Journal ArticleDOI
Prediction of iron losses of wound core distribution transformers based on artificial neural networks
Paul S. Georgilakis,Nikos Hatziargyriou,Nikolaos Doulamis,Anastasios Doulamis,Stefanos Kollias +4 more
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TLDR
An artificial neural network approach to predicting and classifying distribution transformer specific iron losses, i.e., losses per weight unit, is presented, showing that an average absolute error has been achieved in the prediction of individual core specificIron losses and an error of 2.2% in case of transformer specific losses.About:
This article is published in Neurocomputing.The article was published on 1998-12-07. It has received 28 citations till now. The article focuses on the topics: Distribution transformer & Transformer.read more
Citations
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Journal ArticleDOI
Transformer Design and Optimization: A Literature Survey
TL;DR: In this article, the authors conduct a literature survey and reveal general backgrounds of research and developments in the field of transformer design and optimization for the past 35 years, based on more than 420 published articles, 50 transformer books, and 65 standards.
Journal ArticleDOI
Reliability enhancement using optimal distribution feeder reconfiguration
TL;DR: A new multiobjective improved shuffled frog leaping algorithm (ISFLA) is proposed to investigate the distribution feeder reconfiguration (DFR) problem from the reliability enhancement point of view.
Journal ArticleDOI
NSGA-II+FEM Based Loss Optimization of Three-Phase Transformer
TL;DR: Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers.
Journal ArticleDOI
A Novel Octagonal Wound Core for Distribution Transformers Validated by Electromagnetic Field Analysis and Comparison With Conventional Wound Core
TL;DR: In this article, a novel configuration of transformer core, called octagonal wound core (OWC), was analyzed, and showed the minimization of the excitation current and reduction of the eddy-current losses.
Proceedings ArticleDOI
Application Research Based on Artificial Neural Network (ANN) to Predict No-Load Loss for Transformer's Design
TL;DR: A new method for classification of transformer no-load losses is presented and it is shown that ANNs are very suitable for this application since they present classification success rates between 78% and 96% for all the situations examined.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Pruning algorithms-a survey
TL;DR: The approach taken by the methods described here is to train a network that is larger than necessary and then remove the parts that are not needed.
Journal ArticleDOI
Electric load forecasting using an artificial neural network
TL;DR: In this article, an artificial neural network (ANN) approach is presented for electric load forecasting, which is used to learn the relationship among past, current and future temperatures and loads.
Book
Neural Computing - An Introduction
Russell Beale,Thomas Jackson +1 more
TL;DR: The perceptron: a vectorial perspective The perceptron learning rule: proof Limitations of perceptrons The end of the line?