Special Issue "Condition Monitoring and Machine Learning Strategies for Electrical Apparatus"
Deadline for manuscript submissions: closed (30 November 2021).
Interests: high voltage electrical insulation; dielectric materials; condition monitoring of electrical equipment; transformer diagnostics; AIML techniques
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 in Energies: Selected Papers from 2016 IEEE International Conference on High Voltage Engineering (ICHVE 2016)
Special Issue in Energies: Engineering Dielectric Liquid Applications
Special Issue in Energies: Selected Papers from 2018 IEEE International Conference on High Voltage Engineering (ICHVE 2018)
Special Issue in Energies: Outdoor Insulation and Gas Insulated Switchgears
Special Issue in Energies: Selected Papers from 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020)
Special Issue in Energies: Advances in Power Transformers FRA Response for Detection of Windings Interturn Faults
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
Prof. Dr. Issouf Fofana
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Condition Monitoring (Online/Offline)
- Intelligent Monitoring Techniques
- Diagnostic Testings
- Sensors and Signal processing