Improved Feed-Forward Backpropagation Neural Network Based on Marine Predators Algorithm for Tuning Automatic Voltage Regulator

Abstract

This research will discuss the application of an automatic voltage regulator based on the feed-forward back propagation neural network (FFBNN), which is enhanced by the marine predator algorithm (MPA). The marine predators algorithm is a method that adopts marine ecosystem life that is identified in the relationship between predators and prey. MPA is adopting a natural approach to arranging the best food search strategies and finding the latest strategy. The focus of the research is on the performance of speed and rotor angle. The performance of the proposed method will be tested using hidden layer variations. In addition, the proposed method will be compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), and Focused Time Delay neural network (FTDNN). The speed and rotor angle of the proposed method have good values. The MPA-FFBNN results are not much different from other methods. The experimental results show that the performance of the proposed method has promising results.