Geração de modelos sintéticos de topologia de sistemas de distribuição de energia elétrica
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2022-05-23
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Universidade Federal de Goiás
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The development of algorithms to solve problems related to electrical networks has always faced access to data, since, for reasons of security and secrecy, analysts are unable to obtain real data from these networks. This work arises in order to mitigate this problem, allowing these researchers to generate synthetic networks with characteristics close to real networks.
For this, algorithms were developed to generate forests with several rooted trees based on the characteristics of real electrical networks. These algorithms are guided during the search process through the desired topological distributions, allowing the use of characteristics of real networks from any Brazilian state or regions of the world. This study was based on the electrical networks of the state of Paraná. Each proposed algorithm was studied in relation to its tree generation trends allowing a better understanding of its behavior. These algorithms were employed in the generation of electrical networks through a multiobjective evolutionary computation process, using the NSGA-II method, and resulted in networks with degree distribution, number of buses per feeder and number of leaves per feeder similar to the real networks studied. In addition, a method for positioning consumers was determined and a single-objective genetic algorithm was developed for the ideal positioning of normally closed and normally open switches. It was able to evaluate faults in all the buses of an electrical network in a single depth search through the use of dynamic programming techniques.
By using the algorithms and methodology developed in this thesis, the researcher will have a topology containing feeder, buses, sections, normally open and normally closed switches, and the number of consumers in the buses at his disposal for his studies.
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TELES, R. M. Geração de modelos sintéticos de topologia de sistemas de distribuição de energia elétrica. 2022. 145 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2022.