Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

+Advanced Search
Home > Archive>Volume 10, Issue 6, 2022 >1648-1657. DOI:10.35833/MPCE.2021.000444
PDF HTML Export
Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
Author:
Affiliation:

1. Electric and Electronic Engineering Program, Universidad Tecnológica de Bolívar, Cartagena, Colombia
2. Electrical and Electronic Engineering Department, Universidad del Norte, Barranquilla, Colombia
3. Distributed Systems Group, Pacific Northwest National Laboratory, Gainesville, USA

Fund Project:

This work was supported by Universidad del Norte, Fondo Nacional de Financiamiento para la Ciencia, la Tecnología e Innovación FCTEI del sistema general de regalías SGR, and Departamento Administrativo de Ciencia, Tecnología e Innovación–COLCIENCIAS (now Colombian Ministry of Science, Technology, and Innovation–Minciencias) by call contest “Convocatoria 757 de 2017” and “Convocatoria 852–Conectando conocimiento de 2019”–Project Integra2023, code 111085271060, contract 80740-774-2020.

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • |
    Abstract:

    Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive 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 infrastructure. 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 single-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 testing 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.

    Reference
    Related
    Cited by
Get Citation

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:July 01,2021
  • Revised:October 27,2021
  • Online: November 21,2022

Address:No.19 Chengxin Avenue, Jiangning District, Nanjing 211106, China

E-mail: mpce@alljournals.cn

Tel:86-25-81093060

Fax:86-25-81093040

Copyright：Journal of Modern Power Systems and Clean Energy ® 2025 All Rights Reserved
Supported by：Beijing E-Tiller Technology Development Co., Ltd.

ICP：ICP备09008660号