May, NMay, NBokoro, PMay, Siyasanga IMkasi, Hlaluku W2025-05-022025-05-022025-01979-8-3315-3516-2DOI: 10.1109/SAUPEC65723.2025.10944477http://hdl.handle.net/10204/14228This research investigates the effectiveness of machine learning and deep learning models in forecasting voltage swell peak amplitudes within grid-connected photovoltaic (PV) systems, aiming to enhance power quality management. A 24month dataset (January 2022 - December 2023) encompassing power and weather data from a 3.3 kWp PV system at the Council for Scientific and Industrial Research (CSIR) in Pretoria, South Africa, was utilized. Hourly averaged data between 5 am and 6 pm, capturing PV system and weather measurements, was analysed. Five models – Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbours (KNN), and Long Short-Term Memory (LSTM) – were trained and evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The Random Forest model demonstrated superior predictive accuracy, closely aligning with actual peak voltages and achieving the lowest MSE (0.01V²) and RMSE (0.02V). This study highlights the potential of machine learning, particularly Random Forest, in accurately predicting voltage swell peak amplitudes, contributing to improved power quality management in grid-connected PV systems.AbstractenGrid-tied photovoltaic systemVoltage swellPeak amplitude predictionMachine learning algorithmsPower qualityConference Presentationn/a