Machine learning based impedance estimation in power system

Givaki, Kamyab and Seyedzadeh, Saleh and GIvaki, Kamyar (2019) Machine learning based impedance estimation in power system. In: 8th International Conference on Renewable Power Generation, 2019-10-24 - 2019-10-25.

[thumbnail of Givaki-etal-RPG2019-Machine-learning-based-impedance-estimation-in-power-system]
Text (Givaki-etal-RPG2019-Machine-learning-based-impedance-estimation-in-power-system)
Accepted Author Manuscript

Download (901kB)| Preview


    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.

    ORCID iDs

    Givaki, Kamyab ORCID logoORCID:, Seyedzadeh, Saleh ORCID logoORCID: and GIvaki, Kamyar;