Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 1997-2006.doi: 10.13229/j.cnki.jdxbgxb20200848

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Wind speed prediction based on machine learning and new energy pumping unit wind power control

Chun-you ZHANG1,2(),Liang WANG1(),Hong LI1,Tong-yan WU1,Yan LI3   

  1. 1.School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China
    2.College of Engineering，Inner Mongolia University for the Nationalities，Tongliao 028000，China
    3.College of Electronics and Automation，Inner Mongolia Electronic Information Vocational Technical College，Hohhot 010070，China
  • Received:2020-11-03 Online:2021-11-01 Published:2021-11-15
  • Contact: Liang WANG E-mail:zcy19801204@126.com;wangliang@buaa.edu.cn

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Abstract:

This paper takes the new energy pumping unit as the research object. In order to improve the ability of new energy pumping unit，first, the maximum wind energy tracking control strategy of the wind turbine is studied, and a wind speed estimation method-support vector regression is presented. Then, the key parameters in the estimation model are optimized by particle swarm optimization. Finally, a feedback linearized sliding mode controller is designed for speed feedback control. By comparing with PID controller, it is verified that the new controller has the characteristics of rapidity and anti-disturbance. Therefore, the maximum wind power tracking control strategy proposed in this paper meets the energy-saving requirements of new energy pumping units.

Key words: wind energy, new energy pumping unit, support vector regression（SVR）, feedback linearization, power tracking

CLC Number: 

  • TK89

Fig.1

Structure diagram of beam pumping unit"

Fig.2

Structure diagram of wind turbine and electromotor hybrid transmission system"

Fig.3

Curve of the relationship between wind energy utilization coefficient and tip speed ratio"

Fig.4

Wind turbine characteristic curve"

Fig.5

Flow chart of parameter optimization based on PSO"

Fig.6

Fitness curve"

Fig.7

Regression result on test samples for C=142.847， δ2=187.851"

Fig.8

Step wind speed and pipeline pressure signal"

Fig.9

Comparison of wind turbine speed response"

Fig.10

Comparison of wind turbines capture wind power"

Fig.11

Comparison of wind turbine-pumpsystem output power"

Fig.12

Comparison of wind energy utilization coefficient"

Fig.13

Wind speed disturbance signal"

Fig.14

Comparison of speed stability whenwind speed disturbance"

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