A Hierarchical Data-Driven Method for Event-Based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems
Qiaoqiao Li, Yan Hui Xu, Chao Ren · IEEE Transactions on Industrial Informatics · 2020
Load shedding (LS) is an effective control strategy against voltage instability in power systems. With increasing uncertainties and complexity in modern power grids, there is a pressing need for faster and more accurate control decisions. In this article, a hierarchical data-driven method is proposed for the online prediction of event-based load shedding (ELS) against fault-induced delayed voltage recovery. The ELS problem is hierarchically modeled as a multi-output classification subproblem for identifying the best shedding location and a regression subproblem to predict the minimum shedding amount. To solve the two subproblems, the weighted kernel extreme learning machine is adopted to construct a direct mapping between the system pre-fault operating conditions and the corresponding control variables. The method is tested on the ELS database, which is analytically generated via a novel adaptive sensitivity-based process on the New England 39-bus system. Compared with other methods, the proposed method is very accurate in prediction with excellent control performance, which maintains superior prediction ability under an imbalanced data distribution.