Wavelets for the analysis and compression of power system disturbances

Littler, Timothy; Morrow, D. John

doi:10.1109/61.754074

Cited by 178 publications

(73 citation statements)

References 8 publications

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“…It was reviewed and discussed that the compression ratio, defined as the original file size divided by the compressed file size is well proved. These observation and discussions concludes that the Data storage requirement is minimized and transmission time while preserving the reconstructed signal in such a way that it is drastically reduced from the original [21]. Discussed approach found to be quite powerful in detecting and localizing various types of power quality disturbances.…”

Section: Wavelet Based Data Compressionmentioning

confidence: 86%

De-noising of Voltage Sag using Wavelet Transform

Gawre¹,

Patidar²,

Nema³

2013

IJCA

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Wavelet analysis approach is presented in this paper for the automatic detection and analysis of voltage sag in power systems with noise and without noise. Wavelet analysis is used to process the voltage waveform for detection and better estimation of the time-related parameters of a voltage Sag in voltage supply, The method proposed, which can be processed in real-time, has been simulated for a low voltage distribution system, showing the potential in data reduction and accurate characterization of voltage Sag in power systems .

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Section: Wavelet Based Data Compressionmentioning

confidence: 86%

De-noising of Voltage Sag using Wavelet Transform

Gawre¹,

Patidar²,

Nema³

2013

IJCA

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“…In the past two decades many techniques have been proposed to improve the detection of HIF in Power distribution systems. Some of these methods are mechanical methods [5], in these methods some devices are used to provide low impedance by catching the falling conductor. The installation and maintenance cost is very high.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Classification on a High voltage Transmission Line Using Wavelet Transforms

Thokala¹

2017

IJRASET

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Electrical power system consists of many different complex dynamic and interacting elements, which are always prone to disturbance or an electrical fault. Transmission lines are exposed to atmosphere, the faults due to transmission lines are about 50% as compared to different types of faults that occur in the power system. The faults on electrical power system transmission lines are supposed to be first detected and then be classified correctly and should be cleared in least fast as possible time. This paper describes a fault-detection technique of faults in high voltage transmission lines using the wavelet transform. The wavelet transform (WT) has been successfully applied in many fields. The technique is based on using the absolute sum value of coefficients in multi resolution signal decomposition (MSD) based on the discrete wavelet transform (DWT). A fault indicator and fault criteria are then used to detect the faults in the transmission line. Wavelet transform is best suited for fault detection and classification. This paper describes a new fault detection technique which involves capturing the current signals generated in a system under faults. The detection process is based on calculating the absolute sum of the wavelet transform detail coefficients for one period. Wavelet transform is used for the decomposition of signals and feature extraction.

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“…Para compressão de dados,é muito frequente a utilização de métodos também baseados na transformada wavelet (Santoso et al, 1997;Littler e Marrow, 1999). Embora as técnicas citadas acima sejam muito bem conhecidas na comunidade de processamento de sinais, elas têm sido cada vez mais aplicadas para resolver problemas de QEE de forma bastante satisfatória, e têm fornecido novos rumosà análise de dados de QEE.…”

Section: Variações Na Frequência Fundamentalunclassified

Uma nova metodologia para análise da qualidade da energia elétrica sob condições de ocorrência de múltiplos distúrbios

Lima¹

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Trata-se da versão corrigida da tese. A versão original se encontra disponível na EESC/USP que aloja o Programa de Pós-Graduação de Engenharia Elétrica. DedicatóriaDedico este trabalho ao meu avô Raimundo Feliciano Alves (in memoriam), com quem aprendi que as coisas simples da vida são as que trazem a verdadeira felicidade.i ii Agradecimentos A Deus, por ter me proporcionado força, dedicação, respeito e sabedoria para realizar este trabalho.A minha esposa Tatiana, pelo amor, apoio, carinho e bons conselhos.A meus pais, Márcio e Eva, por todo o amor incondicional, apoio, carinho e educação.Ao meu orientador, Prof. Denis Vinicius Coury, e ao meu co-orientador, Prof. Augusto Santiago Cerqueira, por confiarem no meu trabalho e na minha dedicação.Ao Laboratório de Sistemas de Energia Elétrica (LSEE), pela acomodação e infra-estrutura disponibilizados no desenvolvimento deste trabalho.A todos os amigos do LSEE, pela amizade, troca de experiências e momentos de descontração.Em especial, a Daniel Motter, Etienne, Hermes, Janison, Sérgio Pitombo e Ulisses. Ao Departamento de Engenharia Elétrica e de Computação da Escola de Engenharia de SãoCarlos, Universidade de São Paulo.Ao CNPq, pelo suporte financeiro proporcionado para o desenvolvimento deste trabalho. The power system is susceptible to the presence of several sources of disturbances that harm the power quality. In this sense, its voltages and/or currents may contain multiple disturbances with simultaneous occurrence. This work presents a methodology that decomposes the measured signal in components which estimate the waveforms of the individual disturbances followed by their recognition when a multiple disturbance situation occurs. The Independent Component Analysis (ICA) is the main tool in the disturbance decomposition stage. The ICA is originally a technique applied in multivariate data analysis, which means that it requires measurements from multiple sensors allocated in different positions of the system. However, this work proposes its application for a single measured signal available. For this, two methods were developed in order to provide the required diversity to the ICA technique. It is demonstrated that both methods are equivalent to an adaptive linear filter bank capable to perform an unsupervised separation of multiple independent disturbances, if they are spectrally disjoint. A classification system based on artificial neural networks is proposed to identify the disturbances decomposed by the previous stage. The complete system is tested using synthetic and actual data, presenting highly satisfactory results for the decomposition of signals containing multiple disturbances, and precision for the classification task above 97%.

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Wavelets for the analysis and compression of power system disturbances

Cited by 178 publications

References 8 publications

De-noising of Voltage Sag using Wavelet Transform

De-noising of Voltage Sag using Wavelet Transform

Fault Detection and Classification on a High voltage Transmission Line Using Wavelet Transforms

Uma nova metodologia para análise da qualidade da energia elétrica sob condições de ocorrência de múltiplos distúrbios

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