A Probabilistic Data-Driven Method For Response-Based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems

Aiming at timely and adaptive remedial control for the fault-induced voltage recovery (FIDVR) events in power systems, this paper develops a probabilistic data-driven method for response-based load shedding (RLS). In the proposed method, a scalable Gaussian process (SGP) model is developed to estimate the required load shedding (LS) amount and the confidence of the corresponding predictions. Based on the probabilistic information, a 2-stage LS process is designed to enhance the effectiveness and efficiency of the scheme, using the mean value for LS at the first stage and the upper-bound value for LS at the second stage. The 1
^st
stage shed the amount of load with the largest likelihood, aiming to alleviate system stress with the least control cost. The 2
^nd
stage serves as a safety net to ensure system stability considering the possible prediction error. Compared with the conventional RLS schemes and other state-of-the-art approaches, simulation results verify that the proposed method can effectively mitigate FIDVR with a much less load shedding amount.