基于卷积-长短期记忆神经网络的抽水蓄能机组健康性能趋势预测  

A Health Performance Tendency Prediction Model of Pumped Storage Unit Based on Convolution Neural Network-long Short-term Memory Neural Network

作　　者：单亚辉 王浩[1] 吴根平 刘颉[2] SHAN Ya-hui;WANG Hao;WU Gen-ping;LIU Jie(Wuhan Second Ship Design and Research Institute,Wuhan 430064,China;School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]武汉第二船舶设计研究所,湖北武汉430064 [2]华中科技大学土木与水利工程学院,湖北武汉430074

出　　处：《水电能源科学》2023年第8期185-187,184,共4页Water Resources and Power

基　　金：湖北省自然科学基金资助项目(2022CFB935)。

摘　　要：为准确掌握抽水蓄能机组的健康性能水平,提出基于卷积-长短期记忆神经网络(CNN-LSTM)的机组健康性能趋势预测方法。首先,为有效地刻画机组的运行特性,构建基于高斯过程回归的机组健康状态模型;然后,设计可量化机组健康性能的指标因子;进一步融合CNN良好的局部特征提取能力和LSTM在时间序列预测方面的优势,提出基于CNN-LSTM的预测模型。对国内某抽水蓄能电站机组监测数据进行的试验结果表明,所提方法可较好地预测机组健康性能的发展趋势。To accurately obtain the health performance level of a pumped storage unit(PSU),a health performance tendency prediction method based on convolution neural network-long short-term memory neural network(CNN-LSTM)is proposed.Firstly,a unit health state model based on Gaussian process regression was constructed to effectively characterize the operating characteristics of the PSU.Then,an index that can quantify the health performance of the PSU was proposed.Finally,by integrating the good local feature extraction ability of the CNN and the advantage of the LSTM in time series prediction,a prediction model based on CNN-LSTM was proposed.The experiments were conducted using monitoring data from a pumped storage station in China.The results show that the proposed method can betterly predict the future evolution of the PSU's health performance.

关 键 词：抽水蓄能机组 趋势预测 健康性能指标 卷积神经网络 长短期记忆网络 

分 类 号：TV743[水利工程—水利水电工程] TK730[交通运输工程—轮机工程]