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1. In-Stream Hydrokinetic Turbine Fault Detection and Fault Tolerant Control - A Benchmark Model

   Tang, Yufei ; VanZwieten, James ; Dunlap, Broc ; Wilson, David A ; Sultan, Cornel ; Xiros, Nikolas ( July 2019 , Proceedings of the American Control Conference)

   Increased interest in renewable energy production has created demand for novel methods of electricity production. With a high potential for low cost power generation in locations otherwise isolated from the grid, in-stream hydrokinetic turbines could serve to help meet this growing demand. Hydrokinetic turbines possess higher operations and maintenance (O&M) costs due to their isolated nature and harsh operating environment when compared with other sources of renewable energy. As such, techniques must be developed to mitigate these costs through the application of fault-tolerant control (FTC) and machine condition monitoring (MCM) for increased reliability and maintenance forecasting. Hence, the primary objective of this paper is to address a key limitation in hydrokinetic turbine research: the lack of widely available data for use in developing models by which to conduct FTC and MCM. To this end, a 20 kW research hydrokinetic turbine implemented in Fatigue Aerodynamics Structures and Turbulence (FAST) is presented and housed within the Matlab/Simulink environment. This paper details the high-fidelity simulation platform development together with the characteristics of generated data with a focus on future FTC and MCM implementation. 

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2. In-Stream Hydrokinetic Turbine Fault Detection and Fault Tolerant Control - A Benchmark Model

   Tang, Y ; VanZwieten, J. ; Dunlap, B ; Wilson, D ; Sultan, C ; Xiros, N.I. ( July 2019 , American Control Conference)

   Increased interest in renewable energy production has created demand for novel methods of electricity production. With a high potential for low cost power generation in locations otherwise isolated from the grid, in-stream hydrokinetic turbines could serve to help meet this growing demand. Hydrokinetic turbines possess higher operations and maintenance (O&M) costs due to their isolated nature and harsh operating environment when compared with other sources of renewable energy. As such, techniques must be developed to mitigate these costs through the application of fault-tolerant control (FTC) and machine condition monitoring (MCM) for increased reliability and maintenance forecasting. Hence, the primary objective of this paper is to address a key limitation in hydrokinetic turbine research: the lack of widely available data for use in developing models by which to conduct FTC and MCM. To this end, a 20 kW research hydrokinetic turbine implemented in Fatigue Aerodynamics Structures and Turbulence (FAST) is presented and housed within the Matlab/Simulink environment. This paper details the high-fidelity simulation platform development together with the characteristics of generated data with a focus on future FTC and MCM implementation. 

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3. Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver

   https://doi.org/10.2514/6.2018-2018  

   Santhanagopalan, Vignesh ; Letizia, Stefano ; Zhan, Lu ; Al-Hamidi, Louay Yahia ; Iungo, Giacomo Valerio ( January 2018 , 2018 Wind Energy Symposium)

   null (Ed.)

   This work focuses on the optimization of performance and profitability of a wind farm carried out by means of an economic model and Reynolds-Averaged Navier-Stokes (RANS) simulations of wind turbine wakes. Axisymmetric RANS simulations of isolated wind turbine wakes are leveraged with a quadratic super-positioning model to estimate wake interactions within wind farms. The resulting velocity field is used with an actuator disk model to predict power production from each turbine in the wind farm. Design optimization is performed by considering a site in North Texas, whose wind resource statistics are obtained from a meteorological tower. The RANS solver provides capabilities to simulate different incoming wind turbulence intensities and, hence, the wind farm optimization is performed by taking the daily cycle of the atmospheric stability into account. The objective functional of the optimization problem is the levelized cost of energy (LCoE) encompassing capital cost, operation and maintenance costs, land cost and annual power production. At the first level of the optimization problem, the wind farm gross capacity is determined by considering three potential turbine types with different rated power. Subsequently, the optimal wind farm layout is estimated by varying the uniform spacing between consecutive turbine rows. It is found that increasing turbine rated power, the wind farm profitability is enhanced. Substituting a wind farm of 24 turbines of 2.3-MW rated power with 18, 3-MW turbines could reduce the LCoE of about 1.56 $/MWh, while maintaining a similar gross capacity factor. The optimization of the spacing between turbine rows was found to be sensitive to the land cost. For a land cost of 0.05 $/m2, the layout could be designed with a spacing between 6 to 15 rotor diameters without any significant effect on the LCoE, while an increased land cost of 0.1 $/m2 leads to an optimal spacing of about 6 rotor diameters. 

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4. Multiresolution Large‐Eddy Simulation of an Array of Hydrokinetic Turbines in a Field‐Scale River: The Roosevelt Island Tidal Energy Project in New York City

   https://doi.org/10.1029/2018WR023345  

   Chawdhary, Saurabh ; Angelidis, Dionysios ; Colby, Jonathan ; Corren, Dean ; Shen, Lian ; Sotiropoulos, Fotis ( December 2018 , Water Resources Research)

   Marine hydrokinetic (MHK) power generation systems enable harvesting energy from waterways without the need for water impoundment. A major research challenge for numerical simulations of field‐scale MHK farms stems from the large disparity in scales between the size of waterway and the energy harvesting device. We propose a large‐eddy simulation (LES) framework to perform high‐fidelity, multiresolution simulations of MHK arrays in a real‐life marine environment using a novel unstructured Cartesian flow solver coupled with a sharp‐interface immersed boundary method. The potential of the method as a powerful engineering design tool is demonstrated by applying it to simulate a 30 turbine MHK array under development in the East River in New York City. A virtual model of the MHK power plant is reconstructed from high‐resolution bathymetry measurements in the East River and the 30 turbines placed in 10 TriFrame arrangements as designed by Verdant Power. A locally refined, near the individual turbines, background unstructured Cartesian grid enables LES across a range of geometric scales of relevance spanning approximately 5 orders of magnitude. The simulated flow field is compared with a baseline LES of the flow in the East River without turbines. While velocity deficits and increased levels of turbulence kinetic energy are observed in the vicinity of the turbine wakes, away from the turbines as well as on the water surface only a small increase in mean momentum is found. Therefore, our results point to the conclusion that MHK energy harvesting from large rivers is possible without a significant disruption of the river flow.

    

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5. Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

   https://doi.org/10.3389/fenrg.2022.904622  

   Latiffianti, Effi ; Sheng, Shawn ; Ding, Yu ( May 2022 , Frontiers in Energy Research)

   The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time. 

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