PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems

TL;DR

PyProD is a Python-based platform that enables the development and testing of machine learning algorithms for protection system design in modern electric distribution grids with distributed energy resources.

Contribution

It introduces PyProD, a novel test-bed that integrates ML capabilities into protection analytics for distribution systems, bridging the gap between traditional analysis and ML-based decision making.

Findings

PyProD effectively supports the design of ML algorithms for protection.

It facilitates evaluation of protection schemes in complex distribution scenarios.

The platform is adaptable for future grid configurations with distributed energy resources.

Abstract

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.