Dong She
Power system, programmable controller, multi-layer feedforward neural network, improved algorithm, fault diagnosis, precision
As the significant part of the system, the power system bears the function of power transmission and activation. Taking the ship’s power system as an example, the starting and stopping of the entire ship require the power system to function. This research is aimed at the fault analysis of the ship power system. The programmable controller is used to code the algorithm parameters, and an improved algorithm model combining the multi-layer feedforward and radial basis function (RNF) neural networks is built. The new algorithm applies the least square method to calculate the weights of the hidden and the input layers, and adds the genetic algorithm to re-code the threshold of the multi-layer feedforward neural network. The research outcomes indicate that the accuracy of the output value comparison of the improved algorithm is 9% higher than that of the multi-layer feedforward neural network, and 6% higher than that of the radial basis neural network. The approximation error of the improved algorithm is 0.033 less than that of the multi-layer feedforward neural network, and the radial basis neural network is 0.019 less. The improved algorithm has higher accuracy and better precision in power systems’ fault diagnosis, and the diagnostic findings are significantly better than the other two algorithms.
Important Links:
Go Back
