Very Short‐Term Blackout Prediction for Grid‐Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries

Abstract

Sub‐Saharan emerging countries experience electrical shortages resulting in power rationing, which ends up hampering economic activities. This paper proposes an approach for very short‐term blackout forecast in grid‐tied PV systems operating in low reliability weak electric grids of emerging countries. A pilot project was implemented in Arusha‐Tanzania; it mainly comprised of a PV‐inverter and a lead‐acid battery bank connected to the local electricity utility company, Tanzania Electric Supply Company Limited (TANESCO). A very short‐term power outage prediction model framework based on a hybrid random forest (RF) algorithm was developed using open‐source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. Input data sampled at a 15‐minute interval included day of the month, weekday, hour, supply voltage, utility line frequency, and previous days’ blackout profiles. The model was composed of an adaptive similar day (ASD) module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF‐ASD blackout prediction model. Furthermore, the efficacy analysis of the short‐term blackout prediction of the formulated RF, ASD, and RF‐ASD regression and classification algorithms was compared. Considering the stochastic nature of blackouts, their performance was found to be fair in short‐term blackout predictions of the test site’s weak grid using limited input data from the point of coupling of the user. The models developed were only able to predict blackouts if they occurred frequently and contiguously, but they performed poorly if they were sparse or dispersed.