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STTR Phase I: Triboluminescent sensor system for quasi-distributed load sensing on wind blades for active control of wind turbines

STTR Phase I: Triboluminescent sensor system for quasi-distributed load sensing on wind blades for active control of wind turbines
STTR 第一阶段:用于风力叶片准分布式负载传感的摩擦发光传感器系统,用于风力涡轮机的主动控制
批准号:
1549716
负责人:
David Olawale
金额:
$22.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-01-01 至 2016-12-31

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英文摘要
This Small Business Technology Transfer Phase I project is for the development of a triboluminescent sensor system (ITOFPress) for quasi-distributed load sensing along the length of a wind blade for active control of wind turbines. According to experts, this innovation could result in a 20% increase in wind turbine power generation through enhanced inputs for active turbine control and better understanding of the load profile along the span of a blade. Such knowledge is critical as wind blades become larger and are installed higher. This new system will also help to protect the blades and other expensive components from damage and overload, thereby reducing operation & maintenance (O&M) costs and making wind energy more attractive to investors. The installation of the ITOFPress system would increase the initial capital costs of a large wind turbine by a very small amount (about a half a percent), creating a strong value proposition for turbine manufacturers and operators. The estimated market potential for this system is expected to be $1.5 billion in 2018. The intellectual merit of this project addresses the technical hurdles in developing the proposed sensor system. The ITOFPress technology is highly sensitive to deformation-induced excitation and can withstand high loading cycles. The sensor combines the light-emitting property of ZnS:Mn with the highly desirable features of optical fibers (i.e. they are lightweight, smaller, immune to electromagnetic interference, and have the capacity for distributed sensing). The ITOFPress does not require external power at the sensing location or for signal transmission to the blade's hub, which makes it very attractive for use in wind turbines. The sensor can accurately detect loads that are not anticipated by models which rely on inputs from inflow sensors like anemometers. In this effort, increased sensor sensitivity will be achieved through experimental design studies involving critical design factors. The sensor's long term stability will be studied by subjecting samples to flexural and compressive load cycles. Finite element analysis will be performed to establish relationships between sensor response, sensor deformation and applied load. Strain gages and load sensors will be used for sensor calibration. A lab-sized wind blade will also be instrumented and tested under compressive cyclic loading to demonstrate quasi-distributed load sensing with the new sensor system.
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