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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract-The optimal control of electrical drives necessitates to take into account current and voltage limits that are imposed by the power electronics and the electrical machines. Let's cite for example the flux-weakening operation of electrical drives for propulsion. If the control of classical three-phase drives under voltage and current limits are known for a long time, the specific characteristics of multiphase drives open the way to researches on their control under such constraints. This paper aims to explain what are the main differences between three-phase and multiphase drives when they run under voltage and current constraints and try to show what are the scientific and technical problems to be solved. Some first results are given in order to show that Model Predictive Control (MPC) is expected to be a good candidate to answer the proposed challenge.

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID AbstractVolatile productions and consumptions generate a stochastic behavior of distribution grids and make its supervision difficult to achieve. Usually, the Distributed Generators reactive powers are adjusted to perform decentralized voltage control. Industrial controllers are generally equipped with a local affine feedback law, which settings are tuned at early stage using local data. A centralized and more efficient tuning method should aim to maximize the probability that all the node voltages of distribution grids remain within prescribed bounds. When the characteristics of the stochastic power forecasts are known, the centralized algorithm allows to update the settings on a regular time basis. However, the method requires to solve stochastic optimization problem. Assuming that stochastic variables have Gaussian distributions, a procedure is given which guarantees the convergence of the stochastic optimization. Convex problems drastically reduce the difficulty and the computational time required to reach the global minimum, compared to nonconvex optimal power flow problems. The linear controllers with optimized parameters are compared to traditional control laws using simulations of a real distribution grid model. The results show that the algorithm is reliable and moreover fast enough. Hence, the proposed method can be used to update periodically the control parameters.