摘要
泄漏电流适合作为在线监测输电线路绝缘子状态的方法。虽然国内外已有学者对泄漏电流的去噪问题和特征提取问题进行了研究,并采用模式识别方法实现了泄漏电流分类和绝缘子状态预测,但是,这些方法均存在缺陷,仍有许多问题有待解决。为了弥补已有方法的不足,本文对泄漏电流去噪和特征提取,以及采用更完善的模式识别方法实现泄漏电流分类等问题展开了研究。
论文提出了采用小波变换去除输电线路绝缘子泄漏电流噪声时分解层数的计算方法,给出了最优小波基和最佳阈值。根据采用小波阈值法去噪时要对小波系数进行量化、泄漏电流的能量主要集中在低频成分上的事实,提出了小波变换去除泄漏电流噪声时最佳分解层数的判据；并以此判据为基础,提出并证明了计算小波分解层数的公式。根据对泄漏电流的分析,确定了最优小波基和最佳阈值。
为了实现对输电线路绝缘子泄漏电流的自适应去噪,论文对固有时间尺度分解(Intrinsic Time-Scale Decomposition, ITD)进行了改进,并提出了基于改进ITD的泄漏电流去噪方法。通过引入距离尺度解决了ITD方法的旋转混叠问题；通过引入两端延拓解决了ITD方法的端点效应问题。提出了采用改进ITD对泄漏电流去噪时分解次数的计算公式；在借鉴小波阈值合理性的基础上,结合改进ITD方法的特殊性,给出了去除泄漏电流噪声时的阈值和去噪过程。
论文提出了采用时域熵作为输电线路绝缘子泄漏电流时域的新特征量。在采样频率和数据长度一定的情况下,泄漏电流绝对值在不同范围内分布的点数不同。据此,论文提出了分布密度的概念,并根据熵能反映变量分布规律的特点,提出采用时域熵作为泄漏电流时域新特征量,并给出了计算时域熵的过程。
论文提出了采用频域熵作为输电线路绝缘子泄漏电流频域的新特征量。泄漏电流除了在基波和谐波处的幅值特征明显外,在其它频率成分处的幅值也可能剧烈变化。为了自适应反映现场泄漏电流不同频域成分的变化规律,论文提出采用频域熵作为泄漏电流新的频域特征量,并给出了计算频域熵的过程。
论文提出采用多分类相关向量机作为识别输电线路绝缘子泄漏电流波形类别的方法。确定了多分类相关向量机的输入向量、输出向量和核函数,并给出了识别绝缘子泄漏电流所属类别的过程。
The leakage current method is a good way to monitor the states of the online insulators on the transmission line. The de-noising and the feature extracting of the leakage current have been studied by some domestic and foreign scholars. And the pattern recognition methods also have been used to calssify the leakage current and to forecast the states of the insualtors. While, the methods have some faults. And many problems don't be solved. In order to make up the faults of the methods, this paper studies not only the de-noising and the feature extracting of the leakage current but also the better pattern recognition for the leakage current classification.
When the wavelet transform is used to de-noise the leakage current of the insulators on the transmission line, the calculation method of the best decompositon level number is proposed, and the best wavelet basis and the best threshold are given in this paper. During the de-noising with the wavelet threshold method, the wavelet coefficient is truncated with the threshold. The energy of the leakage current concentrates on the low frequency compocents. Based on the above two facts, the criterion judging the best decomposition level number is proposed. Based on this criterion, the formula is proposed and proved, which is used to calcalate the best docomposition level number of the wavelet transform. According to the analysis for the leakage current, the best wavelet basis and the best threshold are given.
In order to de-noise the leakage current of the insulators on the transmission line adaptively, the intrinsic time-scale decomposition (ITD) method is improved, and the de-noising method based on the improved ITD is proposed. The distance scale is introduced to solve the rotation mixing problem of the ITD. And the extension of the two ends is introduced to solve the end effect problem. When the improved ITD is used to de-noise the leakage current, the formula calculating the decomposition number is proposed. Based on the wavelet threshold and the special characteristics of the improved ITD, the threshold and the de-noising process are given.
The time domain entropy is proposed as the new feature of the insulator leakage current on the transmission line in the time domain in this paper. When the sampling frequency and the length of the leakage current are constant, the numbers of the absolute values of the leakage current in the different ranges are different. So, the concept distribution density is proposed. The entropy can reflect the distribution of the variables. Based on this concept "distritution density" and the characteristics of the entropy, the calculation process of the time doman entopy is given, which is proposed as the new method to extract the time domain feature of the leakage current.
The frequency domain entropy is proposed as the new feature of the insulator leakage current on the transmission line in the frequency domain in this paper. The leakage current have obvious characteristics not only on their fundamental and the harmonic but also on their other frequency components. In order to reflect the change of the different frequency components of the field leakage current adaptively, the calcalation process of the frequency domain entropy is given, which is proposed as the method to extract the frequency domain feature of the leakage current.
The multiclass relevance vector machine (m-RVM) is proposed as the mothod to recognize the insulator leakage current. The input vector, the output vector and the kernel function of the m-RVM are given. The process of recognizing the insulator leakage current is given.
引文
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