Journal of
Engineering and Technology Research

  • Abbreviation: J. Eng. Technol. Res.
  • Language: English
  • ISSN: 2006-9790
  • DOI: 10.5897/JETR
  • Start Year: 2009
  • Published Articles: 198

Full Length Research Paper

Hybrid diagnosing techniques for analyzing dissolved gases in power transformers

Alamuru Vani
  • Alamuru Vani
  • Department of Electrical Engineering, VJIT, Hyderabad, India.
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Pessapaty Sree Rama Chandra Murthy
  • Pessapaty Sree Rama Chandra Murthy
  • School of Electrical Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India.
  • Google Scholar

  •  Received: 29 November 2014
  •  Accepted: 08 February 2015
  •  Published: 28 February 2015


Safe operation of elements of power systems plays a crucial role in maintaining the reliability and safety of the system. Transformers being a key element in power systems need to be maintained and monitored on a regular basis. Dissolved gas analysis has been used as a reliable tool in maintaining the safe operation of transformers for a long time. Analysis of dissolved gases is analytical and often interpreted differently by different users and methods. The scope of Artificial Intelligence tools in dissolved gas analysis has become critical with increasing number of transformers being used in power systems coupled with rapid expansion of transmission and distribution components. Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has emerged as one of the soft computing modeling technique for power transformer. An ANFIS model for dissolved gas analysis of power transformers is implemented.  Similarly the GA-based weight optimization during training of an ANN is employed to improve diagnostic accuracy. A Graphical User Interface (GUI) is designed using Matlab to help in the seamless integration of analysis and decision making. The user interface is simple and easy to use providing the user flexibility and wide options for analysis. Traditional methods like Rogers Ratio, Key Gas Method, IEC Ratio Method, Dorenburg Ratio Method, Total Dissolved Combustible Gases Method and Triangle Method. The tools also incorporate fuzzy based analysis based on Rogers’s ratios and Key Gas methods and analysis using Artificial Neural Networks. The primary motivation for the work is to provide a platform for analysis of dissolved gases to help in the early detection and diagnosis of transformer faults. This work is carried out with assistance from Andhra Pradesh State Transmission Corporation (APTRANSCO) in the form of required transformer analysis data and expert opinion for validation of the tool.
Key words: Transformer faults, expert system, Matlab, graphical user interface (GUI), fuzzy, artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm- artificial neural networks (GA-ANN).