Health index calculation for power transformers using technical and economical parameters. Issue 7 (1st October 2016)

• Record Type: Journal Article
• Authors: Zeinoddini‐Meymand, Hamed; Vahidi, Behrooz
• Is Part Of: IET science, measurement & technology. Volume 10:Issue 7(2016)
• Page Start: 823
• Page End: 830
• Publication Date: 2016-10-01
• Subjects: power transformer testing -- artificial intelligence -- chemical analysis -- transformer oil -- power transformer insulation -- investment; health index calculation -- power transformer -- economical lifetime assessment -- artificial intelligence model -- artificial neural network -- adaptive neurofuzzy inference system -- oil characteristics -- dissolved gas analysis -- capital investment -- maintenance; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0184
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : In this study technical diagnostic tests and economical lifetime assessment of transformers are investigated to evaluate the overall health condition of working transformers. Two artificial intelligence models including artificial neural network and adaptive neuro‐fuzzy inference system models are presented to determine the health index for transformers. The technical and economical parameters are used as input parameters to develop the models. Technical parameters are extracted from oil characteristics and dissolved gas analysis of different transformers. Economical parameters are constructed with transformer capital investments, maintenance and operating costs. The models are developed using 226 experimental field datasets of transformers technical and economical parameters. The models are trained using 80% of the experimental datasets. The remaining 20% is used to evaluate the performance and applicability of the models. The results prove that the models can be used to determine the health condition of transformers with high accuracy.