Barati, Masoudباسليمان ، كمال2024-01-182024-01-182024https://hdl.handle.net/20.500.14154/71235Please note that page number 3 is the Electronic Theses and Dissertation (ETD) Approval Form containing signatures of committee members. Also, my official transcript is included at the end of the document (after references).Nonlinear convex optimization has provided a great modeling language and a powerful solution tool for the control and analysis of power systems over the last decade. A main challenge today is solving non-convex problems in real-time. However, if an oracle can guess, ahead of time, a high quality initial solution, then most non-convex optimization problems can be solved in a limited number of iterations using off-the-shelf solvers. In this proposal, we study how deep learning can provide good approximations for real-time power system applications. These approximations can act as good initial solutions to any exact algorithm. Alternatively, such approximations could be satisfactory to carry out real-time operations in power systems. First, we address the problem of joint power system state estimation and bad data identification. We propose a deep learning model that provides high quality approximations in milliseconds. Second, we address the problem multi-step ahead power system state forecasting and advocate sequence-to-sequence models for better representation. Lastly, we study the problem of learning fast approximations to intialize linear programming solvers. We cast the problem as a simple learning task and propose a deep learning model.102en-USDeep LearningSequence-to-SequenceTime-series ForecastingNon-convex OptimizationOperations ResearchArtificial IntelligenceGraph Convolution Neural NetworksMachine LearningMixed Integer Linear ProgrammingThesis