Abstrato

A Hybrid Neuro Genetic Approach for Analyzing Dissolved Gases in Power Transformers

Alamuru Vani., Dr. Pessapaty Sree Rama Chandra Murthy

Transformers being a key element in power systems need to be maintained and monitored on a regular basis. Dissolved Gas Analysis (DGA) has been used as a reliable tool in maintaining the safe operation of transformers for a long time. In this paper, genetic algorithm based back propagation neural network (GA - ANN) has been proposed for analyzing Dissolved Gases in Transformer Oil. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The GA-based weight optimization during training of an ANN is employed to improve diagnostic accuracy. 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 with Andhra Pradesh State Transmission Corporation (APTRANSCO) in the form of required transformer analysis data and expert opinion for validation of the tool.