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.
Performance evaluation of on-line transformer winding short circuit fault detection based on instantaneous voltage and current measurements
- Authors: Masoum, Ali , Hashemnia, Seyednaser , Abu-Siada, Ahmed , Masoum, Mohammad , Islam, Syed
- Date: 2014
- Type: Text , Conference proceedings , Conference paper
- Relation: 2014 IEEE Power and Energy Society General Meeting; National Harbor, United States; 27th-31st July 2014 Vol. 2014, p. 1-5
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- Description: This paper investigates the performance of a recently proposed on-line transformer winding short circuit fault detection approach through detailed nonlinear three-dimensional finite element modelling of windings, magnetic core and transformer tank. The technique considers correlation of instantaneous input and output voltage difference Δ V=(v1(t)-v2(t)) and instantaneous input current I=i(t) at the power frequency as a fingerprint of the transformer. The on-line measured ΔV-I locus of healthy and faulty transformer are compared to detect the internal fault. A detailed three-dimensional finite element transformer models based on the physical dimensions, parameters and magnetic core characteristics are developed and used to emulate internal winding short circuit faults and calculate the corresponding transformer ΔV-I locus. Detailed simulations and some laboratory measurements are performed and analysed to investigate the impact of winding short circuit faults on the on-line transformer ΔV-I locus.
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.
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.
Enhancing dynamic ECG heartbeat classification with lightweight transformer model
- Authors: Meng, Lingxiao , Tan, Wenjun , Ma, Jiangang , Wang, Ruofei , Yin, Xiaoxia , Zhang, Yanchun
- Date: 2022
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 124, no. (2022), p.
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- Description: Arrhythmia is a common class of Cardiovascular disease which is the cause for over 31% of all death over the world, according to WHOs' report. Automatic detection and classification of arrhythmia, as an effective tool of early warning, has recently been received more and more attention, especially in the applications of wearable devices for data capturing. However, different from traditional application scenarios, wearable electrocardiogram (ECG) devices have some drawbacks, such as being subject to multiple abnormal interferences, thus making accurate ventricular contraction (PVC) and supraventricular premature beat (SPB) detection to be more challenging. The traditional models for heartbeat classification suffer from the problem of large-scale parameters and the performance in dynamic ECG heartbeat classification is not satisfactory. In this paper, we propose a novel light model Lightweight Fussing Transformer to address these problems. We developed a more lightweight structure named LightConv Attention (LCA) to replace the self-attention of Fussing Transformer. LCA has reached remarkable performance level equal to or higher than self-attention with fewer parameters. In particular, we designed a stronger embedding structure (Convolutional Neural Network with attention mechanism) to enhance the weight of features of internal morphology of the heartbeat. Furthermore, we have implemented the proposed methods on real datasets and experimental results have demonstrated outstanding accuracy of detecting PVC and SPB. © 2022 Elsevier B.V.