Ce travail de thèse porte sur la conception et le développement de capteurs de courant adaptés aux gammes de tensions et de courants d’une installation électrique tertiaire ou industrielle. Ces nouveaux capteurs permettent d’obtenir un contrôle sur la gestion de la consommation électrique, en caractérisant un réseau grâce à la mesure de courant. Ces données transmises devront restituer les variations sur la courbe de charge de façon suffisamment détaillée pour permettre de reconnaître les équipements en fonctionnement.La première partie de ce mémoire présente les études réalisées afin de concevoir les capteurs innovants; afin de répondre aux problématiques liées aux régimes continus et transitoires. Les solutions retenues sont basées sur la technologie Rogowski qui présente notamment l’avantage d’une excellente linéarité ainsi que la mesure d’une large dynamique avec un seul dispositif. Les caractérisations de ces capteurs ont permis de valider ces modèles. La sensibilité, la linéarité et la mesure de la FFT sont certains des paramètres qu’il faut évaluer afin de caractériser les boucles de Rogowski.Les mesures effectuées sur des bancs de mesure au laboratoire et au sein de la société, avec des essais sur le terrain ont permis de spécifier et de concevoir une électronique de mise en forme, en vue d’une réalisation d’un circuit dédié. La seconde partie de ce travail concerne le conditionnement du signal. L’objectif est de rendre un capteur sans fil à l’aide de la technologie Bluetooth Low Energy et l’utilisation d’un système électronique RF utilisant un transmetteur. / Part of the CIFRE contract in collaboration with Qualisteo company, this thesis focuses on the design and development of current sensors suitable for large voltage and current ranges for a tertiary or industrial or electrical installation. These new sensors allows to obtain control over the management of power consumption, featuring a network through the current measurement. These transmitted data will return the variations of the charging curve with sufficient detail to allow to recognize the equipment in operation, limiting at the same time the size of the information provided by several orders of magnitude compared to the original signal.The first part of this thesis presents the work done in order to develop innovative sensors. The developed sensors will proposed an answer to respond to the problems related to continuous and transient states. The solutions are based on the Rogowski technology which has the advantage of excellent linearity and measuring a wide dynamic with only one device. Coil sensitivity, linearity, time domain and FFT measurements are some of the mains parameters to judge the static characteristics of the Rogowski coil.The response of this new sensors have been increased as the design and technologies have been tested. Measurements on measuring benches made by the laboratory and field trials enabled to specify and design an electronic treatment, for the specific purpose of achieving a dedicated circuit.The second part of this work concerns the signal conditioning. The aim is to make the wireless sensor using Bluetooth Low Energy technology and use of an electronic system including RF transmitter implemented.

NIALM (Non-Intrusive Load Monitoring) est une technologie innovante qui permet de suivre la consommation individuelle en énergie des différents appareils électriques dans un réseau électrique grâce à un seul point de mesure. Ainsi, l’installation et la maintenance du système est très simple. Cependant, le logiciel NIALM nécessite le développement d’algorithmes sophistiqués pour identifier la consommation de chaque appareil avec une bonne précision. Par conséquent, ces algorithmes complexes nécessitent une plate-forme d’exécution puissante et coûteuse. En réponse à ce problème, cette thèse propose un système NIALM innovant fonctionnant en temps réel et à faible coût. Ce système permet de dépasser certaines limites actuelles du NIALM grâce à une extraction d’informations supplémentaires sur les signatures électriques, une détection des transitions lentes et des appareils à multi-états grâce à deux nouvelles fonctions : un algorithme de détection d'événements CUSUM et une ventilation des sommes cumulées en se basant sur un algorithme génétique. La deuxième contribution importante est de proposer une méthodologie utilisant le modèle RPN (Reactive Process Network) pour développer le système NIALM dans un SoC (System on Chip) avec une accélération matérielle de type FPGA. Ce SoC permet ainsi l'exécution en parallèle dans le FPGA de processus de traitement de données avec des algorithmes complexes tout en satisfaisant les contraintes de temps. Les avantages de notre méthode sont : la capacité de développer une spécification exécutable, d’effectuer une exploration d'architecture, et d’obtenir rapidement un prototype du système NIALM à partir d’un même modèle applicatif. / In comparison to conventional smart meters, NIALM (Non-Intrusive Load Monitoring) is an innovative technology because it can monitor power usage on individual appliances in an electrical network using only one sensing node. Thus, setting up and maintaining the system is very simple because of the few of hardware elements. In contrast, the software of NIALM is often very complex and there is still the need in developing more complex algorithms to classify appliances more accurately. These complex algorithms of NIALM require to run on a powerful and expensive hardware platform. In order to overcome this problem, the first contribution of this thesis is to propose a low cost real-time innovative NIALM system to solve some limits of NIALM design by extracting more electrical signatures, detecting slow transition and multi-state appliances, and energy disaggregation in real-time. This is possible by using two new algorithms: CUSUM event detection algorithm and disaggregation based on Genetic Algorithm. Similar to complex DSP systems, a NIALM system contains both event control processes and data streaming processes. The second important contribution of this research is to propose a methodology based on RPN model (Reactive Process Network) to develop a complex NIALM system in SoC with FPGA acceleration. Such SoC allows running data streaming processes with complex algorithms and hard timing constraints in parallel in FPGA while other processes can run in processors. The advantages of our methodology are the ability to develop an executable specification to proceed to architecture exploration, and prototype the NIALM system quickly using the same application model.

Le travail de recherche proposé est relatif au suivi de la consommation par usage afin que les clients puissent mieux maîtriser leurs consommations électriques. Ce projet de recherche consiste à identifier les charges électriques en milieu domestique à partir des mesures non intrusives faites au niveau du compteur électrique. Les informations relevées et traitées sont les courants et les tensions. Nous avons proposé une méthode innovante et performante d'identification, c'est la méthode de Matrix Pencil. Elle s'adapte à la fois aux parties transitoires que permanentes des signatures traitées. Nous proposons deux voies d'investigations complémentaires en basses fréquences et en hautes fréquences auxquelles on appliquera la méthode de Matrix Pencil pour caractériser chaque charge par un ensemble de pôles et de résidus tant en régime transitoire qu'en régime permanent. Cette technique d'identification a été implantée dans le prototype de compteur électrique réalisant les fonctions de reconnaissance et d'identification des signatures. La conception et la réalisation du prototype ont été suivies par la validation de la fonction d'identification et de reconnaissance des usages dans une maison témoin. La capacité d'identification et le niveau du taux d'erreur sont très satisfaisants. Une éventuelle amélioration technologique permettra dans le futur de réaliser une meilleure identification.

There is a continuously growing amount of appliances and energy dependent services in households. To date, efforts have mostly focused on energy efficiency, however behavior changes are required for a more sustainable energy consumption. People therefore need to understand their consumption habits to be able to adapt them. Appliance-specific feedback is probably the most efficient way to impact behaviors, since people need to ‘see’ where their electricity goes. Smart meters, currently being extensively rolled out in Europe and in the U.S. are good potential candidates to provide end-users with<p>energy advice. The required functionalities must however be rapidly defined if they are expected to be integrated in the future massive roll out.<p><p>Nonintrusive appliance load monitoring aims to derive appliance-specific information from the aggregate electricity consumption. While techniques have been developed since the 80’s, those mainly address the identification of previously learned appliances, from a database. Building such a database is an intrusive and tedious process which should be avoided. Whereas most recent efforts have focused on unsupervised techniques to disambiguate energy consumption into individual appliances, they usually rely on prior information about measured appliances such as the number of appliances, the number of states in each appliance as well as the power they consume in each state. This information should ideally be learned from the data. This topic will be addressed in the present research.<p><p>This work will present a framework for unsupervised learning for nonintrusive appliance<p>load monitoring. It aims to discover information about appliances of a household solely from its aggregate consumption data, with neither prior information nor user intervention. The learning process can be segmented into five tasks: the detection of on/off switching, the extraction of individual load signatures, the identification of<p>recurrent signatures, the discovery of two-state electrical devices and, finally, the elaboration<p>of appliance models. The first four steps will be addressed in this paper.<p><p>The suite of algorithms proposed in this work allows to discover the set of two-states electrical loads from their aggregated consumption. This, along with the evaluation<p>of their operating sequences, is a prerequisite to learn appliance models from the data. Results show that loads consuming power down to some dozens of watts can be learned from the data. This should encourage future researchers to consider such an unsupervised learning. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-06T18:59:56Z No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) / Made available in DSpace on 2018-08-06T18:59:56Z (GMT). No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) Previous issue date: 2014-02-27 / Capes / Companhias de energia começaram a substituir os medidores de energia tradicionais pelos Smart Meters, que podem transmitir valores de consumo para as companhias em curtos intervalos de tempo. Com uma insfraestrutura de Smart Meters, existem muitas motivações para as concessionárias de energia coletarem dados de consumo em alta resolução. Entretanto, isto implica em informações bastante detalhadas sobre os consumidores sendo monitoradas. Consequentemente, um problema sério precisa ser resolvido: como preservar a privacidade dos consumidores sem afetar a prestação de certos serviços pelas concessionárias? Claramente, este é um tradeoff entre privacidade e utilidade. Existem diversas abordagens para preservar a privacidade, porém muitas delas afetam a utilidade dos dados ou possuem um alto custo computacional. Neste trabalho, nós propomos e avaliamos uma abordagem computacionalmente barata que preserva a privacidade e utilidade dos dados através de adição de ruído. Para validar a privacidade, nós avaliamos possíveis ataques (tal como Monitoramento Não-Intrusivo de Carga de Eletrodomésticos - NIALM, do inglês Non-Intrusive Appliance Load Monitoring) utilizando dados reais de consumidores. Para validar a utilidade, nós avaliamos a influência da abordagem em vários benefícios que podem ser providos com o uso de Smart Meters. / Power providers have started replacing traditional electricity meters for Smart Meters, which can transmit power consumption levels to the provider within short intervals. With a Smart Metering infrastructure, there are many motivations for power providers to collect highresolution data of electricity usage from consumers. However, this implies in very detailed information about the consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are several approaches for privacy preserving, but many of them affect the data usefulness or are computationally expensive. In this work, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. To validate the privacy, we evaluate possible attacks (such as a NIALM - Non-Intrusive Appliance Load Monitoring) using real consumers' data. To validate the utility, we analyze the influence of the approach in various benefits that can be provided through the use of Smart Meters.