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1. 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, Americn 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. Characterizing Sockeye Salmon Smolt Interactions with a Hydrokinetic Turbine in the Kvichak River, Alaska

   https://doi.org/10.1002/nafm.10806  

   Courtney, Michael B; Flanigan, Austin J; Hostetter, Mary; Seitz, Andrew C (August 2022, North American Journal of Fisheries Management)

   Abstract The development of hydrokinetic turbines has been motivated by the desire to reduce fossil fuel reliance, energy production costs, and greenhouse gas emissions. Detailed information about fish interactions with hydrokinetic turbines is limited; therefore, this study sought to characterize the interactions between a turbine (RivGen; Ocean Renewable Power Company) and Sockeye Salmon Oncorhynchus nerka from one of the most productive populations in the world—that in the Kvichak River, Alaska. By viewing real-time video imagery, our objectives were to quantify the number of Sockeye Salmon smolts that interacted with the turbine and to assess the behaviors/outcomes of these interactions during the species' smolt out-migration. From May 21 to June 10, 2021, a total of 2,374 Sockeye Salmon smolts passed through the field of view of cameras placed immediately downstream of the hydrokinetic turbine. The majority of these observed events occurred over a short (5-d) time period from late May to early June during periods of darkness (0000–0400 hours). Fish were observed passing through the hydrokinetic turbine in both normal and disoriented manners, with the rotational status/speed of the hydrokinetic turbine appearing to influence passage behavior. Blade strikes on fish were also observed, all of which occurred when the turbine was rotating at high “production” speeds. After temporally and spatially extrapolating the observed fish interactions to account for our subsampling, the results suggest that when monitoring was conducted, the hydrokinetic turbine interacted with approximately 200,000 Sockeye Salmon smolts during this species' smolt out-migration period. This study adds to the sparse knowledge base on fish interactions with emerging riverine hydrokinetic devices and may inform strategies to mitigate the impacts of developing energy projects on socially and culturally important fisheries. 

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4. 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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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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