Deep mining of megawatt large wind turbine actual operating data: exploration of accurate modeling & performance optimization

Abstract. The real-time operation data analysis and condition monitoring of large wind turbines are crucial for ensuring the efficient and safe operation of wind farms. In response to this, this paper proposes a precise prediction model architecture based on the multivariate linear regression algorithm to gain a deeper understanding of the actual operation of large wind turbines. By comparing different prediction variable combinations, we confirmed that the average wind direction and average wind speed play a core role in predicting active power, and found that their combined effect can capture more than 70 % of power changes. Furthermore, this paper innovatively introduces Bayesian algorithm for parameter fusion, effectively improving the model's goodness of fit. However, the complexity of the data in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm, suggesting that further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.