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Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system

机译：基于小波变换和自适应神经模糊推理系统的串补输电线路故障诊断系统

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This paper proposes a new fault diagnosis approach based on combined wavelet transform and adaptive neuro-fuzzy inference system for fault section identification, classification and location in a series compensated transmission line. It performs an effective feature extraction approach based on norm entropy in order to obtain the features represented main frequency, harmonic and transient characteristics of the fault signals. The proposed method uses the samples of fault voltages and currents for one cycle duration from the inception of fault. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MAT-LAB/Simulink for a large data set of 23,436 fault cases comprising of all the 10 types of faults. Fault signals varying with fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance are applied to the proposed algorithm. The results also indicate that the proposed method is robust to wide variation in system conditions and has higher fault diagnosis accuracy with regard to the other approaches in the literature for this problem.
机译：提出了一种基于小波变换和自适应神经模糊推理系统相结合的故障诊断新方法，用于串联补偿传输线的故障区间识别，分类和定位。它执行基于范数熵的有效特征提取方法，以获得代表故障信号主频率，谐波和瞬态特性的特征。所提出的方法从故障发生开始的一个周期内使用故障电压和电流样本。已使用MAT-LAB / Simulink在400 kV，300 km串联补偿传输线上针对所有十种类型的故障测试了所提方法的可行性，以处理包含所有10种类型的故障的23,436个故障案例的大型数据集。该算法将故障信号随故障电阻，故障起始角度，故障距离，负载角度，百分比补偿水平和源阻抗而变化。结果还表明，相对于文献中针对该问题的其他方法，所提出的方法对于系统条件的广泛变化具有鲁棒性，并且具有较高的故障诊断准确性。

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