Fault Detection and Classification in Smart Grids Using Wavelet Analysis

2020 ◽  
Author(s):  
Muhammad Ibrahim Munir ◽  
Sajid Hussain ◽  
Ali Al-Alili ◽  
Reem Al Ameri ◽  
Ehab El-Sadaany
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Smart Grids ◽  
Short Circuit ◽  
Low Frequency ◽  
Evaluation Study ◽  
Open Circuit ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Transmission Grid

Abstract One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This paper presents the incorporation of artificial intelligence techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to detect and comprehensively classify the most common fault transients within a reasonable range of accuracy. For gauging the effectiveness of the proposed scheme, a thorough evaluation study is conducted on a modified IEEE-39 bus system. Bus voltage and line current measurements are taken for a range of fault scenarios which result in high-frequency transient signals. These signals are analyzed using continuous wavelet transform (CWT). The measured signals are afterward preprocessed using Discrete Wavelet Transform (DWT) employing Daubechies four (Db4) mother wavelet in order to decompose the high-frequency components of the faulty signals. DWT results in a range of high and low-frequency detail and approximate coefficients, from which a range of statistical features are extracted and used as inputs for training and testing the classification algorithms. The results demonstrate that the trained models can be successfully employed to detect and classify faults on the transmission system with acceptable accuracy.

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

Audio Watermarking Based on Wavelet Transform

2012 ◽  
Vol 229-231 ◽  
pp. 2784-2788 ◽  
Author(s):  
Mahmoud A. Osman ◽  
Nasser H. Ali
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Audio Signal ◽  
Frequency Component ◽  
Binary Image ◽  
Audio Watermarking ◽  
High Frequency Component ◽  
Discrete Wavelet ◽  
Copyright Information

The process of hiding the information like text, binary image, audio etc. into another signal source like image, audio etc. is called watermarking. The approach involved in watermarking the binary image signal in the wavelet domain of the audio signal was implemented using MATLAB. In this paper, we propose a Discrete Wavelet Transform low frequency to high frequency. Besides, the high frequency spectrum is less sensitive to human ear. That is the reason why the high frequency component is usually discarded in the compression process. Therefore, information to be hidden can be embedded into the low frequency component to against the compression attack. The characteristic of this scheme is that the user can not only use the DAW to embed the text file in to the audio but also binary image. In this paper we embeds copyright information into audio files as a proof of their ownership, we propose an effective, robust, and an inaudible audio watermarking algorithm. The effectiveness of the algorithm has been brought by virtue of applying the discrete wavelets transform (DWT) . Experimental results will be presented in this paper to demonstrate the effectiveness of the proposed algorithm.

scholarly journals An Iterative Hybrid Harmonics Detection Method Based on Discrete Wavelet Transform and Bartlett–Hann Window

2020 ◽  
Vol 10 (11) ◽  
pp. 3922 ◽  
Author(s):  
Guishuo Wang ◽  
Xiaoli Wang ◽  
Chen Zhao
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
High Frequency Part

The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

scholarly journals Efficient fault identification scheme of compensated transmission grid based on correlated reactive power measurements and discrete wavelet transform

2021 ◽  
Vol 72 (4) ◽  
pp. 217-228
Author(s):  
Mohamed I. Zaki ◽  
Ragab A. El-Sehiemy ◽  
Ghada M. Amer
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Reactive Power ◽  
Short Circuit ◽  
Measuring System ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
Identification Scheme ◽  
Transmission Grid ◽  
Wide Range

Abstract The early fault identification in high-voltage power systems is a substantial aspect not only to minimize equipment failure but also to increase both the reliability and stability in power system. Subsequently, the aim of this paper is to propose the adaptive fault-identification scheme based on multi-resolution analysis technique. The proposed method is dependent on monitoring both voltages and currents from single-ended measuring system. The correlation among the reactive power computation and discrete wavelet transform is used to generate the significant criteria which are used to discriminate between short-circuit currents and energizing heavy loads behaviour. Different transmission network configurations are investigated to assess the dependability, security, and reliability of fault identification relay as well. The correlative protection scheme attains the accurate results under healthy disturbances, and therefore it is superior to other conventional approaches. In addition, a selective study is applied to different mother wavelets to find the best one. The response of the proposed scheme to the compensated transmission line is also verified at a wide range of compensation levels with faults before and after compensated bank. Simulation tests have been handled via ATP-EMTP to investigate the proper practicability and adaptability of the fault-identication relay.

scholarly journals Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Salim Lahmiri ◽  
Mounir Boukadoum
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Filter Banks ◽  
Gabor Filter ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Biomedical Images ◽  
Automatic Feature Extraction

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

Sign in / Sign up

Export Citation Format

Share Document