Wind turbine generator data driving model prediction control method

Abstract

The invention relates to a novel data-driven model predictive control method, which comprises the following steps of: giving correlation weight between each input variable and output from input variables obtained from a wind power plant by adopting a two-stage fuzzy curve method, and quickly selecting important input variables according to input variable indexes; then, determining prerequisite parameters of a fuzzy model by adopting fuzzy clustering and a Gaussian membership function, and identifying conclusion parameters of the fuzzy model by adopting recursive least squares; and the identified model is used for participating in data-driven model prediction control, so that the wind power plant can smoothly perform primary frequency modulation, the MPC problem for wind power plant frequency control is completed, and a relatively accurate solution can be quickly obtained.

Description

technical field [0001] The invention relates to an automatic power generation control strategy of a wind farm, in particular to a data-driven model predictive control method for a wind turbine. Background technique [0002] With global attention on climate change issues and energy crisis, wind energy has experienced rapid but fairly steady growth for nearly a decade. my country is rich in wind energy resources, and the development of China's wind power technology has made great progress. However, there are still many problems in the development and utilization of wind energy resources in China. [0003] With the increasing penetration of wind power into the grid, it brings a series of challenges to the safe and stable operation of the power system, especially in the aspect of active power control. The purpose of wind farm frequency regulation is that when the grid frequency changes, the synchronous generator will respond to this change, and the wind farm will also quickly ...

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Problems solved by technology

In order to enable the wind farm to have frequency regulation capability and respond to grid frequency changes, the wind farm must reserve some active power, which makes the wind turbine must be transferred from the maximum power tracking state to the second-best power generation, which will inevitably lead to the economic benefit of the wind farm. reduce

[0005] Due to the strong nonlinearity of the dynamic process of wind power conversion, the effectiveness of the dynamic optimal control strategy of the wind farm depends on the accurate and fast solution of the nonlinear optimization problem. The limited response time required by the control strategy makes this difficult to balance

Before the wind farm is integrated into the grid, the frequency regulation problem should be solved first. First, the wind farm must be modeled. However, when modeling a nonlinear system such as a wind farm, if all variables must be considered, then the difficulty of modeling and accuracy will be affected by

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