Power quality (PQ) is crucial parameter in determining the robustness and accuracy of any energy-metering algorithm. It immensely affects the performance of energy meters in terms of measurement accuracy. This study p...
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Power quality (PQ) is crucial parameter in determining the robustness and accuracy of any energy-metering algorithm. It immensely affects the performance of energy meters in terms of measurement accuracy. This study presents digital implementation of fast discrete Stockwell transform (FDST) with automatic scaling for accurate PQ-event detection (ED) and energymetering. Comparative analysis of FDST-based energy-metering algorithm is carried out with existing algorithms such as fast Fourier transform and filter-based design. The results demonstrate that FDST-based energy-metering algorithm surpasses existing algorithms in terms of accuracy, complexity, and adaptability under PQ events. In addition, new statistical features are proposed to classify and measure PQ events. The real-time implementation of the proposed FDST-based smart energy meter (SEM) is performed in national instruments (NI) laboratory virtual instrument engineering workbench (LabVIEW) and validated through field programmable gate array (FPGA)-in-the-loop using MATLAB and Altera Cyclone IV E FPGA board. The experimental results show satisfactory performance in terms of accuracy and resource utilisation. The effect of PQ events over energymetering is evaluated in terms of percentage loss in billing units. The features of the proposed SEM virtual instrument are real-time accurate PQ-ED, energymetering, loss evaluation, data logging, and remote control access.
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