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Explainable Machine Learning Method for Open Fault Detection of NPC Inverter Using SHAP and LIME
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Publication Year
2023-01-01
Journal
2023 IEEE Conference on Energy Conversion, CENCON 2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2023 IEEE Conference on Energy Conversion, CENCON 2023, pp.14-19
Keyword
Fault detectionLIMENPC InverterRandom forestSHAP
Mesh Keyword
Faults detectionInterpretabilityLocal interpretable model-agnostic explanationMachine learning methodsNPC inverteNPC invertersOpen faultsRandom forestsShapleyShapley additive explanation
All Science Classification Codes (ASJC)
Energy Engineering and Power TechnologyRenewable Energy, Sustainability and the EnvironmentAutomotive EngineeringElectrical and Electronic EngineeringMechanical Engineering
Abstract
Machine learning methods (ML) have been widely used for fault detection of NPC inverters. However, ML methods such as deep neural networks or ensemble models often act as black boxes, making it challenging to identify critical factors that significantly affect the performance of these models. The lack of interpretability in these models can be a drawback, especially when there is a need to understand the underlying factors driving their predictions for fault detection of NPC inverter. To address this challenge, there is a need for more reliable and trustworthy ML model. Therefore, this study focuses on analyzing the importance of features for open fault detection using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive explanations (SHAP). SHAP and LIME are popular techniques used to provide interpretability and explainability in machine learning models. The simulation results show that SHAP and LIME methods can identify and analyze the most important features of the open fault of the NPC inverter.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36933
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85182951708&origin=inward
DOI
https://doi.org/10.1109/cencon58932.2023.10368888
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10368382
Type
Conference
Funding
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20225500000110).
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Lee, Kyo-Beum이교범
Department of Electrical and Computer Engineering
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