Avaliação da confiabilidade preventiva de sistemas elétricos de grande porte utilizando redes neurais

The well-being analysis was recently proposed as a new framework to measure the degree of adequacy of power systems, which has as the main objective the incorporation of deterministic criteria into the reliability analysis process. The conceptual basis for this framework is obtained through the classification of the system states into three groups: healthy, marginal and at risk. For the identification of these operation states, the system is submitted to a deterministic criterion. In composite generation and transmission systems, the identification of a healthy or marginal state becomes much more complex than that one used, for example, in generation systems. Any deterministic criterion to be used must consider a list of contingencies. In principle, for each considered operation state, it is necessary to carry out a number of additional performance analyses equal to the number of elements in the list. Moreover, these adequacy analyses involve load flow runs with corrective measure optimizations. Therefore, the major difficulty found in assessing well-being indices consists of conciliating the deterministic criterion and the combinatorial nature of the problem. In this thesis, the evaluation of well-being indices for bulk composite generation and transmission power systems is focused. For this purpose, the following techniques are considered: non-sequential Monte Carlo simulation with a new test function; interior point method for optimal power flow with reduced constraints; probabilistic equivalent network; and incorporation of artificial neural nets in the classification of the operation states. These techniques can provide considerable reductions in the computational cost demanded during the classification of states. In order to verify the proposed concepts and models, the developed methodology is applied to several test systems, including a configuration of the Brazilian power network.A avaliação de índices de confiabilidade preventiva ou de bem-estar (well-being) foi proposta, recentemente, como uma nova ferramenta para se determinar o grau de adequação dos sistemas de potência, tendo como principal objetivo a incorporação de critérios determinísticos ao processo de análise da confiabilidade. A base conceitual para esta técnica é obtida através da classificação dos estados operativos do sistema em três grupos: saudável, marginal e de falha. Para a identificação destes estados, o sistema é submetido a um critério determinístico. Em relação aos sistemas compostos de geração e transmissão, a identificação de um estado saudável ou marginal se torna bem mais complexa que aquela utilizada, por exemplo, em sistemas de geração. Qualquer critério determinístico a ser empregado deve considerar uma lista de contingências. Em princípio, para cada estado operativo considerado, é necessário realizar um número de análises adicionais de desempenho igual ao número de elementos da lista. Acrescenta-se, ainda, a necessidade de análises de adequação dos estados utilizando algoritmos de fluxo de potência, com otimização de medidas corretivas. Portanto, a grande dificuldade encontrada na avaliação de índices de bem-estar consiste em conciliar o critério determinístico e a natureza combinatorial do problema. Esta tese trata da avaliação de índices de bem-estar de sistemas elétricos de potência de grande porte, compostos de geração e transmissão. Para este fim, é proposta a utilização de técnicas como: simulação Monte Carlo não seqüencial com uma nova função teste; modelo de fluxo de potência ótimo baseado em pontos interiores com restrições reduzidas; equivalente probabilístico de rede; e a incorporação de redes neurais artificiais na classificação dos estados operativos. Tais técnicas podem propiciar reduções significativas no custo computacional exigido durante a classificação dos estados. Para a verificação dos conceitos e modelos propostos, a metodologia desenvolvida é aplicada em vários sistemas testes incluindo uma configuração da rede elétrica brasileira

Avaliação da confiabilidade preventiva de sistemas de potência

The regulatory reform of the electric power industry creates an entirely new competitive environment. In this new electricity market, the reliability of services plays a very important role to establish non-deterministic criteria, to be applied to both operation and expansion planning of electric power systems. The utilization of these criteria, however, is being slowly incorporated into the decision-making processes of most utilities. Due to the difficulties of interpreting numerical risk indices, system operators and planners are still averse to the use of probabilistic techniques, being more confident with the traditional deterministic criteria. The well-being analysis has been recently proposed as a new technique to measure the degree of adequacy of power systems, which incorporates deterministic criteria in a probabilistic framework. The combination of the basic deterministic and probabilistic concepts is established through the classification of the system operating states into three categories: healthy, marginal and at risk. In order to identify these states, the system is analyzed according to a deterministic criterion based on a pre-specified list of equipment contingencies. In this dissertation, a new methodology is proposed to evaluate well-being indices considering composite generation and transmission power systems. The new methodology uses a non-sequential Monte Carlo simulation, a non-aggregate Markovian load model and a new process to estimate failure frequency indices, named one step forward state transition process. New test functions are proposed to calculate the well-being indices, including the frequency of marginal states. In order to test the accuracy and efficiency of the proposed methodology, the IEEE Reliability Test System with some modifications is used.O setor elétrico vem experimentando um ambiente de crescente competitividade e desregulamentação. Neste novo mercado de energia, a confiabilidade dos serviços prestados vem se tornando objeto de importantes discussões e recomendações que visam a inclusão de critérios não determinísticos no processo de planejamento da operação e expansão de sistemas elétricos de potência. No entanto, a utilização desses critérios vem sendo lentamente incorporada pela maioria das concessionárias. Devido à dificuldade de interpretação de índices numéricos de risco, operadores e planejadores de sistemas ainda relutam em aplicar técnicas probabilísticas, fazendo maior utilização dos chamados critérios determinísticos. A avaliação da confiabilidade preventiva foi proposta recentemente como uma nova técnica para se determinar o grau de adequação de sistemas de potência, tendo como principal objetivo a incorporação de critérios determinísticos ao processo de análise da confiabilidade. A base conceitual para esta técnica é obtida através da classificação dos estados operativos do sistema em três grupos: saudável, marginal e de falha. Para a identificação destes estados o sistema é submetido a um critério determinístico, usualmente, baseado em uma lista de contingências préespecificadas. Nesta dissertação é proposta uma metodologia para a avaliação da confiabilidade preventiva de sistemas compostos de geração e transmissão. Esta metodologia utiliza a simulação Monte Carlo não-seqüencial, um modelo de carga Markoviano não-agregado e um novo processo de estimação índices de freqüência, denominado processo de transição de estado um passo à frente. Novas funções testes são propostas para a avaliação de índices de confiabilidade preventiva, como por exemplo a freqüência dos estados marginais. Para a verificação dos conceitos propostos, a metodologia desenvolvida é aplicada ao sistema teste IEEE-RTS e modificações no mesmo

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon