Model prediction control method and model prediction control system for all working conditions of wind generating set

Abstract

Disclosed are a model prediction control method and a model prediction control system for all working conditions of a wind generating set. The system comprises an MPC (model prediction control) device, a feedback information measurer, a wind wheel, a driving chain, a tower, a generating unit, a variable propeller driver and a converter. The feedback information measurer is used for detecting status variables of the wind wheel, the driving chain, the tower and the generating unit and transmitting detecting results to the MPC device, the MPC device is used for computing targets of the blade pitch angle and the generator torque, and the variable propeller driver and the converter are used for adjusting the blade pitch angle and the wind generator torque. The method is used for computing control increment by means of a variable propeller control prediction model and a torque control prediction model, takes the status variables including driving chain torsional displacement, driving chain torsional speed, blade plane external first-order flap displacement, blade plane external first-order flap speed, tower front-back first-order swing displacement, tower front-back first-order swing speed, mechanical loads of the unit and the like, and two prediction models can be automatically switched in different working conditions, so that the wind generating set can be operated in all working conditions.

Description

technical field [0001] The invention relates to the technical field of control of wind power generators, in particular to a model predictive control method and system for full working conditions of wind power generators. Background technique [0002] Model predictive control (MPC), also known as model-based predictive control (MBPC), was originally proposed by Richalet and Culter et al. Since the development of the four basic principles of model predictive control in 1968, it has been applied in many industrial control fields. . Model predictive control consists of four principles, predictive model, reference response trajectory, rolling optimization, and feedback correction. [0003] The first principle of model predictive control is the predictive model. Predictive control is an algorithm based on a model called a predictive model. This is the basis of model predictive control, because the quality of the model directly affects the quality of control, and it is also the ...

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Problems solved by technology

[0013] Therefore, the method itself has shortcomings, such as not considering the influence of dynamic characteristic changes and uncertain factors, and may bring about large overshoots, resulting in long-term oscillations

[0022] Its shortcoming is that its control goal only considers the stable and reliable operation of the wind turbine in the entire operating range, and does not consider the optimization of the operation performance of the wind turbine, such as improving the power generation quality of the unit, reducing the mechanical load of the unit, etc.

Moreover, the influence of wind speed disturbance is not considered in its control model, and the control strategy cannot guarantee the stable operation and fast follow-up of the unit when the wind speed is disturbed.

[0023] In the existing technology, wind speed prediction does not consider the influence of dynamic characteristic changes and uncertain factors; no reference trajectory may lead to large overshoot, resulting in long-term oscillation; no consideration is given to the optimization of wind turbine operation performance, such as improving the power generation quality of the unit , reduce the mechanical load of the unit, etc.; without considering the influence of wind speed disturbance, the control strategy cannot guarantee the stable operation and fast follow-up of the unit when the wind speed is disturbed

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