A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems

Abstract

In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.

Keywords

• Convolutional neural networks (CNNs)
• fault identification
• feature extraction
• gated neural networks (GNNs)
• information theory
• loss function