An effective wavelet-based feature extraction method for classification of power quality disturbance signals

Uyar, Murat; Yildirim, Selcuk; Gençoğlu, Muhsin Tunay

doi:10.1016/j.epsr.2008.03.002

Cited by 200 publications

(126 citation statements)

References 19 publications

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“…Wavelet transform has been proven to very efficient in signal analysis [18].The wavelet analysis block transforms the distorted signal into different time-frequency scales. Wavelet analysis employs the expansion and contraction of basis function to detect simultaneously the characteristics of global and local of measured signal [19].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Determining Five Kinds of Power Quality Disturbances by Using Statistical Methods and Wavelet Energy Coefficients

Kocaman¹,

Ozdemir²

2024

RE&PQJ

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Abstract. In this paper, it is tried to compare two methods for determining pure sine and five kinds of power quality disturbances (PQD) such as voltage sag, voltage swell, voltage with harmonics, transients and flicker. These methods are statistical methods and wavelet based effective feature extraction method. Before classifying power quality signals, one of the feature extraction method must be applied. So, these two methods are compared. Firstly, statistical methods are applied to PQD. It is observed that if PQD signal is created at the zero crossing points of the voltage signal, statistical methods give satisfactory result. But occurrence of disturbances at these points is not guaranteed in real systems. It is seen that first method may confuse some PQD according to occurrence place of disturbances. So second method is used for extracting the energy distribution features of PQD constituted in eight different points (0 0 ,

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Determining Five Kinds of Power Quality Disturbances by Using Statistical Methods and Wavelet Energy Coefficients

Kocaman¹,

Ozdemir²

2024

RE&PQJ

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“…Entropy is a measure of irregularities of states, which has been recognized as an ideal parameter for quantifying the ordering of nonstationary signals [18]. The energy entropy based on the wavelet packet decomposition was reported to detect and classify power quality disturbances [19].…”

Section: Wavelet Packet Energy Entropymentioning

confidence: 99%

Gas–liquid Flow Pattern Recognition Based on Wavelet Packet Energy Entropy of Vortex-induced Pressure Fluctuation

Sun¹,

Shao²,

Gong³

2013

Measurement Science Review

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Here we report a novel flow-pattern map to distinguish the gas-liquid flow patterns in horizontal pipes at ambient temperature and atmospheric pressure. The map is constructed using the coordinate system of wavelet packet energy entropy versus total mass flow rate. The wavelet packet energy entropy is obtained from the coefficients of vortex-induced pressure fluctuation decomposed by the wavelet packet transform. A triangular bluff body perpendicular to the flow direction is employed to generate the pressure fluctuation. Experimental tests confirm the suitability of the wavelet packet energy entropy as an ideal indicator of the gas-liquid flow patterns. The overall identification rate of the map is 92.86%, which can satisfy most engineering applications. This method provides a simple, practical, and robust solution to the problem of gas-liquid flow pattern recognition.

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“…Although this classifier has good capabilities for classifying combined PQ disturbances, the analysis stage needs more attention about subclasses of each PQ disturbance, especially sag and swell. A feature extraction method for the automatic classification of PQ disturbances based on wavelet neural network (WNN) has been proposed in [36]. Its main advantage is the reduction of training data size and a good signal characterization.…”

Section: Introductionmentioning

confidence: 99%

Disturbances Classification Based on a Model Order Selection Method for Power Quality Monitoring

Oubrahim

Choqueuse

Benbouzid

2017

IEEE Trans. Ind. Electron.

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Abstract-In this paper, a new technique for power quality disturbances classification is proposed. It focuses on voltage sags and swells that are first pre-classified into four classes that depend on the number of non-zero symmetrical components and can contain different types of sag and swell. Using the estimated symmetrical component values, we can afterward classify the corresponding sag or swell signature. In this study, we show that the pre-classification can be reformulated as a pure model order selection problem. To solve this problem, we propose two preclassifiers based on Information Theoretical Criteria. The former yields the highest statistical performances, while the latter has a lower computation complexity. The performances of the proposed classification algorithms are evaluated using Monte Carlo simulations on synthetic signals and using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed algorithms for voltage sag and swell classification.

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An effective wavelet-based feature extraction method for classification of power quality disturbance signals

Cited by 200 publications

References 19 publications

Determining Five Kinds of Power Quality Disturbances by Using Statistical Methods and Wavelet Energy Coefficients

Determining Five Kinds of Power Quality Disturbances by Using Statistical Methods and Wavelet Energy Coefficients

Gas–liquid Flow Pattern Recognition Based on Wavelet Packet Energy Entropy of Vortex-induced Pressure Fluctuation

Disturbances Classification Based on a Model Order Selection Method for Power Quality Monitoring

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