Offshore wind energy could potentially play a significant role in helping the nation establish a diversified energy-generation portfolio composed of clean, renewable resources. A current obstacle to offshore wind energy is that most projections put offshore wind farm operations and maintenance (O&M) costs between 2 to 5 times the current average O&M costs for onshore wind farms. One way in which those costs may be reduced is through a simple, yet effective structural health monitoring system as part of an overall condition-based maintenance strategy.
Sandia recently completed a four-year research study, funded by the DOE Wind and Water Power Technologies Office, focused on developing and evaluating technical innovations to maximize offshore wind-plant revenues and reduce levelized cost of energy (LCOE) with structural health monitoring and prognostics management (SHPM)-based technologies. The project was led by Sandia and included major contributions from ATA Engineering, Purdue University, Georgia Tech, and Vanderbilt University.
To significantly reduce O&M costs and increase energy capture, the Sandia-led research team developed and evaluated new strategies—robust and cost-effective SHPM strategies that could
- ensure operations in a desired (designed) safe state of health,
- aid in planning of maintenance processes versus more costly unplanned servicing,
- optimize supply-chain management by using prognostics (predictive estimates of damage),
- avoid catastrophic failures through advance warning, and/or
- improve energy capture by avoiding unnecessary shutdown and increasing overall plant availability.
The team used physics-based simulations to create a multiscale modeling and simulation methodology to aid in developing and optimizing an SHPM system for wind-turbine blades. This approach propagates the effects of damage from high-fidelity local simulations to full-turbine simulations using reduced-order models as illustrated in the flow chart (right). The technique can also be used as an initial roadmap for developing future health monitoring systems because it allows for the investigation of the effects of damage on both local and global scales. Globally, the operational responses of the full-turbine models can be analyzed for developing health-monitoring algorithms and identifying optimal measurement types, locations, and directions. The loads from these full-turbine simulations can then be applied to high-fidelity models in order to investigate the localized effects of damage.
In addition to significantly reducing wind turbine O&M costs, a SHPM system could be used as an integral component of health-driven wind-turbine control. A major focus of the final report was to develop damage-detection strategies for the most frequent blade damage conditions and damage-mitigation and life-extension strategies via changes in turbine operations (i.e., smart loads management). Initial simulations found that derating a turbine power production by as little as 5% resulted in a reduction in the equivalent loading by 10% and a blade fatigue life extension of 300%. Therefore, if the health of a turbine is known, the power production of that turbine could be derated slightly to avoid costly unscheduled repairs and coordinate the lower-cost scheduled repair of many turbines. These load-management strategies could prove especially beneficial for offshore turbines where maintenance may be limited by the weather and the increased possibility of servicing multiple turbines during a single visit to the wind plant may result in significantly reduced offshore O&M costs.
The major results/findings of the Sandia SHPM program were
- a roadmap for SHPM technology,
- a multiple-scale damage modeling & simulation methodology,
- damage-detection strategies for common damage types (global operating sensitivity),
- a state-of-health of damaged turbines assessment (local sensitivity),
- the maturation of damage models for wind-turbine blade analysis,
- smart loads management (or derating, damage-mitigating controls, prognostic controls) for wind-turbine rotors,
- optimized maintenance processes,
- SHPM economic calculations, and
- damage detection strategies that were tested under realistic and variable inflow conditions.
More information on the Structural Health Monitoring project can be found here.