Classification of Lightning and Faults in Transmission Line Systems Using Discrete Wavelet Transform

Chiradeja, Pathomthat; Ngaopitakkul, Atthapol

doi:10.1155/2018/1847968

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…It was first shown in [13] that by recording the fault at the two ends of a simple transmission line, regardless of the load along the line, the precise location of the fault can be determined by moving the waves by wavelet transform. In [13] and [4], a method is proposed that uses a wavelet transform to determine the location of a fault using mobile waves in the power system. This method makes it possible to convert a highresolution wavelet to high-frequency components of the fault transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…This method makes it possible to convert a highresolution wavelet to high-frequency components of the fault transmissions. Further, by converting the three-phase voltage signals into the medal components and performing the wavelet transform on the medal signals, a component of regional fashion at high frequencies makes it possible to determine the location of the types of faults [4].…”

Section: Introductionmentioning

confidence: 99%

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Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Dashtdar¹

2019

AJECE

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In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to faultlocation evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Dashtdar¹

2019

AJECE

View full text Add to dashboard Cite

show abstract

“…It was first shown in [10] that by recording the fault at the two ends of a simple transmission line, regardless of the load along the line, the precise location of the fault can be determined by moving the waves by wavelet transform. In [11] and [12], a method is proposed that uses a wavelet transform to determine the location of a fault using mobile waves in the power system. This method makes it possible to convert a high-resolution wavelet to high-frequency components of the fault transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…This method makes it possible to convert a high-resolution wavelet to high-frequency components of the fault transmissions. Further, by converting the three-phase voltage signals into the medal components and performing the wavelet transform on the medal signals, a component of regional fashion at high frequencies makes it possible to determine the location of the types of faults [12].…”

Section: Introductionmentioning

confidence: 99%

Fault Location in the Transmission Network Based on Extraction of Fault Components Using Wavelet Transform

Dashtdar

2019

The Scientific Bulletin of Electrical Engineering Faculty

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In this paper, a Discrete Wavelet Transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault). The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

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“…Apart of analysing the waveform, wavelet transform can also be adopted to extract the energy value from the coefficient. In [6], Mamta et al proposed wavelet transform to extract energy at the third data window. However, the author only analysed short circuit fault and did not do the features extraction in their study to reduce the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Determination of Different Fault Features in Power Distribution System Based on Wavelet Transform

Asman*,

Aziz,

Kadir

et al. 2019

IJRTE

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Nowadays, there are various signal processing methods that have been studied by many researchers in order to detect faults in power lines. From previous literature, signal processing that works based on time frequency analysis has been proven to accurately detect faults at high speed. In this study, wavelet transform is adopted to analyse fault occurrences on power line of distribution network. Three types of faults due to lightning, switching and short circuit fault were analysed based on their voltage waveform profiles. ‘Daubechies’ 4 (db4) mother wavelet and four levels decomposition were implemented to extract the features. Approximation at level 4 (A4) and detail coefficient at level 1 to 4 (D1-D4) were extracted to evaluate the energy, skewness, and kurtosis. Based on the results, lightning showed the highest energy, skewness and kurtosis compared to the short circuit and switching voltage waveform. Therefore, these features can be utilized as the new parameters for fault detection in a power system network

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Classification of Lightning and Faults in Transmission Line Systems Using Discrete Wavelet Transform

Cited by 17 publications

References 22 publications

Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Fault Location in the Transmission Network Based on Extraction of Fault Components Using Wavelet Transform

Determination of Different Fault Features in Power Distribution System Based on Wavelet Transform

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