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access icon openaccess Cyber–physical microgrid components fault prognosis using electromagnetic sensors

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 06/11/2017, Accepted 11/12/2018, Revised 21/10/2018, Published 13/12/2018

Higher operational requirements in cyber–physical microgrid system stress the electrical system and may push it to the edge of stability. Therefore, prognosis of the imminent failures is vital. Accessing stray electromagnetic waves of power components helps in power system protection and non-intrusive prognosis of electric components faults in a cyber–physical microgrid environment. This study implements a cyber–physical approach associated between the electromagnetic waves radiated by components in the microgrid and the communication structure. To verify the same, the entire system is implemented on a real-time lab-based microgrid environment. The major problem with the stray electromagnetic waves is receiving appropriate fields. This is resolved by placing magnetic coil antennas at optimal distances and monitoring the radiated electromagnetic waves and their harmonics. Quick response code recognition technique is used to recognise the source and its corresponding healthy mode while harmonic analysis through artificial neural network helps to find the type and origin of faults. This would be an artificial intelligence-enabled system which self-optimises and acts according to the patterns. The proposed monitoring system can be utilised in any cyber–physical microgrid system especially those located in extreme/remote areas.

Inspec keywords: power generation faults; harmonic analysis; magnetic sensors; neural nets; electromagnetic devices; power engineering computing; distributed power generation; fault diagnosis; cyber-physical systems; coils; QR codes; magnetic field measurement; electric sensing devices

Other keywords: electromagnetic sensors; stray electromagnetic wave radiation; artificial intelligence-enabled system; real-time lab-based microgrid environment; nonintrusive electric components fault prognosis; power system protection; quick response code recognition technique; cyber–physical microgrid components fault prognosis; harmonic analysis; cyber–physical microgrid environment; artificial neural network; magnetic coil antennas; electrical system; fault detection

Subjects: Mathematical analysis; Neural computing techniques; Electromagnetic device applications; Magnetic variables measurement; Distributed power generation; Sensing devices and transducers; Mathematical analysis; Power engineering computing

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