Distributed State Estimation for Medium-Fidelity Wind Farm Models in pursuit of Model-Based Closed-Loop Control

Abstract

In research, the overall power production in a wind farm is typically increased by employing model-based wind farm control. A controller, in an open-loop setting, operates based on the velocity of the wind flow, predicted by a wind farm model. For a controller to achieve the desired level of power production, a wind farm model has to be accurate and computationally tractable.

Generally, high-fidelity wind farm models are accurate but are computationally complex, making real-time control infeasible. This issue can, however, be addressed by employing closed-loop approach. In this approach, low- or medium-fidelity wind farm models, which are computationally tractable, are used, and their accuracy Is improved by employing an estimator.

In the previous researches, a centralized estimation approach was employed. In this framework, a single estimator is employed for estimating all the states (second-to-second wind field at turbine hub height) in a wind farm. Simulation results show that the accuracy of an open-loop model can be improved. However, the problem is the state size of the wind farm models. This leads to the objective of the thesis, which is “Can the accuracy of a wind farm model be improved while maintaining computational tractability?”.

In this thesis, distributed estimation is proposed as a solution to this problem. The basic idea behind distributed estimation is to distribute a wind farm into a number of small spatial domains (subsystems), and define a wind farm model for each of these subsystems to independently predict the wind flow in their respective spatial domain. For estimation, each sub-system employs an estimator to independently estimate their respective states, in parallel. In this thesis, based on the extent to which the states are estimated (measurement-update), model distribution, and size of the subsystems, four types of distributed architectures are devised, using the medium-fidelity model WindFarmSimulator (WFSim). Simulations show negligible loss in performance, and at the same time, the time taken for each iteration decreases drastically, making it computationally tractable.

In conclusion, distributed architectures are capable of improving the accuracy of the open-loop wind farm models, to the same level of accuracy offered by the centralized architecture, while maintaining computational tractability. Additionally, application of these distributed architectures for controller design will be a scope for future research in this topic.