The size, cost and performance of different kinds of electrical machines like transformers, alternators, induction motors, induction generators, synchronous motors, d c motors, exciters, etc. always play crucial role in the operation and reliability of power system. Their replacement costs ranging from a few thousand to millions of rupees. Monitoring and classifying internal faults in these electrical machines are still challenging problems for power system engineers because of the various types of possibility like false tripping caused by the magnetizing effect of the inrush current, mal operation of transducers, etc. The purpose of present invention is to introduce methodology to know the status of the important kind of electrical machines used in power sector. The algorithm is designed base on the availability of minimum online and offline measurable parameters. The existing online parameters like winding temperature, oil temperature, low oil level, bearing temperature, speed etc. applied as online input parameters for the design of condition monitoring assessment algorithm. This Condition Monitoring System (CMS) avoids the unnecessary outages, prevent breakdowns thereby increases the availability of the equipment and cause considerable savings in the economy of utility. The main advantage of the model is that it can be applied for online monitoring to all types of static and dynamic electrical machines. Following invention is described in detail with the help of Fig. 1 showing the algorithm developed for CMS, Fig. 2 showing the connection schematic diagram for this CMS. Fig. 3 shows the online, offline and overall module for this computational intelligence based CMS.
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