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首页> 外文期刊>Asian Journal of Information Technology >Efficient Fault Detection Based on Localization and Classification of TransmissionLine Using Morlet Discrete Neuro Fuzzy Logic Wavelet Transform
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Efficient Fault Detection Based on Localization and Classification of TransmissionLine Using Morlet Discrete Neuro Fuzzy Logic Wavelet Transform

机译：基于Morlet离散神经模糊逻辑小波变换的输电线路定位和分类的有效故障检测。

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Transmission line faults are the main causes of interruption in power supply system. The fault on transmission lines affects the reliability and stability of the system. Fault identification, location using Synchronized Phasor Measurements has become highly significant, as they were capable of locating faults with its proven higher amount of accuracy. However, Synchronized Phasor Measurements have revealed some of its drawbacks in the conventional fault identification, location system. To improve the accuracy of fault detection, localization and classification of transmission line, a method called Morlet Discrete Neuro Fuzzy logic Wavelet Transform (MDFWT) is proposed. Initially, Morlet Envelope Spectrum (MES) Power wavelet transform is applied to detect faults on the measured frequencies and time of the modulating components. MES power wavelet transform in MDFWT method consists of complex exponential carriers with a multiplied Gaussian window to detect the faults. Detected faults are localized using the Discrete Wavelet Transform (DWT). DWT with Boltzmann entropy theory locates accurately the position of the fault on the transmission line. Finally the classification of transmission line faults in the MDFWT power system is carried out using the Neuro Fuzzy logic wavelet transform. The neuro fuzzy logic wavelet transform uses the if-then rules to classify the faults in an efficient manner with the highest reliability. Neuro fuzzy logic set in MDFWT method highlights the acquired knowledge for easy diagnosis of faults on the transmission line. The experiments are conducted on the factors such as fault detected error rate, fault localization accuracy and fault classification rate.
机译：传输线故障是供电系统中断的主要原因。传输线上的故障会影响系统的可靠性和稳定性。使用同步相量测量进行故障识别和定位已经变得非常重要，因为它们能够以其更高的精度来定位故障。然而，同步相量测量已经揭示了传统故障识别定位系统中的一些缺点。为了提高输电线路故障检测，定位和分类的准确性，提出了一种称为Morlet离散神经模糊逻辑小波变换（MDFWT）的方法。最初，使用Morlet包络谱（MES）功率小波变换来检测调制组件的测量频率和时间上的故障。 MDFWT方法中的MES功率小波变换由具有复数高斯窗的复杂指数载波组成，以检测故障。使用离散小波变换（DWT）对检测到的故障进行定位。带有玻尔兹曼熵理论的DWT可以精确定位故障在传输线上的位置。最后，使用神经模糊逻辑小波变换对MDFWT电力系统中的输电线路故障进行分类。神经模糊逻辑小波变换使用if-then规则以最高可靠性的有效方式对故障进行分类。使用MDFWT方法设置的神经模糊逻辑突出了所获得的知识，可轻松诊断传输线上的故障。针对故障检测错误率，故障定位精度和故障分类率等因素进行了实验。

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