Keys to Achieving 20 Percent Wind by 2030

Swisher, Randall

doi:10.1080/15453660909509161

Cited by 3 publications

(2 citation statements)

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“…The Department Of Energy (DOE) has set a goal to provide 20% of US electricity supply from wind energy by 2030. 1 To meet this goal, wind turbines must be extremely efficient and reliable. However, a wind turbine can fail by any structural component, and one of the most common and critical components to fail is the wind turbine blade.…”

Section: Introductionmentioning

confidence: 99%

Wireless monitoring algorithm for wind turbine blades using Piezo‐electric energy harvesters

2016

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Wind turbine blade failure can be catastrophic and lead to unexpected power interruptions. In this paper, a Structural Health Monitoring (SHM) algorithm is presented for wireless monitoring of wind turbine blades. The SHM algorithm utilizes accumulated strain energy data, such as would be acquired by piezoelectric materials. The SHM algorithm compares the accumulated strain energy at the same position on the three blades. This exploits the inherent triple redundancy of the blades and avoids the need for a structural model of the blade. The performance of the algorithm is evaluated using probabilistic metrics such as detection probability (True Positive) and false alarm rate (False Positive). The decision time is chosen to be sufficiently long that a particular damage level can be detected even in the presence of system sensor noise and wind variations. Finally, the proposed algorithm is evaluated with a case study of a utility-scale turbine. The noise level is based on measurements acquired from strain sensors mounted on the blades of a Clipper Liberty C96 turbine. Strain energy changes associated with damage from matrix cracking and delamination are simulated with a finite element model. The case study demonstrates that the proposed algorithm can detect damage with a high probability based on a decision time period of approximately 50-200 days. NOMENCLATUREN B D Average of strain energy one-step increment, [ J/step] B k D Strain energy one-step increment, [ J/step] E D Young's Modulus of a composite blade, [GPa] E 0 D Degraded Young's modulus, [GPa] E EH D Young's modulus of an energy harvester, [GPa] g D D Degradation parameter, [-] k d D Decision time, [step] k f D Last time step of the given time frame, [step] k s D Statistical evaluation time, [step] m D Number of divided windows from the evaluation time, [-] n D Integer, [-] p FP D Possibility of False Positive, [%] p TP D Possibility of True Positive, [%] r .ij/ k D Residual of i, j blades at time k, [ J] T D Threshold, [ J] v D Variational term in B k , [ J] V D Volume of energy harvester, OEm 3 W .i/ k D Strain energy of the EH in the blade i, [-] t D Step time, [sec/step] ı k D Random Variable in the residual, [ J] D Strain of a composite blade, [ -strain] 0 D Strain of a damaged composite blade, [ -strain] 2 D Variance of ı k , [ J 2 ] 2 k D Variance of r k , [ J 2 ]

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Section: Introductionmentioning

confidence: 99%

Wireless monitoring algorithm for wind turbine blades using Piezo‐electric energy harvesters

2016

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“…HE DOE has set a goal of "20% wind energy by 2030". 1 Reduction in operating and maintenance costs for wind turbines has been identified as a major challenge to achieving this goal. Wind turbine maintenance is a particular challenge because wind turbines are often located in remote regions (including offshore).…”

Section: Introductionmentioning

confidence: 99%