Wavelet Transform Analysis to Applications in Electric Power Systems
07 May 2019-
TL;DR: This chapter presents a review on main application of wavelet transform in electric power systems, which has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis.
Abstract: The wavelet transform has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis. This chapter presents a review on main application of wavelet transform in electric power systems. The study areas have been classified as power system protection, power quality disturbances, power system transient, partial discharge, load forecasting, faults detection, and power system measurement. The areas in which more works have been developed are the power quality and protections field, where both cover 51% of the articles analyzed.
Citations
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TL;DR: The statistical analysis revealed that the proposed DWT-PSO-RBFNN method performed better based on MAPE, MAD, and RMSE emphasizing its great potential.
Abstract: Availability of electrical energy affects many facets of an entire economy of a country. This has made short-term electrical load forecasting an important area in recent years for policy makers and academic researchers. However, it has been found that the actual load series exhibit some complex behaviours which are often characterised by nonlinearity, nonstationarity, and temporal variations. In this study, a three-level hybrid ensemble short-term load forecasting method consisting of Discrete Wavelet Transform (DWT), Particle Swarm Optimization (PSO), and Radial Basis Function Neural Network (RBFNN) is proposed. The DWT is applied to decompose the data to get a well-behaved requisite series for forecasting since the data becomes stable before using PSO. PSO is used to obtain the required optimal adjustable parameters of the RBFNN for the forecasting. The proposed hybrid ensemble method (DWT-PSO-RBFNN) was evaluated using Ghana Grid Company daily average demand data from 1 st December 2018 to 30th November 2019. The DWT-PSO-RBFNN approach was compared with three other DWT coupling methods namely RBFNN, Backpropagation Neural Network (BPNN), and Self Adaptive Differential Evolution – Extreme Learning Machine (SaDE-ELM). The statistical analysis revealed that the proposed method performed better based on MAPE, MAD, and RMSE emphasizing its great potential.
51 citations
TL;DR: This paper presents a methodology to evaluate hybrid energy storage systems in hybrid energy systems, using the stretched-thread method to evaluate the influence of energy storage, instead of conventional optimization techniques, conferring a visual approach.
Abstract: This paper presents a methodology to evaluate hybrid energy storage systems in hybrid energy systems. While Wavelet is used to decompose the net load in temporal segments, the stretched-thread method is used to evaluate the influence of energy storage, instead of conventional optimization techniques, conferring a visual approach. Proper selection of energy storage technologies for each time frame, as long as several sizing of different energy storage technologies is evaluated as well. The use of different methods and their application in a hybrid system are the main contributions of this piece.
4 citations
31 Aug 2021
TL;DR: In this paper, a machine learning based model for power quality event classification is developed and tested and 16 categories of the most commonly occurring power quality events are classified by means of wavelet transform and select machine learning-based methods to evaluate the best performing machine learning model.
Abstract: With the penetration of non-linear loads, renewables and distributed generation with power electronic converters, solutions for maintaining good power quality have become a major concern for the stakeholders of electrical power systems. In this paper, a machine learning based model for power quality event classification is developed and tested. 16 categories of the most commonly occurring power quality events are classified by means of wavelet transform and select machine learning based methods to evaluate the best performing machine learning model. The outcome of classifications and effectiveness of machine learning methods is evaluated using the ‘Classification Learners’ application in MATLAB. The selected machine learning model is implemented in Simulink for test distribution grid circuits. The results obtained from simulation showed acceptable accuracy and performance and demonstrated the efficiency of the model in different operating conditions.
4 citations
TL;DR: In this article , an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale semi-autogenous (SAG) mill.
Abstract: The harsh and hostile internal environment of semi-autogenous (SAG) mills renders real-time monitoring of some critical variables practically unmeasured. Typically, feed size fractions are known to cause mill fluctuations and impede the consistent processing behaviour of ores. There is, therefore, the need for continuous monitoring of mill parameters for optimal operation. In this paper, an acoustic-based sensing method is employed to estimate, in real time, a snapshot of the different feed size fractions presented to a laboratory-scale SAG mill. Employing the MATLAB 2020b programme, the mill acoustic signal is processed using various transform techniques such as power spectral density estimate (PSDE) by Welch’s method, discrete wavelet transform (DWT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). Different fractional bandpowers are obtained from the PSDE spectrum, while the statistical root mean square values are further extracted from DWT, WPT, EMD, and VMD as feature vectors. The features are used as input features in different machine-learning classification algorithms for different mill feed size fractions predictions. The various transform techniques and feed size fraction predictions are evaluated using the various performance indicators obtained from the confusion matrix such as accuracy, precision, sensitivity and F1 score. The study showed that the acoustic signal feature extraction techniques used in conjunction with the Support Vector Machine (SVM), linear discriminant analysis (LDA), and ensemble with subclass discriminant machine learning algorithms demonstrated improved performance for predicting feed size variations.
1 citations
10 Jan 2019
TL;DR: This chapter elaborates on signal processing, feature extraction, their application in fault detection and obtaining its location with illustrations.
Abstract: Electrical power is the backbone for the survival of mankind and technological development. The vast span of power system network is expanding its tentacles to all possible corners of the world. Fault is an inevitable phenomenon in every such network. Determination of the type of fault and finding its location is essential to ensure uninterruptible supply of power to the consumers. Conventional methods of fault analysis become more complicated with the increase in complexity of network configuration. They involve modelling of sequence networks, complex mathematical calculations, data of line parameters, fault location and fault resistance. The magnitudes of fault resistance and location are not always accessible. Evolution of soft computational techniques has made the process of power system relaying smarter and faster. These techniques mainly involve signal processing tools like wavelet transform and S-transform. The current and voltage signals are observed at any position of a power system network, and they are processed to extract suitable features. Methods of feature extraction are, therefore, very significant. These features are subsequently analysed by various approaches/methods for determination of the faulty phase and estimating its location. The methods are based on artificial neural network, fuzzy logic, combination of neuro-fuzzy technique, decision tree-based classifier, support vector machine, and so on. The results obtained are promising and independent of the magnitudes of fault resistance, location and line parameters. The networks are simulated in high-end software like MATLAB, EMTP and PSCAD. This chapter elaborates on signal processing, feature extraction, their application in fault detection and obtaining its location with illustrations.
References
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TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.
Abstract: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems. >
6,180 citations
"Wavelet Transform Analysis to Appli..." refers methods in this paper
...The wavelet is derived from operations such as dilating and translating the mother wavelet, ψ, which must satisfy the admissibility criterion given by [7]:...
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CNET1
TL;DR: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes, which includes nonstationary signal analysis, scale versus frequency,Wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing.
Abstract: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown. >
2,945 citations
"Wavelet Transform Analysis to Appli..." refers background in this paper
...The CWT follows exactly these concepts and adds the simplification of the scale, where all the impulse responses of the filter bank are defined as dilated versions of a mother wavelet [10]....
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...There are several ways to introduce the concept of DWT, the main are the decomposition bands and the decomposition pyramid (or Multi-Resolution Analysis -MRA), developed in the late 1970s [10]....
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Book•
01 Jul 1988TL;DR: In this paper, the authors present a comprehensive, up-to-date account of computer relaying in power systems, based in part on the author's extensive experience in the field.
Abstract: This text/reference presents a comprehensive, up-to-date account of computer relaying in power systems, based in part on the author's extensive experience in the field. Provides background material on current relaying practices, and covers the mathematical foundations for relaying algorithms. Each chapter contains helpful illustrations, examples, and problems.
880 citations
01 Apr 1996
TL;DR: The wavelet transform was introduced as a method for analyzing electromagnetic transients associated with power system faults and switching as mentioned in this paper, and it is more appropriate than the familiar Fourier methods for the nonperiodic, wide-band signals associated with EM transients.
Abstract: The wavelet transform is introduced as a method for analyzing electromagnetic transients associated with power system faults and switching. This method, like the Fourier transform, provides information related to the frequency composition of a waveform, but it is more appropriate than the familiar Fourier methods for the nonperiodic, wide-band signals associated with electromagnetic transients. It appears that the frequency domain data produced by the wavelet transform may be useful for analyzing the sources of transients through manual or automated feature detection schemes. The basic principles of wavelet analysis are set forth, and examples showing the application of the wavelet transform to actual power system transients are presented.
550 citations
"Wavelet Transform Analysis to Appli..." refers background in this paper
...This characteristic presents a problem for traditional Fourier analysis because it assumes a periodic signal and a wide-band signal that require more dense sampling and longer time periods to maintain good resolution in the low frequencies [3]....
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TL;DR: A tutorial introduction to the theory, implementation and interpretation of the wavelet transform to the time-scale (time-frequency) analysis of discrete signals.
Abstract: Wavelets and wavelet transforms are a relatively new topic in signal processing. Their development and, in particular, their application remains an active area of research. This paper presents a tutorial introduction to the theory, implementation and interpretation of the wavelet transform. The paper concentrates on the application of the wavelet transform to the time-scale (time-frequency) analysis of discrete signals. Examples are given of the analysis of basic test signals and of an actual electrocardiographic signal.<
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159 citations