Wavelet-Based Intelligent System for Recognition of Power Quality Disturbance Signals

Kaewarsa, Suriya; Attakitmongcol, Kitti; Krongkitsiri, Wichai

doi:10.1007/11760023_199

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…[1] Users can choose different assessment grades of PQ according to their different demands [2] . Some power quality indexes, especially voltage dips and transient interruptions are considered more and more important for estimating PQ.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Power Quality Based on D-S Evidence Theory

Dou

Gui

Yang

et al. 2014

Int. J. Autom. Comput.

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Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer (D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed, and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally, the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.

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Section: Introductionmentioning

confidence: 99%

Assessment of Power Quality Based on D-S Evidence Theory

Dou

Gui

Yang

et al. 2014

Int. J. Autom. Comput.

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“…To get a fast recognition, it is necessary to develop an automatic method to classify the disturbances. Advanced artificial intelligence technologies such as expert system [7,8], support vector machines (SVM) [9] and neural network [10,11], have provided a good platform to develop fast classification approaches.…”

Section: Introductionmentioning

confidence: 99%

Classification of power quality disturbances using quantum neural network and DS evidence fusion

Zhang

Zhao

et al. 2011

Int Trans Elec Energy Syst

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SUMMARY A novel classifier based on Quantum Neural Network (QNN) and Dempster‐Shafer (DS) evidence theory to recognize the types of power quality (PQ) disturbances is presented. According to the Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT) and S‐transform (ST) algorithms, three kinds of feature vectors extracted from the original signals are used to train three different quantum neural networks, then DS evidence theory is used for global fusion at the decision level to gain a unified classification result from the outputs of QNNs. Ten types of disturbances are considered for the classification problem. Simulation results indicate that the classification performance of QNN is better than back propagation neural network (BPNN). The recognition capability of the QNN‐DS classifier is compared with BPNN‐DS, probabilistic neural network with voting rules (PNN‐VR) at the decision level, and only one QNN with information fusion at the feature level. It shows that the proposed classifier has good performance on recognizing single and multiple disturbances under different situations and can achieve a highest accuracy of all. Copyright © 2011 John Wiley & Sons, Ltd.

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