An adaptive Zhang neural network controller for frequency control of renewable energy integrated system
This paper proposes a Zhang neural network (ZNN) designed self-adaptive proportional-integral-derivative (PID) controller for frequency control of renewable energy integrated systems. The network is formulated to minimize the error function that minimizes the area control error of the integrated sys...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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IEEE
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37831/ |
| _version_ | 1848848711761788928 |
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| author | Irudayaraj, Andrew Xavier Raj Abdul Wahab, Noor Izzri Veerasamy, Veerapandiyan Othman, Mohammad Lutfi Singh, Shailendra Gooi, Hoay Beng |
| author_facet | Irudayaraj, Andrew Xavier Raj Abdul Wahab, Noor Izzri Veerasamy, Veerapandiyan Othman, Mohammad Lutfi Singh, Shailendra Gooi, Hoay Beng |
| author_sort | Irudayaraj, Andrew Xavier Raj |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper proposes a Zhang neural network (ZNN) designed self-adaptive proportional-integral-derivative (PID) controller for frequency control of renewable energy integrated systems. The network is formulated to minimize the error function that minimizes the area control error of the integrated system by optimizing the controller. Initially, the control problem is formulated as an error function in terms of area control error associated with gains of PID controller such as Kp, Ki, and Kd. Then, the gradient equations governing the dynamics of Zhang Gradients (ZG) are derived from the error function. The presented method is simulated in MATLAB/Simulink and the results obtained have shown the ZNN-based PID controller gives a smooth and faster response than simple ZG and Hopfield neural network-based PID controllers. To validate the robustness of the controller, the system is tested in the presence of random load disturbance, and the performance of the proposed controller is more predominant. In the case of consecutive changes in load demand, the values of Kp, Ki, and Kd are adapted with respect to the plant dynamics, demonstrating the self-adaptiveness of the controller. |
| first_indexed | 2025-11-15T09:38:51Z |
| format | Conference or Workshop Item |
| id | upm-37831 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:38:51Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-378312023-11-08T02:21:19Z http://psasir.upm.edu.my/id/eprint/37831/ An adaptive Zhang neural network controller for frequency control of renewable energy integrated system Irudayaraj, Andrew Xavier Raj Abdul Wahab, Noor Izzri Veerasamy, Veerapandiyan Othman, Mohammad Lutfi Singh, Shailendra Gooi, Hoay Beng This paper proposes a Zhang neural network (ZNN) designed self-adaptive proportional-integral-derivative (PID) controller for frequency control of renewable energy integrated systems. The network is formulated to minimize the error function that minimizes the area control error of the integrated system by optimizing the controller. Initially, the control problem is formulated as an error function in terms of area control error associated with gains of PID controller such as Kp, Ki, and Kd. Then, the gradient equations governing the dynamics of Zhang Gradients (ZG) are derived from the error function. The presented method is simulated in MATLAB/Simulink and the results obtained have shown the ZNN-based PID controller gives a smooth and faster response than simple ZG and Hopfield neural network-based PID controllers. To validate the robustness of the controller, the system is tested in the presence of random load disturbance, and the performance of the proposed controller is more predominant. In the case of consecutive changes in load demand, the values of Kp, Ki, and Kd are adapted with respect to the plant dynamics, demonstrating the self-adaptiveness of the controller. IEEE 2022 Conference or Workshop Item PeerReviewed Irudayaraj, Andrew Xavier Raj and Abdul Wahab, Noor Izzri and Veerasamy, Veerapandiyan and Othman, Mohammad Lutfi and Singh, Shailendra and Gooi, Hoay Beng (2022) An adaptive Zhang neural network controller for frequency control of renewable energy integrated system. In: 2022 IEEE 10th Power India International Conference (PIICON), 25-27 Nov. 2022, National Institute of Technology Delhi, India. . https://ieeexplore.ieee.org/document/10045183 10.1109/PIICON56320.2022.10045183 |
| spellingShingle | Irudayaraj, Andrew Xavier Raj Abdul Wahab, Noor Izzri Veerasamy, Veerapandiyan Othman, Mohammad Lutfi Singh, Shailendra Gooi, Hoay Beng An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title | An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title_full | An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title_fullStr | An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title_full_unstemmed | An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title_short | An adaptive Zhang neural network controller for frequency control of renewable energy integrated system |
| title_sort | adaptive zhang neural network controller for frequency control of renewable energy integrated system |
| url | http://psasir.upm.edu.my/id/eprint/37831/ http://psasir.upm.edu.my/id/eprint/37831/ http://psasir.upm.edu.my/id/eprint/37831/ |