No abstract

NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Executive SummaryUtility-scale wind turbines have historically experienced premature component failures, which subsequently increase the cost of energy. The majority of these failures are caused by faults in the drivetrain, led by the main gearbox. To understand the possible causes for gearbox failures and recommend practices for improvement, the National Wind Technology Center (NWTC), at the National Renewable Energy Laboratory (NREL), started a project called the Gearbox Reliability Collaborative (GRC). Condition Monitoring (CM) is one research area under the GRC. It is a method to assess a system's health, which enables proactive maintenance planning, reduces downtime and operations and maintenance costs, and, to some extent, increases safety.To understand the dynamic responses of wind turbine gearboxes under different loading conditions, the GRC tested two identical gearboxes. One was tested on the NWTC's 2.5 MW dynamometer and the other was first tested in the dynamometer, and then field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not catastrophic, the test gearbox was removed from the field and retested in the NWTC's dynamometer before it was disassembled. During the dynamometer retest, various condition monitoring systems, e.g., vibration and oil debris, collected data along with testing condition information. The vibrationbased condition monitoring system and the test condition data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project is to evaluate different vibration analysis algorithms used in wind turbine CM and determine whether typical practices are effective. With the involvement of both academic researchers and industrial partners, the Round Robin provides cutting edge research results to industry stakeholders.Under this project, the collected vibration and testing condition data, along with the test gearbox configuration information, were shared with partners who signed memoranda of understand...

No abstract

Wind turbines have historically had reliability issues, which subsequently increase the overall cost of energy. The majority of these issues are caused by faults in the drivetrain, led by the main gearbox. These issues are widespread, existing across all turbine sizes and manufacturers. One means to mitigate the detrimental effect of reliability issues is through condition monitoring. Condition monitoring is a method to assess a system's health; enabling proactive maintenance planning, reducing downtime, reducing operations and maintenance costs and, to some extent, increasing safety. In this report, vibration, acoustic emission (specifically stress wave), electrical signature, oil cleanliness, oil debris, and oil sample analysis condition monitoring techniques were investigated for two identical 750 kW wind turbine gearboxes, in both a dynamometer test cell and field installation. The two test gearboxes are referred to as Gearboxes 1 and 2 in this report. The strengths and weaknesses of the different techniques were assessed. The feasibility of using oil cleanliness monitoring to determine the length of the run-in procedure was investigated on both gearboxes. Both demonstrated that, to make the run-in process sufficient, longer run-in durations at each torque level may be needed as compared to the current standard run-in procedure of prescribed durations at each torque level. Without fully completing the run-in, surface roughness remains excessive leading to increased contact stresses when the gearbox is placed into service and potentially leading to premature failures. Gearbox 1 was installed in a turbine at the Ponnequin Wind Farm and, after 300 hours of operation, it experienced two oil loss events and excessive temperatures that caused damage to some of its internal components. The gearbox was subsequently removed and inspected. Since the damage to the teeth was not severe, gearbox 1 also was installed and retested in the National Wind Technology Center's (NWTC) dynamometer before it was disassembled. Gearbox 2 was tested only in the dynamometer and was undamaged. The results were compared between gearboxes for each monitoring technique and the technique itself was evaluated for its detection capability. Vibration, acoustic emission, and oil debris monitoring all demonstrated the capability of distinguishing between the healthy and damaged gearbox components. It was possible to identify which stage of the gearbox was damaged, but not exactly which component was damaged for some gears and bearings inside the gearbox. Electrical signature analysis did not show any indication of the gear teeth damage, most likely because the damage to the teeth was not severe. To detect dominant failure modes in a gearbox, a combination of vibration or acoustic emission and oil debris monitoring techniques is recommended. Each technique is sensitive to sensor location and even orientation, and maintenance alerts are specific to each component and damaged part. v Table of Contents Acknowledgements .

High operations and maintenance costs for wind turbines reduce their overall cost effectiveness. One of the biggest drivers of maintenance cost is unscheduled maintenance due to unexpected failures. Continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by eliminating unnecessary scheduled maintenance. A SCADA (Supervisory Control and Data Acquisition System) -data based condition monitoring system uses data already collected at the wind turbine controller. It is a cost-effective way to monitor wind turbines for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. Anomalous signatures due to a reported gearbox failure are identified from a set of original measurements including rotor speeds and produced power.