The Compare of Transformer Fault Diagnosis Based on Feature Selection and Parameter Optimization and Fuzzy Reasoning Spiking Neural P Systems
Abstract
Nowadays, Due to existing complexity and changing organizations using accurate and modern tools is compulsory needed on performing the maintenance strategies. Human's improvements during last decades about gathering and storage the results and data cause the organizations have a huge dimension of data related to maintenance. Detection techniques based on dissolved gas analysis (DGA) have been developed to early fault detection in power transformers. Transformer, as one of the most important parts in power supply system, can put the reliability of power supply and the safe operation of electrical system in an entire electricity network into a great danger. Making decision on the strategy requires knowledge that matches reality maintenance organization. On the other hand, it requires a good knowledge and proper analysis of data used. So using data and information and applying them during the strategic follow-up implementation mainstream for Histological maintenance and repair are of great importance. The purpose of current paper is based on a review of 35 refereed article and dissertation that focused on transformer fault diagnosis based on feature selection.
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References
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