Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller
The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the opti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722017164 |
| _version_ | 1797901883077033984 |
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| author | Sheikh Safiullah Asadur Rahman Shameem Ahmad Lone S.M. Suhail Hussain Taha Selim Ustun |
| author_facet | Sheikh Safiullah Asadur Rahman Shameem Ahmad Lone S.M. Suhail Hussain Taha Selim Ustun |
| author_sort | Sheikh Safiullah |
| collection | DOAJ |
| description | The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly. |
| first_indexed | 2024-04-10T09:08:59Z |
| format | Article |
| id | doaj.art-9924aa7ffdaa45d888bb7a922b0daa58 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:08:59Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-9924aa7ffdaa45d888bb7a922b0daa582023-02-21T05:13:12ZengElsevierEnergy Reports2352-48472022-11-0181218612202Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controllerSheikh Safiullah0Asadur Rahman1Shameem Ahmad Lone2S.M. Suhail Hussain3Taha Selim Ustun4Electrical Engineering Department, National Institute of Technology Srinagar, J&K, IndiaElectrical Engineering Department, National Institute of Technology Srinagar, J&K, India; Corresponding authors.Electrical Engineering Department, National Institute of Technology Srinagar, J&K, IndiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaFukushima Renewable Energy Institute, AIST (FREA), Koriyama, Japan; Corresponding authors.The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly.http://www.sciencedirect.com/science/article/pii/S2352484722017164Power system controlArtificial neural network (ANN)Active-disturbance-rejection-controller (ADRC)Electric vehicle (EV)Solar power generationElectrical wind power distribution |
| spellingShingle | Sheikh Safiullah Asadur Rahman Shameem Ahmad Lone S.M. Suhail Hussain Taha Selim Ustun Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller Energy Reports Power system control Artificial neural network (ANN) Active-disturbance-rejection-controller (ADRC) Electric vehicle (EV) Solar power generation Electrical wind power distribution |
| title | Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller |
| title_full | Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller |
| title_fullStr | Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller |
| title_full_unstemmed | Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller |
| title_short | Robust frequency–voltage stabilization scheme for multi-area power systems incorporated with EVs and renewable generations using AI based modified disturbance rejection controller |
| title_sort | robust frequency voltage stabilization scheme for multi area power systems incorporated with evs and renewable generations using ai based modified disturbance rejection controller |
| topic | Power system control Artificial neural network (ANN) Active-disturbance-rejection-controller (ADRC) Electric vehicle (EV) Solar power generation Electrical wind power distribution |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722017164 |
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