Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

Upadhyaya, Swarnabala; Mohanty, S.; Bhende, Chandrashekhar N.

doi:10.1007/s40313-015-0204-4

Cited by 16 publications

(6 citation statements)

References 35 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%

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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“…However, this article considered less number of PQDs and having less classification accuracy as compared proposed algorithm. In [30], maximum overlapping DWT (MODWT), second-generation WT (SGWT) and ST were compared to accomplish the best classification results. ST retained the best classification rate of 97.39%.…”

Section: Resultsmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Abubakar*,

Shahzad,

Rehman

et al. 2019

IJRTE

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In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.

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“…In the wavelet network, combined ability of WT, SVM for analyzing nonstationary in [56] and for multiple signals in [57] have been presented in a real-time environment. The other WT based detection techniques include, interpolated DFT [58], actual data based noise-suppression method using WT and un-decimated WT [59], integrated rule-based approach of DWT-FFT [60], DTCWT and sparse presentation classifier (SRC) [61], combine wavelet packet and t-sallis entropy [62], empirical-WT based time-frequency technique [63], rank wavelet support vector machine (rank-WSVM) [64], wavelet packet decomposition (WPD) [65], combination of WT and SVM [66], WPE and MIST [67], hybridization of daubechies wavelets db2 and db8 [68], multi-flicker source power network using WT [69], variants of WT, namely the maximum overlapping DWT and the second-generation WT [70], threshold selection using WT [71], maximal overlap discrete wavelet transform [72], DB4 wavelet [73], dualtree complex wavelet-based algorithm [74] and harmonic evolution [75]. Power quality disturbances detection using DWT in the utility network with wind energy penetration has been presented in [76].…”

Section: ) Wavelet Transform-based Pqds Detection Techniquementioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

Cited by 16 publications

References 35 publications

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

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

Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

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