Practical Application of Wavelet to Power Quality Analysis. Norman Tse
|
|
- Arron Garrison
- 6 years ago
- Views:
Transcription
1 Paper Title: Practical Application of Wavelet to Power Quality Analysis Author and Presenter: Norman Tse 1
2 Harmonics Frequency Estimation by Wavelet Transform (WT) Any harmonic signal can be described by its frequency, amplitude and phase Wavelet Transform is used to estimate harmonic frequencies amplitudes of the harmonic frequencies initial phase difference 2
3 Harmonic Frequencies Estimation Algorithm Step 1 use Continuous Wavelet Transform (CWT) to extract signal time and scale (frequency) information Step 2 - Normalised scalogram and wavelet ridges are used to find out the scale(s) ) at which the absolute value of the CWT coefficients are maximum Step 3 - the scales at which the wavelet ridges are maximum reveal the frequencies of the harmonics 3
4 Amplitude Estimation Algorithm Step 1 CWT coefficients of scales of the maximum wavelet ridges are extracted (these coefficients contain the time information of the harmonic frequencies) Step 2 the amplitudes of the harmonic frequencies are calculated from these CWT coefficients 4
5 Phase difference Estimation Algorithm Step 1 estimate the instantaneous phase angles from the CWT coefficients of the harmonic frequencies Step 2 initial phase angles (phase difference) of any two harmonic frequencies are calculated accordingly 5
6 What is Continuous Wavelet Transform (CWT)? able to extract signal time and scale (frequency) information simultaneously able to preserve phase information 6
7 Graphical Illustrations of CWT 50Hz Signal Increasing scales (dilating) Shifting 7
8 The wavelet coefficients will be largest when the wavelet oscillation frequency at a particular scale is the same as the signal harmonic frequencies The scale and the signal frequency is related by f 1 = fs fc S1 where f 1 is the signal frequency f c is the wavelet centre frequency f s is the sampling frequency s 1 is the corresponding scale 8
9 Wavelet at scale 5a Signal with mixed frequencies of 50Hz + 250Hz Wavelet at scale a 9
10 Basic Properties Selection of the Wavelet must have finite energy (i.e. finite support) of zero mean, i.e. has no zero frequency component Simplified Complex Morlet Wavelet (CMW) is chosen smooth and harmonic like waveform suitable for harmonic analysis contain phase information 10
11 Complex Morlet Wavelet (CMW) 11
12 Characteristics of the CMW with the centre frequency fc and the bandwidth parameter fb large enough,, the mean of the CMW is practically equal to zero the frequency support of the CMW is not a compact support but the entire frequency axis the time support of the CMW is taken from -88 to 8 the waveform is symmetrical 12
13 Harmonic Frequencies Estimation Algorithm Given a signal f (t) = a(t)cos φ (t) Its wavelet transform is Wf(u,s) = s 2 a(u)e jφ(u) ( ĝ( s[ ξ φ (u)]) + ε(u, ξ )) The corrective term will be appreciable if the waveform f(t) has large variations 13
14 The normalised scalogram is defined as ξ P w f ( u, ξ ) = η Wf ( u,s ) s 2 which is calculated by ξ Pw f η ( u, ξ ) = 1 4 a 2 ( u ) ĝ( η[1 φ ( u ) ξ ]) + ε( u, ξ ) 2 This term is maximum at ω=0 neglect this corrective term 14
15 The normalised scalogram is maximum at η s( u ) = ξ( u ) = φ ( u ) The corresponding points ( u, ξ(u) ) calculated by the above equation are called wavelet ridges. 15
16 Illustrative examples A signal is constructed as x = 10cos(2π50t) + 5cos(2π100t 100t-20 o ) 16
17 Wavelet Ridges Plot Detected frequency = 50Hz Accuracy = 100% Detected frequency = 100Hz Accuracy = 100% 17
18 The analytic amplitude is given by Absolute value of the wavelet coefficient ξ 2 Wf( u,s ) 2 P w f ( u, ξ ) 2 η s a ( u ) = = = ĝ(0 ) 1 2Wf( u,s ) s The scale representing a particular harmonic frequency of the signal 18
19 Absolute Coefficients Plot at 50Hz Detected Amplitude = Accuracy = % 19
20 Absolute Coefficients Plot at 100Hz Detected Amplitude = Accuracy = % 20
21 Detection of Adjacent Frequencies If a signal f is a sum of two sinusoids f (t ) = acos( ω t ) + acos( ω t ) 1 2 The analytic part fa of the signal f is f a ( t ) = ae iω1 t + ae iω2 t = a cos( ω1 + ω2 t 2 ω1 + ω ( i 2 t 2 The instantaneous frequency is the average of the two frequencies ' ω1 + ω2 φ ( t ) = 2 ) e ) 21
22 Illustrative Examples Signal = mix frequencies of 400Hz + 500Hz Detected frequency = Hz (400Hz + 500Hz)/2 = 450Hz 22
23 In frequency domain, the complex morlet wavelet is a bandpass filter centred at the centre frequency fc with bandwidth parameter fb To discriminate adjacent frequencies the bandwidth must be narrow It is estimated that to detect adjacent frequencies, fb and fc must satisfy the inequality f c f b 0.87 x f 2 f 2 + f 1 f 1 23
24 Denoising with Discrete Stationary Wavelet Transform (DSWT) apply to wavelet ridges for frequency detection apply to absolute coefficients for amplitude detection Symlet2 wavelet is used for both wavelet ridges and absolute coefficients Haar wavelet can be used for absolute coefficients 24
25 Without denoising Illustrative Examples (Wavelet Ridges) signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Detected frequency = 50.1Hz Wavelet ridges Detected frequency = 100Hz 25
26 With denoising Illustrative Examples signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Detected frequency = 50Hz Accuracy = 100% Wavelet Ridges Detected frequency = 100Hz Accuracy = 100% 26
27 Without denoising Illustrative Examples (Wavelet Coefficient) signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Detected Amplitude = Accuracy = % Absolute Coefficient Plot for 50Hz frequency 27
28 Illustrative Examples (Wavelet Coefficient) signal = 10cos(2π50t) + 5cos(2π100t-20 o ) With denoising Detected Amplitude = Accuracy = % Absolute Coefficient Plot for 50Hz frequency 28
29 Illustrative Examples (Wavelet Coefficient) signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Without denoising Detected Amplitude = Accuracy = % Absolute Coefficient Plot for 100Hz frequency 29
30 With denoising Illustrative Examples (Wavelet coefficient) signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Detected Amplitude = Accuracy = % Absolute Coefficient Plot for 100Hz frequency 30
31 Conclusions The paper has presented a computational algorithm to estimate frequency contents of a signal together with the respective amplitudes The calculation results show that accuracy in frequency detection is 100% accuracy in amplitude detection is practically 100% 31
32 Further Works Initial phase difference detection Transient detection Increase the computational speed 32
33 Initial Phase (Phase Difference) Detection Given a signal f = a 1 cos( 2πf 1 t+θ 1 ) + a 2 cos( 2πf 2 t+θ 2 ) The instantaneous phase of the signal components are at time t is ph 1 = 2πf 1 t+θ 1 ph 2 = 2πf 2 t+θ 2 The instantaneous phase difference is given by ph 1 ph 2 = 2π(f 1 f 2 )t + (θ 1 -θ 2 ) changes over time t remains constant 33
34 Phase information by CWT with Complex Morlet Wavelet The phase angles represented by the wavelet coefficients are instantaneous phase angles which are dependent on the frequency of the signal component and change over time The initial phase angles (at time = 0) of the signal components are preserved by the instantaneous phase information of the wavelet coefficients 34
35 Accuracy in Initial Phase Angle Detection The accuracy in initial phase angle estimation is dependent on the sampling frequency A 50Hz signal sampled at 4000Hz would have an angular step of o 360 Sampling Frequency x Signal frequency = o x 50 = 4.5 o The higher the sampling frequency, the better the initial phase angle estimation 35
36 Illustrative Examples signal = 10cos(2π50t) + 5cos(2π100t-20 o ) Initial phase difference = 20 o sampling frequency = 4000Hz angular step for 50Hz = 4.5 o angular step for 100Hz = 9 o 36
37 Data Point Estimation Results Instantaneous Phase 50Hz Signal 100Hz Signal Initial Phase Difference rad rad deg rad rad deg Error deg (11.47% ) deg (11.47% ) The error of 2.29 o is approx. half of the angular step size of the 50Hz signal, i.e. 4.5 o /2 = 2.25 o Take this 2.25 o into account, the error in initial phase difference detection can be reduced to 0.216% This estimation approach needs to be verified 37
38 Preliminary Study on Transient Detection Given a 50Hz signal containing a transient as shown 38
39 The 50Hz signal amplitude = 10 Signal Characteristics sampled at 2000Hz No. of data = 2000 The transient duration = 0.5msec magnitude = located at data no
40 Wavelet Ridges Plot Detected frequency = 50Hz Detected Frequency = 1000Hz 40
41 Absolute Coefficients Plot of the Transient 41
42 THE END QUESTIONS ARE WELCOME 42
Introduction to Wavelet Transform. Chapter 7 Instructor: Hossein Pourghassem
Introduction to Wavelet Transform Chapter 7 Instructor: Hossein Pourghassem Introduction Most of the signals in practice, are TIME-DOMAIN signals in their raw format. It means that measured signal is a
More informationLecture 2: SIGNALS. 1 st semester By: Elham Sunbu
Lecture 2: SIGNALS 1 st semester 1439-2017 1 By: Elham Sunbu OUTLINE Signals and the classification of signals Sine wave Time and frequency domains Composite signals Signal bandwidth Digital signal Signal
More informationECE 201: Introduction to Signal Analysis
ECE 201: Introduction to Signal Analysis Prof. Paris Last updated: October 9, 2007 Part I Spectrum Representation of Signals Lecture: Sums of Sinusoids (of different frequency) Introduction Sum of Sinusoidal
More informationTRANSFORMS / WAVELETS
RANSFORMS / WAVELES ransform Analysis Signal processing using a transform analysis for calculations is a technique used to simplify or accelerate problem solution. For example, instead of dividing two
More informationSinusoids. Lecture #2 Chapter 2. BME 310 Biomedical Computing - J.Schesser
Sinusoids Lecture # Chapter BME 30 Biomedical Computing - 8 What Is this Course All About? To Gain an Appreciation of the Various Types of Signals and Systems To Analyze The Various Types of Systems To
More informationApplication of The Wavelet Transform In The Processing of Musical Signals
EE678 WAVELETS APPLICATION ASSIGNMENT 1 Application of The Wavelet Transform In The Processing of Musical Signals Group Members: Anshul Saxena anshuls@ee.iitb.ac.in 01d07027 Sanjay Kumar skumar@ee.iitb.ac.in
More informationIntroduction to Wavelets Michael Phipps Vallary Bhopatkar
Introduction to Wavelets Michael Phipps Vallary Bhopatkar *Amended from The Wavelet Tutorial by Robi Polikar, http://users.rowan.edu/~polikar/wavelets/wttutoria Who can tell me what this means? NR3, pg
More informationHarmonic Analysis of Power System Waveforms Based on Chaari Complex Mother Wavelet
Proceedings of the 7th WSEAS International Conference on Power Systems, Beijing, China, September 15-17, 2007 7 Harmonic Analysis of Power System Waveforms Based on Chaari Complex Mother Wavelet DAN EL
More informationADDITIVE SYNTHESIS BASED ON THE CONTINUOUS WAVELET TRANSFORM: A SINUSOIDAL PLUS TRANSIENT MODEL
ADDITIVE SYNTHESIS BASED ON THE CONTINUOUS WAVELET TRANSFORM: A SINUSOIDAL PLUS TRANSIENT MODEL José R. Beltrán and Fernando Beltrán Department of Electronic Engineering and Communications University of
More informationChapter 1. Electronics and Semiconductors
Chapter 1. Electronics and Semiconductors Tong In Oh 1 Objective Understanding electrical signals Thevenin and Norton representations of signal sources Representation of a signal as the sum of sine waves
More informationVOLD-KALMAN ORDER TRACKING FILTERING IN ROTATING MACHINERY
TŮMA, J. GEARBOX NOISE AND VIBRATION TESTING. IN 5 TH SCHOOL ON NOISE AND VIBRATION CONTROL METHODS, KRYNICA, POLAND. 1 ST ED. KRAKOW : AGH, MAY 23-26, 2001. PP. 143-146. ISBN 80-7099-510-6. VOLD-KALMAN
More informationDetection, localization, and classification of power quality disturbances using discrete wavelet transform technique
From the SelectedWorks of Tarek Ibrahim ElShennawy 2003 Detection, localization, and classification of power quality disturbances using discrete wavelet transform technique Tarek Ibrahim ElShennawy, Dr.
More informationA Comparative Study of Wavelet Transform Technique & FFT in the Estimation of Power System Harmonics and Interharmonics
ISSN: 78-181 Vol. 3 Issue 7, July - 14 A Comparative Study of Wavelet Transform Technique & FFT in the Estimation of Power System Harmonics and Interharmonics Chayanika Baruah 1, Dr. Dipankar Chanda 1
More informationDSP First. Laboratory Exercise #2. Introduction to Complex Exponentials
DSP First Laboratory Exercise #2 Introduction to Complex Exponentials The goal of this laboratory is gain familiarity with complex numbers and their use in representing sinusoidal signals as complex exponentials.
More informationChapter 5. Signal Analysis. 5.1 Denoising fiber optic sensor signal
Chapter 5 Signal Analysis 5.1 Denoising fiber optic sensor signal We first perform wavelet-based denoising on fiber optic sensor signals. Examine the fiber optic signal data (see Appendix B). Across all
More informationSinusoids and Phasors (Chapter 9 - Lecture #1) Dr. Shahrel A. Suandi Room 2.20, PPKEE
Sinusoids and Phasors (Chapter 9 - Lecture #1) Dr. Shahrel A. Suandi Room 2.20, PPKEE Email:shahrel@eng.usm.my 1 Outline of Chapter 9 Introduction Sinusoids Phasors Phasor Relationships for Circuit Elements
More informationCircuit Analysis-II. Circuit Analysis-II Lecture # 2 Wednesday 28 th Mar, 18
Circuit Analysis-II Angular Measurement Angular Measurement of a Sine Wave ü As we already know that a sinusoidal voltage can be produced by an ac generator. ü As the windings on the rotor of the ac generator
More informationChapter 2. Signals and Spectra
Chapter 2 Signals and Spectra Outline Properties of Signals and Noise Fourier Transform and Spectra Power Spectral Density and Autocorrelation Function Orthogonal Series Representation of Signals and Noise
More informationVU Signal and Image Processing. Torsten Möller + Hrvoje Bogunović + Raphael Sahann
052600 VU Signal and Image Processing Torsten Möller + Hrvoje Bogunović + Raphael Sahann torsten.moeller@univie.ac.at hrvoje.bogunovic@meduniwien.ac.at raphael.sahann@univie.ac.at vda.cs.univie.ac.at/teaching/sip/17s/
More informationFault Location Technique for UHV Lines Using Wavelet Transform
International Journal of Electrical Engineering. ISSN 0974-2158 Volume 6, Number 1 (2013), pp. 77-88 International Research Publication House http://www.irphouse.com Fault Location Technique for UHV Lines
More informationPractical Applications of the Wavelet Analysis
Practical Applications of the Wavelet Analysis M. Bigi, M. Jacchia, D. Ponteggia ALMA International Europe (6- - Frankfurt) Summary Impulse and Frequency Response Classical Time and Frequency Analysis
More informationObjectives. Presentation Outline. Digital Modulation Lecture 03
Digital Modulation Lecture 03 Inter-Symbol Interference Power Spectral Density Richard Harris Objectives To be able to discuss Inter-Symbol Interference (ISI), its causes and possible remedies. To be able
More informationTIME FREQUENCY ANALYSIS OF TRANSIENT NVH PHENOMENA IN VEHICLES
TIME FREQUENCY ANALYSIS OF TRANSIENT NVH PHENOMENA IN VEHICLES K Becker 1, S J Walsh 2, J Niermann 3 1 Institute of Automotive Engineering, University of Applied Sciences Cologne, Germany 2 Dept. of Aeronautical
More informationEEE508 GÜÇ SİSTEMLERİNDE SİNYAL İŞLEME
EEE508 GÜÇ SİSTEMLERİNDE SİNYAL İŞLEME Signal Processing for Power System Applications Triggering, Segmentation and Characterization of the Events (Week-12) Gazi Üniversitesi, Elektrik ve Elektronik Müh.
More informationEECS40 RLC Lab guide
EECS40 RLC Lab guide Introduction Second-Order Circuits Second order circuits have both inductor and capacitor components, which produce one or more resonant frequencies, ω0. In general, a differential
More informationChapter 2. Fourier Series & Fourier Transform. Updated:2/11/15
Chapter 2 Fourier Series & Fourier Transform Updated:2/11/15 Outline Systems and frequency domain representation Fourier Series and different representation of FS Fourier Transform and Spectra Power Spectral
More informationLOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION. Hans Knutsson Carl-Fredrik Westin Gösta Granlund
LOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION Hans Knutsson Carl-Fredri Westin Gösta Granlund Department of Electrical Engineering, Computer Vision Laboratory Linöping University, S-58 83 Linöping,
More informationSignals. Periodic vs. Aperiodic. Signals
Signals 1 Periodic vs. Aperiodic Signals periodic signal completes a pattern within some measurable time frame, called a period (), and then repeats that pattern over subsequent identical periods R s.
More informationEvoked Potentials (EPs)
EVOKED POTENTIALS Evoked Potentials (EPs) Event-related brain activity where the stimulus is usually of sensory origin. Acquired with conventional EEG electrodes. Time-synchronized = time interval from
More informationDETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCE WAVEFORM USING MRA BASED MODIFIED WAVELET TRANSFROM AND NEURAL NETWORKS
Journal of ELECTRICAL ENGINEERING, VOL. 61, NO. 4, 2010, 235 240 DETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCE WAVEFORM USING MRA BASED MODIFIED WAVELET TRANSFROM AND NEURAL NETWORKS Perumal
More informationECEGR Lab #8: Introduction to Simulink
Page 1 ECEGR 317 - Lab #8: Introduction to Simulink Objective: By: Joe McMichael This lab is an introduction to Simulink. The student will become familiar with the Help menu, go through a short example,
More informationspeech signal S(n). This involves a transformation of S(n) into another signal or a set of signals
16 3. SPEECH ANALYSIS 3.1 INTRODUCTION TO SPEECH ANALYSIS Many speech processing [22] applications exploits speech production and perception to accomplish speech analysis. By speech analysis we extract
More informationBiomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar
Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative
More informationIntroduction to signals and systems
CHAPTER Introduction to signals and systems Welcome to Introduction to Signals and Systems. This text will focus on the properties of signals and systems, and the relationship between the inputs and outputs
More informationLecture 25: The Theorem of (Dyadic) MRA
WAVELETS AND MULTIRATE DIGITAL SIGNAL PROCESSING Lecture 25: The Theorem of (Dyadic) MRA Prof.V.M.Gadre, EE, IIT Bombay 1 Introduction In the previous lecture, we discussed that translation and scaling
More informationTHE SINUSOIDAL WAVEFORM
Chapter 11 THE SINUSOIDAL WAVEFORM The sinusoidal waveform or sine wave is the fundamental type of alternating current (ac) and alternating voltage. It is also referred to as a sinusoidal wave or, simply,
More informationMULTIRATE SIGNAL PROCESSING AND ITS APPLICATIONS
M.Tech. credit seminar report, Electronic Systems Group, EE Dept, IIT Bombay, submitted November 00 MULTIRATE SIGNAL PROCESSING AND ITS APPLICATIONS Author:Roday Viramsingh Roll no.:0330706 Supervisor:
More informationProject I: Phase Tracking and Baud Timing Correction Systems
Project I: Phase Tracking and Baud Timing Correction Systems ECES 631, Prof. John MacLaren Walsh, Ph. D. 1 Purpose In this lab you will encounter the utility of the fundamental Fourier and z-transform
More information, answer the next six questions.
Frequency Response Problems Conceptual Questions 1) T/F Given f(t) = A cos (ωt + θ): The amplitude of the output in sinusoidal steady-state increases as K increases and decreases as ω increases. 2) T/F
More informationTHE APPLICATION WAVELET TRANSFORM ALGORITHM IN TESTING ADC EFFECTIVE NUMBER OF BITS
ABSTRACT THE APPLICATION WAVELET TRANSFORM ALGORITHM IN TESTING EFFECTIVE NUMBER OF BITS Emad A. Awada Department of Electrical and Computer Engineering, Applied Science University, Amman, Jordan In evaluating
More informationDetection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms
Detection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms Nor Asrina Binti Ramlee International Science Index, Energy and Power Engineering waset.org/publication/10007639 Abstract
More informationDSP First. Laboratory Exercise #7. Everyday Sinusoidal Signals
DSP First Laboratory Exercise #7 Everyday Sinusoidal Signals This lab introduces two practical applications where sinusoidal signals are used to transmit information: a touch-tone dialer and amplitude
More informationFinal Exam Solutions June 14, 2006
Name or 6-Digit Code: PSU Student ID Number: Final Exam Solutions June 14, 2006 ECE 223: Signals & Systems II Dr. McNames Keep your exam flat during the entire exam. If you have to leave the exam temporarily,
More informationCHAPTER 9. Sinusoidal Steady-State Analysis
CHAPTER 9 Sinusoidal Steady-State Analysis 9.1 The Sinusoidal Source A sinusoidal voltage source (independent or dependent) produces a voltage that varies sinusoidally with time. A sinusoidal current source
More informationSound pressure level calculation methodology investigation of corona noise in AC substations
International Conference on Advanced Electronic Science and Technology (AEST 06) Sound pressure level calculation methodology investigation of corona noise in AC substations,a Xiaowen Wu, Nianguang Zhou,
More informationHarmonic Analysis. Purpose of Time Series Analysis. What Does Each Harmonic Mean? Part 3: Time Series I
Part 3: Time Series I Harmonic Analysis Spectrum Analysis Autocorrelation Function Degree of Freedom Data Window (Figure from Panofsky and Brier 1968) Significance Tests Harmonic Analysis Harmonic analysis
More informationEWGAE Latest improvements on Freeware AGU-Vallen-Wavelet
EWGAE 2010 Vienna, 8th to 10th September Latest improvements on Freeware AGU-Vallen-Wavelet Jochen VALLEN 1, Hartmut VALLEN 2 1 Vallen Systeme GmbH, Schäftlarner Weg 26a, 82057 Icking, Germany jochen@vallen.de,
More informationExperiment 1 LRC Transients
Physics 263 Experiment 1 LRC Transients 1 Introduction In this experiment we will study the damped oscillations and other transient waveforms produced in a circuit containing an inductor, a capacitor,
More informationLinear Frequency Modulation (FM) Chirp Signal. Chirp Signal cont. CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis
Linear Frequency Modulation (FM) CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 26, 29 Till now we
More informationOutline. Communications Engineering 1
Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband channels Signal space representation Optimal
More informationChapter-2 SAMPLING PROCESS
Chapter-2 SAMPLING PROCESS SAMPLING: A message signal may originate from a digital or analog source. If the message signal is analog in nature, then it has to be converted into digital form before it can
More informationEE 422G - Signals and Systems Laboratory
EE 422G - Signals and Systems Laboratory Lab 3 FIR Filters Written by Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 September 19, 2015 Objectives:
More informationBasic Signals and Systems
Chapter 2 Basic Signals and Systems A large part of this chapter is taken from: C.S. Burrus, J.H. McClellan, A.V. Oppenheim, T.W. Parks, R.W. Schafer, and H. W. Schüssler: Computer-based exercises for
More informationCMPT 468: Frequency Modulation (FM) Synthesis
CMPT 468: Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University October 6, 23 Linear Frequency Modulation (FM) Till now we ve seen signals
More informationDSP First Lab 03: AM and FM Sinusoidal Signals. We have spent a lot of time learning about the properties of sinusoidal waveforms of the form: k=1
DSP First Lab 03: AM and FM Sinusoidal Signals Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises in the Pre-Lab section before
More informationDigital Video and Audio Processing. Winter term 2002/ 2003 Computer-based exercises
Digital Video and Audio Processing Winter term 2002/ 2003 Computer-based exercises Rudolf Mester Institut für Angewandte Physik Johann Wolfgang Goethe-Universität Frankfurt am Main 6th November 2002 Chapter
More informationWavelet Transform for Classification of Voltage Sag Causes using Probabilistic Neural Network
International Journal of Electrical Engineering. ISSN 974-2158 Volume 4, Number 3 (211), pp. 299-39 International Research Publication House http://www.irphouse.com Wavelet Transform for Classification
More informationCHAPTER 3 WAVELET TRANSFORM BASED CONTROLLER FOR INDUCTION MOTOR DRIVES
49 CHAPTER 3 WAVELET TRANSFORM BASED CONTROLLER FOR INDUCTION MOTOR DRIVES 3.1 INTRODUCTION The wavelet transform is a very popular tool for signal processing and analysis. It is widely used for the analysis
More informationWavelets and wavelet convolution and brain music. Dr. Frederike Petzschner Translational Neuromodeling Unit
Wavelets and wavelet convolution and brain music Dr. Frederike Petzschner Translational Neuromodeling Unit 06.03.2015 Recap Why are we doing this? We know that EEG data contain oscillations. Or goal is
More informationDiscrete Fourier Transform (DFT)
Amplitude Amplitude Discrete Fourier Transform (DFT) DFT transforms the time domain signal samples to the frequency domain components. DFT Signal Spectrum Time Frequency DFT is often used to do frequency
More informationGear Transmission Error Measurements based on the Phase Demodulation
Gear Transmission Error Measurements based on the Phase Demodulation JIRI TUMA Abstract. The paper deals with a simple gear set transmission error (TE) measurements at gearbox operational conditions that
More informationSignal Processing. Introduction
Signal Processing 0 Introduction One of the premiere uses of MATLAB is in the analysis of signal processing and control systems. In this chapter we consider signal processing. The final chapter of the
More informationDo wavelet filters provide more accurate estimates of reverberation times at low frequencies.
INTER-NOISE 216 Do wavelet filters provide more accurate estimates of reverberation times at low frequencies. Manuel A. SOBREIRA SEOANE 1 ; David PÉREZ CABO 2 ; Finn T. AGERKVIST 3 1 AtlanTIC Research
More informationModern spectral analysis of non-stationary signals in power electronics
Modern spectral analysis of non-stationary signaln power electronics Zbigniew Leonowicz Wroclaw University of Technology I-7, pl. Grunwaldzki 3 5-37 Wroclaw, Poland ++48-7-36 leonowic@ipee.pwr.wroc.pl
More informationBode plot, named after Hendrik Wade Bode, is usually a combination of a Bode magnitude plot and Bode phase plot:
Bode plot From Wikipedia, the free encyclopedia A The Bode plot for a first-order (one-pole) lowpass filter Bode plot, named after Hendrik Wade Bode, is usually a combination of a Bode magnitude plot and
More informationAlternating voltages and currents
Alternating voltages and currents Introduction - Electricity is produced by generators at power stations and then distributed by a vast network of transmission lines (called the National Grid system) to
More informationSolution to Chapter 4 Problems
Solution to Chapter 4 Problems Problem 4.1 1) Since F[sinc(400t)]= 1 modulation index 400 ( f 400 β f = k f max[ m(t) ] W Hence, the modulated signal is ), the bandwidth of the message signal is W = 00
More informationThe Principle of Superposition
The Principle of Superposition If wave 1 displaces a particle in the medium by D 1 and wave 2 simultaneously displaces it by D 2, the net displacement of the particle is simply D 1 + D 2. Standing Waves
More informationEE216B: VLSI Signal Processing. Wavelets. Prof. Dejan Marković Shortcomings of the Fourier Transform (FT)
5//0 EE6B: VLSI Signal Processing Wavelets Prof. Dejan Marković ee6b@gmail.com Shortcomings of the Fourier Transform (FT) FT gives information about the spectral content of the signal but loses all time
More informationBakiss Hiyana binti Abu Bakar JKE, POLISAS BHAB
1 Bakiss Hiyana binti Abu Bakar JKE, POLISAS 1. Explain AC circuit concept and their analysis using AC circuit law. 2. Apply the knowledge of AC circuit in solving problem related to AC electrical circuit.
More informationImproved PLL for Power Generation Systems Operating under Real Grid Conditions
ELECTRONICS, VOL. 15, NO., DECEMBER 011 5 Improved PLL for Power Generation Systems Operating under Real Grid Conditions Evgenije M. Adžić, Milan S. Adžić, and Vladimir A. Katić Abstract Distributed power
More informationSpectrum. Additive Synthesis. Additive Synthesis Caveat. Music 270a: Modulation
Spectrum Music 7a: Modulation Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) October 3, 7 When sinusoids of different frequencies are added together, the
More informationChapter 5 Window Functions. periodic with a period of N (number of samples). This is observed in table (3.1).
Chapter 5 Window Functions 5.1 Introduction As discussed in section (3.7.5), the DTFS assumes that the input waveform is periodic with a period of N (number of samples). This is observed in table (3.1).
More informationSpace Vector PWM and Model Predictive Control for Voltage Source Inverter Control
Space Vector PWM and Model Predictive Control for Voltage Source Inverter Control Irtaza M. Syed, Kaamran Raahemifar Abstract In this paper, we present a comparative assessment of Space Vector Pulse Width
More informationEND-OF-YEAR EXAMINATIONS ELEC321 Communication Systems (D2) Tuesday, 22 November 2005, 9:20 a.m. Three hours plus 10 minutes reading time.
END-OF-YEAR EXAMINATIONS 2005 Unit: Day and Time: Time Allowed: ELEC321 Communication Systems (D2) Tuesday, 22 November 2005, 9:20 a.m. Three hours plus 10 minutes reading time. Total Number of Questions:
More informationCharacterization of Voltage Sag due to Faults and Induction Motor Starting
Characterization of Voltage Sag due to Faults and Induction Motor Starting Dépt. of Electrical Engineering, SSGMCE, Shegaon, India, Dépt. of Electronics & Telecommunication Engineering, SITS, Pune, India
More informationSignal Characteristics
Data Transmission The successful transmission of data depends upon two factors:» The quality of the transmission signal» The characteristics of the transmission medium Some type of transmission medium
More informationDSP First Lab 08: Frequency Response: Bandpass and Nulling Filters
DSP First Lab 08: Frequency Response: Bandpass and Nulling Filters Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises in the
More information4.1 REPRESENTATION OF FM AND PM SIGNALS An angle-modulated signal generally can be written as
1 In frequency-modulation (FM) systems, the frequency of the carrier f c is changed by the message signal; in phase modulation (PM) systems, the phase of the carrier is changed according to the variations
More informationSelection of Mother Wavelet for Processing of Power Quality Disturbance Signals using Energy for Wavelet Packet Decomposition
Volume 114 No. 9 217, 313-323 ISSN: 1311-88 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Selection of Mother Wavelet for Processing of Power Quality Disturbance
More informationInstruction Manual for Concept Simulators. Signals and Systems. M. J. Roberts
Instruction Manual for Concept Simulators that accompany the book Signals and Systems by M. J. Roberts March 2004 - All Rights Reserved Table of Contents I. Loading and Running the Simulators II. Continuous-Time
More informationIEEE 802.3aq Task Force Dynamic Channel Model Ad Hoc Task 2 - Time variation & modal noise 10/13/2004 con-call
IEEE 802.3aq Task Force Dynamic Channel Model Ad Hoc Task 2 - Time variation & modal noise 10/13/2004 con-call Time variance in MMF links Further test results Rob Coenen Overview Based on the formulation
More informationLaboratory Assignment 4. Fourier Sound Synthesis
Laboratory Assignment 4 Fourier Sound Synthesis PURPOSE This lab investigates how to use a computer to evaluate the Fourier series for periodic signals and to synthesize audio signals from Fourier series
More informationCMPT 368: Lecture 4 Amplitude Modulation (AM) Synthesis
CMPT 368: Lecture 4 Amplitude Modulation (AM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 8, 008 Beat Notes What happens when we add two frequencies
More informationPHASE DEMODULATION OF IMPULSE SIGNALS IN MACHINE SHAFT ANGULAR VIBRATION MEASUREMENTS
PHASE DEMODULATION OF IMPULSE SIGNALS IN MACHINE SHAFT ANGULAR VIBRATION MEASUREMENTS Jiri Tuma VSB Technical University of Ostrava, Faculty of Mechanical Engineering Department of Control Systems and
More informationEvaluation of Steady-State and Dynamic Performance of a Synchronized Phasor Measurement Unit
Electrical Power and Energy Conference 2012 Resilient Green Energy Systems for a Sustainable Society Evaluation of Steady-State and Dynamic Performance of a Synchronized Phasor Measurement Unit Dinesh
More information3.2 Measuring Frequency Response Of Low-Pass Filter :
2.5 Filter Band-Width : In ideal Band-Pass Filters, the band-width is the frequency range in Hz where the magnitude response is at is maximum (or the attenuation is at its minimum) and constant and equal
More informationIntroduction to Wavelet Transform. A. Enis Çetin Visiting Professor Ryerson University
Introduction to Wavelet Transform A. Enis Çetin Visiting Professor Ryerson University Overview of Wavelet Course Sampling theorem and multirate signal processing 2 Wavelets form an orthonormal basis of
More information1. Explain how Doppler direction is identified with FMCW radar. Fig Block diagram of FM-CW radar. f b (up) = f r - f d. f b (down) = f r + f d
1. Explain how Doppler direction is identified with FMCW radar. A block diagram illustrating the principle of the FM-CW radar is shown in Fig. 4.1.1 A portion of the transmitter signal acts as the reference
More informationYEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS
YEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS EXPERIMENT 3: SAMPLING & TIME DIVISION MULTIPLEX (TDM) Objective: Experimental verification of the
More informationEEG Signal Preprocessing using Wavelet Transform
International Journal of Electronics Engineering, 3 (1), 2011, pp. 5 10 Serials Publications, ISSN : 0973-7383 EEG Signal Preprocessing using Wavelet Transform Arun S. Chavan 1 and Mahesh Kolte 2 1 Vidyalankar
More informationWavelet Transform for Bearing Faults Diagnosis
Wavelet Transform for Bearing Faults Diagnosis H. Bendjama and S. Bouhouche Welding and NDT research centre (CSC) Cheraga, Algeria hocine_bendjama@yahoo.fr A.k. Moussaoui Laboratory of electrical engineering
More informationLecture 3 Complex Exponential Signals
Lecture 3 Complex Exponential Signals Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/1 1 Review of Complex Numbers Using Euler s famous formula for the complex exponential The
More informationBroken Rotor Bar Fault Detection using Wavlet
Broken Rotor Bar Fault Detection using Wavlet sonalika mohanty Department of Electronics and Communication Engineering KISD, Bhubaneswar, Odisha, India Prof.(Dr.) Subrat Kumar Mohanty, Principal CEB Department
More informationEE105 Fall 2015 Microelectronic Devices and Circuits. Amplifier Gain
EE05 Fall 205 Microelectronic Devices and Circuits Prof. Ming C. Wu wu@eecs.berkeley.edu 5 Sutardja Dai Hall (SDH) 2- Amplifier Gain Voltage Gain: Current Gain: Power Gain: Note: A v v O v I A i i O i
More informationImage Denoising Using Complex Framelets
Image Denoising Using Complex Framelets 1 N. Gayathri, 2 A. Hazarathaiah. 1 PG Student, Dept. of ECE, S V Engineering College for Women, AP, India. 2 Professor & Head, Dept. of ECE, S V Engineering College
More informationPower Spectral Density (PSD) for TH-UWB signals using PPM is derived in this
C H A P T E R 3 The PSD of TH-UWB Signals Power Spectral Density (PSD) for TH-UWB signals using PPM is derived in this chapter. The adopted approach (Di Benedetto and Vojcic, 3) follows the analog PPM
More informationTheory of Telecommunications Networks
Theory of Telecommunications Networks Anton Čižmár Ján Papaj Department of electronics and multimedia telecommunications CONTENTS Preface... 5 1 Introduction... 6 1.1 Mathematical models for communication
More informationProceedings of the 29th International Conference on Ocean, Offshore and Arctic Engineering OMAE2010 June 6-11, 2010, Shanghai, China
Proceedings of the 29th International Conference on Ocean, Offshore and Arctic Engineering OMAE2 June 6 -, 2, Shanghai, China OMAE2-299 ON THE VORTEX-INDUCED VIBRATION RESPONSE OF A MODEL RISER AND LOCATION
More informationMETHODS TO IMPROVE DYNAMIC RESPONSE OF POWER FACTOR PREREGULATORS: AN OVERVIEW
METHODS TO IMPROE DYNAMIC RESPONSE OF POWER FACTOR PREREGULATORS: AN OERIEW G. Spiazzi*, P. Mattavelli**, L. Rossetto** *Dept. of Electronics and Informatics, **Dept. of Electrical Engineering University
More information