Fuzzy-based adaptive digital power metering using a genetic algorithm

This paper describes an innovative, fuzzy-based, adaptive approach to the metering of power and rms voltage and current employing a genetic algorithm. The fuzzy-based adaptive metering engine adjusts the number of points per cycle to be processed and the location of these points. Adjustments are based on the optimal fuzzy rules constructed by a genetic algorithm to satisfy overall metering-error criteria under different operating environments 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 that employs a multitasking operating system which exploits the efficiencies achieved by the reduced metering rate. 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