摘要： 成品油管道运行工况变化频繁,难以精准判断管道运行状态,依靠现场人员进行识别监控易造成误判.本文为实现管道运行工况的准确识别,考虑管道的物理空间特性,分析整理各站运行参数(压力、流量、密度);考虑管道运行的时间序列特性,基于SCADA管道数据构造运行数据矩阵,以克服单一时刻的瞬态扰动.针对管道运行数据高维度、非线性的特点,利用卷积自编码器(CAE)强大的特征压缩及重构能力对管道数据做降噪处理;利用T分布邻域嵌入算法(T-SNE)对管道数据做降维聚类处理,最终建立了基于CAE-TSNE的管道运行工况识别模型.以某两条成品油管道为例,对比主流的非线性分类模型(ANN、DT、RF),结果表明基于CAE-TSNE的工况识别模型精度最高,对降噪后的运行数据识别准确率可达到 99%以上,可用于指导现场管道的运行管理.

关键词: 成品油管道, 运行工况识别, 数据矩阵, 卷积自编码器, T分布邻域嵌入

Abstract: The operation conditions of multi-product pipeline changes frequently and it is difficult to judge the operation state accurately.Therefore,the recognition and monitoring by on-site personnel is easy to cause misjudgment.In order to realize the accurate recognition of pipeline operation conditions,considering the physical spatial characteristics of the pipeline,the operation parameters(pressure,flow rate and density)of each station are sorted out.Considering the time series characteristics of pipeline operation,operating data matrix is formed to overcome the transient disturbance at a single moment based on the SCADA data.Aiming at the high-dimensional and non-linear characteristics of pipeline operating data,the powerful feature compression and reconstruction capabilities of the convolutional autoencoder(CAE)are used to reduce the noise of pipeline data.T-distributed stochastic neighbor embedding algorithm(T-SNE)is used to perform dimensionality reduction and clustering processing on pipeline data,and finally the model based on CAE-TSNE for pipeline operation condition recognition is established.Taking two real multi-product pipeline as example,the mainstream machine learning nonlinear classification models(ANN,DT and RF)were compared with the proposed method.The results show that the operating condition identification model based on CAE-TSNE has the highest accuracy,and the recognition rate of clustering identification of operating data after noise reduction can reach 99%,which can guide the operation and management of on-site pipelines.

Key words: multi-product pipeline, operating condition recognition, data matrix, CAE, T-SNE