Transient protection of transmission line using wavelet transform

Solanki, Mehul Dansinh; Song, Yan-Wen; Potts, S.; Perks, A.

doi:10.1049/cp:20010159

Cited by 34 publications

(17 citation statements)

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“…Time resolution capability of the wavelet analysis is employed in (Chaari et al, 1996) for detection of earth faults in case of a 20 kV resonant grounded network. High impedance faults identification and protection of transformers and generators by means of wavelets are also shown in (Solanki et al, 2001) and (Eren & Devaney, 2001) respectively. In this last case, the frequency resolution of wavelets allows the changes of the power signals' spectra to be measured in order to detect the degradation of the insulation and identify internal and external faults.…”

Section: Wavelet Analysis In Power Systemsmentioning

confidence: 99%

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

Pigazo¹,

Moreno²

2011

Discrete Wavelet Transforms - Theory and Applications

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Section: Wavelet Analysis In Power Systemsmentioning

confidence: 99%

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

Pigazo¹,

Moreno²

2011

Discrete Wavelet Transforms - Theory and Applications

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“…Then the translation is used to obtain the time information. In this way a family of scaled and translated wavelets is created that serves as the base for representing the function to be analyzed [13]. The DWT is in the form:…”

Section: Dwt-based Fault Detectionmentioning

confidence: 99%

A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

Elkalashy¹,

Lehtonen²,

Darwish

et al. 2007

Int Trans Elec Energy Syst

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SUMMARYIn this paper, the initial transients due to arc reignitions associated with high impedance faults caused by leaning trees are extracted using discrete wavelet transform (DWT). In this way, the fault occurrence is localized. The feature extraction is carried out for the phase quantities corresponding to a band frequency 12.5-6.25 kHz. The detection security is enhanced because the DWT corresponds to the periodicity of these transients. The selectivity term of the faulty feeder is based on a novel technique, in which the power polarity is examined. This power is mathematically processed by multiplying the DWT detail coefficients of the phase voltage and current for each feeder. Its polarity identifies the faulty feeder. In order to reduce the computational burden of the technique, the extraction of the fault features from the residual components is examined. The same methodology of computing the power is considered by taking into account the residual voltage and current detail coefficients where the proposed algorithm performs best. Test cases provide evidence of the efficacy of the proposed technique.

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“…Por outro lado, a transformada wavelet (TW) também vem sendo largamente empregada, devido a sua aplicabilidade na análise de sinais não estacionários, a exemplo dos sinais transitórios oriundos de faltas e distúrbios de QEE (Solanki et al, 2001;Kim and Aggarwal, 2001). No entanto, esses métodos distinguem a falta apenas da situação normal de operação em regime permanente do sistema, não contemplando as demais situações de operação que podem ser registradas pelos RDPs.…”

Section: Introductionunclassified

Detecção e classificação de faltas a partir da análise de registros oscilográficos via redes neurais artificiais e transformada wavelet

Silva

Souza

Brito

et al. 2007

Sba Controle & Automação

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RESUMOEste artigo apresenta um método de detecção e classificação de faltas em linhas de transmissão, a partir da análise de registros oscilográficos via redes neurais artificiais e transformada wavelet. Realiza-se a detecção da falta e a determinação de sua duração através de um conjunto de regras heurísticas construídas a partir da análise dos sinais de corrente nos domínios do tempo e wavelet. O método diferencia faltas de outras ocorrências relacionadas à qualidade da energia elétrica, tais como afundamentos de tensão e transitórios de chaveamento. Uma rede neural classifica faltas a partir do reconhecimento dos padrões das tensões e correntes relacionados a cada tipo específico. O método proposto foi avaliado para registros simulados e reais em linhas de transmissão do sistema Chesf, apresentando bons resultados em ambos os casos. ABSTRACTThis paper presents a method for fault detection and classification in transmission lines, based on analysis of oscillographic data using artificial neural networks and wavelet transform. The fault detection and its clearing time are determined based on a set of heuristic rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power quality disturbances such as voltage sags and oscillatory transients, which are common in power systems operation. An ANN classifies the fault by the voltage and current waveforms pattern recognition in time domain. The method was used for fault detection and classification from both simulated and real oscillographic data of Chesf, a Brazilian utility company, with excellent results.

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Transient protection of transmission line using wavelet transform

Cited by 34 publications

References 0 publications

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

Discrete Wavelet Transforms for Synchronization of Power Converters Connected to Electrical Grids

A novel selectivity technique for high impedance arcing fault detection in compensated MV networks

Detecção e classificação de faltas a partir da análise de registros oscilográficos via redes neurais artificiais e transformada wavelet

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