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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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809921002708 |
| _version_ | 1818404685928726528 |
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| author | Li Sun Fengqi You |
| author_facet | Li Sun Fengqi You |
| author_sort | Li Sun |
| collection | DOAJ |
| 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era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods.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. |
| first_indexed | 2024-12-14T08:44:05Z |
| format | Article |
| id | doaj.art-b65f85a5c83243b6b2c87839a2f619e0 |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-12-14T08:44:05Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-b65f85a5c83243b6b2c87839a2f619e02022-12-21T23:09:14ZengElsevierEngineering2095-80992021-09-017912391247Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling PerspectiveLi Sun0Fengqi You1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and the Environment, Southeast University, Nanjing 210096, ChinaRobert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2095809921002708Smart power generationMachine learningData-driven controlSystems engineering |
| spellingShingle | Li Sun Fengqi You Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective Engineering Smart power generation Machine learning Data-driven control Systems engineering |
| title | 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_short | 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 |
| topic | Smart power generation Machine learning Data-driven control Systems engineering |
| url | http://www.sciencedirect.com/science/article/pii/S2095809921002708 |
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