Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Abstract

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.