| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 104 === As the power system voltage class is upgraded, the partial discharge may cause insulation deterioration of high voltage power equipments. In recent years, the detection of partial discharge and pattern recognition have become the latest development trend of preventive equipment fault diagnosis. Therefore, if the partial discharge detection can be combined with signal analysis to master the state of insulation of power equipments, the shutdown of power equipments without early warning is supposed to be prevented in time, so as to enhance the reliability of power quality.
This thesis aims to use artificial neural network for recognition of partial discharge pattern of high-voltage motor stator insulation coil bar. First, the partial discharge signal of the test object is measured by using high-frequency current sensor, and the received partial discharge signal is changed into energy spectrum, so as to suppress the external noise. Secondly, the fractal dimension and lacunarity features are extracted from the spectrum by using fractal theory, so as to reduce the dimension for subsequent operation. Finally, the artificial neural network is used for partial discharge pattern recognition. The extracted features vector is used as input and the optimum Artificial Neural Network is determined, so as to obtain the optimum recognition capability. This study uses common high voltage motor stator fault types to experimentally produce four kinds of stator test models with insulation defect, which are compared with healthy motor model. The experimental results show that the artificial neural network-based stator fault diagnosis system proposed in thesis has a recognition rate as high as 90 % when the conjugate gradient algorithm is used and there are 20 neurons on hidden layer.
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