A aplicação de técnicas de Inteligência artificial em sistemas elétricos de potência permite a resolução de problemas onde soluções tradicionais não mostram bons resultados. Por exemplo, após a ocorrência de uma falta, um sistema automatizado toma todas as medidas necessárias para proteger o sistema e assumir um estado seguro de operação. Nessa ocasião, cabe aos operadores verificar todas as mensagens e dados disponíveis, selecionando aquelas relevantes, para então chegar a uma conclusão sobre o evento ocorrido. Apenas após esse diagnóstico, com base em seu conhecimento, o operador pode ordenar o restabelecimento do sistema ou sua manobra para outro estado. Esse processo é lento e passível de falhas, pois as informações são usualmente conflitantes e confusas, devido à avalanche de eventos, alarmes e alertas decorrentes. Diante desta dificuldade, é interessante dispor de uma ferramenta automática de diagnóstico e apoio na tomada de decisão que torne essa tarefa mais ágil e com menores riscos. Este trabalho aborda a integração e a interoperabilidade de equipamentos de uma subestação elétrica em conformidade com a norma IEC 61850, e a utilização de sistemas inteligentes com lógica Fuzzy executados em um controlador programável moderno. O sistema proposto faz uso de recursos de comunicação da IEC 61850, com mensagens prioritárias padrão GOOSE incluindo valores analógicos de corrente de falta, além de mensagens no padrão TCP/IP MMS. Essas informações, alarmes e eventos, são processadas de forma automática por um motor Fuzzy que permite aproximar a decisão computacional à decisão humana, incluindo zonas de incerteza e lógica ponderada, permitindo respostas mais abstratas do que algo puramente binário. O sistema proposto foi submetido a um grande número de eventos com um simulador em tempo real, com equipamentos reais, tais como IEDs e um controlador programável de alto desempenho. Além disso o sistema usou um esquema de comunicação inovador para o módulo do processador de dados em tempo real (RTDP), inteiramente baseado nos protocolos da IEC 61850. / The application of artificial intelligence techniques in electric power systems allows solving problems where traditional solutions do not show good results. For example, in electric power systems, after the occurence of a fault, an automated system takes all necessary measures to protect the system and assume a safe state of operation. On this occasion, it is for operators to review all messages and data available, selecting those relevant to then come to a conclusion about the event that occurred. Only after this diagnosis, based on his knowledge the operator may order the restoration of the system or their operation to another state. This process is therefore slow and subject to failure, because the information is usually conflicting and confusing because of the avalanche of events, alarms and warnings arising. Faced with this difficulty, it is interesting to have an automatic diagnostic tool and support in decision making that makes this task quicker and with less risk. This paper addresses the integration and interoperability of an electrical substation equipment in accordance with IEC 61850, and the use of intelligent systems Fuzzy logic running on a modern programmable controller. The proposed system makes use of the IEC 61850 communication resources with priority GOOSE messages including standard analog values of fault current, as well as messages on standard TCP / IP MMS. This information, alarms and events, are processed automatically by a fuzzy engine that allows the computational approach to human decision making, including areas of uncertainty and weighted logic, allowing more abstract answers than purely binary. The proposed system has undergone a large number of events with a simulator in real time with actual equipment such as a programmable controller IEDs and high performance. In addition the system used an innovative communication scheme for the module\'s real-time data processor ( RTDP ), based entirely on the IEC 61850 standard protocols such as MMS and GOOSE messages.

ABSTRACT EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER G&uuml / ner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoglu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 &amp / M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPDAF) and interactive multiple model filtering are used for this purpose. Keywords: clutter, false alarms, track initiation, data association, gating, target kinematics, IMM, JPDAF

Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the anaesthetists.

In the Diploma Work is described the Simotion commanding system which has been produced by Siemens. Conception of the system and its features are explained. Further, the work deals with the data and alarm transfer among separate systems, an item which has been dealt with by the help of OPC (OLE for Process Control). Its update and specification have been covered with OPC Foundation organization. The alarm report visualization out of the Simotion system to WinnCC environment (which is not a client of OPC Alarms & Events server) is introduced here .

Today’s intensive care units monitor patients through the use of various medical devices, which generate a high ratio of false positive alarms due to a low alarm specificity. The false alarms have resulted in a stressful working environment for healthcare professionals that are getting more desensitized to triggered alarms and causing alarm fatigue. The patient safety is also compromised by having high noise levels in the patient room, which disturbs their sleep. This thesis has developed an intelligent alarm system with an improved alarm management and the use of 23 intelligent algorithms to minimize the number of false positive alarms. The suggested system is capable of improving the alarm situation and increasing the patient safety in critical care. The algorithms were modeled with fuzzy logics consisting of delays and multi parameter validation. The results were iteratively developed by having focus groups with various experts.

Speech intelligibility of voice alarm communication systems is extremely important for proper notification and direction of building occupants. Currently, there is no minimum standard to which all voice alarm communication systems must be held. Tests were conducted to determine how system and room characteristics, and the addition of occupants, affect the intelligibility of a voice signal. This research outlines a methodology for measuring the speech intelligibility of a room and describes the impact of numerous variables on these measurements. Eight variables were considered for this study: speaker quantity and location, speaker power tap, sound pressure level (SPL), number and location of occupants, presence of furniture, location of intelligibility measurements, data collection method, and floor covering. All room characteristics had some affect on the room intelligibility; the sound pressure level of the signal and the number and location of occupants had the greatest overall impact on the intelligibility of the room. It is recommended, based on the results of this study, that further investigation be conducted in the following areas: floor finishes, speaker directivity, various population densities, furniture packages and room sizes.

Cette thèse est composée de deux parties: une première partie traite le problème de changement de régime et une deuxième partie concerne le processusautorégressif à seuil dont les innovations ne sont pas indépendantes. Toutefois, ces deux domaines de la statistique et des probabilités se rejoignent dans la littérature et donc dans mon projet de recherche. Dans la première partie, nous étudions le problème de changements derégime. Il existe plusieurs méthodes pour la détection de ruptures mais les principales méthodes sont : la méthode de moindres carrés pénalisés (PLS)et la méthode de derivée filtrée (FD) introduit par Basseville et Nikirov. D’autres méthodes existent telles que la méthode Bayésienne de changementde points. Nous avons validé la nouvelle méthode de dérivée filtrée et taux de fausses découvertes (FDqV) sur des données réelles (des données du vent sur des éoliennes et des données du battement du coeur). Bien naturellement, nous avons donné une extension de la méthode FDqV sur le cas des variables aléatoires faiblement dépendantes.Dans la deuxième partie, nous étudions le modèle autorégressif à seuil (en anglais Threshold Autoregessive Model (TAR)). Le TAR est étudié dans la littérature par plusieurs auteurs tels que Tong(1983), Petrucelli(1984, 1986), Chan(1993). Les applications du modèle TAR sont nombreuses par exemple en économie, en biologie, l'environnement, etc. Jusqu'à présent, le modèle TAR étudié concerne le cas où les innovations sont indépendantes. Dans ce projet, nous avons étudié le cas où les innovations sont non corrélées. Nous avons établi les comportements asymptotiques des estimateurs du modèle. Ces résultats concernent la convergence presque sûre, la convergence en loi et la convergence uniforme des paramètres. / This thesis has two parts: the first part deals the change points problem and the second concerns the weak threshold autoregressive model (TAR); the errors are not correlated.In the first part, we treat the change point analysis. In the litterature, it exists two popular methods: The Penalized Least Square (PLS) and the Filtered Derivative introduced by Basseville end Nikirov.We give a new method of filtered derivative and false discovery rate (FDqV) on real data (the wind turbines and heartbeats series). Also, we studied an extension of FDqV method on weakly dependent random variables.In the second part, we spotlight the weak threshold autoregressive (TAR) model. The TAR model is studied by many authors such that Tong(1983), Petrucelli(1984, 1986). there exist many applications, for example in economics, biological and many others. The weak TAR model treated is the case where the innovations are not correlated.

Les systèmes d'alarmes jouent un rôle critique dans la bonne exploitation des installations industrielles modernes. Cependant, dans la plupart de ces systèmes, les alarmes ne sont pas toujours traitées correctement par les opérateurs car il y a régulièrement beaucoup trop d’alarmes à gérer, notamment lors des avalanches d’alarmes. Pour réduire le flux d'alarmes, notre approche consiste à détecter des redondances entre alarmes qui pourraient être supprimées. Pour atteindre cet objectif, nous recherchons dans un premier temps les adjacences fréquentes entre les alarmes contenues dans un historique. Ceci est réalisé en adaptant l’algorithme de recherche de motifs fréquents AprioriAll. Nous explorons également une seconde méthode consistant à trouver des précédences systématiques. Pour les découvrir, nous identifions les relations de domination et de mutuelle dépendance contenues dans l’historique des alarmes. Pour faciliter l’analyse experte, les relations découvertes sont traduites sous la forme d’un réseau de Pétri.Ces deux méthodes sont ensuite confrontées à un historique d’alarmes industriel fourni par General Electric. Les résultats obtenus montrent que nos deux méthodes permettent une réduction globale du flux d'alarmes qui est plus importante durant les avalanches. / Alarm systems play an important role for the safe and efficient operation of modern industrial plants. However, in most of industrial alarm systems, alarm flows cannot always be correctly managed by the operators as they often turn into alarm floods, sequences of numerous alarms occurring in a short period of time. To reduce the alarm flows, this report focuses on detection of redundant alarms that could be removed. This objective is met by, first, looking for frequent adjacency in the alarm log. To identify them, the frequent pattern mining algorithm AprioriAll is adapted. Another way to find potentially redundant alarms is to look for systematic predecessors. To discover them, dominations and mutual dependencies contained in the alarm log are identified. To ease this analysis, the discovered relations are depicted in the form of Petri nets.Both those methods are then tested against an industrial alarm log made available by General Electric. The results show that both methods allow a reduction of the alarm flow, with the biggest reduction being during alarm floods.

This work deals with the topic – Heating with the use of large radiating structures. Theoretical part contains distribution underfloor heating, wall heating and ceiling heating. This topic is applied to the specified building. The project deals with preparing of domestic water, wall heating and underfloor heating. Experimental part deals with the measurement of ceiling heating.

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