Advanced Sensors Technologies in Monitoring, Operation and Maintenance of Wind Farms
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4373
Special Issue Editors
Interests: fault diagnosis; wind farms; artificial intelligence; machine learning; smart grid; power electronic technology
Interests: wind and solar dispatch; uncertainty modeling and analysis; energy storage/EV integration; optimization of integrated energy systems; integrated demand response; AI-driven power system analysis; federated learning
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; intelligent systems; wind farms, robotics; their applications in structural and machine health monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the advantages of clean, renewable energy, wind power has become one of the fastest-growing energy sources and one of the most economical solutions for electricity generation. As a consequence, the number and scale of offshore/onshore wind farms have been developing rapidly. There are already 350,000 wind turbines installed globally, with more than 650,000 MW of installed generation capacity. In light of climate change, demand continues to grow. However, to cope with challenges of availability and production, and to reduce costs for wind farm owners, it is considerably urgent to investigate and explore advanced intelligent techniques with respect to the monitoring, operation, and maintenance of wind farms, which will be beneficial for improving the safety and reliability of wind farm systems, reducing operation and maintenance costs, enhancing power generation efficiency, and speeding up the implementation of smart wind farms.
Advanced machine learning techniques have made tremendous progress in the monitoring, operation, and maintenance of wind farms; however, the current machine learning approaches are fully data driven, not capable of coping with the mass of monitoring and detection data, and with little domain knowledge in data processing, fault diagnosis and prediction, health monitoring and management, and maintenance. Additionally, there are often complicated fault mechanisms and fewer fault samples of all key components of wind farms, due to robust manufacturing technologies and continuous data monitoring early warning applied in this type of safety-critical power assets. As a result, current machine learning methods are often characterized by poor generalization performance and not providing untrustworthy diagnostic and predictive decisions for wind farm owners. In this context, it is significantly necessary to explore and examine advanced and innovative paradigms for synergizing new machine learning- and knowledge-driven techniques for data processing, condition monitoring, fault diagnosis, fault prediction, and maintenance decision-making such as deep Gaussian process, causal graphs, and graph neural network, from perfectives of both physical and data modelling. These paradigms provide the crucial potential to substantially strengthen the flexibility and trustworthiness of machine learning approaches, and further enhance their high-reliability applications in the monitoring, operation and maintenance of wind farms.
The main objective of this Research Topic is to provide a collective idea and novel methodology related to advanced machine learning in monitoring, operation and maintenance to enhance the high reliability and low maintenance cost of wind farms. We welcome all types of articles, including original research, reviews and communications, and seek contributions on the monitoring, operation and maintenance of wind farms based on advanced machine learning methods. Topics to consider include, but are not limited to, the following:
- Knowledge-guided machine learning.
- Machine learning for monitoring of wind farms.
- Advanced machine learning for operation and maintenance of wind farms.
- Advanced machine learning-based digital twin approach.
- Data and knowledge augmentation learning.
- Trustworthy decision-making support for operation and maintenance of wind farms.
- Advanced machine learning for prognostics and health management.
- Knowledge-guided few shot learning for fault diagnosis.
- Machine learning for encryption of communication systems.
- Machine learning for wind power forecasting.
- Physical model and knowledge-driven integrated learning.
Dr. Lei Kou
Prof. Dr. Yang Li
Prof. Dr. Teng Li
Guest Editors
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