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Convolutional Neural Network-Based Fault Detection and Classification in Photovoltaic Systems Under Partial Shading Conditions.

Najaad OubeBlika
Volume 5

Abstract

 

Photovoltaic (PV) systems are increasingly deployed as a key renewable energy technology for sustainable electricity generation; however, their operational performance, reliability, and energy yield can be significantly affected by faults and environmental disturbances. Among these disturbances, partial shading conditions represent one of the most challenging scenarios because they create non-uniform irradiance distribution across PV modules, leading to power mismatch, hotspot formation, multiple peaks in the power–voltage characteristics, reduced maximum power extraction, and possible long-term degradation of system components. In addition, partial shading may mask or imitate several electrical and physical faults, making accurate fault detection and classification more difficult using conventional threshold-based monitoring and rule-based diagnostic methods. To address these limitations, this paper proposes a Convolutional Neural Network (CNN)-based framework for automated fault detection and classification in photovoltaic systems operating under partial shading conditions.

The proposed framework is designed to learn discriminative fault-related patterns from PV system data, including current–voltage curves, power–voltage curves, module temperature profiles, irradiance measurements, string current, array voltage, power output, bypass diode behavior, and inverter-level monitoring signals. Depending on the available monitoring infrastructure, the input representation can be constructed from transformed electrical signal maps, time–frequency images, thermal images, electroluminescence images, or two-dimensional feature matrices derived from sensor measurements. A CNN architecture is employed to automatically extract hierarchical spatial and local pattern features associated with different PV abnormalities without relying heavily on handcrafted indicators. The model is trained to distinguish normal operation from multiple fault categories, including partial shading, open-circuit faults, short-circuit faults, line-to-line faults, bypass diode faults, hotspot defects, soiling effects, module degradation, and mismatch faults.

A comprehensive preprocessing pipeline is incorporated to improve data quality and diagnostic robustness. This pipeline includes noise filtering, missing value handling, normalization of electrical variables, irradiance and temperature compensation, image resizing, contrast enhancement for thermal or electroluminescence data, feature scaling, and data augmentation under diverse shading intensities and environmental conditions. To improve the model’s ability to generalize across different PV configurations, the framework considers variations in module technology, array topology, shading pattern, fault severity, load condition, and seasonal weather behavior. Class imbalance caused by the lower occurrence of severe faults is addressed using class-weighted loss functions, oversampling, synthetic sample generation, and balanced mini-batch training.

The proposed CNN-based diagnostic system can be evaluated using experimental PV testbeds, simulation-generated datasets, and real-world monitoring data collected from grid-connected or standalone PV installations. Model performance is assessed using accuracy, precision, recall, sensitivity, specificity, F1-score, confusion matrix analysis, false alarm rate, detection latency, and robustness under changing irradiance and temperature conditions. Comparative analysis is conducted against conventional electrical threshold methods, support vector machine, random forest, k-nearest neighbors, multilayer perceptron, and handcrafted feature-based diagnostic approaches. Experimental evaluation is expected to demonstrate that the proposed CNN framework achieves improved fault classification accuracy and stronger resilience to partial shading effects by learning complex nonlinear relationships between shading patterns, electrical behavior, and fault signatures.

To support practical deployment, the proposed framework can be integrated into PV monitoring platforms, inverter controllers, supervisory control systems, and smart energy management systems. Explainability techniques such as Grad-CAM, saliency mapping, and feature activation visualization are incorporated to highlight the signal regions, thermal hotspots, or electrical pattern segments most responsible for each fault prediction. These interpretability tools help operators verify whether the model focuses on physically meaningful fault indicators, thereby improving trust and supporting maintenance decision-making. The proposed system offers a scalable, accurate, and automated solution for early fault detection and classification in PV systems under partial shading conditions. By enabling timely fault diagnosis, reducing unnecessary maintenance, minimizing energy losses, and preventing severe component damage, the framework contributes to improved PV system reliability, operational safety, and long-term renewable energy performance.

 

Keywords

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