A method based on independent component analysis for single and multiple power quality disturbance classification

Ferreira, Danton Diego; Seixas, J. M.; Cerqueira, A. S.

doi:10.1016/j.epsr.2014.10.028

Cited by 46 publications

(29 citation statements)

References 16 publications

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“…The common method for PQD detection is to perform a frequency spectrum analysis on the power distribution signal [6][7][8][9][10][11], however, this method does not provide information on frequency distribution through time. 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].…”

Section: Introductionmentioning

confidence: 99%

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%

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

et al. 2016

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“…These methods are used to identify and categorize the commonly occurring PQ disturbances. A specific independent component analysis for single and multiple PQ disturbance classification has been discussed by Ferreiraa et al, and they have classified 5 classes of single disturbances and 12 of multiple disturbances. In the study of Barbosa and Ferreira, multiple and single PQ disturbances classification using a decision tree–based approach has been presented, the features are extracted using the principle of divide and conquer, and classification has been done with the help of perceptrons and a Bayesian classifier.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels

Kubendran

Loganathan

2016

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a simple and effective method for detection of complex power quality disturbances using S‐transform amplitude matrix. In this work, classification of complex power quality disturbances has been implemented using a rule‐based decision tree for different noise levels, such as with no noise, 30‐dB noise, and 45‐dB noise. The S‐transform is distinct, which provides a frequency‐dependent resolution with direct relationship to the Fourier spectrum. The features obtained from S‐transform amplitude matrix are dissimilar, clear, and immune to noise. According to a rule‐based decision tree, 7 types of single power disturbance and 16 types of complex power disturbance are well identified in this work. The proposed work is simulated using MATLAB simulation, and the various results are found, which detect the single and complex power quality disturbances; and it proves that the proposed method is effective and unaffected against noise.

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“…Reference proposed a method to identify and classify PQ disturbances based on ICA and support vector machine. In previous works , the authors exploited the ICA decomposition capability to develop a method for multiple and single PQ disturbance classification.…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of the method proposed in this paper are the low cost in the operational stage, because it may be viewed as a low‐order finite‐impulse‐response (FIR) filter, and its good performance. Unlike in and , the method proposed here focuses on transient extraction from power signals by using a simple ICA‐based FIR filter structure. Therefore, the main contribution of this paper lies in providing a good estimation of PQ transients.…”

Section: Introductionmentioning

confidence: 99%

Extracting the transient events from power system signals by independent component analysis

Ferreira

Seixas

Cerqueira

et al. 2015

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a method for extracting transient events from the power system signal based on independent component analysis. The extraction of transients is important to diagnose the system in terms of power quality, because the computation of power quality indices from the extracted events is more efficient and easier to be carried out. Furthermore, the proposed method can be applied as a preprocessing tool for transient characterization in disturbance detection, classification, and identification systems. Both qualitative and quantitative analyses demonstrate the efficiency and the advantages of the proposed method in comparison with other approaches commonly used for the same purpose. Copyright © 2015 John Wiley & Sons, Ltd.

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A method based on independent component analysis for single and multiple power quality disturbance classification

Cited by 46 publications

References 16 publications

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

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

Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels

Extracting the transient events from power system signals by independent component analysis

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