TY - GEN
T1 - A New Transformer FRA Test Setup for Advanced Interpretation and Winding Short-circuit Prediction
AU - Nurmanova, Venera
AU - Akhmetov, Yerbol
AU - Bagheri, Mehdi
AU - Zollanvari, Amin
AU - Gharehpetian, Gevork B.
AU - Phung, Toan
N1 - Funding Information:
This work was supported in part by Faculty Development Competitive Research Grant (Project No. SOE2018018), Nazarbayev University.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6
Y1 - 2020/6
N2 - The significance of the distribution and power transformers as the key components of the electric power grid is not overrated. Therefore, a thorough diagnosis and quality maintenance procedures are matters of the primary interest for power companies. Among most accurate and well-established transformer mechanical condition monitoring methods is the Frequency Response Analysis (FRA). FRA has a quite solid background in terms of research, industry application and international standardization. Nevertheless, the interpretation of the FRA still suffers from the ambiguity of the test results. In this paper, a modified measurement setup for FRA is proposed in order to collect the comprehensive data sets for statistical analysis and machine learning application. Based on the collected data a winding short-circuit detection and fault severity prediction model is developed. The estimated accuracy of the proposed interpretation and classification method demonstrated the validity of the novelty approach.
AB - The significance of the distribution and power transformers as the key components of the electric power grid is not overrated. Therefore, a thorough diagnosis and quality maintenance procedures are matters of the primary interest for power companies. Among most accurate and well-established transformer mechanical condition monitoring methods is the Frequency Response Analysis (FRA). FRA has a quite solid background in terms of research, industry application and international standardization. Nevertheless, the interpretation of the FRA still suffers from the ambiguity of the test results. In this paper, a modified measurement setup for FRA is proposed in order to collect the comprehensive data sets for statistical analysis and machine learning application. Based on the collected data a winding short-circuit detection and fault severity prediction model is developed. The estimated accuracy of the proposed interpretation and classification method demonstrated the validity of the novelty approach.
KW - fault detection
KW - FRA interpretation
KW - Frequency Response Analysis (FRA)
KW - severity prediction
KW - winding short-circuit
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U2 - 10.1109/EEEIC/ICPSEurope49358.2020.9160545
DO - 10.1109/EEEIC/ICPSEurope49358.2020.9160545
M3 - Conference contribution
AN - SCOPUS:85090408547
T3 - Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
BT - Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
A2 - Leonowicz, Zhigniew
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
Y2 - 9 June 2020 through 12 June 2020
ER -