{"title":"Substation grounding system optimization with utilizing a novel MATLAB application","authors":"Xuan Wu, Qianzhi Zhang, Jiahong He","doi":"10.1109/APPEEC.2016.7779580","DOIUrl":null,"url":null,"abstract":"the aim of this work is to develop an application which has the capability of modeling and optimizing regular shape (rectangular, square or L shape) ground grids under a two-layer soil model. The ground grid optimal design is the focus of this paper by using a 3-step optimization method: 1) using IEEE-80 equations to calculate touch and step potentials, which makes constraints continuous and differentiable for gradient descent methods; 2) using the optimal solutions from step 1 as the initial input into a pattern search algorithm by applying a more accurate but non-differentiable touch and step potential calculation method; 3) performing a perturbation test to determine whether the results from step 2 are globally optimal, otherwise using a genetic algorithm to re-optimize the solution from step 2 until it passes the perturbation test.","PeriodicalId":117485,"journal":{"name":"2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APPEEC.2016.7779580","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
the aim of this work is to develop an application which has the capability of modeling and optimizing regular shape (rectangular, square or L shape) ground grids under a two-layer soil model. The ground grid optimal design is the focus of this paper by using a 3-step optimization method: 1) using IEEE-80 equations to calculate touch and step potentials, which makes constraints continuous and differentiable for gradient descent methods; 2) using the optimal solutions from step 1 as the initial input into a pattern search algorithm by applying a more accurate but non-differentiable touch and step potential calculation method; 3) performing a perturbation test to determine whether the results from step 2 are globally optimal, otherwise using a genetic algorithm to re-optimize the solution from step 2 until it passes the perturbation test.