Fault Detection for Grid-Connected Photovoltaic System via Anomaly-Transformer Technique

The fault characteristics of photovoltaic (PV) systems are greatly influenced by environmental factors, which causes grand challenges in PV fault detection. Therefore, this paper proposes an anomaly detection algorithm for grid-connected PV system via anomaly-transformer. Firstly, a PV platform was built to carry out fault experiments under different meteorological conditions, and a total of 218 sets of DC voltage/current datasets were constructed. Aiming at the characteristics of multi-dimensional time series data, the multi-branch anomaly-attention mechanism is used to calculate prior-association and series-association, then use transformer to reconstruct the loss values based on the obtained data. The association discrepancy is calculated as the index of anomaly detection, so as to achieve the goal of time-based localization of PV faults. The experimental results show that compared with graph deviation network (GDN), unsupervised anomaly detection (USAD) and other algorithms, the Precision of anomaly-transformer reaches 76.45% and 95.41% respectively in sunny and cloudy test data sets, and the F1-score reaches 86.65% and 97.65% respectively. It can accurately locate the fault time, which provides an effective method for PV fault detection.