Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems Article Swipe
Slim Abid
,
Ali M. El‐Rifaie
,
Mostafa Elshahed
,
Ahmed R. Ginidi
,
Abdullah M. Shaheen
,
Ghareeb Moustafa
,
Mohamed A. Tolba
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11081796
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11081796
Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques.
Related Topics
Concepts
PID controller
Control theory (sociology)
Controller (irrigation)
Stability (learning theory)
Computer science
Power (physics)
Chaotic
Electric power system
Control engineering
Engineering
Control (management)
Temperature control
Quantum mechanics
Machine learning
Biology
Agronomy
Artificial intelligence
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11081796
- https://www.mdpi.com/2227-7390/11/8/1796/pdf?version=1681115681
- OA Status
- gold
- Cited By
- 34
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4363674208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4363674208Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math11081796Digital Object Identifier
- Title
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Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power SystemsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-10Full publication date if available
- Authors
-
Slim Abid, Ali M. El‐Rifaie, Mostafa Elshahed, Ahmed R. Ginidi, Abdullah M. Shaheen, Ghareeb Moustafa, Mohamed A. TolbaList of authors in order
- Landing page
-
https://doi.org/10.3390/math11081796Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-7390/11/8/1796/pdf?version=1681115681Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/8/1796/pdf?version=1681115681Direct OA link when available
- Concepts
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PID controller, Control theory (sociology), Controller (irrigation), Stability (learning theory), Computer science, Power (physics), Chaotic, Electric power system, Control engineering, Engineering, Control (management), Temperature control, Quantum mechanics, Machine learning, Biology, Agronomy, Artificial intelligence, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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34Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 11, 2023: 12Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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