Wavelet‐based fast discrimination of transformer magnetizing inrush current

Abstract

Recently customers who have sensitive industrial loads have been installing co-generation facilities. They can avoid voltage sags and other distribution system related disturbances by supplying electricity to important load from their generators. For another example, FRIENDS, highly reliable distribution system using semiconductor switches or storage devices based on power electronics technology, is proposed. These examples illustrate that the need for high reliability in distribution system is increasing. In order to realize these systems, fast relaying algorithms are indispensable. The authors propose the necessity of protection devices that have the ability of processing fast relaying algorithms for distribution system. In designing fast protection systems, fast discrimination of magnetizing inrush current is also indispensable. Conventional method of inrush current detection for transformer protection utilizes the amount of second harmonic component of differential current. The author proposes a new method of detecting magnetizing inrush current using discrete wavelet transform. Fig. 1 shows the results of wavelet of inrush current. The upper figure shows a waveform of inrush current and the three figures below indicate wavelet components of level 1, 2 and 3. Wavelet Fig. 1. Wavelet components of inrush current and short-circuit fault current transform provides the function of detecting discontinuity of current waveform. accoding to state transition between non-saturation and saturation of transformer core. The proposed method detects spikes of the wavelet component of level 3 derived from the discontinuity of the current waveform at both the beginning and the end of inrush current. In spike detection algorithm, wavelet thresholding, one of the wavelet-based statistical modeling, was applied to estimate detection function of inrush current. Parameters of detection function, such as standard variation of the wavelet component was determined considering a distribution of wavelet component. The proposed method is verified using experimental data using sigle-phase transformer. The result of the spike detection using a moving window is shown in Fig. 2. In this figure, the result of spike detection is plotted together with original current waveform of each inrush and short-circuit fault. In the proposed method, inrush current is discriminated when the 2nd spike of wavelet component or level 3 disappeared. In this case, inrush current can be discriminated at 11.6 ms after inrush current began to flow. This simulation shows that the propsed method can can detect inrush current in less than 1 cycle and was proved to be effective. Fig. 2. Results of inrush current descrimination by spike detection-1

Time–time‐transform application to fault diagnosis of power transformers

“…It can be seen that unlike the noise‐free signal, spikes have occurred consistently and do not decay to zero rapidly. So, it can be said that wavelet‐based methods such as the methods suggested in [14, 15, 37] can be affected by noise. More details about these methods can be found in [14, 15, 37].…”

Characterization of internal disturbances and external faults in transformers using an S-transform--based algorithm

“…It is found that the noise has an obvious effect on the wavelet transform and it is difficult to distinguish between the internal fault and the inrush current. Hence, the spike-shaped waveforms affect the wavelet-based method (such as in [8], [14], and [32]) in a noisy environment.…”

“…hence, a large computational burden is required. Discrete wavelet transform (DWT)-based algorithms for discriminating between the magnetizing inrush current and short circuit current were suggested in [8] and * Correspondence: az.ashrafian@gmail.com [9]. Moreover, protective methods have been proposed using a combination of DWT and fuzzy logic [10,11], neural networks [5,12], Gaussian mixture model [13], correlation factor [14], and support vector machine [15,16].…”

A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

“…In the literature for fault detection, several decision algorithms 1–42 have been developed to be employed in the protective relay for preventing maloperation of the protective equipment under different nonfault conditions, including magnetizing inrush current, ratio mismatch, through‐fault current, etc. There are many techniques 1–42 for detecting faults, such as artificial neural networks (ANNs) 12,30,39,40, transient‐based protection 13,18–20, finite element 14, fuzzy logic 35, hybrid systems 14,32, and so on. An algorithm for protecting a transformer with three windings using the increments of flux linkages (IFLs) has been proposed by Kang et al 2.…”