Application of Machine Learning Methods for Recognition of Daily Patterns in Power Quality Time Series

Strunz, Elias; Zyabkina, Olga; Meyer, Jan

doi:10.1109/ichqp53011.2022.9808558

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Cited by 2 publications

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“…As it is clear, supervised learning techniques like linear regression, support vector machines, and neural networks are valuable tools for power electronics control and optimization. They enable the prediction of system behavior based on input data and the fine-tuning of system parameters to achieve specific goals in power electronics applications [42][43][44][45].…”

Section: Neural Networkmentioning

confidence: 99%

Review of Machine Learning Techniques for Power Electronics Control and Optimization

Bahrami,

Khashroum

2023

CRPASE

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In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integration of machine learning techniques assumes paramount significance. This article endeavors to present an extensive and comprehensive review of the machine learning techniques that find application in power electronics control and optimization. Through meticulous exploration, we aim to elucidate the profound potential of these methods in shaping the future of power electronics control and optimization.

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Section: Neural Networkmentioning

confidence: 99%