摘要
水电机组的运行状态直接影响电站及电网的安全稳定,预测机组监测的振动信号有助于改善故障诊断的缺陷。为此,将经验模态分解(EMD)和神经网络模型相结合,提出一种基于EMD-LSTM的水轮机组摆度信号预测模型,将该模型应用于国内某水电站的机组摆度信号预测中,并与LSTM、GA-BP和EMD-GABP模型预测结果进行比较。结果表明,该模型在机组摆度信号的预测方面表现出较高的精度,且优于其他模型。
The operating condition of hydropower units is greatly related to the safety and stability of power stations and grids.The prediction of swing signals from unit monitoring can improve the defect of fault diagnosis.So,a combina-tion of empirical modal decomposition(EMD)and neural network model was used to put forward an EMD-LSTM-based model for predicting the swing signal of a hydropower station.The proposed model was applied to predict the swing sig-nal of a hydropower station in China,and the results were compared with those of LSTM,GA-BP and EMD-GABP mod-els.The results show that the model exhibits high accuracy in predicting the unit swing signal,outperforming other models.
作者
吴康平
周建旭
潘伟峰
丁钶铖祺
WU Kang-ping;ZHOU Jian-xu;PAN Wei-feng;DING Ke-cheng-qi(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;NARI Group Corporation,State Grid Electric Power Research Institute,Nanjing 211106,China;College of Electrical and Power Engineering,Hohai University,Nanjing 211100,China)
出处
《水电能源科学》
北大核心
2024年第5期179-182,共4页
Water Resources and Power
关键词
水轮机组
摆度信号
经验模态分解
长短时记忆神经网络
预测精度
hydraulic turbine sets
swing signal
empirical modal decomposition
long and short term memory neural networks
prediction accuracy
