Wind turbine generator set system identification method based on ADALINE technology

Abstract

The invention discloses a wind turbine generator set system identification method based on the ADALINE (ADAptive LINear Element) technology. to The method provided by the invention comprises the following steps: determining the input-output of the system, aiming at the operation mechanism of the wind turbine generator set; based on an LPV (Linear Parameter Varying) method, dividing stable operating points according to effective wind speed; selecting an appropriate excitation signal, and using a blade pitch angle or generator torque as a testing input signal as well as using an output signal as generator speed to perform stimulation on the stable operating points and a transition stage between the stable operating points to obtain universe input-output data; extracting all stable operating point input-output data in the testing data, and using an identification method based on the ADALINE technology to obtain a simple model under the all stable operating points; establishing an LPV model of a global system through a model interpolation method. The method is simple in algorithm, easy to implement, has high operation speed and lower calculated amount and can ensure the model performance and stability.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, intelligent control and system identification, and in particular relates to a system identification method based on ADALINE technology in neural network, which is used in combination with LPV method for system identification of complex and non-linear wind power generators. Background technique [0002] With the continuous development of wind power generation technology, the small fixed-pitch wind turbines in the past are gradually replaced by large variable-pitch and variable-speed wind turbines. This variable pitch and variable speed wind turbine has a more complex structure and a more optimized power output curve, which puts forward higher requirements for the control algorithm, especially in the transition stage of switching between operating points and operating points. The accuracy of the control algorithm strongly depends on the accuracy of the object model. For the wind turb...

AI Technical Summary

Problems solved by technology

To establish a model of each subsystem of the wind turbine system and even the whole system, the solution in the strict theoretical sense is to use the mechanism modeling method or the nonlinear system identification method, but these two methods are not easy to implement

On the one hand, the use of mechanism modeling methods, that is, the use of differential equations and algebraic equations to understand and describe the physical and chemical processes of wind turbine systems requires a lot of manpower and material resources, and it is almost impractical for complex nonlinear processes

On the other hand, using the nonlinear system identification method, empirically relying on the input and output data to identify the nonlinear model strongly depends on the excitation signal, but for the wind turbine system, the excitation test may generate a large number of excessive disturbances and product loss costs, And it is difficult to conduct continuous stimulus testing on the global working domain

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