Online power quality events detection using weighted Extreme Learning Machine

Uçar, Ferhat; Alçın, Ömer Faruk; Dandil, Besir; Ata, Fikret; Cordova, Jose; Arghandeh, Reza

doi:10.1109/sgcf.2018.8408938

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Artificial Neural Networks (ANN), probabilistic neural network (PNN), and retrieved features by Discrete Wavelet Transform (DWT), Hilbert Transform (GT), and S-transform (ST) among others, are some of the existing methodologies. Ferhat Ucar [4] presented the W-ELM driven features extraction approach. further, ELM applied to classify the type of event.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

M.,

G.,

et al. 2023

ECJSE

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The use of sensitive electrical gadgets in industries, buildings, smart cities, and homes has increased drastically in recent years. PQ events such as interruptions, surges, and sags have a high impact on these sensitive devices. The failure of these delicate devices in real-time applications, particularly smart applications, may result in significant damage. The supply quality decreases because of the failure of internal transmission system elements, unbalanced loads, and other outdoor issues such as like weather. Several academics have proposed techniques to analyze these PQ disturbances, including wavelet packets, S-transform, rough sets and neural networks. In all the available algorithms, the classification procedure involves the extraction of a large set of features from the transformed outputs, training the classifier, and finally making a conclusion with the classifier. Because of the involvement of a large number of features, the computational cost of all these methods increases. To reduce complexity and enhance classification efficiency, the proposed method focuses on extracting fewer low-complexity wavelet features from signals. Pattern recognition (PR) methods, such as the wide variety of K-nearest neighbors (KNN) and ensemble classifiers, are used to classify PQ events in this study. The performance of the proposed ML approaches' performance is evaluated at various training and testing rates. Subsequently, the performance of the proposed strategies was compared to that of the current methods to determine the dominance of the proposed approaches.

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Section: Introductionmentioning

confidence: 99%

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

M.,

G.,

et al. 2023

ECJSE

View full text Add to dashboard Cite

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“…Recent works have shown the advantages of using big data and machine learning applications in power quality analysis [5]. There are several intelligent methods presented such as shape-based data analytics of event signals [6,7], non-parametric and partial-knowledge detection [8][9][10], and also preliminary studies including intelligent classifiers [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

et al. 2019

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This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

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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

133

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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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Online power quality events detection using weighted Extreme Learning Machine

Cited by 5 publications

References 18 publications

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

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

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