Application of Frequency Response Analysis Technique to Detect Transformer Tap Changer Faults

This work presents an innovative application of Frequency Response Analysis (FRA) in order to detect early degradation of Metal Oxide Surge Arresters (MOSAs). This technique has been widely used in power transformers, but has never been applied to MOSAs. It consists in comparisons of spectra, measured at different instants of the lifetime of the arrester. Differences between these spectra are an indicator that some electrical properties of the arrester have changed. An incremental deterioration test has been performed on arrester samples (with controlled circulation of leakage current, which increases the energy dissipation over the device), and the FRA spectra correctly identified the progression of damage. Although preliminary, the FRA results seemed promising, and it is expected that this technology could be used as another diagnostic tool for arresters.

Section: Methodsmentioning

confidence: 99%

Detection of Failures in Metal Oxide Surge Arresters Using Frequency Response Analysis

Zacarias

Martins²,

Xavier³

et al. 2023

“…The frequency response analysis (FRA) technique is one main method to detect transformer tap changer faults. The experiment in [8] was conducted on a three-phase 50 Hz, 11/0.415 kV, 500 kVA distribution transformer with a de-energized tap changer and simulated the tap changer fault to use FRA measurements to identify various contact faults such as coking and pitting. 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.…”

Section: Literature Reviewmentioning

confidence: 99%

“…After pretraining and assigning the pseudolabels, we obtain a target model composed of F s θ and G t φ and a target dataset D t = D l t D pl t , the size of which was n + m. To make the distributions of the source and the target domain similar so that the model fit better, we supplement the target dataset with the weak types' fault data from the source domain based on the probability distribution of the number of the fault types, as shown in (8).…”

Section: Supplement Unbalancing Datasetmentioning

confidence: 99%

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Mao

Wei

et al. 2022

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.

“…According to Blanquez et al [ 7 ], despite the great developments in FRA for transformers, its application for rotating machines (motors and generators) is still under research. Additionally, some recent interesting applications of FRA include the work of Guerrero et al [ 8 ] on the detection of fluid degradation, the work of Al-Ameri et al [ 9 ] on the detection of failures in transformer tap changers and the work of Kumar et al [ 10 ] on the identification of the location of an inter-turn insulation fault in a transformer winding.…”

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

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.