Fault diagnosis in transmission lines using wavelet transform analysis

Makming, P.; Bunjongjit, S.; Kunakorn, A.; Jiriwibhakorn, Somchat; Kando, Masaaki

doi:10.1109/tdc.2002.1177813

Cited by 35 publications

(31 citation statements)

References 4 publications

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“…Fault detection decision [5][6] is processed using the positive sequence current signals as illustrated in Fig. 3.…”

Section: Decision Algorithm and Resultsmentioning

confidence: 99%

“…Results obtained from the wavelet transform are shown on both the time domain and the frequency domain. The wavelet transform which has a change in the analysis scale by the factor of two is called discrete wavelet transform (DWT) as in Equation 1 [5][6]. …”

Section: Wavelet Transformmentioning

confidence: 99%

“…During the 1990's, the development in the algorithm for locate of the faults on the transmission lines with the wavelet transform was initially proposed by F. H. Magnago et al [4]. However, the most research works interest only fault location which one-source (singleended) or two-source (two-terminal) [5][6][7] while research work rarely mention about locate of fault on multi-terminal. In fact, transmission lines are connected to each other and become a large grid connected system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of discrete wavelet transforms for fault location on transmission network systems

Pothisarn

Ngaopitakkul

2009

8th International Conference on Advances in Power System Control, Operation and Management (APSCOM 2009)

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This paper proposes a technique to identify locations of fault along the transmission network systems using Discrete Wavelet Transforms (DWT). The maximum coefficients of the positive sequence current obtained from all buses are compared in order to detect the faulty bus on a transmission network and the first peak times obtained from the faulty bus are used as an input data for travelling wave equation. The proposed method gives a satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission network systems.

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“…Fault detection decision [5][6] is processed using the positive sequence current signals as illustrated in Fig. 3.…”

Section: Decision Algorithm and Resultsmentioning

confidence: 99%

Section: Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of discrete wavelet transforms for fault location on transmission network systems

Pothisarn

Ngaopitakkul

2009

8th International Conference on Advances in Power System Control, Operation and Management (APSCOM 2009)

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“…According to [13], [15] for the detection of faults, the proposed method requires the following steps:…”

Section: Detection Of Faultsmentioning

confidence: 99%

A Novel Approach for the Detection, Classification and Localization of Transmission Lines Faults using Wavelet Transform and Support Vector Machines Classifier

S.Wani¹,

Singh²

2018

IJET

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Over the time, techniques have been developed whose approach has been linked mainly to the diagnosis of faults, which is why over the years these techniques have been improved little by little in order to complement or at best cases innovate the traditional methods used for the detection of faults from the mathematical point of view relying mainly on sophisticated methods and some of them related to artificial intelligence. Taking into account the aforementioned, in this paper we propose the use one of the branches of artificial intelligence, specifically automatic learning through the tool known as Support Vector Machines (SVM) to find a method with which is feasible to identify and classify the type of fault. For the creation of the mathematical model it is essential to have a database. The database consists of input data and output data, the input data are the detail coefficients obtained from the decomposition of the current and voltage signals using the Discrete Wavelet Transform (DWT). Meanwhile, the output data are the labels assigned and with which the model can identify and classify the different types of faults. Both current signals and voltage signals are generated based on an extensive simulation of faults along the longest transmission line that has a test system.

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“…This work made extensive use of the simulation software PSCAD/EMTDC [16] which resulted in the fault of the simulation of the transient signal transmission line parallel to the number of data points . For one kind of fault, these data were then transferred to MATLAB with the help of Clarke's transformation to convert the three-phase signal into alpha and beta signals.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

Saini

Zin

Mustafa

et al. 2018

IJECE

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This paper proposed a new algorithm for fault location and classification using wavelet based on Clarke's transformation to obtain the fault current. This novel method of fault current approach is studied by comparing the use of the glide path of the fault voltage. The current alpha and beta (Current Mode) were used to transform the signal using discrete wavelet transform (DWT). The fault location was determined by using the Clarke's transformation, and then turned into a wavelet, which was very precise and thorough. The most accurate was the mother wavelet Db4 which had the fastest time and smallest error detection when compared with the other wavelet mothers. In this study, the Clarke's transformation is also compared with the Karenbauer's, which has produced results with similar error percentage. The simulation results using PSCAD / EMTDC software showed that the proposed algorithm could distinguish internal and external faults to get the current signal in the transformation of a signal fault.

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Fault diagnosis in transmission lines using wavelet transform analysis

Cited by 35 publications

References 4 publications

Application of discrete wavelet transforms for fault location on transmission network systems

Application of discrete wavelet transforms for fault location on transmission network systems

A Novel Approach for the Detection, Classification and Localization of Transmission Lines Faults using Wavelet Transform and Support Vector Machines Classifier

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

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