Machine learning based impedance estimation in power system

Kamyab Givaki, Saleh Seyedzadeh, Kamyar GIvaki

Research output: Contribution to conferencePaper

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Abstract

A passive machine learning based technique to estimate the impedance of the power grid at the point of common coupling of a converter interfaced distributed generation source is proposed. The proposed method is based on supervised learning and provides a fast and accurate estimation of the grid impedance without adversely impacting the power quality of the system. This method does not need an injection of additional signals to the grid and provides an accurate estimation of the grid impedance. Multi-objective NSGA-II algorithm is used for optimisation and tuning the random forest model for accurate estimation of both R and X The resistive and inductive reactance of grid is estimated using Random Forest model due to its capability in the prediction of multiple output values simultaneously.
Original languageEnglish
Number of pages6
Publication statusPublished - 24 Oct 2019
Event8th International Conference on Renewable Power Generation - Shanghai, China
Duration: 24 Oct 201925 Oct 2019
Conference number: 8
http://rpg2019.events.theiet.org.cn/

Conference

Conference8th International Conference on Renewable Power Generation
Abbreviated titleRPG
CountryChina
CityShanghai
Period24/10/1925/10/19
Internet address

Keywords

  • impedance estimation
  • machine learning
  • power system stability
  • random forest model
  • supervised learning

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