Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A mppt control method for wind power system based on the universal gravitational neural network

A neural network and universal gravitational technology, which is applied in the control of wind turbines, wind power generation, wind turbines, etc., can solve the problems of unsatisfactory control effect of hill-climbing search method, power response of rotational speed disturbance, restricting the development of wind power generation, etc., so as to save control costs. , Improve power generation efficiency, fast tracking effect

Inactive Publication Date: 2017-07-14
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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 of the hill-climbing search method.
[0007] In summary, the existing control methods for wind power generation systems have disadvantages such as high cost and poor tracking accuracy, which limit the development of wind power generation, so it is necessary to improve them

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A mppt control method for wind power system based on the universal gravitational neural network
  • A mppt control method for wind power system based on the universal gravitational neural network
  • A mppt control method for wind power system based on the universal gravitational neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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 gravitational neural network, and belongs to the technical field of wind power generation. Background technique [0002] With the continuous development of the social economy and the ever-increasing energy crisis, wind energy as a renewable energy source has received more and more attention from the world today. [0003] The maximum power tracking of a wind turbine means that the maximum power output is obtained by adjusting the wind turbine speed below the rated wind speed to keep the wind energy utilization coefficient at the maximum value. Based on the basic control principle of maximum power tracking, wind turbine control methods 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, the maximum power tracking can...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
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)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products