Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient...

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Autores principales:	Li Sun, Fengqi You
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Smart power generation Machine learning Data-driven control Systems engineering Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b65f85a5c83243b6b2c87839a2f619e0
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spelling	oai:doaj.org-article:b65f85a5c83243b6b2c87839a2f619e02021-11-14T04:32:15ZMachine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective2095-809910.1016/j.eng.2021.04.020https://doaj.org/article/b65f85a5c83243b6b2c87839a2f619e02021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095809921002708https://doaj.org/toc/2095-8099Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the “5-TYs”), respectively. Finally, an outlook on future research and applications is presented.Li SunFengqi YouElsevierarticleSmart power generationMachine learningData-driven controlSystems engineeringEngineering (General). Civil engineering (General)TA1-2040ENEngineering, Vol 7, Iss 9, Pp 1239-1247 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Smart power generation Machine learning Data-driven control Systems engineering Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Smart power generation Machine learning Data-driven control Systems engineering Engineering (General). Civil engineering (General) TA1-2040 Li Sun Fengqi You Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
description	Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the “5-TYs”), respectively. Finally, an outlook on future research and applications is presented.
format	article
author	Li Sun Fengqi You
author_facet	Li Sun Fengqi You
author_sort	Li Sun
title	Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
title_short	Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
title_full	Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
title_fullStr	Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
title_full_unstemmed	Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
title_sort	machine learning and data-driven techniques for the control of smart power generation systems: an uncertainty handling perspective
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/b65f85a5c83243b6b2c87839a2f619e0
work_keys_str_mv	AT lisun machinelearninganddatadriventechniquesforthecontrolofsmartpowergenerationsystemsanuncertaintyhandlingperspective AT fengqiyou machinelearninganddatadriventechniquesforthecontrolofsmartpowergenerationsystemsanuncertaintyhandlingperspective
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Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective

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