国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

The excitation system is directly related to the progress of power production and task completion quality in hydropower plants. Therefore, the fault analysis and improvement measures of excitation system in hydropower plants are proposed. In the phase of fault analysis, the fault recording of magnetic system is carried out by pressure sensor, which is amplified, filtered and isolated. As a sample, it is input into artificial neural network to realize fault identification and analysis of excitation system. In the phase of fault improvement, according to the results of fault analysis, several common faults, namely power cabinet fault, regulator fault, loss of field fault, rectifier power fault, fuse burst fault, inverter de excitation fault, are studied and improved, and improvement measures are put forward. The experimental results show that the method based on neural network algorithm is used to analyze the excitation system fault of hydropower plant, which proves the accuracy of the analysis and provides a reliable basis for the subsequent improvement measures.