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Dynamic fault location method for distribution networks with distributed generation

机译:分布式发电的配电网动态故障定位方法

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This paper presents a new analytical time-based fault location algorithm for radial distribution networks with synchronous generators used for distributed generation (DG). The algorithm uses the voltage and current samples recorded at the main feeder head along with the scheduled injected active and reactive powers by the network synchronous generators to estimate the fault location. A full order synchronous machine model has been used to consider dynamic behaviour of the DG plants during fault transients. The algorithm, therefore, uses time domain analysis and is dynamic in the sense that it uses the updated synchronous machines and network data at each time step. The algorithm is implemented in two sequential steps. In the initialisation step, by using the measured pre-fault voltage at the main feeder head and scheduled power injections by the DG plants, a pre-fault load flow calculation is performed to evaluate different parameters of the synchronous generators, initial values of the network node voltages, line section currents, loads and capacitive charging currents. In the second step, the numerical fault location algorithm is performed by moving the data window from the pre-fault to a post-fault cycle. The accuracy of the proposed algorithm has been validated by several simulated fault studies carried out on a 205-node 20 kV practical radial distribution feeder. The results have shown remarkable accuracy in fault location when considering only the current and voltage measurements at the main feeder head have been used.
机译:本文提出了一种新的基于时间的分析型故障定位算法,该算法具有用于分布式发电(DG)的同步发电机的径向配电网。该算法使用记录在主馈线头上的电压和电流样本以及网络同步发电机计划的注入的有功功率和无功功率来估计故障位置。已使用全序同步电机模型来考虑DG设备在故障瞬变期间的动态行为。因此,该算法使用时域分析,并且在每个时间步均使用更新的同步机和网络数据的意义上是动态的。该算法以两个连续步骤实现。在初始化步骤中,通过使用主馈电头上测得的故障前电压和DG工厂的预定功率注入,执行故障前潮流计算,以评估同步发电机的不同参数,网络初始值节点电压,线段电流,负载和电容性充电电流。在第二步中,通过将数据窗口从故障前循环移动到故障后循环来执行数字故障定位算法。该算法的准确性已通过在205节点20 kV实用径向配电馈线上进行的几次模拟故障研究得到了验证。当仅考虑使用主馈线头上的电流和电压测量结果时,结果表明在故障定位中具有卓越的准确性。

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