Scalable Impedance Identification of Diverse IBRs via Cluster-Specialized Neural Networks
This addresses the scalability challenge in power system impedance identification for heterogeneous IBRs, though it is incremental as it builds on existing clustering and neural network methods.
The paper tackles the problem of efficiently identifying impedance for diverse inverter-based resources (IBRs) in power systems, proposing a cluster-specialized neural network framework that achieves high accuracy with only ten measurement points on an unseen IBR.
Modern machine learning approaches typically identify the impedance of a single inverter-based resource (IBR) and assume similar impedance characteristics across devices. In modern power systems, however, IBRs will employ diverse control topologies and algorithms, leading to highly heterogeneous impedance behaviors. Training one model per IBR is inefficient and does not scale. This paper proposes a scalable impedance identification framework for diverse IBRs via cluster-specialized neural networks. First, the dataset is partitioned into multiple clusters with similar feature profiles using the K-means clustering method. Then, each cluster is assigned a specialized feed-forward neural network (FNN) tailored to its characteristics, improving both accuracy and computational efficiency. In deployment, only a small number of measurements are required to predict impedance over a wide range of operating points. The framework is validated on six IBRs with varying control bandwidths, control structures, and operating conditions, and further tested on a previously unseen IBR using only ten measurement points. The results demonstrate high accuracy in both the clustering and prediction stages, confirming the effectiveness and scalability of the proposed method.