Artificial neural estimator and controller for Field Oriented Control of three-phase I.M.
Автор: Lina J. Rashad, Fadhil A. Hassan
Статья в выпуске: 6 vol.11, 2019 года.
Speed control for an I.M is a few what complex strategies; the complexity is regularly increasing in line with the required system achievement. The main forms of control strategies are scalar, direct torque, adaptive, sensorless, and vector or Field Oriented Control (FOC). The FOC method is the most efficient technique in which machine parameters: Rotor flux, unit vector, and electromagnetic torque, usually are estimated by means of using Digital Signal Processing (DSP). The Artificial Neural Network (ANN) becomes an effective tool for controlling nonlinear device in present time. This paper proposes the using of ANN instead of DSP to estimate the machine parameters in order to reduce the hardware complexity and the Electromagnetic Interference (EMI) impact. Also, it presents the PI-NN controller which is based totally on ANN. The systems simulations for both DSP and ANN are depicted. The performance of the ANN-based system gives excellent results: overshot less than 0.5%, rise time 0.514 s, steady state error less than 0.2%, settling time 0.7 s. in conjunction with that of DSP-based performance: overshot about 2%, rise time 0.64 s, steady state error less than 0.4%, settling time 0.75 s.
Field oriented control, neural control, intelligent estimator, vector control of I.M.
Короткий адрес: https://readera.ru/15016600
IDR: 15016600 | DOI: 10.5815/ijisa.2019.06.04
Список литературы Artificial neural estimator and controller for Field Oriented Control of three-phase I.M.
- Bimal K. Bose, Modern Power Electronic and AC Drives, Prentice Hall, 2002.
- R. Gunabalan, P. Sanjeevikumar, F. Blaabjerg, O. Ojo and V. Subbiah, “Analysis and Implementation of Parallel Connected Two-Induction Motor Single-Inverter Drive by Direct Vector Control for Industrial Application,” in IEEE Trans. on Power Electronics, vol. 30, No. 12, pp. 6472-6475, 2015.
- Y. Song, J. Ma, H. Zhang and N. He, “Digital Implementation of Neural Network Inverse Control for Induction Motor Based on DSP,” 2nd International Conference on Future Computer and Communication, Wuha, pp. V1-174-V1-178, 2010. doi:10.1109/ICFCC.2010.5497810
- P. Girovsky, J. Timko, J. Zilková and V. Fedák, “Neural Estimators for Shaft Sensorless FOC Control of Induction Motor,” Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC, Ohrid, 2010, pp. T7-1-T7-5, 2010. doi:10.1109/EPEPEMC.2010.5606907
- F. Lima, W. Kaiser, I. N. da Silva and A. A. de Oliveira, “Speed Neuro-Fuzzy Estimator for Sensorless Indirect Flux Oriented Induction Motor Drive,” IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, pp. 2926-2931, 2010. doi:10.1109/IECON.2010.5675063
- C. Venugopal, “ANFIS based Field Oriented Control for Matrix Converter fed Induction Motor,” IEEE International Conference on Power and Energy, Kuala Lumpur, pp. 74-78, 2010. doi:10.1109/PECON.2010.5697560
- A. Iqbal and M. R. Khan, “Sensorless Control of A Vector Controlled Three-Phase Induction Motor Drive Using Artificial Neural Network,” Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power India, New Delhi, pp. 1-5, 2010. doi: 10.1109/PEDES.2010.5712474
- M. A. Rafiq, M. Habibullah and B. C. Ghosh, “Artificial Neural Network Based Speed Tracking of A Field Oriented Induction Motor Drive,” 7th International Conference on Electrical and Computer Engineering, Dhaka, pp. 315-318, 2012. doi: 10.1109/ICECE.2012.6471549
- S. Hussain and M. A. Bazaz, “ANFIS Implementation on A Three Phase Vector Controlled Induction Motor with Efficiency Optimization,” International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), Mumbai, pp. 391-396, 2014. doi: 10.1109/CSCITA.2014.6839293
- H. Zhang, G. Liu, L. Qu and Y. Jiang, “A Neural Network Left-Inversion Flux Estimation for Induction Motor Filed-Oriented Control,” International Joint Conference on Neural Networks (IJCNN), Beijing, pp. 1310-1313, 2014. doi:10.1109/IJCNN.2014.6889578
- F. Lftisi, G. H. George, A. Aktaibi, C. B. Butt and M. A. Rahman, “Artificial Neural Network Based Speed Controller for Induction Motors,” IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, pp. 2708-2713, 2016. doi:10.1109/IECON.20 16.7793117
- M. R. Hazari, E. Jahan, M. A. Mannan and J. Tamura, “Artificial Neural Network Based Speed Control of An SPWM-VSI Fed Induction Motor With Considering Core Loss and Stray Load Losses,” 2016 19th International Conference on Electrical Machines and Systems (ICEMS), Chiba, pp. 1-6, 2016.
- S. T. Nguyen, P. H. Pham, T. V. Pham, H. X. Ha, C. T. Nguyen and P. C. Do, “A Sensorless Three-Phase Induction Motor Drive Using Indirect Field Oriented Control and Artificial Neural Network,” 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, pp. 1454-1459, 2017. doi:10.1109/ICIEA.20 17.8283068
- R. Nahavandi, M. Asadi, H. Vazini and H. Moghbeli, “Improving Performance of Sensorless Vector Control Using Artificial Neural Network Against Parametric Uncertainty,” IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), Doha, pp. 1-6, 2018. doi: 10.1109/CPE.2018.8372511
- K. Wang, Y. Li, Q. Ge and L. Shi, “An Improved Indirect Field-Oriented Control Scheme for Linear Induction Motor Traction Drives,” in IEEE Trans. on Industrial Electronics, vol. 65, No. 12: pp. 9928-9937, 2018. doi: 10.1109/TIE.2018.2815940
- H. Sira-Ramírez, F. González-Montañez, J. A. Cortés-Romero and A. Luviano-Juárez, “A Robust Linear Field-Oriented Voltage Control for the Induction Motor: Experimental Results,” in IEEE Trans. on Industrial Electronics, vol. 60, No. 8: pp. 3025-3033, 2013. doi:10.1109/TIE.2012.2201430
- G. C. Konstantopoulos, A. T. Alexandridis and E. D. Mitronikas, “Bounded Nonlinear Stabilizing Speed Regulators for VSI-Fed Induction Motors in Field-Oriented Operation,” in IEEE Trans. on Control Systems Technology, vol. 22, No. 3: pp. 1112-1121, 2014. doi: 10.1109/TCST.2013.2271256
- X. Fu and S. Li, “Control of Single-Phase Grid-Connected Converters With LCL Filters Using Recurrent Neural Network and Conventional Control Methods,” in IEEE Trans. on Power Electronics, vol. 31, No. 7: pp. 5354-5364, 2016.
- T. Wang, H. Gao and J. Qiu, “A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multi-rate Networked Industrial Process Control,” in IEEE Trans. on Neural Networks and Learning Systems, vol. 27, No. 2: pp. 416-425, 2016. doi:10.1109/TNNLS.2015.2411671
- Ahmed S. Al-Araji, Ahmed I. Abdulkareem, “A Nonlinear Neural Controller Design for the Single Axis Magnetic Ball Levitation System Based on Slice Genetic Algorithm,” Engineering And Technology Journal, vol. 34, part (A), 2012.
- Shekhar F. Lilhare, Dr. N. G. Bawane, “Artificial Neural Network Based Control Strategies for Paddy Drying Process,” International Journal of Information Technology and Computer Science, 2014, 11, pp. 28-35. doi:10.5815/ijitcs.2014.11.04.
- Alireza Sahab, Masoud Taleb Ziabari, “Intelligent Controller for Synchronization New Three Dimensional Chaotic System,” International Journal of Modern Education and Computer Science, 2014, 7, pp. 40-46. doi:10.5815/ijmecs.2014.07.06