【作者】 尚海昆；

【导师】 苑津莎；

【作者基本信息】 华北电力大学 ， 电气信息技术， 2014， 博士

【摘要】 电力变压器是电力系统中的关键设备,维护其安全稳定的运行至关重要。局部放电是变压器绝缘劣化的重要征兆及表现形式,有效检测局部放电对于正确评估变压器的绝缘状况具有重要的现实意义。本文基于变压器局部放电的原理及特性,深入研究了局部放电信号的干扰抑制、特征提取以及模式识别方法。主要研究工作如下：提出了基于平移不变小波迹的变压器局部放电信号消噪方法。深入分析了小波迹方法的消噪原理,将其应用于局部放电信号去噪,并利用循环平移方法消除小波基的平移依赖性,抑制小波变换产生的振荡现象,强化消噪效果。仿真及现场信号分析验证了该方法的有效性。针对传统局部放电特征提取方法对噪声信号的高敏感性,提出了基于交叉小波变换的局部放电特征提取方法。研究了交叉小波变换对信号在时频域内的分析特性,采用该方法对局部放电信号进行处理,得到描述交叉谱图特性的放电特征参数,并利用相关系数矩阵对特征参数进行相关性分析。实例分析表明,该特征提取方法可以有效避免噪声信号的影响。提出了基于主动学习SVM的局部放电模式识别方法。将主动学习的思想引用到“一对一”多分类SVM分类器,选用基于后验概率的采样函数对放电样本进行选择,挑选出对分类器最有价值的样本进行训练。实例分析表明,在保证局部放电识别精度的前提下,该方法可以减少训练样本个数,提高样本学习效率。深入分析了主成分分析(PCA)方法的原理,利用PCA对局部放电的高维统计特征参数进行处理,提取出较少的主成分因子来表征原始信号特征,并通过相关向量机(RVM)对降维前后的特征参数进行模式识别。实例分析验证了该降维方法的有效性。提出了基于多核多分类相关向量机(MMRVM)的局部放电模式识别方法。该方法采用不同的核函数融合多个不同的放电数据源信息,并利用粒子群优化算法对组合核参数进行寻优配置,选取出最优的核参数。局部放电实验数据分析表明,设计出的MMRVM分类模型融合了多种放电特征信息,能够较为全面的描述局部放电特性,具有较高的诊断准确率。更多还原

【Abstract】 Power transformer is the key equipment in the electrical system and it is important to maintain its safety and stable operation. Partial discharge is the important symptom and manifestation of insulation deterioration in power transformer. Effective detection of partial discharge has practical significance in insulation evaluation of power transformer. Deep studies on interference suppression method, feature extraction method and pattern recognition method for partial discharge were conducted based on the principles and properties of partial discharge signal. The main work of this dissertation is as follows.A denoising method for partial discharge signal in power transformer based on translation invariant wavelet footprint was proposed. The denoising principle of wavelet footprint was studied and the method was applied in denoising of partial discharge signal. Cycle spanning method was used for eliminating the translation dependence, so as to restrain the oscillation phenomenon and strengthen denoising effection. Effectiveness of the method was validated by case studies.A novel feature extraction method for partial discharge based on cross-wavelet transform was proposed, aimed at the high sensitivity of the traditional feature extraction method to noise. The analytical characteristics of signal in time and frequency domain were described by cross-wavelet transform. The method was used for processing of the partial discharge signal and the feature parameters were obtained which represented the characteristics of the cross diagram. Finally the correlation coefficient matrix method was applied to correlation analysis between feature parameters. Case analysis demonstrated that the proposed method could effectively avoid the influence of noise.A pattern recognition method for partial discharge based on active learning SVM was proposed. The idea of active learning was applied to "one against one" multi-class SVM classifier. The sampling function based on posterior probability was used for sample selection. Those samples which were valuable to the classifier were selected for training. Case analysis showed that, the proposed method could reduce the number of training samples and improve the learning efficiency on the premise of maintaining high recognition accuracy.The theory of principal component analysis (PCA) method was considered for deep analysis. PCA method was used for processing high-dimensional statistical parameters. Fewer principal component factors were extracted to represent original signal characteristics. The relevance vetor machine (RVM) classifier was applied to pattern recognition with the characteristic parameters before and after dimension reduction. The effectiveness of the proposed method for dimension reduction was validated by case analysis.A pattern recognition method for partial discharge based on the multi-kernel multi-class relevance vetor machine (MMRVM) was proposed. The method integrated information of different discharge sources with different kernel functions. Particle swarm optimization algorithm was utilized for optimal design to obtain the optimal combination parameters. Experimental data of partial discharge indicated that the designed classification model integrated various feature information and could represent partial discharge characteristics comprehensively with higher diagnostic accuracy.更多还原