Data-Driven Short-Term Voltage Stability Assessment Considering Sample Imbalance and Overlapping

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, a cascaded LightGBM (CasLightGBM) model is constructed to mine STVS informatization. Based on the LightGBM and cascaded structure, CasLightGBM can enhance the assessment accuracy without sacrificing the assessment earliness. Then, focal loss (FL) is embedded into both offline and online phases of the CasLightGBM to mitigate the loss of accuracy caused by sample imbalance and overlapping, thus deriving a highly comprehensive and reliable classification model for real-time STVS assessment. Extensive numerical tests are conducted on the IEEE 118-bus system, and the simulation results demonstrate that the proposed method outperforms traditional algorithms and exhibits favorable robustness to measurement noise.