A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System

Abstract

In this paper, a complete comparison analysis of two advanced control algorithms, namely robust model predictive control (MPC) and stochastic MPC, is performed in order to optimize the operation of a wind power generation system (WPGS). The power maximization often conflicts with the mechanical load experienced by the turbine in the full-load region (i.e., the higher the power extracted, the higher the load) under the wind speed disturbance, thereby leading to high maintenance cost resulting from the fatigue damage. Thus, a typical 5 MW wind turbine operating in a high-speed region is considered to guarantee system security and economy. The robust MPC is designed by utilizing the min–max framework to track steady-state optimum operating reference trajectory with the deterministic constraint of output power, while the stochastic MPC is constructed by incorporating the invariant set theory to also ensure the system security subjecting to the probabilistic constraint of output power. The relation between the constraints and the implications on optimal performance are also studied. Comprehensive simulations on a mechanism model and FAST simulator are carried out to demonstrate the validation of the two control methods under various scenarios. It is discovered that when wind speed in the near future can be predicted and utilized in controller design, the stochastic MPC can effectively reduce the maintenance cost by suppressing the constraint violation rate compared to robust MPC with a similar energy utilization due to the incorporation of the stochastic characteristics of wind speed.

Robust control of wind turbines to reduce wind power fluctuation

“…In this paper, we provide an in-depth study of a 5 MW horizontal axis wind power system. 41 The turbulent speeds of wind turbines in three directionslongitudinal, lateral, and vertical-are simulated using the Vonkarm model in TurbSim. The scale size of the simulated two-dimensional (2D) wind field created by TurbSim is Y × Z = 145 m × 145 m, and the whole simulated 2D wind field includes the projection region and the tower in the Y × Z plane.…”

“…Therefore, the min-max optimization problem in Equation ( 27) is solved by a dynamic output feedback robust model predictive control method based on quasi-minimunmaximum robust optimization to reduce the computational burden. 41 For unknown system states in constrained linear parameter varying systems with bounded disturbances, a dynamic output feedback robust model predictive control approach via quasi-min-max robust optimization is used. In the optimization problem, the dynamic output feedback controller takes a parameterdependent form, and the optimization control problem can be formulated as convex optimization by the techniques of linear matrix inequalities.…”

“…In this paper, we provide an in‐depth study of a 5 MW horizontal axis wind power system 41 . The turbulent speeds of wind turbines in three directions—longitudinal, lateral, and vertical—are simulated using the Vonkarm model in TurbSim.…”

“…Unfortunately, there is a huge computational burden in finding the perfect address for all points in the forecasting horizon $${W}$$ by enumeration. Therefore, the min–max optimization problem in Equation () is solved by a dynamic output feedback robust model predictive control method based on quasi‐minimun‐maximum robust optimization to reduce the computational burden 41 . For unknown system states in constrained linear parameter varying systems with bounded disturbances, a dynamic output feedback robust model predictive control approach via quasi‐min–max robust optimization is used.…”

Aplicação do controlador preditivo generalizado para rastreamento de referência polinomial considerando perturbações

“…A técnica de MPPT é responsável por fornecer uma referência de tensão para controlar a máxima conversão de potência. Verifica-se o uso de técnicas de controle para rastreamento de referências em outras produções relativas à conversão de energia eólica (CHHIPĄ et al, 2022;LIU;FENG;KONG, 2022).…”