Bayesian Mesh-based Optimization of Transformer Fault Diagnosis
Due to the compounding of the internal operating state parameters of the transformer,there is a large error in the diagnosis result of the specific fault location when the fault diagnosis is carried out directly,so the optimization of the transformer fault diagnosis based on Bayesian mesh is proposed.Based on the transformer node admittance matrix,a mod-el with the magnetic flux leakage admittance array of the transformer in series of the power system was constructed,and the complex transformer was converted into a model composed of several node admittance matrices.In the diagnosis stage,a Bayesian mesh is introduced to determine the specific fault location according to the sparse position vectors randomly and evenly scattered in the Bayesian mesh in the perception area.The test results show that the diagnostic method for the spe-cific fault location not only shows high stability,but also achieves small errors.
Bayesian meshtransformer fault diagnosisnode admittance matrixtransformer modelsparse position vector
万方数据
维普
