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

doi:10.16182/j.issn1004731x.joss.23-1396

Abstract

Abstract:

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.

Key words: wind energy conversion system, nonlinear economic model predictive control, moving horizon estimation, fatigue load, real-time iteration

CLC Number:

TP273

Wang Wenwen, Liu Xiangjie, Kong Xiaobing. Real-time Nonlinear Economic Model Predictive Control of Wind Energy Conversion System[J]. Journal of System Simulation, 2025, 37(3): 679-690.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 1

Fig. 8

Table 2

Comparison of each fatigue load

指标	NMPC	NEMPC
$T t o w e r$	0.138 9	0.007 5
$T g e a r b o x$	2.270 0×10^-6	1.525 1×10^-6
$T p i t c h$	0.339 4	0.197 3
$T t o r q u e$	4.989 7×10⁷	4.966 1×10⁷

Table 2

Fig. 9

Fig. 10

Table 3

Fig. 11

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算法	w_r	w_g
NMPC	0.013 3	1.521 2
NEMPC	0.012 9	1.291 3

求解器/OCP	平均每次迭代求解时间/ms	系统采样时间/ms
HPIPM/NEMPC	142.4	200
IPOPT/NEMPC	234.6	200
HPIPM/MHE	101.2	200
IPOPT/MHE	196.5	200

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