arXiv 2201.082632022-01-20eess.SP

基于包围学习的高压多端直流网络故障测距

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Timothy Flavin, Bhaskar Mitra, Vidhyashree Nagaraju, Rounak Meyur

摘要：

大距离输电网故障的精确定位是快速修复和恢复过程的关键。采用模块化多电平换流器（MMC）技术的高压直流（HVdc）网络在互连多端网络中具有突出的应用。这允许以较低的成本进行大距离的大容量电力传输。但是，它们可以应对直流故障的挑战。在直流故障下，快速有效的隔离网络的方法已被广泛研究和调查。成功隔离后，必须精确定位故障。故障后的电压和电流特征是多种因素的函数，因此在多端网络上准确定位故障是具有挑战性的。在本文中，我们讨论了一种新的数据驱动集成学习的方法，用于准确的故障定位。在这里，我们利用极端梯度提升（XGB）的方法进行准确的故障定位。在电力系统计算机辅助设计（PSCAD）/含直流电磁暂态（EMTdc）软件中设计的径向三端MTdc网络上，对该算法对测量噪声、故障定位、电阻和限流电感的灵敏度进行了分析。

英文摘要：

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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