Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems
ABSTRACT Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in c...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70028 |
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| author | Ali M. El‐Rifaie Slim Abid Ahmed R. Ginidi Abdullah M. Shaheen |
| author_facet | Ali M. El‐Rifaie Slim Abid Ahmed R. Ginidi Abdullah M. Shaheen |
| author_sort | Ali M. El‐Rifaie |
| collection | DOAJ |
| description | ABSTRACT Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in contemporary multi‐area interconnected power systems (MAIPS). This paper introduces an innovative approach to Load Frequency Control (LFC) using a Fractional‐Order PID (FOPID) controller, optimized by a Neural Network Algorithm (NNA). The proposed NNA‐FOPID framework leverages the biological principles of neural networks to dynamically tune controller parameters, significantly enhancing system performance. The solution is tested under various scenarios involving step load changes across multi‐area systems. The proposed method demonstrates marked improvements over traditional PID controllers and advanced optimization techniques such as Differential Evolution (DE) and Artificial Rabbits Algorithm (ARA). The comparisons show that the FOPID controller's NNA‐based design effectively and successfully handles LFC in MAIPSs for ITAE minimizations, and statistical evaluation supports its superiority. |
| format | Article |
| id | doaj-art-b41bde021b4b4f679800f54478b4f5bd |
| institution | DOAJ |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-b41bde021b4b4f679800f54478b4f5bd2025-08-20T03:11:14ZengWileyEngineering Reports2577-81962025-02-0172n/an/a10.1002/eng2.70028Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power SystemsAli M. El‐Rifaie0Slim Abid1Ahmed R. Ginidi2Abdullah M. Shaheen3College of Engineering and Technology American University of the Middle East Egaila KuwaitDepartment of Electrical and Electronic Engineering, College of Engineering and Computer Science Jazan University Jazan Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering Suez University Suez EgyptDepartment of Electrical Engineering, Faculty of Engineering Suez University Suez EgyptABSTRACT Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in contemporary multi‐area interconnected power systems (MAIPS). This paper introduces an innovative approach to Load Frequency Control (LFC) using a Fractional‐Order PID (FOPID) controller, optimized by a Neural Network Algorithm (NNA). The proposed NNA‐FOPID framework leverages the biological principles of neural networks to dynamically tune controller parameters, significantly enhancing system performance. The solution is tested under various scenarios involving step load changes across multi‐area systems. The proposed method demonstrates marked improvements over traditional PID controllers and advanced optimization techniques such as Differential Evolution (DE) and Artificial Rabbits Algorithm (ARA). The comparisons show that the FOPID controller's NNA‐based design effectively and successfully handles LFC in MAIPSs for ITAE minimizations, and statistical evaluation supports its superiority.https://doi.org/10.1002/eng2.70028fractional‐order PID controllergrid stabilitymulti‐area power systemsneural network algorithmoptimization techniques |
| spellingShingle | Ali M. El‐Rifaie Slim Abid Ahmed R. Ginidi Abdullah M. Shaheen Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems Engineering Reports fractional‐order PID controller grid stability multi‐area power systems neural network algorithm optimization techniques |
| title | Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems |
| title_full | Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems |
| title_fullStr | Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems |
| title_full_unstemmed | Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems |
| title_short | Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems |
| title_sort | fractional order pid controller based neural network algorithm for lfc in multi area power systems |
| topic | fractional‐order PID controller grid stability multi‐area power systems neural network algorithm optimization techniques |
| url | https://doi.org/10.1002/eng2.70028 |
| work_keys_str_mv | AT alimelrifaie fractionalorderpidcontrollerbasedneuralnetworkalgorithmforlfcinmultiareapowersystems AT slimabid fractionalorderpidcontrollerbasedneuralnetworkalgorithmforlfcinmultiareapowersystems AT ahmedrginidi fractionalorderpidcontrollerbasedneuralnetworkalgorithmforlfcinmultiareapowersystems AT abdullahmshaheen fractionalorderpidcontrollerbasedneuralnetworkalgorithmforlfcinmultiareapowersystems |