Soft computing approaches of direct torque control for DFIM Motor's
Conventional Direct Torque Control (DTC) is widely used for torque and speed control in doubly-fed induction machines (DFIM). However, it has notable drawbacks, including high torque and flux ripples, which generate acoustic noise and reduce system performance. To address these limitations, several...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Cleaner Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266679082500014X |
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| author | Zakariae Sakhri El-Houssine Bekkour Badre Bossoufi Nicu Bizon Mishari Metab Almalki Thamer A.H. Alghamdi Mohammed Alenezi |
| author_facet | Zakariae Sakhri El-Houssine Bekkour Badre Bossoufi Nicu Bizon Mishari Metab Almalki Thamer A.H. Alghamdi Mohammed Alenezi |
| author_sort | Zakariae Sakhri |
| collection | DOAJ |
| description | Conventional Direct Torque Control (DTC) is widely used for torque and speed control in doubly-fed induction machines (DFIM). However, it has notable drawbacks, including high torque and flux ripples, which generate acoustic noise and reduce system performance. To address these limitations, several advanced approaches have emerged. This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. Results show that DTC-ANN and DTC-SVM stand out for their ripple reduction performance, making them particularly suitable for applications requiring high precision. Additionally, DTC-FL and DTC-SMC excel in robustness against system parameter variations, a valuable asset for evolving industrial environments. Optimization approaches such as DTC-GA, DTC-ACO, and DTC-RTO contribute to reducing switching losses and improving energy efficiency, a crucial aspect for large-scale applications. Finally, P-DTC offers excellent dynamics and precise speed tracking, making it ideal for rapid response systems. These findings provide valuable insights for researchers and engineers seeking to optimize modern DTC system performance according to the specific needs of their applications. |
| format | Article |
| id | doaj-art-072a96886a02420ca67e97222c7f00b0 |
| institution | Kabale University |
| issn | 2666-7908 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Engineering and Technology |
| spelling | doaj-art-072a96886a02420ca67e97222c7f00b02025-01-26T05:05:09ZengElsevierCleaner Engineering and Technology2666-79082025-02-0124100891Soft computing approaches of direct torque control for DFIM Motor'sZakariae Sakhri0El-Houssine Bekkour1Badre Bossoufi2Nicu Bizon3Mishari Metab Almalki4Thamer A.H. Alghamdi5Mohammed Alenezi6Laboratory of Engineering Modeling and Systems Analysis, SMBA University Fez, Morocco; Corresponding author.Laboratory of Engineering Modeling and Systems Analysis, SMBA University Fez, Morocco; SMARTiLab, Moroccan School of Engineering Sciences (EMSI), Rabat, MoroccoLaboratory of Engineering Modeling and Systems Analysis, SMBA University Fez, Morocco; Corresponding author.Faculty of Electronics, Communication and Computers, University of Pitesti, 110040, Pitesti, RomaniaDepartment of Electrical Engineering, Faculty of Engineering, Al-Baha University, Alaqiq, 65779-7738, Saudi ArabiaWolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK; Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, 65779, Saudi ArabiaWolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK; Corresponding author.Conventional Direct Torque Control (DTC) is widely used for torque and speed control in doubly-fed induction machines (DFIM). However, it has notable drawbacks, including high torque and flux ripples, which generate acoustic noise and reduce system performance. To address these limitations, several advanced approaches have emerged. This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. Results show that DTC-ANN and DTC-SVM stand out for their ripple reduction performance, making them particularly suitable for applications requiring high precision. Additionally, DTC-FL and DTC-SMC excel in robustness against system parameter variations, a valuable asset for evolving industrial environments. Optimization approaches such as DTC-GA, DTC-ACO, and DTC-RTO contribute to reducing switching losses and improving energy efficiency, a crucial aspect for large-scale applications. Finally, P-DTC offers excellent dynamics and precise speed tracking, making it ideal for rapid response systems. These findings provide valuable insights for researchers and engineers seeking to optimize modern DTC system performance according to the specific needs of their applications.http://www.sciencedirect.com/science/article/pii/S266679082500014XDFIMDTCDTC-SVMDTC-FLDTC-ANNDTC-GA |
| spellingShingle | Zakariae Sakhri El-Houssine Bekkour Badre Bossoufi Nicu Bizon Mishari Metab Almalki Thamer A.H. Alghamdi Mohammed Alenezi Soft computing approaches of direct torque control for DFIM Motor's Cleaner Engineering and Technology DFIM DTC DTC-SVM DTC-FL DTC-ANN DTC-GA |
| title | Soft computing approaches of direct torque control for DFIM Motor's |
| title_full | Soft computing approaches of direct torque control for DFIM Motor's |
| title_fullStr | Soft computing approaches of direct torque control for DFIM Motor's |
| title_full_unstemmed | Soft computing approaches of direct torque control for DFIM Motor's |
| title_short | Soft computing approaches of direct torque control for DFIM Motor's |
| title_sort | soft computing approaches of direct torque control for dfim motor s |
| topic | DFIM DTC DTC-SVM DTC-FL DTC-ANN DTC-GA |
| url | http://www.sciencedirect.com/science/article/pii/S266679082500014X |
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