Sizing Tool for Quadrotor Biplane Tailsitter UAS

The Quadrotor-Biplane-Tailsitter (QBT) configuration is the basis for a mechanically simplistic rotorcraft capable of both long-range, high-speed cruise as well as hovering flight. This work presents the development and validation of a set of preliminary design tools built specifically for this aircraft to enable its further development, including: a QBT weight model, preliminary sizing framework, and vehicle analysis tools. The preliminary sizing tool presented here shows the advantage afforded by QBT designs in missions with aggressive cruise requirements, such as offshore wind turbine inspections, wherein transition from a quadcopter configuration to a QBT allows for a 5:1 trade of battery weight for wing weight. A 3D, unsteady panel method utilizing a nonlinear implementation of the Kutta-Joukowsky condition is also presented as a means of computing aerodynamic interference effects and, through the implementation of rotor, body, and wing geometry generators, is prepared for coupling with a comprehensive rotor analysis package

MAXIMIZING THE FINANCIAL RETURNS OF USING LIDAR SYSTEMS IN WIND FARMS FOR YAW ERROR CORRECTION APPLICATIONS

Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Ideally, wind turbines have to be aligned with wind flow at all times. However, this is not the case and there exists and angle between a wind turbine nacelle’s central axis and the wind flow. This angle is called yaw error. Yaw error lowers the efficiency of turbines as well as lowers the reliability of key components in turbines. LIDAR devices can correct the yaw error; however, they are expensive and there is a trade-off between their costs and benefits. In this dissertation, a stochastic discrete-event simulation is developed that models the operation of a wind farm. By maximizing the Net Present Value (NPV) changes associated with using LIDAR devices in a wind farm, the optimum number of LIDAR devices and their associated turbine stay time will be determined. These optimum values are a function of number of turbines in the wind farm for specific turbine sizes. The outcome of this dissertation will help wind farm owners and operators to make informed decisions about purchasing LIDAR devices for their wind farms

PHM-BASED PREDICTIVE MAINTENANCE SCHEDULING FOR WIND FARMS MANAGED USING OUTCOME-BASED CONTRACTS

Prognostics and Health Management (PHM) technologies have been introduced into wind turbines to forecast the Remaining Useful Life (RUL), and enable predictive maintenance opportunities prior to failure thus avoiding corrective maintenance that may be expensive and cause long downtimes. For a wind turbine, when an RUL is predicted, a predictive maintenance option is triggered that the maintenance decision-maker has the managerial flexibility to decide if and when to exercise before the turbine fails. By implementing the predictive maintenance, the high cost of corrective maintenance can be avoided; however a portion of the RUL will be thrown away that can be translated into cumulative revenue loss. In this dissertation, a simulation-based European-style Real Options Analysis (ROA) approach is used to schedule the predictive maintenance for a single wind turbine with an RUL prediction managed using an as-delivered payment model. When an RUL is predicted for the wind turbine, the predictive maintenance value paths are simulated by considering the uncertainties in the RUL prediction and wind speeds. By valuating the European-style predictive maintenance option at all possible predictive maintenance opportunities, a series of predictive maintenance option values can be obtained, and the predictive maintenance opportunity with the highest expected predictive maintenance option value can be selected. By extending the approach for a single wind turbine, a wind farm managed using an outcome-based contract, specifically a Power Purchase Agreement (PPA), with multiple turbines indicating RULs concurrently can be analyzed. The predictive maintenance value for each wind turbine with an RUL indication depends on the operational state of all the other turbines, the amount of energy delivered, and the energy delivery target, prices and penalization mechanism for under-delivery defined in the PPA. A case study is provided demonstrating that the selected predictive maintenance opportunity for a PPA-managed wind farm is different from the same wind farm managed using an as-delivered payment model, and also differs from the selected predictive maintenance opportunities for the individual turbines with RULs managed in isolation. Finally, the magnitude of the life-cycle benefit that the developed approach can bring to the wind farm owner is estimated through a simple case study. Using the European-style ROA approach to determine the wind farm maintenance policy, the improvement to the wind farm expected life-cycle net revenue is significant compared with the state-of-art wind farm maintenance policies, i.e., up to 25% higher than the corrective maintenance policy, and up to 83% higher than the predictive maintenance at the earliest opportunity policy

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements