A DEEP LEARNING–EMPOWERED ARTIFICIAL INTELLIGENCE FRAMEWORK FOR INTELLIGENT FAULT DETECTION AND PERFORMANCE OPTIMIZATION IN PHOTOVOLTAIC SOLAR ENERGY SYSTEMS

Abstract

The rapid global expansion of photovoltaic (PV) solar energy systems has intensified the need for intelligent, autonomous, and highly accurate monitoring frameworks capable of mitigating faults and optimizing energy output under diverse environmental and operational conditions. Conventional rule-based and model-driven diagnostic techniques often fail to capture nonlinear interactions among irradiance fluctuations, temperature variations, shading patterns, aging effects, and inverter-level disturbances. To address these limitations, this study proposes a Deep Learning–Empowered Artificial Intelligence Framework for intelligent fault detection, performance prediction, and real-time optimization in grid-connected and standalone PV systems. The framework integrates multilevel sensing, high-resolution current–voltage (I–V) acquisition, inverter diagnostics, and meteorological inputs into a unified data fusion pipeline optimized for deep neural architectures. At the core of the proposed system is a hybrid deep learning engine combining convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal behavior modeling, and an attention-driven fusion module for anomaly localization and severity scoring. The model is trained on multisource datasets capturing normal, partially shaded, soiling-affected, hotspot-induced, bypass-diode-damaged, and degradation-related faults. A hierarchical classification and regression scheme enables simultaneous detection, fault categorization, maximum power point deviation estimation, and performance forecasting. Additionally, a reinforcement-learning–based optimization layer dynamically adjusts MPPT parameters, inverter reference signals, and operational settings to enhance energy yield while ensuring system safety. Experimental validation is performed using both real-time PV testbed measurements and open-source datasets such as the Desert Knowledge Australia Solar Centre (DKASC) and Sandia National Laboratories benchmarking repository. Results demonstrate that the proposed framework achieves significant improvements in detection accuracy, false-alarm reduction, severe-fault localization, and output power prediction compared to classical SVM, random forest, and shallow neural network approaches. The RL-driven optimization further enhances overall energy efficiency and operational stability under rapidly changing conditions, outperforming traditional MPPT methods such as Perturb-and-Observe and Incremental Conductance. The findings highlight the potential of intelligent AI-driven monitoring to revolutionize the reliability, resilience, and performance of next-generation photovoltaic assets. The proposed framework serves as a scalable foundation for future PV smart-grid integration, digital twins, predictive maintenance, and autonomous self-healing solar farms.