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Fiber-Optic Sensing System for Wind Turbine Prognostics Using Artificial Intelligence

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0022624
Agency Tracking Number: 0000266232
Amount: $199,941.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: C54-17a
Solicitation Number: N/A
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-27
Award End Date (Contract End Date): 2023-03-26
Small Business Information
4425 Fortran Drive
San Jose, CA 95134-2300
United States
DUNS: 877452664
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Richard Black
 (408) 565-9000
Business Contact
 Behzad Moslehi
Phone: (408) 565-9004
Research Institution
 National Renewable Energy Laboratory (NREL)
15013 Denver West Pkwy
Golden, CO 80401-3111
United States

 Federally Funded R&D Center (FFRDC)

In order to ensure the reliability and optimum performance of distributed, land-based, and offshore wind turbine assets over at least two to three decade lifetimes, DOE seeks technology innovations to address wind turbine challenges such as analysis of data from Supervisory Control and Data Acquisition (SCADA) systems for prognostic health management or intelligent assessment of environmental sensor data using artificial intelligence.
General statement of how this problem is being addressed
IFOS proposes an innovative, robust and cost-effective end-to-end Fiber Bragg Grating (FBG) integrated sensor system capable of providing real-time, in-situ defect detection, localization, and quantification of damage combined with predictive intelligent diagnostics. This technology allows non-destructive monitoring of wind turbine blades and bearings in real time, during and after manufacturing, transportation, installation and operation. This improves reliability, reduces cost of ownership (CoO), and lowers levelized cost of electricity (LCOE) of wind power systems. The IFOS solution utilizes improvements in sensing and interrogation, such as higher signal to noise ratio and sensitivity. Enhanced data analysis using Wavelet Transform and machine learning / artificial intelligence-based algorithms improve defect and damage location analysis and system prognosis. This improves sensing in strongly attenuating materials. It will provide real-time online monitoring of wind turbine blades and bearings by detecting minor changes in structural and material strain response. Strain data offer vital structural health information for safe wind turbine operation, allowing for informed preventive maintenance decisions as well as avoidance of catastrophic field failures. Accurate real-time measurement of strain values, as an indicator of structural health state, is crucial to the success of the monitoring process. The system will be designed for interfacing with existing sensing arrangements in the turbine, including environmental sensors. The specification of the system will take input from stakeholders such as wind turbine manufacturers and operators. This development will enable performance optimization and improved system reliability of land-based and off-shore wind turbines through early defect and damage detection in turbine blades and bearings. IFOS’ solution can gain broad adoption across the renewable energy industry.
In Phase I, IFOS in collaboration with National Renewal Energy Lab (NREL) will demonstrate feasibility of this innovative health monitoring system on composite test articles including a scaled wind turbine blade and bearing system. In Phase II, IFOS seeks to deploy and field-test an integrated engineering prototype system in collaboration with NREL and a wind turbine manufacturer.
Commercial Applications and Other Benefits
Cost-effective and robust real-time monitoring of the condition of wind turbine blades and bearings is the commercial application of the proposed innovation. Variants of the proposed technology platform could be used to monitor the health of other components of the power distribution infrastructure. Immunity to electromagnetic interference (EMI) makes fiber-optic sensors ideal for monitoring transmission lines, transformers, generators and other electrical systems. The ability to make measurements over extended kilometer-scale distances and safe ignition-proof operation makes them well suited for pipeline monitoring, including natural gas distribution. Finally, extreme high-temperature capability makes the IFOS solutions ideal for monitoring high-temperature assets such as gas turbines and power plants

* Information listed above is at the time of submission. *

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