When a permanent fault occurs in distribution systems, the fault location should be detected, isolated and the un-faulted but out of service areas have to be restored. The outage areas can be minimized by the switching operation based on the system load characteristics. By integrating the Outage Management Information System (OMIS), the connectivity of customers and feeder/transformer, the Customer Information System (CIS), the Automated mapping /Facility Management (AM/FM) with the customer load patterns, the hourly load demand and the service priority index of each distribution feeder and each service zone are calculated. By this way, the service restoration of the most power demand and customers can be obtained for the fault contingency of distribution system. To enhance the effectiveness of switching operation for fault contingency of distribution system, the Expert System with CLIPS has been developed by considering the operation rules in the application software program. A underground distribution system with 26 feeders in Kaohsiung District of Taiwan Power Company has been selected for computer simulation to solve the proper switching operation by taking into account the service priority of customers. It has been verified that the proposed methodology can restore the customer power service effectively by Expert System with distribution operation rules.

L'inférence d’un système, par la reconstruction de la structure à partir de l’analyse de son comportement, est reconnue comme un problème critique. Dans la théorie des systèmes, la structure et le comportement se situent aux extrémités de la hiérarchie qui définit la connaissance du système. L'inférence d’un système peut être également considérée comme l’escalade de la hiérarchie depuis la connaissance de bas niveau vers la connaissance de plus haut niveau. Ceci n'est possible que sous des conditions maitrisées et justifiées. Dans cette thèse, une nouvelle méthode d'inférence de système est proposée. La méthode proposée étend la technique Process Mining pour extraire des connaissances depuis les données des événements du système. Les aspects de modularité, de fréquence et de synchronisation peuvent être extraits des données. Ils sont intégrés ensemble pour construire un modèle Fuzzy-Discrete Event System Specification (Fuzzy-DEVS). La méthode proposée, également appelée méthode D2FD (Data to Fuzzy-DEVS), comprend trois étapes: (1) l’extraction depuis des journaux d’évènements (registres) obtenus à partir des données générées par le système en utilisant une approche conceptuelle; (2) la découverte d'un système de transition, en utilisant des techniques de découverte de processus; (3) l'intégration de méthodes Fuzzy pour générer automatiquement un modèle Fuzzy-DEVS à partir du système de transition. La dernière étape est de l’implémenter cette contribution en tant que plugin dans l'environnement Process Mining Framework (ProM). Afin de valider les modèles construits, une approximation de modèle basée sur le morphisme et une méthode prédictive intégrée à Granger Causality sont proposées. Deux études de cas sont présentées dans lesquelles le modèle Fuzzy-DEVS est déduit à partir de données réelles, où l'outil SimStudio est utilisé pour sa simulation. Les modèles ainsi construits et les résultats de simulation sont validés par comparaison à d'autres modèles. / System inference, i.e., the building of system structure from system behavior, is widely recognized as a critical challenging issue. In System Theory, structure and behavior are at the extreme sides of the hierarchy that defines knowledge about the system. System inference is known as climbing the hierarchy from less to more knowledge. In addition, it is possible only under justifying conditions. In this thesis, a new system inference method is proposed. The proposed method extends the process mining technique to extract knowledge from event data and to represent complex systems. The modularity, frequency and timing aspects can be extracted from the data. They are integrated together to construct the Fuzzy Discrete Event System Specification (Fuzzy-DEVS) model. The proposed method is also called D2FD (Data to Fuzzy-DEVS) method, and consists of three stages: (1) extraction of event logs from event data by using the conceptual structure; (2) discovery of a transition system, using process discovery techniques; (3) integration of fuzzy methods to automatically generate a Fuzzy-DEVS model from the transition system. The last stage is implemented as a plugin in the Process Mining Framework (ProM) environment. In order to validate constructed models, morphism-based model approximation and predictive method integrated with Granger Causality are proposed. Two case studies are presented in which Fuzzy-DEVS model is inferred from real life data, and the SimStudio tool is used for its simulation. The constructed models and simulation results are validated by comparing to other models.

Made available in DSpace on 2014-12-17T14:56:12Z (GMT). No. of bitstreams: 1 DaniloCS_DISSERT.pdf: 3897603 bytes, checksum: cd98fa05a1dee36b5186c50e95b2f03b (MD5) Previous issue date: 2013-02-01 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Operating industrial processes is becoming more complex each day, and one of the factors that contribute to this growth in complexity is the integration of new technologies and smart solutions employed in the industry, such as the decision support systems. In this regard, this dissertation aims to develop a decision support system based on an computational tool called expert system. The main goal is to turn operation more reliable and secure while maximizing the amount of relevant information to each situation by using an expert system based on rules designed for a particular area of expertise. For the modeling of such rules has been proposed a high-level environment, which allows the creation and manipulation of rules in an easier way through visual programming. Despite its wide range of possible applications, this dissertation focuses only in the context of real-time filtering of alarms during the operation, properly validated in a case study based on a real scenario occurred in an industrial plant of an oil and gas refinery / A opera??o de processos industriais vem se tornando mais complexa ao longo dos anos, e um dos elementos que possibilitam este aumento de complexidade ? a integra??o de novas tecnologias e solu??es inteligentes empregadas no setor, como ? o caso dos sistemas de apoio ? decis?o. Neste sentido, esta disserta??o visa o desenvolvimento de um sistema de aux?lio ? opera??o baseado em uma ferramenta computacional chamada de sistema especialista. O objetivo principal ? tornar a opera??o mais confi?vel e segura ao maximizar a quantidade de informa??es relevantes a cada situa??o atrav?s da utiliza??o de um sistema especialista baseado em regras pr?-moldadas para uma determinada ?rea de conhecimento. Para a modelagem de tais regras foi proposto um ambiente de alto-n?vel, que permite a cria??o e manipula??o de regras de forma facilitada atrav?s de programa??o visual. A despeito de sua ampla gama de poss?veis aplica??es, esta disserta??o tem como foco o contexto de filtragem em tempo real de alarmes durante a opera??o, devidamente validada em um estudo de caso baseado em um cen?rio real ocorrido em uma planta industrial de uma refinaria de petr?leo e g?s