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Title	Machine Learning In Power Electronics For Smart Cities
ID_Doc	35995
Authors	Haque A.; Bharat K.V.S.; Zehgeer F.S.; Malik A.
Year	2024
Published	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things
DOI	http://dx.doi.org/10.1201/9781032669809-12
Abstract	The functionality and the development of a smart city can be improved with the applications of the machine learning tools in power electronics. Smart energy management, smart transportation, smart lightning and heating, smart power generation, and smart grid are the some of the building blocks of a smart city, and power electronics plays a central role in all these smart functions. For the reduction in the carbon footprint, the electrification of the transportation system and power generation from the renewable energy such as the solar energy, wind energy has been encouraged in a smart city. Power electronic converters like the DC-DC converters and rectifiers are the interface between the vehicle and the smart gird. For the efficient and desired execution of all such smart city applications machine learning has aided in the design, control, and maintenance of the power electronic converter. The much more efficient and superior control along with the remaining useful life prediction and fault diagnosis of the various power electronic converters has been achieved with the advent of various machine learning methods. Machine learning techniques in the power electronics have been extensively exploited so as to fulfill all the necessary requirements for smart city development. © 2024 selection and editorial matter, Binbing Yu and Kristine Broglio.
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