Machine Learning-based Fault Prediction for Smart Electrical Grids in Nigeria: A Critical Narrative Review of Reliability Potential, Evidence Gaps and Deployment Priorities
Nigeria’s electricity system continues to experience reliability challenges arising from transmission and distribution faults, protection coordination difficulties, voltage and frequency deviations, ageing assets, and uneven network observability. Machine learning (ML) is increasingly proposed as a means of identifying abnormal operating states earlier and supporting faster, more selective intervention. This critical narrative review evaluates the extent to which the international fault-analytics literature can support genuine fault prediction and reliability improvement in the Nigerian smart-grid context. Literature published from 1 January 2010 to 22 June 2026 was examined, while older foundational Nigerian evidence was retained where it remained necessary for context. The evidence shows a pronounced terminological and translational gap. Most studies labelled as predictive apply ML after fault inception to detection, classification or localisation, commonly on simulated or synthetically augmented data. Convolutional, recurrent, ensemble, support-vector and hybrid methods often achieve very high test performance in controlled settings, but these results do not establish prospective prediction, field robustness or reduction in outage burden. Nigeria-specific evidence demonstrates technical feasibility for high-impedance-fault diagnosis and voltage-stability classification, yet field-validated prospective models linked to reliability outcomes remain scarce. The strongest near-term opportunity is therefore not autonomous fault anticipation in the abstract, but staged deployment of ML for condition-based risk scoring, abnormal-event recognition, protection support and maintenance prioritisation using utility data streams. Successful translation will require dependable sensing, labelled event histories, topology-aware modelling, uncertainty estimation, cybersecurity, model-drift governance and prospective evaluation against operational endpoints such as outage frequency, interruption duration, energy not supplied and protection misoperations. ML can contribute meaningfully to Nigerian grid reliability, but only when treated as one component of an engineering, data and governance architecture rather than as a substitute for protection design, asset renewal or network reinforcement.