Open AccessReduction of a noise influence with sinusoidal signal meters
Author(s) -
K. Yu. Solomentsev,
V. I. Lachin,
Д. А. Плотников,
V. E. Samtchenko,
А. А. Ховпачев,
Ya K. Solomentsev
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1582/1/012082
Subject(s) - mathematics , equidistant , signal (programming language) , trigonometric functions , noise (video) , sine wave , polynomial , signal transfer function , acoustics , sine , mathematical analysis , sampling (signal processing) , control theory (sociology) , algorithm , analog signal , digital signal processing , voltage , computer science , electronic engineering , physics , filter (signal processing) , electrical engineering , engineering , geometry , control (management) , artificial intelligence , image (mathematics) , programming language
The purpose of the article is to suggest digital processing methods of sinusoidal signal measurements data as well as compensation of sinusoidal noises. This kind of task arises, for example, in implementation of feeder control of electrical power networks, in which an additional ELF voltage source is included between the controlled network and the ground. A leakage current measuring device for this frequency should be installed on each feeder. The suggestion is to use averaging of the sinusoidal signal over several periods to reduce the effect of random interference. For this purpose, several periods of the signal are stored in digital form, measured after equidistant moments of time. Time sampling frequency is selected at least an order of magnitude higher than the frequency of the signal being measured. Then the values “bound” to a particular signal phase are averaged. In addition, it was proposed to use the approximation of the obtained points by a trigonometric polynomial. A series of averaged values corresponding to one period are approximated for this purpose by the sum of the constant component as well as the cosine and sinusoidal components. For approximation, the least squares method is used, which provides the minimum discrepancy between the measured values and the analytical function. The measurement result is presented in a complex form, i.e. as a sum of cosine (actual) and sinusoidal (imaginary) components. As a result, it is possible to isolate oneself from noise and measure the sinusoidal signal with high precision.