A Gray-Box Approach for Decentralized Grid-Equivalent Model Identification

Abstract

We propose a decentralized, frequency-domain identification algorithm that estimates the grid-equivalent model using local measurements from the perspective of each converter. Since local electric signals in a multi-converter setup are affected by voltage inputs from the grid, estimating a direct equivalent impedance yields biased and inaccurate results. To overcome this, we design a framework that decouples the effect of the equivalent impedance (passive) from that of the equivalent voltage (active). The parameters and equivalent grid voltages are then estimated using a least-squares algorithm and a Kalman filter, respectively, applied across frequency samples, with additional pre-processing techniques to remove the influence of the grid on the locally estimated models. We then demonstrate the accuracy and performance of our algorithm on an interconnected $5-$converter system in grid-forming mode, with minimal voltage excitations and non-nominal operating conditions.