Reliability Assessment of Wind Turbine Based on Fuzzy Markov Theory and Sequential Monte Carlo Multiple Sampling Method

ABSTRACT A reliability analysis approach of wind turbine based on fuzzy Markov theory and sequential Monte Carlo multiple sampling method is proposed in our study to address the problems: (1) the most reliability analysis of wind turbine are implemented based on the assumption that the system operates in healthy operation state or shutdown operation state, neglecting the derating operation state in actual engineering, (2) the uncertainty in transitions between different operational states, as well as the duration of each state, is neglected, and (3) the stochastic variations in the operational states of wind turbine within system is failed to consider. Firstly, the models of wind speed distribution, wind power conversion, wind turbine failure, and wind turbine output are established based on a two‐parameter Weibull distribution model, the relationship between output power and wind speed, and multi‐state theory, respectively. Secondly, the discrete method for wind turbine operational states is proposed based on fuzzy theory and Markov model. Moreover, the reliability assessment for a multi‐state wind turbine is presented by using the sequential Monte Carlo multiple sampling model established in our study. Finally, a case study of a 2 MW wind turbine is conducted to illustrate the validity of the proposed approach, and a multidimensional analysis is executed from the wind turbine fault states, wind turbine output states, and the impact of wind turbine fault state on its output.