Open AccessAnalysis of training techniques of ANN for classification of insulators in electrical power systems
Author(s) -
Frizzo Stefe Stéfano,
Waldrigues Branco Nathielle,
Nied Ademir,
Wildgrube Bertol Douglas,
Cristian Finardi Erlon,
Sartori Andreza,
Henrique Meyer Luiz,
Bartnik Grebogi Rafael
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1579
Subject(s) - artificial neural network , computer science , gradient descent , operator (biology) , electric power system , task (project management) , field (mathematics) , artificial intelligence , convergence (economics) , identification (biology) , machine learning , grid , power (physics) , engineering , mathematics , quantum mechanics , physics , systems engineering , repressor , economic growth , chemistry , biology , biochemistry , geometry , transcription factor , botany , pure mathematics , economics , gene
Identifying defects in electrical power systems during field inspections is a difficult task, since faults are generally not visible and may be intermittent. To find possible adverse conditions, specific inspection equipment is used. The ultrasound detector is the equipment normally used to inspect outdoor insulating systems; however, using it demands operator experience. To improve the defect condition classification, artificial intelligence techniques are applied to assist the operator in the decision task and thereby facilitate the identification of faulty insulating devices in the grid. The training of artificial neural network (ANN) models is an important step in solving the classification problem. This study aims to evaluate the training capacity in terms of the performance of different optimisation methods for the calculation of the mean square error after convergence. Traditional methods such as Gradient Descent and its variations will be presented, as well as methods that employ high computational effort such as quasi‐Newton and Levenberg–Marquardt. In order to base these concepts, a review will be presented on the use of these algorithms and on the problem of classification of insulators in distribution networks. The results show that there is a considerable performance difference between the calculation methods.