水电厂励磁系统故障分析及改进措施研究

Fault analysis and improvement of excitation system in hydropower plant

摘要:

励磁系统直接关系着水电厂电力生产工作进度和任务完成质量,为此,提出水电厂励磁系统故障分析及改进措施研究。在故障分析阶段,通过压力传感器进行磁系统故障录波,并放大、滤波及隔离处理,以此作为样本,输入到人工神经网络当中,实现励磁系统故障识别分析。在故障改进阶段,针对故障分析结果,对常见的几种故障,即功率柜故障、调节器故障、失磁故障、整流电源故障、熔断器爆裂故障、逆变灭磁失败故障等,进行改进研究,提出改进措施。实验结果表明：利用基于神经网络算法的方法分析水电厂励磁系统故障,证明分析的准确性,为后续改进措施的提出提供了可靠依据。

Abstract:

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.