@inproceedings{0e84196e04554953a711bfe8d33b54b2,
title = "Real-time Transformer Diagnosis using Voltage-Current Signal over Cloud Environment",
abstract = "Most transformer diagnosis methods can only be performed off-line when the transformer is taken out of service. This study is specifically focused on the V-I locus method for real-time transformer active part evaluation over the cloud environment and assessment of captured data through cloud computing. Experiments are carried out on a test setup to study turn-to-turn short circuit fault created on small transformers. Transformer mechanical fault recognition is discussed and the voltage/current technique is evaluated. Data obtained from practical measurements is analysed over cloud environment and assessment of the transformer condition is performed via application on mobile device. Also, protection relay connected to the transformer can be activated via cloud-based data assessment.",
keywords = "Cloud computing, Internet of Things (IoT) application, Real-time transformer deformation evaluation, Transformer diagnosis",
author = "Altynay Smagulova and Aigerim Borasheva and Nurtas Moldiyar and Nurbolat Bazarbek and Mehdi Bagheri and Phung, {B. T.}",
note = "Funding Information: This research work was supported by Faculty Development Competitive Research Grant of Nazarbayev University (Grant Award No. 090118FD5318).; 7th International Conference on Condition Monitoring and Diagnosis, CMD 2018 ; Conference date: 23-09-2018 Through 26-09-2018",
year = "2018",
month = nov,
day = "14",
doi = "10.1109/CMD.2018.8535876",
language = "English",
series = "2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Ahmed Abu-Siada",
booktitle = "2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings",
address = "United States",
}