Smart Transformer for Smart Grid - Intelligent Framework and Techniques for Power Transformer Asset Management

View/ Open
File version
Accepted Manuscript (AM)
Author(s)
Ma, Hui
Saha, Tapan
Ekanayake, Chandima
Martin, Daniel
Griffith University Author(s)
Year published
2015
Metadata
Show full item recordAbstract
Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, ...
View more >Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.
View less >
View more >Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.
View less >
Journal Title
IEEE Transactions on Smart Grid
Volume
6
Issue
2
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Electrical and Electronic Engineering not elsewhere classified
Electrical and Electronic Engineering
Interdisciplinary Engineering