A Hybrid Analytical Approach for Voltage Stability Assessment in Microgrids Using Machine Learning

Abstract

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications.