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Performance analysis of a novel AI based approach to fault classification and location in an active distribution network with Type 3 and Type 4 wind turbine connections

机译:具有类型3和类型4风力发电机连接的有源配电网中基于AI的故障分类和定位方法的性能分析

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摘要

Accurate fault location in an electrical power system improves the response time to short circuit faults on the system and increases the system reliability. This paper presents novel Artificial Neural Network algorithms that identify with high accuracy whether a short circuit fault lies on a feeder or on one of the spurs. These algorithms are also able to evaluate the distance to the point of fault on the feeder or a spur using only the phase currents measured at the substation. Further tests demonstrate the robustness of the proposed method to the integration of doubly fed induction generator and permanent magnet synchronous generator wind turbines into the network.
机译:电力系统中准确的故障定位可改善对系统短路故障的响应时间,并提高系统可靠性。本文提出了新颖的人工神经网络算法,该算法可以高精度地识别出短路故障是在馈线上还是在支线之一上。这些算法还能够仅使用在变电站中测量的相电流来评估到馈线或支线上故障点的距离。进一步的测试证明了该方法对于将双馈感应发电机和永磁同步发电机风力发电机集成到网络中的鲁棒性。

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