A new algorithm for automatic classification of power quality events based on wavelet transform and SVM

Eri̇şti̇, Hüseyin; Demir, Yakup

doi:10.1016/j.eswa.2009.11.015

Cited by 90 publications

(48 citation statements)

References 16 publications

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“…The analysis includes the data with noise and without noise which is able to prove the robustness of system. In [124], authors proposed classification system based on the WT and SVM using only single feature vector at 8th decomposition level. The increased decomposition level causes to increase the computational cost and for better classification system selected feature vectors must be strong enough.…”

Section: Neural Network Based Methodsmentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

189

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Section: Neural Network Based Methodsmentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

189

View full text Add to dashboard Cite

“…Moreover, LMTs have the computational complexity of inducing the logistic regression models in a tree. Recently, SVMs have been used as an attractive tool for pattern classification because of their superior features such as producing single, optimum, and automatic sparse solutions by simultaneously minimizing both generalization and training error and separating data by a large margin in high-dimensional space [29]. Furthermore, no study has been reported yet about ST-SVM-based protection schema for SCTLs with fixed capacitors.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, SVMs have been used as effective tools for fault classification in power systems [3,8,18,29,32]. This approach constructs the separating hyperplane for pattern recognition.…”

Section: Fault Classification and Faulty Section Identification By Svmsmentioning

confidence: 99%

A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

Çöteli¹

2013

Turk J Elec Eng & Comp Sci

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Abstract:The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST-neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.

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“…On the contrary, considering the wavelet transform, it is difficult to obtain a reduced set of variables that provide a good classification accuracy. In [120], an approach for automatic classification of power quality events based on the wavelet transform and SVM is proposed. The problem is tackled as a multi-class classification problem with seven classes (corresponding to the seven different power quality disturbances to be detected) that obtains the predictive variables from the wavelet transform.…”

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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A new algorithm for automatic classification of power quality events based on wavelet transform and SVM

Cited by 90 publications

References 16 publications

Classification of power quality events – A review

Classification of power quality events – A review

A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

A Review of Classification Problems and Algorithms in Renewable Energy Applications

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