A constraint-based approach for optimizing the design of overhead lines
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TECHNICAL PAPER
A constraint‑based approach for optimizing the design of overhead lines Paolo Cicconi1 · Steve Manieri2 · Miriam Nardelli2 · Nicola Bergantino3 · Roberto Raffaeli4 · Michele Germani2 Received: 17 May 2020 / Accepted: 24 July 2020 / Published online: 5 August 2020 © The Author(s) 2020
Abstract The use of computational methods in engineering design is a long-standing issue. Several mathematical approaches have been investigated in the literature to support the design optimization of engineering products. Most of them are focused on the optimization of a single structure, without considering a system of structures. The design of supports for electric lines requires tools for the management and sizing of a system of structures that interacts with each other under specific load conditions. This paper deals with a framework to support the design optimization of an overhead line using methods related to the theory of the Constraint Satisfaction Problem. The object-oriented model of a transmission line has been described and then implemented into a prototypical software platform. The parameters to be considered as variables are defined by the designer at the beginning of the optimization process. These variables are geometrical dimensions, poles locations, cable pre-tension, etc. The set of constraints is related to normative, climate conditions, datasheets, material limits, and expert knowledge. To demonstrate the effectiveness of this approach, a case study has been developed considering a variable number of constraints and parameters. In particular, the case study is focused on the design of a low-voltage sub-network between two distribution substations. Keywords Design optimization · Multi-objective optimization · Constraint satisfaction problem · Overhead lines
1 Introduction The use of tools for design optimization is related to the recent improvements in computational methods such as evolutionary algorithms [1]. Evolutionary algorithms are widely applied in multidisciplinary fields for optimizing structures such as steel trusses [2, 3] and towers [4, 5], and processes such as additive manufacturing [6] and machining [7]. In literature, their application is mostly related to the multi-objective optimization (MOO) analysis instead of a problem with linear complexity where maximin fitness function (MFF) * Paolo Cicconi [email protected] 1
Università degli Studi Roma Tre, Via della Vasca Navale, 79, 00146 Rome, Italy
2
Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy
3
NeXT S.r.l, Via Ancona, 55/A, 60030 Castelbellino, AN, Italy
4
Università degli Studi di Modena e Reggio Emilia, Reggio Emilia, Italy
can be used [8]. Genetic Algorithms are typical stochastic evolutionary methods used in design optimization [9]. These optimization methods are robust and cope with multimodal functions. They can efficiently achieve a global minimum or maximum for an optimization function [10]; however, they are also computationally expensive in terms of the necess
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