Dentification of leakage pressure drop rate signal of trunk gas pipeline based on SVM

doi:10.16265/j.cnki.issn1003-3033.2024.06.1137

Abstract

Abstract:

In order to solve the problem that the pressure drop signals caused by compressor suction or upstream block valve cut-off conditions leaded to incorrect shut-off of the block valve, and the problem that the block valve failure due to insignificant pipeline pressure drop caused by small hole leakage, a simulation model was established. Taking a typical gas transmission trunk line as the research object, 300 sets of pressure drop signals under three different working conditions, namely compressor suction, emergency cut-off of the block valve and pipeline leakage, were obtained. The pressure drop rate of the pressure drop signal was calculated by point-to-point detection method. Singular value decomposition(SVD) method was used to extract the characteristics of the pressure drop rate signal, and the min-max normalization method was used to normalize the characteristic values of the pressure drop rate signal. SVM method was used to identify the characteristic value signals of different pressure drop rates, and the corresponding working conditions were obtained. To solve the problem that the unreasonable setting of kernel function parameters and penalty factors in the SVM model affected the accuracy of algorithm recognition, TLBO algorithm was used to optimize the kernel function parameters and penalty factors, and a TLBO-SVM model for intelligent identification of gas pipeline leakage signals was established. The model was applied to classify and identify 300 groups of simulated pressure drop rate signals in three working conditions. The results show that the recognition accuracy of the model is 92.22% for three kinds of pressure drop rate signals under different working conditions. The identification accuracy is 96.67% for small hole leakage with a leakage diameter of 50-125 mm and a pressure drop rate range of 0.01-0.07 MPa/min. For the actual leakage pressure drop rate signal of a main pipeline, the accuracy of TLBO-SVM is 100%.

Key words: support vector machine (SVM), trunk gas pipeline, pressure drop rate signal, pressure signal of leakage, block valve

CLC Number:

X944.4

WU Xia, CHEN Honghuan, JIA Wenlong, SUN Yibin, REN Sibo. Dentification of leakage pressure drop rate signal of trunk gas pipeline based on SVM[J]. China Safety Science Journal, 2024, 34(6): 119-126.

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工况	方向	气量/ (10⁴m³·d^-1)	起点压力/ MPa	终点压力/ MPa	模拟终点压力/ MPa	相对误差/ %
1	正输	1 712	7.83	7.56	7.55	0.13
2	正输	2 089	7.40	6.72	6.65	1.04
3	反输	1 161	8.39	7.63	7.67	0.52
4	反输	1 118	7.93	7.19	7.24	0.70
5	反输	893	8.81	8.03	8.19	1.99

序号	泄漏信号	压缩机抽吸信号	截断阀截断信号
1	0.221 2	5.348 6	1.487 1
2	0.042 9	0.876 1	0.332 4
3	0.012 0	0.190 7	0.102 2
4	0.002 9	0.037 4	0.022 3
5	0.000 7	0.011 5	0.012 1
6	0.000 4	0.005 8	0.005 1
7	0.000 4	0.001 8	0.001 4
8	0.000 4	0.001 4	0.001 0
9	0.000 2	0.001 0	0.000 8
10	0.000 2	0.000 6	0.000 5

序号	泄漏信号	压缩机抽吸信号	截断阀截断信号
1	-0.93	0.95	-0.46
2	-0.91	0.98	-0.25
3	-0.88	0.98	0.06
4	-0.87	0.98	0.17
5	-0.96	-0.10	-0.06
6	-0.93	0.63	0.42
7	-0.87	-0.11	-0.32
8	-0.77	0.15	-0.26
9	-0.87	-0.17	-0.32
10	-0.86	-0.35	-0.46