A novel method for voltage-sag source location using a robust machine learning approach

Mohammadi, Younes; Moradi, Mohammad H.; Leborgne, Roberto Chouhy

doi:10.1016/j.epsr.2016.12.028

Cited by 33 publications

(7 citation statements)

References 30 publications

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“…In ref. [131], SVM classifiers are explored for recognition of power quality events. A hybrid method of a priori algorithm with least square SVM is explored for the feature section and identification of PQ disturbances.…”

Section: T T Transformmentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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The issue of power quality (PQ) has become more prominent over the last few decades as the demand of clean and high quality power is increasing around the globe. The effect of power quality disturbances on the equipment most of the time is very destructive, usually generates disruptions, which consecutively affects the other load connected to the power systems. The main purpose of this article is to present a comprehensive review of various power quality analysis techniques such as heuristic optimization, signal processing, machine learning, neural networks, artificial intelligence, and hardware implementation, so that a brief overview will be presented to the researcher and power engineers working in the field of power quality. Additionally, a comparative analysis is also reported on various methods based on several criteria including timing and accuracy, use of industrial and non-industrial data set, noisy and noiseless conditions, and finally single and multiple power quality events for analysis. More than 200 research publications are included for analysis and listed in reference so that it will be easy for the researcher in the domain of power quality to explore the possibility of further improvement in this field.

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Section: T T Transformmentioning

confidence: 99%

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Rahul

2020

Advcd Theory and Sims

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“…Therefore, accurate detection of VS causes (VSCs) and location can avoid the loss and problems instigated by VS in an electric power system (EPS). 5,6 This work confers the aspects of VSCs identification and their classification.…”

Section: Introductionmentioning

confidence: 99%

“…VS can trigger periods of downtime, considerable damage of product, and moreover, it can attribute to malfunctions, instabilities, and decrease the lifespan of the connected loads. Therefore, accurate detection of VS causes (VSCs) and location can avoid the loss and problems instigated by VS in an electric power system (EPS) 5,6 . This work confers the aspects of VSCs identification and their classification.…”

Section: Introductionmentioning

confidence: 99%

Advanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system

Mishra

Panigrahi

2019

Int Trans Electr Energ Syst

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Summary In this manuscript, voltage sag causes (VSCs) detection in an electric power system is studied using S‐transform (ST) and variational mode decomposition (VMD) techniques. The advantages of these approaches are compared in detail against earlier implemented wavelet transform (WT), empirical mode decomposition (EMD), and Hilbert‐transform (HT) techniques, as a novel contribution to previous studies. Voltage sag can trigger periods of downtime, considerable damage of product and moreover, it can attribute to malfunctions, instabilities, and decrease the lifespan of the connected loads. Therefore, accurate detection of VSCs can avoid the loss and problems instigated by voltage sag in an electric power system. In this work, initially, the extracted disturbance voltage signals from the relaying point are passed through the aforementioned signal processing algorithms to extract unique features. Then, these extracted features are used as inputs to the extreme learning machine (ELM) classifier for recognition of VSCs. Additionally, to augment the precision of the output result, the performance of ELM classifier is compared with the artificial neural network (ANN), K‐nearest neighbors (KNN), and support vector machine (SVM) classifier. Three types of VSCs are used in this analysis such as (a) sag due to incipient fault, (b) sag due to induction motor stating, and (c) sag due to transformer energization. The simulation results show good performance and feasibility of the stated methods for VSCs recognition even under in cooperation of noisy condition.

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“…Under the second sub-category, one of the main techniques proposed for detection of sag source location is instantaneous current and voltage vectors in Clark’s transform (Mohammadi et al, 2017; Polajzer et al, 2015). Thakur et al (2013) introduced phase-to-neutral and phase-to-phase voltage indices for distinguishing the voltage sag even after having same sag type and same zero sequence component of voltage.…”

Section: Introductionmentioning

confidence: 99%

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Saini

Kapoor

Beniwal

et al. 2019

Transactions of the Institute of Measurement and Control

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In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.

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A novel method for voltage-sag source location using a robust machine learning approach

Cited by 33 publications

References 30 publications

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

Advanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

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