A 20 KW open ocean current test turbine

Driscoll, Frederick; Alsenas, Gabriel; Beaujean, Pierre‐Philippe; Ravenna, Shirley; Raveling, J.; Busold, E.; Slezycki, C. R.

doi:10.1109/oceans.2008.5152104

Cited by 27 publications

(12 citation statements)

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“…As a part of its research efforts, the center has developed a computer-controlled 20 kW dynamometer ( Figure 1) for the purpose of testing the components of the ocean turbine [7]. The structure of the dynamometer is discussed in greater depths in section 2.1.…”

Section: Figure 1 Dynamometermentioning

confidence: 99%

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

Duhaney

Khoshgoftaar

Sloan

et al. 2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. This paper presents an onshore test platform for an ocean turbine as well as a case study showing how machine learning can be used to detect changes in the operational state of this plant based on its vibration signals.In the case study, seven widely used machine learners are trained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machine's state. The classification models generated by these classifiers are being considered as possible components of the state detection module of an MCM/PHM system for ocean turbines, and would be used for fault prediction. Experimental results presented here show the effectiveness of decision tree and random forest learners on distinguishing between faulty and normal states based on vibration data preprocessed by a wavelet transform.

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Section: Figure 1 Dynamometermentioning

confidence: 99%

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

Duhaney

Khoshgoftaar

Sloan

et al. 2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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“…Initially described in [1], this prototype is planned to operate off the Southeast coast of Florida, converting the momentum flux generated by the Gulf Stream into electricity.…”

Section: Case Studymentioning

confidence: 99%

“…* John C. Sloan, Florida Atlantic University, Boca Raton, Florida 33431, jsloan11@fau.edu † Taghi M. Khoshgoftaar, Florida Atlantic University, Boca Raton, Florida 33431, khoshgof@fau.edu. 1 For an introduction to the ocean turbine project at the (U.S.) Southeast National Marine Renewable Energy Center, see [1,3], and for a reliability assessment see [9].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Coordination for Discrete Event Control

Sloan

Khoshgoftaar

2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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An ensemble is a collection of independent processes, each tasked with drawing potentially differing conclusions about the same data. Using Petri nets, this paper formally describes how ensembles are organized and their behavior coordinated to effect distributed discrete event control of an ocean turbine prototype. Compositions, duals, reverses, and cliques formed over known Petri net graphs comprise the building blocks of the proposed ensemble coordination strategy. The behavior of an ensemble of controllers tasked with fault triage are subject to constraints formulated herein. The controller tasked with prognosis and health management (PHM) itself uses an ensemble of classifiers to detect faults. This ensemble is subject to constraints imposed by stream processing, which require a nonblocking form of rendezvous synchronization. Furthermore, results from each classifier must be fused in a manner that rewards that classifier's ability to predict faults. We identify two competing merit schemes -one based on individual classifier performance and the other on performance of the sub-ensembles to which that classifier participates. Finally, we model check these Petri nets and report their results.

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“…The length for the ocean current turbine towline has not yet been determined, and to further investigate this, comparisons are made for the forces on the turbine and towline for north and east wind/wave directions. The turbine towline is designed to position the turbine behind the OCDP when the buoy is surfaced [2], and therefore the OCDP towline length should also be considered when considering various turbine towline lengths. Though not directly connected to the turbine, the OCDP is towed by the MTB, and also plays a role in the dynamically coupled behavior of the MTB and turbine, via the flounder plate.…”

Section: A Turbine and Ocdp Towline Lengthmentioning

confidence: 99%

“…The developed numeric model includes all of the components that are to be placed offshore including the prototype turbine with a rotating rotor, of which the rpm may either be specified or left to rotate based on the forces from the surrounding flow; and two surface buoys: the Mooring and Telemetry Buoy (MTB), which contains several environmental sensors, and the Observation, Controls, and Deployment Platform (OCDP) (Figure 1). The OCDP is towed to the test site providing transportation, housing, launching, and recovery for the turbine, and is tethered behind the MTB during testing [2]. The mooring line has an anchor chain to develop a catenary making the pull on the anchor tangential to the roughly 330 m deep sea floor, while mooring the MTB on site for several years.…”

Section: Introductionmentioning

confidence: 99%

Global numeric analysis of a moored ocean current turbine testing platform

Cribbs

VanZwieten

2010

Oceans 2010 MTS/Ieee Seattle

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In response to Florida's growing energy needs and drive to develop renewable power, Florida Atlantic University's Center for Ocean Energy Technology (COET) plans to deploy a mooring near the core of the Gulf Stream off southeast Florida's coast. This mooring system testing platform will be used to evaluate the performance of prototype ocean current turbines including COET's 20 kW turbine that is currently under development. No mooring systems for deepwater hydrokinetic turbines have been constructed, tested and installed; therefore little is known about the performance of these moorings. To investigate this, a numeric model is used to predict the static and dynamic behavior of the mooring system and attachments, quantifying sensitivity of the system's response to parametric variations. This numeric model of COET's proposed mooring system has been created in OrcaFlex to model static and dynamic effects of the system. It includes numerical models of its two surface buoys, a rotating ocean current turbine, and the lines associated with the system. Wind, wave, and current profiles represent the environmental conditions the system is expected to experience.

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A 20 KW open ocean current test turbine

Cited by 27 publications

References 0 publications

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

Ensemble Coordination for Discrete Event Control

Global numeric analysis of a moored ocean current turbine testing platform

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