Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

Eri̇şti̇, Hüseyin; Yıldırım, Özal; Eristi, Belkis; Demir, Yakup

doi:10.1016/j.ijepes.2012.12.018

Cited by 110 publications

(66 citation statements)

References 30 publications

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“…Different signal processing methods will affect the classification accuracy of PQ disturbance signals. Therefore, after the new feature selection method and RF classifier are proved to be effective, the classification accuracy of discrete wavelet transform (DWT) [37] and wavelet package transform (WPT) [38] are compared to ST. The new method is chosen as the feature selection and classification method.…”

Section: Affection Of Signal Processing Methods On Classification Accumentioning

confidence: 99%

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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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Section: Affection Of Signal Processing Methods On Classification Accumentioning

confidence: 99%

“…In the contrast experiment, the features of DWT based method are extracted refers to literature [37]. The fourth-order Daubechies wavelet (db-4) was chosen as the mother wavelet function.…”

Section: Affection Of Signal Processing Methods On Classification Accumentioning

confidence: 99%

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

Huang

Cai

et al. 2016

Entropy

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“…A nine-level Discrete Wavelet Transform (DWT) using Daubechies number four wavelet mother was selected [37].…”

Section: Signal Processingmentioning

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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“…Energy of local matrix 2 (E2) [22] The relevant calculation approaches of these features mainly refer to [36,37], as shown in Table 4. The voltage amplitude of a sampling point is set to S (k), where 0 ≤ k ≤ N − 1, and N is the number of sampling points.…”

Section: F57mentioning

confidence: 99%

Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

et al. 2016

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Abstract:In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine.

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Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

Cited by 110 publications

References 30 publications

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

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

Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

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