Grid impedance estimation based Kalman Filter

TL;DR

This paper introduces a real-time grid impedance estimation method using a Discrete Fourier Transform embedded within a Kalman Filter framework, enhancing inverter stability in modern power systems with dynamic grid conditions.

Contribution

It presents a novel impedance estimation algorithm integrated into an advanced angle estimation Kalman filter with LQR control, improving stability under weak grid conditions.

Findings

Robust impedance estimation during grid variations

Enhanced system stability with the proposed method

Swift and accurate impedance updates in simulations

Abstract

Modern power systems face new operational hurdles due to the increasing adoption of inverter-coupled distributed energy resources, which impact system stability and control. Central to these challenges is the dynamic nature of grid impedance. To address this, a novel real-time estimation algorithm based on the Discrete Fourier Transform is proposed. This algorithm is embedded within an Advanced Angle Estimation Kalman Filter framework that employs a Linear Quadratic Regulator for current control (AAEKF-LQR). The impedance data directly informs and refines the controller's phase angle estimation. Simulation analyses demonstrate robust collaboration between the estimator and controller, sustaining system stability under weak grid conditions. The technique proves capable of delivering swift and accurate impedance updates during grid variations, which is crucial for maintaining stable inverter operation