Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

Borges, Fábbio A. S.; Fernandes, Ricardo Augusto Souza; Silva, Ivan N.; Silva, Cintia B. S.

doi:10.1109/tii.2015.2486379

Cited by 210 publications

(105 citation statements)

References 18 publications

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“…Matz et al 136 presented a digital filter and MM technique to detect and classify the transient and waveform distortion. 154 Teager energy operator, 155 principle curves (PCs), 155 spectral kurtosis (SK), 156 amplitude and frequency demodulation (AFD) with FPARR classifier, 157 adaptive local iterative filter decomposition (ALIFD), 158 delay small angle dq transform, 159 generalized morphological filter, smart meter, 160 PLL, 161 ensemble EMD (EEMD), 162 morphological lifting wavelet (AMLW), 163 pencil matrix method, 164 and information theory. Based on MM and grille fractal, a new method on PQD detection and location was presented in Li et al 139 In Huang et al, 140 the authors presented a technique using MM and HHT, which was operated for the detection and analysis of PQDs.…”

Section: Mathematical Morphology-based Methodsmentioning

confidence: 99%

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

Mishra

2019

Int Trans Electr Energ Syst

137

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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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Section: Mathematical Morphology-based Methodsmentioning

confidence: 99%

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

Mishra

2019

Int Trans Electr Energ Syst

137

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“…Application of Hilbert transform to these IMFs can indicate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF). Therefore, HHT can give a reasonably better time-frequency pattern representations for non-stationary signals [6,11,12,13,18,19,21]. In the light of this information, we used the HHT for generating IA signals from the voltage sag signal Firstly, for 3 phases (L1-N, L2-N, L3-N) real time processing the first intrinsic mode function is removed with the addition (superposition) of remain components to reconstruct the analyzed signal.…”

Section: Voltage Sagmentioning

confidence: 99%

“…When the signal is turn into IMFs, the Hilbert transform can formerly be carried out to each IMF giving the IA and IF versus time plot. This merging of EEMD process and Hilbert transform is known as the HHT [6,11,12,13,18,19,21]. IF signal can be used for separation for two cases but there is end effect problem that has to be solved.…”

Section: Ensemble Emd (Eemd) Algorithmmentioning

confidence: 99%

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Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid

Yalçin¹,

Ozdemir²

2017

Adv. sci. technol. eng. syst. j.

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“…However, the active distribution network is a complex stochastic system, where disturbances, faults, and other uncertain factors cause the system inevitably to have a variety of complex and diverse processes. The spectral leakage phenomenon may appear in the asynchronous sampling of DFT; this adversely affects the measurement accuracy [4,5]. Wavelet transform overcomes …”

Section: Introductionmentioning

confidence: 99%

Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

et al. 2018

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Micro phasor measurement units (µPMU) installed in active distribution networks are very useful for improving observability by acquiring system real-time data. However, three-phase imbalance and harmonic power flows adversely impact the accuracy of synchronous measurements, which implies the importance of phasor estimation errors. This paper proposes a new phasor estimation algorithm for µPMU in active distribution networks that uses a conditional maximum likelihood (CML) estimation method. Firstly, the signal model of three-phase, three-wire and four-wire imbalance systems is established. Then, the probability distributions of the magnitude and phase angles are derived from the geometric characteristics of the CML method by solving the geometric equation. Simulation results show that the proposed CML based method is effective for estimating phasor and impedance models of active distribution networks by using µPMU measurement data.

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Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

Cited by 210 publications

References 18 publications

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

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

Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid

Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

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