Wake field prediction of a wind farm based on a physics-informed neural network with different spatiotemporal prediction performance improvement strategies  

作　　者：Junyong Song Lei Wang Zhiqiang Xin Hao Wang 

机构地区：[1]Department of Engineering Mechanics,College of Mechanics and Engineering Science,Hohai University,Nanjing 211100,China

出　　处：《Theoretical & Applied Mechanics Letters》2025年第2期141-153,共13页力学快报(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12072105,11932006,and 52308498);the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220976).

摘　　要：Dynamic wake field information is vital for the optimized design and control of wind farms.Combined with sparse measurement data from light detection and ranging(LiDAR),the physics-informed neural network(PINN)frameworks have recently been employed for forecasting freestream wind and wake fields.However,these PINN frameworks face challenges of low prediction accuracy and long training times.Therefore,this paper constructed a PINN framework for dynamic wake field prediction by integrating two accuracy improvement strategies and a step-by-step training time saving strategy.The results showed that the different performance improvement routes significantly improved the overall performance of the PINN.The accuracy and efficiency of the PINN with spatiotemporal improvement strategies were validated via LiDAR-measured data from a wind farm in Shandong province,China.This paper sheds light on load reduction,efficiency improvement,intelligent operation and maintenance of wind farms.

关 键 词：Dynamic wake prediction LiDAR measurements Physics-informed neural network Accuracy improvement strategy Step-by-step time saving strategy 

分 类 号：TM614[电气工程—电力系统及自动化]