Hybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems

Abstract

With a large number of inverter-interfaced renewable power generation, fault-induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data-driven method based on probabilistic prediction, ensemble learning, and multi-objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real-time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data-driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time-adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10-machine 39-bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment.