoltaic fault classifications under normal and shading conditions using machine learning models

This study suggested PV monitoring systems with lower maintenance costs and energy losses, and an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest, K-Nearest Neighbors, Decision Tree, and SVM are proposed.

Integrating local photovoltaic and meteorological data for enhanced forecasting precision using machine learning

Data-driven fault detection in renewable energy systems using hybrid machine learning techniques

A new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner is suggested.

Machine Learning Based Predictive Maintenance for Improved Reliability and Efficiency of Solar Farms

The increasing deployment of solar photovoltaic (PV) systems has intensified the need for intelligent approaches that ensure reliable operation, minimize performance losses, and improve economic viability. Conventional maintenance strategies in solar farms are largely reactive or scheduled, often leading to delayed fault detection, reducing energy yield, and increased operational costs. This study presents a machine learning-based predictive maintenance and optimization framework aimed at enhancing the operational efficiency of a solar photovoltaic power plant, using Azari Farm as a case study. Operational data such as PV array voltage, current, irradiance, temperature, and power output are analyzed to identify performance degradation patterns and detect potential system faults before failure occurs. Machine learning algorithms are applied to monitor system behavior, predict anomalies, and optimize maintenance scheduling. The results demonstrate that integrating machine learning with predictive maintenance strategies significantly improves system reliability, reduces downtime, and enhances overall solar farm efficiency. The study highlights the potential of intelligent monitoring and optimization techniques to improve energy generation performance and sustainability in large scale solar photovoltaic installations.