Abstract

This paper introduces a novel approach to high-speed fault protection strategy for bipolar Current Source Converter (CSC) based high voltage DC transmission line. The Bagged-Tree Ensemble-Classifier (BTEC) method is employed to detect the presence of a fault and categorizes the various types of faults in the DC transmission line. The implementation of the Wavelet Transform (WT) technique assists in diminishing the effect of deviation in the fault resistance value, fault location, and disturbances created due to noise on the performance BTEC method. The development of the predictive model through concatenating individual models is the prime objective of the ensemble schemes. Ensemble procedure assists to enhance the performance accuracy of the fault protection strategy. The local measurement at the rectifier end side is enough for the proposed strategy to realize the protection of the entire line with no use of communication channels. The AC sinusoidal voltage, DC current and DC voltage are recorded at the rectifier end terminal and sampled at 1 kHz, which is very less as compared to the other existing techniques. The proposed strategy detects the fault in the least possible time of 4 ms and able to classify all the tested faults with reasonable accuracy. The results convey that the proposed strategy offers high-speed detection of fault and preciseness in the classification of all types of faults, despite variation in the fault resistance up to 180 Ω. The proposed scheme is applicable for DC faults and also detects the presence of AC faults in either rectifier end as reverse fault and in inverter end as sub-sequent forward section fault.

摘要

本文介绍了一种基于双极电流源换流器 (CSC) 的高压直流输电线路的高速故障保护策略的新方法。Bagged-Tree Ensemble-Classifier (BTEC) 方法用于检测故障的存在并对直流输电线路中的各种类型的故障进行分类。小波变换 (WT) 技术的实施有助于减少故障电阻值偏差、故障位置和由于噪声对性能 BTEC 方法产生的干扰的影响。通过连接单个模型来开发预测模型是集成方案的主要目标。集成过程有助于提高故障保护策略的性能准确性。整流器端的本地测量足以使所提出的策略在不使用通信通道的情况下实现整条线路的保护。AC 正弦电压、DC 电流和 DC 电压在整流器末端记录并以 1 kHz 采样，与其他现有技术相比，这是非常少的。所提出的策略在 4 ms 的最短时间内检测到故障，并且能够以合理的精度对所有测试过的故障进行分类。结果表明，尽管故障电阻变化高达 180 Ω，但所提出的策略提供了对所有类型故障的高速故障检测和精确性分类。