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Adaline and FIS Techniques for Fault Identification in HV Transmission Line

机译：用于高压输电线路故障识别的Adaline和FIS技术

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This paper is to identify and classify the various types of shunt and line faults in transmission line. The faults may be an insulation failure, lightning or accidental faulty operation. In a transmission line protection important factor is identifying a fault because if any error occurs in finding fault may leads to abnormal operation of the protection system. So either a disturbance or steady state variation is called power quality variation. The proposed test system is modeled based on the neural network and fuzzy algorithm. The online symmetrical components are extracted by this above algorithm. The fuzzy is used to separate the oscillating components and average components. Here input for the fuzzy is trained by using neural network. It is based on current samples and very effective in fault classifier using rule base. This method is very much suitable for online implementation.
机译：本文旨在识别和分类输电线路中各种类型的并联故障和线路故障。这些故障可能是绝缘故障，雷击或意外故障操作。在传输线保护中，重要的因素是识别故障，因为如果在发现故障时发生任何错误，可能会导致保护系统的异常运行。因此，干扰或稳态变化都称为电能质量变化。所提出的测试系统是基于神经网络和模糊算法建模的。通过以上算法提取在线对称分量。模糊用于分离振荡成分和平均成分。在这里，通过使用神经网络来训练用于模糊的输入。它基于当前样本，在使用规则库的故障分类器中非常有效。此方法非常适合在线实施。

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