| As the main equipment of power system, shipboard power generator plays a crucial role of the security and stability for entire power system. Along with the increase of the capacity of shipboard power generator, people's demand for safe operation and reliability of generators is greater and greater. Among all the shipboard generator faults, single-phase stator ground fault, the terminal phase short-circuit fault, Magnetic loss fault and single-phase stator windings short-circuit fault are common faults of shipboard generator. So diagnosis for these faults of shipboard generator has important theoretical and practical value.Based on the research of ship generators mathematical mechanism, the in-depth analysis of internal structure of generator model in Simulink and the reason of typical faults of synchronous generator, this paper has done the modeling and simulation. RMS current phase was collected as a sample. After several sample processing means such as Fast Fourier Transform (FFT), this paper got the study samples of neural network for input vectors.By using the associative memory capacity of Discrete Hopfield Neural Network(DHNN) as a content - addressed memory(CAM)device, DHNN saved study samples of 5 faults as 5 typical styles and, found the corresponding one which was most similar to the given test samples in study samples saved before. So that the style of fault that test samples stand for were identified and, the purpose of fault diagnosis was also achieved. Test results show that the network can effectively identify several common faults of shipboard generator.The paper also analyzed the fault-tolerant ability of discrete Hopfield network. Based on polluting sample testing by varied degrees, this paper got the probability of success diagnosis. In this way, this paper can show the superior fault-tolerant ability of Discrete Hopfield Neural Network in the one hand; in the other hand, it can also support the relevant data for further optimization in the future. |
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