ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||7|
|Section||Control Technology and Robotics Technology|
|Published online||23 June 2022|
A hybrid artificial bee colony algorithm for transformer vibration fundamental frequency amplitude prediction
State Grid Xinzhou Power Supply Company, Shanxi, China
* Corresponding author: email@example.com
The fundamental frequency amplitude of transformer surface vibration signal is an important basis for judging transformer status. It is very important to predict the amplitude of fundamental frequency quickly and accurately. In this paper, a method is proposed to optimize the prediction of the transformer vibration fundamental frequency amplitude by modifying the artificial bee colony algorithm. An opposition-based learning mechanism is introduced and the search formula of each bee species is improved at the initial stage of the artificial bee colony algorithm. The performance of the proposed method is evaluated by five standard test functions and transformer vibration fundamental frequency amplitude prediction. Experimental results show that the proposed method is much better than the original artificial bee colony algorithm in search accuracy, convergence speed, and robustness, and improve the prediction accuracy.
Key words: Vibration signal / Fundamental frequency amplitude / Modified artificial bee colony algorithm
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.