V. Jayashankar

Bio: V. Jayashankar is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Partial discharge & Impulse (physics). The author has an hindex of 1, co-authored 1 publications receiving 35 citations.

Impulse testing of power transformers - a model reference approach

Abstract: An objective classification of faults that can occur during impulse tests on power transformers is proposed. It includes nonlinear elements, breakdown, and partial discharge events. An analysis of a layer winding, in the time and frequency domains, with these faults shows that current assessment methods must be used with caution. A model reference approach is proposed to distinctly improve recognition in such cases. The method is immune to changes of wave shape and the instant of fault is available by observation.

Transformer Design and Optimization: A Literature Survey

Abstract: With the fast-paced changing technologies in the power industry, new references addressing new technologies are coming to the market. Based on this fact, there is an urgent need to keep track of international experiences and activities taking place in the field of modern transformer design. The complexity of transformer design demands reliable and rigorous solution methods. A survey of current research reveals the continued interest in application of advanced techniques for transformer design optimization. This paper conducts a literature survey and reveals general backgrounds of research and developments in the field of transformer design and optimization for the past 35 years, based on more than 420 published articles, 50 transformer books, and 65 standards.

Mathematical Comparison Methods to Assess Transfer Functions of Transformers to Detect Different Types of Mechanical Faults

Abstract: The transfer function (TF) these days is a well-known method to detect different types of mechanical damage in power transformers. The most important mechanical faults mentioned by the authors and researchers, which are most likely to be detected using the TF and occur frequently in transformers, are disc-space variation, radial deformation, and axial displacement. These faults are investigated in this paper using three different similar-size test objects. Since the TF method is a comparative method and the measured results should be compared with the reference results, some mathematical methods are studied to compare different TFs. A complete fault detection, which means determining the type, location, and level of the faults by using TF analyses is the main aim of this paper.

Virtual Instrument for Lightning Impulse Tests

Abstract: We propose an objective formulation of the impulse analysis problem from a signal analysis viewpoint. The winding response is quintessentially that of a deterministic network to a finite energy signal, with breakdown and partial discharge being inherently nonlinear events. A significant improvement to the acquisition of waveforms is demonstrated by a virtual instrument approach. It retains the advantages of the time- and frequency-domain methods. The drawbacks of the transfer function method are highlighted and a new piecewise linear approach is proposed for analysis. Experiments on a discrete lumped parameter model of the winding are used to validate the PXI based instrument.

Cross-correlation aided wavelet network for classification of dynamic insulation failures in transformer winding during impulse test

Abstract: Wavelet network based approach for identification of fault characteristics of dynamic insulation failure during impulse test has been proposed. The network identifies the fault characteristics using the significant features extracted from cross-correlation sequence of winding currents of no-fault as well as impulse faulted winding insulation. The required winding current waveforms to extract significant features for identification of various fault characteristics are acquired by emulating different dynamic insulation failures in the analog model of 33 kV winding of 3 MVA transformer using developed analog fault simulator. The results show that the wavelet network using cross-correlation features has successfully identified the dynamic insulation failure characteristics, viz. fault type, condition and location of occurrence of failure along the length of the winding with acceptable accuracy. The efficacy of extracted features and developed wavelet network for fault characteristics identification is also compared with artificial neural network classifier. The concept of emulation of dynamic insulation failure, cross-correlation based feature extraction and wavelet based fault characteristics identification methods are explained.