Condition Monitoring and Machine Learning Strategies for Electrical Apparatus
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 17221
Special Issue Editors
Interests: High voltage electrical insulation; dielectric materials; Condition monitoring of electrical equipment; Transformer diagnostics; AIML Techniques
Special Issues, Collections and Topics in MDPI journals
Interests: Electromagnetic compatibility; Electrical engineering; Electrical insulation; Dielectric materials; Problems associated with the transport and distribution of electrical energy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This special issue is intended to expand the existing knowledge on advanced condition monitoring methodologies and inclusion of computational techniques for effective monitoring. The majority of the electrical apparatus are mostly involved with high voltages, high cost, and possible risk of failures. The failure of an electrical apparatus is majorly due to vulnerable operating conditions, insulation failures, electrical and thermal stresses. Thus it is essential and customary to adopt efficient condition monitoring techniques (online and offline) for the successful operation of the electrical power network. Starting from generating stations, grid parameters, distribution aspects, and utilization, condition monitoring is of very high engineering importance. Some of them are very complex (high dimensional/ ambiguity) and challenging to handle and make decision on prognosis. Thus, adopting artificial intelligence and machine learning (AIML) techniques, sensor technologies, advanced diagnostic approaches are potential avenues of research for future grid operations. We therefore invite contributions on technical developments, regular research problems, critical reviews, and industrial case studies from the electrical engineering communities. Studies pertinent to condition monitoring, insulation failures, intelligent monitoring ideas, and AIML for precise monitoring are invited.
Dr. U. Mohan Rao
Guest Editor
Prof. Dr. Issouf Fofana
Co-Guest Editor
Manuscript Submission Information
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Keywords
- Condition Monitoring (Online/Offline)
- Intelligent Monitoring Techniques
- Diagnostic Testings
- Sensors and Signal processing