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High impedance fault identification using FIR filters, wavelet transforms and computational intelligence.

机译：使用FIR滤波器，小波变换和计算智能的高阻抗故障识别。

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摘要

This thesis deals with a computer-based simulation study of high impedance fault detection on power distribution systems. High impedance faults (HIFs) are difficult to detect. When a conductor such as a distribution line makes contact with a poor conductive surface or substance the resulting level of fault current is usually lower than the nominal current of the system at the fault location. Therefore, conventional protection relay system will not be able to detect the HIFs and trip the protection relay. The failure of HIF detection leads to potential hazards to human beings and potential fire hazards.; HIFs on electrical transmission and distribution networks involve arcing and/or nonlinear characteristics of fault impedance which cause cyclical pattern acid distortion. Therefore, the objective of most detection schemes is to evaluate the special features in patterns of the voltages and currents in HIFs.; Some researchers proposed various detection schemes based on fractal techniques, digital signal processing, expert systems, neural networks, crest factor, wavelet transform in high frequency noise patterns and dominant harmonic vectors. They offer potential solutions to these problems currently associated with conventional schemes. While direct calculation of fractal dimensions is not effective due to relatively short data sets available for estimation, the use of high frequency harmonics is not feasible in practical measurement because of the filtering by substation current transformers.; Simulations using the Electromagnetic Transients Program (EMTP) and Matlab were employed to perform a stochastic study of the nature and waveforms of the fault voltages and currents in the AC supply. This thesis proposes a method which incorporates the statistical nature of the high impedance faults and fault locations in order to recognize the characteristic distortions on voltage and current waveform in the electrical supply network. The immunity of the proposed detection to confounding from contingencies such as load and capacitor switching in electrical networks is evaluated through simulation. After capturing the voltage and current waveforms from the power system simulations, they were analyzed by finite impulse response (FIR) filter bank and discrete wavelet transform (DWT) followed by rms conversion to produce rms values under different frequency ranges. The rms values of these voltage and current waveforms in their various frequency ranges were fed into the pattern classifier such as nearest neighbour rule (NNR) method and artificial neural network (ANN) to determine the fault or non-fault situations. The sensitivity test of above simulations was also performed to investigate the lower current HIFs. Three types of power distribution systems were used for the demonstration of the high impedance fault analyses.
机译：本文涉及基于计算机的配电系统高阻抗故障检测仿真研究。高阻抗故障（HIF）很难检测。当诸如配电线之类的导体与不良的导电表面或物质接触时，产生的故障电流水平通常低于故障位置系统的标称电流。因此，传统的保护继电器系统将无法检测HIF并使保护继电器跳闸。 HIF检测失败会导致对人类的潜在危害和潜在的火灾隐患。电力传输和配电网络上的HIF包括故障阻抗的电弧和/或非线性特性，这些特性会导致循环模式酸变形。因此，大多数检测方案的目的是评估HIF中电压和电流模式的特殊特征。一些研究人员基于分形技术，数字信号处理，专家系统，神经网络，波峰因数，高频噪声模式中的小波变换和主要谐波矢量提出了各种检测方案。它们为当前与常规方案相关的问题提供了潜在的解决方案。分形维数的直接计算由于可用于估计的相对较短的数据集而无效，但由于变电站电流互感器的滤波，在实际测量中使用高频谐波是不可行的。使用电磁暂态程序（EMTP）和Matlab进行的仿真对交流电源中的故障电压和电流的性质和波形进行了随机研究。本文提出了一种方法，该方法结合了高阻抗故障和故障位置的统计性质，以便识别供电网络中电压和电流波形的特征失真。通过仿真评估了所提出的检测方法不受诸如负载和电容器切换等突发事件影响的混杂性。从电力系统仿真中捕获电压和电流波形后，通过有限脉冲响应（FIR）滤波器组和离散小波变换（DWT）进行均方根转换以产生不同频率范围内的均方根值，从而对它们进行了分析。将这些电压和电流波形在其各个频率范围内的均方根值输入到模式分类器中，例如最近邻法则（NNR）方法和人工神经网络（ANN），以确定故障或非故障情况。还进行了上述模拟的灵敏度测试，以研究较低电流的HIF。三种类型的配电系统用于高阻抗故障分析的演示。

著录项

  • 作者

    Lai, Tsz Ming Terence.;

  • 作者单位

    Hong Kong Polytechnic University (People's Republic of China).;

  • 授予单位 Hong Kong Polytechnic University (People's Republic of China).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 202 p.
  • 总页数 202
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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