Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System
This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Fractal and Fractional |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3110/7/4/315 |
| _version_ | 1797605406330060800 |
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| author | Guoqiang Zhang Amil Daraz Irfan Ahmed Khan Abdul Basit Muhammad Irshad Khan Mirzat Ullah |
| author_facet | Guoqiang Zhang Amil Daraz Irfan Ahmed Khan Abdul Basit Muhammad Irshad Khan Mirzat Ullah |
| author_sort | Guoqiang Zhang |
| collection | DOAJ |
| description | This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure. |
| first_indexed | 2024-03-11T05:00:39Z |
| format | Article |
| id | doaj.art-f17c31b7dbd14485a29ef07fa39884db |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-11T05:00:39Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-f17c31b7dbd14485a29ef07fa39884db2023-11-17T19:19:27ZengMDPI AGFractal and Fractional2504-31102023-04-017431510.3390/fractalfract7040315Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power SystemGuoqiang Zhang0Amil Daraz1Irfan Ahmed Khan2Abdul Basit3Muhammad Irshad Khan4Mirzat Ullah5School of Information Science and Engineering, NingboTech University, Ningbo 315100, ChinaSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, ChinaDepartment of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Federal Territory of Kuala Lumpur 50603, MalaysiaSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, ChinaCollege of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210000, ChinaGraduate School of Economics and Management, Ural Federal University, Yekaterinburg 620002, RussiaThis work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure.https://www.mdpi.com/2504-3110/7/4/315renewable energy resourcesoptimization techniquesfractional order controllerpower systemload frequency controlheuristic techniques |
| spellingShingle | Guoqiang Zhang Amil Daraz Irfan Ahmed Khan Abdul Basit Muhammad Irshad Khan Mirzat Ullah Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System Fractal and Fractional renewable energy resources optimization techniques fractional order controller power system load frequency control heuristic techniques |
| title | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
| title_full | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
| title_fullStr | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
| title_full_unstemmed | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
| title_short | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
| title_sort | driver training based optimized fractional order pi pdf controller for frequency stabilization of diverse hybrid power system |
| topic | renewable energy resources optimization techniques fractional order controller power system load frequency control heuristic techniques |
| url | https://www.mdpi.com/2504-3110/7/4/315 |
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