Abstract
This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power system, the simulation model of the ship electrical power system is built using the MATLAB/Simulink simulation software platform in order to understand the normal working state and fault state of the generator and load in the power system. Then, the model is simulated to generate the fault response curve, and the picture dataset of the network model is obtained. Second, a CNN fault diagnosis model is designed using TensorFlow, an open-source tool for deep learning. Finally, network model training is performed, and the optimal diagnosis results of the ship electrical power system are obtained to realize structural parameter optimization and diagnosis. The diagnosis results show that the established simulation model and improved CNN can provide support for fault diagnosis of the ship electrical power system, improve the operation stability and safety of the ship electrical power system, and ensure safety of the crew.
Highlights
With the continuous development of modern technology, ship electrical power systems that can realize overall coordination of the energy of the entire ship are expected to constitute the development trend of ships in the future [1,2]
This paper proposes an improved network fault diagnosis model based on a convolutional neural network (CNN)
Compared with the original CNN model proposed, the average accuracy of the identification and classification of the ship electrical power system is up to 99%
Summary
With the continuous development of modern technology, ship electrical power systems that can realize overall coordination of the energy of the entire ship are expected to constitute the development trend of ships in the future [1,2]. Ship electrical power systems are significantly different from land power systems [3]. Ship electrical power systems are regarded as the core of the entire ship. They are independent and have high requirements in terms of safe operation and fault diagnosis. They need faster and more accurate fault diagnosis than land power systems in case of system faults [4]. Fault diagnosis technologies are necessary to study ship electrical power systems [5]
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