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Title: Data-driven fault diagnosis of power converters
Authors: Shwe, Yee Win
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Shwe, Y. W. (2021). Data-driven fault diagnosis of power converters. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: P1031-192
Abstract: Development of machine learning algorithms for multi-classification makes many unsolved classification problems approachable. The only way to find the best classifier is not just to get the best accuracy level by using it. So, we considered some more statistical measures to prove the efficiency of our model. Considering the wide use of neural network architectures in different fields we have selected one of the emerging architectures, Stochastic Configuration Network (SCN). It not just influentially impacts the accuracy and outperforms compared with some traditional algorithms in term of time complexity. In our work of classifying converter open circuit faults where we deal with 22 types of fault classes, SCN shows an efficient result to be used with. In this final year project, MATLAB-based programming will be used to conduct the system models and simulate the test results.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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