Itron Gmbh

Michael E Moore

Michel Gervais

Isabelle B Snyder

Daniel M Lakich

Brent Martin

Jocelyn Bilodeau

Signal filtering removes 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. 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.

Filtering techniques to remove noise from a periodic signal and IRMS calculations

Methodology and corresponding apparatus ensure the integrity of utility metering data during power outages while providing improved memory integrity. An initial step includes determining and storing an energy top value that is representative of metered energy, and also includes establishing an update pattern. Accumulated energy is then monitored until a predetermined energy threshold is crossed, at which point a selected state in the update pattern is stored into one of a plurality of energy increment cells. Such stored value can be validated at some point after the data read. The steps of monitoring accumulated energy and storing a selected value/state of the update pattern into additional energy increment cells continue until each of the plurality of energy increment cells is updated with a new value. Upon completion, the energy top value is updated with a new energy total and the process of cycling through each energy increment cell is repeated.

System and method for storing metering data while increasing memory endurance

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.

Filtering techniques to remove noise from a periodic signal and Irms calculations

A voltage acquisition circuit for sensing input voltage signals in each phase of an electrical service includes a non-active current transformer configuration that is operable over a wide input voltage range. The current transformer configuration includes primary and secondary windings, an input resistor and a burden resistor across which an output voltage is defined. The input resistor may be relatively large, such as on the order of about one M&#x3a9; and the transformer core may have a nanocrystalline core characterized by predictable magnetic properties versus varied transformer flux levels. This operational predictability facilitates phase compensation via digital filtering or otherwise for any phase shift induced between input and output voltages of the current transformer.

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Michael E Moore