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Parameter estimation of horizontal multilayer earth based on complex image method and improved particle swarm optimization
Author(s) -
He Wei,
Zhang Ruiqiang,
Zhu Liwei,
Chen Tao,
Yang Fan,
Shi Qingyan
Publication year - 2013
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21880
Subject(s) - particle swarm optimization , function (biology) , inverse , mean squared error , lipschitz continuity , algorithm , grid , fault (geology) , mathematics , computer science , mathematical analysis , geometry , geology , statistics , evolutionary biology , seismology , biology
Grounding grids are buried underground to reduce the earth resistance and provide ways for overvoltage protection and current leakage. With regard to the safety evaluation and fault diagnosis of substations, it is vital to know the accurate earth parameters before detecting the fault sites of grounding grid in substations. The estimation of earth parameters is used to solve the inverse problem of calculating apparent resistivity using given earth parameters. First, complex image methods are used to solve the improper integration with the assistance of Prony's expansion which is utilized to calculate the coefficients for the Lipschitz's expansions in this article. Also, by considering the root mean square error and absolute error (AbsE), we focus on different aspects of the effectiveness of the estimation. The root mean square error between the measured and calculated apparent resistivity ( \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\rho^a_{\mathrm{md}}$ \end{document} and \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\rho^a_{\mathrm{cd}}$ \end{document} , respectively) is set up as the main objective function, and AbsE as a supplemental criterion for measuring the estimation. Afterwards, improved particle swarm optimization is adopted to minimize the objective function with a constraint function. Resulting from the outstanding global searching characteristics, accurate soil parameters are much easier to obtain. A method to artificially regulate the initial values is proposed in this article, and two cases are introduced to validate the effectiveness. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.