A variable ratio gearbox (VRG) can enable small wind turbines to operate at discrete variable rotor speeds. This reliable, low-cost, alternative does not require power conversion equipment, as is the case with conventional variable speed. Previous work conducted by the author has demonstrated that a VRG can increase the power production for a fixed-speed system with passive blades. The current study characterizes the performance of a wind turbine equipped with a VRG and active blades. The contribution of this work is an integrative framework that optimizes power production with blade root stress. It works by defining a set of control rules that specify the VRG gear ratio and pitch angle that will be used in relation to wind speed. Three ratios are selected through the proposed procedure. A case study based on the simulation of a 300-kW wind turbine model is performed to demonstrate the proposed technique. The model is constructed with aerodynamic, mechanical, and electrical submodels. These drivetrain components work together to simulate the conversion of moving air to electrical power. The blade element momentum (BEM) technique is used here to compute the blade loading. The resulting torque and rotor speed are reduced and increased, respectively, through the mechanical system gearbox. The output from this is then applied to the electrical generator. The BEM technique is also used here to determine the bending and thrust and loads that are applied to the blade. The stress in the root of the blade is then determined based on these loads, and that caused by centrifugal force and gravity. The proposed method devises a VRG design and control algorithm based on the unique wind conditions at a given installation site. Two case studies are conducted using wind data sets provided by the National Renewable Energy Laboratory (NREL). Low and high-speed data set are selected as inputs to demonstrate the versatility of the proposed method. Dynamic programming is used to reduce the computational expense. This enables the simulation of an exhaustive set of potential VRG combinations over each set of recorded wind data. Each possible combination is evaluated in terms of the total energy production and blade-root stress produced over the simulation period. A set of weights is applied to a multi-objective function that computes the cost associated with each combination. A Pareto analysis is then used to identify the VRG combination and establish the control algorithm for both systems. The results suggest that the VRG can improve energy production in the partial-load region by roughly 10% in both cases. Although stress increases in Region 2, it decreases in Region 3, and overall, through the optimal selection of gear combinations.

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