CN113685736A - Method and system for detecting leakage of gas pipeline network based on pressure parameter analysis - Google Patents
Method and system for detecting leakage of gas pipeline network based on pressure parameter analysis Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/005—Protection or supervision of installations of gas pipelines, e.g. alarm
Abstract
The invention relates to a method and a system for detecting leakage of a gas pipeline network based on pressure parameter analysis, wherein the method comprises the following steps: step S1: establishing a field pipe section model, and setting a detection point to obtain a normal point curve; step S2, formulating a leakage judgment standard; step S3: and leakage detection and alarm are carried out according to the real-time operation data. The model leakage alarm timeliness and the balance of the leakage-free false alarm rate are comprehensively considered, leakage determination is carried out based on scientific quantitative calculation, financial resources and material resources for detecting leakage by physical appliances are saved, the requirements of a management system adopted by a current actual gas company can be better met, and the processing space and the degree of freedom are high; by setting an alarm mode corresponding to the values of the delta percent and the n, a user can know the current alarm level and possible error conditions of the alarm level, and the user can quickly perform leakage processing.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of energy engineering automation, and particularly relates to a method and a system for detecting leakage of a gas pipeline network based on pressure parameter analysis.
[ background of the invention ]
In recent years, urban gas pipe network systems are continuously developed, and the urban gas pipe network systems relate to all corners of our production and life. The gas pipeline is distributed throughout the city underground, and the gas pipeline is influenced by various factors such as surrounding soil corrosion, stray current, self use aging, third party damage and the like, so that leakage often occurs. After leakage, the fuel gas is flammable and explosive, and the personal and property safety of surrounding people is seriously threatened. In addition, the waste caused by leakage is also remarkable, the output difference of individual gas companies can reach more than 20%, and the operation of the companies can be reluctantly maintained only by collecting account-opening fees. The influence of gas pipeline leakage on safety and economy is large, so that gas leakage detection is always a hot topic at home and abroad. The gas pipeline leakage has a great influence on safety and economy. Firstly, leakage causes serious impact on explosion accidents and secondly, waste of gas in the pipe. Therefore, leak detection plays a significant role in gas delivery and sales. Gas leakage detection methods are also constantly being developed. At present, common gas leakage detection technologies are mainly divided into hardware detection and software detection. The hardware detection is commonly used, such as detection by using acoustics, ultrasonic and camera shooting technology, optical cable fiber technology and the like; in addition, the gas concentration is acquired in a passing area by a person who carries detection equipment such as a handheld detector or vehicle-mounted intelligent detection technology. The hardware technology has the defects that on one hand, the efficiency is low, the defects cannot be found in time, the coverage range is limited when the traditional bicycle or vehicle is used, and the requirements on manpower, material resources and financial resources are high. On the one hand, the requirements for equipment are high, and high sensitivity is required. Underground pipe networks have a plurality of interference factors and are generally difficult to judge. In the developing stage of software detection, common pressure parameter analysis methods, mass balance methods, mathematical model methods, neural network methods, statistical decision methods and the like are mainly utilized; at present, software technology is continuously developed, and with the continuous improvement of computer technology and the strengthening of informatization level, the software technology is a future trend and needs a great amount of experiments and verification. The model leakage alarm timeliness and the balance of the leakage-free false alarm rate are comprehensively considered, leakage determination is carried out based on scientific quantitative calculation, financial resources and material resources for detecting leakage by physical appliances are saved, the requirements of a management system adopted by a current actual gas company can be better met, and the processing space and the degree of freedom are high; by setting an alarm mode corresponding to the values of the delta percent and the n, a user can know the current alarm level and possible error conditions of the alarm level, and the user can quickly perform leakage processing.
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides a method and a system for detecting gas pipeline network leakage based on pressure parameter analysis, wherein the method comprises:
step S1: establishing a field pipe section model, and setting a detection point to obtain a normal point curve;
step S2, formulating a leakage judgment standard;
step S3: and leakage detection and alarm are carried out according to the real-time operation data.
Further, the step S1 is specifically:
step S11: establishing a field pipe section model and setting a detection point;
step S12: collecting operation data of a time period by using a meter at a detection point;
step S13: removing abnormal data;
step S14: and (5) re-fitting the normal data to obtain a normal point curve.
Further, the period of time is one week.
Further, the time is dynamically set, and the length of the time period is set according to the obtained data amount.
Further, the monitoring method is started when the monitoring data change occurs.
A model prediction based heating system hydraulic balance adjustment system, the system comprising a server and a client; the client accesses the server to obtain a leakage detection judgment result; and the client sends the acquired leakage detection result to the server for subsequent analysis and data storage, and the server performs leakage processing according to the leakage detection result and the feedback of the client.
Further, a processor in the server is used for executing the method for detecting the leakage of the natural gas pipeline network based on the pressure parameter analysis.
Further, one or more field pipe section models are pre-stored in the server.
Further, the client sends real-time detection data of the pipe section model while accessing the server.
Further, the server stores one or more templates of the field pipe section model in advance.
The beneficial effects of the invention include: (1) the balance between the timeliness of model leakage alarm and the leakage-free false alarm rate is fully and comprehensively considered, leakage determination is carried out based on scientific quantitative calculation, the financial and material resources for detecting leakage of physical appliances are saved, the requirements of a management system adopted by the current actual gas company can be better met, and the processing space and the degree of freedom are very high. (2) By setting an alarm mode corresponding to the values of the delta percent and the n, a user can know the current alarm level and possible error conditions of the current alarm level.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
fig. 1 is a schematic diagram of a gas pipeline network leakage detection method based on pressure parameter analysis according to the present invention.
FIG. 2 is a schematic diagram of an experimental pipeline according to the present invention.
FIG. 3 is a schematic diagram of removing outliers according to the present invention.
[ detailed description ] embodiments
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, the method for detecting leakage of gas pipeline network based on pressure parameter analysis according to the present invention
Step S1: establishing a field pipe section model, and setting a detection point to obtain a normal point curve; the method specifically comprises the following steps:
step S11: and establishing a field pipe section model and setting a detection point. Arranging a pressure gauge at an inlet (serving as a source point) of the pipe section, and detecting the pressure value of the inlet; arranging a pressure gauge at the outlet (as a junction) of the pipe section, and detecting the pressure value of the outlet; a flow meter is arranged at the outlet (as a junction) of the pipe section, and the outlet flow is detected.
According to the field situation, a pipe section model is established, as shown in fig. 2, the pipe section model of one embodiment is that a pipe diameter De200 is set, the pipe length is 400m, and a detection point is set.
Step S12: and collecting operation data of a time period by using the meter at the detection point.
Preferably: the period of time is one week.
Step S13: and removing abnormal data. And (4) making the collected operation data into a scatter diagram, wherein the x axis is flow and has the unit of Nm3/h, and the y axis is the pressure difference of an inlet and an outlet of the pipe section and has the unit of kPa. All data were fitted to obtain a polynomial curve, and points outside of ± α% deviation were regarded as outliers and removed.
Wherein: the selection of alpha% requires that abnormal points are removed and simultaneously as much data as possible is reserved;
preferably: α is 20.
Step S14: and (5) re-fitting the normal data to obtain a normal point curve. As shown in fig. 3, the normal point curve obtained in the above embodiment is:
y＝-0.000000007116x3+0.000037603973x2-0.000718810171x+1.377870876337，
R20.964484510880(R2 is the fit of the fitted curve to the reality).
That is, the normal point curve is a curve obtained by fitting the differential pressure flow and the differential pressure, and can be used for calculating or predicting the differential pressure, wherein the calculated differential pressure is called as standard differential pressure;
step S2, formulating a leakage judgment standard; specifically, setting the variation range of the standard pressure difference value as +/-delta% and continuous suspected leakage data n; corresponding to the leakage judgment standard, judging that the pressure difference value is suspected to be leaked when the pressure difference value outside the range of the standard pressure difference value +/-delta percent appears; and when the continuous suspected leakage data jump out by more than n pieces, determining that the suspected leakage occurs. The selection of delta percent and n needs to meet the requirement of finding a suspected leakage point in time and reducing the false alarm rate.
The step S2 specifically includes the following steps:
step S21: leakage is simulated at a diffusing point, and the method specifically comprises the following steps: and performing a leakage simulation experiment based on the pipe section model, diffusing at a diffusing point, simultaneously recording each detection point data, calculating a standard pressure difference value under actual measurement flow through a normal point curve formula, respectively selecting different delta% and n values to perform 'suspected leakage' judgment, and correspondingly recording each detection point data, the standard pressure difference value and 'suspected leakage' judgment conditions in an analysis table.
The leakage simulation experiment is performed twice aiming at the embodiment, the diffusion is performed at the diffusion point, the data of each detection point is recorded at the same time, and the checking calculation is performed by using the established model. The experimental data results are shown in tables 1 and 2, the second and third columns of tables 1 and 2 are measured flow and pressure difference data (pipe section inlet and outlet pressure difference values), the fourth column of tables is standard pressure difference values under measured flow calculated by a normal point curve formula, the fifth, sixth and seventh columns of tables are set different pressure difference standard value ranges, and delta% is respectively +/-8%, +/-9% and +/-10% of the standard pressure difference. And when the measured differential pressure value is out of the range of the differential pressure standard value, judging that the pressure is suspected to leak, marking the data of the analysis table as 1, and otherwise, marking the data as 0. When n pieces of data marked as 1 continuously appear, the data is judged to be suspected to be leaked, and n is respectively 2, 3 and 4.
Table 1 first time leakage simulation results and analysis table
TABLE 2 simulation results and analysis Table for second time leakage
Step S22: setting suspected leakage judgment conditions with different values of delta% and n based on analysis table analysis, and calculating a leakage report rate;
analysis of table 1 in the above example reveals that:
a) when the delta% value is 8%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 1 time in the first leakage simulation process, and carrying out 14: 17; if the leakage alarm is continuously carried out for 3 times, the leakage alarm is carried out for 3 times at the time points of 13:37, 14:16 and 14: 17; if 2 continuous leakage alarms are given, 9 alarms are given at the time points 13: 3613: 37, 13:41, 13:56, 14:02, 14:06, 14:15, 14:16 and 14:17 respectively.
b) When the delta% value is 9%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 0 time in the first leakage simulation process; if the leakage alarm is continuously carried out for 3 times, the leakage alarm is carried out for 2 times at the time 13:37 and 14:17 respectively; if 2 continuous leakage alarms are given, 8 continuous leakage alarms are given at the time points 13: 3613: 37, 13:41, 13:56, 14:02, 14:06, 14:16 and 14: 17.
c) When the delta% value is 10%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 0 time in the first leakage simulation process; if the leakage alarm is continuously carried out for 3 times, the leakage alarm is carried out for 2 times at the time 13:37 and 14:17 respectively; if 2 consecutive leakage alarms are issued, 6 alarms are issued at times 13:41, 13:56, 14:02, 14:15, 14:16, 14:17, respectively.
Analysis of table 2 in the above example reveals that:
a) when the delta% value is 8%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 1 time in the first leakage simulation process, and judging that the leakage is suspected to be leaked at the moment 15: 02; if the leakage alarm is continuously carried out for 3 times, the leakage alarm is carried out for 4 times at the time 14:48, 14:54, 15:01 and 15:02 respectively; if 2 consecutive leakage alarms are issued, 8 alarms are issued at the time points of 14:46, 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07 respectively.
b) When the delta% value is 9%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 1 time in the first leakage simulation process, and judging that the leakage is suspected to be leaked at the moment 15: 02; if 3 continuous leakage alarms are given, 2 continuous leakage alarms are given, and the alarm time is respectively set at the time 15: 01. 15: 02; if 2 consecutive leakage alarms are issued, 7 alarms are issued at the time 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07 respectively.
c) When the delta% value is 10%, judging that the leakage is suspected, if 4 times of suspected leakage alarm are carried out continuously, alarming for 1 time in the first leakage simulation process, and judging that the leakage is suspected to be leaked at the moment 15: 02; if the leakage alarm is continuously carried out for 3 times, the leakage alarm is carried out for 3 times at the time 14:54, 15:01 and 15:02 respectively; if 2 consecutive leakage alarms are issued, 7 alarms are issued at the time 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07 respectively.
Step S23: simulating and calculating the false alarm rate condition through normal working conditions; specifically, the method comprises the following steps: carrying out a simulation experiment of on-site normal work, recording data of each monitoring point, calculating a standard pressure difference value under actual measurement flow through a normal point curve formula, analyzing and selecting the condition of being judged to be 'suspected leakage' under different delta% and n values, and recording the condition in an analysis table; analyzing and calculating the situation of false alarm rate when different delta% n values are set based on an analysis table; namely analyzing the relationship between the false alarm condition and the value of delta percent and n under the condition of no leakage; as shown in table three, the third table is obtained by simulating a scene (i.e., normal working condition) where no leakage occurs, and includes false alarm rates under different values of δ% and n, where an alarm occurring under normal working condition is a false alarm, counting the number of false alarms, and calculating the false alarm rates under different values of δ% and n.
Preferably: the false alarm rate epsilon refers to the ratio of the alarm times to the total data volume in a selected period of time, and the formula is as follows:
analysis table 3 gave: two times of suspected leakage alarm are carried out continuously, multiple false alarms exist in three ranges, and the false alarm rates are respectively 15.3% (+ -8%), 11.5% (+ -9%) and 7.7% (+ -10%); three times of suspected leakage alarm are carried out continuously, and one false alarm exists under the condition that the normal range is +/-8 percent, wherein the ratio is 13: 18; four suspected leakage alarms are carried out continuously, and no false alarm exists in the three ranges.
TABLE 3 Normal simulation results and analysis Table
Step S24: determining the values of delta% and n to ensure that the sum of the rate of missing report and the rate of false report is the lowest; specifically, the method comprises the following steps: selecting a value pair of delta% and n, so that the sum of the missing report rate and the false report rate is the lowest under the condition of the value pair;
in the above embodiment, the suspected leakage data is significantly increased when δ% is low, and the leakage alarm condition is increased when n is low. When n is 2, false alarm exists under normal working conditions; when the value is 4, the leakage is not reported. Therefore, it is reasonable to take 3 out. Under the condition that n is 3, false alarm exists when delta% takes 8%; when 10% of the leakage is taken, the alarm time is 14:17 in the first leakage simulation, and the leakage is not timely enough; and when the leakage simulation is carried out for 9 percent, false alarm does not exist, the alarm is timely carried out, and the alarm time is 13:37 when the leakage simulation is carried out for the first time. Therefore, in this embodiment, δ% is 9% and n is 3.
Step S3: detecting and alarming according to real-time operation data leakage; the method specifically comprises the following steps:
step S31: and acquiring real-time operation data. After the pipe section model is built, an interface is built with a pipeline data monitoring system, and field real-time operation data are obtained; the real-time operation data comprises an actually measured flow value, an actually measured pressure value and the like.
Preferably: the pipeline monitoring system is an SCADA system;
step S32: and calculating a standard pressure difference value, and judging whether leakage occurs according to a leakage standard. And inputting the acquired measured flow and the acquired differential pressure value in the pipe section model to obtain a standard differential pressure value. Judging whether the standard pressure difference obtained by continuous n times of detection meets the pressure difference range delta%, if so, judging that no leakage exists, otherwise, judging that leakage exists;
step S33: when leakage occurs, a leakage alarm is carried out;
preferably: the alarm mode is manual feedback, and the leakage related analysis table is sent to a user while the manual feedback is carried out;
preferably: sending the current values of delta% and n to the user;
preferably: the alarm mode is a whistle registration mode;
preferably: different values of δ% and n correspond to different alarm modes, for example: the whistling mode is different when alarming; setting an alarm mode corresponding to the values of the delta percent and the n, so that a user can know the current alarm level and possible error conditions of the current alarm level; as will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Those skilled in the art will appreciate that all or part of the steps in the above method embodiments may be implemented by a program to instruct relevant hardware to perform the steps, and the program may be stored in a computer-readable storage medium, which is referred to herein as a storage medium, such as: ROM/RAM, magnetic disk, optical disk, etc.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A gas pipeline network leakage detection method based on pressure parameter analysis is characterized by comprising the following steps:
step S1: establishing a field pipe section model, and setting a detection point to obtain a normal point curve;
step S2, formulating a leakage judgment standard;
step S3: and leakage detection and alarm are carried out according to the real-time operation data.
2. The gas pipeline network leakage detection method based on pressure parameter analysis as claimed in claim 1, wherein the step S1 specifically is:
step S11: establishing a field pipe section model and setting a detection point;
step S12: collecting operation data of a time period by using a meter at a detection point;
step S13: removing abnormal data;
step S14: and (5) re-fitting the normal data to obtain a normal point curve.
3. The gas pipeline network leakage detection method based on pressure parameter analysis as claimed in claim 2, wherein the period of time is one week.
4. The method for detecting gas pipeline network leakage based on pressure parameter analysis as claimed in claim 3, wherein the time is dynamically set, and the length of the time period is set according to the obtained data amount.
5. The gas pipeline network leakage detection method based on pressure parameter analysis as recited in claim 4, characterized in that the monitoring method is started when monitoring data change occurs.
6. A model prediction based heating system hydraulic balance adjustment system adopting the gas pipeline network leakage detection method based on pressure parameter analysis according to any one of claims 1-5, characterized in that the system comprises a server and a client; the client accesses the server to obtain a leakage detection judgment result; and the client sends the acquired leakage detection result to the server for subsequent analysis and data storage, and the server performs leakage processing according to the leakage detection result and the feedback of the client.
7. The system for detecting leakage of gas pipeline network based on pressure parameter analysis as claimed in claim 6, wherein the processor in the server is used for executing the method for detecting leakage of gas pipeline network based on pressure parameter analysis.
8. The gas pipeline network leakage detection system based on pressure parameter analysis as recited in claim 7, wherein one or more field pipeline section models are pre-stored in the server.
9. The gas pipeline network leakage detection system based on pressure parameter analysis as claimed in claim 8, wherein the client sends real-time detection data of the pipeline section model while accessing the server.
10. The gas pipeline network leakage detection system based on pressure parameter analysis as recited in claim 9, wherein the server stores in advance templates of one or more on-site pipe segment models.
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| CN114964432A (en) * | 2022-07-29 | 2022-08-30 | 杭州先锋电子技术股份有限公司 | Method for judging tiny leakage of intelligent diaphragm gas meter |
| CN115095798A (en) * | 2022-06-08 | 2022-09-23 | 盐城旭东机械有限公司 | High-pressure straight pipe high-pressure anti-drop and high-pressure early warning method and system |
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Denomination of invention: A Leakage Detection Method and System for Gas Pipeline Networks Based on Pressure Parameter Analysis Granted publication date: 20230704 Pledgee: Changning Sub Branch of Shanghai Rural Commercial Bank Co.,Ltd. Pledgor: Shanghai guanran Intelligent Technology Co.,Ltd. Registration number: Y2024310000160 |