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A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator
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 Title & Authors
A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator
Wang, Chao; Liu, Xiao; Liu, Hui; Chen, Zhe;
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 Abstract
Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.
 Keywords
Extreme learning machine;Fault diagnostics;Finite element analysis;Switched reluctance generator;
 Language
English
 Cited by
 References
1.
D. A. Torrey, “Variable-reluctance generators in wind-energy systems,” in Proc. IEEE PESC’93, 1993, pp. 561-567.

2.
R. Cardenas, W. F. Ray, and G. M. Asher, “Switched reluctance generators for wind energy applications,” in Proc. IEEE PESC’95, 1995, pp. 559-564.

3.
K. Park and Z. Chen, “Self-tuning fuzzy logic control of a switched reluctance generator for wind energy applications,” in Proc. IEEE 3rd Int. Symp. Power Electron. Distrib. Gener. Syst., 2012, pp. 357-363.

4.
R. Cardenas, R. Pena, M. Perez, J. Clare, G. Asher, and P. Wheeler, “Control of a switched reluctance generator for variable-speed wind energy applications,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 781-791, Dec. 2005. crossref(new window)

5.
E. Echenique, J. Dixon, R. Cardenas, and R. Pena, “Sensorless control for a switched reluctance wind generator, based on current slopes and neural networks,” IEEE Trans. Ind. Electron., vol. 56, no. 3, pp. 817-825, Mar. 2009. crossref(new window)

6.
S. Mendez, A. Martinez, W. Millan, C. E. Montano, and F. Perez-Cebolla, “Design, Characterization, and Validation of a 1-kW AC Self-Excited Switched Reluctance Generator,” IEEE Trans. Ind. Electron., vol. 61, no. 2, pp. 846-855, Feb. 2014. crossref(new window)

7.
D. A. Torrey, “Switched reluctance generators and their control,” IEEE Trans. Ind. Electron., vol. 49, no. 1, pp. 3-14, Feb. 2002. crossref(new window)

8.
X. Liu, K. Park and Z. Chen, “A Novel Excitation Assistance Switched Reluctance Wind Power Generator,” IEEE Trans. on Magn., vol. 50, no. 11, pp. 1-4, Nov. 2014.

9.
H. Chen and S. Lu, “Fault diagnosis digital method for power transistors in power converters of switched reluctance motors,” IEEE Trans. Ind. Electron., vol. 60, no. 2, pp. 749-763, 2013. crossref(new window)

10.
S. Gopalakrishnan, A. M. Omekanda, and B. Lequesne, “Classification and Remediation of Electrical Faults in the Switched Reluctance Drive”, IEEE Trans. Ind. Appl., vol. 42, no. 2, 2006, pp.479-486. crossref(new window)

11.
B. Schinnerl and D. Gerling, “Analysis of winding failure of switched reluctance motors,” in Proc. IEEE IEMDC’09, 2009, pp. 738-743.

12.
J. F. Marques, J. O. Estima, N. S. Gameiro, and A. J. M. Cardoso, “A New Diagnostic Technique for Real-Time Diagnosis of Power Converter Faults in Switched Reluctance Motor Drives,” IEEE Trans. Ind. Appl., vol. 50, no. 3, pp. 1854-1860, May./Jun. 2014. crossref(new window)

13.
H. Torkaman and E. Afjei, “Comprehensive detection of eccentricity fault in switched reluctance machines using high frequency pulse injection,” IEEE Trans. Power Electron., vol. 28, no. 3, pp.1382 -1390, 2013. crossref(new window)

14.
J. Cai, Z. Q. Deng, and R. G. Hu, “Position Signal Faults Diagnosis and Control for Switched Reluctance Motor,” IEEE Trans. Magn., vol. 50, no. 9, 2014.

15.
M. Ehsani and B. Fahimi, “Elimination of position sensors in switched reluctance motor drives: State of the art and future trends,” IEEE Trans. Ind. Eletron., vol. 49, no. 1, pp. 40-47, Feb. 2002. crossref(new window)

16.
I. H. Al-Bahadly, “Examination of a sensorless rotor position measurement method for switched reluctance drive”, IEEE Trans. Ind. Eletron., vol. 55, no. 1, pp. 288-295, 2008. crossref(new window)

17.
L. Xu and C. Wang, “Accurate rotor position detection and sensorless control of SRM for super-high speed operation,” IEEE Trans. Power Electron., vol. 17, no. 5, pp. 757-763, 2002. crossref(new window)

18.
J. P. Lyons, S. R. MacMinn, and M. A. Preston, “Flux-current methods for SRM rotor position estimation,” in Proc. Conf. Rec. IEEE-IAS Annu. Meeting, 1991, pp. 482-487.

19.
A. D. Cheok and N. Ertugrul, “High robustness and reliability of fuzzy logic based position estimation for sensorless switched reluctance motor drives,” IEEE Trans. Power Electron., vol. 15, no. 2, pp. 319-334, 2000.

20.
A. D. Cheok and Z. F. Wang, “Fuzzy logic rotor position estimation based switched reluctance motor DSP drive with accuracy enhancement,” IEEE Trans. Power Electron., vol. 20, no. 4, pp. 908-921, 2005. crossref(new window)

21.
N. Ertugrul and A. D. Cheok, “Indirect angle estimation in switched reluctance motor drive using fuzzy logic based motor model,” IEEE Trans. Power Electron., vol. 15, no. 6, pp. 1029-1044, 2000. crossref(new window)

22.
A. D. Cheok and N. Ertugrul, “Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives,” IEEE Trans. Ind. Electron., vol. 46, no. 6, pp. 1207-1224, 2000.

23.
A. D. Cheok and N. Ertugrul, “High robustness of an SR motor angle estimation algorithm using fuzzy predictive filters and heuristic knowledge-based rules,” IEEE Trans. Ind. Electron., vol. 46, no. 5, pp. 904-916, 2000.

24.
E. Mese and D. A. Torrey, “An approach for sensorless position estimation for switched reluctance motors using artificial neural networks,” IEEE Trans. Power Electron., vol. 17, no. 1, pp. 66-75, 2002. crossref(new window)

25.
L. Henriques , L. Rolim , W. Suemitsu , J. Dente and P. Branco, “Development and experimental tests of a simple neuro-fuzzy learning sensorless approach for switched reluctance motors, ” IEEE Trans. Power Electron., vol. 26, no. 11, pp. 3330-3344, 2011. crossref(new window)

26.
S. Paramasivam, S. Vijayan, M. Vasudevan, R. Arumugam, and R. Krishnan, “Real-time verification of AI based rotor position estimation techniques for a 6/4 pole switched reluctance motor drive,” IEEE Trans. Magn., vol. 43, no. 7, pp. 3209-3221, 2007. crossref(new window)

27.
C. A. Hudson, N. S. Lobo, and R. Krishnan, “Sensorless control of single switch-based switched reluctance motor drive using neural network,” IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 321-329, 2008. crossref(new window)

28.
C. Wang, X. Liu, and Z. Chen, “Rotor Position Estimation for Switched Reluctance Wind Generator Using Extreme Learning Machine,” Proc. of WEGAT 2014, 2014, pp. 1-8.

29.
R. Isermann, “Model-based fault-detection and diagnosis-Status and applications,” Annu. Rev. Control, vol. 29, no. 1, pp.71-85, 2005. crossref(new window)

30.
G. Scelba, G. De Donato, F. Bonaccorso, G. Scarcella, F. Giulii Capponi, “Fault Tolerant Rotor Position and Velocity Estimation Using Binary Hall-Effect Sensors for Low Cost Vector Control Drives,” IEEE Trans. Ind. Appl., vol. 50, no. 5, pp. 3403-3413, Sept.-Oct. 2014. crossref(new window)

31.
G. B. Huang, Q. Y. Zhu, and C. K. SiewK, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, nos. 1-3, pp. 489-501, Dec. 2006. crossref(new window)

32.
N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1411-1423, Nov. 2006. crossref(new window)

33.
G. B. Huang, H. M. Zhou, X. J. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. Syst., Man, Cybern., B, Cybern., vol. 42, no. 2, pp. 513-529, Apr. 2012. crossref(new window)

34.
C. Wan, Z. Xu; P. Pinson, Z. Y. Dong, and K. Wong, “Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine”, IEEE Trans. Power Syst., pp. 1033-1044, vol. 29, no. 3, May 2014. crossref(new window)

35.
A. H. Nizar, Z. Y. Dong, and Y. Wang, “Power utility nontechnical loss analysis with extreme learning machine method,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 946-955, Aug. 2008. crossref(new window)

36.
F. Deng and Z. Chen, “Power Control of Permanent Magnet Generator Based Variable Speed Wind Turbines,” in Proc. ICEMS’09, 2009, pp. 1-6.