Study of approaches to incipient fault detection in power transformer by using dissolved gas analysis

  • Ruta Liepniece
  • Sandra Vitolina
  • Janis Marks
Keywords: power transformers, dissolved gas analysis, incipient fault, reliability

Abstract

To maintain the reliability of power transmission it is important to detect the incipient fault of power transformer as early as possible. If the fault of a power transformer is not detected promptly, it can evolve resulting in high repair costs or even failure of the power transformer and decreasing reliability of power transmission. The most commonly used method for power transformer fault detection is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret the data of dissolved gas analysis, but not many are applicable for the detection of the incipient fault. The detection of the incipient fault of a power transformer is included in both IEEE C57.104-2008 “Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers” and Standard of Latvian Electrotechnical Committee LEK 118 “Transformer Oil Inspection Standards”. In both standards, the limits of dissolved gases in transformer oil are divided into levels, each corresponding to different technical conditions of the power transformer including the level that indicates the incipient fault. However, these approaches vary to a great degree – one approach mostly indicates that transformers are in good condition with several cases that must be additionally evaluated, but the second approach mostly results in warning about the incipient fault, which must be confirmed by additional evaluation. The objective of this paper is to determine the most suitable approach to detect the incipient fault of power transformers. A case study is provided, which includes analysis of DGA data of 48 power transformers installed in the transmission network in Latvia with both methodologies mentioned above for detecting the incipient fault.
Published
2017-09-04
Section
Best papers of CYSENI 2017 international conference