Abstract
This study presents a novel method to detect and classify power quality disturbances using wavelets. The proposed algorithm uses different wavelets each for a particular class of disturbance. The method uses wavelet filter banks in an effective way and does multiple filtering to detect the disturbances. A qualitative comparison of results shows the advantages and drawbacks of each wavelet when applied to the detection of the disturbances. This method is tested for a large class of test conditions simulated in MATLAB. Power quality monitoring together with the ability of the proposed algorithm to classify the disturbances will be a powerful tool for the power system engineers.
Highlights
Electric power quality is an important issue in power systems nowadays .The demand for clean power has been increasing in the past several years
An input signal having all the five disturbances simulated in MATLAB considered as Case (i) is given as input to the detection algorithm
In the output of the detection algorithm for case (i), Pulse amplitude= 10 indicates the presence of voltage swell
Summary
Electric power quality is an important issue in power systems nowadays .The demand for clean power has been increasing in the past several years. A method of detecting power quality disturbances based on neural networks and wavelets has been proposed[7]. The fundamental component is removed using wavelets and the remaining signal corresponding to disturbances is processed and given as input to ANN. This paper presents novel algorithm, which overcomes all these difficulties and can accurately detect and classify the disturbances present in the Determine N filter outputs from wavelet filter bank, where N
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
