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首页> 外文期刊>Science, Measurement & Technology, IET >Data-mining-based fault during power swing identification in power transmission system
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Data-mining-based fault during power swing identification in power transmission system

机译：输电系统功率摆幅辨识中基于数据挖掘的故障

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摘要

This study proposes a decision-tree-based scheme for detection and classification of fault during power swing in double circuit transmission lines. The power swing may result due to switching in/out of heavy loads, switching of lines, clearance of short-circuit faults, generator tripping or load shedding. The proposed decision tree approach makes the discrimination among no fault situation/power swing and fault during power swing. The fundamental components of currents and voltages and zero sequence currents measured at only one end of the double circuit line are used as input to decision tree. To ascertain validity of the proposed scheme, it is tested for variation in fault type, fault inception angle, fault location and fault resistance. The main advantage of this proposed scheme is that it detects fault during power swing within half cycle time and classify the type of fault and identify the faulty phase also.
机译：这项研究提出了一种基于决策树的方案，用于在双回输电线路的功率摆动期间对故障进行检测和分类。功率波动可能是由于重负载的切入/切出，线路的切入，短路故障的清除，发电机跳闸或甩负荷引起的。所提出的决策树方法可以在无故障情况/无动力摆幅和无动力摆幅期间进行区分。仅在双回路线路的一端测得的电流和电压以及零序电流的基本分量用作决策树的输入。为了确定所提出方案的有效性，测试了该方案在故障类型，故障起始角度，故障位置和故障电阻方面的变化。该方案的主要优点是，它可以在半个周期内检测出电源摆幅期间的故障，并对故障的类型进行分类并确定故障相。

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