Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Neto, Nemesio Fava Sopelsa; Stefenon, Stéfano Frizzo; Meyer, Luiz Henrique; Ovejero, Raúl García; Leithardt, Valderi Reis Quietinho

doi:10.3390/s22166121

Cited by 38 publications

(24 citation statements)

References 118 publications

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“…Other analyses using equivalent signals can be found in the work of Sopelsa Neto et al [ 4 ]. One difference that should be noted, compared with our work, is that Sopelsa Neto et al [ 4 ] did not use the MAPE in percent to present the results. Comparatively, all the MSE results were lower than the proposed optimized EWT-Seq2Seq-LSTM with the attention model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…When there is a missing or partially broken insulator, this can be easily noticed, and finding the location of a fault in which the insulator is complete is more difficult; for this reason, specific types of equipment are used such as ultrasound detector, radio interference, ultraviolet camera, among others. One way to assess the impact of contamination on the grid insulation is the leakage current [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Klaar

Stefenon

Seman

et al. 2023

Sensors

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Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict whether a shutdown may occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with a long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called optimized EWT-Seq2Seq-LSTM with attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Klaar

Stefenon

Seman

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The hybrid model improved the forecast accuracy by reducing the average absolute percentage error rate when compared to the base model [20]. The use of filters for noise reduction can improve the model's ability to make predictions, and besides seasonal filters, the wavelet transform shows promise for this purpose [21], and can be combined with several state-of-the-art models [22], or classical methods such as neuro-fuzzy systems [23].…”

Section: Related Workmentioning

confidence: 99%

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

et al. 2023

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The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.

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“…In this paper, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R

) were considered, given by

where

is the observed value,

is the predicted output, and

is the average of the observed value [ 81 ].…”

Section: Wavelet Lstmmentioning

confidence: 99%

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Branco

Cavalca

Stefenon

et al. 2022

Sensors

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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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Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Cited by 38 publications

References 118 publications

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

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