Commande non linéaire à modèle prédictif pour une machine asynchrone

La machine asynchrone est un système multivariable, non linéaire, fortement couplé, à dynamique rapide et à paramètres variant dans le temps. Vu les avantages qu'elle a sur les autres types de machines électriques, parmi lesquels nous pouvons citer : robustesse, entretien moins fréquent et faible coût, la machine asynchrone est de loin la plus utilisée dans les applications requérant la variation de vitesse. Cependant, sa nature non linéaire rend sa commande compliquée. Le but de cette thèse est la mise en oeuvre d'une loi de commande non linéaire prédictive de haute performance pour un moteur asynchrone, avec comme objectifs : améliorer la poursuite de trajectoires, garantir la stabilité, la robustesse aux variations des paramètres et le rejet de perturbation. L'élément de base dans une commande prédictive est le modèle pour prédire le comportement du système. Deux méthodes sont utilisées pour la conception du modèle de prédiction ; l'une est une conception à partir de réseaux de neurones pour une commande prédictive neuronale, et l'autre utilise les outils mathématiques de la géométrie différentielle pour la commande non linéaire prédictive. Dans la commande prédictive neuronale, une modélisation multivariable de la machine par un réseau de neurones de type multicouches est présentée pour la conception d'un prédicteur non linéaire. La commande optimale est obtenue en minimisant un critère quadratique. Un modèle de référence, calculé à partir de l'inversion du modèle de la machine, est inclus dans le critère à optimiser. Ce modèle permet d'améliorer l'optimisation. Pour la commande non linéaire prédictive, deux variantes sont proposées dans ce travail. Une commande multivariable pour le contrôle d'un système carré avec la vitesse rotorique et la norme carré du flux rotorique comme sorties, et une commande en cascade pour le contrôle du couple électromagnétique, de la norme carré du flux et de la vitesse. Le choix de ces sorties est pris pour contrôler la vitesse et simplifier le calcul différentiel lors du développement de ces lois de commande. Le modèle de prédiction est obtenu en utilisant une expansion en série de Taylor. La perturbation (couple de charge) est estimée par un observateur. L'intégration de la commande prédictive dans la structure de l'observateur de perturbation lui permet de se comporter comme un contrôleur PID ou PI de vitesse selon le degré relatif de la sortie vitesse par rapport à la commande. Cette combinaison aboutit à une commande non linéaire PID (ou PI) prédictive. La commande non linéaire prédictive est détaillée, d'abord dans le cas où l'état est supposé entièrement mesurable, puis lorsqu'un observateur doit être utilisé. Le problème de la stabilité globale est alors pris en compte. La méthode de Lyapunov est utilisée pour prouver la stabilité globale du schéma complet de commande (procédé + commande + observateur d'état). Les performances de poursuite de trajectoires, de robustesse aux variations de paramètres et de rejet de perturbation, sont améliorées par ce contrôleur non linéaire prédictif. Nous croyons que ce que nous avons réalisé avec la commande prédictive non linéaire constitue une contribution majeure au domaine des entraînements à vitesse variable par machines asynchrones

Cascade Second Order Sliding Mode Control for Permanent Magnet Synchronous Motor Drive

Published VersionThis paper presents a cascade second-order sliding mode control scheme applied to a permanent magnet synchronous motor for speed tracking applications. The control system is comprised of two control loops for the speed and the armature current control, where the command of the speed controller (outer loop) is the reference of the q-current controller (inner loop) that forms the cascade structure. The sliding mode control algorithm is based on a single input-output state space model and a second order control structure. The proposed cascade second order sliding mode control approach is validated on an experimental permanent magnet synchronous motor drive. Experimental results are provided to validate the effectiveness of the proposed control strategy with respect to speed and current control. Moreover, the robustness of the second-order sliding mode controller is guaranteed in terms of unknown disturbances and parametric and modeling uncertainties

Power Management Strategy for Solar-Wind-Diesel Stand-Alone Hybrid Energy System

This paper presents a simulation and mathematical model of stand-alone solar-wind-diesel based hybrid energy system (HES). A power management system is designed for multiple energy resources in a stand-alone hybrid energy system. Both Solar photovoltaic and wind energy conversion system consists of maximum power point tracking (MPPT), voltage regulation, and basic power electronic interfaces. An additional diesel generator is included to support and improve the reliability of stand-alone system when renewable energy sources are not available. A power management strategy is introduced to distribute the generated power among resistive load banks. The frequency regulation is developed with conventional phase locked loop (PLL) system. The power management algorithm was applied in Matlab®/Simulink® to simulate the results

Real-Time Control of Active and Reactive Power for Doubly Fed Induction Generator (DFIG)-Based Wind Energy Conversion System

Publisher's Version/PDFThis paper presents the modeling, rapid control prototyping, and hardware-in-the-loop testing for real-time simulation and control of a grid-connected doubly fed induction generator (DFIG) in a laboratory-size wind turbine emulator for wind energy conversation systems. The generator is modeled using the direct-quadrature rotating reference frame circuit along with the aligned stator flux, and the field-oriented control approach is applied for independent control of the active and reactive power and the DC-link voltage at the grid side. The control of the active, reactive power and the DC-link voltage are performed using a back-to-back converter at sub- and super-synchronous as well as at variable speeds. The control strategy is experimentally validated on an emulated wind turbine driven by the Opal-RT real-time simulator (OP5600) for simultaneous control of the DC-link voltage, active and reactive power

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Adaptive Sliding Mode Speed Control for Wind Energy Experimental System

In this paper, an adaptive sliding mode speed control algorithm with an integral-operation sliding surface is proposed for a variable speed wind energy experimental system. In the control design, an estimator is designed to compensate for the uncertainties and the unknown turbine torque. In addition, the bound of the sliding mode is investigated to deal with uncertainties. The stability of the system can be guaranteed in the sense of the Lyapunov stability theorem. The laboratory size DC generator wind energy system is controlled using a buck-boost DC-DC converter interface. The control system is validated by experimentation and results demonstrate the achievement of favorable speed tracking performance and robustness against parametric variations and external disturbances

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA