To operate in open world environments a symbolic Artificial Intelligence (AI) to be able to adapt and incorporate new objects and relations in its Knowledge Base (KB). Symbolic AI use the objects and relations in their KB to navigate the world and create plans. These KB are filled with knowledge in advance, so they can not add new knowledge when encountering novel situations. This thesis presents Heuristics-Based Causal Discovery (HBCD), a method that identifies and labels causal relations and transforms those causal relations into logic statements, which can be inserted into a KB autonomously.

HBCD can operate in a partially-observable environment by performing actions and observing the effects of its actions. The actions are chosen by heuristics, which are modelled after human strategies for causal discovery. The discovered causal relations are tested for the properties necessity and sufficiency. These properties provide information on the completeness of the result, whether there are any missing causal relations. HBCD uses this information to adjust its search space by moving variables in and out of it during the search. If there are no more missing causal relations HBCD stops the search. The method was tested in two simulated environments and the results are promising.

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.