Abstract
In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm.
Highlights
Nowadays, it is quite strange to find wind turbines operating isolated into a geographical scheme.they are conveniently arranged in groups known as wind farms, which inject power into the electrical grid in a way and magnitude comparable to non-renewable energy sources.Initially, control strategies designed for wind farms were mainly based on aggregate models, which represent those arrays as a large equivalent wind turbine [1,2,3,4]
The free-stream wind speed was above rated value and the total power production should be close to rated values
The wake effects induce the reduction the wind speed faced by the set of last wind turbines, causing a total power lower than 160 MW
Summary
It is quite strange to find wind turbines operating isolated into a geographical scheme. The use of safe experimentation dynamics to design a distributed control strategy aimed at maximizing the energy capture considering local information and the total power amount produced by the wind farm is reported in [2] In this proposed approach, control variables are randomly perturbed in order to optimize such total power. The algorithm proposed in this paper uses historical information of the system evolution to compute multiple directions of the gradient estimations in a decentralized fashion and for every single wind turbine, i.e., it is not necessary to share information among turbines, and the proposed approach produces global solutions due to the availability of the total generated power amount. Discrete time is denoted as a sub-index, e.g., Sk , whereas continuous time is denoted as an argument, i.e., pi (t)
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