-Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.
Wireless sensor network (WSN) has emerged as a promising technology thanks to the recent advances in electronics, networking, and information technologies. The data which flows through the wireless sensor network has great impact on the link load. The handling this data against the congestion, its reliability, and loss recovery is very tough task. In this paper we propose the scheme to detect and overcome the congestion (EDCAM). The main feature of proposed algorithm is early detection of the congestion. Rather to take the corrective action, here we prevent to happening the congestion.
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