Condition monitoring of electrical supply voltage quality to electrical machines using RBF neural network

Three phase electrical machines are normally exposed to lowered levels of supply voltage quality conditions which can appear simultaneously due to voltage disturbances of overvoltage or undervoltage, voltage unbalance and voltage waveform distortions. These voltage disturbances can cause effects of seriously overheating winding insulation resulting in degradation and reduced lifespan of the machines. The supply of electrical power with proper rated voltages and acceptable voltage waveforms can significantly improve the satisfactory operation and life span of the machines. 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 control methodology for the early detection and classification of the electrical voltage supply condition in electrical machines based on radial based function (RBF) neural networks is presented. The condition of the supply voltage quality to electrical machines is diagnosed and classified using RBF neural networks. 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 able to correctly identify supply voltage conditions, and the adopted RBF neural network condition monitoring based method is efficient.