ReviewSensors (Basel, Switzerland)2022
Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models.
Review 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 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- AI-optimized BPNN model for port safety risk prediction and management.Scientific reports · 2026Article
- Hybrid machine learning predictions of high voltage polymeric insulator pollution.Scientific reports · 2025Article
- A novel hybrid mathematical deep learning technique for early warning of flashover in composite insulators.Scientific reports · 2025Article
- Experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current.Scientific reports · 2025Article
- Classification of RTV-coated porcelain insulator condition under different profiles and levels of pollution.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
- Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks.Scientific reports · 2023Article
- 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
- Wavelet LSTM for Fault Forecasting in Electrical Power Grids.Sensors (Basel, Switzerland) · 2022Article
- Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
Abstract
To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 ×10-3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 ×10-19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.
Indexed as
Identifiers
What OpenQuestion holds
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.