Multiresolution Signal Decomposition Technique Research Articles

Investigation on Surface Condition of the Corona-Aged Silicone Rubber Nanocomposite Adopting Wavelet and LIBS Technique

Silicone rubber nanocomposites were prepared using 1, 3, 5, and 10 wt% of alumina nanofiller. Corona inception voltage (CIV) due to water droplets was measured using fluorescence technique, and it has decreased for alumina-added silicone rubber nanocomposites and with corona-aged samples. The effect of the corona discharges on the surface morphology characteristics of the nanocomposites was analyzed by measuring contact angle, surface profile measurement using atomic force microscopy (AFM), and characterized by adopting multiresolution signal decomposition (MRSD) technique. The addition of the alumina nanofiller suppressed the surface roughness for the corona-aged nanocomposites and 5-wt% sample observed to have the lowest surface roughness values. Recovery of the surface roughness caused by corona discharges was analyzed at different time intervals, and it was observed that alumina nanoparticle-added silicone rubber showed an incremental reduction in the roughness recovery rate. The laser-induced breakdown spectroscopy (LIBS) technique was adopted to analyze the nanocomposites after the corona aging, and further plasma temperature was evaluated at different recovery times. The addition of an alumina nanofiller increased the plasma temperature, and 5-wt% samples have the lowest plasma temperature recovery rate after corona aging. The analysis of the study indicates that the corona-aged silicone rubber specimen surface properties recover in 8 h of resting time period.

Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems

This paper presents a detection scheme for DC side short-circuit faults of photovoltaic (PV) arrays that consist of multiple PV panels connected in a series/parallel configuration. Such faults are nearly undetectable under low irradiance conditions, particularly, when a maximum power point tracking algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. The proposed fault detection scheme is based on a pattern recognition approach that employs a multiresolution signal decomposition technique to extract the necessary features, based on which a fuzzy inference system determines if a fault has occurred. The presented case studies (both simulation and experimental) demonstrate the effective and reliable performance of the proposed method in detecting PV array faults.

Pollution Severity Analysis of Electric Power Transmission Tower Insulators using K-Means Clustering and Multi Resolution Signal Decomposition Technique

Pollution due to various sources deposits on the surface of insulators used in the electric power transmission towers. Under wet and highly polluted conditions, this pollution layer causes the formation of severe arcing on the surface of insulator which in turn leads to flashover of insulator. Failure of insulators due to pollution based flashover is a major issue faced by electrical utilities and hence accurate prediction of pollution on the surface of insulator using advanced techniques is always a hot research area. This paper deals with the development of a high performance diagnostic system to predict the pollution severity of tower insulators based on the multi resolution signal decomposition technique and k-means clustering approach. Laboratory experiments were carried out on porcelain insulators, which are conventionally used in power transmission system, under AC voltages at different pollution conditions and corresponding leakage current signals were measured. Multi resolution signal decomposition technique is employed to extract important features of leakage current signals. Variation of leakage current magnitude and distortion ratio at different pollution levels were analyzed. K-means algorithm is used to cluster the extracted features of the leakage current data. Test results clearly show that the pollution severity of power transmission tower insulators can be effectively realized through k-means clustering technique and it will be useful to carry out preventive maintenance work.

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation.

We propose a new algorithm to predict the outcome of direct-current electric (DCE) cardioversion for atrial fibrillation (AF) patients. AF is the most common cardiac arrhythmia and DCE cardioversion is a noninvasive treatment to end AF and return the patient to sinus rhythm (SR). Unfortunately, there is a high risk of AF recurrence in persistent AF patients; hence clinically it is important to predict the DCE outcome in order to avoid the procedure's side effects. This study develops a feature extraction and classification framework to predict AF recurrence patients from the underlying structure of atrial activity (AA). A multiresolution signal decomposition technique, based on matching pursuit (MP), was used to project the AA over a dictionary of wavelets. Seven novel features were derived from the decompositions and were employed in a quadratic discrimination analysis classification to predict the success of post-DCE cardioversion in 40 patients with persistent AF. The proposed algorithm achieved 100% sensitivity and 95% specificity, indicating that the proposed computational approach captures detailed structural information about the underlying AA and could provide reliable information for effective management of AF.

The Study of the Deforming Regime of AC/AC Converter using Fourier and Multiresolution Analysis

For electric power quality improvement should be identified and followed the deforming consumers. This is necessary, because the number of equipments producing harmonic distortion is increasing, primarily by expanding the use of static power converters. This paper presents the study of the deforming state generated by the AC/AC converter with full-wave AC control using two SCRs (silicon – controlled rectifier) connected in inverse parallel. The harmonic spectrums of distorted current and voltage signals are determined by the load and the thyristors delay angle. For this study, we use Fourier and Wavelet transform. A multiresolution signal decomposition technique is presented in the analysis of the deforming signals from the AC/AC converter. Ill. 10, bibl. 8 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.121.5.1643

Denoising and Frequency Analysis of Noninvasive Magnetoencephalography Sensor Signals for Functional Brain Mapping

Magnetoencephalography (MEG) is an important noninvasive, nonhazardous technology for functional brain mapping, measuring the magnetic fields due to the intracellular neuronal current flow in the brain. However, most often, the inherent level of noise in the MEG sensor data collection process is large enough to obscure the signal(s) of interest. In this paper, a denoising technique based on the wavelet transform and the multiresolution signal decomposition technique along with thresholding is presented, substantiated by application results. Thereafter, different frequency analysis are performed on the denoised MEG signals to identify the major frequencies of the brain oscillations present in the denoised signals. Time-frequency plots (spectrograms) of the denoised signals are also provided.

Theoretical and Experimental Aspects Concerning Fourier and Wavelet Analysis for Deforming Consumers in Power Network

This paper presents the application of Fourier and Wavelet transforms in analysis of the deforming consumers. Fourier transform is probably the most popular transform being use in the analysis of the deforming state, but it is not the only one. The Wavelet transform is introduced as a powerful tool for monitoring the deforming state. We present a comparative study between the Fourier transform and Wavelet transform. It is presents a multiresolution signal decomposition technique in analysis of the deforming signals from the three-phase rectifier with thyristors. Also we illustrate a virtual instrument for Fourier analysis of some deforming consumers Data acquisition, display and analysis are making with the PCI 6251 board acquisition, the digital oscilloscope DPO 3014 and power quality analyzer C.A. 8332 B. Ill. 7, bibl. 12 (in English; abstracts in English, Russian and Lithuanian).

Wavelet‐based neural networks applied to automatic detection of road surface conditions using tire noise from vehicles.

The detection of road surface conditions is an important process in efficient road management. In particular, in snowy seasons, prior information about the road conditions such as an icy state, helps road users or automobile drivers to obviate serious traffic accidents. This paper proposes a novel approach for automatically detecting the states of the road surface from tire noises of vehicles. The method is based on a wavelet transform analysis, artificial neural networks, and the mathematical theory of evidence. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques. The proposed classification is carried out in sets of multiple neural networks using learning vector quantization networks. The outcomes of the networks are then integrated using the voting decision‐making scheme. It seems then feasible to detect passively and readily the states of the surface, i.e., as a rule of thumb, the dry, wet, snowy, and slushy state, automatically.

Understanding the hydrophobic characteristics of epoxy nanocomposites using wavelets and fractal technique

In the present study, the influence of water ageing on the surface characteristics of epoxy nanocomposites was analyzed through atomic force microscopy (AFM) studies. The hydrophobic properties of the epoxy nanocomposite material were analyzed through contact angle and diffusion coefficient measurements. Fractal dimension were calculated by adopting a multi resolution signal decomposition (MRSD) to the 1D-AFM signal through power spectral density calculations at each level of decomposition. 2D-MRSD studies were applied to AFM pictures to understand the surface characteristics of the material. The result of the study shows the presence of high and low frequency signals indicating characteristic variation in surface roughness of the material. The surface roughness parameter measured through the MRSD technique indicates that the surface roughness is lower for epoxy nanocomposites (upto 5Wt%) compared to pure epoxy resin. It is observed that the fractal dimension and the root mean square roughness values vary proportionately. Also it is realized that diffusion coefficient and surface roughness of the material shows an inverse relationship.

WNN based intelligent energy meter

This paper presents a novel method for estimation of average power based on multiresolution signal decomposition technique and neural network. Proposed method allows the measurement form the samples of voltage and current signal over a cycle. Significance of this approach is we have not considered the phase factor for measuring average power as in conventional method. A prototype model for on line measuring and estimating the average power with high accuracy is presented using TMS320C3X starter kit.

Recognition of power quality events by using multiwavelet-based neural networks

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.

고압전동기 고정자권선의 절연결함에 대한 특징추출기법

Multi-resolution Signal Decomposition (MSD) Technique of Wavelet Transform has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge sources present in multi-source PD pattern, usually encountered during practical PD measurement. Employing the Daubechies wavelet, feature were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources and multi-PD soruces.

Investigations Into the Surface Condition of Silicone Rubber Insulation Material Using Multiresolution Signal Decomposition

In the present paper, the tracking phenomena of silicone rubber material has been studied under ac voltages, with ammonium chloride as a contaminant. The characteristic changes in the tracking time of the aged specimens are analyzed through leakage current measurement. The observed trends in the leakage current during the tracking study are analyzed using a moving average current technique. The contact angle of the specimen varies with the type of aging. multiresolution signal decomposition technique was adopted to identify the characteristic change in the hydrophobicity levels of the specimens using the standard deviation multiresolution analysis curve. It is concluded that the material with less contact angle where the leakage current magnitude is high, allows tracking to occur early.

Wavelet-based neural network for power quality disturbance recognition and classification

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, voltage swell, interruption, notching, impulsive transient, and harmonic distortion. The results show that the classifier can efficiently detect and classify different types of power quality disturbance.

Abrupt change detection in power system fault analysis using adaptive whitening filter and wavelet transform

This paper describes the application of the adaptive whitening filter and the wavelet transform used to detect the abrupt changes in the signals recorded during disturbances in the electrical power network in South Africa. Main focus has been to estimate exactly the time-instants of the changes in the signal model parameters during the pre-fault condition and following events like initiation of fault, circuit-breaker opening, auto-reclosure of the circuit-breakers. The key idea is to decompose the fault signals, de-noised using the adaptive whitening filter, into effective detailed and smoothed version using the multiresolution signal decomposition technique based on discrete wavelet transform. Then we apply the threshold method on the decomposed signals to estimate the change time-instants, segmenting the fault signals into the event-specific sections for further signal processing and analysis. This paper presents application on the recorded signals in the power transmission network of South Africa.

Diagnostic study of the surface condition of the insulation structure using wavelet transform and neural networks

In the present work, tracking phenomenon in high density polyethylene material has been studied under AC voltage, with ammonium chloride as a contaminant. It is observed that the tracking time depends on both conductivity and flow rate of the contaminants. The surface conditions of the insulation structure such as normal conditions and surface discharges or tracking were characterized by using the leakage current measurement, utilizing the multi resolution signal decomposition technique. The process of identification of surface condition of the insulation structure was automated using Artificial Neural Network.

Application of multiresolution signal decomposition for monitoring short-duration variations in distribution systems

The multiresolution signal decomposition (MRA) technique is introduced as a powerful tool for detecting, classifying and quantifying the short duration variations in an electrical distribution system. Using the proposed technique, one will have the ability to measure the magnitude and duration of the signal during SDV events. Furthermore, the total harmonic distortion and the DC offset can also be measured. The information gained at different resolution levels and the measured values by the proposed technique can be used to distinguish among similar power quality phenomena and may help in finding the source of disturbance.

Wavelet analysis for classification of multi-source PD patterns

Multi-resolution signal decomposition (MSD) technique of wavelet transforms has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge (PD) sources present in multi-source PD patterns, usually encountered during practical PD measurements. Employing the Daubechies wavelet, features were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources. Suitability of these extracted features for classification was further verified using a radial basis function neural network (NN). Successful recognition was achieved, even when the constituent sources produced partially and fully overlapping patterns, thus demonstrating the applicability of the proposed novel approach for the task of multi-source PD classification.

Power quality detection and classification using wavelet-multiresolution signal decomposition

The wavelet transform is introduced as a powerful tool for monitoring power quality problems generated due to the dynamic performance of industrial plants. The paper presents a multiresolution signal decomposition technique as an efficient method in analyzing transient events. The multiresolution signal decomposition has the ability to detect and localize transient events and furthermore classify different power quality disturbances. It can also be used to distinguish among similar disturbances.