Wind driven generator stability control method based on hybrid neural network

Abstract

The invention relates to a wind driven generator stability control method based on a hybrid neural network, which comprises the following steps of: (1) collecting the wind speed and corresponding active power; (2) preprocessing the filtering wave; (3) debugging the GA-BP (Genetic Algorithm - Back Propagation) algorithm; (4) training the neural network; (5) calculating the result on the display screen. The method has the advantages that the accuracy is high so that the performance of the wind driven generating system can be well fitted; the computing speed is high so that the system meets the requirement of real time property; and the realization of the GA-BP neural network algorithm through programming by using a DSP (Digital Signal Processor) is effective to increase the speed of the GA-BP neural network algorithm and better exert the parallelism of the GA-BP neural network algorithm.

Description

(1) Technical field: [0001] The invention relates to the fields of wind power generation, intelligent control and power electronics technology, and digital signal processing technology, in particular based on a hybrid neural network-based stability control method for wind power generators. (two) background technology: [0002] With the continuous introduction of China's renewable energy stimulus policies and the improvement of power grid supporting construction, China's huge wind power development potential will be further stimulated, and China has the ability to become a world's largest renewable energy country. From the characteristics of wind power generation itself, it can be seen that the magnitude and direction of natural wind speed are random and uncontrollable, so the wind energy generated by acting on the blades of the wind turbine is also random and uncontrollable; The complex mechanism makes the modeling of large wind turbines very difficult, and it is difficult t...

AI Technical Summary

Problems solved by technology

The artificial neural network has the ability to simulate multiple variables without making complex assumptions about the input variables. Many fields have begun to use this theoretical modeling, but most of them are computer simulations and few hardware implementations.

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