AI-Based Impedance Encoding-Decoding Method for Online Impedance Network Construction of Wind Farms

TL;DR

This paper introduces an AI-based impedance encoding-decoding approach that enables efficient online construction of impedance network models for wind farms, improving transmission speed and accuracy in oscillation analysis.

Contribution

It presents a novel AI-driven method to compress and reconstruct impedance curves, facilitating real-time impedance network modeling for wind farms.

Findings

The method achieves accurate impedance curve reconstruction.

It enables fast transmission of impedance data.

The approach is validated through simulations.

Abstract

The impedance network (IN) model is gaining popularity in the oscillation analysis of wind farms. However, the construction of such an IN model requires impedance curves of each wind turbine under their respective operating conditions, making its online application difficult due to the transmission of numerous high-density impedance curves. To address this issue, this paper proposes an AI-based impedance encoding-decoding method to facilitate the online construction of IN model. First, an impedance encoder is trained to compress impedance curves by setting the number of neurons much smaller than that of frequency points. Then, the compressed data of each turbine are uploaded to the wind farm and an impedance decoder is trained to reconstruct original impedance curves. At last, based on the nodal admittance matrix (NAM) method, the IN model of the wind farm can be obtained. The proposed method is validated via model training and real-time simulations, demonstrating that the encoded impedance vectors enable fast transmission and accurate reconstruction of the original impedance curves.