Fan active power tracking method based on dynamic multi-model prediction controller

Abstract

The invention provides a fan active power tracking method based on a dynamic multi-model prediction controller. The method comprises the steps that nonlinear models of a fan are built and subjected to linearization and discretization at working condition points, and a linear multi-working-condition multi-model of the fan is obtained; a model prediction controller is built according to the linear multi-working-condition multi-model of the fan; parameters of models in the model prediction controller are updated and corrected according to the actual running state of the fan, and a dynamic multi-model prediction controller is built; and the effectiveness of the dynamic multi-model prediction controller in the aspect of fan active power tracking is verified based on an actual experiment platform or high-fidelity simulation software. The defects of a traditional fan active power tracking strategy are overcome, fluctuation during fan output power tracking is restrained on the basis of the dynamic multi-model prediction controller, and the economic performance of the fan is improved.

Description

technical field [0001] The disclosure belongs to the technical field of wind turbine active power tracking control, and in particular relates to a wind turbine active power tracking method based on a dynamic multi-model predictive controller. Background technique [0002] Since the energy output by the wind turbine comes from the wind in the environment, and the wind in the environment is difficult to be accurately predicted, and there will be turbulence and rapid changes, the output power of the wind turbine is prone to fluctuations, and it is difficult to accurately control the power of the wind turbine , not only affects the economic operation of the wind turbine, but also affects the frequency of the power grid, posing a huge challenge to the operation of the wind turbine and the power grid. [0003] Therefore, it is necessary to find effective means to improve the power tracking ability of wind turbines, reduce power fluctuations, and improve the quality of wind energy ...

AI Technical Summary

Problems solved by technology

The PI control strategy is mainly to adjust the pitch angle through the PI controller to keep the generator speed at the rated speed when the wind speed is above the rated wind speed, and the electromagnetic torque controller cooperates with the generator speed to realize the power tracking of the rated power; the gain scheduling PI control strategy is to solve Under different wind speeds, the output power of the generator has different sensitivity to the pitch angle, so the gain compensation and scheduling of the PI controller are performed based on the sensitivity; the traditional model predictive control strategy can be based on the fan model for real-time optimal control to meet the power requirements. Output requirements, but the accuracy and precision of the control are closely related to the accuracy of the model. At the same time, because the states of the fans are coupled with each other and are greatly affected by the input wind speed, the model is difficult to establish accurately. If the model is more realistic If the model gap is large, the power of the fan will not meet the control requirements

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