Machine Learning-Based Data Mining and Fault Identification for Condition Monitoring of Multi-Dimensional Anthropomorphic Wind Turbines

Due to the complex structure of multi-dimensional anthropomorphic wind turbine and the harsh operating environment, in order to reduce its maintenance cost, it has become a popular research hotspot to get fast and effective condition diagnosis and fault early warning through big data mining and analysis of wind turbine condition monitoring.The article clarifies the basic mechanism and typical faults of multi-dimensional anthropomorphic wind turbine, and after analyzing the characteristic frequency of faults on the transmission chain of multi-dimensional anthropomorphic wind turbine, it proposes the anomaly detection method of wind turbine condition monitoring data based on the auxiliary eigenvectors improved density clustering (DBSCAN), which realizes the accurate identification of different types of normal data, valid anomalous data containing fault information, and invalid anomalous data in the monitoring data.It realizes the accurate identification of different types of normal data, valid abnormal data with fault information, and invalid abnormal data in monitoring data.Subsequently, the actual historical data of the wind farm is used as the experimental data set to realize the identification of the operating status of the wind turbine.Finally, the DBN-Dropout wind turbine fault identification method is proposed by combining Deep Confidence Network and Dropout technique.The experimental results indicate that the recognition rate of this paper's model for nine faults is as high as 99.88%, and the superiority and accuracy of this paper's model in feature extraction and fault diagnosis are verified by comparing its performance with other fault detection models.