水电能源科学

为了提高水电机组的故障诊断效率与精准度，研究利用多重分形去趋势波动分析算法，结合概率神经网络，构建了一个水电机组振动信号特征提取与识别模型，并利用二进制引力搜索算法对其参数进行优化。结果显示，经过二进制引力搜索算法优化后，研究设计的特征提取与识别分类模型的分类识别准确率可提升至99%,同时将信号处理时间降至1.3 s左右。研究设计的水电机组振动信号特征提取与识别模型可显著区分水电机组的正常工作状态与故障工作状态，实现利用振动信号特征对水电机组故障进行诊断的目的。

To improve the efficiency and accuracy of fault diagnosis for hydroelectric units, combination of multifractal detrended fluctuation analysis algorithm and probabilistic neural network was used to establish a vibration signal feature extraction and recognition model. The binary gravity search algorithm was used to optimize its parameters. The results show that the classification accuracy of the feature extraction and recognition classification model can be improved to 99% and reduce the signal processing time to about 1.3 seconds after optimizing by the binary gravity search algorithm. The proposed vibration signal feature extraction and recognition model for hydroelectric units can significantly distinguish between the normal working state and the fault working state of hydroelectric units, achieving the purpose of using vibration signal features to diagnose faults in hydroelectric units.