Research on fault diagnosis for power transformer based on random forests and wavelet transform Online publication date: Fri, 31-May-2024
by Ming Zhang; Chongfeng Fang; Shuang Ji
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 26, No. 4, 2024
Abstract: Transformers are electrical equipment widely used in power systems and electronic circuits. In order to improve the accuracy of power transformer fault diagnosis and condition monitoring, this paper proposes a fault diagnosis method for power transformers based on Random Forests (RFs) and wavelet transform. Firstly, the wavelet transform method is adopted to decompose the noisy vibration signal into multi-scales, and then the detailed signals at different scales are processed to achieve fault feature extraction of the power transformer vibration signals. Secondly, the mapping relationship between fault features and fault types of vibration signals is established by RFs algorithm, and the fault diagnosis model is trained by RFs algorithm. Finally, by identifying the experimental data of the normal and fault states of the power transformers, the accuracy reached 96.52%, which is suitable for monitoring and diagnosing the different working states of the power transformers.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook