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Handle process constraints during learning Evaluate the trained controllers on a more detailed process model to assess how well the learned policies carry over beyond their training environment Validated
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include: Developing and analyzing representative dynamic simulation scenarios for electrical power systems Extending kernel-based Gaussian Process methods for the estimation of electrical and control
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neural networks, and physics-informed machine learning in power systems Set up benchmark grids and generate or process suitable simulation data for power-flow, voltage-prediction, or state-estimation
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