Maximum power point tracing control method based on neural network optimization starting rotating speed

Abstract

The invention provides a maximum power point tracing control method based on the neural network optimization starting rotating speed. According to the method, on the basis of an existing MPPT control method, a neural network is adopted to obtain the best starting generator rotating speed according to wind speed condition dynamic optimization compensation factors, and then wind energy capture efficiency is further improved. The adopted neural network takes the average wind speed and turbulence intensity as input, and takes the best compensation factor as output. Mass draining data obtained through the traversal algorithm are used for training the neural network, the trained neural network is adopted for obtaining the corresponding best compensation factor through calculation according to the changed wind speed conditions, and then the best compensation factor is used for optimizing the starting generator rotating sped, so that the best MPPT tracking interval is obtained, and the wind energy capture efficiency is further improved. Compared with various traditional MPPT control methods, the effectiveness and superiority of the algorithm are verified.

Description

technical field [0001] The invention belongs to the field of wind power generation, in particular to a maximum power point tracking control method based on a neural network to optimize the initial rotational speed. Background technique [0002] In order to improve the wind energy capture efficiency below the rated wind speed range, the variable speed constant frequency wind turbine generally adopts the Maximum Power Point Tracking (MPPT) control strategy. Power curve method (also known as power signal feedback method or torque curve method) is one of the most widely used MPPT control methods. [0003] The traditional MPPT control, especially the power curve method, is mostly based on the steady-state design of the system, while ignoring the dynamic process of the fan system tracking between different steady-state operating points. However, in the face of the ever-increasing moment of inertia of the wind rotor and its increasingly slow dynamic response performance due to the...

AI Technical Summary

Problems solved by technology

However, the improved maximum power point tracking control based on the shrinkage tracking interval does not give the optimal tracking interval (that is, the best compensation coefficient). Since the setting of the tracking interval will significantly affect the tracking effect of MPPT, the optimization of the tracking interval seems to very important

But there is no relevant description in the prior art

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