Study on locating transformer internal faults using sweep frequency response analysis

Khanali, Mahdi; Hayati-Soloot, Amir; Høidalen, Hans Kristian; Jayaram, S.

doi:10.1016/j.epsr.2016.11.016

Cited by 21 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, to prove the powerful ability of the proposed method to monitor and locate minor latent faults, the fault severity is set to vary randomly from 10% to 50%. The formulas for calculating the component parameters in fault state are shown in equation ( 2)- (2). In this way, even for samples with the same label (ie, the same fault location), due to the randomness of the type and severities of the fault occurring at the fault location, the fault characteristics are not exactly the same.…”

Section: Proposed Methods a The Prinsiple Of Dataset Aquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Most of the power transformer fault diagnostic researches so far focuses on its fault type diagnosis, but there are less related researches on fault positioning, and the diagnostic methods of which are still less intelligent. This paper proposes a two-dimensional Hilbert ID considering multi-window feature extraction for deep vision fault positioning of the transformer winding. Firstly, sweep frequency response data containing complex fault characteristics is obtained based on pspice simulation. Next, a multi-window feature extraction method with logarithmic constraints is introduced to process the original data to obtain feature sequences. Then the proposed Hilbert visualization is used to further highlight the graphic feature of the feature sequences, and obtain Hilbert ID (MAPE) dataset. Finally, it is used to conduct transfer learning on the convolutional neural network. Different intelligent positioning methods are compared, and the proposed deep vision fault positioning method is 6.51% higher than other methods on average. What's more, the positioning effects based on different data processing methods are also compared. The accuracy of the proposed Hibert ID (MAPE) dataset is 10.35% higher than the other data processing methods on average. Finally, the positioning accuracy of Hilbert ID (MAPE+CC) combining two feature sequences can reach 96.09%, having an increase of 2.50%.INDEX TERMS Convolutional neural network (CNN), deep transfer learning (DTL), fault positioning, Hilbert visualization, multi-window feature extraction, power transformer, sweep frequency response analysis (SFRA).

show abstract

Section: Proposed Methods a The Prinsiple Of Dataset Aquisitionmentioning

confidence: 99%

“…Fault diagnosis of key power equipment such as power transformer is an important part of power systems for its safe and economic operation [1], [2]. With the further construction of smart grid, power equipment will continue to develop in the direction of intelligence and high integration.…”

Section: Introductionmentioning

confidence: 99%

Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The hardware of the SFRA system is meticulously engineered to identify winding displacements and potential faults in the magnetic core of power transformers. In the philosophy of testing, a voltage wave is applied to one of the transformer coils, where this wave is a small amplitude sinusoidal wave (2-15V) and variable frequency (from 20 Hz to 2 MHz) [9], [10], [11], according to the standards of the International Electrotechnical Commission [12]. Then, this applied voltage is measured to serve as a reference wave, and the output voltage is measured to be the response wave as shown in Fig.…”

Section: Experimental Workmentioning

confidence: 99%

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Salim,

Rouabhi,

Ayad

et al. 2024

Diagnostyka

View full text Add to dashboard Cite

A several diagnostic methods used to evaluate and detect any potential issues or faults in the transformer before they cause significant problems. The research proposal focuses on employing the SFRA (Sweep Frequency Response Analysis) technique, renowned for its exceptional sensitivity and diagnostic capabilities. This method serves to identify the mechanical integrity of the transformer's core, winding distortion, and clamping structures by analyzing their electrical transfer functions across a broad frequency spectrum. By utilizing SFRA, the study aims to accurately predict the internal physical condition of the transformer, making it a highly effective and reliable indicator for assessing its overall health. The motivation of this present work is using all experiments of SFRA were conducted and validated on a three-phase 30kV/0.4KV voltage transformer with 50kVA at the laboratory of Msila university. The result of these experiments is presented and discussed in terms of interpretation the analyses based on different standards.

show abstract

“…The leakage flux fault detection strategies are classified into the vibration-based method (VBM) and search coil-based method (SCBM), and flux leakage-based methods; they were studied and compared. This modelling aims to achieve an FRA -The capacitive effect can be detected at high frequency -This method needs previous data on the transformer -This method needs complicated tools for detection -Offline method -Needs expert's opinion and sophisticated instruments [12,[16][17][18] Negative sequence -The signal for fault detection is available -Unable to locate the fault [19,20] Partial discharge -Well-established method in power utilities -This method is under the influence of tank and windings [21][22][23] Flux-based method -Precise and accurate -Exact fault location detection -Changes in the transformer structure -Models developed for verification purposes -Requires the details of the transformer structure and sensors [7,8,11,24] Voltage and current measurement -Models developed for verification purpose -Unable to locate the fault [9,25,26] Differential protection -Classical robust method -Online monitoring is possible -Unable to detect inter-turn faults at initial levels -Mainly depends on the precision of the current transformer -Sensitive to winding insulations breakdown -Sensitive to the structure of the transformer [27][28][29] Intelligent approach -Detect minute faults -Robust against missing data…”

Section: Introductionmentioning

confidence: 99%

Transformer inter‐turn failure detection based on leakage flux and vibration analysis

Hajiaghasi

Ahmadi

Goleij

et al. 2021

IET Electric Power Appl

View full text Add to dashboard Cite

Inter‐turn winding fault is one of the most critical failures of the transformers. Here, a comprehensive transformer leakage flux analysis under inter‐turn winding fault is presented, and the feasibility and sensitivity of leakage flux‐based fault detection methods are investigated. The leakage flux fault detection strategies are classified into vibration‐based methods and search coil‐based methods. Flux leakage‐based methods have been studied and compared. Moreover, the effects of various important factors, such as fault severity, fault location, load power factor, and loading rate in transformer under inter‐turn faults are studied. Furthermore, two new online methods based on leakage flux and vibration analysis are proposed. In the first method, search coils voltage analysis is used for fault detection. In the second one, the vibration of the transformer is measured using the Digital Accelerometer (ADXL) sensor and compared with the vibration of the normal conditions, and based on the amount of vibration, fault detection is performed. The simulation and experimentation results on 20/0.4 kV, 50 kVA distribution transformer demonstrated that the implementation of leakage flux‐based methods provides valuable information on the transformer behaviour, which can be used to detect transformer winding faults.

show abstract

Study on locating transformer internal faults using sweep frequency response analysis

Cited by 21 publications

References 9 publications

Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Testing and diagnostic out of the service power transformer using a sweep frequency response analysis (SFRA) comparing with China standard

Transformer inter‐turn failure detection based on leakage flux and vibration analysis

Contact Info

Product

Resources

About