Machine Learning and Data-Driven Techniques for the Control of SmartPower 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 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era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods.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. (C) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.

Automated summary

This paper discusses the applications and potential benefits of smart power generation control technology, focusing on the use of machine learning and data-driven control techniques in monitoring, optimizing, and controlling power generation systems, as well as their ability to counter uncertainties. A comprehensive view from the regulation level to the planning level is provided, highlighting the potential value of ML and DDC techniques in improving visibility, maneuverability, flexibility, profitability, and safety.