Neural network prediction of electromagnetic field strength in hybrid micro-grid system
Location of any Hybrid Micro-Grid System requires efficiently prediction of the electromagnetic field strength. This study proposes a novel Electromagnetic Field Strength (EFS) predication based on Probabilistic Neural Network (PNN). Learning data sets have been generated using Electromagnetic Transients Program EMTP. The PNN model has three input nodes representing the Switching Distance, Busbar Interference Voltage and Current waveforms, the output node representing the EFS. Testing datasets have deliberately been chosen outside the region of the learning datasets so as to check the performance of the neural network. The results indicate that the proposed technique can be used successfully to detect the maximum Electromagnetic Field Strength Level at any Location.
Haidar, A. M., Ahmed, I. & Abdalla, A. (2010). Neural network prediction of electromagnetic field strength in hybrid micro-grid system. International Journal of Soft Computing, 5 (2), 62-66.