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Optimal Control of a Vertical Axis Wind Turbine with a Varying Structure Four-Bar Linkage Mechanism

Graduate #68
Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering
Session: 3
Room: Senate

Alexis Ruiz - California State University, Los Angeles
Co-Author(s): .



Vertical axis wind turbines have many advantages over their horizontal counterparts, such as their simplicity in design, installation, and maintenance. However, they also suffer from problems of not being able to self-start and having low power generating efficiency. Existing studies have shown that these problems can be addressed by using pitching controls. By controlling the blade pitch angle according to the wind conditions and operation states of a wind turbine, the power generation can be significantly improved. In this research, an optimal control method is proposed for an H-rotor Darrieus vertical axis wind turbine to maintain high-efficiency energy generation by using an online reinforcement learning control to optimize a VAWT’s blade pitch trajectory. The mechanism used to control the blade pitching is a varying structure four bar linkage mechanism. This allows for simultaneous control of all blades with a single actuator. First, the optimum four bar linkage structure is found by the interior point method using nonlinear programming. The aerodynamics of the turbine is modeled by the double multiple stream tube model. Second, through adjusting the input link length of the four-bar linkage mechanism, the proposed pitching control mechanism can create changing pitching trajectories. Third, an online policy gradient with parameter-based exploration reinforcement learning agent is used to adapt the varying link length to improve the power generating efficiency. Finally, a simulation is created to verify the effectiveness of the proposed control method, and it shows the efficiency can be improvement by more than 30.5% comparing to the same model with a passive four bar linkage pitching mechanism.

Funder Acknowledgement(s): National Science Foundation with Award No. HRD-1547723

Faculty Advisor: .,

Role: Contributed to the theories used, created studies through simulation and prepared most results.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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