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Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks [article]

Alireza Sadeghi
<span title="2022-02-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The present thesis first develops principled methods to make generic machine learning models robust against distributional uncertainties and adversarial data.  ...  To account for unanticipated and rapidly changing renewable generation and load consumption scenarios, we specifically delegate reactive power compensation to both utility-owned control devices (e.g.,  ...  To overcome these hurdles, this thesis adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.05395v1">arXiv:2202.05395v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5v3awpoiizaxjjam5deq6yadpa">fatcat:5v3awpoiizaxjjam5deq6yadpa</a> </span>
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