Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Decanini, José Guilherme Magalini Santos; Tonelli‐Neto, Mauro S.; Malange, Fernando Cezar Vieira; Minussi, Carlos R.

doi:10.1016/j.epsr.2011.07.018

Cited by 101 publications

(60 citation statements)

References 22 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%

“…On the other hand, in [28], just the overall percentage of effectiveness during PQD classification is reported. These figures were obtained from the best performance of the corresponding methodology [17,28,[39][40][41], considering the lowest SNR treated on each of them. From Table 7, it is clear that the proposed methodology is able to correctly classify more different PQDs than any other method from the reviewed literature, using less features from the analyzed voltage/current signal.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the proposed methodology extracts just three classification features from each of the five IMF to carry out the PQD identification; whereas, in [39], a 10-level wavelet decomposition is performed on the voltage signal to extract 11 features from each decomposition; hence, 121 classification features are input to an SVM for categorizing seven different PQD. In [40], one feature vector, with nine different characteristics obtained from an 8-level wavelet decomposition, is extracted using the voltage signal. The vector is used as an input to two ANN linked through an Inter-ART module to perform the PQD classification.…”

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%

See 4 more Smart Citations

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: Resultsmentioning

confidence: 99%

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

Mishra

2019

Int Trans Electr Energ Syst

119

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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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Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Cited by 101 publications

References 22 publications

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

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

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

Classification of multiple power quality events via compressed deep learning

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