Application of near perfect reconstruction filter banks in power quality disturbances classification methods

Koleva, Ljubica; Taskovski, Dimitar; Milchevski, Aleksandar; Dimchev, Vladimir

doi:10.1109/amps.2012.6343995

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Researchers have considered diverse types of disturbances and they trained their models based on their predefined types. 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.…”

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

Polyphase representation of QMF filter bank for power systems harmonics analysis

Markovska

Taskovski

2015

IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON)

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Near Perfect Reconstruction Filter Banks for Power Quality Analysis

Taskovski¹,

Koleva²,

Milchevski³

et al. 2013

Metrology and Measurement Systems

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The wavelet transform has been successfully used in the area of power quality analysis. There are many published papers with methods for power quality disturbance classification or harmonics measurement, which use wavelet transform. However, the properties of the wavelet transform can drastically vary from the choice of the wavelet. In this paper we analyze the influence of the choice of the wavelet to the accuracy of the power quality classification method and to high frequency harmonics measurements. Additionally to the well known wavelet filters we introduce near perfect reconstruction filter banks. The simulation results indicate that these filter banks are a good choice for classification of power quality disturbances, especially in the presence of noise and for high frequency harmonics measurements.

show abstract

Application of near perfect reconstruction filter banks in power quality disturbances classification methods

Cited by 3 publications

References 10 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

Polyphase representation of QMF filter bank for power systems harmonics analysis

Near Perfect Reconstruction Filter Banks for Power Quality Analysis

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