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The development of signal processing techniques for the noise reduction and classification of partial discharge

机译:降噪和局部放电分类的信号处理技术的发展

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摘要

Power outages often happen as a result of electrical insulation breakdown in power equipment. Partial discharge is a key indicator of the occurrence of insulation deterioration. As global efforts are focused on creating a smart grid, online condition monitoring of power equipment is an area of interest to both utilities and researchers. Identifying the type of partial discharge can also determine the nature of the repair required. However, one of the challenges with online condition monitoring is background noise, making partial discharge difficult to detect.This thesis presents the investigation and development of efficient digital signal processing techniques to de-noise the signal and extract robust features for the classification of a variety of partial discharge types. It also analyses existing methods of de-noising partial discharge and develops a new adaptive thresholding algorithm that uses the arithmetic and geometric means in the discrete wavelet domain. To complement this, a Fourier Transform based signal boosting technique is also proposed, to de-noise partial discharge. Results demonstrate that the new algorithms outperform existing techniques. At a feature level, this thesis proposes two novel spectral features called Octave Frequency Moment Coefficients (OFMC) and Octave Frequency Cepstral Coefficients (OFCC) as the front-end to a classifier. In addition, a wavelet transform based feature called Time-Frequency Domain Coefficients (TFDC) is also introduced. These three spectral features provide a greater robustness and better classification accuracy compared to the well-known higher order statistical features.This thesis also introduces the use of the sparse representation classifier and compares its classification performance of partial discharge against existing classifiers such as the probabilistic neural network and support vector machine. Additionally, the use of Prony’s method (pole-zero model) is introduced, to estimate the system transfer function such that the impulse response of the estimated transfer function approximately matches the original partial discharge waveform. Step-response features were extracted from the estimated system transfer function to discriminate between various types of partial discharge. Prony’s method is also compared with an all-pole model to fit a digital transfer function to a partial discharge waveform. The all-pole model based step response features provided a better classification accuracy compared to the pole-zero model based features. Overall, the results presented in this thesis indicate that the novel techniques developed provide a robust solution that can be considered in online condition monitoring systems for implementation in the smart grid.
机译:电源设备中的电气绝缘击穿通常会导致断电。局部放电是绝缘劣化发生的关键指标。随着全球致力于创建智能电网的努力,电力设备的在线状态监控成为公用事业和研究人员关注的领域。确定局部放电的类型也可以确定所需维修的性质。然而,在线状态监测的挑战之一是背景噪声,使得局部放电难以检测。本文介绍了有效的数字信号处理技术的研究和发展,该技术可以对信号进行去噪并提取鲁棒的特征以进行各种分类。部分放电类型。它还分析了现有的局部放电降噪方法,并开发了一种新的自适应阈值算法,该算法在离散小波域中使用了算术和几何手段。为了补充这一点,还提出了一种基于傅立叶变换的信号增强技术,以对局部放电进行消噪。结果表明,新算法优于现有技术。在功能层面上,本文提出了两个新颖的频谱特征,称为八度频矩系数(OFMC)和八度频谱倒谱系数(OFCC)作为分类器的前端。此外,还引入了基于小波变换的功能,称为时频域系数(TFDC)。与众所周知的高阶统计特征相比,这三个频谱特征提供了更高的鲁棒性和更好的分类精度。本文还介绍了稀疏表示分类器的使用,并比较了局部放电与现有分类器(例如概率神经网络)的分类性能。网络和支持向量机。此外,还引入了Prony方法(零极点模型)的使用,以估算系统传递函数,以使估算的传递函数的脉冲响应与原始局部放电波形近似匹配。从估计的系统传递函数中提取了阶跃响应特征,以区分各种类型的局部放电。 Prony的方法还与全极点模型进行了比较,以使数字传递函数适合局部放电波形。与基于零极点模型的功能相比,基于全极点模型的阶跃响应功能提供了更好的分类精度。总体而言,本文提出的结果表明,开发的新技术提供了一种可靠的解决方案,可以在在线状态监测系统中考虑将其应用于智能电网。

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