PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey
Version 1
: Received: 5 April 2018 / Approved: 9 April 2018 / Online: 9 April 2018 (15:15:26 CEST)
Version 2
: Received: 10 April 2018 / Approved: 11 April 2018 / Online: 11 April 2018 (08:58:29 CEST)
How to cite:
Cheng, L.; Yu, T. Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey. Preprints2018, 2018040109. https://doi.org/10.20944/preprints201804.0109.v1
Cheng, L.; Yu, T. Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey. Preprints 2018, 2018040109. https://doi.org/10.20944/preprints201804.0109.v1
Cheng, L.; Yu, T. Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey. Preprints2018, 2018040109. https://doi.org/10.20944/preprints201804.0109.v1
APA Style
Cheng, L., & Yu, T. (2018). Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey. Preprints. https://doi.org/10.20944/preprints201804.0109.v1
Chicago/Turabian Style
Cheng, L. and Tao Yu. 2018 "Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey" Preprints. https://doi.org/10.20944/preprints201804.0109.v1
Abstract
Compared with conventional methods in fault diagnosis of power transformers, which have defects such as imperfect encoding and too absolute encoding boundary, this paper systematically reveals various intelligent approaches applied in fault diagnosing and decision making of large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one certain aspect, causing some shortcomings in various degrees cannot be revealed effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.
Keywords
power transformer; fault diagnosis and decision; dissolved gas analysis; intelligent algorithms; reliability assessment; hybrid network; preventive electrical tests
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.