Root cause analysis improved with machine learning for failure analysis in power transformers. (September 2020)

• Record Type: Journal Article
• Authors: Arias Velásquez, Ricardo Manuel; Mejía Lara, Jennifer Vanessa
• Is Part Of: Engineering failure analysis. Volume 115(2020)
• Publication Date: 2020-09
• Subjects: Artificial neural network -- Health index -- Neural network classifier -- Power transformer; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2020.104684
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The root cause analysis (RCA) is fundamental for transformers' diagnosis. This paper proposes a methodology for to RCA with Machine learning. High correlation is achieved with Machine learning techniques. A case study is implemented with 868 data sets. Abstract: The root cause analysis, diagnosis and classification of faults in power transformers with high accuracy and efficiency is the fundamental key to ensure reliability and power quality with least interruptions. In this research, a new proposal was developed for an intelligent Genetic Algorithm tuned artificial neural network (ANN) classifier for transformer faults for to improve the root cause analysis, in this case, this new proposal is able to segregate all fault types using Dissolved Gas Analysis (DGA) samples from power transformers of a large range of providers and from other research papers, these input data have been pre-processed using the Fast Decision tree learner (FDTL), tree learner advanced, and M5 Rule (M5R) algorithm and NN. We replace the conventional action selection procedure of Reinforcement Learning (RL) by a machine learning based optimizer. In this research, a new proposal for computationally least expensive in comparison to other approaches is presented. Our proposed classifier could serve as an important tool in ensuring healthy operation of power transformers, the correlation is higher than 0.98 with tree learner classifier, with a validation of the over-fitting perspective.