Abstract:In recent years, data-driven methods have shown great potential for the practical application of short-term voltage stability (STVS) assessment. However, most existing research works overlook the problem of sample imbalance and overlap in STVS assessment. To tackle this issue, a novel self-adaptive data-driven method for real-time STVS is proposed in this study. First, min-redundancy and max-relevance (mRMR) is employed for feature selection to reduce the computational burden. Taking the key features as inputs… Show more
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