Data Analytics for Low Voltage Electrical Grids

Maria Stefan, Jose G. Lopez, Morten H. Andreasen, Ruben Sanchez, Rasmus L. Olsen

2018

Abstract

At the consumer level in the electrical grid, the increase in distributed power generation from renewable energy resources creates operational challenges for the DSOs. Nowadays, grid data is only used for billing purposes. Intelligent management tools can facilitate enhanced control of the power system, where the first step is the ability to monitor the grid state in near-real-time. Therefore, the concepts of smart grids and Internet of Things can enable future enhancements via the application of smart analytics. This paper introduces a use case for low voltage grid observability. The proposal involves a state estimation algorithm (DSSE) that aims to eliminate errors in the received meter data and provide an estimate of the actual grid state by replacing missing or insufficient data for the DSSE by pseudo-measurements acquired from historical data. A state of the art of historical and near-real-time analytics techniques is further presented. Based on the proposed study model and the survey, the team near-real-time is defined. The proposal concludes with an evaluation of the different analytical methods and a subsequent set of recommendations best suited for low voltage grid observability.

Paper Citation

in Harvard Style

Stefan M., G. Lopez J., H. Andreasen M., Sanchez R. and L. Olsen R. (2018). Data Analytics for Low Voltage Electrical Grids.In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-296-7, pages 221-228. DOI: 10.5220/0006694802210228

in Bibtex Style

@conference{iotbds18,
author={Maria Stefan and Jose G. Lopez and Morten H. Andreasen and Ruben Sanchez and Rasmus L. Olsen},
title={Data Analytics for Low Voltage Electrical Grids},
booktitle={Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2018},
pages={221-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006694802210228},
isbn={978-989-758-296-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Data Analytics for Low Voltage Electrical Grids
SN - 978-989-758-296-7
AU - Stefan M.
AU - G. Lopez J.
AU - H. Andreasen M.
AU - Sanchez R.
AU - L. Olsen R.
PY - 2018
SP - 221
EP - 228
DO - 10.5220/0006694802210228