Open AccessGrounding system cost reduction using multi‐objective optimisation method
Author(s) -
Aboura Faouzi,
Touhami Omar
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2019.0575
Subject(s) - particle swarm optimization , ground , electric power system , mathematical optimization , reliability engineering , grid , earthing system , voltage , computer science , power grid , process (computing) , function (biology) , engineering , power (physics) , electrical engineering , mathematics , physics , geometry , quantum mechanics , evolutionary biology , biology , operating system
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.