Original scientific paperThe new grid regulations require that a grid-connected wind farm acts as a single controllable power producer. To meet this requirement a traditional wind farm control structure, which allowed individual wind turbines to internally define their power production, has to be modified. This paper investigates the opportunity for wind turbine load reduction that arises from dynamic power control of wind turbines. The wind farm controller design is proposed that utilizes coordinated power control of all wind turbines to achieve the wind farm regulation requirements and to minimize the wind turbine loads.Key words: Wind turbine control, Wind farm control, Model predictive control, Structural loads Reference snage vjetroagregata u koordiniranom upravljanju vjetroelektranama. Nova mrežna pravila zahtijevaju da vjetroelektrane spojene na električnu mrežu djeluju kao jedinstveni upravljivi proizvoač električne energije. Da bi se zadovoljio takav zahtjev, tradicionalni način upravljanja vjetroelektranama, koji dozvoljava da vjetroagregati interno definiraju svoju referencu snage, treba biti modificiran. U ovom radu proučavaju se mogućnosti smanjenja opterećenja vjetroagregata korištenjem dinamičkog upravljanja snage vjetroagregata. Predložen je koncept regulatora vjetroelektrane koji koristi koordinirano upravljanje snagom vjetroagregata u svrhu zadovoljenja mrežnih pravila i smanjenja opterećenja vjetroagregata.Ključne riječi: upravljanje vjetroagregatom, upravljanje vjetroelektranom, modelsko prediktivno upravljanje, strukturna opterećenja

We consider a wind power plant of megawatt wind turbines operating in derated mode. When operating in this mode, the wind power plant controller is free to distribute power set-points to the individual turbines, as long as the total power demand is met. In this work, we design a controller that exploits this freedom to reduce the fatigue on the turbines in the wind power plant. We show that the controller can be designed in a decentralized manner, such that each wind turbine is equipped with a local low-complexity controller relying only on few measurements and little communication. As a basis for the controller design, a linear wind turbine model is constructed and verified in an operational wind power plant of megawatt turbines. Due to limitations of the wind power plant available for tests, it is not possible to implement the developed controller; instead the final distributed controller is evaluated via simulations using an industrial wind turbine model. The simulations consistently show fatigue reductions in the magnitude of 15-20 %.

SUMMARYThis paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm-wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite-horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm-wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint.

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