Multiresolution S-Transform-Based Fuzzy Recognition System for Power Quality Events

Chilukuri, M.V.; Dash, P.K.

doi:10.1109/tpwrd.2003.820180

Cited by 246 publications

(130 citation statements)

References 11 publications

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“…Expert System Based Classifier Santoso et al, 2000b;Styvaktakis et al, 2001;Styvaktakis et al, 2002;Chung et al, 2002;Reaz et al, 2007. Fuzzy Expert System Based Classifiers Dash et al, 2000Thapar et al, 2003;Zhu et al, 2004;Chilukuri et al, 2004;Ortiz et al, 2006;Bizjak et al, 2006. Adaptive Neuro-Fuzzy System Based Classifier Ibrahim et al, 2001.…”

Section: Summary Of Pq Classification Methodsmentioning

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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Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…In (Chilukuri et al, 2004), the features of seven simulated signals have been extracted by calculating the minima and maxima of the S-transform absolute matrix. Similar to the STFT, S-transform also requires significant amount of computational resources.…”

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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“…For this purpose, such signal processing approaches as Fourier transformation [9,10], wavelet transformation (WT) [11,12], Kalman filtering [13,14] and -transformation [15,16] are commonly used. The least squares fitting is one of the commonly used methods to find the fitting function, and it is widely used in practical engineering [17].…”

Section: Introductionmentioning

confidence: 99%

Power quality prediction based on least squares method

Lu¹

2017

Vib. proced.

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Abstract. In the current high degree of popularity of power products, power abnormalities on the production of more and more. Therefore, the prediction of power quality is of great significance. The method of prediction is generally to find the fitting function, the least squares fitting is the commonly used method to find the fitting function. In this paper, the least square method is used to fit the data of power grid. The factors influencing power quality were analyzed from four aspects, and the conclusions were obtained by fitting with real data points. In order to further improve the power quality prediction.

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“…There have been many methods presented, such as Fourier transform [1], wavelet transform [2]- [4], S transform and so on. The Fourier transform is suitable to analyze stationary signals, and has a good effect on stationary disturbance like harmonics; the wavelet transform has advantage of analyzing singularity and nonstationary signals [5] [6]; S transform is the inheritance and development of short-time Fourier transform and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%