This paper describes an innovative fuzzy-based adaptive approach to the metering of power and RMS voltage and current employing the genetic algorithm. The fuzzy-based adaptive metering engine adjusts the number of points per cycle to be processed and the location of these points based on the optimal fuzzy rules constructed bp the genetic algorithm to satisfy overall metering error criteria under different operating environment while minimizing the number of points actually employed in the metering computation. This results in a reduction in the metering computation effort which frees up the processor for other tasks such as communication or power quality measurements. The fuzzy-based adaptive metering algorithm has been implemented on a microcontroller-based power metering system which operates under a multi-tasking operating system which exploits the efficiencies achieved by the reduced metering rite. The fuzzy-based adaptive metering algorithm has been tested with a variety of actual and synthesized power system waveforms and the experimental evaluations have demonstrated excellent accuracy in the metered power system quantities.
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