Cyber–physical microgrid components fault prognosis using electromagnetic sensors

• Main Authors: Tanushree Agarwal, Payam Niknejad, Abolfazl Rahimnejad, M.R. Barzegaran, Luigi Vanfretti
• Format: Article
• Language: English
• Published: Wiley 2018-12-01
• Series: IET Cyber-Physical Systems
• Subjects: distributed power generation; harmonic analysis; electromagnetic devices; neural nets; fault diagnosis; power generation faults; cyber-physical systems; coils; magnetic sensors; magnetic field measurement; electric sensing devices; QR codes; power engineering computing; artificial intelligence-enabled system; cyber–physical microgrid components fault prognosis; electromagnetic sensors; electrical system; power system protection; cyber–physical microgrid environment; real-time lab-based microgrid environment; stray electromagnetic wave radiation; nonintrusive electric components fault prognosis; fault detection; magnetic coil antennas; quick response code recognition technique; artificial neural network
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5043

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.