Inverter Output Impedance Estimation in Power Networks: A Variable Direction Forgetting Recursive-Least-Square Algorithm Based Approach

TL;DR

This paper introduces a non-invasive, real-time impedance estimation method for inverter-based power systems using a novel variable direction forgetting RLS algorithm with specialized signal conditioning, improving accuracy especially under low excitation.

Contribution

It proposes a new impedance estimation approach combining signal conditioning in the dq frame with a VDF-RLS algorithm, enhancing stability and accuracy without network disturbance.

Findings

Over three times lower error in low-excitation conditions

Significant improvement over constant forgetting RLS and Kalman filter

Effective non-invasive impedance estimation demonstrated in simulations

Abstract

As inverter-based loads and energy sources become increasingly prevalent, accurate estimation of line impedance between inverters and the grid is essential for optimizing performance and enhancing control strategies. This paper presents a non-invasive method for estimating output-line impedance using measurements local to the inverter. It provides a specific method for signal conditioning of signals measured at the inverter, which makes the measured data better suited to estimation algorithms. An algorithm based on the Variable Direction Forgetting Recursive Least Squares (VDF-RLS) method is introduced, which leverages these conditioned signals for precise impedance estimation. The signal conditioning process transforms measurements into the direct-quadrature (dq) coordinate frame, where the rotating frame frequency is determined to facilitate a simpler and more accurate estimation. This frequency is implemented using a secondary Phase-Locked Loop (PLL) to attenuate grid voltage measurement variations. By isolating the variation-sensitive q-axis and relying solely on the less sensitive d-axis, the method further minimizes the impact of variations. The VDF-RLS estimation method achieves rapid adaptation while ensuring stability in the absence of persistent excitation by selectively discarding outdated data during updates. Proposed conditioning and estimation methods are non-invasive; estimations are solely done using measured outputs, and no signal is injected into the power network. Simulation results demonstrate a significant improvement in impedance estimation stability, particularly in low-excitation conditions, where the VDF-RLS method achieves more than three time lower error compared to existing approaches such as constant forgetting RLS and the Kalman filter.