【摘要】 在线识别局部放电模式可以有效地判断局部放电对变压器绝缘的危害程度。文中根据局部放电超声波信号存在的非线性和非平稳特性,提出利用分形理论对局部放电超声波信号的时域脉冲波形进行分析。对分形理论及其参数计算方法进行了简单的介绍,在自制的几种典型变压器局部放电模型上进行实验,计算了所得到的局部放电超声波信号的分形参数(分维数和空缺率),得到不同放电形 式的分形特征,利用人工神经网络对所得到的分形特征进行模式识别。结果表明利用超声波信号可以有效地判断变压器局部放电模式,为变压器局部放电信号的特征提取和模式识别提供了一种新的研究方法。更多还原

【Abstract】 Online partial discharge pattern recognition can be applied to analyze the possible damages produced by partial discharge. Based on non-linear and non-stationary characteristics of partial discharge acoustic signal, fractal theory is adopted to extract fractal parameters from acoustic signal of transformer partial discharge in this paper. The fractal theory and the approach to calculating relevant parameters are introduced. Several typical kinds of partial discharge model are designed in laboratory, which include point to plane discharge, void discharge, floating discharge and surface discharge. In order to simulate partial discharge in transformer, all these models are immersed into transformer oil. Acoustic signals of these models are obtained by digital measuring device. Then the fractal parameters (box counting dimension and lacunarity) are calculated, the calculated result show that fractal parameters are dissimilarity of different partial discharge acoustic signals. Finally these fractal parameters form the characteristic vectors, which are inputted into BP artificial neural networks for recognition. The result shows the fractal theory can be exploited to obtain the features of acoustic signals for accomplishing pattern recognition. Acoustic signal processing research provides us with a novel approach to study partial discharge, and it has a promising application future.更多还原