Transformer Leakage Flux Frequencies Analysis under Internal Windings Faults

Hajiaghasi, Salman; Ahmadi, Mohammad; Rafiee, Zahra; Selemnia, Ahmad

doi:10.1109/iraniancee.2019.8786753

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…In the vibration-based method, the sensor measures the amount of vibration. First, the amount of electromagnetic force is obtained using Equation (5); afterwards, the flux density amount will be obtained using Equation (3). In this case, the differences between the vibration of the healthy state and the faulty state are used to calculate the leakage flux.…”

Section: Discussionmentioning

confidence: 99%

“…In the second case, the fault is in the middle of the winding, and the DC amount is larger than the healthy state. Figure 28 shows that at a frequency of 100 Hz with increasing fault severity, the winding vibration increases based on Equation (3).…”

Section: Fault Detection Based On Vibration Analysismentioning

confidence: 99%

“…Studies demonstrate that 70%-80% of the power transformer damages arise from internal faults [1]. At the low current side, inter-turn faults detection is a critical issue that has been surveyed in various research [2][3][4][5]. The incidence percentage of different fault types in a transformer is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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

Hajiaghasi

Ahmadi

Goleij

et al. 2021

IET Electric Power Appl

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Fault Detection Based On Vibration Analysismentioning

confidence: 99%

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

Hajiaghasi

Ahmadi

Goleij

et al. 2021

IET Electric Power Appl

Self Cite

View full text Add to dashboard Cite

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Transformer Fault Diagnosis Method based on PSO-GMNN Model

2023

EEENG

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Background: Oil-immersed distribution transformer is an important power transmission and distribution equipment in the power system. If it fails, it will cause huge economic losses and safety hazards. It is of great significance to identify and diagnose its faults, find potential faults in time, and restore normal operation. Objective: To detect transformer fault, a transformer fault diagnosis method based on Graph Markov Neural Networks for Particle Swarm Optimization algorithm (PSO-GMNN) is proposed. Methods: Five common dissolved gases in transformer oil are used to construct a 22-dimensional feature set to be selected, and then the similarity between each feature vector is calculated by using Mahalanobis Distance. The graph structure is constructed with feature vectors as vertices and similarities as edges. Finally, the Particle Swarm Optimization algorithm is used to optimize the initial weights of Graph Markov Neural Networks, and then transformer fault diagnosis is realized. Results: The experiments are performed in the environment of Python 3.7, PyTorch 1.6.0, and the validity of the proposed method is verified by a comparative analysis of the detection accuracy between the proposed method and existing mainstream methods. Conclusion: A transformer fault diagnosis method based on Graph Markov Neural Networks for Particle Swarm Optimization algorithm is proposed to detect transformer fault, and the experimental results demonstrate the effectiveness and advantage of the proposed method.

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Transformer Leakage Flux Frequencies Analysis under Internal Windings Faults

Cited by 2 publications

References 9 publications

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

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

Transformer Fault Diagnosis Method based on PSO-GMNN Model

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