%0 Journal Article
%T Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System
%A Nandkumar Wagh
%A D. M. Deshpande
%J Applied Computational Intelligence and Soft Computing
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/845815
%X Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented. 1. Introduction Power transformer is of prime importance and costly element of the power system and the reliability of the system then depend upon its well-being. Close and continuous monitoring and maintenance of it restore the service conditions. Thermal and electrical stresses can cause the incipient faults which further lead to failure of the equipment. Fault detection at the early stage can save the equipment. The important tool to diagnose the faults is DGA. Rogers ratio, Doernenburg ratio, IEC ratio, and Duval triangle are some of the standards established for diagnosis. The ratio methods are based on the single fault prediction but there are the situations of multiple faults and the diagnosis becomes erroneous. Among the existing methods for identifying the incipient faults, DGA is the most popular and successful method [1¨C3]. When there is any kind of fault, such as overheating or discharge fault inside the transformer, it will produce a corresponding characteristic amount of gases in the transformer oil. This concept is the underlying principle of DGA. Through the analysis of the concentrations of dissolved gases, their gassing rates, and the ratio of certain gases, the DGA method can determine the type of fault of the transformer. The commonly collected and analyzed gases are H2, CH4, C2H2, C2H4, C2H6, CO2, and CO. An ANSI/IEEE standard and IEC publication 599 [4, 5] describes three DGA approaches such as (1) key gas method; (2) Rogers ratio method; and (3) Doernenburg ratio method. All three methods are computationally straightforward. However, these methods, in some cases, provide
%U http://www.hindawi.com/journals/acisc/2014/845815/