Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

Oleskovicz, Mário; Coury, Denis Vinicius; Felho, Odilon Delmont; Usida, Wesley F.; Carneiro, A.A.F.M.; Pires, Leandro R.S.

doi:10.1016/j.ijepes.2009.01.012

Cited by 82 publications

(43 citation statements)

References 12 publications

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“…Exhaustive training phase is required in [99]. In [100], authors provided a hybrid methodology with WT and ANN for recognition of synthetic data through graphical user interface. In [101], authors investigated the comparison between logistic regression (LR) and ANN for recognition of practical noisy PQ events.…”

Section: Neural Network Based Methodsmentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

188

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Section: Neural Network Based Methodsmentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

188

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“…The typical pattern recognition methods, such as fuzzy rules (FR) [7], decision tree (DT) [8] and neural networks (NNs) [9][10][11], have been widely used for PQ disturbances classification. FR and DT methods for PQ disturbances recognition are very effective and easy to achieve.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Classification Based on A Novel Fourier Neural Network and Hyperbolic S-transform

Lin¹,

Wu²,

Qi³

et al. 2016

IJSIP

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Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.

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“…Among the main disturbances that indicate a poor power quality, the following can be highlighted: voltage sag/swell, overvoltage, undervoltage, interruption, oscillatory transient, noise, flicker and harmonic distortion (Dugan et al, 2003). Actually, in literature, a diversity of papers can be found concerning detection and identification of power quality disturbances by applying intelligent systems, such as Artificial Neural Networks (ANN) (Janik & Lobos, 2006;Oleskovicz et. al., 2009;Jayasree, Devaraj & Sukanesh, 2010) and Fuzzy Inference Systems (Zhu, Tso & Lo, 2004;Hooshmand & Enshaee, 2010;Meher & Pradhan, 2010;Behera, Dash & Biswal, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…However, only some papers use data pre-processing tools before the application of intelligent systems. Among these papers, the use of Discrete Wavelet Transform (DWT) (Zhu, Tso & Lo, 2004;Uyar, Yildirim & Gencoglu, 2008;Oleskovicz et. al., 2009) and Discrete Fourier Transform (DFT) (Zhang, Li & Hu, 2011) can be highlighted in the pre-processing stage.…”

Section: Introductionmentioning

confidence: 99%