Amit Bakshi

Bio: Amit Bakshi is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Transformer & Buckling. The author has an hindex of 4, co-authored 7 publications receiving 75 citations. Previous affiliations of Amit Bakshi include Shiv Nadar University.

Analysis of Buckling Strength of Inner Windings in Transformers Under Radial Short-Circuit Forces

Abstract: The buckling of conductors of inner windings in transformers is one of the major causes of their failures. It can occur when a large magnitude of radial short-circuit electromagnetic force acts on them. In this paper, initially, mechanical strains developed during winding processes and due to radial short-circuit forces have been determined. The two mechanical strains viz. the short-circuit induced strain and the winding process-induced strain are algebraically added to obtain their resulting strain. The stress corresponding to the resulting strain has been determined by using the Ramberg-Osgood stress-strain relation. The critical buckling stress has been calculated and compared with the resulting stress. The analytically obtained result of the strain induced in the winding conductor during its winding process has been verified using the finite-element method. A case study has been described in which the factor of safety against the buckling strength is determined.

Coupled Electromagnetic-Structural Analysis of the Spiraling Phenomenon in a Helical Winding of a Power Transformer

Abstract: Spiraling of conductors in a helical winding of a transformer may lead to a catastrophic failure under the action of torsional short-circuit electromagnetic forces. Torsional forces are produced by the interaction of the axial component of the short-circuit current and the radial component of the leakage magnetic flux density. In this paper, 3-D magnetostatic analysis has been performed to determine the leakage magnetic-field distribution in a 130-MVA power transformer. Torsional electromagnetic forces are calculated from the analysis to determine circumferential displacements of conductors of its low-voltage helical winding. Furthermore, mechanical stress components are computed and a factor of safety is defined, which gives an indication as to whether the winding conductors are in the elastic or plastic zone. The numerical results thus obtained by the finite-element method are verified using the first principles from mechanics. The effect of looseness in the inner support structure on the von-Mises equivalent stress and, hence, on the factor of safety has been studied.

Eigenvalue Analysis for Investigation of Tilting of Transformer Winding Conductors Under Axial Short-Circuit Forces

Abstract: The tilting phenomenon may lead to a catastrophic failure, such as an interturn fault in transformers, under the action of axial short-circuit electromagnetic forces. In this paper, first, a state-space approach has been used to determine the eigenvalues of various winding configurations used in practice by assuming a small value of tilt angle up to 10°. These eigenvalues decide the critical tilting forces and the natural frequencies of windings. The four cases involving disk, layer, helical windings (all these with strip conductor), and layer winding with continuously transposed cable conductor have been analyzed. Further, initial critical tilt angles are determined for various magnitudes of the axial force in a typical 5-MVA transformer.

An Investigation of Winding Curvature Effect on the Mechanical Strength of Transformer Windings

Abstract: During the winding process of a transformer, a residual strain is induced in its windings conductors. In this paper, the effect of the strain on the mechanical strength of transformer windings under the worst case condition of the short circuit has been investigated. The distribution of the residual strain in the conductors during its winding process has been determined analytically. The strain induced in the conductors under the action of radial short-circuit forces has been computed. The two strains are then added to obtain a resulting strain. By using the Ramberg-Osgood stress-strain relation of the conductor (copper) material, resulting stress is determined. A case study has been given where the presence of residual strain in the conductors of a power transformer has been taken into account for determining its mechanical strength under the worst case condition of short circuit.

Effect of Width of Axial Supporting Spacers on the Buckling Strength of Transformer Inner Winding

Abstract: During a short circuit, the transformer's inner winding, which is supported by the inner support structure, may fail due to the buckling phenomenon. This occurs due to the generation of large radially inward electromagnetic force on the inner winding. In the literature, the effect of the number of axial supporting spacers on the critical buckling stress is reported, but there is no investigation about the effect of their circumferential width on the value of the critical buckling stress. In this letter, the effect of the circumferential width of the axial supporting spacers on the value of critical buckling stress of the transformer inner winding has been investigated. An algorithm has been given to calculate the critical buckling stress of the inner winding. A case study has been taken from the published work, and by varying the width of the spacers, the factor of safety against buckling has been determined.

Short-Circuit Strength of Power Transformer Windings-Verification of Tests by a Finite Element Analysis-Based Model

Abstract: A test stand to evaluate the radial buckling strength of power transformer windings under the influence of electromagnetic forces is presented. The resulting conductor deformation can be measured in parallel to the sinusoidal test current. The proposed test results focus on the inception of forced buckling for continuously transposed conductors (CTCs), which are a special type of conductor often used in power transformer windings. In the literature, there barely exist calculations to the radial buckling withstand capability of CTCs. Therefore, a finite-element analysis-based simulation model for this kind of conductor is proposed and verified by the tests. For this verification, three different CTC types are used. The final simulation model is based on a coupled analysis of magnetostatic force calculation and static structural deformation analysis. It suitably reproduces the measurement results from the dynamic short-circuit tests. Furthermore, a standard formula describing radial buckling phenomena inside power transformers is adapted for use with CTCs.

Research of Short-Circuit Performance of a Split-Winding Transformer With Stabilizing Windings

Abstract: Short-circuit faults are inevitable in split-winding transformers with stabilizing windings, and the resulting transient electromagnetic force may cause detrimental damages to the equipment. This paper focuses on a comprehensive analysis of the characteristics of the split-winding transformer with stabilizing windings under different short-circuit faults. In this regard, a FEM based on a field-circuit coupled approach is proposed. Also, a SFFZ10-88000-kVA split-winding transformer with stabilizing windings is used as a prototype to investigate its transient performances with both full-crossing and half-crossing conditions under different short-circuit faults. The symmetrical component method is presented to compute short-circuit currents to compare with the simulation ones, and a prototype test model is established to verify the correctness of the proposed method. The results reveal that the axial forces exerted on the winding in half-crossing short-circuit faults are generally larger than those in full-crossing short-circuit faults. Moreover, there is a considerable short-circuit force in the stabilizing winding in cases of a single-phase earthed fault and a two-phase earthed fault and there is no current in the stabilizing winding under other short-circuit fault cases. The numerical modeling approach dealt with in this paper is expected to be useful in the design of the split-winding transformer with stabilizing windings.

Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach

Abstract: This paper introduces a novel fault diagnosis approach for transformer based on self-powered radio-frequency identification (RFID) sensor and deep learning technique. The exploited RFID sensor tag with functionalities of signal collection, data storage, and wireless transmission employs surrounding electromagnetic field as power source. A customized power management circuit, including ac–dc converter, supercapacitor, and its corresponding charging circuit, is presented to guarantee constant dc power for the sensor tag. The measured vibration signal contains miscellaneous noises and is characterized as nonlinearity and nonstationarity, so it is difficult to extract robust and useful features by using traditional feature extraction approaches. As one of the deep learning techniques, stacked denoising autoencoder (SDA) shows satisfactory performance in learning robust features from complex signal. Hence, in this paper, SDA approach is employed to learn robust and discriminative features from measured signals. The experimental results show that the presented power supply can generate 2.5-V dc voltage, which is the rated operating voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.3 m in the test environment. Furthermore, the SDA approach shows satisfactory performance in learning robust and discriminative features. Experimental results indicate that the presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core.

Analysis of Structural Behavior of Transformer's Winding Under Inrush Current Conditions

Abstract: The objective of this paper is to investigate the behavior of electromagnetic forces during the occurrence of faults inside transformers as result of transients in the electrical systems. The methodology is based on the modeling of a single-phase 50-MVA transformer subjected to inrush currents through finite-element method. In this perspective, the values of inrush currents, obtained by the alternative transient program software, are used to estimate the magnetic field density dispersion in the transformer, and to find the values of forces in axial and radial directions. These forces components are distributed along the energized windings for observing the loads behavior in high-voltage windings. This paper will thus present investigations of electromagnetic forces, structural deformation, stresses, and safety factor on transformer's winding when subjected to inrush current.

Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM

Abstract: An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.