Condition Monitoring for Renewable Energy Systems, Volume II

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Yusen He

Grinnell College

Alan Wai Hou Lio

Technical University of Denmark

Tinghui Ouyang

National Institute of Advanced Industrial Science and Technology (AIST)

Xun Shen

Osaka University

Shuang Zhao

Hefei University of Technology

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This Research Topic is Volume II of a series. The previous volume, can be found here: Condition Monitoring for Renewable Energy Systems

The utility of renewable energy resources, such as wind and solar, has grown rapidly throughout the globe in the past few decades. They convert the green resources into the usable energy which accounts for a significant portion of supply in the grid network. However, the energy converters such as wind turbines, solar panels, water dam, biofuel combustors, and other power supply infrastructures are usually complex systems and are assembled with various kinds of components. This system complexity may easily lead to system failure via different operation conditions. Thus, the condition monitoring for the whole renewable energy system is a challenging task.

Recently, sensors and related measurement technologies enabled huge progress in the research topics of condition monitoring in both online and offline. The application of data-driven approaches using machine learning and AI has offered many innovative solutions to the condition monitoring issues surrounding the renewable energy system.

The main objective of this Research Topic is to invite high-quality papers written by researchers and experts from all over the globe. The topics can cover techniques related to real-time online & offline condition monitoring, components failure prediction, system vulnerability analysis, hazard prevention, IoT based condition monitoring, diagnosis and prognosis, and others in renewable energy systems and grid networks.

Multiple aspects of the energy system applications can be included in the list of topics. The topics of interest include, but not limited to:

• Statistical anomaly analysis

• Data mining techniques

• Data cleaning and preprocessing

• Deep-learning theories and applications

• Deep reinforcement learning

• Graph theory and applications

• Real-time condition monitoring techniques

• Fatigue analysis

• Wind, solar, and hydropower system analysis

• Big data and cloud computing.

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Editors

Yusen He

Grinnell College

Alan Wai Hou Lio

Technical University of Denmark

Tinghui Ouyang

National Institute of Advanced Industrial Science and Technology (AIST)

Xun Shen

Osaka University

Shuang Zhao

Hefei University of Technology

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Frontiers in Energy Research

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