Earticle

초록

영어

The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back Propagation (BP) neural network algorithm is respectively combined with genetic algorithm (GA) and particle swarm optimization (PSO) to establish the combination prediction model of the short-term wind farm output power based on meteorological data collected by Wireless Sensor Network (WSN). The meteorological data is used to determine the input variables of the BP neural network. Meanwhile, the GA and the PSO is respectively used to adjust the value of BP's connection weight and threshold dynamically. Then the trained GA-BP and PSO-BP neural network are used to predict the wind power by combination method. The experiment results show that our method has better prediction capability compared with that using BP neural network, GA-BP neural network and PSO-BP neural network alone.

목차

Abstract
 1. Introduction
 2. Meteorological Data Collected by WSN
 3. Data and Methods
  3.1 The Selection of Data
  3.2 The Determination of BP Neural Network Structure
  3.3 The Connection Weights and Thresholds of BP Neural Network Adjusted by GA and PSO
  3.4 The Combination Prediction Model
 4. Experiment Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Li Ma [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 , Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Bo Li [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044 2 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Zhen Bin Yang [ CMA Public Meteorological Service Centre, Beijing 100081 ]
• Jie Du [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044 2 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Jin Wang [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044 2 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605