NEURAL NETWORK ESTIMATION OF MUTUAL INDUCTANCE VARIATION FOR A SHADED-POLE INDUCTION MOTOR

Year 2018, Volume: 3 Issue: 2, 36 - 45, 01.01.2019

Emre Çelik

Abstract

Shaded-pole
induction motors (SPIMs) are often preferred in small power applications owing
to their ability to work with single-phase power source, simple structure and
low-cost properties. Such motors are within the class easy to manufacture, but
the most difficult to analyze mathematically due to the fact that they have a
variable air gap and elliptical rotating magnetic field, which leads to highly
complex inductance calculations. Considering that the identification accuracy
of phase variables is directly related to the correct knowledge of inductances
in AC machines, the authors of this article attempt to realize a neural network
(NN)-based inductance estimation in-between the stator and rotor, and also
in-between the shading ring (shaded-pole winding) and rotor loop for an
industrial SPIM. For this aim, corresponding inductance measurements are made
first experimentally in terms of each 3.6º electrical position, and as such, a
total of 101 data samples have been collected. %70 of them are considered as
training data to train the NN while the remainder is adopted for testing the
generation capability of NN. Results in comparison with the actual values have
affirmed the excellence performance of the introduced NN in simultaneous
estimation of the concerned two important inductances.

Keywords

Shaded-pole induction motors, shaded-pole, rotor loop, neural network, inductance estimation

References

• Dalcalı, A. (2017). Gölge kutuplu asenkron motorların yeni bir matematiksel modeli ve uzay harmonikli eşdeğer devresi, Doktora tezi, Karabük Üniversitesi, Karabük.
• Dalcalı A, Akbaba A. Detection of the space harmonics of the shaded pole induction motor. Journal of Engineering Research 2017; 5: 95-105.
• Çelik E, Gör H, Öztürk N, Kurt E. Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator. International Journal of Hydrogen Energy 2017; 42: 17692-17699.
• Wang K, Gelgele H, Wang Y, Yuan Q, Fang M. A hybrid intelligent method for modelling the EDM process. International Journal of Machine Tools and Manufacture 2003; 43: 995-999.
• Shabgard MR, Badamchizadeh MA, Ranjbary G, Amini K. Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes. Journal of Manufacturing Systems 2012; 32: 32-39.
• Simoes MG, Bose BK. Application of fuzzy logic in the estimation of power electronic waveforms. In: IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting; 2-8 Oct. 1993; Toronto, Ontario, Canada. pp. 853-861.
• Yilmaz O, Eyercioglu O, Gindy NZ. A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. Journal of Materials Processing Technology 2006; 172: 363-371.
• Rodic D, Gostimirovic M, Kovac P, Radovanovic M, Savkovic B. Comparison of fuzzy logic and neural network for modelling surface roughness in EDM. International Journal of Recent advances in Mechanical Engineering 2014; 3: 69-78.
• Karanayil B, Rahman MF, Grantham C. Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drive. IEEE Transactions on Industrial Electronics 2007; 54: 167-176.
• Hambli R. Prediction of burr height formation in blanking processes using neural network. International Journal of Mechanical Sciences 2002; 44: 2089-2102.
• Kim MH, Simoes MG, Bose BK. Neural network based estimation of power electronic waves. In: 21st Annual Conference on IEEE Industrial Electronics; 6-10 Nov. 1995; Orlando, FL, USA. pp. 353-358.
• Li S, Wunsch DC, O'Hair EA, Giesselmann MG. Using neural networks to estimate wind turbine power generation. IEEE Transactions on Energy Conversion 2001; 16: 276-282.
• Paulson F, Prabhu VV. Back propagation based ANN technique for rotor position estimation of 8/6 switched reluctance motor. In: International Conference on Innovations in Information, Embedded and Communication Systems; 19-20 March 2015; Coimbatore, India. pp. 1-5.
• Chatterjee A, Keyhani A. Neural network estimation of microgrid maximum solar power. IEEE Transactions on Smart Grid 2012; 3: 1860-1866.
• Çelik E, Uzun Y, Kurt E, Öztürk N, Topaloğlu N. A neural network design for the estimation of nonlinear behavior of a magnetically-excited piezoelectric harvester. Journal of Electronic Materials 2018; 47: 4412-4420.
• Çelik E, Çavuşoğlu O, Gürün H, Öztürk N. Estimation of the clearance effect in the blanking process of CuZn30 sheet metal using neural network−A comparative study. International Journal of Informatics Technologies 2018; 11: 187-193.
• Saygın A, Ocak C, Dalcalı A, Çelik E. Optimum rotor design of small PM BLDC motor based on high efficiency criteria. ARPN Journal of Engineering and Applied Sciences 2015; 10: 9127-9132.
• Parasiliti F, Villani M, Castello M. PM brushless DC motor with exterior rotor for high efficiency household appliances. In: International Conference on Electrical Machines; 2-5 Sept. 2014; Berlin, Germany. pp. 623-628.
• Gao Y, Chau KT, Ye S. A novel chaotic-speed single-phase induction motor drive for cooling fans. In: Fortieth IAS Annual Meeting; 2-6 Oct. 2005; Hong Kong, China. pp. 1337-1341.
• Dalcalı A, Akbaba M. Comparison of the performance of bridge and bridgeless shaded pole induction motor using FEM. International Journal of Applied Electromagnetics and Mechanics 2017; 54: 341-350.
• Akcayol MA. Anahtarlamalı relüktans motorun endüktans değı̇şı̇mı̇nı̇n sı̇nı̇rsel-bulanık modellenmesi̇. Politeknik Dergisi 2002; 5: 287-292.