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Artificial Neural Network Based Fault Classification and Location for Transmission Lines

机译:基于人工神经网络的传输线路故障分类和位置

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Due to various faults occur to transmission lines and because it was necessary to find and recover these faults quickly as possible. This paper discussing fault detection, classification and determining fault location as fast as possible via Artificial Neural Network (ANN) algorithm. The software used for modeling the proposed network is a MATLAB/SIMULINK software environment. The training, testing and evaluation of the intelligent locator processes are done based on a multilayer Perceptron feed forward neural network with back propagation algorithm. Mean Square Error (MSE) algorithm is used to evaluate the performance of the detector/classifier as well as fault locator. The results show that the validation performance (MSE) for the fault detector/classifier is 2.36e-9 and for fault locator is 2.179e-5. The system can detect if there is a fault or not, can classify the fault type and determine the fault location very precisely.
机译:由于各种故障发生到传输线,因为有必要尽可能快地查找和恢复这些故障。本文通过人工神经网络(ANN)算法尽快讨论故障检测,分类和确定故障位置。用于建模所提出的网络的软件是Matlab / Simulink软件环境。智能定位器工艺的训练,测试和评估是基于具有背传播算法的多层的Perceptron馈送神经网络完成的。均方误差(MSE)算法用于评估检测器/分类器的性能以及故障定位器。结果表明,故障检测器/分类器的验证性能(MSE)为2.36E-9,故障定位器为2.179E-5。系统可以检测是否存在故障,可以分类故障类型并非常精确地确定故障位置。

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