Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine
- Authors: Liu, Jiangnan , Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 112494-112504
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- Description: As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.
Understanding online frequency response signatures for transformer winding deformation: Axial displacement simulation
- Authors: Zhao, Zhongyong , Islam, Syed , Hashemnia, Naser , Hu, Di , Yao, Chenguo
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2016 International Conference on Condition Monitoring and Diagnosis, CMD 2016; Xi'an, China; 25th-28th September 2016 p. 404-407
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- Description: The power transformer is considered as the most critical and expensive device in substation, however, the irreversible transformer winding mechanical deformation can eventually develop into catastrophic failure if no further steps are taken in a proper way, which would cause the outage of transformer and the significant economic losses. Online frequency response analysis (FRA) has been proven to be a promising tool for condition monitoring and diagnosing of winding deformation. Online FRA relies on graphic comparison of signatures, but up to now, there is no standard and practical interpretation code for signatures classification and quantification. This paper particularly studies the characteristic of online FRA signatures under the winding axial displacement mode, in which the 3D finite element electromagnetic analysis and online transformer equivalent high frequency electrical model are established as auxiliary tools to precisely emulate winding axial displacement. Results of this simulation will provide guidance on understanding online frequency response signatures.
Detection of power transformer winding deformation using improved FRA based on binary morphology and extreme point variation
- Authors: Zhao, Zhongyong , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 65, no. 4 (2018), p. 3509-3519
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- Description: IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.