JETI Admin2
Abstract
Power Quality (PQ) concerns and incidents can adversely affect the efficacy, efficiency, quantity, and cleanliness of grid-supplied energy. This paper is aimed at developing an intelligent model to predict a power quality event called voltage sag on Adebayo feeder located within Ado-Ekiti network. A portable network analyser (Circutor) was logged for 90 days at 10-minute intervals to collect power quality data, which tracked 12,960 data sets of frequency, current, voltage, voltage sag (Vsag), and power factor. A Recurrent Neural Network (RNN) model with four inputs made up of frequency, power factor, voltage and current, five hidden neurons and one output which produced a 4 by 5 by 1 matrix was set up. The Vsag prediction was implemented based on training, testing datasets and data validation at the 70%, 20% and 10% proportion respectively for the RNN model. The results of the target and predicted Vsag were coded with R 4.4.1 package version. The plot and assessment were performed by Matlab 2023a. The results showed that the target and predicted Vsag in the feeder were within the acceptable limit of the nominal voltage by 10 to 90 percent by the IEEE standard. The target Vsag ranges from 209 V to 211V while the predicted Vsag ranges from 209. 03V to 211.04 V depicting good PQ. The contributors to the prediction were frequency, power factor, current and voltage with 100 %, 66.1%, 59.2% and 39.9 respectively. It is important to recommend the incorporation of techniques like deep learning, transfer learning to improve the accuracy and robustness of power PQ event prediction models. Future trends of this research tilts towards cloud computing and edge computing for real time data analytics, machine learning, and predictive modeling for power quality event prediction.
References
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