Multilabel Classification of Complex Power Quality Disturbances via Label-Semantics Fusion and Transformer Encoder

The increasing penetration of renewable energy sources and the widespread use of power electronic devices have introduced complex power quality disturbances (PQDs), which are essentially caused by the superposition and coupling of multiple single disturbances. Accurate identification and classification of complex PQDs are crucial prerequisites for effective power quality assessment and control. However, existing classification methods often neglect the correlations among disturbance category labels, limiting their applicability under multi-disturbance conditions. To address these limitations, this paper proposes a Transformer-based multi-label classification framework that integrates label semantics fusion for complex PQDs. Specifically, a multi-scale dilated residual convolution module is employed to extract discriminative features across multiple temporal scales; the label semantics fusion module then embeds disturbance label semantics into the signal features; finally, a Transformer encoder is introduced along the category dimension to explicitly model inter-label dependencies, thereby enhancing multi-label classification accuracy. Furthermore, extensive validation on both the standard IEEE PES dataset and a self-developed physical experimental platform confirms the reliability and robust generalization capability of the proposed approach across heterogeneous measurement scenarios.