Abstract:

Accurate localization of natural gas pipeline leakage points is of great significance for emergency response to accidents. In view of the defects of existing leakage location methods that rely on instantaneous signals, they are easily disturbed by pipeline operation noise, and have insufficient positioning accuracy, a pipeline leakage location method based on TPE (tree-structured Parzen estimator)-XGBoost (eXtreme gradient boosting) model is proposed. This method utilizes pressure drop rate data collected at 5-second intervals after leakage to train and predict the model. The hyperparameter optimization performance of TPE is compared with PSO (particle swarm optimization), BOA (Bayesian optimization algorithm), and Optuna methods, addressing the challenge of hyperparameter search in the model. Additionally, the prediction accuracies of the TPE-XGBoost model are compared with those of SVM (support vector machine), CNN (convolutional neural network), and CNN-LSTM-Attention models. The effects of dataset time series length and superimposed running noise on localization accuracy are also analyzed. The results demonstrate that the TPE optimization algorithm enhances the accuracy of the XGBoost model, outperforming PSO, BOA, and Optuna in hyperparameter optimization. Compared to other localization models, the TPE-XGBoost model achieves the highest prediction accuracy, with an R² value of 0.9835 and a localization error of only 3.77% on the test set. In contrast, the R² values for SVM, CNN, and CNN-LSTM-Attention models are 0.3684, 0.9285, and 0.9821, respectively. Analysis of the impact of time series length reveals that, within the data length range of 30 to 150 seconds, model decision coefficients increase with longer time series, and significant differences in model hyperparameters across time lengths are observed. When the experimental group with pipeline running noise is introduced, all models' accuracy decreases, while the model configuration adapts by adjusting hyperparameters to extract valid information through increased segmentation. In this noise-added experimental group, the R² of the XGBoost model is 0.9580 and the localization error is 7.58%.

Key words: natural gas, pipeline, neural networks, algorithm, prediction, leak localization

摘要：

精准定位天然气管道泄漏点对事故应急处置具有重要意义。针对现有泄漏定位方法依赖瞬时信号、易受管道运行噪声干扰、定位精度不足的缺陷，提出了基于TPE(tree-structured Parzen estimator)-XGBoost（eXtreme gradient boosting）模型的管道泄漏定位方法。通过采集持续200 s的压降速率时间序列数据构建训练集，对比了TPE、PSO（粒子群算法）、BOA（贝叶斯优化）和Optuna四种优化算法对XGBoost模型精度的影响；分析了TPE-XGBoost与SVM、CNN及CNN-LSTM-Attention模型的定位准确性；探究了数据时间窗（30~200 s）与叠加运行噪声对定位准确性的影响规律。研究结果表明：（1）经TPE优化后，XGBoost模型的性能显著提升，在测试集上决定系数R²达0.9835；（2）在泄漏定位任务中，TPE-XGBoost模型的定位偏差仅为3.77%，优于SVM、CNN及CNN-LSTM-Attention等对比模型；（3）当数据时间窗长度在30~200 s变化时，模型在测试集上的R²始终高于0.96；（4）在叠加管道运行噪声场景下，模型定位偏差为7.58%。

关键词: 天然气, 管道, 神经网络, 算法, 预测, 泄漏定位