Abstract
Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM).
Highlights
Current electrical demands are growing and it is getting harder to keep the electrical system running, a possible solution to better meet these demands would be to increase the quality in the evaluation of the electrical power system [1]
Based on the promising applications of the wavelet transform for feature extraction, as well as the ensemble learning model for time series forecasting, this paper aims to evaluate the forecasting effectiveness of a hybrid wavelet stacking ensemble learning model for ultrasonic signal detection from a contaminated insulator, which may develop a failure over time
For a comparative analysis in relation to variation of the algorithm, we present in Table 7 a statistical evaluation of the adaptive neuro-fuzzy inference system (ANFIS) model and in Table 8 of the long-term short-term memory (LSTM) model, both from the best configuration found for the models in question
Summary
Current electrical demands are growing and it is getting harder to keep the electrical system running, a possible solution to better meet these demands would be to increase the quality in the evaluation of the electrical power system [1]. To maintain transmission and distribution systems working satisfactorily it is necessary to have accurate and comprehensive information on the service performance of the insulators. The use of artificial intelligence to predict adverse conditions in the electrical system is a promising alternative to the problem in question, as it can improve the quality of electricity, reducing possible failures. According to Santos and Barros [5] a stochastic approach can be used to predict the amplitude and duration of voltage sags when planning the electrical network. This analysis needs to be performed as a network planning criterion, considering the stochastic nature of the energy system failures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
