Leveraging EMI signals for appliance detection and energy harvesting

Abstract

Electromagnetic interference (also known as EMI) is a byproduct of high-speed switching circuits used inside most of present-day electrical and electronic appliances. EMI propagates through conduction along the power lines and through radiation to limited distances. Due to its intrusive nature, EMI signals are generally suppressed or filtered out. Despite this, these signals are fairly ubiquitous. Hence, we explore the possibility of leveraging the weak EMI signals for two applications - appliance detection and energy harvesting. There has been increased research, in recent years, in appliance detection for nonintrusive load monitoring(NILM). NILM facilitates consumers with direct energy feedback, information regarding daily activities, and supports data-driven load scheduling for realizing the long-term goal of optimization of energy consumption in buildings. Traditionally, appliance detection has relied on low-frequency smart meter data. How-ever, in current literature, NILM has been unsuccessful in identifying many information technology loads - such as laptops, desktop computers, modems, and projectors- due to their complex time-varying power consumption patterns. In our thesis, we have investigated the use of conducted and radiated EMI, arising from the switching circuits within these loads, as unique and time-invariant features for detection and classification. Differential mode (DM) conducted EMI signals were first proposed in 2010 as possible features for identifying appliances having complex power consumption patterns. However, these EMI signals were not robustly characterized to ascertain their effectiveness in real world scenarios. In my thesis, we conducted an in-depth study of DMEMI signals through both measurements and simulations for 24 different appliances. Our studies showed that the performance of DM EMI is impacted significantly by the power line impedance, the filters present in the switching power supply circuitry of neighboring appliances on a common power line and power line harmonics. Based on our findings with DM EMI, our follow-up work proposed common-mode(CM) conducted EMI for appliance detection. CM EMI originates from capacitive coupling from the switching circuitry and flows along the earth conductor. Hence, the signal is not affected by power line harmonics. Also, most appliances are not fitted with common mode chokes because of which the signals from multiple appliances do not interfere with each other. Hence, the CM EMI is a far more robust feature for appliance detection. In order to experimentally test our hypothesis, we designed an EMI sensor to simultaneously monitor both DM and CM EMI from appliances. We evaluated the detection performance, across multiple instances of five commonly used electronic appliances typically found in office setups. We used statistical features de-rived from the histograms of measured EMI signals to differentiate across the various classes of appliances. We found that the CM EMI indeed serves as a superior feature, having higher detection accuracy of 87% in comparison to lower accuracy of 45% in the case of DM EMI. Expanding on this line of work, we envision CM EMI data to be combined with instantaneous, low-frequency power data gathered from smart meters to provide actionable insights to energy stakeholders. Along with conducted EMI, we also explored radiated emissions (also known as RFI) from appliances with an end goal of providing personalized energy apportionment(PEA). PEA is a process of attributing energy consumption to individual stakeholders in a shared space. As radiated emissions can propagate as far as 30cm, they can be leveraged using a wearable sensing device for mapping appliance usage to the instantaneous power data from smart meters. In our study, we characterized RFI from 10 electrical and electronic appliances, in multiple test scenarios, at variable distances. Our test setup consisted of custom-off-the-shelf components like software defined radio and ultra-wideband antennas. We found that a simple peak finder algorithm yielded 72% accuracy for detecting these appliances using RFI signals. Taking our initial exploration with EMI signals one step further, we employed low-frequency stray emissions from AC power lines for energy harvesting. Energy harvesting is a process of scavenging energy from ambient physical sources - such as mechanical load, vibrations, temperature gradients, and light - to support battery-less low-power sensing in the nW-mW range. Since the advent of cyber-physical systems and the internet of things, energy harvesting has been a topic of interest. However, the intermittent nature of existing natural sources restricted the applications of energy harvesting. In this thesis, we leveraged the ubiquitous and continuous nature of stray electric fields from power lines, for facilitating 24x7 energy harvesting for long-term, self-powered deploy and forget sensor networks. Stray electric field signals do not require an isolating wire bundle or an active appliance for harvesting, unlike stray magnetic field signals. We proposed a novel capacitive energy harvester (CapHarvest) with an ultra-low-power management circuit connected to the harvesting electrodes to effectively gather energy from this nano-watt source. Furthermore, we demonstrated the efficiency of our circuit for powering two applications. The first application, called Appliance Tag, is a new stick-on sensing system which monitors appliance state using stray magnetic field signals present around the power line. The second application, called Farm Check, monitors all the ambient physical parameters like temperature, light intensity, and relative humidity for vertical farming applications. This thesis paves a new dimension of sensing and repurposing the otherwise ignored ubiquitous EMI signals for appliance detection and energy harvesting to support the long-term goal of energy sustainability. In the future, the blend of simultaneous sensing and energy harvesting - as demonstrated with Cap Harvest - may enable more such exciting applications.