A Comprehensive Review of Machine Learning Applications in State Assessment and Control of Power Electronic Converters

DOI

10.2991/978-94-6463-602-4_3How to use a DOI?

Keywords

Machine Learning (ML); State Assessment and Control; Power Electronic Converters; Physics Informed Machine learning (PIML)

Abstract

World wise consumption of electrical energy has led to the integration of renewable energy resources into the power grids. These renewable energy resources are interfaced to power grids via power electronic converters because of their abilities of precise control, high efficiency, and sustainability, however, they are also nonlinear, they change the dynamics of the power grids, and require to be operated in aperiodic and unbalanced regime. They often trigger instability in the power grids via interaction through various phenomena including sub synchronous oscillations, intermittent nature of renewable energy resources, harmonic pollutions, and nonlinear dynamics of constant power loads, all requiring appropriate diagnosis and control methods. Several machine learning algorithms have shown remarkable achievements in the quantification of nonlinearity in power electronic converters. This paper describes the comprehensive reviews of various machine learning approaches in the field of state assessment and control of power electronic converters.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yasir Rizwan
AU  - Gulistan Raja
PY  - 2024
DA  - 2024/12/24
TI  - A Comprehensive Review of Machine Learning Applications in State Assessment and Control of Power Electronic Converters
BT  - Proceedings of the 4th International Conference on Key Enabling Technologies (KEYTECH 2024)
PB  - Atlantis Press
SP  - 14
EP  - 25
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-602-4_3
DO  - 10.2991/978-94-6463-602-4_3
ID  - Rizwan2024
ER  -