AI-based static voltage stability analysis of power grids - Conrad, Timon (2024)
The objective of this thesis was to implement an AI system to analyse the static voltage stability of power grids and assess its ecacy in achieving faster computation times com- pared to existing approaches. To carry out this implementation, a training dataset of randomly generated cases for a 5 bus grid was created using the load ow calculation and dV ◁dQ sensitivity analysis in the PowerFactory program. Various articial neural networks were optimised using the pruning function in the DataEngine programme and implemented using the Python library Keras. The evaluation focused on applicability, the impact of reducing features and cleaning the dataset, as well as examining variations in power grid size. The thesis indicated that, despite obtaining high-accuracy results during training, the models lacked sucient generalisation ability. Expanding the dataset did not enhance this ability and the model necessitated more time for both data production and training when the power grid was expanded. Regardless of the utilization of diverse approaches, including MLPs and Kohonen maps, adequate generalisation was not achieved. Future research should concentrate on ex- panding the features, testing multiple articial neural networks and integrating load ow calculations with AI. It is suggested that the methodology and approach to data cleaning should be re-evaluated and further research should explore contemporary optimisers or additional articial neural network models.
Keywords: AI (articial intelligence); ML (machine learning); ANN (articial neural networks); Power Grids; Smart Grid; Voltage Stability Analysis; dV/dQ Sensitivity Anal- ysis; PowerFactory; Keras