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

• 作者: Li Sun Fengqi You
• 来源期刊: 工程（英文）
• 年，卷（期）: 2021,（9）
• 所属期刊栏目: AI Energizes Process Manufacturing—Review
• 页码范围: 1239-1247
• 页数: 9页
• 语种: 英文

摘要

Due to growing concerns regarding climate change and environmental protection,smart power genera-tion 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burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitor-ing,control,optimization,and fault detection of power generation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 gen-eration,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(abbre-viated as the"5-TYs"),respectively.Finally,an outlook on future research and applications is presented.