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Wavelet LSTM for Fault Forecasting in Electrical Power Grids.

Nathielle Waldrigues Branco1, Mariana Santos Matos Cavalca1, Stefano Frizzo Stefenon2,3

  • 1Department of Electrical Engineering, Santa Catarina State University, R. Paulo Malschitzki 200, Joinville 89219-710, Brazil.

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PubMed
Summary
This summary is machine-generated.

This study forecasts electrical power failures using a wavelet long short-term memory (LSTM) model. The approach enhances grid reliability by predicting faults for better maintenance planning.

Keywords:
electrical power gridsfault forecastinglong short-term memorytime series forecastingwavelet transform

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Time Series Analysis

Background:

  • Electric power utilities must ensure continuous energy supply.
  • Grid failures decrease reliability and performance.
  • Accurate fault prediction is crucial for rapid power restoration.

Purpose of the Study:

  • To assess the feasibility of time series forecasting for electrical fault prediction.
  • To evaluate the long short-term memory (LSTM) model for fault forecasting.
  • To investigate the use of wavelet transform to enhance LSTM predictive capabilities.

Main Methods:

  • Utilized time series data of electrical power failures in Brazil during 2020.
  • Implemented and evaluated the long short-term memory (LSTM) neural network model.
  • Integrated wavelet transform with LSTM (wavelet-LSTM) to improve prediction accuracy.

Main Results:

  • The wavelet-LSTM model demonstrated superior performance in fault prediction compared to standard LSTM.
  • The proposed approach showed reduced prediction error and enhanced robustness.
  • Statistical analysis confirmed the model's reliability for practical utility application.

Conclusions:

  • Wavelet-enhanced LSTM is a feasible and effective tool for electrical fault prediction.
  • This predictive capability aids electric power utilities in optimizing maintenance and improving grid reliability.
  • The study validates the utility of advanced forecasting models in critical infrastructure management.