论文标题
一种基于机器学习的方法，用于识别瞬态稳定性研究的关键距离继电器
A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies
论文作者
论文摘要
建模保护性继电器对于执行准确的稳定性研究至关重要，因为它们在定义干扰过程中动力系统的动态响应中起着至关重要的作用。然而，由于当前稳定软件的局限性以及跟踪数千个保护性继电器的设置信息中的变化的挑战，对散装电力系统中的所有保护继电器进行建模是一项艰巨的任务。距离继电器是关键保护方案之一，在当前的稳定研究实践中，这些方案未正确建模。本文提出了一种基于机器学习的方法，该方法使用早期终止稳定性研究的结果来确定这些研究中需要建模的临界距离继电器。使用的算法是随机森林（RF）分类器。 GE阳性序列载荷分析（PSLF）软件用于执行稳定性研究。该模型在西方电力协调委员会（WECC）系统数据中进行了训练和测试，该系统代表了该系统在不同的操作条件和拓扑结构下的2018年夏季峰值负载。结果表明，该方法在识别临界距离继电器方面的表现出色。结果还表明，仅对已确定的临界距离继电器进行建模足以进行准确的稳定性研究。
Modeling protective relays is crucial for performing accurate stability studies as they play a critical role in defining the dynamic responses of power systems during disturbances. Nevertheless, due to the current limitations of stability software and the challenges of keeping track of the changes in the settings information of thousands of protective relays, modeling all the protective relays in bulk power systems is a challenging task. Distance relays are among the critical protection schemes, which are not properly modeled in current practices of stability studies. This paper proposes a machine learning-based method that uses the results of early-terminated stability studies to identify the critical distance relays required to be modeled in those studies. The algorithm used is the random forest (RF) classifier. GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The model is trained and tested on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer peak load under different operating conditions and topologies of the system. The results show the great performance of the method in identifying the critical distance relays. The results also show that only modeling the identified critical distance relays suffices to perform accurate stability studies.
