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Journal ArticleDOI

Exploring the power of wavelet analysis

TL;DR: Here, the authors describe how wavelets may be used in the analysis of power system transients using computer implementation.
Abstract: Wavelets are a recently developed mathematical tool for signal analysis. Informally, a wavelet is a short-term duration wave. Wavelets are used as a kernel function in an integral transform, much in the same way that sines and cosines are used in Fourier analysis or the Walsh functions in Walsh analysis. To date, the primary application of wavelets has been in the areas of signal processing, image compression, subband coding, medical imaging, data compression, seismic studies, denoising data, computer vision and sound synthesis. Here, the authors describe how wavelets may be used in the analysis of power system transients using computer implementation.
Citations
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Journal ArticleDOI
TL;DR: The fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors are introduced.
Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.

1,396 citations

Proceedings ArticleDOI
31 Aug 1998
TL;DR: In this article, the authors present a tutorial overview of induction motors signature analysis as a medium for fault detection, and introduce the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of inductive motors.
Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis (MCSA) which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state of the art" terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing.

612 citations


Cites methods from "Exploring the power of wavelet anal..."

  • ...To date, the primary application of wavelets has been mainly in the areas of signal processing and medical imaging [ 36 ]....

    [...]

Journal ArticleDOI
TL;DR: In this article, a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology is presented. And a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors is provided.
Abstract: Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30-40% of faults of induction motors are stator faults. This work presents a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology. It is aimed at providing a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors. A list of 183 research publications on the subject is appended for quick reference.

541 citations

Journal ArticleDOI
TL;DR: The literature for current applications of advanced artificial intelligence techniques in power quality, including applications of fuzzy logic, expert systems, neural networks, and genetic algorithms, are surveyed.
Abstract: Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

234 citations

Journal ArticleDOI
TL;DR: In this paper, the Hilbert-Huang method is presented with modifications, for time-frequency analysis of distorted power quality signals, and the empirical mode decomposition (EMD) is enhanced with masking signals based on fast Fourier transform (FFT), for separating frequencies that lie within an octave.
Abstract: The Hilbert-Huang method is presented with modifications, for time-frequency analysis of distorted power quality signals. The empirical mode decomposition (EMD) is enhanced with masking signals based on fast Fourier transform (FFT), for separating frequencies that lie within an octave. Further, the instantaneous frequency and amplitude of the constituent modes obtained by Hilbert spectral analysis are improved by demodulation. The method shows promising time-frequency-magnitude localization capabilities for distorted power quality signals. The performance of the new technique is compared with that of another multiresolution analysis tool, the S-transform-a phase corrected wavelet transform. Analysis on actual measurements of transformer inrush current from an existing laboratory setup is used to demonstrate this technique.

215 citations


Cites methods from "Exploring the power of wavelet anal..."

  • ...The wavelet transform is a mathematical tool proposed to identify modes of nonperiodic oscillations, as well as those that evolve in time [8]....

    [...]

References
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Journal ArticleDOI
01 Jun 1995
TL;DR: The mathematics have been worked out in excruciating detail, and wavelet theory is now in the refinement stage, which involves generalizing and extending wavelets, such as in extending wavelet packet techniques.
Abstract: Wavelets were developed independently by mathematicians, quantum physicists, electrical engineers and geologists, but collaborations among these fields during the last decade have led to new and varied applications. What are wavelets, and why might they be useful to you? The fundamental idea behind wavelets is to analyze according to scale. Indeed, some researchers feel that using wavelets means adopting a whole new mind-set or perspective in processing data. Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. Most of the basic wavelet theory has now been done. The mathematics have been worked out in excruciating detail, and wavelet theory is now in the refinement stage. This involves generalizing and extending wavelets, such as in extending wavelet packet techniques. The future of wavelets lies in the as-yet uncharted territory of applications. Wavelet techniques have not been thoroughly worked out in such applications as practical data analysis, where, for example, discretely sampled time-series data might need to be analyzed. Such applications offer exciting avenues for exploration. >

3,022 citations

Journal ArticleDOI
TL;DR: In this article, the basic ideas of discrete wavelet analysis are presented, and a variety of actual and simulated transient signals are then analyzed using the Discrete Wavelet Transform (DWT).
Abstract: Wavelet analysis is a new method for studying power system transients. Through wavelet analysis, transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. This paper presents the basic ideas of discrete wavelet analysis. A variety of actual and simulated transient signals are then analyzed using the discrete wavelet transform that help demonstrate the power of wavelet analysis.

297 citations