Voltage Stability Analysis in Transmission and Distribution Network using Random Forest

The reliable operation of modern power systems relies upon voltage stability analysis, especially when load demands and the integration of renewable energy sources increase. Conventional methods struggle with real-time and large-scale analysis. In this study, Modal Analysis and Random Forest machine learning model are combined to propose a novel method to evaluate voltage stability and to detect the most vulnerable branch in the network with contingency analysis and load variation. The feasibility of the proposed work is evaluated in IEEE 14 bus transmission system and IEEE 33 bus radial distribution networks, respectively. The results demonstrate the promising potential of machine learning for voltage stability prediction