Real-Time Anomaly Detection for Nigerian Power Grid Stability: An Integrated Time Series and Machine Learning Approach
DOI:
https://doi.org/10.54536/ajase.v4i1.5421Keywords:
Anomaly Detection, Grid Stability, Grid, Modelling, Statistical OutlierAbstract
Among other endemic problems with the nation’s grid, including frequent grid failure, load-shedding, poor generation, and old equipment, the Nigerian Electricity Regulatory Commission (NERC) has documented over 300 system breakdowns annually. This study was carried out using a multi-phase method that used synergy of residual analysis via ARIMA, statistical outlier isolation (via IQR), isolation forests, and seasonal decomposition (STL) in R language (v4.3.0) to detect anomalies in this sector. Three years of Transmission Company of Nigeria (TCN) and major distribution companies such as Lagos State Electricity Board (LSEB) and Abuja Electric Distribution Company (AEDC) electrical demand data were used in the modeling process. Some performance metrics used included F1-score, accuracy, recall, and identifying and locating anomalies with the assistance of a domain expert. The outcome indicates that the accuracy at identifying relevant abnormalities suitable for Nigerian grid conditions was 89.4%, and the recall rate was 84.2%. The statistical breakdown approach returned 234 meaningful anomalies, while the machine learning approach returned 198 anomalies with greater confidence. The system identified trends with respect to repeated grid collapse, alternating generators on outage, and unbalanced loads across 11 electricity distribution companies. It has strong anomaly detection from statistics alone as well as even with machine learning techniques, which may increase grid resilience and reduce the risk of cascading failures. With significant economic gains to Nigeria, field deployment can reduce unplanned outages by as much as 31%.
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