To benchmark the current U.S. wind turbine fleet reliability performance and identify the major contributors to component-level failures and other downtime events, the Department of Energy funded the development of the Continuous Reliability Enhancement for Wind (CREW) database by Sandia National Laboratories. This report is the third annual Wind Plant Reliability Benchmark, to publically report on CREW findings for the wind industry. The CREW database uses both high resolution Supervisory Control and Data Acquisition (SCADA) data from operating plants and Strategic Power Systems ORAPWindª (Operational Reliability Analysis Program for Wind) data, which consist of downtime and reserve event records and daily summaries of various time categories for each turbine. Together, these data are used as inputs into CREWs reliability modeling. The results presented here include: the primary CREW Benchmark statistics (operational availability, utilization, capacity factor, mean time between events, and mean downtime); time accounting from an availability perspective; time accounting in terms of the combination of wind speed and generation levels; power curve analysis; and top system and component contributors to unavailability.
The Blade Reliability Collaborative (BRC) was started by the Wind Energy Technologies Department of Sandia National Laboratories and DOE in 2010 with the goal of gaining insight into planned and unplanned O&M issues associated with wind turbine blades. A significant part of BRC is the Blade Defect, Damage and Repair Survey task, which will gather data from blade manufacturers, service companies, operators and prior studies to determine details about the largest sources of blade unreliability. This report summarizes the initial findings from this work.
This report addresses the general data requirements for reliability analysis of fielded wind turbines and other wind plant equipment. The report provides a rationale for why this data should be collected, a list of the data needed to support reliability and availability analysis, and specific data recommendations for a Computerized Maintenance Management System (CMMS) to support automated analysis. This data collection recommendations report was written by Sandia National Laboratories to address the general data requirements for reliability analysis of operating wind turbines. This report is intended to help develop a basic understanding of the data needed for reliability analysis from a Computerized Maintenance Management System (CMMS) and other data systems. The report provides a rationale for why this data should be collected, a list of the data needed to support reliability and availability analysis, and specific recommendations for a CMMS to support automated analysis. Though written for reliability analysis of wind turbines, much of the information is applicable to a wider variety of equipment and analysis and reporting needs. The 'Motivation' section of this report provides a rationale for collecting and analyzing field data for reliability analysis. The benefits of this type of effort can include increased energy delivered, decreased operating costs, enhanced preventive maintenance schedules, solutions to issues with the largest payback, and identification of early failure indicators.
Operators interested in improving reliability should begin with a focus on the performance of the wind plant as a whole. To then understand the factors which drive individual turbine performance, which together comprise the plant performance, it is necessary to track a number of key indicators. Analysis of these key indicators can reveal the type, frequency, and cause of failures and will also identify their contributions to overall plant performance. The ideal approach to using data to drive good decisions includes first determining which critical decisions can be based on data. When those required decisions are understood, then the analysis required to inform those decisions can be identified, and finally the data to be collected in support of those analyses can be determined. Once equipped with high-quality data and analysis capabilities, the key steps to data-based decision making for reliability improvements are to isolate possible improvements, select the improvements with largest return on investment (ROI), implement the selected improvements, and finally to track their impact.