Single Point Conducted EMI Sensor With Intelligent Inference for Detecting IT Appliances

Abstract

Electrical grids need to embed smartness not just at the generation and distribution side but also at the consumption side. Specifically, in regard to office buildings, information technology (IT) loads such as desktops and printers, operating in non-working hours, can lead to significant energy wastage. Detailed understanding and quantification of this wastage can lead to motivational insights for reduction in this wastage. However, it is impractical to monitor such a large number of loads individually. In this paper, we propose a single point smart sensor to detect and track the operation of these IT appliances. Existing methods, based on state-of-the art sensors, have been ineffective at detecting IT loads that have time-varying power consumption patterns. Our proposed sensor detects IT loads using their common mode electromagnetic emissions (CM EMI) injected on the grid. The sensor is low cost, portable, and built using commercial off-the-shelf components. We use a nearest neighbor-based classification algorithm on the statistical features extracted from histograms of the measured CM EMI. Experimental evaluations carried out with multiple instances of commonly found IT appliances display up to 87% detection accuracy, thus validating the real world applicability of our proposed system.