Mandar CHAUDHARI, Anandita CHOWDHURY, Gajanan DHOLE

Effect of Flux Barrier Shape on Performance of Single-Phase Line Start Synchronous Reluctance Motor Modified from Single Phase Induction Motor

Single-phase induction motors (SPIM) are extensively used in many industrial, commercial, and household applications. These motors are therefore required to be highly efficient and cost-effective. As the efficiency is inherently compromised for these motors, enhancement in the operating efficiency is of prime importance. This work dealt with the single-phase induction motor for increasing the efficiency by converting it to a line-start synchronous reluctance motor (LS-SynRM). However, the effect of change in the barrier shapes, such as line angle or arc shape, and the introduction of a hyperbolic barrier in the rotor are considered. The performance is determined by simulations with the FEM Ansys Maxwell software. The 0.5HP SPIM is considered for analysis. The detailed comparative parametric sensitivity analysis is carried out on rotor parameters such as barrier shape, barrier position, barrier width, rib width, and pole arc to pole pitch ratio. The results demonstrate that the efficiency of the motor is considerably dependent on these prescribed parameters, and the optimum performance is noted for the barrier position of 25 mm and barrier width of 2 mm for the designated cases. This paper also provides a detailed comparative statistical analysis of torque pulsations.

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1. Global Wind Energy Council, “GWEC global wind report,” Wind Energy Technol., 2018. Available online: https://gwec.net/wp-content/uploads/2019/04/GWEC-Global-Wind-Report-2018.pdf (accessed on 1 April 2021).
2. Turkish Wind Energy Association, Turkish Wind Energy Statistic Report. Ankara, Turkey: Turkish Wind Energy Association, 2018.
3. Z. Su, J. Wang, H. Lu and G. Zhao, “A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting,” Energy Conversion and Management, vol. 85, pp. 443–452, 2014.
4. R. Ata, “An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines,” Istanbul University – Journal of Electrical and Electronics Engineering, vol. 9, no. 1, pp. 905–912, 2009.
5. W. Zhang, Z. Qu, K. Zhang, W. Mao, Y. Ma and X. Fan, “A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting,” Energy Conversion and Management, vol. 136, pp. 439–451, 2017.
6. H. Liu, C. Yu, H. Wu, Z. Duan and G. Yan, “A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting,” Energy, vol. 202, 2020.
7. Y. Zhang, S. Gao, M. Ban and Y. Sun, “A method based on Lorenz disturbance and variational mode decomposition for wind speed prediction,” Advances in Electrical and Computer Engineering, vol. 19, no. 2, pp. 3–12, 2019.
8. H. Liu, H. Q. Tian, D. F. Pan and Y. F. Li, “Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks,” Applied Energy, vol. 107, pp. 191–208, 2013.
9. H. Liu, X. Mi and Y. Li, “An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm,” Renewable Energy, vol. 123, pp. 694–705, 2018.
10. Z. Guo, W. Zhao, H. Lu and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,” Renewable Energy, vol. 37, no. 1, pp. 241–249, 2012 .
11. J. Hu, J. Wang and G. Zeng, “A hybrid forecasting approach applied to wind speed time series,” Renewable Energy, vol. 60, pp. 185–194, 2013 .
12. H. Liu, C. Chen, H. Q. Tian and Y. F. Li, “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks,” Renewable Energy, vol. 48, pp. 545–556, 2012.
13. M. Dehghani, A. Seifi and H. Riahi-Madvar, “Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization,” Journal of Hydrology, vol. 576, pp. 698–725, 2019.
14. M. Rezakazemi, A. Dashti, M. Asghari and S. Shirazian, “H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS,” International Journal of Hydrogen Energy, vol. 42, no. 22, 15211–15225, 2017.
15. M. O. Gulbahce and D. A. Kocabas, “High-speed solid rotor induction motor design with improved efficiency and decreased harmonic effect,” IET Electric Power Applications, vol. 12, no. 8, pp. 1126–1133, 2018.
16. C. Wu, J. Wang, X. Chen, P. Du and W. Yang, “A novel hybrid system based on multi-objective optimization for wind speed forecasting,” Renewable Energy, vol. 146, pp. 149–165, 2020.
17. E. Dokur, M. Kurban and S. Ceyhan, “Hybrid model for short term wind speed forecasting using empirical mode decomposition and artificial neural network,”, in ELECO9th International Conference on Electrical and Electronics Engineering, Vol. 2016, 2015 - , pp. 420–423.
18. J. -S. R. Jang and R. Jang, “ANFIS : Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, 665–685, 1993.
19. D. Karaboga, “An idea based on Honey Bee Swarm for Numerical Optimization,” Tech. Rep. TR06, Erciyes Univ., Vol. TR06, 2005, p. 10.
20. D. Karaboga and B. Basturk, “Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems,”, in Lecture Notes in Computer Science, vol. 4529, pp. 789–798, 2007.
21. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008.
22. R. Storn and K. Price, “Differential Evolution - A simple evolution strategy for fast optimization,” Journal of Global Optimization, vol. 11. 341–359 1997.
23. R. Storn and K. Price, “Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
24. K. Price, D. Corne, M. Dorigo and F. Glover, “An introduction to differential evolution,” in New Ideas in Optimization, London, UK: McGraw-Hill, 1999.
25. U. Yüzgeç, “Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker’s yeast fermentation process,” ISA Transactions, vol. 49, no. 1, pp. 167–176, 2010.
26. D. Goldberg, Genetic algorithms in search, optimization, and machine learning. Addison Wesley, 1989. You can access in this paper at https://www.sciencedirect.com/science/article/pii/ S0960148118302362
27. D. E. Goldberg and K. Deb, “A comparative analysis of selection schemes used in genetic algorithms,”, Foundations of Genetic Algorithms, pp. 69–93, 1991.
28. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” Proceedings of the International Symposium on Micro Machine And Human Science, pp. 39–43, 1995.
29. R. Poli, J. Kennedy and T. Blackwell, “Particle swarm optimization: An overview,” Swarm Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
30. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, vol. 1,pp. 81–86, 2001.