Data-Driven Methods for Voltage Regulator Identification and Tap Estimation

Accurate models of voltage regulating devices are required for reliably executing distribution system analysis and planning tasks. However, these models are often created and updated manually, meaning they are prone to input errors and outdated settings. In this paper, data-driven methods are presented to characterize several physical parameters of voltage regulators and estimate their historical tap position states by leveraging existing measurement infrastructure on distribution grids. Specifically, the methods can differentiate if voltage regulation follows per phase or gang-operated load tap changers (LTCs) operation, estimate the spacing between tap positions, and estimate the total number of tap positions. The methods are tested on actual utility datasets from two different distribution feeders and the results are compared to the utility-verified characteristics where applicable. The impacts of different types of measurements (instantaneous vs. averaged) are also analyzed to identify the data best suited for deploying these methods by utility operators. Overall, the proposed methods are intuitive, effective, and require minimal prior knowledge of the underlying system, making them practical and useful tools for improving model fidelity.