Reconciling motor performance indicators from theoretical calculations and laboratory tests

Quite often because of the complexity in the design of large industrial motors, the theoretical motor parameter calculations do not match actual results from laboratory tests. Thus, it becomes important to predict the amount of discrepancy between the two methods to develop confidence in the motor parameter calculations. This paper discusses the development of multiple artificial neural networks (ANNs) designed to predict the ratios of measured parameters to calculated parameters, given the geometry and construction of the motor. These ratios represent correction factors which can be applied to the values calculated from the theoretical program, which, in this case, is a software package known as MPE program.