Abstract
Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.
Original language | English |
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Article number | 8682123 |
Pages (from-to) | 1508-1519 |
Number of pages | 12 |
Journal | IEEE Transactions on Power Delivery |
Volume | 34 |
Issue number | 4 |
DOIs | |
Publication status | Published - Aug 1 2019 |
Funding
Manuscript received October 29, 2018; revised February 17, 2019; accepted March 20, 2019. Date of publication April 9, 2019; date of current version July 23, 2019. This work was supported by Faculty Development Competitive Research Grant of Nazarbayev University under Project SOE2018018. Paper no. TPWRD-01290-2018. (Corresponding author: Mehdi Bagheri.) V. Nurmanova, M. Bagheri, A. Zollanvari, K. Aliakhmet, and Y. Akhme-tov are with the Electrical and Computer Engineering Department, Nazarbayev University, Astana 01000, Kazakhstan (e-mail:, [email protected]; [email protected]; [email protected]; kamilla.aliakhmet@ nu.edu.kz; [email protected]).
Keywords
- FRA interpretation
- Frequency response analysis (FRA)
- online monitoring
- short-circuit detection
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering