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Combining a HMM with a Genetic Algorithm for the Fault Diagnosis of Photovoltaic Inverters

  • Zheng, Hong (School of Electrical and Information Engineering, Jiangsu University) ;
  • Wang, Ruoyin (School of Electrical and Information Engineering, Jiangsu University) ;
  • Xu, Wencheng (School of Electrical and Information Engineering, Jiangsu University) ;
  • Wang, Yifan (School of Electrical and Information Engineering, Jiangsu University) ;
  • Zhu, Wen (School of Electrical and Information Engineering, Jiangsu University)
  • Received : 2017.02.09
  • Accepted : 2017.05.08
  • Published : 2017.07.20

Abstract

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

References

  1. S. Peuget, S. Courtine, and J. P. Rognon, "Fault detection and isolation on a PWM inverter by knowledge-based model," IEEE Trans. Ind. Appl., Vol. 34, No. 6, pp. 1318-1326, Nov. 1997.
  2. R. L. de Araujo Ribeiro, C. B. Jacobina, E. R. C. da Silva, and A. M. N. Limam "Fault detection of open-switch damage in voltage-fed PWM motor drive systems," IEEE Trans. Power Electron., Vol. 18, No. 2, pp. 587-593, Mar. 2003.
  3. H. Keskes and A. Braham, "DAG SVM and pitch synchronous wavelet transform for induction motor diagnosis," in Proc. Iet International Conference on Power Electronics, Machines and Drives , pp. 0166-0166, Apr. 2014.
  4. D. J. Chen and Y. Z. Ye, "Open circuit fault diagnosis method for three level inverter based on multi neural network," Transactions of China Electrotechnical Society, Vol. 28, No. 6, pp. 120-126, Jun. 2013.
  5. G. S. Hu, J. Xie, and F. F. Zhu, "Classification of power quality disturbances using wavelet and fuzzy support vector machines," in Proc. International Conference on Machine Learning and Cybernetics IEEE, pp. 3981-3984, Vol. 7, Aug. 2005.
  6. S. Xu, W.X. Huang, Y.W. Hu, W.T. Yu, and Z.Y. Hao, "A novel six phase permanent magnet fault tolerant motor system and its fault diagnosis method," in Proc. Annual meeting of China Power Supply Society, May 2009.
  7. A. Bouzida, O. Touhami, R. Ibtiouen, A. Belouchrani, M. Fadel, and A. Rezzoug, "Fault diagnosis in industrial induction machines through discrete wavelet transform," IEEE Trans. Ind. Electron., Vol. 58, No. 9, pp. 4385-4395, Sep. 2011. https://doi.org/10.1109/TIE.2010.2095391
  8. M. Pineda-Sanchez, M. Riear-Guasp, J.A. Antonino-Daviu, J. Roger-Folch, J. Perez-Cruz, and R. Puche-Panadero, "Diagnosis of induction motor faults in the fractional fourier domain," IEEE Trans. Instrum. Meas. Vol. 59, No, 8, pp. 2065-2075, Aug. 2010. https://doi.org/10.1109/TIM.2009.2031835
  9. Y. F. Yin, J.W. Yang, G.Q. Cai, and D.C. Yao, "Fault Diagnosis of Rolling Bearing Based on Wavelet Packet and Fourier Analysis," in Proc. Computational Aspects of Social Networks, pp. 703-706, Sep. 2010.
  10. Z. M. Wu, "Analog circuit fault diagnosis based on information fusion and extreme learning machine," M.S. Thesis, Hunan University, China, 2011.
  11. Y. P. Bao, J. Zheng, and X.G. Wu, "Speech recognition system based on HMM and genetic neural network," Computer engineering and Science, Vol. 33, No. 4, pp. 139-144, Apr. 2011.
  12. C. L. Zhang, and X. Yue, "Fault Diagnosis of Rotating Machinery Based on Energy Moment and HMM," Key Engineering Materials, Vol. 455, pp. 558-564, Dec. 2010. https://doi.org/10.4028/www.scientific.net/KEM.455.558
  13. H. Ocak, and K.A. Loparo, "A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals." in Proc. Acoustics, Speech, and Signal Processing, Vol. 5, pp. 3141-3144, May. 2001.
  14. X. Yue, "Research on fault diagnosis of complex condition based on HMM," M.S. Thesis, South China University of Technology, China, 2012.
  15. R.F. Han, Principle and application of genetic algorithm, Weapon Industry Press, 2010.
  16. J. J. Fu, "Study on the Fault Diagnosis System of Active Neutral Point Clamped Three Level Inverter Based on Neural Network," M.S. Thesis, China Mining University, China, 2016.
  17. F, F. Xie, "Fault Diagnosis Method Based on Support Vector Machine," M.S. Thesis, Hunan University, China, 2006.
  18. A. Widodo, B.S. Yang, "Support vector machine in machine condition monitoring and fault diagnosis," Mechanical Systems & Signal Processing, Vol. 21, pp. 2560-2574, Aug. 2007. https://doi.org/10.1016/j.ymssp.2006.12.007