This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The estimation of spectral information can be obtained from TFR in order to identify the characteristics of the signals. The detection and classification of harmonic signals for 100 unique signals with numerous characteristic of harmonics with support of rule-based classifier and threshold setting that been referred to IEEE standard 1159 2009. The accuracy of proposed method is determined by using MAPE and the outcome demonstrate that the method gives high accuracy of harmonic signals classification. Additionally, Gabor transform also gives 100 percent correct classification of harmonic signals. It is verified that the proposed method is accurate and cost efficient in detecting and classifying harmonic signals in distribution system.
An Accurate Classification Method of Harmonic Signals in Power Distribution S...TELKOMNIKA JOURNAL
This document presents a method for classifying harmonic signals in power distribution systems using S-transform (ST). ST represents signals in the time-frequency domain and is used to estimate spectral parameters for classification. A rule-based classifier classifies 100 unique signals according to IEEE standards. The accuracy of signal parameter estimation using ST is high, with mean absolute percentage errors below 0.06. ST also achieves 100% correct classification of harmonic versus inter-harmonic signals in testing. The proposed ST-based classification method accurately detects and classifies harmonic signals in power distribution systems.
A Critical Review of Time-frequency Distribution Analysis for Detection and C...IJECEIAES
This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...Yayah Zakaria
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can
be observed and estimated plainly from TFR due to identify the
characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
Analysis and Estimation of Harmonics Using Wavelet TechniqueRadita Apriana
The paper develops an approach based on wavelet technique for the evaluation and estimation of
harmonic contents of power system waveform. The proposed algorithm decomposes the signal waveforms
into the uniform frequency sub-bands corresponding to the odd harmonic components of the signal. The
proposed implementation of algorithm determines the frequency bands of harmonics which retain both the
time and frequency relationship of the original waveforms and uses a method to suppress those
harmonics.Thewaveletalgorithm is selected to obtain compatible output bands with the harmonic groups
defined in the standards for power-supply systems. A comparative analysis will be done with the input and
the results obtained from the wavelet transform (WT) for different measuring conditions and Simulation
results are given.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper proposes a wavelet technique to analyze harmonics in power system signals. The algorithm uses Daubechies 20 wavelet and decomposes signals into different frequency sub-bands corresponding to harmonic components. Simulation results on test signals containing various harmonic distortions show the wavelet technique can identify the time and frequency of harmonic disturbances with errors less than 1.2%. The wavelet approach provides an alternative for harmonic analysis that overcomes limitations of conventional Fourier-based methods.
Voltage variations identification using Gabor Transform and rule-based classi...IJECEIAES
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, timefrequency 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.
Accurate harmonic source identification using S-transformTELKOMNIKA JOURNAL
This document discusses accurate harmonic source identification in power distribution systems using time-frequency analysis with the S-transform. The key points are:
1. The S-transform is proposed for harmonic source identification as it provides better frequency resolution for low frequencies and time resolution for high frequencies compared to other time-frequency analysis techniques.
2. The method involves measuring voltage and current at the point of common coupling and applying the S-transform to obtain the time-frequency representation of the signals.
3. Spectral impedances are extracted from the time-frequency impedance representation, including the fundamental impedance and harmonic impedances. The relationship between these impedances is then used to identify the harmonic source location.
Application of Hilbert-Huang Transform in the Field of Power Quality Events A...idescitation
This paper discusses using the Hilbert-Huang Transform (HHT) to analyze power quality events. HHT can be applied to non-stationary and non-linear signals. It decomposes signals into Intrinsic Mode Functions (IMFs) using Empirical Mode Decomposition and then applies the Hilbert Transform to obtain the time-frequency-energy representation. The paper applies HHT to voltage sag, swell, and harmonics with sag signals. It shows the IMFs, instantaneous frequency, amplitude, and phase obtained from HHT have potential to better analyze power quality events compared to other time-frequency methods.
An Accurate Classification Method of Harmonic Signals in Power Distribution S...TELKOMNIKA JOURNAL
This document presents a method for classifying harmonic signals in power distribution systems using S-transform (ST). ST represents signals in the time-frequency domain and is used to estimate spectral parameters for classification. A rule-based classifier classifies 100 unique signals according to IEEE standards. The accuracy of signal parameter estimation using ST is high, with mean absolute percentage errors below 0.06. ST also achieves 100% correct classification of harmonic versus inter-harmonic signals in testing. The proposed ST-based classification method accurately detects and classifies harmonic signals in power distribution systems.
A Critical Review of Time-frequency Distribution Analysis for Detection and C...IJECEIAES
This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...Yayah Zakaria
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can
be observed and estimated plainly from TFR due to identify the
characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
Analysis and Estimation of Harmonics Using Wavelet TechniqueRadita Apriana
The paper develops an approach based on wavelet technique for the evaluation and estimation of
harmonic contents of power system waveform. The proposed algorithm decomposes the signal waveforms
into the uniform frequency sub-bands corresponding to the odd harmonic components of the signal. The
proposed implementation of algorithm determines the frequency bands of harmonics which retain both the
time and frequency relationship of the original waveforms and uses a method to suppress those
harmonics.Thewaveletalgorithm is selected to obtain compatible output bands with the harmonic groups
defined in the standards for power-supply systems. A comparative analysis will be done with the input and
the results obtained from the wavelet transform (WT) for different measuring conditions and Simulation
results are given.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper proposes a wavelet technique to analyze harmonics in power system signals. The algorithm uses Daubechies 20 wavelet and decomposes signals into different frequency sub-bands corresponding to harmonic components. Simulation results on test signals containing various harmonic distortions show the wavelet technique can identify the time and frequency of harmonic disturbances with errors less than 1.2%. The wavelet approach provides an alternative for harmonic analysis that overcomes limitations of conventional Fourier-based methods.
Voltage variations identification using Gabor Transform and rule-based classi...IJECEIAES
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, timefrequency 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.
Accurate harmonic source identification using S-transformTELKOMNIKA JOURNAL
This document discusses accurate harmonic source identification in power distribution systems using time-frequency analysis with the S-transform. The key points are:
1. The S-transform is proposed for harmonic source identification as it provides better frequency resolution for low frequencies and time resolution for high frequencies compared to other time-frequency analysis techniques.
2. The method involves measuring voltage and current at the point of common coupling and applying the S-transform to obtain the time-frequency representation of the signals.
3. Spectral impedances are extracted from the time-frequency impedance representation, including the fundamental impedance and harmonic impedances. The relationship between these impedances is then used to identify the harmonic source location.
Application of Hilbert-Huang Transform in the Field of Power Quality Events A...idescitation
This paper discusses using the Hilbert-Huang Transform (HHT) to analyze power quality events. HHT can be applied to non-stationary and non-linear signals. It decomposes signals into Intrinsic Mode Functions (IMFs) using Empirical Mode Decomposition and then applies the Hilbert Transform to obtain the time-frequency-energy representation. The paper applies HHT to voltage sag, swell, and harmonics with sag signals. It shows the IMFs, instantaneous frequency, amplitude, and phase obtained from HHT have potential to better analyze power quality events compared to other time-frequency methods.
1) Empirical Mode Decomposition (EMD) is a data-driven method that decomposes complicated data sets into intrinsic mode functions (IMFs) that admit well-behaved Hilbert Transforms. 2) EMD decomposes data through a sifting process that identifies intrinsic modes in the data based on the number of extrema and zero-crossings. 3) Hilbert transforms of the IMFs can then be used to determine instantaneous frequencies to analyze nonlinear and non-stationary signals in the time-frequency domain.
Implementation of adaptive stft algorithm for lfm signalseSAT Journals
Abstract
Normally Time-Frequency analysis is done by sliding a window through the time domain data and computing the Fourier
Transform of the data within the window. The choice of the window length determines whether specular or resonant information
will be emphasized. A narrow window will isolate specular reflections but will not be wide enough to accommodate the slowly
varying global resonances; a wide window cannot temporally separate resonance and specular information. So we will adapt
window length according to changes in frequencies. In this case we are realizing the specifications of Linear Frequency
Modulation (LFM) signal.
Index Terms—LFM, FFT, DFT, STFT and ASTFT.
This document discusses using wavelet transforms to analyze vibration signals from bearings for condition monitoring. It describes performing discrete wavelet transforms and wavelet packet transforms on bearing vibration data to extract statistical features like wavelet energy, entropy, and FFT magnitudes. These features are then used as inputs to an artificial neural network to classify signals as normal or faulty. The results show wavelet-based vibration monitoring can successfully detect and classify bearing faults.
Hilbert Huang Transform (HHT) and Empirical Mode Decomposition (EMD) are algorithms for analyzing data from non-linear and non-stationary systems. EMD decomposes signals into Intrinsic Mode Functions (IMFs) through a process called sifting. The sifting process identifies local extrema and decomposes the signal into IMFs until a monotonic residual function remains. HHT then applies the Hilbert transform to each IMF to obtain the instantaneous frequency. While traditional methods assume linearity and stationarity, HHT is suitable for real-world non-linear and non-stationary signals through the EMD sifting process.
This document presents a methodology for detecting epileptic seizures using statistical features in the empirical mode decomposition (EMD) domain. The objective is to transform EEG signals into the EMD domain, extract statistical and chaotic features, and design an artificial neural network classifier to diagnose epilepsy and detect seizures. Statistical features like variance, skewness, and kurtosis show larger differences between healthy and epileptic EEG signals in the EMD domain compared to the original signals. Chaotic features like largest Lyapunov exponent and correlation dimension also differ more between healthy and epileptic signals for some intrinsic mode functions. The proposed method achieves 100% accuracy for seizure detection and epilepsy diagnosis using only 3 statistical features from selected IMFs. Including additional
Empirical mode decomposition and normal shrink tresholding for speech denoisingijitjournal
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the
noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic
oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic
principle of the method is to decompose a speech signal corrupted by additive white Gaussian random
noise into segments each frame is categorised as either signal-dominant or noise-dominant then
reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on
the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of
signal degradation. The denoising method is applied to real signal with different noise levels and the
results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and
hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.
The document discusses using Haar wavelets to solve optimal control problems numerically. It begins by explaining the limitations of indirect methods for solving optimal control problems and advantages of direct methods. It then provides background on wavelet theory, describing different types of wavelets and their properties. Haar wavelets are discussed as being useful for solving variational problems by reducing differential equations to algebraic equations. The document concludes that the Haar wavelet approach provides an effective numerical method for solving variational and optimal control problems compared to other existing techniques.
ECG SIGNAL DENOISING USING EMPIRICAL MODE DECOMPOSITIONSarang Joshi
The document presents a method for denoising ECG signals corrupted with power line interference using empirical mode decomposition and thresholding. It provides background on sources of power line interference in ECG signals and existing approaches to remove it. The proposed approach decomposes noisy ECG signals into intrinsic mode functions using EMD, then applies various thresholding techniques to the IMFs to remove noise before reconstructing the signal. It tests the method on signals from the MIT-BIH Arrhythmia Database corrupted with 10-50% noise and evaluates performance based on correlation coefficient and SNR improvement. Results show Donoho’s thresholding and hard thresholding achieved the best denoising based on these metrics.
Transient Monitoring Function based Fault Classifier for Relaying Applications IJECEIAES
This paper proposes Transient monitoring function (TMF) based fault classification approach for transmission line protection. The classifier provides accurate results under various system conditions involving fault resistance, inception angle, location and load angle. The transient component during fault is measured by TMF and appropriate logics applied for fault classification. Simulation studies using MATLAB ® /SIMULINK ™ are carried out for a 400 kV, 50 Hz power system with variable system conditions. Results show that the proposed classifier has high classification accuracy. The method developed has been compared with a fault classification technique based on Discrete Wavelet Transform (DWT). The proposed technique can be implemented for real time protection schemes employing distance relaying.
Performance analysis of wavelet based blind detection and hop time estimation...Saira Shahid
This document discusses a wavelet-based algorithm for blind detection and hop time estimation of frequency hopping signals in the HF band.
The algorithm first uses the temporal correlation function (TCF) of the received signal to extract phase information. Discrete wavelet transform (DWT) is then applied to the TCF to detect frequency transition points, which indicate the time of hopping between frequencies. Simulation results showing the detection performance at different signal-to-noise ratios are presented.
The key steps are: 1) Extracting the phase from the TCF of the received signal. 2) Applying DWT as a de-noising technique to detect the times of hopping between frequencies. 3) Presenting simulation
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
This paper reviews various techniques used for detection and classification of power quality events. It divides the techniques into those for single events versus combined events. For single events, techniques like wavelet transforms, statistical estimators, and intelligent methods are discussed. For combined events, papers addressing harmonic disturbances combined with others are summarized. The paper also includes a table providing a comparative analysis of several references based on aspects like classification accuracy, noise tolerance, and computation time. It concludes that the field is growing and future work could address techniques for large data and detection of both single and combined disturbances simultaneously.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Analysis of the Waveform of the Acoustic Emission Signal via Analogue Modulat...IOSRjournaljce
The acoustic emission (AE) technique is a non-destructive testing technique applied to pressurized rigid pipelines in order to identify metallurgical discontinuities. This study analyses the dynamic behavior of the propagation of discontinuities in cracks via AE, in the following propagation classes: no propagation (NP), stable propagation (SP), and unstable propagation (UP). The methodology involves applying the concept of modulation of analogue signals, which are used in signal transmission in telecommunications, for the development of a neural network in order to determine new parameters for the AE waveform. The classification of AE signals into propagation classes occurs, therefore, through the extraction of information related to the dynamics of AE signals, by means of the parameters of the analogue carriers of the modulations (in amplitude and in angle) that make up the AE signal. This set of parameters enables an efficient classification (average of 90%) through the identification of patterns from the AE signals in the classes for monitoring the state of the discontinuity, through the use of computational intelligence techniques (artificial neural networks and nonlinear classification).
Comparison and analysis of orthogonal and biorthogonal wavelets for ecg compr...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Load Frequency Control, Integral control, Fuzzy Logic.IJERA Editor
An analysis of digital Phase-modulated signals is performed based on frequency spectrum which consists of a continuous and a number of discrete components at multiples of clock frequencies. The analysis shows that these components depend on the pulse shape function of multi-level digital signals to be phase modulated. In this paper, the effect of duty cycle, rise and fall times of these multi-level digital signals, on the frequency spectrum is studied. It is observed that the duty cycle variation of 10% results 30 dB increase in undesired component and the 10% increase in rise & fall times increase the power of undesired component by 12 dB. The theoretical observations of the effects are applied on the Binary Offset Carrier (BOC) modulated signals as a case study, to discuss their effects in Global Navigation Satellite Systems (GNSS).
Cyclostationary analysis of polytime coded signals for lpi radarseSAT Journals
This document discusses cyclostationary analysis of polytime coded signals for low probability of intercept (LPI) radars. It begins with an introduction to LPI radars and their modulation and detection techniques, focusing on polytime codes. It then describes cyclostationary signal processing methods like the direct frequency smoothing method (DFSM) and fast Fourier transform accumulation method (FAM) that can be used to extract parameters from polytime coded signals. The document analyzes example polytime coded signals with and without noise using these cyclostationary techniques and accurately extracts key parameters like carrier frequency, bandwidth, and code rate. It finds the FAM method has better computational efficiency than DFSM for long signals.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
This document describes ensemble empirical mode decomposition (EEMD), an adaptive method for noise reduction in signals. EEMD is an improvement over empirical mode decomposition (EMD) that can overcome the problem of mode mixing. EEMD works by decomposing the signal into intrinsic mode functions (IMFs) in the presence of added white noise, which is then averaged out. The algorithm adds white noise to the target signal multiple times, applies EMD each time, and takes the mean of the IMFs as the final result. This process separates different scales present in the signal and reduces noise. The document evaluates EEMD on electrocardiogram and other non-stationary signals, demonstrating its effectiveness in noise reduction.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...IJECEIAES
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Packets Wavelets and Stockwell Transform Analysis of Femoral Doppler Ultrasou...IJECEIAES
Ultrasonic Doppler signals are widely used in the detection of cardiovascular pathologies or the evaluation of the degree of stenosis in the femoral arteries. The presence of stenosis can be indicated by disturbing the blood flow in the femoral arteries, causing spectral broadening of the Doppler signal. To analyze these types of signals and determine stenosis index, a number of time-frequency methods have been developed, such as the short-time Fourier transform, the continuous wavelets transform, the wavelet packet transform, and the S-transform
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document provides an overview of the contents and structure of a Digital Signal Processing lab manual for an undergraduate course. The manual covers experiments and programs that will be implemented in both software (using MATLAB, LabVIEW, or C programming) and hardware (using DSP boards from TI, Analog Devices, or Motorola). The experiments are intended to help students learn digital signal processing concepts and gain practical skills in implementing DSP algorithms and applications. Some example topics mentioned include discrete time signals and systems, Fourier analysis, FIR and IIR filter design, and speech/image processing. The manual provides a framework for students to complete their DSP lab assignments throughout the semester.
1) Empirical Mode Decomposition (EMD) is a data-driven method that decomposes complicated data sets into intrinsic mode functions (IMFs) that admit well-behaved Hilbert Transforms. 2) EMD decomposes data through a sifting process that identifies intrinsic modes in the data based on the number of extrema and zero-crossings. 3) Hilbert transforms of the IMFs can then be used to determine instantaneous frequencies to analyze nonlinear and non-stationary signals in the time-frequency domain.
Implementation of adaptive stft algorithm for lfm signalseSAT Journals
Abstract
Normally Time-Frequency analysis is done by sliding a window through the time domain data and computing the Fourier
Transform of the data within the window. The choice of the window length determines whether specular or resonant information
will be emphasized. A narrow window will isolate specular reflections but will not be wide enough to accommodate the slowly
varying global resonances; a wide window cannot temporally separate resonance and specular information. So we will adapt
window length according to changes in frequencies. In this case we are realizing the specifications of Linear Frequency
Modulation (LFM) signal.
Index Terms—LFM, FFT, DFT, STFT and ASTFT.
This document discusses using wavelet transforms to analyze vibration signals from bearings for condition monitoring. It describes performing discrete wavelet transforms and wavelet packet transforms on bearing vibration data to extract statistical features like wavelet energy, entropy, and FFT magnitudes. These features are then used as inputs to an artificial neural network to classify signals as normal or faulty. The results show wavelet-based vibration monitoring can successfully detect and classify bearing faults.
Hilbert Huang Transform (HHT) and Empirical Mode Decomposition (EMD) are algorithms for analyzing data from non-linear and non-stationary systems. EMD decomposes signals into Intrinsic Mode Functions (IMFs) through a process called sifting. The sifting process identifies local extrema and decomposes the signal into IMFs until a monotonic residual function remains. HHT then applies the Hilbert transform to each IMF to obtain the instantaneous frequency. While traditional methods assume linearity and stationarity, HHT is suitable for real-world non-linear and non-stationary signals through the EMD sifting process.
This document presents a methodology for detecting epileptic seizures using statistical features in the empirical mode decomposition (EMD) domain. The objective is to transform EEG signals into the EMD domain, extract statistical and chaotic features, and design an artificial neural network classifier to diagnose epilepsy and detect seizures. Statistical features like variance, skewness, and kurtosis show larger differences between healthy and epileptic EEG signals in the EMD domain compared to the original signals. Chaotic features like largest Lyapunov exponent and correlation dimension also differ more between healthy and epileptic signals for some intrinsic mode functions. The proposed method achieves 100% accuracy for seizure detection and epilepsy diagnosis using only 3 statistical features from selected IMFs. Including additional
Empirical mode decomposition and normal shrink tresholding for speech denoisingijitjournal
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the
noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic
oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic
principle of the method is to decompose a speech signal corrupted by additive white Gaussian random
noise into segments each frame is categorised as either signal-dominant or noise-dominant then
reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on
the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of
signal degradation. The denoising method is applied to real signal with different noise levels and the
results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and
hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.
The document discusses using Haar wavelets to solve optimal control problems numerically. It begins by explaining the limitations of indirect methods for solving optimal control problems and advantages of direct methods. It then provides background on wavelet theory, describing different types of wavelets and their properties. Haar wavelets are discussed as being useful for solving variational problems by reducing differential equations to algebraic equations. The document concludes that the Haar wavelet approach provides an effective numerical method for solving variational and optimal control problems compared to other existing techniques.
ECG SIGNAL DENOISING USING EMPIRICAL MODE DECOMPOSITIONSarang Joshi
The document presents a method for denoising ECG signals corrupted with power line interference using empirical mode decomposition and thresholding. It provides background on sources of power line interference in ECG signals and existing approaches to remove it. The proposed approach decomposes noisy ECG signals into intrinsic mode functions using EMD, then applies various thresholding techniques to the IMFs to remove noise before reconstructing the signal. It tests the method on signals from the MIT-BIH Arrhythmia Database corrupted with 10-50% noise and evaluates performance based on correlation coefficient and SNR improvement. Results show Donoho’s thresholding and hard thresholding achieved the best denoising based on these metrics.
Transient Monitoring Function based Fault Classifier for Relaying Applications IJECEIAES
This paper proposes Transient monitoring function (TMF) based fault classification approach for transmission line protection. The classifier provides accurate results under various system conditions involving fault resistance, inception angle, location and load angle. The transient component during fault is measured by TMF and appropriate logics applied for fault classification. Simulation studies using MATLAB ® /SIMULINK ™ are carried out for a 400 kV, 50 Hz power system with variable system conditions. Results show that the proposed classifier has high classification accuracy. The method developed has been compared with a fault classification technique based on Discrete Wavelet Transform (DWT). The proposed technique can be implemented for real time protection schemes employing distance relaying.
Performance analysis of wavelet based blind detection and hop time estimation...Saira Shahid
This document discusses a wavelet-based algorithm for blind detection and hop time estimation of frequency hopping signals in the HF band.
The algorithm first uses the temporal correlation function (TCF) of the received signal to extract phase information. Discrete wavelet transform (DWT) is then applied to the TCF to detect frequency transition points, which indicate the time of hopping between frequencies. Simulation results showing the detection performance at different signal-to-noise ratios are presented.
The key steps are: 1) Extracting the phase from the TCF of the received signal. 2) Applying DWT as a de-noising technique to detect the times of hopping between frequencies. 3) Presenting simulation
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
This paper reviews various techniques used for detection and classification of power quality events. It divides the techniques into those for single events versus combined events. For single events, techniques like wavelet transforms, statistical estimators, and intelligent methods are discussed. For combined events, papers addressing harmonic disturbances combined with others are summarized. The paper also includes a table providing a comparative analysis of several references based on aspects like classification accuracy, noise tolerance, and computation time. It concludes that the field is growing and future work could address techniques for large data and detection of both single and combined disturbances simultaneously.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Analysis of the Waveform of the Acoustic Emission Signal via Analogue Modulat...IOSRjournaljce
The acoustic emission (AE) technique is a non-destructive testing technique applied to pressurized rigid pipelines in order to identify metallurgical discontinuities. This study analyses the dynamic behavior of the propagation of discontinuities in cracks via AE, in the following propagation classes: no propagation (NP), stable propagation (SP), and unstable propagation (UP). The methodology involves applying the concept of modulation of analogue signals, which are used in signal transmission in telecommunications, for the development of a neural network in order to determine new parameters for the AE waveform. The classification of AE signals into propagation classes occurs, therefore, through the extraction of information related to the dynamics of AE signals, by means of the parameters of the analogue carriers of the modulations (in amplitude and in angle) that make up the AE signal. This set of parameters enables an efficient classification (average of 90%) through the identification of patterns from the AE signals in the classes for monitoring the state of the discontinuity, through the use of computational intelligence techniques (artificial neural networks and nonlinear classification).
Comparison and analysis of orthogonal and biorthogonal wavelets for ecg compr...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Load Frequency Control, Integral control, Fuzzy Logic.IJERA Editor
An analysis of digital Phase-modulated signals is performed based on frequency spectrum which consists of a continuous and a number of discrete components at multiples of clock frequencies. The analysis shows that these components depend on the pulse shape function of multi-level digital signals to be phase modulated. In this paper, the effect of duty cycle, rise and fall times of these multi-level digital signals, on the frequency spectrum is studied. It is observed that the duty cycle variation of 10% results 30 dB increase in undesired component and the 10% increase in rise & fall times increase the power of undesired component by 12 dB. The theoretical observations of the effects are applied on the Binary Offset Carrier (BOC) modulated signals as a case study, to discuss their effects in Global Navigation Satellite Systems (GNSS).
Cyclostationary analysis of polytime coded signals for lpi radarseSAT Journals
This document discusses cyclostationary analysis of polytime coded signals for low probability of intercept (LPI) radars. It begins with an introduction to LPI radars and their modulation and detection techniques, focusing on polytime codes. It then describes cyclostationary signal processing methods like the direct frequency smoothing method (DFSM) and fast Fourier transform accumulation method (FAM) that can be used to extract parameters from polytime coded signals. The document analyzes example polytime coded signals with and without noise using these cyclostationary techniques and accurately extracts key parameters like carrier frequency, bandwidth, and code rate. It finds the FAM method has better computational efficiency than DFSM for long signals.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
This document describes ensemble empirical mode decomposition (EEMD), an adaptive method for noise reduction in signals. EEMD is an improvement over empirical mode decomposition (EMD) that can overcome the problem of mode mixing. EEMD works by decomposing the signal into intrinsic mode functions (IMFs) in the presence of added white noise, which is then averaged out. The algorithm adds white noise to the target signal multiple times, applies EMD each time, and takes the mean of the IMFs as the final result. This process separates different scales present in the signal and reduces noise. The document evaluates EEMD on electrocardiogram and other non-stationary signals, demonstrating its effectiveness in noise reduction.
An Improved Detection and Classification Technique of Harmonic Signals in Pow...IJECEIAES
This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Packets Wavelets and Stockwell Transform Analysis of Femoral Doppler Ultrasou...IJECEIAES
Ultrasonic Doppler signals are widely used in the detection of cardiovascular pathologies or the evaluation of the degree of stenosis in the femoral arteries. The presence of stenosis can be indicated by disturbing the blood flow in the femoral arteries, causing spectral broadening of the Doppler signal. To analyze these types of signals and determine stenosis index, a number of time-frequency methods have been developed, such as the short-time Fourier transform, the continuous wavelets transform, the wavelet packet transform, and the S-transform
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document provides an overview of the contents and structure of a Digital Signal Processing lab manual for an undergraduate course. The manual covers experiments and programs that will be implemented in both software (using MATLAB, LabVIEW, or C programming) and hardware (using DSP boards from TI, Analog Devices, or Motorola). The experiments are intended to help students learn digital signal processing concepts and gain practical skills in implementing DSP algorithms and applications. Some example topics mentioned include discrete time signals and systems, Fourier analysis, FIR and IIR filter design, and speech/image processing. The manual provides a framework for students to complete their DSP lab assignments throughout the semester.
An Algorithm Based On Discrete Wavelet Transform For Faults Detection, Locati...paperpublications3
Abstract: An electric power distribution system is the final stage in the delivery of electric power; it carries electricity from the transmission system to individual consumers. Fault classification and location is very important in power system engineering in order to clear fault quickly and restore power supply as soon as possible with minimum interruption. Hence, ensuring its efficient and reliable operation is an extremely important and challenging task. With availability of inadequate system information, locating faults in a distribution system pose a major challenge to the utility operators. In this paper, a faults detection, location and classification technique using discrete wavelet multi-resolution approach for radial distribution systems are proposed. In this distribution network, the current measurement at the substation have been utilized and is demonstrated on 9-bus distribution system. Also in this work distribution system model was developed and simulated using power system block set of MATLAB to obtain fault current waveforms. The waveforms were analyzed using the Discrete Wavelet Transform (DWT) toolbox by selecting suitable wavelet family. It was estimated and achieved using Daubechies ‘db5’ discrete wavelet transform.
Signal Processing of Radar Echoes Using Wavelets and Hilbert Huang Transformsipij
Atmospheric Radar Signal Processing is one field of Signal Processing where there is a lot of scope for development of new and efficient tools for spectrum cleaning, detection and estimation of desired parameters. The wavelet transform and HHT (Hilbert-Huang transform) are both signal processing methods. This paper is based on comparing HHT and Wavelet transform applied to Radar signals. Wavelet analysis is one of the most important methods for removing noise and extracting signal from any data. The de-noising application of the wavelets has been used in spectrum cleaning of the atmospheric radar signals. HHT can be used for processing non-stationary and nonlinear signals. HHT is one of the timefrequency analysis techniques which consists of two parts: Empirical Mode Decomposition (EMD) and instantaneous frequency solution. EMD is a numerical sifting process to decompose a signal into its fundamental intrinsic oscillatory modes, namely intrinsic mode functions (IMFs). A series of IMFs can be obtained after the application of EMD. In this paper wavelets and EMD has been applied to the time series data obtained from the mesosphere-stratosphere-troposphere (MST) region near Gadanki, Tirupati for 6 beam directions. The Algorithm is developed and tested using Matlab. Moments were estimated and analysis has brought out improvement in some of the characteristic features like SNR, Doppler width, Noise power of the atmospheric signals. The results showed that the proposed algorithm is efficient for dealing non-linear and non- stationary signals contaminated with noise. The results were compared using ADP (Atmospheric Data Processor) and plotted for validation of the proposed algorithm.
Detection of Transmission Line Faults by Wavelet Based Transient ExtractionIDES Editor
This paper proposes a novel technique to detect faults in transmission lines using wavelet transform. Three-phase currents are monitored at both ends of the transmission line using GPS synchronization. Wavelet transform is used to extract transients from the current signals, which are indicative of faults. A fault index is calculated based on the detail coefficients of the wavelet transform and compared to a threshold value to detect faults. Simulation results demonstrate the technique can detect various faults at different locations and inception angles on the transmission line.
Signal classification and characterization using S-Transform and Empirical Wa...shantanu Chutiya begger
Detailed comparison between S-Transform and Empirical Wavelet Transform via signal simulation in terms of classification and characterization.Limitation of both and resulting new transform called THE GENERALIZED EMPIRICAL WAVELET TRANSFORM.
A Systematic Approach to Improve BOC Power Spectrum for GNSSIJERA Editor
An analysis of digital Phase-modulated signals is performed based on frequency spectrum which consists of a continuous and a number of discrete components at multiples of clock frequencies. The analysis shows that these components depend on the pulse shape function of multi-level digital signals to be phase modulated. In this paper, the effect of duty cycle, rise and fall times of these multi-level digital signals, on the frequency spectrum is studied. It is observed that the duty cycle variation of 10% results 30 dB increase in undesired component and the 10% increase in rise & fall times increase the power of undesired component by 12 dB. The theoretical observations of the effects are applied on the Binary Offset Carrier (BOC) modulated signals as a case study, to discuss their effects in Global Navigation Satellite Systems (GNSS).
1) OFDM uses multiple carriers to transmit data in parallel. It can be described mathematically using the Fourier transform which relates events in the time and frequency domains.
2) At the transmitter, the signal is defined in the frequency domain using a discrete Fourier transform and generated using the inverse discrete Fourier transform. This allows the carriers to be orthogonal.
3) A guard interval is added between symbols to prevent intersymbol interference from multipath distortion. This increases the symbol duration and provides timing tolerance at the receiver.
Peak detection using wavelet transformIJCNCJournal
A new work based-wavelet transform is designed to overcome one of the main drawbacks that found in the
present new technologies. Orthogonal Frequency Division Multiplexing (OFDM)is proposed in the
literature to enhance the multimedia resolution. However, the high peak power (PAPR) values will obstruct
such achievements. Therefore, a new proposition is found in this work, making use of the wavelet
transforms methods, and it is divided into three main stages; de-noising stage, thresholding stage and then
the replacement stage.
In order to check the system stages validity; a mathematical model has been built and its checked after
using a MATLAB simulation. A simulated bit error rate (BER) achievement will be compared with our
previously published work, where an enhancement from 8×10-1 to be 5×10-1 is achieved. Moreover, these
results will be compared to the work found in the literature, where we have accomplished around 27%
PAPR extra reduction.
As a result, the BER performance has been improved for the same bandwidth occupancy. Moreover and
due to the de-noise stage, the verification rate has been improved to reach 81%. This is in addition to the
noise immunity enhancement.
Characterization of transients and fault diagnosis in transformer by discreteIAEME Publication
This document discusses using discrete wavelet transform (DWT) and artificial neural networks (ANN) to characterize transients and diagnose faults in transformers. It begins with an introduction to the problem and background on using the second harmonic component for discrimination. It then discusses why time-frequency information is needed and the advantages of wavelet transforms over Fourier transforms. The document describes collecting data from a test transformer under normal and faulted conditions. It explains using DWT for feature extraction and visualizing the wavelet decomposition levels to characterize magnetizing inrush versus inter-turn faults. Finally, it proposes using ANN trained on the wavelet spectral energies for automated discrimination between fault cases.
The document describes an experimental study of a radio frequency interference (RFI) detection algorithm for microwave radiometry. The algorithm uses 2D wavelet-based filtering on a spectrogram to detect sinusoidal and impulse RFI. It then applies frequency and time averaging to remove residual RFI. Testing was performed on L-band radiometer measurements containing continuous and impulse RFI. The algorithm successfully identified and removed RFI from the spectrogram through 2D wavelet filtering and further frequency/time averaging.
The document compares different algorithms for detecting voltage sags and swells in power systems. It describes RMS voltage detection, peak voltage detection, and discrete Fourier transform (DFT) methods. RMS detection uses historical data so it can be slow for mitigation. Peak detection requires only single-phase values but DFT is best for steady signals and not fast transients. The paper proposes a novel real-time algorithm to rapidly detect voltage sags and compares it to existing methods.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
For ease of analog or digital information transmission and reception, modulation is the foremost important technique. In the present project, we’ll discuss about different modulation scheme in digital mode done by operating a switch/ key by the digital data. As we know, by modifying basic three parameters of the carrier signal, three basic modulation schemes can be obtained; generation and detection of these three modulations are discussed and compared with respect to probability of error or bit error rate (BER).
This document discusses signal denoising techniques, specifically wavelet-based techniques and empirical mode decomposition (EMD). Wavelet-based denoising employs wavelet transforms to distribute signal energy into coefficients, then thresholds coefficients to remove noise. EMD decomposes signals into intrinsic mode functions and adapts wavelet thresholding. EMD is useful for non-stationary, multicomponent signals and lacks a theoretical basis but has diverse applications. The document reviews denoising ECG signals using various filters and techniques like EMD, concludes iterative EMD outperforms wavelets, and adaptive filtering best removes low frequency noise from ECGs.
A JOINT TIMING OFFSET AND CHANNEL ESTIMATION USING FRACTIONAL FOURIER TRANSFO...IJCNCJournal
This paper proposes a new method for joint timing offset and channel estimation in OFDM systems using fractional Fourier transform and constant amplitude zero autocorrelation sequences. After estimating the timing offset in the frequency domain, compensation is performed in the time domain. Channel estimation then uses least squares with discrete Fourier transform and linear interpolation to recover the transmitted signal. Simulation results show the proposed method achieves good performance in terms of mean square error for timing offset estimation compared to other techniques, especially for fast fading channels. It also estimates the channel well while maintaining throughput.
Similar to A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
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An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
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Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
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introduced, whereby STFT divides the signal into small segments. Thus these signal segments can be
assumed to be stationary. Moreover, the STFT works well providing that the window is short enough
compared to the oscillation rate. Nevertheless, during high rates of oscillation, it can lead to significant
errors [4]. Due to this limitation, the Discrete Fourier transform (DFT) is presented for analysis of frequency
content in steady state and suitable for harmonic analysis. However, DFT is not capable to detect sudden
changes in waveform. Furthermore, the DFT has major problems such as spectrum leakage and
resolution [5]. The limitation in DFT is overcome by using Wavelet analysis. Wavelet analysis is an approach
to vary the window length. Nevertheless, wavelet reveals some disadvantages such as its complex
computation and sensitive to noise level [6]. With regards to the above discussion, the constraint of previous
techniques can be overcome by introducing Gabor transform.
In this paper, the Gabor transform (GT) is one of time frequency distribution (TFD) technique and
good in distinguishing the harmonic and inter-harmonic signals in power distribution system. The GT is the
windowed Fourier transform and the representation of a signal in a jointly time-frequency domain [7]. The
harmonic signals are analyzed and represented in time frequency representation (TFR). Furthermore, the
parameters such as RMS and fundamental value, total harmonic distortion (THD), total non-harmonic
distortion (TnHD) and total waveform distortion (TWD) for voltage and current are evaluated from TFR and
used for the classification of harmonic and inter-harmonic signals. The performance of the proposed
technique is verified by classifying 100 signals with numerous characteristics for every type of voltage
variation signals and also based on IEEE Std. 1159-2009.
2. RESEARCH METHOD
Time-frequency distribution (TFD) are superior methods that represent signals in time and
frequency representation (TFR). Furthermore, from TFR, spectral parameters can be seen with the changes of
time. In this manner, the TFD are significant to classify harmonic and inter-harmonic signals in the system.
Linear TFD which is Gabor transform is proposed as in Figure 1 for the signal classification technique.
Figure 1. Implementation Chart for Harmonic and Inter-harmonic Signals Detection and Classification
2.1. Gabor Transform
Gabor transform (GT) is an expanding of signal into a set of functions that are concentrated in both
the time and frequency domains and then use the coefficients as the descriptors of the signal’s local property.
Gabor transform is defined as [7].
(1)
where x( is the signal under analysis and h(n,k) is the set of elementary function and is defined as
3. IJECE ISSN: 2088-8708
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and .... (M. H Jopri)
23
( ( nT (2)
where ( is the observation window. Tw and f0 are the time and frequency sampling interval, respectively,
that must satisfy the Heisenberg uncertainty relationship as follows:
(3)
For Gabor transform, hanning window is selected same as spectrogram but different in terms of
window length which is 480 samples. The time and frequency resolution for Gabor transform is fixed for all
frequency shown in Figure 2. The vertical axis (x-axis) shows the time resolution and horizontal axis (y-axis)
shows the frequency resolution.
Figure 2. Resolution of Gabor Transform
2.2. Signal Parameters
Parameters of harmonic and inter-harmonic are assessed from the TFR. The spectral parameters
consist of RMS fundamental voltage, momentary RMS voltage, instantaneous total inter-harmonic distortion
(TnHD), instantaneous total harmonic distortion (THD) and instantaneous total waveform distortion (TWD).
Below are the signal parameters that been estimated from TFR [8].
Instantaneous RMS Voltage, dfftPtV
sf
xrms
0
),()(
(4)
Instantaneous RMS Fundamental Voltage,
hi
lo
f
f
xrms
dfftPtV ),(2)(1
(5)
Instantaneous Total Waveform Distortion,
tV
tVtV
tTWD
rms
rmsrms
)(
)()(
)(
1
2
1
2
(6)
Instantaneous Total Harmonic Distortion,
(t)V
(t)V
tTHD
rms
H
h rmsh
1
2
2
,
)(
(7)
Instantaneous Total Nonharmonic Distortion, (8)
)(
)()(
1
0
2
,
2
tV
tVtV
TnHD(t)
rms
H
h rmshrms
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where Px(t, f) is the TFR of a signal, fs is sampling frequency, f0 is the fundamental frequency, V1rms(t) is
instantaneous RMS fundamental voltage, Vrms(t) is instantaneous RMS voltage and Vh,rms (t) is RMS harmonic
voltage. fhi=f0+25Hz, flo=f0-25Hz, 25 Hz is chosen for fhi and flo, it can represent the fundamental frequency
value and use for calculate the value of the frequency element.
2.3. Signal Characteristic
The identification of signal characteristics is obtained from the calculated spectral parameters.
Moreover, by utilizing the instantaneous RMS voltage, the signal properties for instance the average of RMS
voltage can be computed utilizing Equation 9 [9]. The data of the signal characteristics are used as input for
classifier to identify the harmonic and inter-harmonics signal.
(9)
Furthermore, total nonharmonic distortion, TnHDave and average of total harmonic distortion,
THDave can be computed from instantaneous total nonharmonic distortion, TnHD(t) and instantaneous total
harmonic distortion, THD(t) individually.
∫ ( (10)
∫ ( (11)
2.4. Signal Classification
The rule-based classifier will be utilized for signal classification and this classifier is a deterministic
grouping technique that has been utilized as a part of genuine application [20]. The performance of
classification is much dependent on the principles and threshold values. Since in this research, all previous
information that been obtained from power quality signals gave good information, this classifier is suitable to
be used for of signal characterization. The utilization of rule-based classifier agreeing below
requirements [10].
> and < (12)
>= and < (13)
Figure 3. Rule-based Classifier Flow Chart
T
rmsaverms dttV
T
V
0
, )(
1
5. IJECE ISSN: 2088-8708
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and .... (M. H Jopri)
25
In the meantime, flow chart of rule-based classifier for signal classification clearly shown in
Figure 3. THDthres and TnHDthres are set according results of numerous investigation of these signals. The
classification of signals can be done by observing the significant relationship concerning THD or TnHD.
Hence, harmonic signal is classified when the signal has only THD whereas inter-harmonic signal has just
TnHD.
2.5. Performance Verification
Keeping in mind the end goal to check the performance of proposed technique, the outcomes were
assessed regarding accuracy and classification correctness during analysis. Subsequently, the execution and
feasibility of proposed method relies on upon these said particulars.
2.5.1. Accuracy of the Analysis
The accuracy of the proposed technique is distinguished by measuring the measurement accuracy of
signal characteristics. To quantify the accuracy of the outcome, mean absolute percentage error (MAPE) is
utilized as benchmark and can be defined as [11],
(14)
where xi(n) is an actual value, xm(n) is measured value and N is the number of data. The smaller value of
MAPE offers more accurate results.
3. RESULTS AND ANALYSIS
In this section, it is clarified the results of research and at the same time is given the comprehensive
discussion.
3.1. Harmonic Signals Detection
Figure 4(a) and (b) present harmonic signal in time domain and its TFRs using Gabor transform,
repectively. The TFR indicates that the signal consists of two frequency component: fundamental frequency
(50Hz) and 7th harmonic component (350Hz). Signal parameters estimated from the TFR using Gabor
transform with OSWS are shown in Figure 4(b). Figure 4(c) shows that the harmonic voltage increases the
RMS voltage from normal voltage which is 1.0 to 1.17 pu. However, it does not change the RMS
fundamental voltage which remains constant at 1.0 pu. Besides that, the signal also results the magnitude of
TWD and THD remains constant at 60% and zero percent for the TnHD as shown in Figure 4(d). Thus, the
parameters clearly show that the harmonic signal consists only harmonic frequency components.
The results for interharmonic signal and its TFR using Gabor transform is shown in Figure 5(a) and
(b) respectively. The signal has two frequency components which are at fundamental frequency (50 Hz) and
interharmonic frequency of 375 Hz. Signal parameters estimated from the TFR using Gabor transform with
OSWS are shown in Figure 5(b). Figure 5(c) shows that the interharmonic voltage increases the RMS voltage
from normal voltage which is 1.0 to 1.17 pu. However, it does not change the RMS fundamental voltage
which remains constant at 1.0 pu. Besides that, the signal also results the magnitude of TWD and TnHD
remains constant at 60% and zero percent for the THD as shown in Figure 5(d). Thus, the parameters clearly
show that the interharmonic signal consists only nonharmonic frequency components.
%100
)(
)()(1
1
x
nx
nxnx
N
MAPE
N
n i
mi
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26
(a) (b)
(c) (d)
Figure 4. (a) Harmonic Signal from Simulation and its, (b) TFR using Gabor Transform, (c) Instantaneous
of RMS and Fundamental RMS Voltage, (d) Instantaneous of total Harmonic Distortion, Total
Nonharmonic Distortion and Total Waveform Distortion
(a) (b)
(c) (d)
Figure 5. (a) Interharmonic Signal from Simulation and its, (b) TFR using Gabor Transform,
(c) Instantaneous of RMS and Fundamental RMS Voltage, (d) Instantaneous of Total Harmonic Distortion,
Total Nonharmonic Distortion and Total Waveform Distortion
7. IJECE ISSN: 2088-8708
A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and .... (M. H Jopri)
27
3.2. The accuracy of the Analysis
Harmonic and inter-harmonics signals are tested and mean absolute percentage errors (MAPE) of
the signal properties are calculated. Then, the results are averaged to identify the accuracy of the
measurements as shown in Table 1. The table shows that Gabor transform gives good accuracy for an
average of RMS voltage, THD and TnHD. In addition, Gabor transform has a good accuracy in harmonic and
inter-harmonic signals detection.
Table 1. MAPE Simulation Result for Gabor Transform Analysis
Signal Characteristics MAPE (%)
Vrms,ave 0.5293
THDave 0.9967
TnHDave 0.9331
3.3. Classification of Harmonic and Inter-harmonic Signals
As shown in the previous section, Gabor transform has less computational complexity and high
accuracy. The performance results of the signals classification using the Gabor transform are shown in
Table 2. 100 signals with various characteristics for each type of voltage signal is generated and classified.
The table shows that the classification results using Gabor transform give 100% correct classification for all
signals. From the results, it can be concluded that the Gabor-transform is magnificently a good method for
harmonic and inter-harmonic signals classification.
Table 2. Performance of Harmonic and Inter-Harmonic Signals Classification
Signal
Gabor Transform
Number of data sets % Correct Classification
Harmonics 100 100
Inter-harmonics 100 100
Normal 100 100
4. CONCLUSION
The performance evaluation of the signal analysis using Gabor transform in terms of accuracy and
classification correctness have been shown clearly in results section. The performance of the proposed
technique is verified by using MAPE in classifying 100 signals with various characteristics of voltage
signals. The proposed method also gives 100 percent correctness of signals classification. Hence, it is
concluded that the proposed method is a good technique for harmonic and inter-harmonic signals detection
and classification in power distribution system.
ACKNOWLEDGEMENTS
This research is supported by Advance Digital Signal Processing Laboratory (ADSP Lab). Special
thanks also to the Faculty of Electrical Engineering and Engineering Technology of Universiti Teknikal
Malaysia Melaka (UTeM), Ministry of Higher Education Malaysia (MOHE) and Ministry of Science,
Technology and Innovation (MOSTI) for giving the cooperation and funding for this research with grant
number 06-01-14-SF00119 L00025. Their support is gratefully acknowledged.
REFERENCES
[1] M. S. Manikandan, et al., “Detection and Classification of Power Quality Disturbances Using Sparse Signal
Decomposition on Hybrid Dictionaries,” IEEE Trans. Instrum. Meas., vol/issue: 64(1), pp. 27–38, 2015.
[2] N. H. H. Abidullah, et al., “Real-Time Power Quality Disturbances Detection and Classification System,” World
Appl. Sci. J., vol/issue: 32(8), pp. 1637–1651, 2014.
[3] M. Gupta, et al., “Neural Network Based Indexing and Recognition of Power Quality Disturbances,”
TELKOMNIKA (Telecommunication Comput. Electron. Control., vol/issue: 9(2), pp. 227–236, 2011.
[4] F. Choong, et al., “Advances in Signal Processing and Artificial Intelligence Technologies in the Classification of
Power Quality Events: A Survey,” Electr. Power Components Syst., vol/issue: 33(12), pp. 1333–1349, 2005.
[5] S. A. Deokar and L. M. Waghmare, “Integrated DWT–FFT approach for detection and classification of power
quality disturbances,” Int. J. Electr. Power Energy Syst., vol. 61, pp. 594–605, 2014.
[6] R. Kumar, et al., “Recognition of Power Quality Events Using S- Transform Based ANN Classifier and Rule Based
Decision Tree,” 2013 IEEE Ind. Appl. Soc. Annu. Meet., pp. 1–8, 2013.
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[7] L. Debnath and F. A. Shah, “The Gabor Transform and Time–Frequency Signal Analysis,” in Wavelet Transforms
and Their Applications, Boston, MA: Birkhäuser Boston, pp. 243–286, 2015.
[8] A. R. Abdullah, et al., “Power Quality Monitoring System Utilizing Periodogram And Spectrogram Analysis
Techniques,” Int. Conf. Control. Instrum. Mechatronics Eng., no. August, pp. 770–774, 2007.
[9] N. A. Abidullah, et al., “Real-time power quality signals monitoring system,” Proceeding - 2013 IEEE Student
Conf. Res. Dev. SCOReD 2013, no. December, pp. 433–438, 2015.
[10] A. R. Abdullah, et al., “Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform,”
Appl. Mech. Mater., vol. 752–753, pp. 1343–1348, 2015.
[11] K. R. Cheepati and T. N. Prasad, “Performance Comparison of Short Term Load Forecasting Techniques,” Int. J.
Grid Distrib. Comput., vol/issue: 9(4), pp. 287–302, 2016.
BIOGRAPHIES OF AUTHORS
Mohd Hatta Jopri was born in Johor, Malaysia on 1978. He received his B.Sc from Universiti
Teknologi Malaysia in 2000 and Msc. in Electrical Power Engineering from Rheinisch-
Westfälische Technische Hochschule Aachen (RWTH), Germany in 2011. Since 2005, he has
been an academia staff in the Universiti Teknikal Malaysia Melaka (UTeM) and currently he
pursuing his PhD in the area of power quality.
Dr. Abdul Rahim Abdullah was born in Kedah, Malaysia on 1979. He received his B. Eng.,
Master Eng., PhD Degree from Universiti Teknologi Malaysia in 2001, 2004 and 2011 in
Electrical Engineering and Digital Signal Processing respectively. He is currently an Associate
Professor with the Department of Electrical Engineering, Chief of Advanced Digital Signal
Processing (ADSP) Lab and Center of Excellent (COE) Coordinator for Universiti Teknikal
Malaysia Melaka (UTeM).
Tole Sutikno is an Associated Professor in Department of Electrical Engineering at Universitas
Ahmad Dahlan (UAD), Indonesia. He is Editor-in-Chief, Principle Contact and Editor for some
Scopus indexed journals. He is also one of founder of the Indonesian Publication Index (IPI). He
has completed his studies for B.Eng., M.Eng., Ph.D. in Electrical Engineering from Diponegoro
University, Gadjah Mada University and Universiti Teknologi Malaysia, respectively. His
research interests include the field of industrial electronics and informatics, power electronics,
FPGA applications, embedded systems, data mining, information technology and digital library.
Mustafa Manap was born in Kuala Lumpur, Malaysia on 1978. He received his B.Sc from
Universiti Technologi Malaysia in 2000 and Msc. in Electrical Engineering from Universiti
Teknikal Malaysia Melaka (UTeM) 2016. Since 2006, he has been an academia staff in the
Universiti Teknikal Malaysia Melaka (UTeM). His research interests are power electronics and
drive, instrumentation, and DSP application.
Mohd Rahimi bin Yusoff was born in Kelantan, Malaysia on 1967. He received his M.Sc. in
Electrical Engineering from University of Nottingham, England in 1992 and B.Eng. in Electrical
& Electronics from University of Wales, Swansea, UK in 1990. Since 2010, he has been an
academia staff in Universiti Teknikal Malaysia Melaka (UTeM) and currently holding the post
as Dean of Faculty of Engineering Technology. His research interests are power quality, signal
processing, and DSP application.