Adaptive protection in active distribution networks using local information

Abstract

This thesis proposes two adaptive protection approaches to protect MG/ADN in an autonomous way and without robust communication. The first approach presents an adaptive protection method based on an intelligent fault detector, which uses local measurements. Additionally, this solution was implemented in an online grid, where it used data-driven models running on Jetson Nano-system intended to run machine/deep learning loads at the edge. The second solution presents a decentralized adaptive protection scheme and introduces a data-driven and communication-less approach. The present solution uses an Artificial Neural Network to train Intelligent Electronic Devices as fault classifiers and brings backup protection to adjacent devices; also, it uses a cuckoo search metaheuristic to its quasi-optimal adjustment. The approaches were validated on several modified IEEE test feeders such as IEEE 13, IEEE 34, and IEEE 123. The results of the adaptive protection scheme show values of accuracy above 96% and dependability of 99%. In addition, the solution shows a correlation between the location and the combination of features and hyper-parameters. The implementation of the fault detector model into a physical low voltage network located at Universidad del Norte Colombia showed outstanding results. This test network is based on the IEEE-13 Node Test Feeder scaled to 220V.