Abstract
In the electric power business, power transformers are one of the most common and expensive components. The conventional diagnostic tool for understanding insulation incipient failures is the dissolved gas analysis (DGA) of transformers. Nonetheless, interpreting DGA fault gases remains a significant difficulty for engineers. Along with offline methods, a number of computational approaches have been created for DGA fault classification. Still there exist significant hurdles in applying those methods for DGA fault classification. To establish an effective fault classification system, very diversified and massive DGA data sets of in-service transformers were collected from various utilities for this study. The dataset consists of 3147 instances with four classes: No fault, Thermal fault, Low energy discharge and high energy discharge. Models were built using various machine learning approaches like Quadratic Discriminant Analysis (QDA), Gradient Boosting (GB), Extra Trees (ET), Light Gradient Boosting Machine(LGBM), Random Forest (RF), K Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), Ada Boost (AB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Ridge Classifier, and SVM - Linear Kernel. The proposed work adopts Analytic Hierarchy Process (AHP) technique for the estimation of weights of the criteria. Based on the generated weights, the performance of the various classifiers is assessed and ranked using Multi-Objective Optimization based on the Ratio Analysis (MOORA) approach. QDA is selected as the best classifier model by the proposed technique.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Dielectrics and Electrical Insulation
Disclaimer: 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.