Fault Diagnosis Method for Sub-Module Open-Circuit Faults in Photovoltaic DC Collection Systems Based on CNN-LSTM

To diagnose open-circuit faults (OCFs) in sub-module switching devices within input-independent output-series (IIOS) photovoltaic DC collection systems, this paper presents a hybrid diagnostic method combining convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The method utilizes a sliding window technique to segment sub-module capacitor voltage signals into time-series samples. Initially, CNN automatically extracts local features from the samples, followed by LSTM for capturing temporal dependencies and extracting global time series features, enabling effective fault detection under complex conditions. This approach eliminates the need for manual feature extraction and complex system modeling. By leveraging the model’s learning capabilities, it mitigates the impact of solar irradiance fluctuations on diagnostic accuracy. After training, the model performs real-time fault diagnosis with high precision using voltage data, offering fast, efficient, and reliable performance. The effectiveness of the method was validated through both simulation and experimental results.