Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD operates on stochastic data streams, distinguishing pre- and post-change regimes using the ratio of their respective probability density functions. To derive the density functions for normal and post-outage market signals, we exploit multi-parametric programming to decompose complex market signals into parametric random variables with a known density. These densities are then used to construct a QCD-based statistic that triggers an alarm as soon as the statistic exceeds an appropriate threshold. Numerical experiments on a stylized PJM testbed demonstrate rapid line outage identification from public streams of electricity demand and price data.

翻译：电力系统中断若未能及时检测与对冲，将使市场参与者面临重大财务风险。本文基于参数化最快变化检测理论，开发了一种从公开市场信号中识别中断的方法。参数化QCD作用于随机数据流，通过比较变化前后概率密度函数的比值来区分两种状态。为推导正常与中断后市场信号的密度函数，我们利用多参数规划将复杂市场信号分解为具有已知密度的参数化随机变量。这些密度函数随后用于构建基于QCD的统计量，当该统计量超过适当阈值时立即触发警报。在简化PJM测试平台上的数值实验表明，该方法能够从公开的电力需求与价格数据流中快速识别线路中断。