Wavelet transform and neural networks for fault location of a teed-network

Lai, Loi Lei; Rajkumar, N.; Vaseekar, E.; Subasinghe, H.; Carter, A.; Gwyn, B.

doi:10.1109/icpst.2000.897125

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…[4][5][6][7][8][9][10][11][12] The second group named traveling wave based methods uses traveling wave generated by fault to pinpoint the fault location [13][14][15][16][17][18] and the last one is artificial intelligence methods uses neural networks and fuzzy logic systems for determining the location of the fault. [19][20][21][22][23] Traditional methods demand measurements from one end or both ends of the faulted line (or from all terminals of multiterminal lines). From practical aspects due to economical and/or technical reasons the number of measurements is limited in the network.…”

Section: In This Condition Accuratementioning

confidence: 99%

“…In the next part the signal processing is done to extract the phasor representation of the input signals for pre-and post-fault conditions(here a cycle interval which begins two cycle before and after fault inception are considered for pre-and post-fault phasor calculation respectively) which the phasor extraction process has been described in the first paragraph of this section (section 3) in details (ie, applying Butterworth filter, down sampling, DC removal filter, and DFT respectively).After that the output phasors enter to the Gauss-Newton part. At the beginning the equation set of (24) is formed using (20)-(23), (11)-(12), (15)- (16) and 18- (19) which the magnitude and phase angle of the differences between pre-and post-fault extracted phasors in previous step are considered on their left hand side of(20)- (23).If the synchronized measurement is not possible in a bus, the angle related rows in (24) are omitted. Then the Jacobian matrix (see (28)) elements are calculated using (29)-(40) which (11)-(12), (15)- (16), and (18)- (19) are again used in them.…”

Section: Fl Estimation Error %mentioning

confidence: 99%

“…At the beginning the equation set of (24) is formed using (20)-(23), (11)-(12), (15)- (16) and 18- (19) which the magnitude and phase angle of the differences between pre-and post-fault extracted phasors in previous step are considered on their left hand side of(20)- (23).If the synchronized measurement is not possible in a bus, the angle related rows in (24) are omitted. Then the Jacobian matrix (see (28)) elements are calculated using (29)-(40) which (11)-(12), (15)- (16), and (18)- (19) are again used in them. In this way the Gauss-Newton can be applied according to (26)- (27) and its iterative process of calculating the unknown vector (X) is repeated until the error of its first element (x) which is defined the FL between two consecutive iteration exceeds a predefined value which usually is considered in range of 1e-3 to 1e-6.…”

Section: Fl Estimation Error %mentioning

confidence: 99%

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Wide area impedance‐based fault location using synchronized/unsynchronized current measurements

Chafi

Afrakhte

2020

Int Trans Electr Energ Syst

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Global positioning system (GPS) as an advanced technology provides synchronized measurements through the network. In spite of enhancing measurement accuracy provided by GPS, losing its signal, using slow communication link for measurement or lack of essential infrastructure for synchronized sampling make the synchronized measurement unavailable across the grid. In these conditions some fault location algorithms are not applicable and some others eliminate the corresponding measurements in their fault location process. However valuable information lies in the eliminated measurements magnitude which can improve the fault location accuracy. In this paper a system of equations is developed based on magnitudes of all current measurements and phase angle of synchronized current measurements. Then it is solved using an iterative approach with the aim of finding the location of the fault. To obtain more accurate results the distributed parameter model of line is used. The proposed method is validated by WSCC 9 bus and IEEE 39 bus test systems. The results reveal accurate estimation even in conditions that the synchronized measurements are not available in the grid entirely.

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Section: In This Condition Accuratementioning

confidence: 99%

Section: Fl Estimation Error %mentioning

confidence: 99%

Section: Fl Estimation Error %mentioning

confidence: 99%

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Wide area impedance‐based fault location using synchronized/unsynchronized current measurements

Chafi

Afrakhte

2020

Int Trans Electr Energ Syst

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“…No levantamento da literatura existente, foram encontrados algoritmos bem consolidados para a localização de faltas em linhas de transmissão com apenas uma derivação, (Aggarwal et al, 1993;Girgis et al, 1992;Yu et al, 2001;Tziouvaras et al, 2001;Lin et al, 2002;Lai et al, 2000). Entretanto, para linhas de transmissão com várias subestações conectadas em derivação há poucas publicações, (Abe et al, 1995;Brahma, 2005;Nagasawa et al, 1992;Funabashi et al, 2000).…”

Section: Revisão Da Literaturaunclassified

Sistema para localização de faltas em linhas de transmissão com subestações conectadas em derivação

Manassero¹,

Senger²,

Rodrigues³

et al. 2010

Sba Controle & Automação

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Este trabalho tem por objetivo apresentar o desenvolvimento e a implementação de algoritmos para a localização de faltas em linhas de transmissão com subestações conectadas em derivação. O sistema proposto é capaz de identificar corretamente o ponto de ocorrência do defeito utilizando as componentes de fase dos fasores de tensões e correntes, calculadas através dos registros desses sinais, efetuados por oscilógrafos digitais e/ou relés de proteção instalados nos terminais da linha de transmissão. Os algoritmos que integram este sistema têm acesso aos parâmetros elétricos da linha e informações sobre o tipo de ligação e o carregamento dos transformadores conectados aos terminais das derivações. Este trabalho apresenta também o desenvolvimento dos modelos para os elementos do sistema de transmissão, em componentes de fase. Estes modelos são utilizados pelos algoritmos que integram a metodologia para localização de faltas.

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“…[18], the technique uses principal component analysis (PCA) to identify the dominant pattern of the voltage and current signals preprocessed by the WT at different scales. In addition, use of artificial intelligence (AI) has also been reported in the literature for fault location [8,20,21]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Surface photografting polymerization of vinyl acetate, maleic anhydride, and their charge‐transfer complex. IX. Charge‐transfer complex (5)

Deng

Yang

2005

J of Applied Polymer Sci

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The two‐step method was employed to obtain insight into the grafting mechanism of (MAH‐VAC)/LDPE film in the preceding investigation, where photoreduction of BP (the first step) was investigated and the subsequent grafting polymerization (the second step) was studied from the viewpoint of kinetics. The present article is devoted to investigation of the living polymerization performance, the compositions of the grafted chains, and the relevant grafting copolymerization mechanism of the (MAH‐VAC)/LDPE system. Both grafting copolymerization and nongrafting copolymerization performed living polymerization characteristics to some degree. Regarding the nongrafting copolymerization at a monomer concentration, [MAH] = [VAC] = 2M, MAH and VAC mainly underwent alternating copolymerization, especially at low temperatures; however, the grafting copolymerization mostly exhibited random copolymerization, which became obvious with the elevation of temperature. The composition of the monomer feed largely affected the composition of the grafted copolymer and the nongrafted copolymer. When [MAH]/[VAC] was either 2.5/1.5, 2/2, or 1.5/2.5, MAH/VAC in nongrafted copolymers stayed approximately at 1/1; while when [MAH]/[VAC] was 2.5/1.5, MAH/VAC in the grafted copolymer was nearly 1/1; but in the case of [MAH]/[VAC] being 2/2 and 1.5/2.5, the content of MAH in grafted chains was somewhat lower than that of VAC. All these results demonstrated that the grafting copolymerization on the substrate occurred randomly, which was greatly affected by the affinity of the monomer toward the substrate. This performance of the grafting copolymerization was much different from the nongrafting copolymerization in the solution. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 2230–2237, 2005

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Wavelet transform and neural networks for fault location of a teed-network

Cited by 12 publications

References 4 publications

Wide area impedance‐based fault location using synchronized/unsynchronized current measurements

Wide area impedance‐based fault location using synchronized/unsynchronized current measurements

Sistema para localização de faltas em linhas de transmissão com subestações conectadas em derivação

Surface photografting polymerization of vinyl acetate, maleic anhydride, and their charge‐transfer complex. IX. Charge‐transfer complex (5)

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