Automation and Optimisation of Wavelet Transform Techniques for Partial Discharge Denoising, and Pulse Shape Classification, in Power Plant

发电厂局部放电去噪和脉冲形状分类的小波变换技术的自动化和优化

• 批准号: EP/D048133/2
• 负责人: Chengke Zhou
• 金额: --
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD048133%2F2
• 关键词: Automation; Optimisation; Wavelet; Transform; Techniques

Before high voltage plant fails there is generally a period when degradation of the insulation system occurs, this may be a number of years. The key to improving the assessment of the equipment condition and life expectancy lies in identifying and characterising the stages of degradation. It is widely recognised that the degradation phase, irrespective of the cause, results in small sparks being generated at the site(s) of degradation. These electric sparks are generally referred to as partial discharges(PD). The characteristics of the sparks are influenced by the materials and stresses at the fault site. Improvement in their detection and characterisation will provide information on the location, nature, form and extent of degradation.The current detection process is severely compromised in practical on-site testing. These PD pulses are extremely small and hence, irrespective of the particular strategy being applied to detect them(electrical or acoustic), detection equipment must be very sensitive. In the field, this makes it prone to the influence or external interference or 'noise' from the surrounding environment and electrical/mechanical infrastructure. At best, this results in data corruption and compromises the efficiency of the condition assessment. At worst, it stops the technique from being of any use as the 'noise' signal exceeds the level of partial discharge activity.To solve the problems associated with noise a number of methods have been tried such as: screening and filtering, the application of analogue band-pass filtering, matched filters, polarity discrimination circuitry, time-windowed methods and digital filters. Each of these is, however, applicable to only certain types of noiseIn a recent study the author compared the matched filter, the traditional filter and the Discrete Wavelet Transform (DWT) in PD measurement denoising and has proven DWT provides the best solution in practical measurement when strong noise is in presence. Furthermore, DWT is the only method which allows reconstruction of the PD pulse.Having evolved from the Fourier Transform(FT), WT is particularly designed to analyse transient, irregular and non-periodic signals. Ideally, if a wavelet can be selected to match the PD pulse shape, the PD pulse could be extracted from any strong noise signals. Though the WT generates more information than the FT, it is inherently more complex than the FT and involves procedures dependent on the shape of the signals to be extracted from noisy data, the record length and the sampling rate. Dr. Zhou in the Insulation Diagnostics Group at the GCU was the first to study the optimal selection of the most appropriate wavelets, the optimal number of levels and level-dependent thresholding algorithm for automatic PD pulse extraction from electrically noisy environments using DWT. This innovative work has been proved to be effective in a number of measurement platforms. However, the application of DWT still requires significant experience at the moment when pulses of different shapes exist. The proposed research is to build on the experience and success already gained at GCU and to develop a methodology which allows the DWT to be applied to various PD measurement systems irrespective of their mechanism and bandwidth for PD data denoising and PD pulse reconstruction and classification.The outcome of the proposed research will be algorithms which can identify all types of transient pulses contained in data under analysis and present them separately in time domain. This would allow the identification and classification of various PD activities from PD measurements and production of phi-q-n diagrams which, in conjunction with pulse shapes, provides significantly improved means for plant diagnosis.

在高压发电厂失效之前，通常会有一段时间发生绝缘系统的劣化，这可能是几年的时间。改进对设备状况和预期寿命的评估的关键在于确定和确定退化阶段的特征。人们普遍认为，无论原因如何，退化阶段都会在退化现场(S)产生小火花。这些电火花通常被称为局部放电(PD)。火花的性质受断层部位的材料和应力的影响。改进它们的检测和表征将提供关于降解的位置、性质、形式和程度的信息。目前的检测过程在实际现场测试中严重受损。这些局部放电脉冲非常小，因此，无论采用何种策略来检测它们(电的或声的)，检测设备必须非常灵敏。在现场，这使得它很容易受到周围环境和机电基础设施的影响或外部干扰或‘噪音’。在最好的情况下，这会导致数据损坏，并影响状态评估的效率。为了解决与噪声相关的问题，已经尝试了许多方法，例如：筛选和滤波、应用模拟带通滤波、匹配滤波器、极性辨别电路、时间窗方法和数字滤波器。在最近的一项研究中，作者比较了匹配滤波、传统滤波和离散小波变换(DWT)在局部放电测量去噪中的作用，证明了离散小波变换在强噪声存在的实际测量中提供了最佳解决方案。此外，小波变换是唯一可以重建局部放电脉冲的方法。小波变换是从傅立叶变换(FT)发展而来的，专门用于分析暂态、不规则和非周期信号。理想情况下，如果可以选择与局部放电脉冲形状匹配的小波，则可以从任何强噪声信号中提取局部放电脉冲。尽管小波变换比FT产生更多的信息，但它本质上比FT更复杂，涉及的程序取决于要从噪声数据中提取的信号的形状、记录长度和采样率。GCU绝缘诊断组的周博士是第一个研究利用离散小波变换从电噪声环境中自动提取局部放电脉冲的最合适小波的最佳选择、最佳电平数目和电平相关阈值算法的第一人。这一创新工作已在多个测量平台上被证明是有效的。然而，在存在不同形状的脉冲的情况下，DWT的应用仍然需要丰富的经验。建议的研究是建立在GCU已获得的经验和成功的基础上，并开发一种方法，允许将离散小波变换应用于各种局部放电测量系统，而不考虑其用于局部放电数据去噪和局部放电脉冲重建和分类的机制和带宽。建议研究的结果将是能够识别所分析数据中包含的所有类型的瞬时脉冲并在时间域中分别呈现它们的算法。这将允许从局部放电测量中识别各种局部放电活动并对其进行分类，并产生与脉冲形状相结合的Phi-Q-n图，为植物诊断提供显著改进的手段。