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Wavelet-Based Sparse Representation of Waveforms for Type-Testing of Static Electricity Meters

Lodetti, S; Ritzmann, D; Davis, P; Wright, P; Van den Brom, H; Marais, Z; ten Have, B (2022) Wavelet-Based Sparse Representation of Waveforms for Type-Testing of Static Electricity Meters. IEEE Transactions on Instrumentation and Measurement, 71. 9001010

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Abstract

This article presents a strategy for the description of new test waveforms for static electricity meters to be included in international standards. The need of extending the existing standardization frame arises from several recent studies that have reported conducted electromagnetic interference problems of type-approved static electricity meters, resulting in significant errors in the measured electricity consumption. The proposed method is based on discrete wavelet transform and allows for a compact and parsimonious representation of test waveforms, suitable for inclusion in standards. Very few wavelet parameters are concentrating the relevant information to accurately reproduce all the characteristics that the meters need to be tested against. The same parsimonious description cannot be performed with the current practices based on Fourier transform methods since the new test signals need to be highly non-sinusoidal. The discrete wavelet transform is proposed as a more effective tool to sparsely describe the most relevant waveform features. The effect of different discrete wavelet transform decomposition settings on compactness and reconstruction accuracy is studied using suitable metrics. Finally, results from experimental validation with several different waveforms are presented to demonstrate that the error-inducing features can be preserved using only 0.1% of the original signal information.

Item Type: Article
Keywords: Data compression, discrete wavelet transform, electromagnetic interference (EMI), metering errors, static energy meters, waveform model.
Subjects: Electromagnetics > Electrical Measurement
Divisions: Electromagnetic & Electrochemical Technologies
Identification number/DOI: 10.1109/TIM.2021.3128963
Last Modified: 10 Nov 2022 11:41
URI: http://eprintspublications.npl.co.uk/id/eprint/9583

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