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


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.


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