Abstract
Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind farms. On this basis, the present study is devoted to the formulation of reliable methodologies for the supervision of wind turbine bearings, which possibly can be integrated in the industrial practice. For this reason, this study is a collaboration between a company (ENGIE Italia), the University of Perugia and the Politecnico di Torino. The analysis is based on the exploitation of the data types which are available to wind farm managers from industrial control systems: SCADA (Supervisory Control And Data Acquisition) and TCM (Turbine Condition Monitoring). Due to the intrinsic sampling time difference between SCADA and TCM data (a few minutes the former, up to the millisecond for the latter), the proposed methodology is designed as multi-scale. At first, historical SCADA data are processed and the behavior of the oil filter pressure is analyzed for all the wind turbines in the farm: this provides preliminary advice for identifying presumably healthy wind turbines from those suspected of damage. A second step for the SCADA analysis is then represented by the study of the temperature trends of the bearings through a Support Vector Regression: the incoming damage is individuated from the analysis of the mismatch between measurements and estimates provided by the normal behavior model. Finally, the healthy units are selected as the reference and the faulty as the target for the analysis of TCM vibration data in the time domain: statistical features are computed on independent chunks of the signals and, using a Novelty Index, it was possible to distinguish the damaged wind turbines with respect to the reference ones. In light of the interest in application of the proposed methodology, good practice criteria in selecting and managing the data are discussed as well.
Highlights
Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind farms
Historical SCADA data are processed and the behavior of the oil filter pressure is analyzed for all the wind turbines in the farm: this provides preliminary advice for identifying presumably healthy wind turbines from those suspected of damage
Multi-scale wind turbine monitoring has become a standard in the wind energy practice: recent wind turbine installations are typically equipped of SCADA and TCM control systems
Summary
The widespread exploitation of wind turbines in mountainous areas or offshore poses issues of limited turbine accessibility and overall cost and complexity of maintenance; the relevance of these problems grows as the rotor size increases, as is a recent trend in wind energy technology [1,2].Early detection of gear and bearing damages [3,4] is a keystone for improving wind turbine availability and eventually decreasing the cost of energy: for example, in [5] it is estimated that at least 20% of the non-availability time of a wind turbine is caused by a gearbox failure.Multi-scale wind turbine monitoring has become a standard in the wind energy practice: recent wind turbine installations are typically equipped of SCADA and TCM control systems. Their simplicity has boosted their diffusion in the industry for real-world applications for, for example, temperature trend analysis [9,10,11,12]
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