DC appliance classification and identification using k-Nearest Neighbours technique on features extracted within the 1st second of current waveforms

Quek, Yang Thee, Woo, Wai Lok and Logenthiran, T. (2015) DC appliance classification and identification using k-Nearest Neighbours technique on features extracted within the 1st second of current waveforms. In: EEEIC 2015 - 15th IEEE International Conference on Environment and Electrical Engineering, 10th - 13th June 2015, Rome, Italy.

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Abstract

The commonly used identification techniques for appliances in a household are usually performed on the AC power supply side. However, as more household appliances and gadgets are now being DC powered, it is more accurate to perform the measurement and identification on the DC demand side. In addition, the AC identification method is not applicable for the DC household-grid. This paper discusses the application of a computational intelligence technique, k-Nearest Neighbours, to classify and identify DC appliances in a low voltage DC household through their 1st second of DC demand-side waveforms, sampled at 500Hz. Voltage and current waveforms were collected from an experiment conducted using this technique and it has been observed from the data collected that DC appliances generate unique current waveforms, similar to signatures, during the 1st second of operation. This time window can be spilt further into an inrush current stage and a steady-state stage. Two primary features and three secondary features of the waveforms were extracted and employed as attributes in the kNN technique, which was successfully used to classify and identify three appliances: a Peltier technology fridge, LED lights and a DC motor fan.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Direct Current, Load classification, Appliance recognition, Machine Learning, kNN
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	05 Apr 2019 11:03
Last Modified:	10 Oct 2019 20:34
URI:	http://nrl.northumbria.ac.uk/id/eprint/38792

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