Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines

Eri̇şti̇, Hüseyin; Uçar, Ayşegül; Demir, Yakup

doi:10.1016/j.epsr.2009.09.021

Cited by 143 publications

(63 citation statements)

References 27 publications

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“…Discussion Table 7 summarizes a performance comparison of the proposed method against previous works in reviewed literature, through percentage of effectiveness. In [17,[39][40][41], the PQD H and I in Table 1 are considered as the same disturbance; therefore, these two classes are not included in the comparison table. On the other hand, in [28], just the overall percentage of effectiveness during PQD classification is reported.…”

Section: Resultsmentioning

confidence: 99%

“…In reviewed literature [17,28,[39][40][41]45,46], computer models of treated PQD are obtained by using parametric descriptions as shown in Table 1, which provides the mathematical descriptions for the corresponding real-life phenomenon. Table 1, A and ω are the amplitude and angular frequency of the signal, respectively.…”

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

“…Hence, in this paper, a novel methodology for detecting and classifying PQD through minimum parameter estimation is proposed. Different from recently introduced methodologies in reviewed literature where PQD are detected by extracting several characteristics from the analyzed signal, which are processed by the combination of diverse techniques [39][40][41]. The introduced approach takes advantage of the EMD merits by extracting a reduced number of components from voltage/current signal to obtain the IMF, from which Entropy and the third and fourth central moments (i.e., kurtosis, and skewness) are obtained as classification parameters.…”

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: Resultsmentioning

confidence: 99%

Section: Computer Simulation Of Pqdmentioning

confidence: 99%

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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“…Therefore, the feature extraction approach is necessary before the feature selection. There are many statistical characteristics of discrete data, such as mean, standard deviation, energy, entropy, kurtosis and form factor [13]. They are utilized to extract the statistical characteristics from the results of wavelet MRA, which not only simplify data from wavelet MRA but also enrich and sum up the information amount of every decomposition level.…”

mentioning

confidence: 99%

“…, Daubechies(db) wavelets are widely used in analyzing signals, because the decomposition order can be controlled for specific requirements [13]. Among the different dbN wavelets, db4 is the most widely adopted with its advantage of practicality [14].…”

mentioning

confidence: 99%

Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra: Wavelet Decomposition and Support Vector Machine Classifier

Jia¹,

Yu²,

Du³

et al. 2018

Proceedings of the 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)

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Abstract-Gas acoustic spectrum represents properties of acoustic propagation, which can distinguish gas compositions. However in few existing methods of gas-composition-unknown recognition, the approaches of programmatically processing the acoustic spectrum curves have not yet been presented. We propose a method for gas-composition-unknown recognition by analyzing gas acoustic relaxation absorption spectrum (GARAS) based on wavelet multi-resolution analysis (MRA) and multi-class support vector machine (SVM). Features of GARAS are extracted by wavelet MRA, and then selected to obtain a few feature coefficients which are utilized to train and test multi-class SVM. Simulation results show that the proposed method completely classifies four examples of gas mixtures, and that it recognizes the mixtures with the same and similar concentration or temperature. This method realizes the numerically extracting and programmatically processing the information of GARAS, and implements gas-composition-unknown sensing based on acoustic spectrums.

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Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines

Cited by 143 publications

References 27 publications

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

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

Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra: Wavelet Decomposition and Support Vector Machine Classifier

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

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