EXPLORING THE APPLICATION OF STATISTICAL LEARNING THEORY TO VOLTAGE STABILITY ANALYSIS ON THE BIRNIN KEBBI 330 KV POWER TRANSMISSION NETWORK: A COMPREHENSIVE REVIEW

Voltage instability remains a persistent challenge in Nigeria’s high-voltage grid, including the Birnin Kebbi 330 kV corridor. Aging infrastructure, increasing load, and limited high-resolution telemetry hinder conventional analysis. This review explores how Statistical Learning Theory (SLT), with key concepts like empirical risk minimization, structural risk minimization, margin maximization, and capacity control, can enable robust voltage stability analysis amid data scarcity and noise. We relate SLT ideas to practical tasks such as regression of stability indices, binary and ordinal classification of stability states, and early-warning forecasting. The review covers model families (SVMs, kernel methods, regularized regression), feature extraction from SCADA-like signals, handling class imbalance, and methods for uncertainty quantification. It outlines evaluation protocols, using MSE as the main regression metric, proposes a Birnin Kebbi–specific workflow, and addresses open challenges like domain shift, missing data, and causal confounding. The conclusion presents a roadmap that combines SLT principles with modern techniques (e.g., GAN-generated data) to produce interpretable, capacity-controlled, offline monitoring suitable for under-instrumented networks.