A Novel Self-Decision Fault Diagnosis Model Based on State-Oriented Correction for Power Transformer

Abstract

Dissolved gas analysis can provide powerful technical support for assessment and fault diagnosis of power transformers. However, due to inadaptability of model structure, the performance of diagnosis model can hardly be improved with theoretical support. To fill the gap, a novel self-decision fault diagnosis model for power transformer has been proposed, taking into consideration of both the characteristics of faults and adaptability of conventional deep brief network. Specifically, an active-correction unit, as the core of this novel model, was established to detect the real-time state of the diagnosis model and then select the corresponding correction strategy. An error neuron is also placed to serve as the starting point of the state-oriented correction and load the correction signal that is sent from the active-correction unit. Verifications in the field indicates that the fault correlation extracted by the sparse autoencoder can eliminate error in the training process. When the results of IEC three-ratios method is integrated into the input neuron, the accuracy of proposed model can reach more than 92.3%. Based on the proven effectiveness and feasibility of the proposed method in state-oriented correction, this method can provide a reliable support for substation preventive maintenance.