风力发电控制系统的实时非线性经济模型预测控制  

Real-time Nonlinear Economic Model Predictive Control of Wind Energy Conversion System

作　　者：王文文 刘向杰[1] 孔小兵 Wang Wenwen;Liu Xiangjie;Kong Xiaobing(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206

出　　处：《系统仿真学报》2025年第3期679-690,共12页Journal of System Simulation

基　　金：国家重点研发计划(2021YFE0190900);国家自然科学基金(62073136);辽宁省自然科学基金(2022-KF-21-05);中央高校基本科研业务费专项基金(2023JC002,2023YQ002)。

摘　　要：为应对风电系统经济效益控制和实时性要求的新挑战,针对风力发电控制系统,提出了一种非线性经济模型预测控制策略。在最大化发电功率的同时减少了塔架和齿轮箱等关键结构的疲劳负荷,设计了一个滚动时域估计器为优化提供有意义的初始化。优化问题求解方面,利用相邻采样时刻间非线性规划程序的相似性,实现了算法的实时迭代求解。以5 MW风电机组作为研究对象,在ACADOS框架下实时迭代求解最优控制问题。仿真结果表明:所提控制策略能够有效提高系统运行经济效益并且能够实现实时控制。To address the new challenges of economic control and real-time requirements in wind energy conversion systems(WECS),this study proposes a nonlinear economic model predictive control(NEMPC)strategy.This strategy aims to maximize power generation and while reducing fatigue loads on critical structures,such as towers and gearboxes.Additionally,a moving horizon estimator(MHE)has been designed to provide an effective initialization for optimization.By exploiting the similarity of nonlinear programs between adjacent sampling moments,the algorithm achieves real-time iterative(RTI)solutions.Using a 5 MW wind turbine as the research object,the proposed strategy is implemented in the ACADOS framework for real-time optimization.Simulation results demonstrate that the strategy effectively improves the economic performance of the system while ensuring real-time control.

关 键 词：风力发电控制系统 非线性经济模型预测控制 滚动时域估计 疲劳负荷 实时迭代 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]