Machine Learning for Voltage Stability in Nigeria’s Power Network: A Comprehensive Review

Abstract

The foundation of power system reliability is voltage stability, which is required to promise a secure and stable supply of electricity. Insufficient generating capacity, a timeworn transmission facility, inadequate reactive power compensation, and the increasing integration of renewable energy sources are the main foundations of the existing voltage instability of Nigeria's power grid, specifically in the northern regions. Through evaluating contemporary disturbances such as smart grid technology and intelligent machine learning (ML) drives up for real-time voltage security evaluation and predictive analytics, this study provides a technically enhanced examination of these effects. Contrasting machine learning models, such as deep learning (DL), supervised learning, unsupervised learning, and reinforcement learning (RL), are explored for their capabilities in time-varying voltage prediction, robotic grid control, and anomaly detection. Also, it highlights the transformative impact of machine learning in improving voltage stability management and outlines strategic recommendations relating to guiding principle reforms and infrastructure transformation. The article intends to provide a forward-looking structure for deploying adaptive Machine stakeholder engagement e-learning-powered solutions to achieve resilient and voltage security in the Nigerian power system that structures long-term economic sustainability.