Abstract This article presents a new and fast approach for detection and classification of the fault on a transmission line. The proposed method can effectively detect and classify a fault on a regular uncompensated transmission line. Moreover, the same can detect and classify faults when these lines are subjected to fixed series compensation, with the same accuracy and without any functional customization of the algorithm. Therefore, this method gets rid of the new setting requirement for the inclusion of series compensation. Vice versa can provide protection to a series compensated line during maintenance as well as in the eventuality of bypass of the compensator. This makes the developed algorithm versatile. The proposed algorithm normally converges for fault detection within half cycle from the fault inception, and requires no data after generating fault detection signal for fault classification; which enables the application of faster circuit breaking devices. The two-stage approach uses Wavelet Transform (WT) with Chebyshev Neural Network (ChNN) and uses only measured three-phase current signals at relaying end. The accuracy, speed, and effectiveness of the scheme have been verified with a fault data generation system developed on PSCAD/EMTP with different system parameter variations like fault resistance, load angle, fault inception angle, and types of faults. The results obtained show that the proposed scheme is accurate and fast as well.

中文翻译：

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用于输电线路故障检测和分类的通用中继算法

摘要 本文提出了一种新的、快速的输电线路故障检测和分类方法。所提出的方法可以有效地检测和分类规则未补偿输电线路上的故障。此外，当这些线路受到固定串联补偿时，同样可以检测和分类故障，具有相同的精度并且无需对算法进行任何功能定制。因此，该方法摆脱了包含串联补偿的新设置要求。反之亦然，可以在维护期间以及在补偿器旁路的情况下为串联补偿线路提供保护。这使得开发的算法具有通用性。所提出的算法通常在故障开始后的半个周期内收敛以进行故障检测，产生故障检测信号后无需数据进行故障分类；这使得应用更快的断路设备成为可能。两阶段方法使用小波变换 (WT) 和切比雪夫神经网络 (ChNN)，仅在中继端使用测量的三相电流信号。该方案的准确性、速度和有效性已经通过在 PSCAD/EMTP 上开发的具有不同系统参数变化（如故障电阻、负载角、故障起始角和故障类型）的故障数据生成系统得到验证。获得的结果表明，所提出的方案是准确和快速的。两阶段方法使用小波变换 (WT) 和切比雪夫神经网络 (ChNN)，并且仅在中继端使用测量的三相电流信号。该方案的准确性、速度和有效性已经通过在 PSCAD/EMTP 上开发的具有不同系统参数变化（如故障电阻、负载角、故障起始角和故障类型）的故障数据生成系统得到验证。获得的结果表明，所提出的方案是准确和快速的。两阶段方法使用小波变换 (WT) 和切比雪夫神经网络 (ChNN)，并且仅在中继端使用测量的三相电流信号。该方案的准确性、速度和有效性已经通过在 PSCAD/EMTP 上开发的具有不同系统参数变化（如故障电阻、负载角、故障起始角和故障类型）的故障数据生成系统得到验证。获得的结果表明，所提出的方案是准确和快速的。