Machine Learning-Assisted Advanced Power Converter Sensing Technologies and Health Management
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 15 May 2024 | Viewed by 1778
Special Issue Editor
Special Issue Information
Dear Colleagues,
Power-converter design is moving toward a new era, utilizing advanced sensors, computing techniques, optimization approaches, and machine learning. Such techniques have led to modern power converters that are more compact, lighter, and more efficient than ever before. Furthermore, with the high proliferation of converters in modern applications, the improvement of reliability and health management among power electronics has come to the forefront in research focus in recent years. Machine learning techniques play a vital role in this aspect.
This Special Issue on “Machine-Learning-assisted Advanced Power-Converter Sensing Technologies and Health Management” aims to collect the latest technical advancements and knowledge in research areas related to the subject. We invite researchers to submit original research and survey articles related to power-converter design and health management assisted by modern sensors and machine learning techniques. The sensors and the associated conditioning circuits could be used for the measurement or characterization of power-converter-related parameters, such as voltage, current, temperature, and vibration. This Special Issue also accepts related topics in power electronics, including, but not limited to, health analytics, reliability prediction, fault identification, prevention, and diagnosis, the Internet of Things (IoT), and energy harvesting.
Dr. Harish S. Krishnamoorthy
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.
Keywords
- sensing technologies in power converters
- machine-learning-assisted sensing and processing techniques
- power electronics reliability prediction using machine learning
- health management of power converters
- energy harvesting
- sensors for Internet of Things (IoT) in power conversion
- active fault identification, prevention, and diagnosis
