An Integrative Framework for Design and Control Optimization of a Variable-Ratio Gearbox in a Wind Turbine With Active Blades

A variable ratio gearbox (VRG) provides discrete variable rotor speed operation, and thus increases wind capture, for small fixed-speed wind turbines. It is a low-cost, reliable alternative to conventional variable speed operation, which requires special power-conditioning equipment. The authors’ previous work has demonstrated the benefit of using a VRG in a fixed-speed system with passive blades. This work characterizes the performance of the VRG when used with active blades. The main contribution of the study is an integrative design framework that maximizes power production while mitigating stress in the blade root. As part of the procedure, three gear ratios are selected for the VRG. It establishes the control rules by defining the gear ratio and pitch angle used in relation to wind speed and mechanical torque. A 300 kW wind turbine model is used for a case study that demonstrates how the framework is implemented. The model consists of aerodynamic, mechanical, and electrical submodels, which work collaboratively to convert kinetic air to electrical power. Blade element momentum theory is used in the aerodynamic model to compute the blade loads. The resulting torque is passed through a mechanical system and subsequently to the induction machine model to generate power. The BEM method also provides the thrust and bending loads that contribute to blade-root stress. The stress in the root of the blade is also computed in response to these loads, as well as those caused by gravity and centrifugal force. Two case studies are performed using wind data that was obtained from the National Renewable Energy Laboratory (NREL). Each of these represents an installation site with a unique set of wind conditions that are used to customize the wind turbine design. The framework uses dynamic programming to simulate the performance of an exhaustive set of combinations. Each combination is evaluated over each set of recorded wind data. The combinations are evaluated in terms of the total energy and stress that is produced over the simulation period. Weights are applied to a multi-objective cost function that identifies the optimal design configurations with respect to the design objectives. As a final design step, a VRG combination is selected, and a control algorithm is established for each set of wind data. During operation, the cost function can also be used to bias the system towards higher power production or lower stress. The results suggest a VRG can improve wind energy production in Region 2 by roughly 10% in both the low and high wind regions. In both cases, stress is also increased in Region 2, as the power increases. However, the stress in Region 3 may be reduced for some wind speeds through the optimal selection of gear combinations.