Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

Mishra, Sukumar; Bhende, Chandrashekhar N.; Panigrahi, B. K.

doi:10.1109/tpwrd.2007.911125

Cited by 447 publications

(173 citation statements)

References 13 publications

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“…The strength and weakness of different artificial techniques such as expert system (ES), fuzzy system (FS), neural network (NN), genetic algorithm (GA) and support vector machine (SVM) has been presented in Table 3 (Negnevitsky, 2004). Perunicic et al, 1998;Santoso et al, 2000c;Gaouda et al, 2002a;Giang, 2004;He et al, 2006;Mishra et al, 2008. Expert System Based Classifier Santoso et al, 2000b;Styvaktakis et al, 2001;Styvaktakis et al, 2002;Chung et al, 2002;Reaz et al, 2007.…”

Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…However, the accuracy has been dropped to 75% if contaminated noise strength has increased. Mishra et al (2008) proposed an S-transform based probabilistic neural network (PNN) classifier for classification of 11 types PQ disturbances with only four extracted features. Integrating S-transform with PNN can effetely detect and classify PQ disturbances even under noisy condition.…”

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: Artificial Neural Network Based Classifiersmentioning

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

“…Beberapa penelitian telah melaporkan penggunaan JST yang terbukti efektif dalam pengenalan pola sinyal yang mengandung masalah KD listrik [11,12].…”

Section: B Metode Klasifikasiunclassified

Deteksi dan Klasifikasi Sinyal Listrik Non-Stasioner yang Mengandung Masalah Kualitas Daya Menggunakan Transformasi Hilbert Huang

Afroni

Melfazen

2017

Jurnal Teknik Elektro UNISLA

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AbstrakMakalah ini menyajikan klasifikasi sinyal listrik non-stasioner yang mengandung masalah Kualitas Daya (KD) berbasis Transformasi Hilbert Huang (THH) dan Jaringan Saraf Tiruan (JST). Transformasi Hilbert Huang (THH) adalah metode analisis sinyal yang relatif baru, yang terdiri dari dua tahap -Dekomposisi Mode Empiris (DME) dan Transformasi Hilbert (TH). Proses DME menguraikan sinyal menjadi komponen -komponennya dalam bentuk Fungsi Mode Intrinsik (FMI) sementara TH digunakan untuk memperoleh amplitudo dan frekuensi sesaat dari masing-masing FMI. Fitur yang diekstraksi melalui THH digunakan untuk melatih JST guna mengenali kelas sinyal yang diproses. Kelas sinyal yang diidentifikasi mulai dari sinyal normal, interupsi, sag, swell, harmonisa, transien, dan gabungannya. Hasil simulasi menunjukkan bahwa kombinasi THH dan JST dapat secara efektif mendeteksi dan mengklasifikasikan sinyal yang mengandung berbagai masalah KD.

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“…Feature extraction is done by applying standard statistical techniques onto the S-matrix. Many features such as amplitude, slope (or gradient) of amplitude, time of occurrence, mean, standard deviation and energy of the transformed signal are widely used for proper classification [10,11]. Since, the aim is to obtain a satisfactory classification accuracy, features based on standard deviation (S.D.)…”

Section: B Feature Extraction Using S-transformmentioning

confidence: 99%

Fast SVMS for Power Quality Data Mining

Manimala¹,

Selvi²,

Ahila³

2009

Int. Jour. Info. Sci. Comp.

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Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. Support Vector Machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of classification applications. Although SVMs have shown potential and promising performance in power disturbances classification, they have been limited by speed particularly when the training data set is large. The hyper plane constructed by SVM is dependent on only a portion of the training samples called support vectors that lie close to the decision boundary (hyper plane). Thus, removing any training samples that are not relevant to support vectors might have no effect on building the proper decision function. We propose the use of clustering techniques such as K-mean to find initial clusters that are further altered to identify non-relevant samples in deciding the decision boundary for SVM. This will help to reduce the number of training samples for SVM without degrading the classification result and classification time can be significantly reduced.

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Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

Cited by 447 publications

References 13 publications

Power quality event classification: an overview and key issues

Power quality event classification: an overview and key issues

Deteksi dan Klasifikasi Sinyal Listrik Non-Stasioner yang Mengandung Masalah Kualitas Daya Menggunakan Transformasi Hilbert Huang

Fast SVMS for Power Quality Data Mining

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