Aiming at the problem that Anomaly Transformer model is difficult to detect multiple types of anomaly photovoltaic power data at the same time, this paper proposes an anomaly detection method for photovoltaic power generation data based on improved Anomaly Transformer (IAT). Firstly, the highly relevant features were filtered by Spearman feature selector. Secondly, the Wasserstein distance was introduced to improve the calculation method of prior association and sequence association to solve the problem of overlapping distribution between them. Finally, an anomaly evaluation strategy was proposed to smooth the abnormal reconstruction loss, so that it was easier to identify abnormal data. The experimental results show that the improved Anomaly Transformer improves the recall rate by 1.06% on average compared with other anomaly detection methods.