Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier

Biswal, Milan; Dash, P.K.

doi:10.1016/j.dsp.2013.02.012

Cited by 151 publications

(79 citation statements)

References 28 publications

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“…Biswal, & Dash, [16], propose a methodology to extract the features based on the ST and a classification technique based on a decision tree. This approach uses seven decision steps to obtain the results and seems to achieve a very high accuracy level for a decision tree based classifier.…”

Section: Complex Power Quality Disturbancesmentioning

confidence: 99%

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Yong¹,

Bhowmik²,

Magnago³

2017

EPE

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Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a "One Vs Rest" architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

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Section: Complex Power Quality Disturbancesmentioning

confidence: 99%

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Yong¹,

Bhowmik²,

Magnago³

2017

EPE

View full text Add to dashboard Cite

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“…In [53], a DT is applied to a PQ disturbance detection in power signals. The input variables of the system are obtained by applying the S-transform to the original power signal.…”

Section: Classification Problems and Algorithms In Power Quality Distmentioning

confidence: 99%

“…Problem Specific Methodology Used [52] 2015 Power disturbance Classification SVM [53] 2013 Power disturbance Classification DT, ANN, neuro-fuzzy, SVM [54] 2014 Power disturbance Classification DT, SVM [55] 2014 Power disturbance Classification DT [56] 2012 Power disturbance Classification DT, DE [57] 2004 Power disturbance Classification Fuzzy expert, ANN [58] 2010 Power disturbance Classification Fuzzy classifiers [59] 2010 Power disturbance Classification GFS [48] 2006 Appliance load monitoring Classification ANN [60] 2009 Appliance load monitoring Classification k-NN, DTs, naive Bayes [61] 2010 Appliance load monitoring Classification k-NN, DTs, naive Bayes [62,63] 2010 Appliance load monitoring Classification LR, ANN [64] 2012 Appliance load monitoring Classification SVM [65] 2013 Solar Classification, regression SVM, ANN, ANFIS, wavelet, GA [66] 2008 Solar Classification, regression ANN, fuzzy systems, meta-heuristics [67] 2004 Solar Classification PNN [68] 2006 Solar Classification PNN [69] 2009 Solar Classification PNN, SOM, SVM [70] 2004 Solar Classification SVM [71] 2014 Solar Classification SVM [72] 2015 Solar Classification SVM [73] 2006 Solar Classification Fuzzy rules [74] 2013 Solar Classification Fuzzy classifiers [75] 2014 Solar Classification Fuzzy rules…”

Section: Ref Year Application Fieldmentioning

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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“…The typical pattern recognition methods, such as fuzzy rules (FR) [7], decision tree (DT) [8] and neural networks (NNs) [9][10][11], have been widely used for PQ disturbances classification. FR and DT methods for PQ disturbances recognition are very effective and easy to achieve.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Classification Based on A Novel Fourier Neural Network and Hyperbolic S-transform

Lin¹,

Wu²,

Qi³

et al. 2016

IJSIP

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Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.

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Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier

Cited by 151 publications

References 28 publications

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Power Quality Disturbance Classification Based on A Novel Fourier Neural Network and Hyperbolic S-transform

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