Comparison Of S-Transform And Wavelet Transform In Power Quality Analysis (original) (raw)
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Visualizing time-varying power quality indices using generalized empirical wavelet transform
Electric Power Systems Research, 2017
Tracking of instantaneous power quality (PQ) indices is very essential for better characterization of the time-varying voltage and current signals. This paper presents the estimation of time-varying PQ indices for accurate interpretation of disturbances using a generalized empirical wavelet transform (GEWT). This approach is based on adaptive segmentation of Fourier spectrum and followed by an appropriate filter design to extract the individual frequency components. The main emphasis of this work is to estimate the actual frequencies present in the signal by overcoming the problem of spectral leakage. A preliminary investigation of time domain amplitude variation of the signal helps in realizing the presence of low-frequency interharmonics and thereby permitting to extract the fundamental frequency component perfectly. Hence, the GEWT is employed to assess successfully all sorts of power signals having disturbances such as voltage fluctuation, sag, swell, interruption, transients, harmonics, and interharmonics. The robustness and applicability of the GEWT for PQ analysis have been verified by analyzing several distorted signals generated using PSCAD, recorded waveforms available in IEEE database and a few real signals. Finally, the estimated GEWT-based time-varying single phase PQ indices are compared with the indices obtained from IEC defined fast Fourier transform (FFT) and fast S-transform (FST).
Undecimated Wavelet Packet Transform Based Visualization of Time-Varying Power Quality Disturbance
2018
In this paper, a method is presented for visualization of time-varying power quality disturbances in electrical power distribution system using undecimated wavelet packet transform (UWPT). The proposed method decomposes the input signal in various frequency bands and provides clear visualization of fundamental and each harmonic components. The UWPT is a time-invariant transform, which can lead to better understanding of time-dependent power quality disturbances. Various type of stationary and time-varying waveforms have been used to show the effectiveness of the proposed scheme. The results confirm that the proposed technique based on UWPT efficiently decomposes the fundamental and harmonics component from the distorted signal, reflecting its suitability for power quality monitoring and analysis.
2016
With the increase of non linear load such as a range of electronic and microprocessor base equipment power quality becomes the prominent issue now a day. In order to improve the power quality problem, detection of power quality problem must be done first. This paper presents a literature review of the application of wavelet transforms in the detection and analysis of power quality disturbances. The PQ disturbances include wide range of PQ phenomena namely transient (impulsive and oscillatory), short duration variations (interruption, sag and swell), power frequency variations, long duration variations (sustained under voltages and sustained over voltages) and steady state variations (harmonics, notch, flicker etc.) with time scale ranges from tens of nanoseconds to steady sate in this condition extraction become difficult task. This paper presents a comprehensive review of different techniques based on wavelet transform to detect and classify power quality problems and advantages of...
POWER QUALITY ANALYSIS VIA WAVELET TRANSFORM
The dependence of modern life upon the continuous supply of electrical energy makes power quality of utmost importance in the power systems area. In this paper work, a new approach to detect, localize and investigate the feasibility of classifying various types of power quality disturbances is presented, wavelet transform analysis is done as well as the concept of mother wavelet is also explained. In quality of power, the current state of art is the use of Daubechies wavelets. Daubechies wavelets belong to a special class of mother wavelet and actually they are the most used for detection, localization and classification of disturbances. The key idea underlying the approach is to decompose the disturbance signal developed with the help of matlab 7.0.5 version simulink into other signals which represent a approximated version and a detailed version of the original signal by using the wavemenu toolbox. The signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The signal firstly separated and then analysed using different techniques step by step. The decomposition is performed using multi-resolution signal decomposition techniques. The demonstration is done with the distribution system to detect and localize disturbance with actual power line disturbances. In order to enhance the detection outcomes, utilization of wavelet transform coefficients of the analysed power line signals. The results of various other methods are compared and presented the best suitable method. The simulation results clearly demonstrate the superiority and effectiveness of the wavelet transform in both current and voltage signal noise reduction.
Application of wavelet Transform in power Quality: A Review
International Journal of Computer Applications, 2012
From last decades the objective of Power quality (PQ) monitoring and analysis has drastically. Generally the power quality problem covers the time scales range from tens of nanoseconds to steady state to describe different events. Well discussed in various international standards (IEEE, IEC, EN etc) and also give various acceptability curves to quantify and classify different Power Quality phenomenon (CIBMA and ITC) according to amplitude and time frame. It is observed that different tools and methods are always been used to detect and classify the power Quality events. The whole advance tends to process the raw data and extract the information in order to make decision. And further move towards real time monitoring, protection and control. This paper presents a comprehensive review of different techniques based on wavelet transform to detect and classify power quality problems.
Two Aplications for Power Quality Analysis using the Matlab Wavelet Toolbox
Renewable Energy and Power Quality Journal, 2005
Nowadays there is an increasing number of electric and electronics equipment that has carried out a growing number of problems that affect to the power quality. Several studies on power quality have been made in order to detect and localise that kind of problems. For such objective it has been used the Wavelet Transform that allows the detection and localisation of disturbances in the voltage waveform. At laboratory level it is possible to use several tools, such as the Matlab Wavelet Toolbox that includes basic commands and a graphic environment. To begin with the study of this tool and its capability in detecting disturbances in the voltage waveform we need several test signals with known disturbances. This paper presents two applications developed for the study of Power Quality based on the Matlab environment.
DIAGNOSIS OF POWER QUALITY DISTURBANCES USING WAVELET TRANSFORMS
A new method for detection of power quality disturbance is proposed: first, the original signals are de-noised by the wavelet transform; second, the beginning and ending time of the disturbance can be detected in time, third, determining the cause of power quality disturbances using various approaches such as Multi Resolution Analysis (MRA) or Discrete Wavelet Transforms (DWT) In this paper, wavelet transform is proposed to identify the power quality disturbance at its instance of occurrence. Power quality disturbances like sag, swell, interruption, DC offset, frequency variation and harmonics are considered and are decomposed up to 4 levels using Db4 wavelet. For some disturbances it is sufficient to have only second or third level of decomposition. The exact location of the disturbance can also be found on the time scale. The application to a case study shows that this method is fast, sensitive, and practical for detection and identification of power quality disturbance.
Detection of Power Quality Disturbances Using Wavelet Transforms
A new method for detection of power quality disturbance is proposed: first, the original signals are de-noised by the wavelet transform; second, the beginning and ending time of the disturbance can be detected in time, third, determining the cause of power quality disturbances using various approaches such as Multi Resolution Analysis (MRA) or Discrete Wavelet Transforms (DWT) In this paper, wavelet transform is proposed to identify the power quality disturbance at its instance of occurrence. Power quality disturbances like sag, swell, interruption, DC offset, frequency variation and harmonics are considered and are decomposed up to 4 levels using Db4 wavelet. For some disturbances it is sufficient to have only second or third level of decomposition. The exact location of the disturbance can also be found on the time scale. The application to a case study shows that this method is fast, sensitive, and practical for detection and identification of power quality disturbance.