ArticleSensors (Basel, Switzerland)2022
Wavelet LSTM for Fault Forecasting in Electrical Power Grids.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Multi-family wavelet-based feature engineering method for short-term time series forecasting.Scientific reports · 2025Article
- A deep learning-based approach for axle counter in free-flow tolling systems.Scientific reports · 2024Article
- Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid.Sensors (Basel, Switzerland) · 2024Article
- A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.Sensors (Basel, Switzerland) · 2024Review
- Video-Based Human Activity Recognition Using Deep Learning Approaches.Sensors (Basel, Switzerland) · 2023Article
- Group Method of Data Handling Using Christiano-Fitzgerald Random Walk Filter for Insulator Fault Prediction.Sensors (Basel, Switzerland) · 2023Article
- Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model.Sensors (Basel, Switzerland) · 2023Article
- Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.