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Data-Driven Transient Stability Boundary Generation for Online Security Monitoring
arXiv - CS - Systems and Control Pub Date : 2020-04-03 , DOI: arxiv-2004.01369
Rong Yan and Guangchao Geng and Quanyuan Jiang

Transient stability boundary (TSB) is an important tool in power system online security monitoring, but practically it suffers from high computational burden using state-of-the-art methods, such as time-domain simulation (TDS), with numerous scenarios taken into account (e.g., operating points (OPs) and N-1 contingencies). The purpose of this work is to establish a data-driven framework to generate sufficient critical samples close to the boundary within a limited time, covering all critical scenarios in current OP. Therefore, accurate TSB can be periodically refreshed by tracking current OP in time. The idea is to develop a search strategy to obtain more data samples near the stability boundary, while traverse the rest part with fewer samples. To achieve this goal, a specially designed transient index sensitivity based search strategy and critical scenarios selection mechanism are proposed, in order to find out the most representative scenarios and periodically update TSB for online monitoring. Two case studies validate effectiveness of the proposed method.

中文翻译:

用于在线安全监控的数据驱动暂态稳定性边界生成

瞬态稳定边界 (TSB) 是电力系统在线安全监控的重要工具,但实际上它使用时域仿真 (TDS) 等最先进的方法承受着高计算负担,并考虑了许多场景帐户(例如,操作点 (OP) 和 N-1 意外事件)。这项工作的目的是建立一个数据驱动的框架,以在有限的时间内生成足够接近边界的关键样本,涵盖当前 OP 中的所有关键场景。因此,可以通过及时跟踪当前 OP 来定期刷新准确的 TSB。这个想法是开发一种搜索策略,在稳定边界附近获得更多的数据样本,同时用更少的样本遍历其余部分。为了实现这一目标,提出了一种专门设计的基于瞬态索引敏感性的搜索策略和关键场景选择机制,以找出最具代表性的场景并定期更新TSB以进行在线监控。两个案例研究验证了所提出方法的有效性。
更新日期:2020-04-06
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