Neuro-Wavelet Based Critical Firing Angle Determination of Phase Controlled DC Motor Drive

Bu çalışmada Dalgacık Sinir Ağı (DSA) yaklaşımı ile üç fazlı kontrollü doğrultucu ile beslenen DA motorlar için kritik tetikleme açısının belirlenmesi önerilmiştir. İlk olarak farklı çalışma durumlarında kritik tetikleme açıları hesaplanmıştır. Sonrasında DSA bu veriler ile eğitilmiştir. Herhangi bir çalışma durumu için kritik tetikleme açısı DSA’ dan üretilmektedir. Benzetim çalışması ile önerilen yöntemin etkinliğini belirlenmiştir. Sürücü sistemin benzetim sonuçları, kritik tetikleme açısının DSA ile kesin bir şekilde hesaplanacağını göstermektedir.

Anahtar Kelimeler:

Neuro-wavelet ağlar, Faz kontrollü DC motor sürücüsü, Kritik tetikleme açısı, Süreksiz akım çalışma

Neuro-Wavelet Based Critical Firing Angle Determination of Phase Controlled DC Motor Drive

In this work, a Neuro-Wavelet Network (NWN) based method is proposed to calculate the critical triggering angle of DC motor fed by the three phase controlled rectifier. Firstly, the critical triggering angles for DC motor drive system are computed at different operation conditions. Afterwards, the NWN is trained with this data. The critical triggering angle is derived from NWN for any operation condition. The several simulation examples have been given to illustrate the performance and effectiveness of the proposed method. The simulation results of the drive system show that the critical firing angle is determined precisely with the NWN.

Keywords:

Neuro-Wavelet Networks, Phase Controlled DC Motor Drive, Critical Triggering Angle, Discontinuous Current Conduction,

PDF

___

Abulafya, N. 1995. Neural Networks for System Identification and Control. Ph.D. Thesis, Imperial College of Science, Technology and Medicine, University of London.
Azman, M.A.H., Aris, J.M. , Hussain, Z., Samat, A.A.A., Nazelan, A.M. 2017. A comparative study of fuzzy logic controller and artificial neural network in speed control of separately excited DC motor. 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE).
Bilgin, M.Z. 2008. Denetimli Doğrultucu ile Beslenen DC Motor için Kritik Tetikleme Açısının Yapay Sinir Ağı Yardımı ile Belirlenmesi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi 30.Yıl Sempozyumu. 16-17 Ekim, Adana, Turkey.
Dmytro, K., Ivan, T., Andriy, Z., Vasyl, T. 2018. Model of the regional energy efficiency analysis based on the artificial neural networks. XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH).
Guney, E., Dursun, M., Demir, M. 2017. Artificial neural network based real time speed control of a linear tubular permanent magnet direct current motor. International Conference on Control, Automation and Diagnosis (ICCAD).
Guru, B.S., Hızıroğlu, H.R. 2001. Electric Machinery and Transformers. Oxford Univ. Press.
Hafidz, I. Nofi P.E., Anggriawan, D.O., Priyadi, A., Pumomo, M.H. 2017. Neuro wavelet algortihm for detecting high impedance faults in extra high voltage transmission systems. 2nd International Conference Sustainable and Renewable Energy Engineering (ICSREE).
Isa, H., Elyza, N., Dimas, O.A., Ardyono, P., Mauridhi, H.P. 2017. Neuro wavelet algortihm for detecting high impedance faults in extra high voltage transmission systems. 2nd International Conference Sustainable and Renewable Energy Engineering (ICSREE).
Jiang, C., Chau, K., Liu, C., Han, W. 2017. Time-division multiplexing wireless power transfer for separately excited DC motor drives. IEEE Inter. Magnetics Conf. (INTERMAG).
Joshi, M.C., Samanta, S., Srungavarapu, G. 2017. Battery ultracapacitor based DC motor drive for electric vehicles. nIEEE Region 10 Symposium (TENSYMP).
Karas, P., Kozák, Š. 2018. Highly nonlinear process model using optimal artificial neural network. Cybernetics & Informatics (K&I), 1 – 7 Khan, M.A.S.K., Rahman, M.A. 2010. A Novel Neuro-Wavelet-Based Self Tuned Wavelet Controller for IPM Motor Drives. IEEE Transactions on Industry Applications, 46(3), 1194-1203.
Khosravi, M., Heshmatian, S., Khaburi, D.A., Garcia, C., Rodriguez, J. 2017. A novel hybrid model-based MPPT algorithm based on artificial neural networks for photovoltaic applications. IEEE Southern Power Electronics Conference (SPEC).
Kumar, V.S., Prasad J., Narasimhan, V.L., Ravi, S. 2017. Application of artificial neural networks for prediction of solar radiation for Botswana. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).
Kundu, S., Chatterjee, D., Chakrabarty, K. 2017. Effect of smoothing reactor on the performance of a PWM chopper fed Dc motor drive. IEEE Calcutta Conference (CALCON).
Kurnia, D.W., Kautsar, S., Etikasari, B., Khafidurrohman, A. 2017. A control scheme for typist robot using Artificial Neural Network. International Conference on Sustainable Information Engineering and Technology (SIET).
Krishnan, R. 2001. Electric Motor Drives. Prentice Hall.
Nahavandi, R., Asadi M., Vazini, H.H. 2018. 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).
Naresh, M., Triphati, R.K. 2016. Power flow control and implementation of PFC rectifier for DC motor drive. IEEE 7th Power India International Conference (PIICON).
Lin, C.M., Hung, K.N., Hsu, C.F. 2007. Adaptive Neuro-Wavelet Control for Switching Power Supplies. IEEE Transactions on Power Electronics, 22(1), 87-95.
Lin, F.J., Wai, R.J., Huang, P.K. 2004. Two-axis Motion Control System Using Wavelet Neural Network for Ultrasonic Motor Drives. IEE Proceedings-Electric Power Applications, 151(5), 613-621.
Lin, C.L., Shieh, N.C. and Tung, P.C. 2002. Robust wavelet neuro control for linear brushless motors. IEEE Transactions on Aerospace and Electronic Systems , 38(3), 918–932.
Omidvar, O., Elliott, D.L. 1997. Neural Systems for Control. New York: Academic Press.
Narendra, K.S., Campagna, D.P. 1990. Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks, 1(1).
Parasuraman, K., Elshorbagy, A. 2005. Wavelet Networks: An Alternative to Classical Neural Networks. Proceedings of International Joint Conference on Neural Networks. Montreal, Canada,.
Popov, A.V., Sayarkin, K.S., Zhilenkov, A.A. 2018. Analysis of perspective models of artificial neural networks for control of robotic objects. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)
Prema, V., Jnaneswa, B.S., Badarish, C.A. 2015. Novel training strategies for wavelet-neuro models for wind speed prediction. TENCON 2015 Conference.
Rabiah, B., Saad, D. 2016. Type-II neuro fuzzy wavelet control for power system stability enhancement using STATCOM. 19th International Multi-Topic Conference (INMIC).
Singh, S., Swain, S.C., Dash, R., Roy P. 2017. Current control strategies for SPV grid interconnection based on artificial neural network. Innovations in Power and Advanced Computing Technologies (i-PACT)
Son, T., Nguyen, Phi H.P. 2017. 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)
Sridhar, S., K. Uma, R., Sukrutha, J. 2015. Identification of PQ disturbances and degree of loading in induction motor using neuro-wavelets. TENCON 2015 IEEE Reg.10 Conf.
Verma, M., Singh, Agrawal K.K. 2017. Investigation of multiple models of artificial neural networks”. International Conference on Intelligent Sustainable Systems (ICISS), 1062- 1067.