Wind turbine temperature prediction based on stochastic differential equation + Markov combination model

Zonghao Ding; Hongsheng Su; Xingsheng Wang; ZhiWen Dong

doi:10.1117/12.3013643

29 November 2023 Wind turbine temperature prediction based on stochastic differential equation + Markov combination model

Zonghao Ding, Hongsheng Su, Xingsheng Wang, ZhiWen Dong

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370Z (2023) https://doi.org/10.1117/12.3013643
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zonghao Ding, Hongsheng Su, Xingsheng Wang, and ZhiWen Dong "Wind turbine temperature prediction based on stochastic differential equation + Markov combination model", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370Z (29 November 2023); https://doi.org/10.1117/12.3013643

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KEYWORDS

Stochastic processes

Differential equations

Wind turbine technology

Data modeling

Matrices

Statistical modeling

Automation

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