Wavelet-based signal processing for disturbance classification and measurement

Gaouda, A.M.; Kanoun, S.H.; Salama, M.M.A.; Chikhani, A.Y.

doi:10.1049/ip-gtd:20020119

Cited by 73 publications

(30 citation statements)

References 18 publications

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“…Several signal processing methods have been proposed for feature extraction, like Fourier and wavelet transforms, combined with neural networks, fuzzy expert systems, kalman filter or pattern recognition methods [8] [56]. For detection and location of power quality disturbances, wavelet transform is far better as compare to Fourier transform [1], [2] , [23], [60] and [62]. Various Software packages are available for wavelet analysis, M ATLAB is one of them where wavelet toolbox provide platform for wavelet analysis [24].…”

Section: Wavelet Transformmentioning

confidence: 99%

Application of wavelet Transform in power Quality: A Review

Gawre¹,

Patidar²,

Nema³

2012

IJCA

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From last decades the objective of Power quality (PQ) monitoring and analysis has drastically . Generally the power quality problem covers the time scales range from tens of nanoseconds to steady state to describe different events. Well discussed in various international standards (IEEE, IEC, EN etc) and also give various acceptability curves to quantify and classify different Power Quality phenomenon (CIBM A and ITC) according to amplitude and time frame. It is observed that different tools and methods are always been used to detect and classify the power Quality events. The whole advance tends to process the raw data and extract the information in order to make decision. And further move towards real time monitoring, protection and control. This paper presents a comprehensive review of different techniques based on wavelet transform to detect and classify power quality problems.

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Section: Wavelet Transformmentioning

confidence: 99%

Application of wavelet Transform in power Quality: A Review

Gawre¹,

Patidar²,

Nema³

2012

IJCA

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“…The kNN technique requires a large capacity of memory to store the training data. In (Gaouda et al, 2002b), three pattern recognition techniques (minimum Euclidean distance, kNN, and neural network) have been proposed to automatically classify PQ disturbances. The difference in energy distribution between the distorted power signal and the pure one, and the measured duration of the distortion event have been used to form feature vector for the proposed pattern recognition techniques.…”

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

“…Electrical noise riding on the PQ waveform data also modulates wavelet domain energy distribution patterns of disturbances which, in turn, degrade performance of the existing classification schemes utilizing wavelets for feature extraction, under practical conditions. Wavelet based denoising techniques to remove the effect of noise on PQ waveform data (Yang et al, 2000;Elmitwally et al, 2001;Yang et al, 2001;Gaouda et al, 2002b) have been proposed in the literature but, their performance degrades with decrease in the signal to noise ratio (SNR). Among these, (Yang et al, 2001) proposed a promising method for denoising of PQ waveform data to improve the performance of wavelet based PQ monitoring systems.…”

Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

“…But, there are fundamental problem in the formulation of the proposed technique. Most of the above works generally deal with the denoising of PQ signals from detection and localization point of view (Yang et al, 2000;Gaouda et al, 2002b;Elmitwally et al, 2001). However, the robustness of the energy features, extracted for classification using DWT in the presence of noise and its effect on classification accuracy is rarely addressed.…”

Section: Effect Of Noise On Pq Event Classifiersmentioning

confidence: 99%

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Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…O método proposto na abordagem apresentada em Gaouda et al (2002) consiste em utilizar a técnica de wavelets para detectar, classificar e medir as perturbações que incidem nos sistemas de distribuição. Um sinal f s (t) de duração finita com uma distorção aditiva s d (t) pode ser representado matematicamente por:…”

Section: Classificação E Medição Dos Níveis De Perturbação Em Sistemaunclassified

Detecção e localização de faltas em sistemas elétricos de distribuição usando abordagem inteligente baseada em análise espectral de sinais

Zamboni¹

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2013Trata-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. O objetivo deste trabalho é estudar a identificação, classificação, localização e setorização de faltas em redes de distribuição radiais, verificar a maneira de aplicar e integrar diversas ferramentas numéricas convencionais, assim como ferramentas de sistemas inteligentes, visando identificar a ocorrência de uma falta, classificar as fases envolvidas com a mesma, e aplicar as diversas ferramentas existentes a fim de localizar em tempo real o eventual local onde houve a ocorrência da falta, permitindo que a mesma possa ser setorizada dentro do sistema da concessionária e informada ao centro de operações, usando uma nova abordagem inteligente baseada em análise espectral de sinais. Lucca Zamboni Detecção e Localização de Faltas em Sistemas Elétricos de Distribuição Usando Abordagem Inteligente Baseada em Análise Espectral de Sinais

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Wavelet-based signal processing for disturbance classification and measurement

Cited by 73 publications

References 18 publications

Application of wavelet Transform in power Quality: A Review

Application of wavelet Transform in power Quality: A Review

Power quality event classification: an overview and key issues

Detecção e localização de faltas em sistemas elétricos de distribuição usando abordagem inteligente baseada em análise espectral de sinais

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