A
signal filtering technique is designed to remove the effects of a periodic, low-
frequency noise signal from a
signal of interest. A signal waveform is sampled at different points of a number of consecutive periodic
noise signal cycles and the collected samples are averaged to produce a corrected signal. The number of consecutive cycles in which samples are taken and averaged is inversely related to the signal amplitude such that as the
signal level decreases, the number of cycles examined increases. The technique is particularly applicable to periodic signals associated with the output of
Hall effect sensors in an electrical
metrology environment. Improved RMS calculations are obtained for filtering low-frequency
random noise from Hall sensors by averaging samples at different points of a signal cycle to create a composite desired signal cycle to facilitate other signal calculations. In a given
electricity utility meter incorporating
solid state circuitry, such
metrology RMS calculations may be implemented in a
metrology section of
solid state devices provided on printed circuit boards, such as utilizing programmable
integrated circuit components. By varying the number of cycles summed, the
algorithm will adapt to amplitude changes more quickly. By using time averaged samples to filter
random noise from the periodic
signal of interest, the overall requirements for complex filtering is reduced. Instead, the technique relies on buffering and averaging synchronized samples for a given number of line cycles, so that by increasing the buffer size, larger numbers of line cycles can be accumulated and the filter
cut-off frequency reduced.