Abstract
The traditional fault diagnosis method for photovoltaic (PV) inverters has had a difficult time meeting the requirements of the current complex systems. The main weakness lies in the study of nonlinear systems, but the diagnosis time is also long, and the accuracy is low. To solve these problems, we use a hidden Markov model (HMM) that has unique advantages in its training model and recognition for diagnosing faults. In this paper, the HMM is trained for PV inverter fault diagnosis. We first use MATLAB to simulate and extract the fault information, and then we use the Baum-Welch algorithm for iterative training, and finally we use the Viterbi algorithm for fault identification. The experimental results show that the correct PV inverter fault recognition rate by HMM is 20% higher than that of traditional methods, and the diagnosis time is greatly reduced. Therefore, it is faster and more accurate to use HMM in diagnosing PV inverter faults.
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