A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Abstract

Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

Recent advances in data-driven prediction for wind power

“…A single wind turbine covers a small area and can be disturbed or destroyed by meteorological conditions or local random disturbance factors easily. It is difficult to predict WP accurately (Li and Pan, 2017). Therefore, the research on WPP for a single wind turbine becomes particularly important, but in practice, the cost of WPP for every wind turbine in the whole wind farm is high, and many meteorological variables such as wind speed in the same range are highly similar, so at present, there are few types of research on AI-based WPP models for a wind turbine.…”

Probabilistic prediction of wind power based on improved Bayesian neural network

“…Wind power has attracted much attention worldwide due to its non-polluting and inexhaustible advantages. However, the volatility of wind power generation poses considerable challenges to the stable operation of power grids, and accurate wind power forecasting reduces the negative impact on the grid (Li et al, 2017;Harrou et al, 2023).…”

Comparative Study of Control Methods for a 2MW Doubly-Fed Induction Generator Wind Turbine

“…Wind turbines are a crucial component of wind power generation, converting the kinetic energy of wind into electrical energy. Wind turbines come in different sizes and types, ranging from small-scale turbines used for residential or community power generation to large-scale turbines used for utility-scale wind farms Li et al, [1]. Wind turbines are subject to a range of dynamic loads and disturbances that can affect their performance and structural integrity.…”