Automatic voltage disturbance detection and classification using wavelets and multiclass logistic regression

Kostadinov, Dimce; Taskovski, Dimitar

doi:10.1109/i2mtc.2012.6229122

Cited by 4 publications

(3 citation statements)

References 9 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

“…Statistical analysis [39]; • Logistic regression [40]; • Principal component analysis [41]; • K-nearest neighbors method [42]; • Wald's sequential analysis [43];…”

mentioning

confidence: 99%

“…These methods should ensure that a decision on PQI deviation from standard values is made in real time. In this context, the implementation of methods for PQI control based on machine learning [33][34][35][36][37][38][40][41][42] is difficult due to the complex process of simulation modeling of the power supply system of an industrial consumer. Statistical methods [39,43] show great promise.…”

mentioning

confidence: 99%

Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices

Kulikov,

Ilyushin,

Sevostyanov

et al. 2024

Energies

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Deviations of power quality indices (PQI) from standard values in power supply systems of industrial consumers lead to defective products, complete shutdown of production processes, and significant damage. At the same time, the PQI requirements vary depending on the industrial consumer, which is due to different kinds, types, and composition of essential electrical loads. To ensure their reliable operation, it is crucial to introduce automatic PQI control devices, which evaluate the extent of distortion of the sinusoidal voltage waveform of a three-phase system. This allows the power dispatchers of grid companies and industrial enterprises to quickly make decisions on the measures to be taken in external and internal power supply networks to ensure that the PQI values are within the acceptable range. This paper proposes the use of an integrated indicator to assess the extent of distortion of the sinusoidal voltage waveform in a three-phase system. This indicator is based on the use of the magnitude of the ratio of complex amplitudes of the forward and reverse rotation of the space vector. In the study discussed, block diagrams of algorithms and flowcharts of automatic PQI control devices are developed, which implement parametric and nonparametric multiple-hypothesis sequential analysis using an integrated indicator. In this case, Palmer’s algorithm and the nearest neighbor method are used. The calculations demonstrate that the developed algorithms have high speed and high performance in detecting deviations of the electrical power quality.

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

Koleva

Taskovski

Milchevski

et al. 2012

2012 IEEE International Workshop on Applied Measurements for Power Systems (AMPS) Proceedings

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Automatic voltage disturbance detection and classification using wavelets and multiclass logistic regression

Cited by 4 publications

References 9 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

Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices

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

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