A model-free distributed approach for wind plant control

By extracting kinetic energy from the wind flow, a wind turbine reduces the wind speed in the wake downstream of the wind turbine rotor. In a wind power plant, this wake effect reduces the power production of downstream turbines. This paper presents a control scheme for optimizing the total power output of a wind power plant by taking into account the wake effect. It is a distributed control scheme in which each wind turbine adapts its control settings based on information that it receives from neighbouring turbines. The total power optimization is performed using gradient-based optimization. The optimization is done in a model-free, data-driven manner, as the gradients are estimated from the past control actions, the measured power response of the turbine itself, and the power response of neighbouring turbines. The time-efficiency of the optimization scheme was improved by exploiting information on the locations of the turbines in the wind plant, and an estimate of the wind direction. The method is tested in a simulation of the Princess Amalia Wind Park. To be able to evaluate the time-efficiency of the scheme, in the simulation model a delay structure was included that models the wake traveling from one turbine to the next. The new control method results in a much faster convergence of the power optimization when compared with an existing model-free wind plant power optimization method that uses a game theoretic approach.