This paper proposes a control strategy for robust estimation of the transformation angle error despite variations in motor parameters. The method focuses on identifying and compensating angle errors in Indirect Rotor Flux-Oriented Control (IRFOC). The main objective is to correct the misalignment between the (d, q) reference frame and the actual magnetic flux. In contrast to conventional methods that depend on adaptive techniques and real-time parameter estimation, the proposed strategy avoids the need of an online identification of motor parameters. To improve the accuracy of the transformation angle error estimation, the approach operates on multiple reference frames, including (α, β) frame, (d, q) frame, and a deviated rotating frame referred as the (d′, q′). It relies on a robust Sliding-Mode observer based on the Super-Twisting algorithm. Simulation results using the IRFOC scheme are provided to validate the performance of the proposed method.

In this paper, the problem of electric vehicle (EV) charging at the workplace is addressed via a two-layer predictive algorithm. We consider a time of use (TOU) pricing model for energy drawn from the grid and try to minimize the charging cost incurred by the EV charging station (EVCS) operator via an economic layer based on dynamic programming (DP) approach. An adaptive prediction algorithm based on a non-parametric stochastic model computes the projected EV load demand over the day which helps in the selection of optimal loading policy for the EVs in the economic layer. The second layer is a scheduling algorithm designed to share the allocated power limit (obtained from economic layer) among the charging EVs during each charge cycle. The modeling and validation is performed using ACN data-set from Caltech. Comparison of the proposed scheme with a conventional DP algorithm illustrates its effectiveness in terms of supplying the requested energy despite lacking user input for departure time.

Reducing greenhouse gas (GHG) emissions from Information and Communication Technology (ICT) has become a hot topic since the Paris Agreement. Data centers are one of the most impactful ICT energy consumers since they are built to run 24 hours / 7 days. An emerging discussion is switching their power supply from brown to green energy, using Renewable Energy Sources (RES). However, this change introduces uncertainties linked to production intermittence. This work is part of the Datazero2 project. This project designs a data center powered only by renewable production, adding storage elements to reduce the impact of the intermittence. A clean-by-design data center requires several decisions at different levels of management. To do so, it uses predictions to plan the actions for the next few days. However, it also needs to react to the actual events that can vary from the forecast. This work presents the BEASY heuristic. BEASY mixes power and scheduling decisions in a renewable-only data center, seeking to reduce the number of killed jobs and wasted energy. The results demonstrate that BEASY reduced wasted energy by up to 35.33% in critical cases. Considering the killed jobs, it also kills fewer jobs than the state of art algorithms in all executions.

Many power electronic converters can be modeled using the theoretical framework of switched affine systems, which considers the instantaneous dynamics of the converter instead of its averaged model. In this class of systems, a certain number of subsystems is present and a control law is designed to orchestrate the switching among them. When the switching law is state-dependent, the closed-loop system is stabilized at the desired equilibrium through sliding mode dynamics, leading to infinite-rate switching. In this paper, a control strategy is proposed using constant-width hysteresis as a way of bounding the switching frequency at a finite value when two subsystems are present. In addition, an upper bound on the hysteresis width is provided so that the system is not stabilized at the equilibrium of a subsystem. Finally, simulation results illustrating the application of the proposed method to power electronic converters are presented. The relation between the switching law parameters and the current ripple is discussed for a dc-dc Boost converter and a Buck converter.

International audience

Both academics and industry have engaged their efforts in reducing greenhouse gas (GHG) emissions of Information and Communications Technology (ICT). Data centers are one of the most electricity-expensive ICT actors due to their uninterrupted service. Reducing the usage of brown energy or migrating to green energy using renewable sources (RES) is a way to reduce these emissions. However, this migration is not straightforward due to intermittence from these sources. This work is part of Datazero 2 ANR project. This project aims to design a data center powered only by RES production and storage elements. This architecture requires several decisions at different levels of management. This project divides the problem into two groups: offline and online. On the offline side, it uses renewable and workload predictions to prepare an offline plan with a power envelope (power delivered to the servers) and hints on how to manage the storage (batteries and hydrogen). Given the offline plan, the article's contribution is how to deal with real and dynamic power constraints online while keeping the planned storage level at the end. So, this article proposes policies to modify the plan according to the changes in predictions. We evaluate these policies in a homogeneous and heterogeneous data center. The results demonstrate that our policies could approach the storage level and improve Quality of Service (QoS) independently of data center infrastructures.

Data centers are considered nowadays as the factories of the digital age, being currently responsible for consuming more energy than the entire United Kingdom. On the other side, the global total capacity of renewable power increases continuously. The combination of these two factors calls for new approaches in designing data centers powered only by renewable energy sources. Our work focuses on task scheduling optimization under a power envelope, and on the way to handle power starvation, i.e. when the available power does not provide sufficient resources to execute a given workload. To do so we utilize the concept of task degradation through cross-correlation to find where to place the tasks in order to reduce the data center profit degradation. The results show that our algorithm could obtain more than 34% increase in profit when compared to algorithms from the literature, while fulfilling the power profile and resources constraints.

International audience

International audience

This paper presents a complete modeling of two types of multi-phase induction heating systems and the application of resonant control to achieve a perfect current reference tracking. It proposes a global solution including electrical and thermal modeling of the whole system in PSim software, with a reduced simulation time. The paper presents simplified equivalent model based on data extracted from finite element software, for the modeling of energy transfer between the coil currents and the piece to be heated. The required current density distributions are extracted from the finite element software Flux2D® and the impedance matrix that describes the electrical behavior of the coils supplied by the inverters from Inca3D®. A specific optimization procedure leads to the optimal reference inverter currents in order to obtain a uniform heating of the work piece. In that case, the coupling terms between the phases are not negligible and the sampling frequency/switching frequency ration of the resonant inverter is very low. Then, some specific stability zones are defined for the tuning gains of the resonant controllers.