Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques
Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Curre...
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AIMS Press
2024-02-01
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| Series: | AIMS Electronics and Electrical Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/electreng.2024002 |
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| author | Shaswat Chirantan Bibhuti Bhusan Pati |
| author_facet | Shaswat Chirantan Bibhuti Bhusan Pati |
| author_sort | Shaswat Chirantan |
| collection | DOAJ |
| description | Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories. |
| format | Article |
| id | doaj-art-f2a54394e62e4d6385ba69775588138f |
| institution | Kabale University |
| issn | 2578-1588 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj-art-f2a54394e62e4d6385ba69775588138f2025-01-24T01:10:21ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882024-02-0181285210.3934/electreng.2024002Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniquesShaswat Chirantan0Bibhuti Bhusan Pati1Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, IndiaDepartment of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, IndiaEmphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories.https://www.aimspress.com/article/doi/10.3934/electreng.2024002predictive current controlmodel predictive controlinduction motorsequential modelant colony optimizationnelder-mead method |
| spellingShingle | Shaswat Chirantan Bibhuti Bhusan Pati Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques AIMS Electronics and Electrical Engineering predictive current control model predictive control induction motor sequential model ant colony optimization nelder-mead method |
| title | Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| title_full | Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| title_fullStr | Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| title_full_unstemmed | Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| title_short | Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| title_sort | torque and d q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques |
| topic | predictive current control model predictive control induction motor sequential model ant colony optimization nelder-mead method |
| url | https://www.aimspress.com/article/doi/10.3934/electreng.2024002 |
| work_keys_str_mv | AT shaswatchirantan torqueanddqaxiscurrentdynamicsofaninverterfedinductionmotordrivethatleveragescomputationalintelligenttechniques AT bibhutibhusanpati torqueanddqaxiscurrentdynamicsofaninverterfedinductionmotordrivethatleveragescomputationalintelligenttechniques |