Jul 2026· Engineering Research Express· Vol 8, pp. 145501· 0 citations· 24 references
Physics
Abstract
Nowadays, a big photovoltaic (PV) farm is operating to use solar energy as a source of electricity. Finding and estimating electrical problems on these farms is crucial to ensure the system is reliable, extract the maximum energy from it, and minimize maintenance costs. Machine learning algorithms are the tools that enable the detection of faults in the panel, thereby minimizing downtime. However, changes in PV technologies or environmental conditions make model use difficult because models must be updated frequently to be accurate. This paper presents an ensemble approach of machine learning to tackle this issue. A dataset obtained from a 25 KW PV power farm is used to categorize panels in four classes, including three fault types: string fault, string-ground fault, and string–string fault, and forth one is without fault. Initially, a feature reduction technique is employed, reducing the feature size from 30 to 4. Subsequently, a Bayesian-optimized ensemble approach utilizing the bagging method is applied to identify three types of faults, as well as normal conditions. Experimental evaluation suggested that even with just 4 features, the overall classification rate is maintained at 100% accuracy on the training dataset and at 95% on the test dataset.
This study aimed to develop and implement a cloud-enabled intelligent fault inference system for a photovoltaic installation, using locally acquired electrical and environmental data, preprocessed prior to cloud-based inference.
Claudemiro de Lima Júnior, Mariana da S. M. Sobral, Paulo F. C. Barbosa· Revista Brasileira de Engenh...· 0 citations
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.
Aafaque Ali, M. A. Raza, Muneera Altayeb et al.· Discover Sustainability· 0 citations
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.
Oyiogu Dennis, Nwokporo Sunday Celestine· International journal of re...· 0 citations
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.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
The increasing integration of Inverter-Based Resources (IBRs) into modern power grids has introduced new challenges for conventional fault-location techniques, particularly for renewable sources such as wind and solar. To address this issue, this paper proposes a robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks. The proposed methodology comprises multiple stages. Initially, 14 different regression models were implemented and evaluated using the Scikit-Learn library to assess their suitability for the task. Subsequently, a comprehensive hyperparameter optimization phase was conducted using Optuna, aiming not only to enhance model accuracy but also to reassess previously under-performing algorithms. The resulting models were then validated under a wide range of operating conditions, including variations in fault resistance, fault location, fault inception angle, and wind farm generation level. By exposing these models to 18,600 distinct fault scenarios, the simulations provide a comprehensive assessment of their generalization capability and demonstrate the effectiveness of the proposed approach. Among all models tested, the Multi-Layer Perceptron Regressor (MLP), Support Vector Regressor (SVR), and Kernel Ridge Regression (KRR) achieved the highest accuracy, with prediction errors not exceeding 2%. Conversely, ensemble-based methods such as Random Forest, AdaBoost, and Gradient Boosting exhibited noticeable limitations, struggling to capture the complex nonlinear relationships between fault characteristics and their corresponding locations in the context of IBRs.
Miguel R. Fonseca, M. Davi, M. Oleskovicz· IEEE Access· 0 citations
A hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy, and demonstrates that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photovoltaic forecasting systems.
Khawla Oufrit, A. Mouadili, M. Zazoui· EPJ Web of Conferences· 0 citations
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