数据驱动的在线安全监测暂态稳定边界生成
Data-Driven Transient Stability Boundary Generation for Online Security Monitoring
摘要：
暂态稳定边界（TSB）是电力系统在线安全监测的重要工具，但在实际应用中，采用时域仿真（TDS）等现有方法计算量大，需要考虑多种情况（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，以在有限的时间内产生足够的临界样本接近边界，覆盖当前OP中的所有关键场景。因此，准确的TSB可以通过及时跟踪当前OP定期刷新。其思想是开发一种搜索策略，以获得更多的数据样本附近的稳定性边界，而遍历其余部分与较少的样本。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在线监测。两个算例验证了该方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
