An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Ekonomou, Lambros; Christodoulou, Christos; Mladenov, Valeri
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1143
• Page End: 1148
• Publication Date: 2016-07
• Subjects: Artificial neural networks -- Electric field -- Measurements -- Surge arresters; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1969-x
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract The paper presents an artificial neural network (ANN) software tool that has been developed in order to assess the electric field around medium-voltage surge arresters. The knowledge of the electric field around gapless metal oxide surge arresters is very useful for diagnostic tests and design procedures. For the training, validating, and testing of the ANNs, real data have been used collected from hundreds of measurements. The developed ANN software can be used by electric utilities in order to diagnose the condition of the surge arresters without stopping their operation and by laboratories and manufacturing/retail companies dealing with medium-voltage surge arresters and either face a lack of suitable measuring equipment or want to compare/verify their own measurements.