Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT)

Abstract

In this paper, a supervisory control concept for wind farms is proposed based on the neighboring wind turbines control functions in localized areas for power optimization considering wake effects. The flow control in wind farms to maximize power production is a challenging problem due to its time-varying nonlinear wake dynamics. Hence, we develop a method that authorizes coordination in a wind farm for a squarely payoff-based scenario where the turbines have access only to measurements from their neighbors via repeated interactions. Therefore, in order to maximize output power in a wind farm, an Adaptive Learning Game Theory (ALGT) method is introduced. This control scheme provides an interaction framework that constructs a series of common control functions. Here, in every iteration, each turbine chooses an independent decision according to a localized control law. The control objective of wind turbine [Formula: see text] determines how each turbine adjusts a decision at each iteration by processing available information.

Optimizing Wind Farm Efficiency through Active Yaw Control: A Neural Network-Aided Game Theory Approach

“…Game-theoretic algorithms are among the various control methods developed to optimize wind farm power performance [15,16]. Using game theoretic algorithms for wind farm power maximization presents several key advantages, which is particularly suitable for the complex environment of wind farms [10,17].…”