A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine Generators

Abstract

Changes in sensor measurement parameters of wind turbine SCADA systems usually do not provide reliable early alarms. To detect early faults or abnormal conditions of wind turbine generator components, a wind turbine generator condition monitoring framework based on the fusion of cascaded SAE abnormal condition monitoring and LightGBM abnormal condition classification is proposed. The framework consists of two parts. The first part is a strong anti-interference cascade SAE anomaly condition monitoring method considering that early anomalies are easily flooded. The cascade SAE is trained with polynomial features and original features. The isolated forest is used to determine the alarm threshold of reconstruction error between the input and output of the cascade SAE. The operating condition of the wind turbine is judged by comparing the magnitude between the reconstruction error and this threshold. The second part is the anomaly condition classification based on LightGBM. The optimal parameters of LightGBM are searched by Bayesian optimization to build a LightGBM multi-classification anomaly condition classification model. The results of the case study show that the proposed condition monitoring has high anomaly recognition capability: the cascaded SAE method has strong anti-interference properties and can capture the early abnormal conditions of wind turbine generators; LightGBM has a faster training speed than other classifiers with guaranteed abnormality classification accuracy.