Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to ensure an uninterrupted power supply. Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of transformer, uncertain and incomplete information may be produced. Based on these points, this paper presents an intelligent fault diagnosis method of power transformer using fuzzy fault tree analysis (FTA) and beta distribution for failure possibility estimation. By using the technique we proposed herein, the continuous attribute values are transformed into the fuzzy numbers to give a realistic estimate of failure possibility of a basic event in FTA. Further, it explains a new approach based on Euclidean distance between fuzzy numbers, to rank the basic events in accordance with their Fuzzy Importance Index

ABSTRACT. The aim of this paper is to develop the membership functions of the system characteristic of a queuing g model with an unreliable server, in which the arrival rate, service rate, breakdown rate and repair rate are all fuzzy numbers. The -cut approach is used to transform a fuzzy queue with an unreliable server into a family of conventional crisp queues with an unreliable server. By using membership functions, a set of parametric nonlinear programmes are developed to describe the family of crisp queues with an unreliable server. An efficient algorithm is developed to find the optimal solutions at or different possibility level . Numerical examples are solved successfully. Since the system characteristics being expressed and governed by membership functions, more information is provided for the management.

Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of any system, uncertain and incomplete information may be produced. Based on these points, present paper extends our study for the development of a vague scheme for fault tree analysis of any general system. The functioning of the developed vague scheme is demonstrated for diagnosis of cannula fault in power transformers using vague fault tree analysis (VFTA) and beta distribution for failure possibility estimation. By using these techniques we have proposed herein, the extension of Pandey et al., in 2011 to define the continuous attribute values transformed into the vague numbers to give a realistic estimate of failure possibility of a basic event in VFTA. Further, it explains a new approach based on Euclidean distance between vague numbers, to rank the basic events in accordance with their Vague Importance Index (VII). 852 M. K. Sharma et al.

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