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Fault classification of a long transmission line using nearest neighbor algorithm and boolean indicators

机译:使用最近邻算法和布尔指标的长输电线路故障分类

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This paper proposes a plain-sailing yet powerful method of classification of 3 phase faults in a long transmission line using k-Nearest Neighbor algorithm approach and Boolean Indicators so as to correct the error in the former approach. Discrete Wavelet transform is carried out of the post fault current signal obtained from the model and standard deviations of the approximate coefficients of the each phase are obtained and accordingly a training matrix including the aforesaid feature along with its target and a sample matrix and its target are formed and classified using k-NN algorithm described in the paper. The error thus obtained is rectified using Boolean Indicators and comparing them with suitable thresholds.
机译:本文提出了一种使用k-最近邻算法和布尔指标对长输电线路中的三相故障进行分类的简单,有效的方法,以纠正前一种方法中的误差。对从模型获得的故障后电流信号进行离散小波变换,得到各相的近似系数的标准偏差,从而得到包括上述特征及其目标,样本矩阵和目标的训练矩阵。使用本文描述的k-NN算法进行分类和分类。由此获得的错误使用布尔指示符进行纠正,并将其与适当的阈值进行比较。

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