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Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring

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Pubblicato in:PLoS One vol. 20, no. 9 (Sep 2025), p. e0331213
Autore principale: Li, Renjie
Altri autori: Lu, Xiangxing, Zhao, Jizhang, Chen, Weibing, Huanwei Wei, Liu, Cong
Pubblicazione:
Public Library of Science
Soggetti:
Wind power
Accuracy
Performance evaluation
Wind farms
Concrete
Wind turbines
Machine learning
Standard scores
Monitoring
Learning algorithms
Strain gauges
Turbines
Data integrity
Renewable energy
Missing data
Data loss
Axial forces
Interpolation
Root-mean-square errors
Sensors
Neural networks
Onsite
Data collection
Engineering
Anomalies
Robustness (mathematics)
Structural reliability
Real time
Statistical methods
Environmental
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Abstract:In engineering structure performance monitoring, capturing real-time on-site data and conducting precise analysis are critical for assessing structural condition and safety. However, equipment instability and complex on-site environments often lead to data anomalies and gaps, hindering accurate performance evaluation. This study, conducted within a wind farm reinforcement project in Shandong Province, addresses these challenges by focusing on anomaly detection and data imputation for weld nail strain, anchor cable axial force, and concrete strain. We propose an innovative iterative rolling difference-Z-score method for anomaly detection and a machine learning-based imputation framework combining linear interpolation with LightGBM. Experimental results show that the iterative rolling difference-Z-score method detects single-point and clustered anomalies with a Z-score threshold of 4, achieving robust performance even with 80% data loss. The imputation framework maintains low mean squared error (MSE) of 0.0214–0.0227 and root mean squared error (RMSE) of 0.14–0.15 for continuous missing data scenarios (60–200 points), with reliable reconstruction up to 50% data loss. This research provides a robust solution for ensuring the precision and integrity of wind farm monitoring data, enhancing long-term structural reliability in renewable energy applications.
ISSN:1932-6203
DOI:10.1371/journal.pone.0331213
Fonte:Health & Medical Collection