Abstract
This paper presents an artificial neural network (ANN) based direct torque control (DTC) drive for three-level VSI. Traditional look-up table (LUT)-based DTC algorithms have high output torque ripple and flux ripple. Additionally, CDTC is difficult to practically implement in multi-phase or multi-level systems due to exponential increases in memory requirements. The proposed ANN-based approach offers better generalization, reduced memory usage, and lower computational complexity. The presented ANN-based DTC eliminates hysteresis controllers, leading to improved control of torque and flux and better transient and steady-state response. The proposed ANN-based DTC is verified through simulations and experimental tests. Comparative results are presented.
| Original language | British English |
|---|---|
| Title of host publication | IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781665464543 |
| DOIs | |
| State | Published - 2024 |
| Event | 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 - Chicago, United States Duration: 3 Nov 2024 → 6 Nov 2024 |
Publication series
| Name | IECON Proceedings (Industrial Electronics Conference) |
|---|---|
| ISSN (Print) | 2162-4704 |
| ISSN (Electronic) | 2577-1647 |
Conference
| Conference | 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 |
|---|---|
| Country/Territory | United States |
| City | Chicago |
| Period | 3/11/24 → 6/11/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- Artificial neural network
- direct torque control
- electric vehicle
- induction motor
- voltage source inverter
Fingerprint
Dive into the research topics of 'ANN-Based DTC for Three level VSI Powered Induction Motor'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver