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Review of AI applications in harmonic analysis in power systems by Ahmadreza Eslami; Michael Negnevitsky; Evan Franklin; Sarah Lyden is a Engineering article available to read on EtoBox.

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Harmonics and waveform distortion is a significant power quality problem in modern power systems with high penetration of Renewable Energy Sources (RES). This problem has attracted more attention in recent decades, owing to the increasing integration of power electronic devices and nonlinear loads into power systems. In this paper, Artificial Intelligence (AI) techniques used in different aspects of analyzing harmonics in electrical power networks are reviewed. The tasks of spectrum analysis and waveform estimation or prediction, harmonic source classification, harmonic source location and estimation, determination of harmonic source contributions, harmonic data clustering, filter-based harmonic elimination, and Distributed Generation (DG) hosting capacity in the context of harmonics are considered. The applications of AI in these tasks have been addressed within the literature and are reviewed in this paper. Different AI techniques applied in the study of harmonics such as artificial neural networks, fuzzy systems, support vector machine and decision tree are reviewed. AI techniques mostly outperformed traditional methods in harmonic analysis, particularly under varying operating

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Author

Ahmadreza Eslami; Michael Negnevitsky; Evan Franklin; Sarah Lyden

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)