Transmission Line Fault Location Using MFCC and LS-SVR

Hermes Manoel Galvão Castelo Branco, James Blayne Oliveira Reis, Luan M. M. Pereira, Lucas da Costa Sá, R. A. L. Rabelo
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引用次数: 1

Abstract

The location of Transmission Line (TL) Faults is a major problem in Electrical Power Systems (EPSs), since precisely identifying the point of occurrence of a fault in a TL it is possible to perform a faster restoration of the operation to the desired normal conditions. In this work we used a Least-Squares Support Vector Regression (LS-SVR) to locate faults in a TL with inputs provided by MFCC (Mel-Frequency Cepstral Coefficients) obtained from voltage signals during the fault. A modelled line based on parameters of a real line was used, with a total of 4008 fault situations being simulated on this Transmission Line. It is important to point out that MFCC are not used in applications involving EPS’s, and, according to the bibliographic research conducted by the team so far, no application of this feature extraction tool has been detected for the TL fault location problem. 3006 faults were used to train the model with cross-validation by the k-fold method, and 1002 faults were used for testing. The proposed methodology presented a good performance in the tests carried out, with a mean relative error of 0.000419 ±0.000640% when models are trained and tested with noiseless voltage signals. For models trained with voltage signals that present SNR ranging from 100 dB to 25 dB, the relative mean error ranged from 0.00334 ±0.00459%, in the first case, to 0.030580±0.043160% in the last.
基于MFCC和LS-SVR的输电线路故障定位
输电线路(TL)故障的定位是电力系统(eps)中的一个主要问题,因为准确识别输电线路故障的发生点,可以更快地将运行恢复到所需的正常状态。在这项工作中,我们使用最小二乘支持向量回归(LS-SVR)来定位TL中的故障,该TL的输入由故障期间从电压信号中获得的MFCC (Mel-Frequency Cepstral Coefficients)提供。采用基于实际线路参数的建模线路,对该输电线路共4008种故障情况进行了仿真。需要指出的是,MFCC并没有被用于涉及EPS的应用中,并且根据团队目前进行的文献研究,还没有检测到该特征提取工具在TL故障定位问题中的应用。使用3006个故障通过k-fold方法进行交叉验证训练模型,使用1002个故障进行测试。在无噪声电压信号下训练和测试模型时,该方法的平均相对误差为0.000419±0.000640%。对于信噪比为100 ~ 25 dB的电压信号训练模型,相对平均误差为0.00334±0.00459%至0.030580±0.043160%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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