An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

Yong, D. De; Bhowmik, S.; Magnago, Fernando

doi:10.1016/j.eswa.2015.04.002

Cited by 149 publications

(80 citation statements)

References 36 publications

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“…On the other hand, there are few methodologies that consider time-frequency analysis allowing the detection and classification of two or more PQD [12][13][14][15][16][17][18][19][20][21][22]. For instance, in [23], a research on voltage fluctuation and flicker measurement based on fast Fourier transform (FFT), is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…This method uses energy-spectrum information obtained by a fast discrete S-transform for current differential protection on a transmission line fed from both ends. In [20], a method based on the combination of binary classifiers and wavelet transform is proposed for PQD classification. The methodology is able to classify voltage sags/swells, harmonic distortions and interruptions.…”

Section: Introductionmentioning

confidence: 99%

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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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show abstract

“…Previous studies have carried out a lot of in-depth research on TFA of PQ signals, including Hilbert-Huang transform (HHT) [5,6], S-transform (ST) [7][8][9] and discrete wavelet transform (DWT) [10][11][12]. In the current research results, the environmental noise is the main factor which affects the PQ classification accuracy, especially in the distribution network.…”

Section: Introductionmentioning

confidence: 88%

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

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“…To test the algorithm for a complex power quality scenario, a set 1200 waveforms are generated with a combination of simulated waveforms with real waveforms measured in an oil factory [39]. Similar to scenario 1, the parameters that govern the event are randomly selected.…”

Section: Scenario 2: Complex Power Quality Eventsmentioning

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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An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

Cited by 149 publications

References 36 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

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

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