Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-03-17T15:55:04Z No. of bitstreams: 1 Iverson Farias Costa_.pdf: 1955231 bytes, checksum: 026a292f3b36f9d497620ffb02e502b4 (MD5) / Made available in DSpace on 2016-03-17T15:55:04Z (GMT). No. of bitstreams: 1 Iverson Farias Costa_.pdf: 1955231 bytes, checksum: 026a292f3b36f9d497620ffb02e502b4 (MD5) Previous issue date: 2012-03-29 / Nenhuma / A importância de um suprimento confiável de energia elétrica, para a sociedade industrial atual, é inquestionável. Em um centro de controle de uma rede elétrica, uma importante tarefa computacional é a Análise de Segurança. Nesta tarefa, uma contingência é saída de operação de um ou mais equipamentos, enquanto que a seleção de contingências é a determinação das contingências mais severas existentes no sistema. A despeito dos avanços tecnológicos atuais, uma análise de todas as contingências possíveis é impraticável. Neste trabalho, um método é apresentado para realizar, de forma eficiente, a seleção de contingências múltiplas. O problema é modelado como um problema de otimização combinatória, e é resolvido através de dois algoritmos genéticos. Um método robusto, que considera aspectos de fluxo de potência e tensão, é apresentado e testado em uma rede IEEE e em um sistema real de grande porte, considerando saídas duplas de linha. Os resultados apresentados, obtidos através da varredura de porções reduzidas do espaço de busca, demonstram uma acurácia próxima a 100%, quando comparados a um método exato. / The importance of a reliable supply of electricity to the industrial society is unquestionable. In a control center of an electrical utility, an important computational task is the Security Analysis. In this task, contingency is the out of operation of one or more devices, and contingencies selection is the determination of the most severe contingencies for the system. Despite the current technological advances, an analysis of all possible contingencies, for a real grid, is impracticable. In this work, a method to efficiently perform the selection of multiple contingencies is presented. The problem is modeled as a combinatorial optimization problem, and solved by two genetic algorithms. A robust method, which considers aspects of power flow and voltage, is presented and tested over an IEEE test system and a large real network, considering double outages of branches. The results presented, obtained with scans of reduced portions of search space, shows accuracy close to 100%, when compared with an exact method.

Abstract The goal of this research is to advance the state of the art in bulk power system reliability assessment. Bulk power system reliability assessment is an important procedure at both power system planning and operating stages to assure reliable and acceptable electricity service to customers. With the increase in the complexity of modern power systems and advances in the power industry toward restructuring, the system models and algorithms of traditional reliability assessment techniques are becoming obsolete as they suffer from nonrealistic system models and slow convergence (even non-convergence) when multi-level contingencies are considered and the system is overstressed. To allow more rigor in system modeling and higher computational efficiency in reliability evaluation procedures, this research proposes an analytically-based security-constrained adequacy evaluation (SCAE) methodology that performs bulk power system reliability assessment. The SCAE methodology adopts a single-phase quadratized power flow (SPQPF) model as a basis and encompasses three main steps: (1) critical contingency selection, (2) effects analysis, and (3) reliability index computations. In the critical contingency selection, an improved contingency selection method is developed using a wind-chime contingency enumeration scheme and a performance index approach based on the system state linearization technique, which can rank critical contingencies with high accuracy and efficiency. In the effects analysis for selected critical contingencies, a non-divergent optimal quadratized power flow (NDOQPF) algorithm is developed to (1) incorporate major system operating practices, security constraints, and remedial actions in a constrained optimization problem and (2) guarantee convergence and provide a solution under all conditions. This algorithm is also capable of efficiently solving the ISO/RTO operational mode in deregulated power systems. Based on the results of the effects analysis, reliability indices that provide a quantitative indication of the system reliability level are computed. In addition, this research extends the proposed SCAE framework to include the effects of protection system hidden failures on bulk power system reliability. The overall SCAE methodology is implemented and applied to IEEE reliability test systems, and evaluation results demonstrate the expected features of proposed advanced techniques. Finally, the contributions of this research are summarized and recommendations for future research are proposed.