Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines

Risk management of electric power transmission lines requires knowledge from different areas such as the 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 challen...

Full description

Saved in:
Bibliographic Details
Published inApplied sciences Vol. 12; no. 11; p. 5292
Main Authors Pereira, Lucas H., Pereira, Rafael B., Prado, Pedro H. S., Cunha, Felipe D., Góes, Fabrício, Fiusa, Roger S., da Silva, Lorrany Fernanda Lopes
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Big Data
Costs
Data analysis
Data mining
Electric power
Electric power grids
Electric power transmission
Electricity distribution
Energy industry
Energy transmission
Failure analysis
Hydroelectric plants
natural language processing
Power lines
Probability
Risk analysis
Risk management
Stock exchanges
Structured data
Transmission lines
Unstructured data
Brazil
Online AccessGet full text

Cover

More Information
Summary:Risk management of electric power transmission lines requires knowledge from different areas such as the 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 propose a novel method to calculate risk probabilities on the implementation of transmission lines based on unstructured text from a single source. It uses the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contain decisions about the electrical grid comprising most of the aforementioned areas. Since the data are 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. This method differs from previous works, which were based on structured data (numerical or categorical) from single or multiple sources. Our results show that we were able to extract relevant keywords from the ANEEL reports that enabled our proposed method to estimate the probability of 97 risks out of 233 listed by an expert.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12115292