Open AccessHybrid randomised learning‐based probabilistic data‐driven method for fault‐induced delayed voltage recovery assessment of power systems
Author(s) -
Ren Chao,
Zhang Rui,
Zhang Yuchen,
Dong Zhao Yang
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0402
Subject(s) - probabilistic logic , computer science , fault (geology) , electric power system , ensemble learning , machine learning , process (computing) , stability (learning theory) , artificial intelligence , voltage , power (physics) , data mining , reliability engineering , engineering , physics , electrical engineering , quantum mechanics , seismology , geology , operating system
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.