Study On Reliability Assessment Method Of Wind Turbine Based On Poisson Process

Through the study of deep fault data mining and analysis techniques of wind turbine,we can further understand the performance of the whole machine and its components,effectively improve the quality of unit reliability assessment technology,and provide technical support for unit maintenance strategy formulation.At present,there have been some operating data research based on the reliability of wind turbine,due to the shorter development time,though to be able to get a preliminary analysis of some problems,such as unit maintenance strategies,there are still problems on data fitting precision,so in-depth and systemic research is needed.Poisson process in wind turbines reliability statistical analysis has showed the good results and a large number of applications,therefore,this paper based on the application of the Poisson process in wind turbine reliability statistical analysis will make some related research,aiming at improving the deficiency in the unit reliability assessment.Firstly,this paper analyses the Poisson process theory and its applications in the equipment reliability statistics analysis.we can find that the traditional homogeneous Poisson process assessment model to evaluate the failure rate of the volatile data has limitations by analysing the failure data of a certain wind farm in north China.Secondly,this paper uses the power law process to fit the number of fault occurrence,uses the gray correlation analysis method to study the distribution type of time between failures,and establishes a fault sample simulation model.Finally,this paper establishes a reliability evaluation model of wind turbine turbine based on power law process to improve the traditional evaluation model,and preliminarily develops unit reliability assessment software based on the assessment model of power law process by using the MATLAB.