An Effective Ensemble Learning approach-Based Grid Stability Assessment and Classification

Abstract

This article proposes an accurate Stacking Ensemble Classifier (SEC) for decentral Smart Grid control Stability Prediction. The proposed SEC consists of stacking two base classifiers; specifically, eXtreme Gradient Boosting machine (XGBoost) and Categorical boosting (Catboost), and one meta-classier, Light Gradient Boosting Machine (LGBM). The proposed technique shows an excellent ability to classify the grid instabilities using a supervised learning approach accurately. Extensive experiments have been conducted, demonstrating the superiority of the proposed SEC model over multiple benchmarks. In summary, this paper's main contributions consist of 1) proposing a new model-based ensemble learning 2) tailoring an efficient data-driven technique for grid stability detection and classification. Numerical results are to validate the proposed model's high effectiveness.