Densely-connected Decoder Transformer for unsupervised anomaly detection of power electronic systems

Reliable electricity infrastructure is critical for modern society, highlighting the importance of securing the stability of fundamental power electronic systems. However, as such systems frequently involve high-current and high-voltage conditions, there is a greater likelihood of failures. Consequently, anomaly detection of power electronic systems holds great significance, which is a task that properly-designed neural networks can well undertake, as proven in various scenarios. Transformer-like networks are promising for such application, yet with its structure initially designed for different tasks, features extracted by beginning layers are often lost, decreasing detection performance. Also, such data-driven methods typically require sufficient anomalous data for training, which could be difficult to obtain in practice. Therefore, to improve feature utilization while achieving efficient unsupervised learning, a novel model, Densely-connected Decoder transformer (DDformer), is proposed for unsupervised anomaly detection of power electronic systems in this paper. First, efficient label-free training is achieved based on the concept of autoencoder with recursive-free output. An encoder-decoder structure with densely-connected decoder is then adopted, merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty. Both simulation and real-world experiments are conducted to validate the capabilities of DDformer, and the average FDR has surpassed baseline models, reaching 89.39%, 93.91%, 95.98% in different experiment setups respectively.