Open AccessReal‐time approach for oscillatory stability assessment in large‐scale power systems based on MRMR classifier
Author(s) -
Li Xin,
Zheng Zhiyi,
Ma Zhicheng,
Guo Panfeng,
Shao Kaixuan,
Quan Siping
Publication year - 2019
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.2019.0180
Subject(s) - computer science , data mining , classifier (uml) , redundancy (engineering) , computation , feature (linguistics) , mutual information , stability (learning theory) , artificial intelligence , electric power system , pattern recognition (psychology) , machine learning , algorithm , power (physics) , linguistics , philosophy , physics , quantum mechanics , operating system
An integrated approach for real‐time oscillatory stability assessment (OSA) based on mutual information theory is proposed in this study. An advanced maximum‐relevance minimum‐redundancy (MRMR) ensemble scheme is designed to explore the internal relations between operation variables and the oscillatory stability margin (OSM). Multiple MRMR procedures are generated in parallel to select multiple different feature subsets, in which each feature presents a relevant and complementary description of OSM. The functional expression of the relationships is obtained by curve fitting. The 21‐bus system and 1648‐bus system are implemented to test the performance of the proposed approach. A compared investigation is made with some other data mining methods. The impacts of the number of feature sets, size of feature sets, size of training set, invalid data and computation time are studied. Experimental results reveal that the proposed approach provides faster and more accurate assessment results and is a real‐time adaptive approach for OSA.