Detection and classification of power quality event using wavelet transform and weighted extreme learning machine

Mishra, Siddharth; Ipsita, Ananya; Upadhyay, Binayak

doi:10.1109/iccpct.2016.7530177

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Uyar et al [47], Koleva [48] and Kostadinov [49] defined 6 classes of disturbances: sag, swell, outage, harmonic, swell with harmonic and sag with harmonic. Sahani [50] composed 9 classes, including momentary interruption, sag, swell, harmonics, flicker, notch, spike, transient, and sag with harmonics. Khokhar [51] presented 6 more categories in addition to [52] which are swell with harmonics, interruption and harmonics, impulsive transient, flicker with harmonics, flicker with swell, and flicker with sag.…”

Section: Pq Issuesmentioning

confidence: 99%

Online power quality disturbance detection by support vector machine in smart meter

Parvez

Aghili

Sarwat

et al. 2018

J. Mod. Power Syst. Clean Energy

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Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

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Section: Pq Issuesmentioning

confidence: 99%

Online power quality disturbance detection by support vector machine in smart meter

Parvez

Aghili

Sarwat

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…In [20], the authors presented a basic Extreme Learning Machine (ELM) classifier with S-transform-based features. In [21], the authors used Weighted (W)-ELM for classification of power quality events with a conventional WT method. Synthetic data were used to validate the proposed system.…”

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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“…Authors present detection and classification of islanding condition and non‐islanding PQ disturbances (capacitor switching, load rejection, line‐to‐line fault, 3‐phase fault, single line fault, voltage sag, swell, and DG tripping) using ST for feature extraction and extreme learning classifier (ELM) for classification. For classification of PQ disturbances, modified ST and ELM are used in Zhang et al while WT and weighted ELM are used in Sahani et al…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Saini

Beniwal

2018

Int Trans Electr Energ Syst

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The proposed work accomplishes detection and classification of simple and complex power quality disturbances occurring in the realm of wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Taking the advantage of time-time transform, this work has further tailored time-time transform by accommodating the dyadic scaling to make it a computationally less complex and faster technique. Here, threephase power quality signals are first segmented into single phases to be analyzed with fast time-time transform for getting the event information in timetime domain. The event information is further passed through two-stage feature extraction process to bring out twenty two characteristic curves; each of them is further used to compute five statistical features to produce 107 features after ignoring three redundant features. Thus, a high-dimensional feature set of size 321 is obtained corresponding to three phases of voltage signal. Because, high-dimensional feature set demands a robust classifier that can accurately classify the disturbances even in the presence of noise, small residual-extreme learning machine has been trained to classify the power quality signal into 12 classes (symmetrical sag, asymmetrical sag, swell, unbalance, harmonics, notch, momentary interruption, normal voltage signal, sag with harmonics, swell with harmonics, sag and notch with harmonics, and swell and notch with harmonics). This simulation-based study has been proved effective in dealing with noisy power quality signals also with reduced computational complexity and higher classification rate. KEYWORDS fast time-time transform, power quality, small residual-extreme learning machine classifier, wind-grid integrated system List of symbols and abbreviations: h(t), a time signal; w(t), Gaussian window; σ, standard deviation; f, frequency; β, output weight of SR-ELM network; G, output of hidden layer of SR-ELM network; X, input to SR-ELM; L, number of neurons in hidden layer of SR-ELM; M, number of neurons in output layer of SR-ELM; e, error; C. B.

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Detection and classification of power quality event using wavelet transform and weighted extreme learning machine

Cited by 4 publications

References 15 publications

Online power quality disturbance detection by support vector machine in smart meter

Online power quality disturbance detection by support vector machine in smart meter

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

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