Development of two indices based on discrete wavelet transform for transformer differential protection

Zendehdel, Mohammad Hossein; Sanaye‐Pasand, M.

doi:10.1002/etep.626

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…[11] As a modern transformer diagnostic technique, stochastic Petri Nets are proposed for modeling of fault diagnosis process of transformers. [7] Although there are a lot of studies about transformer protection methods that distinguish between transformer internal faults and inrush currents, [12][13][14][15][16] there are few discussions about the protection of EAF transformer. However, there are several studies that discuss protection of EAF transformer against switching transients.…”

Section: Introductionmentioning

confidence: 99%

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

Marvasti

Samet

2013

Int. Trans. Electr. Energ. Syst.

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This paper presents a novel methodology for detecting fault conditions in the secondary side of electric arc furnace (EAF) transformer. The major focus of this study is to design a digital algorithm which uses solely the primary side current. Therefore, in this new methodology, only the primary side current is measured, and the fault presence in the secondary side is investigated. Here, there are two main objectives. The first objective is minimizing the fail-to-trip functioning probability, and the second one is minimizing the Mal-Trip operation probability. The fault characteristics are extracted using accurate simulation of EAFs installed in the Mobarakeh Steel Company (MSC) in Isfahan, Iran to fulfill the first objective. Three-phase field data of instantaneous current from the primary side of eight EAF transformers of MSC is also provided to evaluate performance of the proposed algorithm in the normal conditions and to achieve the second objective. The proposed algorithm is based on the sudden reduction of current harmonics in the fault conditions which is evaluated by a proposed special index which is called "the difference function" in this paper. Overall reliability assessment (dependability and security) of the proposed protective scheme demonstrates that even in this highly varying and unpredictable environment, the proposed algorithm is so precise and fast in fault detection. Figure 14. Harmonic components of one of the measured records in the normal conditions. a) Fundamental. b) Second. c) Third. d) Fourth.

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Section: Introductionmentioning

confidence: 99%

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

Marvasti

Samet

2013

Int. Trans. Electr. Energ. Syst.

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“…Also, in , a frequency curves analysis‐based method for transformers differential protection, but operation time of this method is long because of calculation the frequency curves. Also , a technique for discrimination between inrush current and internal faults based on discrete wavelet transform is presented, and in , a runs test‐based method for discrimination between internal faults and inrush currents is introduced. But in noisy condition, these methods have difficulty, and also implementations of these methods are not simple.…”

Section: Introductionmentioning

confidence: 99%

A novel technique for internal fault detection of power transformers based on moving windows

Abniki

Sanaye‐Pasand

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY Power transformer differential relays may incorrectly operate due to inrush condition or transformer external faults lead to current transformer (CT) saturation. In this paper, a new technique is presented to distinguish internal fault current from magnetizing inrush current in power transformers. The proposed technique applies two moving windows which both of them estimate magnitude of transformer differential current using different methods. The first moving window method is based on full‐cycle Fourier algorithm (long window) which uses one cycle samples of the current and provides precise estimation. The second one is originated from least error square (LES) method that has five sample point input data (short window). LES method has fast response, but its estimation might not be quite accurate. The proposed technique (which is a combination of two mentioned methods) presents a new criterion for discrimination between internal fault currents and inrush currents using the differences between output values of these two estimators. Meanwhile, obtained results demonstrate precise operation of the proposed algorithm for different conditions such as CT saturation and over‐flux condition; therefore, it can be a multi‐objective technique for transformer protection. Also, a general scheme is presented in order to improve differential protection. In this paper, a real part of 400/230 kV Iranian national grid is simulated using PSCAD software for evaluating the performance of the proposed algorithm. Also, additional validation of the proposed technique is tested offline using data collected from a three‐phase, 5 kVA, 60 Hz, laboratory prototype transformer. Copyright © 2013 John Wiley & Sons, Ltd.

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“…However, the use of this method has drawbacks, which mainly include that there are no accurate rules to set the parameters of neural network as well as the so long time that is taken for pattern learning process. Wavelet Transform (WT) and Discrete Wavelet Transform (DWT) were also used to distinguish between internal faults and inrush case [4], [17]. Wavelet transform is a mathematical expression that can be used for analysing the frequency of the signal when it changes with respect to time.…”

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confidence: 99%

Current signal processing-based methods to discriminate internal faults from magnetizing inrush current

Etumi

Anayi

2020

Electr Eng

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Two new methods, Current Change Ratio (CCR) and Percentage Area Difference (PAD) were proposed to solve a problem of how to distinguish between internal faults and inrush condition when transformer is switched on. This problem may delay operation or may mal-operate some protection schemes like deferential protection. The methods were concluded after observing and analyzing the behavior and shape of large number of both inrush and internal fault signals that had been obtained using a model transformer in a laboratory. The methods were practically tested on a three phase transformer with rated power of 20kVA at Cardiff University's laboratory and the data were processed using LabVIEW and MATLAB programs. The results showed that internal faults can be correctly distinguished from inrush condition within a short time (from 5ms to 10ms), particularly the minor internal faults such as the inter-turn fault which is submerged to inrush current and make it is too difficult to be detected. The advantages of these algorithms are simple in design and faster than the second harmonic method which is the most popular method used for solving this problem.

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Development of two indices based on discrete wavelet transform for transformer differential protection

Cited by 5 publications

References 33 publications

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

A novel technique for internal fault detection of power transformers based on moving windows

Current signal processing-based methods to discriminate internal faults from magnetizing inrush current

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