Power prediction of wind turbine in the wake using hybrid physical process and machine learning models

Abstract

Precise power prediction for wind turbines under wake effects is requisite for wind farm wake control to increase the energy production and economic benefits. Wind farm wake effects that have spatial and temporal characteristics significantly complicate the physical modelling. To overcome the modelling difficulties, this paper proposes two new coupled structures of physical process and machine learning to predict wind turbine output power under wake effects. Their structures are fully presented and verified by comparisons to the pure physical model, neural network model alone and existing physical-guided neural network models. One of the new models that couples physical model and transfer learning approach shows the best prediction performance. It uses data generated from physical model and small size real data to establish the model, which makes it adaptive to data with different distribution. The results confirm that a robust hybrid physical and machine learning model can simultaneously inherit the advantages from developments of physical models and machine learning approaches. Important insights into HPML models for output power prediction of WTs under wake effects are provided.