Voltage Variation Analysis by Using Gabor Transform

Voltage variations which include voltage sag, swell and interruption are simulated and analyzed in this paper. Various types of parametric equation are generated with the help of MATLAB software. Simulated signals are studies by using time-frequency distribution (TFD) technique. The TFD method used in this paper is the Gabor transform which is less applied by the researchers. The signal parameters used in this paper are the RMS voltage and instantaneous power can be extracted from the TFR to study the distinctives of the voltage variations. The parameters extracted can detect the voltage variation signals successfully. The voltage variation signals are successfully detected by using the K-Nearest Neighbors (kNN) algorithm with the implementation of signal parameters extracted as the input on the classifier. The voltage variations waveforms as well as the signal parameters obtained are suitable to be further analyzed

A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram

In this paper, the power quality (PQ) disturbance which is the voltage variations consist of voltage swell, sag and interruption are model and analyze. Different types of voltage variations PQ disturbances models are developed and created by using MATLAB/Simulink as well as mathematical models. The mathematical and Simulink model are used to compare in terms of time-frequency representation (TFR). The Simulink models include shutting down enormouscapacities from system to resemble voltage swell, large loads energizing and three-phase fault to simulate voltage sag as well as implementing permanent three-phase fault to simulate voltage interruption. The signals generated are analyzed by using linear time-frequency distribution (TFD). The signal parameters such as root mean square voltage (Vrms), total harmonic distortion (THD) and power value are estimated from the TFR to identify the characteristics of the voltage variation. The results of analysis on the PQ disturbance waveforms generated are identical to the actual real-time PQ signals and the models can be modified to any desired situation respectively. The PQ waveforms obtained are suitable to be further analyzed

Voltage variations identification using gabor transform and rule-based classification method

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method. The technique was tested by using mathematical model of Power Quality (PQ) disturbances based on the IEEE Std 519-2009. The PQ disturbances focused were the voltage variations, which included voltage sag, swell and interruption. A total of 80 signals were simulated from the mathematical model in MATLAB and used in this study. The signals were analyzed by using Gabor Transform and the signal pattern, time-frequency representation (TFR) and root-mean-square voltage graph were presented in this paper. The features of the analysis were extracted, and rules were implemented in rule-based classification to identify and classify the voltage variation accordingly. The results showed that this method is easy to be used and has good accuracy in classifying the voltage variation

Spectrogram Based Window Selection for the Detection of Voltage Variation

This paper presents the application of spectrogram with K-nearest neighbors (KNN) and Support Vector Machine (SVM) for window selection and voltage variation classification. The voltage variation signals such as voltage sag, swell and interruption are simulated in Matlab and analyzed in spectrogram with different windows which are 256, 512 and 1024. The variations analyzed by spectrogram are displayed in time-frequency representation (TFR) and voltage per unit (PU) graphs. The parameters are calculated from the TFR obtained and be used as inputs for KNN and SVM classifiers. The signals obtained are then added with noise (0SNR and 20SNR) and used in classification. The tested data contain voltage variation signals obtained using the mathematical models simulated in Matlab and the signals added with noise. Classification accuracy of each window by each classifier is obtained and compared along with the TFR and voltage PU graphs to select the best window to be used to analyze the best window to be used to analyze the voltage variation signals in spectrogram. The results showed window 1024 is more suitable to be used

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application