Detection and classification of electrical supply voltage quality to electrical motors using the Fuzzy-Min-Max neural network

Generally electrical motors have a designed lifespan of more than 20 years when supplied with the proper rated voltage and acceptable voltage waveforms. However when motors are subjected to lowered levels of supply voltage quality conditions appearing simultaneously due to disturbances of overvoltage or undervoltage, voltage unbalance and voltage waveform distortions, it causes motor windings to be seriously overheated and reduce significantly the lifespan of the motor. Detection and classification systems are expensive compared to the low cost of motors as such detection systems are seldom integrated together with the motor. In this paper, a control methodology to enable a simple low cost for the proper detection and classification of electrical supply voltage condition to electrical machines using Fuzzy-Min-Max neural network to significantly improve the satisfactory operation and life span of the electrical machines is presented. The proper application of supply voltage quality levels can reduce the downtime and operating expenses of the electrical machines, thus improving return of investment on assets managed by the organization. In this paper, a simple control methodology for the early stage detection and classification of the electrical voltage supply condition in electrical machines based on Fuzzy-Min-Max neural network is presented. The condition of the supply voltage quality to electrical machines is diagnosed and classified using Fuzzy Min-Max neural network. It will be shown that the developed method is simple in dealing with any supply voltage condition to detect and allows for the ease in classification of the supply voltage pattern. Test results for the classified patterns have shown that the method used for this classification scheme is able to correctly identify supply voltage conditions, and the adopted Fuzzy-Min-Max neural network condition monitoring based method is efficient.