Journal ArticleDOI
Application of SVM and KNN to Duval Pentagon 1 for transformer oil diagnosis
TLDR
The Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) algorithms combined to the Duval method may complement theDuval Pentagon 1 diagnosis method.Abstract:
The carried out investigations deal with the application of machine learning algorithms to Duval Pentagon 1 graphical method for the diagnosis of transformer oil. In fact, combined to graphical methods, pattern recognition aims to may complement. For this purpose, we have used the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) algorithms combined to the Duval method. The SVM parameters have been optimized with the Particle Swarm Optimization (PSO). Inspired from IEC and IEEE, five classes namely PD, D1, D2, T1&T2, and T3 have been adopted. The combined algorithms were verified using 155 samples from IEC TC 10 and related databases. We found that KNN, SVM may complement the Duval Pentagon 1 diagnosis method.read more
Citations
More filters
Journal ArticleDOI
A Review of Graph Neural Networks and Their Applications in Power Systems
TL;DR: A comprehensive overview of graph neural networks (GNNs) in power systems is proposed, and several classical paradigms of GNNs structures are summarized, and key applications inPower systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail.
Journal ArticleDOI
Power Transformer Fault Diagnosis Based on DGA Using a Convolutional Neural Network With Noise in Measurements
TL;DR: In this article, a convolutional neural network (CNN) model is proposed based on the DGA approach to accurately predict transformer fault types under different noise levels in measurements, which is applied with three categories of input ratios: conventional ratios (Rogers 4 ratios, IEC 60599 ratios, Duval triangle ratios), new ratios (five gas percentage ratios and new form six ratios), and hybrid ratios (conventional and new ratios together).
Journal ArticleDOI
Adaptive Dynamic Meta-Heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults
Sherif S. M. Ghoneim,Tamer Ahmed Farrag,A. Ali Rashed,El-Sayed M. El-kenawy,Abdelhameed Ibrahim +4 more
TL;DR: In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques' parameters that were used to enhance the transformer diagnostic accuracy.
Journal ArticleDOI
Accuracy Improvement of Power Transformer Faults Diagnostic Using KNN Classifier With Decision Tree Principle
TL;DR: In this paper, a KNN algorithm is combined with the decision tree principle as an improved DGA diagnostic tool to improve the diagnostic accuracy of power transformer faults using artificial intelligence and a total of 501 dataset samples are used to train and test the proposed model.
Journal ArticleDOI
Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
TL;DR: The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers.
References
More filters
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Related Papers (5)
Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases
Michel Duval,A. dePabla +1 more