Abstract
Abstract. We develop an automated controller tuning procedure for wind turbines that uses the results of nonlinear, aeroelastic simulations to arrive at an optimal solution. Using a zeroth-order optimization algorithm, simulations using controllers with randomly generated parameters are used to estimate the gradient and converge to an optimal set of those parameters. We use kriging to visualize the design space and estimate the uncertainty, providing a level of confidence in the result. The procedure is applied to three problems in wind turbine control. First, the below-rated torque control is optimized for power capture. Next, the parameters of a proportional–integral blade pitch controller are optimized to minimize structural loads with a constraint on the maximum generator speed; the procedure is tested on rotors from 40 to 400 m in diameter and compared with the results of a grid search optimization. Finally, we present an algorithm that uses a series of parameter optimizations to tune the lookup table for the minimum pitch setting of the above-rated pitch controller, considering peak loads and power capture. Using experience gained from the applications, we present a generalized design procedure and guidelines for implementing similar automated controller tuning tasks.
Highlights
In this article, we present a data-driven, simulation-based optimization procedure for tuning wind turbine controllers using measures that are directly related to component design
By using a zeroth-order optimization algorithm, random samples are generated near an initial guess, which are used to compute the local gradient
We use ordinary kriging to visualize the design space and its uncertainty to provide a level of confidence in the optimized result
Summary
We present a data-driven, simulation-based optimization procedure for tuning wind turbine controllers using measures that are directly related to component design. Our approach to sampling the parameter space is based on stochastic approximation or “zeroth-order optimization,” which uses the sampled cost function to estimate the local gradient and optimizes the function with proven convergence results (Ghadimi and Lan, 2013). Previous work in controller optimization usually only provides the cost function and goals of the optimization, whereas this work explicitly details the method for determining the sample simulations and how their results are used to iterate on control designs. 2. Applications of the algorithm for wind turbine control tuning are presented, followed by a generalization of the design procedure and guidelines for parameter selection in Sect. If the power of any value is computed, the base will appear in parentheses: e.g., (γ )r
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