Machine learning-based spatiotemporal fusion method for non-intrusive charging pile fault identification

Fault detection in charging piles is crucial for the widespread adoption of electric vehicles and the reliability of charging infrastructure. Currently, due to the lack of sufficient fault data for charging piles, achieving stable and accurate fault identification is challenging. Moreover, distinctive fault features are key to accurate fault recognition. To address this, we designed a simulated charging pile system and collected fault data at multiple power levels by manually introducing faults. Furthermore, we proposed a fault identification algorithm based on spatiotemporal feature fusion using machine learning. This algorithm first collects fault data through a sliding window and utilizes Fourier transform to extract frequency domain information to construct temporal features. These features are then fused with spatial current amplitude information to form a distinctive feature set, enabling fault identification based on a machine learning model. Extensive experiments conducted on the constructed dataset show that this method can accurately identify charging pile faults. Compared with random forest and gradient boosted decision tree, the proposed method improves the macro-average score by 2.99% and 7.28%, respectively. We also explored the importance of each feature for fault identification results and the impact of window length on identification outcomes, demonstrating the necessity of the extracted features and the robustness of the proposed method to data resolution.