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Topology and parameter estimation in power systems through inverter-based broadband stimulations

机译:通过基于逆变器的宽带激励在电力系统中进行拓扑和参数估计

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An increasing number of inverter-based power generators have been connected to the distribution network in recent years. This phenomenon coupled with the adoption of open energy markets has significantly complicated the power-flows on the power networks, requiring advanced and intelligent parameter knowledge to optimise the efficiency, quality and reliability of the system. This study describes a method for identifying parameters associated with the power system model. In particular, the proposed algorithm in this study addresses the line parameter and topology identification task in the scope of state estimation. The goal is to reduce the a priori knowledge for state estimation, and to obtain online information on the power system network. The proposed parameter estimation method relies on injected stimulations in the network. Broadband stimulation signals are injected from distributed generators and their effects are measured at various locations in the grid. To process and evaluate this data, a novel aggregation method based on weighed least squares will be proposed in this study. It combines and correlates various measurements in order to obtain an accurate snapshot of the power network parameters. To test its capabilities, the performance of this algorithm is evaluated on a small-scale test system.
机译:近年来,越来越多的基于逆变器的发电机已连接到配电网络。这种现象与开放式能源市场的采用相结合,已使电网上的潮流大大复杂化,需要先进的智能参数知识来优化系统的效率,质量和可靠性。这项研究描述了一种识别与电力系统模型相关的参数的方法。特别是,本研究中提出的算法在状态估计的范围内解决了线路参数和拓扑识别任务。目的是减少用于状态估计的先验知识,并获得有关电力系统网络的在线信息。所提出的参数估计方法依赖于网络中注入的刺激。从分布式发生器注入宽带激励信号,并在电网的各个位置测量其影响。为了处理和评估这些数据,将在本研究中提出一种基于加权最小二乘的新型聚集方法。它组合并关联了各种测量,以便获得电网参数的准确快照。为了测试其功能,在小型测试系统上评估了该算法的性能。

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