T-S Controllers For Photovoltaic-Grid Connected System Through DC-DC Boost Converter and Three Phase Inverter

Ce document présente deux contrôleurs flous TS en ligne pour contrôler l'extraction de puissance optimale et son transfert du système PV via deux convertisseurs statiques vers le réseau public. Le premier contrôleur est appliqué sur le convertisseur élévateur pour calculer, à chaque instant, le rapport cyclique permettant de suivre le point de puissance maximale du panneau sous les variations climatiques et d'atteindre un rendement élevé pour la récolte d'énergie solaire. Alors que le second ajuste les états de commutation des branches de l'onduleur triphasé à deux niveauxtransistors pour un transfert maximal de la puissance active produite par le panneau vers le réseau de distribution avec compensation de puissance réactive lors de l'établissement de la synchronisation.Après présentation de la structure du système de connexion au réseau et modélisation mathématique des convertisseurs côté PV et côté réseau, les contrôleurs flous TS sont détaillés. La synthèse de ces contrôleurs est basée sur la subdivision de l'espace d'états du système non linéaire à contrôler en un ensemble de sous-systèmes linéaires. Pour assurer le rejet des perturbations et garantir la stabilité du contrôleur flou,  Le critère et la fonction de stabilité quadratique de Lyapunov sont considérés. Les gains du contrôleur sont calculés en utilisant la solution d'inégalité de matrice linéaire (LMI). Les résultats de la simulation numérique sur l'environnement Matlab-Simulink montrent l'efficacité et les performances des contrôleurs proposés

Soft sensor approach based on magnetic Barkhausen noise by means of the forming process punch-hole-rolling

The relevance of the magnetic Barkhausen noise (MBN) and the non-destructive characterization of material properties in near surface layers, has increased in recent years.With the development of new signal processing techniques, the method was further developed into a powerful evaluation technique and is used in various areas of online and offline measurement. In addition to the established use in the detection of grinding burn, the method is increasingly used in the context of soft sensors for property controlled processes, due to its short analysis times. By a detailed description of a soft sensor concept for the novel forming process punch-hole-rolling this work focuses on the offline characterization of the process specific cause-effect relationships. This is done by analyzing the process interactions as well as the surface layer state by a metallographic investigation. Additionally a non-destructive characterization by means of MBN was done and correlated with the surface layer state. This provides important findings for the use of a MBN-sensor in a soft sensor concept and the potential integration into the forming process

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down

Non-stationary dynamic analysis of a wind turbine power drivetrain: Offshore considerations

This paper presents a multi-body model for studying the non-stationary dynamic behaviour of a wind turbine power drivetrain. The model includes some offshore considerations, such as the extra degrees of freedom and boundary conditions that installation on an offshore floating platform can add. The studied problem is an offshore implementation, with seafloor depths of the order of a hundred metres, making it necessary to use a floating platform. Special attention is paid to the characteristics of the combined offshore buoy support and detailed model of the power train, in order to assess the impacts of buoy movement on forces on gears and bearings. A multi-body analysis code was used to develop the model, and a conventional wind turbine set-up was implemented as an example. Gearbox dynamic behaviour was simulated for common manoeuvres such as a start-up and an emergency stop, and the results are presented and discussed.The authors like to thanks the company Apia XXI for supporting part of the research presented by the Project DINAER. Moreover, some parts of the developments presented have been made in the framework of Project DPI2006-14348 funded by the Spanish Ministry of Science and Technology

A System for Historic Document Image Indexing and Retrieval Based on XML Database Conforming to MPEG7 Standard

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Soft Sensor approach based on magnetic barkhausen noise by means of the forming process punch-hole-rolling

The relevance of the magnetic Barkhausen noise (MBN) and the non-destructive characterization of material properties in near surface layers, has increased in recent years.With the development of new signal processing techniques, the method was further developed into a powerful evaluation technique and is used in various areas of online and offline measurement. In addition to the established use in the detection of grinding burn, the method is increasingly used in the context of soft sensors for property controlled processes, due to its short analysis times. By a detailed description of a soft sensor concept for the novel forming process punch-hole-rolling this work focuses on the offline characterization of the process specific cause-effect relationships. This is done by analyzing the process interactions as well as the surface layer state by a metallographic investigation. Additionally a non-destructive characterization by means of MBN was done and correlated with the surface layer state. This provides important findings for the use of a MBN-sensor in a soft sensor concept and the potential integration into the forming process

Modelisation du comportement et des contraintes residuelles introduites dans un materiau soumis a un grenaillage

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations