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Wind field adaptive power tracking control method based on neural network

A tracking control and neural network technology, applied in the field of wind farm adaptive power tracking control, can solve the problems of difficult parameter tuning of PID control, complex dynamic model of variable speed wind turbines, and difficulty in accurately predicting the trajectory of power demand at the load end.

Inactive Publication Date: 2017-09-19
STAR (CHONGQING) INTELLIGENT EQUIP TECH RES INST CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the variable speed fan dynamics model is complex, there are many uncertain parameters, showing strong nonlinear dynamic behavior
In the face of highly nonlinear and uncertain systems, traditional PID control is difficult to achieve fast and optimal real-time parameter tuning
[0003] At the same time, we hope that the total power output by the wind farm can just meet the power demand of the user, but the power demand (ideal power) trajectory from the load end (user) is difficult to be accurately predicted

Method used

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  • Wind field adaptive power tracking control method based on neural network
  • Wind field adaptive power tracking control method based on neural network
  • Wind field adaptive power tracking control method based on neural network

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0040] Such as figure 2 As shown, the dynamic model of the wind turbine system with high nonlinear uncertainty is:

[0041]

[0042] in The i-th wind turbine model in the wind farm:

[0043]

[0044] where d i (·)=c i (ω r1 ,ω r2 ,…ω r(i-1) ,ω r(i+1) ,…ω rn ) is the bounded disturbance of the wind turbine by the speed of other wind turbines around it in the wind field. Output power: P gi =T emi ω gi =n gi T emi ω ri ,but

[0045]

[0046] In this embodiment, the neural network-based adaptive power tracking control method for wind farms includes the following steps:

[0047] Step 1: Establish the analysis model of ideal power tracking curve and estimated power tracking curve, including

[0048] 1) Establish the dynamic model of wind farm output power as:

[0049]

[0050] where P=(P g1 ,P g2 ,…P gn ) Τ ∈ R n is the...

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Abstract

The invention discloses a wind field adaptive power tracking control method based on a neural network. The method comprises the steps of (1) establishing an analysis model of an ideal power tracking curve and an estimated power tracking curve, (2) estimating an upper bound value of an uncertain item of a wind field output power trace tracking system based on a radial basis neural network, (3) using limited Lyapunov to control a computable error em into a compact set, thus the input z of the neural network is controlled into a limited area, (4) designing a proportional controller U, and (5) inputting the proportional controller into an air blower system, and controlling wind field output power. According to the wind field adaptive power tracking control method based on a neural network, in the conditions of unknown wind field power tracking trace, model uncertainty and external disturbance, the output power of a whole wind field can be dynamically adjusted, and stable power output to a load end is maintained.

Description

technical field [0001] The invention relates to the technical field of wind farm power control, in particular to a wind farm adaptive power tracking control method. Background technique [0002] Variable-speed fans in wind farms are increasingly favored by researchers because they can capture the maximum power point and weaken output power fluctuations. When the structure of the fan is determined and the parameters are known, the system dynamics model is constructed to describe the complete dynamic characteristics of the system, and various automatic control theories are applied to design a model-based controller to realize power tracking control. However, the dynamic model of the variable speed fan is complex, and there are many uncertain parameters, showing strong nonlinear dynamic behavior. In the face of highly nonlinear and uncertain systems, traditional PID control is difficult to achieve fast and optimal real-time parameter tuning. [0003] At the same time, we hope...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 宋永端梁丽源方觅赖俊峰
Owner STAR (CHONGQING) INTELLIGENT EQUIP TECH RES INST CO LTD