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Deep learning methods for predicting flows in power grids : novel architectures and algorithms

Abstract : This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid.
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Submitted on : Friday, February 22, 2019 - 1:12:07 PM
Last modification on : Saturday, June 25, 2022 - 10:35:55 PM
Long-term archiving on: : Thursday, May 23, 2019 - 2:20:23 PM


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  • HAL Id : tel-02045873, version 1


Benjamin Donnot. Deep learning methods for predicting flows in power grids : novel architectures and algorithms. Machine Learning [stat.ML]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS060⟩. ⟨tel-02045873⟩



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