Técnicas de aprendizaje automático en el diagnóstico de aerogeneradores

Abstract

The Mexican Center for Innovation in Wind Energy (CEMIE-Eólico) designed a wind turbine diagnostic system based on turbine behavior models using the signals of the Supervisory Control and Data Acquisition system (SCADA). The system provides a pattern of variables that exhibit abnormal behavior in the presence of a fault. The patterns are formed with the detection of the abnormal behavior of the variables during a time window in which the failure manifests itself. This paper presents the application of machine learning techniques for the identification of faults in wind turbines after the diagnostic system. The training and validation data were obtained from the simulation of six different faults in the wind turbine using the Mexican Wind Machine (MEM) designed at the National Institute of Electricity and Clean Energy (INEEL). The diagnostic system was applied, profiles of abnormal behavior were generated and experiments were carried out for the multiclass classification of fault patterns using the "Random Forest" algorithm. Finally, the algorithm performance was evaluated using accuracy and precision metrics achieving 91% in the classification of patterns to identify the root failure.