Data-driven Model Generation Process for Thermal Monitoring of Wind Farm Main Components through Residual Indicators Analysis
Résumé
Most of the Supervisory Control and Data Acquisition (SCADA) fault indicators proposed in the literature to detect a fault that induces a temperature increase of the physical components of a wind turbine are temperature residuals. Temperature residuals measure the difference between the current value of the temperature of a component and its prediction by a normal behavior model. In the literature, normal behavior models built from variable selection algorithms are ad-hoc models, designed to correctly predict the temperature of a specific component of a specific turbine of a specific wind farm. In practice, these models cannot be used to predict the temperature of a component of another turbine, let alone a turbine in a different wind farm, because the sensors used by wind turbine manufacturers are not the same. It is therefore impossible for an industrial wind farm manager to deploy a residual-based fault detection system on a wind farm scale. In order to make it possible to deploy these methods in an industrial context, we propose in this paper a methodology to automatically build linear models capable of predicting all temperatures of any component of any turbine of a given wind farm. The method is designed to be easy to implement, interpretable by the operator, and fast to execute to meet industrial constraints. The set of models obtained allows to build a network of thermal state indicators, which can be used for fault isolation. The method is applied to the monitoring of the thermal condition of a real French wind farm for illustration.
Origine | Fichiers éditeurs autorisés sur une archive ouverte |
---|