The advent of electricity market deregulation has placed great emphasis on the availability of information, the analysis of this information, and the subsequent decision-making to optimize system operation in a competitive environment. This creates a need for better ways of correlating the market activity with the physical grid operating states in real time and sharing such information among market participants. Choices of command and control actions may result in different financial consequences for market participants and severely impact their profits. This work provides a solution, the Intelligent Economic Alarm Processor to be implemented in a control center to assist the grid operator in rapidly identifying the faulted sections and market operation management. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Next, in order to better feed the FRPN model with more accurate inputs, the failure rates of the protections devices are analyzed. A new approach to assess the circuit breaker’s life cycle or deterioration stages using its control circuit data is introduced. Unlike the traditional “mean time” criteria, the deterioration stages have been mathematically defined by setting up the limits of various performance indices. The model can be automatically updated as the new real-time condition-based data become available to assess the CB’s operation performance using probability distributions. The economic alarm processor module is discussed in the end. This processor firstly analyzes the fault severity based on the information retrieved from the fault section estimation module, and gives the changes in the LMPs, total generation cost, congestion revenue etc. with electricity market schedules and trends. Then some suggested restorative actions are given to optimize the overall system benefit. When market participants receive such information in advance, they make estimation about the system operator's restorative action and their competitors' reaction to it.

With the rapid growth of load demand, the distribution system is becoming more and more complicated, and the operational efficiency and service quality deteriorated. Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. The distribution system containing numerous protective facilities and switch equipment ranges over wide boundary. It becomes very complicated for dispatchers to obtain restoration plan for out-of-service areas. To cope with the problem, an effective tool is helpful for the restoration. This thesis proposes the use of Bi-directional associative memory networks (BAMN) to develop alarm processing. And use of Probabilistic Neural Network (PNN) to develop fault section detection, fault isolation, and restoration system. A distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed system. The thesis proposes to use Bi-directional Associative Memory Network¡]BAMN¡^ to pre-process the signal gained from SCADA Interface, and transmit correct signal to Probabilistic Neural Network (PNN) for restoration plan . Computer simulation shows a simplified model to shorten the processing time in this study.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. Dispatchers are use the changed statuses of protection devices from the Supervisory Control and Data Acquisition (SCADA) system to identify the fault. To reduce the outage duration and promptly restore power services, fault section detection has to be done effectively and accurately with fault alarms. In this thesis, artificial neural networks (ANN) and Grey Relational Analysis (GRA) are used to develop the restoration schemes for emergency operation in a power system including fault section detection (FSD), restoration strategy(RS), and voltage correction(VC). The optimal power flow (OPF) is responsible for verifying the proposed schemes by off-line analysis. With a IEEE 30-Bus power system, computer simulations were conducted to show the effectiveness of the proposed restoration schemes.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. Dispatchers could study the changed statuses of primary/back-up relays and circuit breakers to identify the fault section and fault types. It is difficult to process too many alarms under various conditions in a large power system. Single fault, multiple faults, single and multiple faults could coexist with the failed operation of relays and circuit breakers, or with the erroneous data communication. Dispatchers need more time to process the many uncertainties before identifying the fault. This thesis presents the use of artificial intelligence for fault section detection in substation with neural networks. Probabilistic Neural Networks (PNN) are proposed for fault detection system in substation. The proposed methodology will use primary/back-up information of protective relays and circuit breakers to detect the fault sections involving single fault, multiple faults, or fault with the failure operation of the relays and circuit breakers. This paper also presents a fuzzy theory-based method to identify fault types. It is derived to improve the inadequacy of making decisions by selecting a fixed threshold value and has the capability of non-deterministic decision making with a prior knowledge of uncertainties in fault location, fault resistance and the a size of loads. The proposed approach has been tested on a typical taipower system with accurate results.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively and accurately with fault alarms. Dispatchers study the changed statuses of protection devices from the Supervisory Control and Data Acquisition (SCADA) system to identify the fault. Single and multiple faults could coexist with the failed operation of relays and circuit breakers, or with the erroneous data communication. It needs a long time to process a large number of alarms under various conditions involving multiple faults and many uncertainties. To cope with the problem, an effective tool is helpful for the fault section estimation and alarm processing. Besides, power transformer plays a major role in a power system. For a better service quality, it is important to be routinely examined for detecting incipient faults inside transformers. Preventive techniques for early detection can find out the incipient faults and avoid outages. Power quality is another issue to considerable attentions from utilities and customers due to the popular uses of many sensitive electronic equipment. Harmonics, voltage swell, voltage sag, and, power interruption could downgrade the service quality. To ensure the power quality, detecting harmonic and voltage disturbances becomes important. A detection method with classification capability will be helpful for detecting disturbances. This dissertation developed various algorithm for detection including fault section detection, alarm processing, transformer fault diagnosis, and power quality detection. For a well-dispatched power system, the adaptive detection idea will be used, and the existing SCADA/EMS will be integrated without extra devices.

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No. of bitstreams: 1 figueroaescoto_e_me_ilha.pdf: 1574120 bytes, checksum: f8fdbd286a015766cb08bf2cc57ac193 (MD5) Previous issue date: 2016-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O problema de estimação de faltas ou diagnóstico de alarmes em sistemas de energia elétrica é identificar faltas em seções ou falhas em dispositivos através dos alarmes dos relés de proteção, disjuntores e outras informações recebidas pelo Supervisory Control and Data Acquisition System (SCADA). Este trabalho apresenta uma metodologia para resolver o problema de diagnóstico de alarmes em sistemas de energia, através de um modelo de otimização de programação binária irrestrita. Este modelo é desenvolvido com base no conjunto de coberturas mínimas que abrange a lógica e a filosofia dos projetos de proteção empregados por empresas de energia elétrica. A ideia principal é associar os alarmes dos relés de proteção relatados pelo sistema SCADA e com os estados esperados das funções de relé de proteção. Os estados esperados são modelados usando a filosofia de proteção utilizada por especialistas em empresas de energia elétrica. Um Algoritmo Genético Adaptativo (AGA) é desenvolvido para resolver o modelo de otimização de programação binária irrestrita. O AGA proposto tem a característica de usar somente dois parâmetros de controle, ou seja, o número de indivíduos na população e o número máximo de gerações. O algoritmo tem taxas de recombinação e mutação calibradas de forma dinâmica com base na saturação da população atual, tendo uma resposta imediata a possível convergência prematura para ótimos locais. A metodologia proposta para resolver o problema da localização de faltas foi implementada na linguagem de programação C ++ e os testes foram feitos em um computador com processador Intel Core i7 com 2,2 GHz e 12 GB de memória RAM. O desempenho do algoritmo foi testado usando dados de um sistema elétrico real da região sul brasileira. A fim de mostrar o desempenho do AGA, os resultados do algoritmo foram comparados com um algoritmo genético clássico e um algoritmo imune. Os resultados mostraram que o AGA é superior aos algoritmos genético clássico e imune apresentando robustez e eficiência computacional. Além disso, a metodologia provou ser rápida e robusta e tem grande potencial para a localização de faltas em tempo real. / The fault section estimation in electric power systems is to identify faults in sections or devices using information from protective relays, circuit breakers and other information received from Supervisory Control and Data Acquisition Systems (SCADA). This work presents a methodology to solve the fault section estimation problem in power systems, through a model based on an unconstrained binary programming optimization model. This model is developed based on the parsimonious set covering theory and the protection philosophy logic employed by electric companies. The main idea is to associate the alarms of the relay protection functions reported by the SCADA system with the expected states from the protective relay functions. The expected states are modeled using protection philosophy logic employed by experts in electric companies. An Adaptive Genetic Algorithm (AGA) is developed to solve the unconstrained binary programming optimization model. The proposed AGA has the characteristic to use only two control parameters, i.e., number of individuals in the population and maximum number of generations. The algorithm has automatic and dynamically calibrated recombination and mutation rates based on the saturation of the current population, having an immediate response to possible premature convergence to local optima. The methodology proposed to solve the problem of shortages sections location was implemented in the C ++ programming language and the tests are done on a computer with Intel Core 7 Processor with 2.2 GHz and 12 GB of memory. The algorithm’s performance was tested using data from the Brazilian Southern electric power system. In order to show the AGA performance, the algorithm results was compared with a classical genetic algorithm and an immune algorithm. The results have shown that AGA presents robustness and the efficiency was successfully verified. Considering the solutions of the tests, the AGA demonstrates better computational processing time, getting the right solution for every simulation. Furthermore, the method was proven to be fast and robust and has great potential for locating faults in electric power systems in real time.

Orientador: Fábio Bertequini Leão / Resumo: O problema de estimação de faltas ou diagnóstico de alarmes em sistemas de energia elétrica é identificar faltas em seções ou falhas em dispositivos através dos alarmes dos relés de proteção, disjuntores e outras informações recebidas pelo Supervisory Control and Data Acquisition System (SCADA). Este trabalho apresenta uma metodologia para resolver o problema de diagnóstico de alarmes em sistemas de energia, através de um modelo de otimização de programação binária irrestrita. Este modelo é desenvolvido com base no conjunto de coberturas mínimas que abrange a lógica e a filosofia dos projetos de proteção empregados por empresas de energia elétrica. A ideia principal é associar os alarmes dos relés de proteção relatados pelo sistema SCADA e com os estados esperados das funções de relé de proteção. Os estados esperados são modelados usando a filosofia de proteção utilizada por especialistas em empresas de energia elétrica. Um Algoritmo Genético Adaptativo (AGA) é desenvolvido para resolver o modelo de otimização de programação binária irrestrita. O AGA proposto tem a característica de usar somente dois parâmetros de controle, ou seja, o número de indivíduos na população e o número máximo de gerações. O algoritmo tem taxas de recombinação e mutação calibradas de forma dinâmica com base na saturação da população atual, tendo uma resposta imediata a possível convergência prematura para ótimos locais. A metodologia proposta para resolver o problema da localização de... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The fault section estimation in electric power systems is to identify faults in sections or devices using information from protective relays, circuit breakers and other information received from Supervisory Control and Data Acquisition Systems (SCADA). This work presents a methodology to solve the fault section estimation problem in power systems, through a model based on an unconstrained binary programming optimization model. This model is developed based on the parsimonious set covering theory and the protection philosophy logic employed by electric companies. The main idea is to associate the alarms of the relay protection functions reported by the SCADA system with the expected states from the protective relay functions. The expected states are modeled using protection philosophy logic employed by experts in electric companies. An Adaptive Genetic Algorithm (AGA) is developed to solve the unconstrained binary programming optimization model. The proposed AGA has the characteristic to use only two control parameters, i.e., number of individuals in the population and maximum number of generations. The algorithm has automatic and dynamically calibrated recombination and mutation rates based on the saturation of the current population, having an immediate response to possible premature convergence to local optima. The methodology proposed to solve the problem of shortages sections location was implemented in the C ++ programming language and the tests are done ... (Complete abstract click electronic access below) / Mestre

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This dissertation proposes a methodology for alarm processing aiming to solve the fault section estimation in electrical power system. The main motivation for this study is the fact that operators of control centers being subject to information overload during great contingencies. Accordingly, this work aims to support the operator decisions in order to enhance the service reliability and reduce the power restoration time. The approach integrates a legacy SCADA interpretation system working together with a new network topology processor to determine the protection alarms (circuit breakers, switches, protective relays and protection logical schemes), and the set of disconnected equipment after occurrence of fault. The fault diagnosis has been treated as an optimization problem, solved through two stages: event classification at equipment level, based on Bayes s Theorem; and the fault section estimation, which is formulated as a mixed integer programming problem, using the commercial software CPLEX to solve instances. The developed approach also identifies the malfunctioned protective devices as well the missing and false alarms. Possible fault scenarios were considered in part of a real Brazilian power system to validate the methodology. The results show that the proposed approach can find the optimal solution even in case of multiple faults or in case of failure of protection devices. / Esta dissertação propõe uma metodologia para o processamento de alarmes visando à estimação de secção em falta em sistemas elétricos de potência. A principal motivação para este estudo reside no fato de os operadores dos centros de controle estarem sujeitos a sobrecarga de informação durante grandes contingências. Deste modo, o trabalho pretende auxiliar o operador na tomada de decisão, favorecendo a confiabilidade do serviço e a redução do tempo de reestabelecimento. A abordagem integra a interpretação de dados históricos do SCADA em conjunto com um novo configurador de redes para determinar os alarmes de proteção (disjuntores, chaves seccionadoras, relés de proteção e esquemas lógicos de proteção), e o conjunto de equipamentos desligados após a falta. O diagnóstico sobre a falta é tratado como um problema de otimização, resolvido por meio de duas etapas: classificação de eventos em nível de equipamento, fundamentado no Teorema de Bayes; e a estimação da secção em falta, formulada como um problema de programação inteira mista, empregando o otimizador comercial CPLEX para resolver as instâncias. A abordagem proposta também identifica o mau funcionamento de dispositivos de proteção, bem como os alarmes falsos e falhos. Possíveis cenários de falta foram considerados em parte de um sistema de potência real brasileiro a fim de validar a metodologia. Os resultados mostram que a abordagem pode encontrar a solução ótima, mesmo em casos de múltiplas faltas ou em casos de falha em dispositivos de proteção.

This work proposes a dynamic tool for solving the problem of fault section estimation in power systems. A Binary Integer Problem (BIP) is used to identify the faulty section trough analyses of circuit breakers states and power system protection devices signalizations. For the knowledge base construction it is proposed an innovative tool able to automatically generate the pattern of events and alarms for each topology configuration change. The outputs obtained from the pattern generator are used as base of cause end effect by the BIP model. Received alarms on SCADA are used as parameters in model the restrictions. The BIP model, based on the parsimonious set covering problem, is solved by CPLEX commercial software and it is able to deal with protection devices failure, data acquisition problems and occurrence of multiple events. To validate the approach it was used part of a brazilian power system, through the proposed technique the patterns were automatically generated and several faults situations were simulated. The proposed methodology achieved optimum results to all tests applied, being capable to deal with topology changes on power systems and with the problems inherent to the fault section estimation. / Este trabalho propõe uma ferramenta dinâmica para solução do problema de estimação da seção em falta em sistemas elétricos de potência. Um modelo de programação inteira binária (PIB) é usado para identificar a seção em falta através da análise do estado dos disjuntores e sinalizações de disparo das proteções de cada equipamento do sistema elétrico. Para a montagem da base de conhecimento é proposta uma ferramenta inovadora, capaz de gerar padrões de eventos e alarmes automaticamente, cada vez que a configuração da rede é alterada. O algoritmo usa como entrada o cadastro de conexões do sistema (secções e disjuntores) e a leitura do estado dos disjuntores. A saída obtida pelo gerador de padrões é usada como base de conhecimento pelo modelo de programação inteira binária. Os alarmes recebidos no SCADA são usados como parâmetros nas restrições do modelo. O modelo de PIB, fundamentado nos princípios de recobrimento parcimonioso de conjuntos, é resolvido através do otimizador comercial CPLEX, e é capaz de lidar com falhas nos dispositivos de proteção, problemas na aquisição de dados e múltiplas ocorrências. A metodologia foi validada tendo como base parte de um sistema elétrico de potência brasileiro, para o qual, por meio da técnica proposta, os padrões foram gerados automaticamente e simuladas diversas situações de faltas. A metodologia proposta apresentou ótimos resultados para todos os testes aplicados, sendo a ferramenta capaz de lidar com as alterações topológicas dos sistemas elétricos de potência e com os problemas inerentes à estimação da seção em falta.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This thesis proposes an Intelligent Alarm Processor for fault diagnosis in electrical power systems. The objective is to develop a methodology for automatic fault analysis using reported alarms from Supervisory Control and Data Acquisition (SCADA) to allow the use of diagnosis systems in large power systems. The proposal can be used in real-time decision support systems to assist control center‟s operators during the decision-making after unscheduled contingencies with relevant information to power system restoration. This work expects to contribute to the development of advanced alarm management logics that allow modifying the chronological sequence of reported alarms, event mapping and the generation of operating patterns of protection systems according to topology network. Still, mathematical programming models have been formulated as a parsimonious set covering problem to fault section estimation and identification of protective devices with improper operation. Among these models, it stands out the model that deals with integrated analysis of reported alarms, events and diagnosis that better explain the alarms. The proposed approach has been tested in different portions of the Southern Brazilian power system. The results show that alarm processing allows the practical implementation of intelligent diagnosis methods in existing supervisory systems. The proposed diagnosis methods show better performance and accurate solutions than other methods presented in literature. / Esta tese propõe um Processador Inteligente de Alarmes para diagnóstico de faltas em sistemas elétricos de potência. O objetivo é desenvolver uma metodologia para a análise automática de faltas a partir dos alarmes reportados no sistema de supervisão e aquisição de dados (SCADA) que possibilite o uso de métodos de diagnóstico em sistemas de potência de grande porte. Essa proposta pode ser empregada em sistemas de apoio à decisão em tempo real, que auxiliem operadores de centros de controle do sistema (COS) na tomada de decisão após desligamentos não programados, com informações pertinentes para o restabelecimento do sistema. O trabalho espera contribuir com o desenvolvimento de lógicas avançadas de gerenciamento de alarmes que possibilitem a reordenação cronológica dos alarmes reportados, o mapeamento dos eventos e a geração de padrões de funcionamento de sistemas de proteção de acordo à topologia da rede. Além disso, os modelos de programação matemática foram formulados como um problema de recobrimento de conjuntos parcimonioso, para estimação da seção em falta e identificação dos dispositivos de proteção com atuação indevida. Dentre esses modelos, destaca-se o modelo que analisa, de forma integrada, os alarmes reportados e determina os eventos e diagnósticos que melhor explicam os alarmes. A abordagem proposta foi testada em diferentes porções do sistema sul do sistema interligado nacional (SIN). Os resultados mostram que as rotinas desenvolvidas para o processamento de alarmes permite a implantação prática de métodos inteligentes de diagnóstico em sistemas supervisórios existentes. Os métodos propostos para diagnóstico de faltas mostraram desempenhos e precisão nos resultados superiores a outros métodos presentes na literatura.