Anomaly Warning and Fault Detection in DC Pico-grid with enhanced k-Nearest Neighbours Technique

Quek, Y. T., Woo, Wai Lok and Logenthiran, Thillainathan (2018) Anomaly Warning and Fault Detection in DC Pico-grid with enhanced k-Nearest Neighbours Technique. In: ISGT Asia 2018 - International Conference on Innovative Smart Grid Technologies, 22nd - 25th May 2018, Singapore.

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Abstract

The k-nearest neighbours (kNN) algorithm, which is usually used for classification, is presented in this paper to detect faults and trigger anomaly warnings in a single sensor multiple loads dc pico-grid. Anomalies warning is getting more attention in the recent years as it can used as a trigger for predictive maintenance, which is preferred over repair work after a fault detection. On top of performing its usual duty of load classification in the circuit during normal operation, the kNN algorithm is enhanced with 3 additional techniques to set 3 anomaly criteria for the triggering of alarm when the extracted features of the test object exhibit abnormal behaviours. The experiment is set in a dc pico-grid as there is a growing interest and demand in dc loads. Experiments with various anomalies show that the proposed enhanced algorithm can effectively detect anomalies and faults.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	anomaly warning, computational intelligence, dc pico-grid, fault detection, k-nearest neighbours
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	25 Mar 2019 13:02
Last Modified:	10 Oct 2019 21:03
URI:	http://nrl.northumbria.ac.uk/id/eprint/38530

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