Enhanced power grid performance through Gorilla Troops Algorithm-guided thyristor controlled series capacitors allocation Article Swipe
Mohammed H. Alqahtani
,
Sulaiman Z. Almutairi
,
Ali S. Aljumah
,
Ahmed R. Ginidi
,
Abdullah M. Shaheen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e34326
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e34326
This article introduces an innovative application of the Enhanced Gorilla Troops Algorithm (EGTA) in addressing engineering challenges related to the allocation of Thyristor Controlled Series Capacitors (TCSC) in power grids. Drawing inspiration from gorilla group behaviors, EGTA incorporates various methods, such as relocation to new areas, movement towards other gorillas, migration to specific locations, following the silverback, and engaging in competitive interactions for adult females. Enhancements to EGTA involve support for the exploitation and the exploration, respectively, through two additional strategies of periodic Tangent Flight Operator (TFO), and Fitness-based Crossover Strategy (FCS). The paper initially evaluates the effectiveness of EGTA by comparing it to the original GTA using numerical CEC 2017 single-objective benchmarks. Additionally, various recent optimizers are scrutinized. Subsequently, the suitability of the proposed EGTA for the allocation of TCSC apparatuses in transmission power systems is assessed through simulations on two IEEE power grids of 30 and 57 buses, employing various TCSC apparatus quantities. A comprehensive comparison is conducted between EGTA, GTA, and several other prevalent techniques in the literature for all applications. According to the average attained losses, the presented EGTA displays notable reductions in power losses for both the first and second systems when compared to the original GTA. Specifically, for the first system, the proposed EGTA achieves reductions of 1.659 %, 2.545 %, and 4.6 % when optimizing one, two, and three TCSC apparatuses, respectively. Similarly, in the second system, the suggested EGTA achieves reductions of 6.096 %, 7.107 %, and 4.62 %, respectively, when compared to the original GTA's findings considering one, two, and three TCSC apparatuses. The findings underscore the superior effectiveness and efficiency of the proposed EGTA over both the original GTA and several other contemporary systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.heliyon.2024.e34326
- http://www.cell.com/article/S240584402410357X/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 107
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400495880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400495880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.heliyon.2024.e34326Digital Object Identifier
- Title
-
Enhanced power grid performance through Gorilla Troops Algorithm-guided thyristor controlled series capacitors allocationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-01Full publication date if available
- Authors
-
Mohammed H. Alqahtani, Sulaiman Z. Almutairi, Ali S. Aljumah, Ahmed R. Ginidi, Abdullah M. ShaheenList of authors in order
- Landing page
-
https://doi.org/10.1016/j.heliyon.2024.e34326Publisher landing page
- PDF URL
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https://www.cell.com/article/S240584402410357X/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://www.cell.com/article/S240584402410357X/pdfDirect OA link when available
- Concepts
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EGTA, Computer science, Power (physics), Capacitor, Woodchips, Control theory (sociology), Engineering, Electrical engineering, Chemistry, Artificial intelligence, Voltage, Physics, Waste management, Organic chemistry, Quantum mechanics, Calcium, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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107Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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