论文标题
数据驱动的瞬态稳定性边界生成用于在线安全监控
Data-Driven Transient Stability Boundary Generation for Online Security Monitoring
论文作者
论文摘要
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，使用最新方法（例如时域模拟（TDS）），它遭受了高计算负担，其中考虑了许多方案（例如，操作点（OPS）和N-1偶然性）。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靠近边界的足够关键样本，涵盖当前OP中的所有关键方案。因此，可以通过及时跟踪电流OP来定期刷新准确的TSB。这个想法是制定搜索策略，以在稳定边界附近获取更多数据样本，同时以更少的样本遍历其余部分。为了实现这一目标，提出了一个专门设计的基于启示性搜索策略和关键方案选择机制的特殊设计的暂时索引，以找出最具代表性的方案并定期更新TSB以进行在线监视。两项案例研究证明了所提出的方法的有效性。
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.
