Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution net-work(ADN)and microgrid(MG)play an important role in power system operation.The design and implementation of ap-propriate protection systems for MG and ADN must be ad-dressed,which imposes new technical challenges.This paper presents the implementation and validation aspects of an adap-tive fault detection strategy based on neural networks(NNs)and multiple sampling points for ADN and MG.The solution is implemented on an edge device.NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infra-structure.Multiple sampling points are used to derive a data-driven model,which allows the generalization considering the implementation in physical systems.The adaptive fault detector model is implemented on a Jetson Nano system,which is a sin-gle-board computer(SBC)with a small graphic processing unit(GPU)intended to run machine learning loads at the edge.The proposed method is tested in a physical,real-life,low-voltage network located at Universidad del Norte,Colombia.This test-ing network is based on the IEEE 13-node test feeder scaled down to 220 V.The validation in a simulation environment shows the accuracy and dependability above 99.6％,while the real-time tests show the accuracy and dependability of 95.5％and 100％,respectively.Without hard-to-derive parameters,the easy-to-implement embedded model highlights the potential for real-life applications.