  This paper introduces PyProD, a Python-based machine learning (ML)-compatible
test-bed for evaluating the efficacy of protection schemes in electric
distribution grids. This testbed is designed to bridge the gap between
conventional power distribution grid analysis and growing capability of
ML-based decision making algorithms, in particular in the context of protection
system design and configuration. PyProD is shown to be capable of facilitating
efficient design and evaluation of ML-based decision making algorithms for
protection devices in the future electric distribution grid, in which many
distributed energy resources and pro-sumers permeate the system.
