Grounding system cost reduction using multi-objective optimisation method

作     者：Aboura, Faouzi Touhami, Omar 

作者机构：Ecole Natl Polytech Lab Rech Electrotech LRE El Harrach 16200 Algeria Univ Sci & Technol Houari Boumediene Lab Syst Elect & Ind LSEI Bab Ezzouar 16123 Algeria 

出 版 物：《IET SCIENCE MEASUREMENT & TECHNOLOGY》 (IET科学、测量与技术)

年 卷 期：2020年第14卷第10期

页      面：893-900页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：substation protection particle swarm optimisation evolutionary computation cost reduction Pareto optimisation IEEE standards power grids earthing system cost reduction multiobjective optimisation method safe power system reliable power system grounding network multiobjective particle swarm optimisation MOPSO optimum grounding grid design objective function grounding resistance step voltage touch voltage ground potential rise optimal solutions multiobjective problem objective functions electrical cost functions voltage 220 0 kV 

摘      要：Grounding grid is an important process in design substation and power plant that makes a safe and reliable power system. It is therefore necessary to optimise the design of the grounding network, and to seek to minimise costs. To this effect, this study deals with a new method based on multi-objective particle swarm optimisation (MOPSO) to find optimum grounding grid design for a new 220 kV power plant with substation based in Adrar (south of Algeria). First, the authors present a contribution of a generalisation of methods using one objective function or cost function of the grounding grid with constraints of the grounding resistance like the step voltage and touch voltage and ground potential rise, accordingly to IEEE Std.80.2013. The main advantage of the proposed method is the simultaneous location of all optimal solutions on only one analysis. The problem is while transformed to multi-objective problem, and the evolutionary algorithms proved to be particularly effective in solving this type of problem. In this study, the authors present also a new objective functions or cost functions that are developed to distinguish between the construction and the electrical cost functions. The four objective functions obtained are minimised by MOPSO to determine the optimal solutions.