Otimização de torres treliçadas de linhas de transmissão através do método teaching learning based optimization algorithm

• Autor(a) principal: Weber, Guilherme Adriano
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• dARK ID: ark:/26339/0013000006wd3
• Texto Completo: http://repositorio.ufsm.br/handle/1/24213
• topic: Otimização; TLBO; Torres de linhas de transmissão; Optimization; Transmission line towers; CNPQ::ENGENHARIAS::ENGENHARIA CIVIL

In the engineering field, the search for the best performance of a structure in terms of economy, efficiency and safety is one of the main objectives. In this sense, in relation to transmission line towers (TLT), it should be noted that any savings in the cost of a tangent-type structure should be considered, given that a large number of these structures are built from the same project in a transmission line (LT). In this way, new methods and techniques are developed to achieve an ideal structure. The Teaching Learning-Based Optimization (TLBO) is based on the structure of a class, simulating the influence of the teacher on the students and the collaborative learning process among the students. TLBO stands out for its versatility in different types of optimization problems due to its intuitive implementation and for not requiring any specific type of parameter when compared to other metaheuristics. Thus, the main objective of this work is to implement and perform the parametric optimization of TLT using TLBO as an optimization method. For this, three numerical examples studied in Cigré (2009) were used. All computational implementation took place through MATLAB software. The mechanical model, validated and adopted for the structures, is composed of spatial truss elements and resolved through a static and linear analysis using the Finite Element Method (FEM). The weight of the structures was used as an objective function to optimize the structures, and the restrictions imposed were the design strength (compression and traction) and the slenderness index of the bars that make up the towers, as established by ASCE 10 (2015). The proposed optimization methodology was able to reduce up to 16.45% compared to the original weight of one of the towers. Furthermore, the restrictions imposed were perfectly respected by the algorithm.