A review of artificial intelligence applications in wind turbine health monitoring

ABSTRACT

Wind energy is a promising renewable source, necessitating effective monitoring of wind turbine (WT) conditions for reliable and cost-effective energy production, amidst environmental challenges. Condition monitoring of WTs employs traditional methods, signal processing, and emerging artificial intelligence (AI) approaches. AI-driven techniques excel in data-driven decision-making, addressing big data challenges in condition monitoring. This review paper presents a comprehensive overview of all streams of condition monitoring associated with WT, offering detailed insights into the related tasks. It also provides details on AI-based approaches and their application in executing various tasks within condition monitoring for WT. Finally, the study summarises the current trends, advantages, and disadvantages of AI-based techniques for real-world decision making in condition monitoring. This systematic review covers fundamentals to future developments in AI-driven approaches in condition monitoring for WT, serving as a valuable resource for readers and novice researchers in this field.

KEYWORDS:

• Wind turbine
• condition monitoring
• signal processing
• artificial intelligence
• machine learning
• big data

Acknowledgement

The authors thank VIT for providing ‘VIT RGEMS SEED GRANT’ for carrying out this research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data

Data sharing not applicable – no new data generated.

Additional information

Funding

This work was supported by VIT RGEMS SEED GRANT: [Grant Number SCOPE07].