Machine learning based impedance estimation in power system

Kamyab Givaki; Saleh Seyedzadeh; Kamyar GIvaki

Machine learning based impedance estimation in power system

Kamyab Givaki, Saleh Seyedzadeh, Kamyar GIvaki

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

41 Downloads (Pure)

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 language	English
Number of pages	6
Publication status	Published - 24 Oct 2019
Event	8th International Conference on Renewable Power Generation - Shanghai, China Duration: 24 Oct 2019 → 25 Oct 2019 Conference number: 8 http://rpg2019.events.theiet.org.cn/

Conference

Conference	8th International Conference on Renewable Power Generation
Abbreviated title	RPG
Country/Territory	China
City	Shanghai
Period	24/10/19 → 25/10/19
Internet address	http://rpg2019.events.theiet.org.cn/

Keywords

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

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Givaki-etal-RPG2019-Machine-learning-based-impedance-estimation-in-power-systemAccepted author manuscript, 881 KB

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title = "Machine learning based impedance estimation in power system",

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.",

keywords = "impedance estimation, machine learning, power system stability, random forest model, supervised learning",

author = "Kamyab Givaki and Saleh Seyedzadeh and Kamyar GIvaki",

year = "2019",

month = oct,

day = "24",

language = "English",

note = "8th International Conference on Renewable Power Generation, RPG ; Conference date: 24-10-2019 Through 25-10-2019",

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TY - CONF

T1 - Machine learning based impedance estimation in power system

AU - Givaki, Kamyab

AU - Seyedzadeh, Saleh

AU - GIvaki, Kamyar

N1 - Conference code: 8

PY - 2019/10/24

Y1 - 2019/10/24

N2 - 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.

AB - 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.

KW - impedance estimation

KW - machine learning

KW - power system stability

KW - random forest model

KW - supervised learning

M3 - Paper

T2 - 8th International Conference on Renewable Power Generation

Y2 - 24 October 2019 through 25 October 2019

ER -

Machine learning based impedance estimation in power system

Abstract

Conference

Keywords

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