Published in: Proceedings of PMAPS 2016 (Beijing, China, 16-20 Oct. 2016)
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Pages: 6
ISBN: 978-1-5090-1970-0
Conference Location: Beijing, China
Year: 2016
Link: Link DOI: 10.1109/PMAPS.2016.7764057

Hits: 905

Abstract

The integrity of wind power output data is of great significance for the accurate prediction of wind power and the utilization of wind energy. In this paper, it is found that the power output affected by many factors, through the analysis of the mathematical model of wind turbine, and the solution of the specific expressions of the relationship with the traditional mathematical methods is hard to find. Based on the measured data of wind field, such as fan current, rotor speed, wind direction, and so on, a kind of model based on adaptive BP neural network is proposed to fill the missing wind power data. The simulation experiment shows that the accuracy rate and the average relative error of complete data get better results, besides the quality of completed data is improved effectively.

Next Event

PMAPS 2024
24-27 June 2024
Auckland, NZ

About Us

We are PMAPS IS - The International Conference on Probabilistic Methods Applied to Power Systems fills a needed role in the power engineering community by providing a regular forum for engineers and scientists worldwide.

Stay Connected on:

    

Contacts

For general information about the event/expo/conference, including registration, please contact us at:
 info (@) pmaps.world
  +123.123.1234
  +123.123.1234
  2nd Ave and Canada
We use cookies to improve your experience on our website. By browsing this website, you agree to our use of cookies.