Wind turbine temperature is one of the indicators for determining whether the wind turbine is working properly or not. Accurate prediction of wind turbine temperature can predict the status of the wind turbine in advance and actively take preventive maintenance measures, thus reducing losses. In this paper, based on the stochastic differential equation model, a "stochastic differential equation + Markov" combination model is developed to predict the wind turbine temperature. Where the stochastic differential equation predicts the overall trend of wind turbine temperature change and Markov corrects for the effects generated by stochastic disturbances. The fitting and testing errors of the stochastic differential equation, the grey GM(1,1) model and the "stochastic differential equation + Markov" combined model were calculated by collecting data from the temperature of a wind turbine in a wind power plant in Jiuquan, respectively. The results show that the combination model "Stochastic Differential Equation + Markov" is more accurate than the single model in terms of fitting and generalization accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.