Research On Condition Recognition And Maintenance Decision Of Wind Turbine Based On Data Driven

At present,wind power is one of the most mature technologies in renewable energy field,with the fastest growth rate and best commercial development.In recent years,China’s wind power industry has achieved leap-forward development,with installed capacity ranking first in the world.At the same time,China’s wind turbines are also facing problems such as high failure rate.This is partly because that the urgent need for equipment at the beginning of wind power industry led to the design and finalization of wind turbines in a hurry,and technology is not mature.On the other hand,the research on condition assessment and maintenance decision-making of the operating units is seriously lacking.In addition,there are a lot of problems such as idle operation data and inadequate utilization of current operating units.Therefore,aiming at maintenance decision-making of wind turbine,using the massive operation and maintenance data of the units in operation,this paper carried out the research on importance evaluation of components,condition evaluation,condition prediction and condition identification of wind turbine,which provided a basis for maintenance decision-making.First of all,in view of the uncertainty in the process of importance evaluation,a grey fuzzy importance evaluation method of interval value was proposed.The special fuzzy number-interval number-was used to instead the traditional fuzzy membership degree to represent the fuzziness in the evaluation process.The interval grey number was used to represent the gray of the evaluation process,in order to solve the problem that the interval number is not easy to sequence,OWA operator was used to aggregate the module part and the gray part.The evaluation results have obvious discreteness and are not easily affected by incomplete information.Considering the accuracy of condition evaluation model and the objectivity of condition evaluation process,a condition evaluation method of wind turbine based on the normal cloud model was proposed.Based on the selection of SCADA data and the importance evaluation of components,a condition evaluation index system consisting of 5 core components and 14 indicators was constructed.Through the transformation and correction of temperature index,the influence of output power and ambient temperature on the temperature index of non forced cooling parts is eliminated,and reasonable correction threshold and deterioration degree of temperature index were determined.Entropy weight was used to reflect the objective difference between evaluation index data,DSmT evidence fusion criterion was adopted to balance the conflict between subjective factors and objective data to obtain the DSmT fusion weight.Cloud membership degree was used to replace fuzzy membership degree.A method to determine normal cloud digital,which effectively solves the problems of too much overlap and relatively close was proposed.The study shows that the model can track the condition deterioration trend of the unit and realize early warning.Secondly,in order to realize the prediction of condition parameters,a deep learning condition parameter prediction method based on phase space reconstruction was proposed,which was used to establish the prediction models of oil temperature and vibration parameters.The results,which were compared with LSSVM prediction results,neural network prediction results and field test data,showed that the absolute error and relative error were significantly lower than those of other prediction methods by more than 50%,and the superiority of this method in the condition parameter prediction was significant.In order to realize the identification of the specific condition of the components,an improved convolution neural network model was proposed.Taking the matrix reflecting the condition of gearbox as the learning object,an improved convolution neural network(CNN)model based on VGGNet structure was established.The CNN model learned the ability to extract the condition features of gearbox by deep learning the condition matrix.The trained CNN model was used to detect and analyze the condition of the same type gearbox of other unit.It was proved that the accuracy of the model can reach 95.3%,and it had certain generalization ability.Finally,on the basis of the above research.the research work was carried out for the decision-making of component maintenance mode,the optimization of interval of time based maintenance and the decision-making of opportunity of condition based maintenance.First of all,on the basis of component importance evaluation,combined with the application degree of condition monitoring.the logical decision on component maintenance mode was made.The decision results show that condition-based maintenance should be used for the core components with complete condition monitoring means,such as gearbox,and time-based maintenance should be used for the important components and the core components without condition monitoring means.Secondly,the imperfect preventive maintenance strategy should be used for the repairable components with time-based maintenance.The age correction factor and failure rate growth factor were used to modify the failure rate function to characterize the maintenance effect of each preventive imperfect maintenance,and the maintenance interval is dynamically adjusted according to the change of maintenance times.The empirical research results show that the model can take into account the reliability and economy.Finally,the proportional hazard model was applied to the decision-making of condition-based maintenance,which not only considered the running time of components,and more attention was paid to the influence of condition monitoring parameters on the determination of maintenance time.In the empirical study,aiming at the minimum average maintenance cost,the decision curve of condition based maintenance was obtained by solving the threshold value of condition based maintenance,which was tested with the measured data and the predicted data.The calculation results show that the model can accurately determine the maintenance opportunity and reduce the blindness of maintenance.