Lookup NU author(s): Yang Quek,
Dr Wai Lok Woo,
Dr Thillainathan Logenthiran
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2017.
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Detection of steady states and identification of small electrical loads in a household or office grid are important in efficient smart energy management. This paper proposes a method that combines two machine learning techniques, unsupervised K-means clustering, and supervised k-Nearest Neighbours classification techniques, to train a system that can effectively identify the low voltage DC electrical load, and at the same time detect whether it is in its steady state. This is done by comparing the features extracted from signatures of the electric current waveforms of equipment. The combination of K-means and kNN in the initialisation stage removes the need to know all the training elements beforehand, and thus, considerably simplifies the process. In the normal operation stage, kNN was used to identify the new unknown test element to the cluster that has the majority votes from its nearest neighbours. The centroids obtained from the K-means clustering aided in the determination of whether the system is in steady state. The method has been successfully implemented on a low voltage DC office grid, with commonly used office equipment.
Author(s): Quek YT, Woo WL, Logenthiran T
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2016 IEEE Region 10 Conference (TENCON)
Year of Conference: 2017
Online publication date: 09/02/2017
Acceptance date: 01/08/2016
Publisher: Institute of Electrical and Electronics Engineers
Library holdings: Search Newcastle University Library for this item