Wildfire Forecasting near Transmission Lines via Artificial Neural Networks

EasyChair Preprint no. 11091

Abstract

Wildfires are one of the main causes of unscheduled shutdowns in Brazil's power grid. Therefore, tools that help predict these events in the vicinity of transmission lines (TLs) can significantly collaborate in operation planning tasks. In this context, the present work proposes a methodology that employs the artificial neural network Multi-Layer Perceptron in order to carry out wildfire forecasting in the vicinity of TLs based on meteorological data from the region of interest, representing an important indicator of aid in decision-making in relation to planning the operation of electrical power systems. A case study carried out to forecast fires in the region of the city of Uberlândia - MG, through which a 500 kV TL of the national interconnected system passes, points to a promising performance of the proposed method. Accuracy rates of 82% and 84% are obtained for forecasts in the year 2019 for the evaluated region.

Keyphrases: Desligamentos de linhas de transmissão, Previsão de queimadas, Rede Neural Artificial, Sistema Elétrico de Potência

@Booklet{EasyChair:11091,
  author = {Lucas B. Cruz and Carlos Alexandre M. Nascimento and Fernando A. Assis and Rodolfo A. R. Moura and Marco Aurélio O. Schroeder},
  title = {Wildfire Forecasting near Transmission Lines via Artificial Neural Networks},
  howpublished = {EasyChair Preprint no. 11091},

  year = {EasyChair, 2023}}