Purpose

The purpose of this study is to develop an algorithm to accurately detect faults in series capacitor compensated (SCC) power transmission lines. The line fault must be distinguished from stable power swing, compensating unit malfunction and defects on other lines sharing the same bus (external faults).

Design/methodology/approach

In this regard, an effective fault feature extractor based on the cumulative sum (CUSUM) of the amplified second harmonic of the phase currents is suggested. The features are then applied to an artificial neural network for classification. No-fault cases include stable power swing and several disturbances. Due to the independent analysis of each phase, faulty phase detection is also a by-product.

Findings

Various fault scenarios are defined, and the algorithm success rate is compared with some newly published methods. Extensive simulations performed over a single-machine infinite bus, a 3-machine, 9-bus and the large-scale New England IEEE 39-Bus networks all indicate that the proposed algorithm can trip the faulty line more quickly and accurately than the contestant algorithms.

Originality/value

Suggestion of a new algorithm based on the CUSUM of the amplified second harmonic of the phase current for the fault feature extraction that is able to isolate the transmission line internal faults from stable poser swing, line compensating unit malfunction and faults on the adjacent lines connected to the same bus.

中文翻译：

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电力摆动期间的准确故障检测：补偿线路研究

目的

本研究的目的是开发一种算法来准确检测串联电容器补偿 (SCC) 输电线路中的故障。线路故障必须与稳定的功率摆幅、补偿单元故障和共用同一母线的其他线路上的缺陷（外部故障）区分开来。

设计/方法/方法

在这方面，建议基于相电流的放大二次谐波的累积和 (CUSUM) 的有效故障特征提取器。然后将这些特征应用于人工神经网络进行分类。无故障情况包括稳定的功率摆动和若干干扰。由于每相独立分析，故相检测也是一个副产品。

发现

定义了各种故障场景，并将算法成功率与一些新发表的方法进行了比较。在单机无限总线、3 机、9 总线和大规模新英格兰 IEEE 39 总线网络上执行的大量模拟都表明，所提出的算法可以比参赛算法更快、更准确地跳闸故障线路.

原创性/价值

提出一种基于相电流放大二次谐波 CUSUM 的故障特征提取新算法的建议，该算法能够将输电线路内部故障与稳定摆摆、线路补偿单元故障和连接到的相邻线路上的故障隔离开来。同一辆巴士。