Open AccessUsing dissolved gas analysis results to detect and isolate the internal faults of power transformers by applying a fuzzy logic method
Author(s) -
Noori Masoud,
Effatnejad Reza,
Hajihosseini Payman
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0028
Subject(s) - dissolved gas analysis , transformer oil , transformer , fuzzy inference system , fault detection and isolation , power grid , fuzzy logic , engineering , reliability engineering , petroleum engineering , computer science , electrical engineering , power (physics) , fuzzy control system , adaptive neuro fuzzy inference system , artificial intelligence , voltage , physics , quantum mechanics , actuator
One of the most important tools for condition monitoring is the gas chromatography test of transformer oil, which is known as dissolved gas analysis (DGA). In this research, the DGA results of >3000 power transformers operating in Iran's power grid were carefully studied and from among them, the results related to transformers suspicious of being faulty were used to validate the fault detection accuracy of the presented fuzzy inference system (FIS). In most of the previously published papers, the detection and isolation of transformer faults has been based on one or two of the following parameters: absolute concentrations of free and dissolved gases in transformer oil, total dissolved combustible gases, total combustible gases, ratios of some gases to each other, and the rates of gas increase. However, in this research, most of these parameters have been used for fault detection and isolation, according to the IEC 60599 standards. Also, no attempt has been previously made to detect the decomposition of insulation papers of transformers; but the presented FIS is able to detect this fault as well. The overall performance accuracy of the presented system is F 1 = 91.2%, which seems to be a suitable value.