Analysis of RMS Measurements Based on the Wavelet Transform

The monitoring of voltage and current signals in the electric power system (EPS) is a fundamental step for studies on power quality. Thus, the root mean square (RMS) value of a signal is one of the crucial quantities in the analysis of EPS. In the presence of distortions, voltage and current signals...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2021-12, Vol.32 (6), p.1588-1602
Main Authors: Coelho, Rodrigo de Almeida, Brito, Núbia Silva Dantas
Format: Article
Language:English
Subjects:
Control
Control and Systems Theory
Discrete Wavelet Transform
Electric filters
Electric potential
Electric power systems
Electrical Engineering
Electromagnetic wave filters
Engineering
Fourier transforms
Frequency analysis
Frequency ranges
Frequency response
Frequency variation
Harmonic distortion
Mechatronics
Robotics
Robotics and Automation
Signal monitoring
Voltage
Waveforms
Wavelet transforms
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Summary:The monitoring of voltage and current signals in the electric power system (EPS) is a fundamental step for studies on power quality. Thus, the root mean square (RMS) value of a signal is one of the crucial quantities in the analysis of EPS. In the presence of distortions, voltage and current signals are discrepant from the sinusoidal waveform, which motivates the monitoring of signals’ harmonic distortion. Therefore, it is necessary to perform a correct estimation of RMS values in each frequency component that composes the signals. This work presents a study aiming to evaluate the application of Stationary Discrete Wavelet Transform (SDWT) and Stationary Discrete Wavelet Packet Transform (SDWPT) for estimation of RMS values in different frequency ranges. From synthetic signals, the accuracy of the estimation of RMS values obtained via SDWT and SDWPT is analyzed. Additionally, the performance of SDWT and SDWPT was compared to Discrete Fourier Transform (DFT). The effects of mother wavelet choice are assessed from the frequency response of wavelet filters. Furthermore, the effects of the sampling frequency choice, magnitude variation, and noisy conditions on the estimation of RMS values are also evaluated. The results indicate the existence of discrepancies between estimated values via SDWT and SDWPT and exact values when the estimation is applied to individual frequency components. In contrast, as the DFT analyzes each frequency component individually, its performance was superior to that of the SDWT and SDWPT to estimate RMS values.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-021-00770-5