A Fourier Based Wavelet Approach Using Heisenberg’s Uncertainty Principle and Shannon’s Entropy Criterion to Monitor Power System Small Signal Oscillations

Hosseini, Seyyed Ahmad; Amjady, Nima; Velayati, Mohammad Hossein

doi:10.1109/tpwrs.2014.2377180

Cited by 37 publications

(21 citation statements)

References 37 publications

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“…Generally, the use of transient features requires the implementation of signal processing such as DWT or S-transform at the high sampling rate to capture the transient effects. In electrical transient analyses, these wavelet multi-resolution analysis (WMRA) techniques are useful to monitor power system small signal oscillations, power quality (PQ), and electric power disturbance [16][17][18][19]. For example, Energies 2017, 10, 611 3 of 20 the authors [19] employed harmonic voltages and wavelet coefficients as PQ features for placement of power quality measurement facilities to identify PQ problems.…”

Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

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This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

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Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

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“…WE is a new method developed to analyze transient features of complicated signals (Hosseini et al, 2015; Phillips et al, 2015; Sun, 2015), such as the brain image in this study. The value of WE has a physical meaning of the order/disorder degree of the signal with multiscale time-frequency resolution.…”

Section: Methodsmentioning

confidence: 99%

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Wang

Yang

et al. 2016

Front. Comput. Neurosci.

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Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way.Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC).Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM).Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier.Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

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“…We employed WE to extract features from car images. WE combines wavelet transform and entropy calculation [7]. The continuous wavelet transform (CWT) is defined as follows:…”

Section: A Wavelet Entropymentioning

confidence: 99%

Ford vehicle identification via shallow neural network trained by particle swarm optimization

Yang¹,

Wang²,

Jiang³

2018

Proceedings of the 2018 3rd Joint International Information Technology，Mechanical and Electronic Engineering Conference (JIMEC

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Automatic identification of the car manufacturer is difficult to achieve because of the similarity among the different brands. In this work, we propose a new system of Ford vehicle identification. Firstly, we captured the side view of the car image. Secondly, we employed the wavelet entropy (WE) to extract efficient features from car images. Thirdly, we employed a shallow neural network (SNN) as a classifier. Finally, we used the particle swarm optimization to train the classifier. The 10 10-fold cross validation on a data set containing 220 vehicle images showed that our Ford vehicle identification system obtained the overall sensitivity of 83.27±1.61%. The overall specificity is 83.91± 1.87%, the overall accuracy is 83.59± 0.94%. Experiment result show that the proposed system is effective for Ford vehicle identification.

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A Fourier Based Wavelet Approach Using Heisenberg’s Uncertainty Principle and Shannon’s Entropy Criterion to Monitor Power System Small Signal Oscillations

Cited by 37 publications

References 37 publications

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Ford vehicle identification via shallow neural network trained by particle swarm optimization

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