Variable propeller pitch control method based on proportion-extreme learning machine steady state estimation

An extreme learning machine and pitch variable technology, which is applied in the control of wind turbines, engine control, proportional integral algorithm, etc., can solve problems such as unstable output power of wind turbines, nonlinear pitch control system parameters, etc.

Active Publication Date: 2019-03-19
HUNAN UNIV OF TECH
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Problems solved by technology

[0003] Due to the randomness of wind speed, time-varying parameters of wind turbines, and the inertial link driving the load of large-mass impellers, the variable pitch control system has the characteristics of parameter nonlinearity, time-varying parameters, and hysteresis, resulting in unstable output power of wind turbines.

Method used

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  • Variable propeller pitch control method based on proportion-extreme learning machine steady state estimation
  • Variable propeller pitch control method based on proportion-extreme learning machine steady state estimation
  • Variable propeller pitch control method based on proportion-extreme learning machine steady state estimation

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specific Embodiment approach

[0029] Specific implementation method: Take a certain type of 2MW wind turbine as an example. The rated wind speed of its work is 13m / s, and the cut-out wind speed is 25m / s. Select the wind speed data x selected during training. i For 13m / s, 13.1m / s, 13.2m / s, 13.3m / s...24.9m / s, select wind speed x i The corresponding PI controller steady-state output value y i , put x i and y i As the training data of the extreme learning machine, there are 129 sets of data in total, 103 sets of which are randomly selected as training data, and the remaining 26 sets of data are used as test data. The extreme learning machine is used to fit the relationship between each wind speed and the steady-state output value of the PI controller at the rated wind speed. The single hidden layer feedforward neural network model with F hidden neurons can be expressed as:

[0030]

[0031] In the formula: β i is the output weight of the i-th hidden layer node; ω i is the input weight of the i-th hidd...

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Abstract

Aiming at the variable propeller pitch control problem of a wind power generation system, the invention provides a variable propeller pitch control method based on proportion-extreme learning machinesteady state estimation. According to the variable propeller pitch control method based on the proportion-extreme learning machine steady state estimation, firstly, steady-state output of a PI controller of a wind generation set under various wind speeds is leaned through an ELM, and then variable propeller control of the wind generation set is performed adopting a method of combining the trainedELM and a proportional controller. By means of the variable propeller pitch control method based on the proportion-extreme learning machine steady state estimation, the defect of lag of traditional PIvariable propeller pitch control can be improved, and the stability of the output power of the wind generation set is facilitated.

Description

technical field [0001] The present invention relates to a control method in the technical field of wind power generation, in particular to a pitch control method based on proportional-extreme learning machine steady-state estimation. Background technique [0002] The pitch control system of the wind turbine completes the control of the pitch angle of the blades through the pitch controller, and keeps the pitch angle of the wind turbine unchanged when the cut-in wind speed is above the range below the rated wind speed, and the wind turbine runs at The maximum wind energy tracking control can be achieved with the best blade tip speed ratio; when the wind speed is above the rated wind speed to the cut-out wind speed, the speed is maintained near the rated speed, and the output of the generator set is kept constant by adjusting the pitch angle. When the wind speed is greater than the cut-out wind speed , perform shutdown protection. [0003] Due to the randomness of wind speed,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F03D7/00
CPCF03D7/00F05B2270/32F05B2270/328F05B2270/335F05B2270/70F05B2270/705F05B2270/709Y02E10/72
Inventor 秦斌王欣陈金林
Owner HUNAN UNIV OF TECH
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