Smart Sensing of Loads in an Extra Low Voltage DC Pico-Grid Using Machine Learning Techniques

Y. T. Quek, W. L. Woo, T. Logenthiran

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Aiming at the rising number of dc appliances and the growing interest in their monitoring systems, this paper describes the injection of intelligence into dc pico-grids that are made up of “dumb” appliances and loads. Due to reality of economic, dc appliances and loads are usually low in cost and lack intelligence and communication features for effective monitoring and management. This paper proposes a smart sensor design for dc pico-grid with the use of a single sensor multiple loads and states detection in monitoring the “dumb” appliances. This eliminates the need to have intelligence and communication features for every appliance. With the smart sensor, several such smart dc pico-grids can be bundled into bigger scale of smart nano-grid or micro-grid. In addition to knowing how much energy or power the pico-grid is using, the smart sensor also provides load disaggregation and state-change detection. The states of the loads can be learned and detected via the signatures and features obtained from the transient state or the steady state of the entire grid’s current waveform. Computational intelligence techniques, $k$ -nearest neighbors, $K$ -means clustering, and other algorithms are used in the system for loads classification and state-change detection. Working together with the software in the smart sensor is hardware implementation of low cost operational amplifiers and logic gates; these hardware help to share the burden on the controller and release resources for the controller to perform more advanced processes. Experimental results are presented to demonstrate the operation of the smart sensor in dc pico-grid.
Original languageEnglish
Pages (from-to)7775-7783
Number of pages9
JournalIEEE Sensors Journal
Volume17
Issue number23
Early online date6 Jul 2017
DOIs
Publication statusPublished - 1 Dec 2017

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