Two-stage transient stability assessment using ensemble learning and cost sensitivity

This paper proposes a novel two-stage transient stability assessment (TSA) model that integrates ensemble learning with cost sensitivity to address the challenges posed by the integration of renewable energy and load fluctuations. The model employs CNNs as positive and negative classifiers to initially evaluate samples, with consistent results output directly. In cases of inconsistency, the sample is evaluated by a fair classifier, specifically an ELM, trained on critical samples. This approach significantly enhances the classification performance and credibility of the fair classifier, especially under imbalanced conditions, thereby improving the overall efficiency and accuracy of TSA. The proposed model demonstrates superior performance compared to single-stage models and other two-stage models, achieving high accuracy and robustness in transient stability assessment, particularly for critical samples.