Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines

Lucas Pereira; Rafael Pereira; Pedro Prado; Felipe Cunha; Fabrício Góes; Roger Fiusa; Lorrany Silva

doi:10.20944/preprints202204.0016.v1

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preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints202204.0016.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines

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Version 1 : Received: 30 March 2022 / Approved: 4 April 2022 / Online: 4 April 2022 (11:26:15 CEST)

A peer-reviewed article of this Preprint also exists.

Pereira, L.H.; Pereira, R.B.; Prado, P.H.S.; Cunha, F.D.; Góes, F.; Fiusa, R.S.; Silva, L.F.L. Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines. Appl. Sci. 2022, 12, 5292. Pereira, L.H.; Pereira, R.B.; Prado, P.H.S.; Cunha, F.D.; Góes, F.; Fiusa, R.S.; Silva, L.F.L. Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines. Appl. Sci. 2022, 12, 5292.

Abstract

Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

Keywords

natural language processing; risk management; transmission lines; unstructured data

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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