Characterization of distribution power quality events with Fourier and wavelet transforms

Santoso, Surya; Grady, W.M.; Powers, E.J.; Lamoree, J.; Bhatt, S.C.

doi:10.1109/61.847259

Cited by 372 publications

(147 citation statements)

References 2 publications

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“…The same authors previously had achieved performance in second-order computing, using 2-D wavelets and compression techniques [10]; finding, despite the promising results, the limits of the procedure and quantifying its heavy computational cost. Alienated to this work, the researches of Poisson et al and Santoso et al [11] also reported a wavelet-based method, finding the potential and the drawbacks of the technique in order to implement it in an intelligent meter. The direct antecedent of the present research is the work by J.J.G.…”

Section: Introductionmentioning

confidence: 99%

Advanced methods of signal processing for Power Quality assessment.

Montero¹,

Agüera-Pérez²,

Rosa³

et al. 2024

RE&PQJ

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Abstract. The aim of this work is by using artificial neural networks (ANNs) compare six regression algorithms supported by 14 power-quality features, based on higher-order statistics (HOS). In addition, we have combined time and frequency domain estimators to deal with non-stationary measurement sequences; the final target is to implement the system in a smart grid to guarantee compatibility between all the equipment connected. The main results were based on spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. Through these results we have verified that the developed technique is capable of offering interesting results at classifying power quality (PQ) disturbance. We can conclude that using radial basis networks, generalized regression and multilayer perceptron, we have obtained the best results mainly due to the non-linear nature of data.

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Section: Introductionmentioning

confidence: 99%

Advanced methods of signal processing for Power Quality assessment.

Montero¹,

Agüera-Pérez²,

Rosa³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…Though WT exhibits notable capabilities for detection and localization of disturbances, however, its compatibilities are often degraded due to the presence of noise mixed with measured waveform (Dwivedi et al, 2008;Santoso et al, 2000a;Santoso et al, 1996;Gaouda et al, 1999;Lee et al, 1997;Lin et al, 1998;Yang et al, 2001;McGranaghan et al, 2007). In particular when the spectrum of noise coincides with that of transient signals, the effects of the noise cannot be excluded by the filters without affecting the performance of these advanced digital signal processing (DSP) based method.…”

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

“…To resolve the fixed resolution problem of STFT, many researchers have proposed the use of the WT approach to analyzing the power system disturbances (Santoso et al, 1996;Perunicic et al,1998;Santoso et al, 2000a;Ibrahim et al, 2002;Zhu et al, 2004;Abdel-Galil et al, 2004). The WT approach prepares a window that automatically adjusts to give proper resolutions of both the time and the frequency.…”

Section: Features Extractionmentioning

confidence: 99%

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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“…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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Characterization of distribution power quality events with Fourier and wavelet transforms

Cited by 372 publications

References 2 publications

Advanced methods of signal processing for Power Quality assessment.

Advanced methods of signal processing for Power Quality assessment.

Power quality event classification: an overview and key issues

Application of wavelet Transform in power Quality: A Review

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