Understanding the Influence of Power Transformer Faults on the Frequency Response Signature Using Simulation Analysis and Statistical Indicators

Al-Ameri, Salem Mgammal; Kamarudin, M. S.; Yousof, Mohd Fairouz Mohd; Salem, Ali Ahmed; Banakhr, Fahd; Mosaad, Mohamed I.; Abu-Siada, A.

doi:10.1109/access.2021.3076984

Cited by 27 publications

(11 citation statements)

References 30 publications

(29 reference statements)

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“…-Frequency response analysis FRA involves calculating the impedance of a transformer winding across a wide range of frequencies and the results are compared with a reference set [20], [21]. This test method is being gradually introduced in the field of power transformer testing and diagnosis [22].…”

Section: Rated Transformer Voltage Ratio =mentioning

confidence: 99%

Comparative study of electrical test methods on detecting transformer faults

Ghani

Khiar

Chairul

et al. 2022

IJEECS

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Condition monitoring of distribution and power transformers is of utmost importance to utilities due to cost effectiveness concerns. The common faults that occur in transformers are short circuits and winding deformation. To date, there are many established test methods used to detect these faults. In this study, three test methods (insulation resistance (IR), transformer turn ratio (TTR), and frequency response analysis (FRA)) were compared to assess their effectiveness in detecting short circuit and winding deformation in a three-phase transformer. Based on the results, the three test methods were found to be capable of detecting short circuits. However, only TTR and FRA can detect winding deformation, and FRA can further indicate which phase is faulty. Therefore, it is concluded that FRA is more effective in detecting short circuit and winding deformation of a three-phase transformer.

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Section: Rated Transformer Voltage Ratio =mentioning

confidence: 99%

Comparative study of electrical test methods on detecting transformer faults

Ghani

Khiar

Chairul

et al. 2022

IJEECS

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“…The work in [9] was conducted on a three-phase 11/0.433 kV, 500 kVA distribution transformer and calculated the correlation coefficient of the deviation from the fingerprint signature to identify the fault type. The work in [10] used the correlation between physical circuit parameters and various faults and quantified the impact of each fault with respect to the healthy signatures at different frequency regions. The work in [11] proposed a deep-learning-based framework named SigdetNet, which takes the power spectrum as the network's input to localize the spectral locations of the signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Mao

Wei

et al. 2022

Sensors

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The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy.

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“…A literature survey on these indexes was written by us [ 17 ]. However, according to Al-Ameri et al [ 18 ], consistent interpretations of FRA signatures are still challenging due to the lack of widely accepted FRA codes. Recent efforts in order to improve the interpretation of FRA signatures have been reported, usually using some form of machine learning, such as the works of Ferreira et al [ 19 ] and Li et al [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Online Frequency Response Analysis of Electric Machinery through an Active Coupling System Based on Power Electronics

Sant’Ana¹,

Lambert‐Torres²,

Bonaldi³

et al. 2021

Sensors

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This paper presents an innovative concept for the online application of Frequency Response Analysis (FRA). FRA is a well known technique that is applied to detect damage in electric machinery. As an offline technique, the machine under testing has to be removed from service—which may cause loss of production. Experimental adaptations of FRA to online operation are usually based on the use of passive high pass coupling—which, ideally, should provide attenuation to the grid voltage, and at the same time, allow the high frequency FRA signals to be injected at the machine. In practice, however, the passive coupling results in a trade-off between the required attenuation and the useful area obtained at the FRA spectra. This paper proposes the use of an active coupling system, based on power electronics, in order to cancel the grid voltage at the terminals of FRA equipment and allow its safe connection to an energized machine. The paper presents the basic concepts of FRA and the issue of online measurements. It also presents basic concepts about power electronics converters and the operating principles of the Modular Multilevel Converter, which enables the generation of an output voltage with low THD, which is important for tracking the grid voltage with minimum error.

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Understanding the Influence of Power Transformer Faults on the Frequency Response Signature Using Simulation Analysis and Statistical Indicators

Cited by 27 publications

References 30 publications

Comparative study of electrical test methods on detecting transformer faults

Comparative study of electrical test methods on detecting transformer faults

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Online Frequency Response Analysis of Electric Machinery through an Active Coupling System Based on Power Electronics

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