Surrogate Model Training Data for FIDVR-related Voltage Control in Large-scale Power Grids

This work presents an effective machine learning (ML) data set related to the short-term voltage dynamics in power systems. Power systems dynamics are highly nonlinear and intricate. Model designs/specifications in power systems need expertise to capture dynamic phenomena. ML has become an important tool for analyzing complex behaviors of physical systems, but ML models need quality data sets for training and testing. Learning surrogate models to replicate certain dynamic behaviors of power systems is a growing area of interest; however, building required data sets can be challenging. We utilize the high performance computing (HPC)-based grid simulator GridPACK to create voltage dynamics of a bulk power system, namely the IEEE 300 bus test system, capturing fault-induced delayed voltage recovery (FIDVR) phenomenon. This FIDVR is generally mitigated by the under voltage load shedding (UVLS)-based control strategy. The data set created here contains the trajectory data of voltage dynamics under different control actions generated by standard UVLS strategy and random noise. We present the structure of the data set and its application in learning a dynamic surrogate model. Finally, other suitable ML-based applications of the given data set are discussed, thereby helping to strengthen reusable science practices.