Abstract
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.
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