# Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines +
Footnote †: This work was supported by the project R&D ANEEL-PD-05456-0001/ 2019, AVSystemGeo, Equatorial, National Council for Scientific and Technological Development-CNPq, Coordination for the Improvement of Higher Education Personnel-CAPES, Federal University of Minas Gerais-UFMG and Pontifical Catholic University of Minas Gerais (PUC Minas).  
[PERSON]  
1 Department of Electrical Engineering, Pontifical Catholic University of Minas Gerais, Minas Gerais 3053-901, Brazil; lucasbreerias.sgmail.com  
2 Department of Electrical Engineering, Federal University of Minas Gerais, Minas Gerais 31270-901, Brazil; bambr  
In the last decade, a large amount of information was made available on the web to help managers make decisions based on data analysis. These databases come from various sources such as weather forecasts, energy regulatory reports, social media, energy market shares, concession contracts, and internal management system companies. It is a daunting task to combine data from all those different sources into a unique model to estimate the probability of potential risks for the implementation of transmission lines, since data usually come in different formats.  
Data can be structured, for example, in dashboards monitoring transmission projects, where they present information to monitor compliance with concession contracts. They can also be presented in an unstructured way as texts with information related to decisions about transmission projects. An important unstructured database is the weekly reports made available by the Brazilian National Electric Energy Agency (ANEEL) on its website ([[https://antigo.aneel.gov.br/pautas-e-atas](https://antigo.aneel.gov.br/pautas-e-atas)]([https://antigo.aneel.gov.br/pautas-e-atas](https://antigo.aneel.gov.br/pautas-e-atas)) (accessed on 12 May 2022)). Although it is challenging to extract accurate information from this database due to its unstructured nature, it is a single source that comprises most phases and areas involved in the management of transmission lines. Thus, it relieves the burden of integrating multiple database sources into a single risk management model.  
This paper uses natural language processing (NLP) techniques to extract keywords from ANNEL's weekly reports and models to estimate the probability of risks in implementing electric power transmission lines based on the National Electric Energy Agency (ANEEL, a Brazilian regulator) weekly reports. To the best of our knowledge, this is the first scientific work to use those particular weekly reports to estimate the probability of risks in the implementation of transmission lines. After extracting the data from ANEEL's website, we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks. These risks are related to weather, environmental conditions, land constraints, costs, and investments (e.g., non-approval of the study in the regions with indigenous settlements, an increase in the number of malaria cases registered, or political changes in local governments). Based on specialist knowledge, 233 risks were identified during the acquisition and implementation of energy transmission lines. The specialist wrote down a list of possible causes related to each risk. Our results show that we could predict the probability of 97 risks out of the 233 listed by a company using our NLP approach.  
This article is organized as follows. Section 3 presents the background for understanding the electric power transmission line implementation. Section 2 discusses the related work. Then, Section 4 introduces the research methodology. Section 5 presents and discusses the experimental results. Finally, Section 6 presents the conclusion and new directions for this research.