A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation
<p>This paper presents a comprehensive evaluation of the effect of quasi oppositional - based learning method utilization in output tracking control through a swarm-based multivariable Proportional-Integral-Derivative (SMPID) controller, which is tuned by a novel performance index based on the...
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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| _version_ | 1864513560298651648 |
|---|---|
| author | Iman M. Hosseini Naveh (16891482) |
| author2 | Elyas Rakhshani (16891485) Hasan Mehrjerdi (16869957) Mohamed A. Elsaharty (16891488) |
| author2_role | author author author |
| author_facet | Iman M. Hosseini Naveh (16891482) Elyas Rakhshani (16891485) Hasan Mehrjerdi (16869957) Mohamed A. Elsaharty (16891488) |
| author_role | author |
| dc.creator.none.fl_str_mv | Iman M. Hosseini Naveh (16891482) Elyas Rakhshani (16891485) Hasan Mehrjerdi (16869957) Mohamed A. Elsaharty (16891488) |
| dc.date.none.fl_str_mv | 2021-05-17T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3080704 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Quasi-Oppositional_Method_for_Output_Tracking_Control_by_Swarm-Based_MPID_Controller_on_AC_HVDC_Interconnected_Systems_With_Virtual_Inertia_Emulation/24042450 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Distributed computing and systems software Optimization HVDC transmission Power system stability Emulation Interconnected systems MIMO communication Tuning Frequency control Virtual inertia Quasi oppositional-based learning method (QO-BL) Grey wolf optimization (GWO) algorithm Grasshopper algorithm (GOA) Artificial fish swarm algorithm (AFSA) Artificial bee colony (ABC) Particle swarm optimization (PSO) Quasi oppositional-based GWO (QO-GWO) Quasi oppositional-based GOA (QO-GOA) Quasi oppositional-based AFSA (QO-AFSA) Quasi oppositional-based PSO (QO-PSO) Multivariable proportional–integral–derivative (MPID) controller Conventional tuned MPID (C-MPID) Swarm–based MPID controller (SMPID) Swarm–based optimization algorithms (SBOAs) AC/HVDC interconnected system with energy storage systems (AC/HVDC with ESS) |
| dc.title.none.fl_str_mv | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This paper presents a comprehensive evaluation of the effect of quasi oppositional - based learning method utilization in output tracking control through a swarm-based multivariable Proportional-Integral-Derivative (SMPID) controller, which is tuned by a novel performance index based on the step response characteristics in multi-input multi-output (MIMO) system. The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). These methods are used in the tuning process of multivariable PID (MPID) controller for output tracking control of an interconnected AC/DC system with virtual inertia emulation-based HVDC capabilities. The virtual inertia-based HVDC model, which is using a derivative technique, is attached for enhancing the system frequency dynamics with fast power injection during the contingency. The potential possibility for achieving a suitable assessment about the velocity reaction, the flexibility response, and the accuracy of the tracking process is provided by four different scenarios which are operated by step load changes as essential inputs in AC/HVDC interconnected MIMO system. Also the proposed fitness function, as deviation characteristics of the step response in MIMO transfer function in virtual inertia emulation based HVDC model, is compared with integral time absolute error (ITAE), as the standard performance index in the optimization process. The results are compared with the conventional tuned MPID (C - MPID) controller using MATLAB software. The obtained analysis emphasizes how the tuned SMPID can significantly increase the capability of tracking control on the proposed AC/HVDC interconnected model.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3080704" target="_blank">https://dx.doi.org/10.1109/access.2021.3080704</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ec93c4df97aead7d92e6e9b8df469062 |
| identifier_str_mv | 10.1109/access.2021.3080704 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042450 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia EmulationIman M. Hosseini Naveh (16891482)Elyas Rakhshani (16891485)Hasan Mehrjerdi (16869957)Mohamed A. Elsaharty (16891488)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringInformation and computing sciencesDistributed computing and systems softwareOptimizationHVDC transmissionPower system stabilityEmulationInterconnected systemsMIMO communicationTuningFrequency controlVirtual inertiaQuasi oppositional-based learning method (QO-BL)Grey wolf optimization (GWO) algorithmGrasshopper algorithm (GOA)Artificial fish swarm algorithm (AFSA)Artificial bee colony (ABC)Particle swarm optimization (PSO)Quasi oppositional-based GWO (QO-GWO)Quasi oppositional-based GOA (QO-GOA)Quasi oppositional-based AFSA (QO-AFSA)Quasi oppositional-based PSO (QO-PSO)Multivariable proportional–integral–derivative (MPID) controllerConventional tuned MPID (C-MPID)Swarm–based MPID controller (SMPID)Swarm–based optimization algorithms (SBOAs)AC/HVDC interconnected system with energy storage systems (AC/HVDC with ESS)<p>This paper presents a comprehensive evaluation of the effect of quasi oppositional - based learning method utilization in output tracking control through a swarm-based multivariable Proportional-Integral-Derivative (SMPID) controller, which is tuned by a novel performance index based on the step response characteristics in multi-input multi-output (MIMO) system. The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). These methods are used in the tuning process of multivariable PID (MPID) controller for output tracking control of an interconnected AC/DC system with virtual inertia emulation-based HVDC capabilities. The virtual inertia-based HVDC model, which is using a derivative technique, is attached for enhancing the system frequency dynamics with fast power injection during the contingency. The potential possibility for achieving a suitable assessment about the velocity reaction, the flexibility response, and the accuracy of the tracking process is provided by four different scenarios which are operated by step load changes as essential inputs in AC/HVDC interconnected MIMO system. Also the proposed fitness function, as deviation characteristics of the step response in MIMO transfer function in virtual inertia emulation based HVDC model, is compared with integral time absolute error (ITAE), as the standard performance index in the optimization process. The results are compared with the conventional tuned MPID (C - MPID) controller using MATLAB software. The obtained analysis emphasizes how the tuned SMPID can significantly increase the capability of tracking control on the proposed AC/HVDC interconnected model.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3080704" target="_blank">https://dx.doi.org/10.1109/access.2021.3080704</a></p>2021-05-17T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3080704https://figshare.com/articles/journal_contribution/A_Quasi-Oppositional_Method_for_Output_Tracking_Control_by_Swarm-Based_MPID_Controller_on_AC_HVDC_Interconnected_Systems_With_Virtual_Inertia_Emulation/24042450CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240424502021-05-17T00:00:00Z |
| spellingShingle | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation Iman M. Hosseini Naveh (16891482) Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Distributed computing and systems software Optimization HVDC transmission Power system stability Emulation Interconnected systems MIMO communication Tuning Frequency control Virtual inertia Quasi oppositional-based learning method (QO-BL) Grey wolf optimization (GWO) algorithm Grasshopper algorithm (GOA) Artificial fish swarm algorithm (AFSA) Artificial bee colony (ABC) Particle swarm optimization (PSO) Quasi oppositional-based GWO (QO-GWO) Quasi oppositional-based GOA (QO-GOA) Quasi oppositional-based AFSA (QO-AFSA) Quasi oppositional-based PSO (QO-PSO) Multivariable proportional–integral–derivative (MPID) controller Conventional tuned MPID (C-MPID) Swarm–based MPID controller (SMPID) Swarm–based optimization algorithms (SBOAs) AC/HVDC interconnected system with energy storage systems (AC/HVDC with ESS) |
| status_str | publishedVersion |
| title | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| title_full | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| title_fullStr | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| title_full_unstemmed | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| title_short | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| title_sort | A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Distributed computing and systems software Optimization HVDC transmission Power system stability Emulation Interconnected systems MIMO communication Tuning Frequency control Virtual inertia Quasi oppositional-based learning method (QO-BL) Grey wolf optimization (GWO) algorithm Grasshopper algorithm (GOA) Artificial fish swarm algorithm (AFSA) Artificial bee colony (ABC) Particle swarm optimization (PSO) Quasi oppositional-based GWO (QO-GWO) Quasi oppositional-based GOA (QO-GOA) Quasi oppositional-based AFSA (QO-AFSA) Quasi oppositional-based PSO (QO-PSO) Multivariable proportional–integral–derivative (MPID) controller Conventional tuned MPID (C-MPID) Swarm–based MPID controller (SMPID) Swarm–based optimization algorithms (SBOAs) AC/HVDC interconnected system with energy storage systems (AC/HVDC with ESS) |