Accurate Identification of Harmonic Distortion for Micro-Grids Using Artificial Intelligence-Based Predictive Models Article Swipe
Ahmed M. Abed
,
Ragab A. El‐Sehiemy
,
Bachir Bentouati
,
Hasnaa M. El-Arwash
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3400038
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3400038
This paper proposes an accurate harmonic identification strategy for microgrids and distributed power systems. The harmonic identification strategy is one of the complex tasks in microgrids due to the need of high computational burden in terms of memory and computational time. The complexity of the considered problem is resulted from solving the transcendental nonlinear equations that characterize harmonics especially in real time is considered as a highly challenged problem. The proposed identification strategy aims at detecting individual and total harmonic distortion levels that is generated from several harmonic sources. In the current paper, the Machine Learning Regression Analysis (MLRA) including location-specific data and the Artificial Neural Networks (ANNs) are proposed to identify the harmonic distortion. To enhance the identification and prediction performance, the standard IEEE 34-bus test feeder with verified harmonic sources power system is emulated for various scenarios using Electric Transient Analysis Program (ETAP). An extracting procedure for individual and total harmonic components are employed. The higher reduction level of error with approximately maximum median of 3.7127e% assures the accurate prediction of harmonic components. In addition, this work investigates the impact of several practical cases when the voltages of the renewable clean energy source arrays vary (e.g., solar cell, wind turbine, and EV), and a double-stage topology is needed to have the same amount of voltage at the input of the inverter before inversion.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3400038
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529291.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396877931
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- OpenAlex ID
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https://openalex.org/W4396877931Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3400038Digital Object Identifier
- Title
-
Accurate Identification of Harmonic Distortion for Micro-Grids Using Artificial Intelligence-Based Predictive ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Ahmed M. Abed, Ragab A. El‐Sehiemy, Bachir Bentouati, Hasnaa M. El-ArwashList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3400038Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529291.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529291.pdfDirect OA link when available
- Concepts
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Total harmonic distortion, Computer science, Harmonics, Control theory (sociology), Algorithm, Harmonic, Harmonic analysis, Voltage, Electronic engineering, Topology (electrical circuits), Artificial intelligence, Engineering, Electrical engineering, Physics, Quantum mechanics, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 2Per-year citation counts (last 5 years)
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77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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