Uncertainty Quantification of Capacitor Switching Transient Location Using Machine Learning

Identification of capacitor switching transient location provides valuable insight into the state of the associated equipment. Machine learning (ML) models, and convolutional neural networks (CNNs) in particular, have demonstrated remarkable performance in signal location. However, ML models are dat...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 39; no. 2; pp. 2410 - 2420
Main Authors Wilson, Aaron J., Tran, Hoang A., Lu, Dan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Capacitors
Extrapolation
Machine learning
Neural networks
Noise prediction
Predictive models
situational awareness
Smart grid
Smart grids
Switching
Transient analysis
transients
Uncertainty
uncertainty quantification
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Summary:Identification of capacitor switching transient location provides valuable insight into the state of the associated equipment. Machine learning (ML) models, and convolutional neural networks (CNNs) in particular, have demonstrated remarkable performance in signal location. However, ML models are data driven whose predictions are affected by noise in data and may also suffer from large extrapolation errors when applied to new conditions. Uncertainty quantification (UQ) is necessary to ensure model trustworthiness and avoid overconfident predictions in extrapolation. In this work, we propose a novel UQ method, called PI3NN, to quantify prediction uncertainty of ML models and integrate the method with CNNs for transient source location. PI3NN calculates Prediction Intervals by training 3 Neural Networks and uses root-finding methods to determine the interval precisely. Additionally, PI3NN can identify out-of-distribution (OOD) data in a nonstationary condition to avoid overconfident prediction. Results indicate that with PI3NN, transient signals are not only correctly identified, but when said signals are subject to corruptions characteristic of an actual power monitoring system (e.g non-ideal sensors), the model recognizes when it is uncertain about its predictions, effectively letting the user know when to accept or discard the results.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2023.3286173