Oil-immersed Power Transformer Internal Fault Diagnosis Research Based on Probabilistic Neural Network

Abstract

Oil-immersed power transformer is one of the key devices in power system. And the reliability of power grid is guaranteed by its safe operation. Therefore, it is necessary to reduce transformer failures with precautionary measures. Nowadays, three-ratio method of dissolved gas analysis (DGA) is the most effective and convenient method in transformer fault diagnosis. However, when using three-ratio method as the judgment, it exists some disadvantages such as coding defects and threshold criterion defect. A new way for this problem is provided by artificial neural network, which has the advantages such as parallel processing, self-adaptation self-study, association memory, non-linear mapping and other features. Oil-immersed transformer internal faults are predicted in this paper by using probabilistic neural network algorithm, which brings its ability of processing non-linear problem into full play. What's more, the DGA judgment process is optimized and convenient setting of parameters is achieved. The high accuracy of diagnosis is confirmed by simulation results in KNIME platform.