Enhancing Protection Coordination Robustness in DER-Rich Grids Through Deep Learning-Based Preventive Relay Setting Calibration

The increasing penetration of distributed energy resources (DERs) introduces significant operational uncertainties, challenging the reliability and robustness of conventional protection coordination in distribution networks. Traditional optimization methods can obtain high-quality relay settings, but their iterative computational burden limits their direct use for real-time adaptation. To address this issue, this paper proposes a deep learning-based preventive relay setting calibration framework for enhancing protection coordination robustness in DER-rich distribution networks. The proposed method adopts an offline–online architecture. In the offline stage, a refined heuristic algorithm is integrated with ETAP-based fault analysis to generate a comprehensive dataset of high-quality optimized time-multiplier settings (TMSs) and pickup current settings (PCSs) under a wide range of DER-generation and load-demand scenarios. Subsequently, a convolutional neural network (CNN) is trained to learn a nonlinear mapping from multidimensional fault-current signatures to the corresponding optimized relay-setting vectors. In the online stage, the trained CNN serves as a predictive surrogate model, rapidly recommending coordinated relay settings for the current operating condition. The framework is validated using a 16-bus distribution system and the IEEE 37-bus test feeder. The results show that the proposed method can restore correct primary–backup relay operating sequences and maintain coordination time intervals (CTIs) within the required 0.2–0.4 s range under the studied DER-rich operating scenarios. This CNN-based preventive calibration approach provides a rapid, adaptive decision-support tool to improve protection coordination robustness against DER-induced operating uncertainties.