Automated Classification of Power-Quality Disturbances Using SVM and RBF Networks

Janík, P.; Łobos, T.

doi:10.1109/tpwrd.2006.874114

Cited by 217 publications

(103 citation statements)

References 12 publications

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“…Support Vector Machines Moulin et al, 2004;Lin et al, 2006;Axelberg et al, 2007. Artificial Neural Network & Support Vector Machines Thukaram et al, 2005Janik et al, 2006. …”

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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“…Support Vector Machines Moulin et al, 2004;Lin et al, 2006;Axelberg et al, 2007. Artificial Neural Network & Support Vector Machines Thukaram et al, 2005Janik et al, 2006. …”

Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…Applications within power systems using SVM have been reported in (Moulin et al, 2004;Thukaram et al, 2005;Janik et al, 2006). In (Bishop, 2008), a classifier based on radial basis function (RBF) network and SVM has been proposed and compared for classification of four classes of PQ disturbances.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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show abstract

“…In equation 12, the parametric equations used to simulate the disturbed signals, which will be used in processing and classification, are presented. Table 1: Parameters of parametric equations [3].…”

Section: Simulationmentioning

confidence: 99%

“…The poor quality of electric power signals is attributed due to various disorders, such as sags, elevations, interruptions, switching transients, impulses, flicker, harmonics, and notches [3]. Such disturbances may be highly prejudicial to the power grid users.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Packet Energy-Entropy Feature Extraction and Principal Component Analysis for Signal Classification

Varanis

Pederiva²

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…Among the main disturbances that indicate a poor power quality, the following can be highlighted: voltage sag/swell, overvoltage, undervoltage, interruption, oscillatory transient, noise, flicker and harmonic distortion (Dugan et al, 2003). Actually, in literature, a diversity of papers can be found concerning detection and identification of power quality disturbances by applying intelligent systems, such as Artificial Neural Networks (ANN) (Janik & Lobos, 2006;Oleskovicz et. al., 2009;Jayasree, Devaraj & Sukanesh, 2010) and Fuzzy Inference Systems (Zhu, Tso & Lo, 2004;Hooshmand & Enshaee, 2010;Meher & Pradhan, 2010;Behera, Dash & Biswal, 2010).…”

Section: Introductionmentioning

confidence: 99%