IoT Load Classification and Anomaly Warning in ELV DC Picogrids Using Hierarchical Extended ${k}$ -Nearest Neighbors

Abstract

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc picogrids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data, and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended ${k}$ -nearest neighbors (HE- ${k}$ NNs) technique that exploits the use of distance in ${k}$ NN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This article presents the successful implementation of the HE- ${k}$ NN technique in three different circuits: 1) lighting; 2) air-conditioning; and 3) multiple load dc picogrids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data.