TLM modeling of transformer with internal short circuit faults

COMPEL International Journal of Computations and Mathematics in Electrical (Impact Factor: 0.37). 11/2007; 26(5). DOI: 10.1108/03321640710823037


Purpose – The purpose of this paper is to describe a technique for modeling transformer internal faults using transmission line modeling (TLM) method. In this technique, a model for simulating a two winding single phase transformer is modified to be suitable for simulating an internal fault in both windings. Design/methodology/approach – TLM technique is mainly used for modeling transformer internal faults. This was first developed in early 1970s for modeling two-dimensional field problems. Since, then, it has been extended to cover three dimensional problems and circuit simulations. This technique helps to solve integro-differential equations of the analyzed circuit. TLM simulations of a single phase transformer are compared to a custom built transformer in laboratory environment. Findings – It has been concluded from the real time studies that if an internal fault occurs on the primary or secondary winding, the primary current will increase a bit and secondary current does not change much. However, a very big circulating current flows in the shorted turns. This phenomenon requires a detailed modeling aspect in TLM simulations. Therefore, a detailed inductance calculation including leakages is included in the simulations. This is a very important point in testing and evaluating protective relays. Since, the remnant flux in the transformer core is unknown at the beginning of the TLM simulation, all TLM initial conditions are accepted as zero. Research limitations/implications – The modeling technique presented in this paper is based on a low frequency (up to a few kHz) model of the custom-built transformer. A detailed capacitance model must be added to obtain a high-frequency model of the transformer. A detailed arc model, aging problem of the windings will be applied to model with TLM þ finite element method. Originality/value – Using TLM technique for dynamical modeling of transformer internal faults is the main contribution. This is an extended version of an earlier referenced paper of the authors and includes inductance calculation, leakages calculation, and BH curve simulation while the referenced paper only includes piecewise linear inductance values. This modeling approach may help power engineers and power system experts understand the behavior of the transformer under internal faults.

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    • "As a result, some improved methods such as short-time correlation (Zhang et al., 2002), correlation analysis of waveform (Lin et al., 2002; Bi et al., 2007), morphological scheme (Lu et al., 2009), and method based on error estimation (He et al., 2006) have been proposed to obtain better identification results. Other algorithms have taken advantage of wavelet transform (WT) (Ozgonenel, 2006; Samantaray et al., 2007), artificial neural network (ANN) (Tripathy et al., 2008), the fuzzy inference system (FIS) (Shin et al., 2003), the transmission line method (TLM) (Ozgonenel et al., 2007; 2008), hidden Markov models (HMM) (Ma and Shi, 2000), and principal component analysis (PCA) (Kilic et al., 2009). Some of the conventional methods require the equivalent circuit parameters as well as iron-core constructions and winding connections of the transformer . "
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    ABSTRACT: We propose a new scheme for transformer differential protection. This scheme uses different characteristics of the differential currents waveforms (DCWs) under internal fault and magnetizing inrush current conditions. The scheme is based on choosing an appropriate feature of the waveform and monitoring it during the post-disturbance instants. For this purpose, the signal feature is quantified by a discrimination function (DF). Discrimination between internal faults and magnetizing inrush currents is carried out by tracking the signs of three decision-making functions (DMFs) computed from the DFs for three phases. We also present a new algorithm related to the general scheme. The algorithm is based on monitoring the second derivative sign of DCW. The results show that all types of internal faults, even those accompanied by the magnetizing inrush, can be correctly identified from the inrush conditions about half a cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Furthermore, the proposed algorithm does not require burdensome computations.
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    ABSTRACT: Interturn short circuit faults are the leading cause of power transformer failures. If not quickly detected, these faults usually develop into more serious faults that would result in irreversible damage to the transformer, unexpected outages and the consequential costs. This contribution is aimed at obtaining a better understanding of physical behaviour of power transformers in the presence of interturn faults as well as extracting several features that would be useful to specify the faults. To this end, a circuit-coupled time-stepping finite-element model (FEM) of power transformer has been developed to characterise the transient behaviour of a real power transformer when the transformer is working under winding short circuit fault conditions. An experimental set-up consisting of the same transformer used with the FEM transformer simulation was used to validate the FEM of the faulty transformer. The results of the experiments demonstrate the remarkable ability of the model to reproduce the real behaviour of the transformer with interturn winding faults. The study characterises the faulty transformer behaviour under varying conditions of load, supplying voltage, location of the fault, and fault severity and size. Useful characteristic signatures associated with interturn faults extracted from the transformer behaviour under varying fault and transformer operating conditions are expected to yield insights into developing reliable and sensitive fault detection and localisation methods in power transformers.
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