A New Artificial Neural Network Model for the Output Voltage and Power Predictions of Permanent Magnet Generators with Variable Air Gaps

An artificial neural network (ANN) model is recommended for the analysis and prediction of the output results of a recently designed and constructed generator known as a permanent magnet generator with axial flux. The generator is considered to generate a power scale of P = 3 kW, especially for off-grid household usage far from the network. This machine has an adjustable air gap mechanism, and P = 3 kW is the maximum power; thus, this power can be decreased comfortably by increasing the air gap further to 7 mm In this case, the maximum power rate can be decreased to P = 1.5 kW according to the experimental confirmation. As a new technique, an ANN approach is introduced for the predictions of voltage and power obtained output for various electrical loads, rotor speeds, and air gaps because it is difficult to get output values for all those machine parameters. In addition, this ANN technique enables one to find the generated voltage and power for specific extremely low air gap values (i.e., 0.2 mm, 0.4mm, 0.6mm), which is hard in experimental work due to construction difficulties. The ANN model is used to predict the output values of the generator before any laboratory practice successfully, and the users can determine the correct air gap ranges to their required power regimes for this generator.