Alghamdi, Thamer A. H. and Anayi, Fatih ![]() |
Abstract
Practically, it is difficult to estimate the real harmonic emissions of a grid-connected solar Photovoltaic (PV) power inverter using conventional metering systems. As a result, the authors have recently suggested a strategy that uses an Artificial Neural Network (ANN) system and incorporates location-specific data. This paper compares the performance and computational requirements of various neural networks, including Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Echo State Networks (ESN), Linear Auto-Regressive eXogenous (NARX), and Adaptive Wavelet Neural Network (AWNN) systems, to assess the effectiveness of various neural network structures for this application. It has been shown that compared to the NARX and AWNN, the MLP, RNN, and ESN have superior prediction accuracy and require less training and prediction time with a comparatively fewer number of neurons.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Publisher: | IEEE |
ISBN: | 9781665464765 |
Last Modified: | 06 Apr 2023 11:15 |
URI: | https://orca.cardiff.ac.uk/id/eprint/158388 |
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