Universal gravitation neural network based wind power system MPPT control method

A neural network and universal gravitational technology, applied in the control of wind turbines, wind power generation, wind turbines, etc., can solve the problems of restricting the development of wind power generation, unsatisfactory control effect of hill-climbing search method, and high cost

Inactive Publication Date: 2015-07-29
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

In addition, this method requires the speed to have a good instantaneous response to wind speed changes. When the fan capacity is large, due to the inertia of the system, the speed disturbance cannot get a timely power response, resulting in the actual control effect o

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  • Universal gravitation neural network based wind power system MPPT control method
  • Universal gravitation neural network based wind power system MPPT control method
  • Universal gravitation neural network based wind power system MPPT control method

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[0063] reference figure 1 The hardware structure adopted by the present invention includes an MPPT controller, a rectifier, a first voltage sensor, a second voltage sensor, a current sensor, a DC-DC converter, a driving module, a first capacitor and a second capacitor. The device combines the fan and the load to form a permanent magnet direct-drive wind power generation system. Among them, the three-phase input terminal of the rectifier is connected to the three-phase output terminal of the fan, the single-phase output positive terminal is connected to the positive terminal of the first capacitor, and the single-phase output negative terminal is grounded; the measurement positive terminal of the first voltage sensor is connected to the A phase of the fan The output terminal is connected, the measuring negative terminal is connected to the B-phase output terminal of the fan, and the measuring output terminal is connected to the MPPT controller; the negative terminal of the first...

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Abstract

Provided is a universal gravitation neural network based wind power system MPPT control method. According to the method, on the basis that a large amount of power-rotation speed-wind speed samples are established, a universal gravitation neural network prediction model of wind speed is established and is utilized to perform wind speed estimation, then an optimum tip speed ratio method is adopted to predict an optimal fan rotation speed corresponding to a maximum power point, the fan rotation speed is adjusted to be the predicted optimal fan rotation speed, the rotation speed is used as an initial value, and a duty ratio perturbation and observation method is adopted to set perturbation step length for tracing the maximum power of a fan. The estimation method is adopted to obtain the wind speed without a wind speed sensor, the control cost of the system can be effectively saved, and the reliability of the system can be improved. The method utilizes a universal gravitation search algorithm to optimize a neural network model, and wind speed estimation accuracy can be effectively improved. In addition, the universal gravitation neural network based wind power system MPPT control method further has the advantages of being high in tracing speed and capable of improving the powder generating efficiency of the fan.

Description

technical field [0001] The invention relates to a maximum power tracking control method of a wind power generation system based on a universal gravitational neural network, and belongs to the technical field of wind power generation. Background technique [0002] With the continuous development of social economy and the aggravation of energy crisis, wind energy, as a renewable energy, has been paid more and more attention in the world today. [0003] The maximum power tracking of the wind turbine means that below the rated wind speed, by adjusting the speed of the wind rotor, the wind energy utilization coefficient is kept at the maximum value to obtain the maximum power output. Based on the basic control principle of maximum power tracking, the control methods of wind turbines can be roughly divided into three categories: tip speed ratio method, power feedback method and hill-climbing search method. [0004] Tip speed ratio method: Under a certain fixed wind speed, by adju...

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Application Information

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IPC IPC(8): F03D7/04
CPCY02A30/00Y02E10/72
Inventor 马良玉李强刘卫亮刘长良林永君陈文颖马进马永光
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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