SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 15 Aug 2020 11:04:23 GMT2020-08-15T11:04:23ZConfidence Level Optimization of DG Piecewise Affine Controllers in Distribution Grids
http://hdl.handle.net/10985/17725
Confidence Level Optimization of DG Piecewise Affine Controllers in Distribution Grids
BUIRE, Jerome; COLAS, Frédéric; DIEULOT, Jean-Yves; DE ALVARO, Leticia; GUILLAUD, Xavier
Distributed generators (DGs) reactive powers are controlled to mitigate voltage overshoots in distribution grids with stochastic power production and consumption. Classical DGs controllers may embed piecewise affine laws with dead-band terms. Their settings are usually tuned using a decentralized method which uses local data and optimizes only the DG node behavior. It is shown that when short-term forecasts of stochastic powers are Gaussian and the grid model is assumed to be linear, nodes voltages can either be approximated by Gaussian or sums of truncated Gaussian variables. In the latter case, the voltages probability density functions (pdf) that are needed to compute the overvoltage risks or DG control effort are less straightforward than for normal distributions. These pdf are used into a centralized optimization problem which tunes all DGs control parameters. The objectives consist in maximizing the confidence levels for which voltages and powers remain in prescribed domains and minimizing voltage variances and DG efforts. Simulations on a real distribution grid model show that the truncated Gaussian representation is relevant and that control parameters can easily be updated even when extra DGs are added to the grid. The DG reactive power can be reduced down to 50% or node voltages variances can be reduced down to 30%.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/177252019-01-01T00:00:00ZBUIRE, JeromeCOLAS, FrédéricDIEULOT, Jean-YvesDE ALVARO, LeticiaGUILLAUD, XavierDistributed generators (DGs) reactive powers are controlled to mitigate voltage overshoots in distribution grids with stochastic power production and consumption. Classical DGs controllers may embed piecewise affine laws with dead-band terms. Their settings are usually tuned using a decentralized method which uses local data and optimizes only the DG node behavior. It is shown that when short-term forecasts of stochastic powers are Gaussian and the grid model is assumed to be linear, nodes voltages can either be approximated by Gaussian or sums of truncated Gaussian variables. In the latter case, the voltages probability density functions (pdf) that are needed to compute the overvoltage risks or DG control effort are less straightforward than for normal distributions. These pdf are used into a centralized optimization problem which tunes all DGs control parameters. The objectives consist in maximizing the confidence levels for which voltages and powers remain in prescribed domains and minimizing voltage variances and DG efforts. Simulations on a real distribution grid model show that the truncated Gaussian representation is relevant and that control parameters can easily be updated even when extra DGs are added to the grid. The DG reactive power can be reduced down to 50% or node voltages variances can be reduced down to 30%.Investigation on Model Predictive Control of a Five-Phase Permanent Magnet Synchronous Machine under Voltage and Current limits
http://hdl.handle.net/10985/9946
Investigation on Model Predictive Control of a Five-Phase Permanent Magnet Synchronous Machine under Voltage and Current limits
KESTELYN, Xavier; GOMOZOV, Oleg; BUIRE, Jerome; COLAS, Frédéric; NGUYEN, Ngac Ky; SEMAIL, Eric
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.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/99462015-01-01T00:00:00ZKESTELYN, XavierGOMOZOV, OlegBUIRE, JeromeCOLAS, FrédéricNGUYEN, Ngac KySEMAIL, EricThe 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.