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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. 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. 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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5. Cyber Threat Analysis Framework for the Wind Energy Based Power System

   https://doi.org/10.1145/3140241.3140247  

   Datta, Amarjit; Rahman, Mohammad Ashiqur (November 2017, Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy - CPS '17)

   Wind energy is one of the major sources of renewable energy. Countries around the world are increasingly deploying large wind farms that can generate a significant amount of clean energy. A wind farm consists of many turbines, often spread across a large geographical area. Modern wind turbines are equipped with meteorological sensors. The wind farm control center monitors the turbine sensors and adjusts the power generation parameters for optimal power production. The turbine sensors are prone to cyberattacks and with the evolving of large wind farms and their share in the power generation, it is crucial to analyze such potential cyber threats. In this paper, we present a formal framework to verify the impact of false data injection attack on the wind farm meteorological sensor measurements. The framework designs this verification as a maximization problem where the adversary's goal is to maximize the wind farm power production loss with its limited attack capability. Moreover, the adversary wants to remain stealthy to the wind farm bad data detection mechanism while it is launching its cyberattack on the turbine sensors. We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on synthetic test cases. 

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