Modified Gradient-Based Algorithm for Distributed Generation and Capacitors Integration in Radial Distribution Networks Article Swipe
Ali M. El‐Rifaie
,
Abdullah M. Shaheen
,
Mohamed A. Tolba
,
Idris H. Smaili
,
Ghareeb Moustafa
,
Ahmed R. Ginidi
,
Mostafa Elshahed
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3326758
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3326758
With the use of cutting-edge demand management at the system or home level and powerful network reconfiguration tools, smart grids are expected to introduce advanced hardware and software resources to strengthen the operation of power systems. This article describes a Modified Gradient-Based Optimization (MGBO) algorithm for Distributed Generation (DG) and capacitors integration in distribution feeders. To increase the variety of the produced searching individuals, the suggested MGBO combines the basic Gradient Searching Method (GSM) and Local Escape Mechanism (LEM) with a binomial crossover strategy. This combined cross-over strategy upgrades the forthcoming searching individuals to be more random. The LEM assists in evading local optima, whereas the GSM guides the searching scan to promising regions and facilitates its convergence to the optimum solution. The suggested MGBO method is designed and implemented to improve the performance of radial distribution networks by reducing technical power losses while taking into account the peak loading. Its relevance is tested on a practical radial 59-bus Cairo distribution feeder in Egypt and a large-scale radial 135-bus distribution feeder. The proposed MGBO is compared with the original GBO, Manta ray foraging optimization (MRFO) and honey badger algorithm (HBA). The whole comparison of the suggested MGBO with the original GBO and the newly developed optimization algorithms demonstrates that the suggested MGBO derives the best performance in all of the cases studied. For the practical radial 59-bus Cairo distribution feeder in Egypt, the proposed MGBO shows great improvement of 18.40%, 20.17%, and 2.29% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively. For the large-scale radial 135-bus distribution feeder, the proposed MGBO shows great improvement of 46.92%, 62.94%, and 67.87% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3326758
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10290877.pdf
- OA Status
- gold
- Cited By
- 25
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387886022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387886022Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2023.3326758Digital Object Identifier
- Title
-
Modified Gradient-Based Algorithm for Distributed Generation and Capacitors Integration in Radial Distribution NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Ali M. El‐Rifaie, Abdullah M. Shaheen, Mohamed A. Tolba, Idris H. Smaili, Ghareeb Moustafa, Ahmed R. Ginidi, Mostafa ElshahedList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2023.3326758Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10290877.pdfDirect link to full text PDF
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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/ielx7/6287639/6514899/10290877.pdfDirect OA link when available
- Concepts
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Computer science, Control reconfiguration, GSM, Capacitor, Mathematical optimization, Local optimum, Crossover, Algorithm, Distributed computing, Embedded system, Telecommunications, Engineering, Mathematics, Electrical engineering, Voltage, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
25Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 14Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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