지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.12 No.4
- 발행연도
- 2017.7
- 수록면
- 1,548 - 1,555 (8page)
이용수
초록· 키워드
오류제보하기
This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.
목차
Abstract
1. Introduction
2. Non-linear Modeling of SRM
3. Neural Approach for Non-linear Modeling of SRM
4. Results and Discussions
5. Conclusion
References
참고문헌 (36)
참고문헌 신청
Khwaja M. Rahman, and Steven E. Schulz, IEEE Trans. Ind. Appl. 6, 38 (2002).
Saurabh P. Nikam, Vandana Rallabandi, and B. G. Fernandes, IEEE Trans. Ind. Appl. 6, 48 (2012).
Jaehyuch Kim, Yong-hoe Jeong, Yong-Hee Jeon, Jun- Ho Kang, Seunghun Lee, Jang-Yeop Park, J. Magnetics 3,19 (2014).
Heon-Hyeong Lee, Qi Wang, Se-Joo Kim, Woongchul Choi, Geun-Ho Lee, J. Magnetic 3, 17 (2012).
P. Chancharoensook, IEEE Power Electronics and Drive Systems, pp: 770-777 (2009).
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