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The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.
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A Safety‐Aware Framework for Offshore Wind Turbine Maintenance
In this paper, we suggest a framework for determining the best operation and maintenance strategies for offshore wind turbines. The framework takes into account both quantitative and qualitative data gathered from the wind turbines. The proposed framework consists of a simulation‐optimization approach for designing, planning, and scheduling maintenance operations for offshore wind farms and finding the optimal intervention solution for minimizing costs while keeping a high availability of wind turbines and guaranteeing safety standards for workers. Several parameters and constraints are addressed to account for the realistic complexity of the problem, such as weather conditions, resource cost, and maintenance duration. A numerical case study focusing on offshore wind turbine blade maintenance is presented to demonstrate the implementation of the proposed framework. The example simulates realistic defect progression scenarios, stratified by severity level, and incorporates empirically grounded estimates of failure rates, repair costs, technician requirements, and vessel logistics. The study illustrates how the simulation‐optimization approach integrates economic considerations, resource constraints, and safety risk factors to support data‐informed maintenance scheduling decisions under uncertainty.
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- Award ID(s):
- 2230630
- PAR ID:
- 10662995
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Wind Energy
- Volume:
- 28
- Issue:
- 12
- ISSN:
- 1095-4244
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/we.70065
