CN113685736B - Gas pipe network leakage detection method and system based on pressure parameter analysis - Google Patents
Gas pipe network leakage detection method and system based on pressure parameter analysis Download PDFInfo
- Publication number
- CN113685736B CN113685736B CN202110977523.1A CN202110977523A CN113685736B CN 113685736 B CN113685736 B CN 113685736B CN 202110977523 A CN202110977523 A CN 202110977523A CN 113685736 B CN113685736 B CN 113685736B
- Authority
- CN
- China
- Prior art keywords
- leakage
- delta
- data
- alarm
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 43
- 238000004458 analytical method Methods 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 32
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 238000004088 simulation Methods 0.000 claims description 16
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000009792 diffusion process Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 10
- 238000007726 management method Methods 0.000 abstract description 3
- 239000007789 gas Substances 0.000 description 20
- 230000008569 process Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000002737 fuel gas Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Examining Or Testing Airtightness (AREA)
Abstract
The invention relates to a gas pipe network leakage detection method and system based on pressure parameter analysis, wherein the method comprises the following steps: step S1: establishing a field pipe section model, and setting detection points to obtain a normal point curve; step S2, making a standard for judging leakage; step S3: and detecting and alarming according to the real-time operation data leakage. The invention comprehensively considers the balance of the leak alarm timeliness and the leak-free false alarm rate of the model, carries out leak determination based on scientific quantitative calculation, omits the detection of leaked financial resources by physical appliances, and can also better meet the requirements of a management system adopted by the current actual gas company, so that the processing space and the degree of freedom are high; by setting the alarm mode corresponding to the delta% and n values, the user can know the current alarm level and the possible error condition thereof, and can quickly perform leakage processing.
Description
[ field of technology ]
The invention belongs to the technical field of energy engineering automation, and particularly relates to a gas pipe network leakage detection method and system based on pressure parameter analysis.
[ background Art ]
In recent years, urban gas pipe network systems are continuously developed, and the urban gas pipe network systems relate to all corners of production and living of people. The gas pipeline is spread underground in cities, and is influenced by various factors such as surrounding soil corrosion, stray current influence, self-use aging, third party damage and the like, so that leakage can occur frequently. After leakage, the fuel gas belongs to inflammable and explosive gas, and seriously threatens personal and property safety of surrounding people. In addition, the waste caused by leakage is remarkable, the transmission difference of individual gas companies can reach more than 20%, and the operation of the companies can be only barely maintained by collecting the account opening cost. The gas pipeline leakage has great influence on safety and economy, so gas leakage detection is always a hot spot problem at home and abroad. The gas pipeline leakage has great influence on safety and economy. Firstly, the influence of explosion accidents caused by leakage is serious, and secondly, the waste of fuel gas in the pipe is caused. Thus, leak testing is important in both gas delivery and sales. Gas leakage detection methods are also continually evolving. The common gas leakage detection technology at present is mainly divided into hardware detection and software detection. Hardware detection is commonly used, such as detection and ultrasound by using acoustics, shooting technology, optical cable fiber-optic technology and the like; in addition, the manual inspection technology is also provided, and a person carries detection equipment such as a handheld detector or vehicle-mounted intelligent detection to acquire the gas concentration in a passing area. The hardware technology has defects, on one hand, the efficiency is low, the coverage range can not be found in time, the traditional hiking or vehicle-mounted technology is limited by the coverage range, and the requirements on manpower, material resources and financial resources are high. On the one hand, high demands are made on the device, which requires a high sensitivity. Underground pipe network interference factors are many, and are generally difficult to judge. In the development stage of software detection, a common pressure parameter analysis method, a mass balance method, a mathematical model method, a neural network method, a statistical decision method and the like are mainly utilized; at present, software technology is continuously developed, and with the continuous improvement of computer technology and the enhancement of informatization level, the software technology is a future trend, and a large amount of experiments and verification are required. The invention comprehensively considers the balance of the leak alarm timeliness and the leak-free false alarm rate of the model, carries out leak determination based on scientific quantitative calculation, omits the detection of leaked financial resources by physical appliances, and can also better meet the requirements of a management system adopted by the current actual gas company, so that the processing space and the degree of freedom are high; by setting the alarm mode corresponding to the delta% and n values, the user can know the current alarm level and the possible error condition thereof, and can quickly perform leakage processing.
[ invention ]
In order to solve the above problems in the prior art, the present invention provides a method and a system for detecting gas pipe network leakage based on pressure parameter analysis, wherein the method comprises:
step S1: establishing a field pipe section model, and setting detection points to obtain a normal point curve;
step S2, making a standard for judging leakage;
step S3: and detecting and alarming according to the real-time operation data leakage.
Further, the step S1 specifically includes:
step S11: establishing a field pipe section model and setting detection points;
step S12: collecting operation data of a period of time by using a meter at a detection point;
step S13: removing abnormal data;
step S14: and 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 detection method is started when the monitored data change occurs.
A model prediction-based hydraulic balance adjustment system for a heating 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 a 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, the processor in the server is used for executing the gas pipe network leakage detection method based on the pressure parameter analysis.
Further, one or more field pipe section models are pre-stored in the server.
Further, the client transmits real-time detection data of the pipe section model while accessing the server.
Further, templates of one or more field pipe section models are pre-stored in the server.
The beneficial effects of the invention include: (1) The method has the advantages that the balance of the timeliness of the model leakage alarm and the leakage-free false alarm rate is fully considered and fully combined, the leakage determination is carried out based on scientific quantitative calculation, the leaked financial resources and physical appliance detection are omitted, the requirements of a management system adopted by the current actual gas company can be met, and the processing space and the degree of freedom are high. (2) By setting the alarm mode corresponding to the delta% and n values, the user can know the current alarm level and the possible error condition thereof.
[ 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, illustrate and together with the description serve to explain the invention, if necessary:
FIG. 1 is a schematic diagram of a gas pipe network leakage detection method based on pressure parameter analysis according to the present invention.
FIG. 2 is a schematic diagram of an experimental pipeline of the present invention.
FIG. 3 is a schematic view of removing outliers according to the present invention.
[ detailed description ] of the invention
The present invention will now be described in detail with reference to the drawings and the specific embodiments thereof, wherein the exemplary embodiments and the description are for the purpose of illustrating the invention only and are not to be construed as limiting the invention.
As shown in FIG. 1, the gas pipe network leakage detection method based on pressure parameter analysis
Step S1: establishing a field pipe section model, and setting detection points to obtain a normal point curve; the method specifically comprises the following steps:
step S11: and establishing a field pipe section model and setting detection points. Setting a pressure gauge at the inlet (as a source point) of the pipe section, and detecting an inlet pressure value; setting a pressure gauge at the outlet (as a sink) of the pipe section, and detecting an outlet pressure value; and arranging a flow meter at the outlet (as a sink) of the pipe section, and detecting the outlet flow.
A pipe section model is built according to the site situation, as shown in fig. 2, and the pipe section model of one embodiment is that a pipe diameter De200 is set, the pipe length is 400m, and detection points are set.
Step S12: operational data is collected with the meter at the detection point for a period of time.
Preferably: the period of time is one week.
Step S13: and removing the abnormal data. And (3) making the acquired operation data into a scatter diagram, wherein the x-axis is the flow, the unit is Nm3/h, the y-axis is the inlet and outlet pressure difference of the pipe section, and the unit is kPa. Fitting all the data to obtain a polynomial curve, and taking points except the deviation + -alpha% as abnormal points and removing the abnormal points.
Wherein: the selection of alpha% requires that as much data as possible is reserved while abnormal points are removed;
preferably: α=20.
Step S14: and 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，
R 2 = 0.964484510880 (R2 is the fit of the fitted curve to the actual)。
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 differential pressure calculation or prediction, and the calculated differential pressure is called as a standard differential pressure;
step S2, making a standard for judging leakage; specifically, setting the variation range of the differential pressure standard value as + -delta, and continuously suspected leakage data n; corresponding to the leakage judgment standard, judging that the leakage is suspected when the differential pressure value is out of the range of the differential pressure standard value +/-delta%; and when the continuous suspected leakage data jump out of more than n pieces, determining that the suspected leakage occurs. The selection of delta% and n is required to meet the requirement of timely finding out suspected leakage points and reducing false alarm rate.
The step S2 specifically includes the following steps:
step S21: the leakage is simulated at the diffusing point, specifically: based on a pipe section model, performing leakage simulation experiments, performing diffusion at a diffusion point, recording data of each detection point, 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 the data of each detection point, the standard pressure difference value and the 'suspected leakage' judgment condition in an analysis table.
Two leakage simulation experiments are performed for the above embodiments, and the diffusion is performed at the diffusion point, and meanwhile, the data of each detection point is recorded, and the built model is used for accounting. The experimental data results are shown in tables 1 and 2, the second and third columns of tables 1 and 2 are actual measurement flow and differential pressure data (pipe section inlet and outlet pressure difference), the fourth column of tables is standard differential pressure value under the actual measurement flow calculated by a normal point curve formula, the fifth, sixth and seventh columns are set different differential pressure standard value ranges, and delta% respectively takes +/-8%, +/-9% and +/-10% of standard differential pressure. When the measured differential pressure value is out of the differential pressure standard value range, judging that the differential pressure value is suspected to be leaked, marking the data of the analysis table as 1, otherwise, marking the data as 0. When n pieces of data marked 1 appear continuously, the suspected leakage is judged, and n is respectively 2, 3 and 4.
TABLE 1 simulation results of first leakage and analysis Table
TABLE 2 simulation results and analysis Table for second leakage
Step S22: based on the analysis table, the suspected leakage judgment conditions with different delta% and n values are analyzed and set, and the leakage report rate is calculated;
analysis of table 1 in the above examples can find that:
a) If the delta% value is 8%, if the leakage is judged to be suspected to be leaked, carrying out leakage alarm for 4 times continuously, and in the first leakage simulation process, carrying out alarm for 1 time, wherein the time is 14:17; if the leakage alarm is continuously given for 3 times, the leakage alarm is given for 3 times, and the leakage alarm is respectively given at the time points 13:37, 14:16 and 14:17; if 2 leakage alarms are consecutively performed, 9 alarms are performed, and the leakage alarms are respectively performed at the time points 13:3613:37, 13:41, 13:56, 14:02, 14:06, 14:15, 14:16 and 14:17.
b) If the delta% value is 9%, if the leakage is judged to be suspected, carrying out leakage alarm for 4 times continuously, and then carrying out alarm for 0 times in the first leakage simulation process; if the leakage alarm is continuously given for 3 times, the leakage alarm is given for 2 times, and the leakage alarm is respectively given at the time points 13:37 and 14:17; if 2 leakage alarms are consecutively performed, 8 alarms are performed, and the leakage alarms are respectively performed at the time points 13:3613:37, 13:41, 13:56, 14:02, 14:06, 14:16 and 14:17.
c) If the delta% value is 10%, if the leakage is judged to be suspected, carrying out leakage alarm for 4 times continuously, and if the leakage is judged to be suspected to be leaked for 0 times in the first leakage simulation process; if the leakage alarm is continuously given for 3 times, the leakage alarm is given for 2 times, and the leakage alarm is respectively given at the time points 13:37 and 14:17; if 2 leakage alarms are consecutively performed, 6 alarms are performed, and the time points are 13:41, 13:56, 14:02, 14:15, 14:16 and 14:17 respectively.
Analysis of table 2 in the above examples can find that:
a) If the delta% value is 8%, if the leakage is judged to be suspected to be leaked, carrying out leakage alarm for 4 times continuously, and in the first leakage simulation process, carrying out alarm for 1 time, wherein the time is 15:02; if the leakage alarm is continuously given for 3 times, the leakage alarm is given for 4 times, and the leakage alarm is given at the time points 14:48, 14:54, 15:01 and 15:02 respectively; if 2 leakage alarms are consecutively performed, 8 alarms are performed, and the leakage alarms are respectively performed at the time points 14:46, 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07.
b) If the delta% value is 9%, if the leakage alarm is continuously carried out for 4 times, the leakage alarm is carried out for 1 time in the first leakage simulation process, and the time is 15:02; if the leakage alarm is continuously performed for 3 times, the alarm is performed for 2 times, and the time 15 is respectively: 01. 15:02; if 2 leakage alarms are consecutively performed, 7 alarms are performed, and the leakage alarms are respectively performed at the time points 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07.
c) If the delta% value is 10%, if the leakage is judged to be suspected to be leaked, carrying out leakage alarm for 4 times continuously, and in the first leakage simulation process, carrying out alarm for 1 time, wherein the time is 15:02; if the leakage alarm is continuously given for 3 times, the leakage alarm is given for 3 times, and the leakage alarm is respectively given at the time points 14:54, 15:01 and 15:02; if 2 leakage alarms are consecutively performed, 7 alarms are performed, and the leakage alarms are respectively performed at the time points 14:47, 14:53, 14:54, 15:00, 15:01, 15:02 and 15:07.
Step S23: calculating the false alarm rate through normal working condition simulation; specific: performing a simulation experiment of normal operation on site, recording data of each monitoring point, analyzing and selecting the conditions of which the delta% and the n are judged to be 'suspected leakage' under different delta% and n values according to a standard pressure difference value under the measured flow obtained through calculation of a normal point curve formula, and recording the conditions in an analysis table; analyzing and calculating false alarm rate conditions when different delta% n values are set based on an analysis table; namely, analyzing the relation between false alarm condition and delta% and n values under the condition of no leakage; as shown in the third table, the third table is obtained by simulating a scene (namely a normal working condition) without leakage, and comprises false alarm rates under different delta% and n values, wherein the false alarm rate under the normal working condition is the false alarm, the false alarm times are counted, and the false alarm rates under the different delta% and n values are calculated.
Preferably: the false alarm rate epsilon refers to the ratio of the alarm times to the total data amount in a selected period of time, and the formula is as follows:
analysis of table 3 gave: the method comprises the following steps of continuously carrying out two suspected leakage alarms, wherein multiple false alarms exist in three ranges, and the false alarm rates are 15.3% (+/-8%), 11.5% (+/-9%), and 7.7% (+/-10%), respectively; three continuous suspected leakage alarms exist, and a false alarm exists under the condition that the normal range is +/-8%, and the false alarm is 13:18; four suspected leakage alarms are continuous, and no false alarm exists in all three ranges.
TABLE 3 Normal simulation results and analysis Table
Step S24: determining the delta% and n values so as to enable the sum value of the missing report rate and the false report rate to be the lowest; specific: selecting a delta% and n value pair, 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 obviously increased when delta% is low, and the leakage alarm condition is increased when n is low. When the value of n is 2, false alarm exists under normal working conditions; when the value is 4, leakage is leaked. Therefore, the taking of 3 is reasonable. Under the condition that n takes 3, when delta% takes 8%, false alarm exists; when 10% is taken, the alarm time is 14:17 when the leakage is simulated for the first time, and the time is not enough; when 9% is taken, false alarm does not exist, the alarm is timely, and the alarm time is 13:37 when the first leakage is simulated. Thus, δ% =9% and n=3 are taken in this embodiment.
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 establishing a riser section model, establishing an interface with a pipeline data monitoring system to acquire on-site real-time operation data; the real-time operation data are real measured flow value, 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 differential pressure value into 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 the leakage is not caused, otherwise, judging that the leakage is caused;
step S33: when leakage occurs, a leakage alarm is carried out;
preferably: the alarm mode is fed back manually, and the leakage correlation analysis table is sent to a user while the manual feedback is carried out;
preferably: the current delta% and the value of n are sent to a user together;
preferably: the alarm mode is a whistling alarm mode;
preferably: different delta% and n values correspond to different alarm modes, for example: the mode of whistling during alarming is different; the user can know the current alarm level and possible error conditions thereof by setting an alarm mode corresponding to the delta% and n values; it will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
Those of ordinary skill in the art will appreciate that implementing all or part of the steps in the above-described method embodiments may be accomplished by programming instructions in a computer readable storage medium, such as: ROM/RAM, magnetic disks, optical disks, etc.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (4)
1. A gas pipe network leak detection method based on pressure parameter analysis, the method comprising:
step S1: establishing a field pipe section model, and setting detection points to obtain a normal point curve;
step S11: establishing a field pipe section model and setting detection points;
step S12: collecting operation data of a period of time by using a meter at a detection point;
step S13: removing abnormal data;
step S14: fitting the normal data again to obtain a normal point curve;
step S2, making a standard for judging leakage;
step S21: the leakage is simulated at the diffusing point, specifically: performing a leakage simulation experiment based on a pipe section model, performing diffusion at a diffusion point, recording data of each detection point, 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 the data of each detection point, the standard pressure difference value and the 'suspected leakage' judgment condition in an analysis table;
step S22: based on the analysis table, the suspected leakage judgment conditions with different delta% and n values are analyzed and set, and the leakage report rate is calculated;
step S23: calculating the false alarm rate through normal working condition simulation; specific: performing a simulation experiment of normal operation on site, recording data of each monitoring point, analyzing and selecting the conditions of which the delta% and the n are judged to be 'suspected leakage' under different delta% and n values according to a standard pressure difference value under the measured flow obtained through calculation of a normal point curve formula, and recording the conditions in an analysis table; analyzing and calculating false alarm rate conditions when different delta% n values are set based on an analysis table; namely, analyzing the relation between false alarm condition and delta% and n values under the condition of no leakage;
step S24: determining the delta% and n values so as to enable the sum value of the missing report rate and the false report rate to be the lowest; specific: selecting a delta% and n value pair, so that the sum of the missing report rate and the false report rate is the lowest under the condition of the value pair; step S3: detecting and alarming according to real-time operation data leakage;
step S31: acquiring real-time operation data; after establishing a riser section model, establishing an interface with a pipeline data monitoring system to acquire on-site real-time operation data; the real-time operation data is a real measured flow value and a real measured pressure value;
step S32: calculating a standard pressure difference value, and judging whether leakage occurs according to a leakage standard; inputting the acquired actual measurement flow and 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 the leakage is not caused, otherwise, judging that the leakage is caused;
step S33: and when leakage occurs, a leakage alarm is carried out.
2. The method for detecting leakage of a gas pipe network based on pressure parameter analysis according to claim 1, wherein the period of time is one week.
3. The method for detecting leakage of a gas pipe network based on pressure parameter analysis according to claim 2, wherein the time is dynamically set, and the length of the time period is set according to the obtained data amount.
4. A gas pipe network leak detection method based on pressure parameter analysis according to claim 3, wherein the detection method is started when a change in monitoring data occurs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110977523.1A CN113685736B (en) | 2021-08-24 | 2021-08-24 | Gas pipe network leakage detection method and system based on pressure parameter analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110977523.1A CN113685736B (en) | 2021-08-24 | 2021-08-24 | Gas pipe network leakage detection method and system based on pressure parameter analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113685736A CN113685736A (en) | 2021-11-23 |
| CN113685736B true CN113685736B (en) | 2023-07-04 |
Family
ID=78582119
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110977523.1A Active CN113685736B (en) | 2021-08-24 | 2021-08-24 | Gas pipe network leakage detection method and system based on pressure parameter analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113685736B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114334194B (en) * | 2021-11-24 | 2023-03-28 | 华能核能技术研究院有限公司 | High-temperature gas cooled reactor helium gas leakage early warning method, device, equipment and storage medium |
| CN115095798B (en) * | 2022-06-08 | 2023-10-31 | 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 |
| CN114964432B (en) * | 2022-07-29 | 2022-10-25 | 杭州先锋电子技术股份有限公司 | Method for judging tiny leakage of intelligent diaphragm gas meter |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1184931A (en) * | 1996-12-11 | 1998-06-17 | 唐秀家 | Method and apparatus for detecting and positioning leakage of fluid transferring pipeline |
| CN1138085C (en) * | 1999-05-10 | 2004-02-11 | 北京昊科航科技有限责任公司 | Method and device for monitoring and locating leakage of fluid delivering pipeline |
| CN103939749B (en) * | 2014-04-28 | 2016-08-24 | 东北大学 | Flow circuits based on big data leakage intelligent adaptive monitoring system and method |
| CN105388884A (en) * | 2015-11-05 | 2016-03-09 | 天津大学 | Alarm system for detecting leakage fault of heat supply network based on identification algorithm driven by data and method |
| CN105927863B (en) * | 2016-05-07 | 2018-06-29 | 大连理工大学 | DMA subregions pipeline network leak on-line checking alignment system and its detection localization method |
| CN110319982B (en) * | 2019-06-03 | 2021-03-30 | 清华大学合肥公共安全研究院 | Buried gas pipeline leakage judgment method based on machine learning |
| CN110263416A (en) * | 2019-06-17 | 2019-09-20 | 北京讯腾智慧科技股份有限公司 | A kind of gas ductwork leakage detection method and device based on emulation technology |
| CN110332465A (en) * | 2019-06-27 | 2019-10-15 | 中石化川气东送天然气管道有限公司 | A kind of long distance gas pipeline leakage monitoring decision-making technique and system |
| CN111706785B (en) * | 2020-07-28 | 2021-06-18 | 西南石油大学 | Natural gas dendritic pipe network leakage pipe section identification method |
| CN112443763A (en) * | 2020-12-14 | 2021-03-05 | 福州大学 | Pipe network on-line detection system based on Internet of things platform |
-
2021
- 2021-08-24 CN CN202110977523.1A patent/CN113685736B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113685736A (en) | 2021-11-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113685736B (en) | Gas pipe network leakage detection method and system based on pressure parameter analysis | |
| Mashford et al. | An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine | |
| Sanz et al. | Leak detection and localization through demand components calibration | |
| Farley et al. | Development and field validation of a burst localization methodology | |
| Farley et al. | Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network | |
| Lijuan et al. | A leak detection method based on EPANET and genetic algorithm in water distribution systems | |
| CN102072409B (en) | Pipe network leakage monitoring method combining leakage probability calculation and recorder monitoring | |
| CN112097126B (en) | Water supply network pipe burst pipeline accurate identification method based on deep neural network | |
| Özdemir | Water leakage management by district metered areas at water distribution networks | |
| Xue et al. | Application of acoustic intelligent leak detection in an urban water supply pipe network | |
| Yan et al. | Reliability-based crack threat assessment and management | |
| JP2009216471A (en) | Flow measuring device | |
| WO2012050744A2 (en) | Prediction of remaining life in a heat exchanger | |
| CN112052457A (en) | Security condition evaluation method and device of application system | |
| Stephens et al. | Field tests for leakage, air pocket, and discrete blockage detection using inverse transient analysis in water distribution pipes | |
| Okeya et al. | Locating pipe bursts in a district metered area via online hydraulic modelling | |
| Fantozzi et al. | Experience and results achieved in introducing district metered areas (DMA) and pressure management areas (PMA) at Enia utility (Italy) | |
| Kariyawasam et al. | Effective improvements to reliability based corrosion management | |
| JP2009216472A (en) | Flow measuring device | |
| Chang et al. | Quantification of the head-outflow relationship for pressure-driven analysis in water distribution networks | |
| Mantilla‐Peña et al. | Evaluation of in‐service residential nutating disc water meter performance | |
| Kępa et al. | A hydraulic model as a useful tool in the operation of a water-pipe network. | |
| CN103776652B (en) | A kind of high-pressure heater method for testing performance and system | |
| Terpstra | Use of statistical techniques for sampling inspection in the oil and gas industry | |
| CN112231957B (en) | Structure fracture evaluation processing method suitable for discontinuous region |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
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 |