Parameter identification method for permanent magnet synchronous generator based on polynomial evolution model

Abstract

The invention discloses a parameter identification method for a permanent magnet synchronous generator based on a polynomial evolution model. The method comprises the steps of obtaining an electrical part equation and a mechanical part equation of the permanent magnet synchronous generator under a d-q coordinate system; when the permanent magnet synchronous generator operates stably, establishing an open-loop speed control model of the permanent magnet synchronous generator; or when the operation of the permanent magnet synchronous generator is unstable, establishing a closed-loop speed control model of the permanent magnet synchronous generator; inputting the polynomial excitation signal into the open-loop speed control model or the closed-loop speed control model to obtain a measurable signal curve of the permanent magnet synchronous generator; and obtaining a correlation polynomial coefficient of the measurable signal curve, and carrying out matrix change on the coefficient to obtain a parameter identification result of the permanent magnet synchronous generator. According to the parameter identification method provided by the invention, the motor parameters can be identified under the condition that the motor operates relatively stably and is subjected to unstable interference. The method has the advantages of being high in speed, high in stability and high in identification result accuracy.

Description

technical field [0001] The invention relates to the technical field of generators, in particular to a parameter identification method of a permanent magnet synchronous generator based on a polynomial evolution model. Background technique [0002] The use of renewable energy plays an important role in the power grid. The wind power generation system directly driven by the permanent magnet synchronous generator is based on the primary grid and equipment, through AC / DC conversion, DC / AC conversion and transformers. Transformation and other processes to achieve effective utilization and control of wind energy, and speed signal acquisition and control through the motor control system to achieve reliability and economical management of the wind power generation system. Among them, the motor parameter identification technology can quickly detect the relevant parameters of the permanent magnet synchronous generator, and feed back to the motor control system when the parameters devia...

AI Technical Summary

Problems solved by technology

However, these methods have their own shortcomings. For example, the recursive least squares method is prone to "data saturation" with the increase of the amount of data, and when the recognition results are refreshed during the recognition process, the new data will be overwhelmed by the old data, and thus the recognition cannot be guaranteed. The accuracy of the results; the main limitation of the frequency domain response method is that the frequency domain analysis is based on a linear system, so it cannot reflect the nonlinearity in the dynamic process; the advantage of the model reference adaptive method is that it can guarantee parameter convergence, but there is an adjustable model Problems such as complex setting, difficulty in determining the adaptive speed, and measurement noise easily affect the recognition results; the Kalman filter method not only has a large amount of calculation in the algorithm, but is only suitable for linear systems; the artificial intelligence algorithm model is not only complex, the calculation Carry out self-learning to reduce error

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