CN109268692A - A kind of heating network well formula compensator leakage monitoring system and monitoring method - Google Patents
A kind of heating network well formula compensator leakage monitoring system and monitoring method Download PDFInfo
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- CN109268692A CN109268692A CN201810961516.0A CN201810961516A CN109268692A CN 109268692 A CN109268692 A CN 109268692A CN 201810961516 A CN201810961516 A CN 201810961516A CN 109268692 A CN109268692 A CN 109268692A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
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- F17D5/02—Preventing, monitoring, or locating loss
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Abstract
The invention discloses a kind of heating network well formula compensator leakage monitoring systems, including main control unit, main control unit is connected with A/D conversion module by conducting wire, A/D conversion module is connected separately with temperature-measuring module, conductivity sensor and liquid level sensor by conducting wire, main control unit is connected with GPRS wireless module by conducting wire, and GPRS wireless module is connect with remote monitoring center.Its monitoring method is, firstly, establishing sample input matrix, to be normalized, and establishes heating network well formula compensator leakage condition predicting model, and heating network well formula compensator leakage situation finally can be obtained using measurement parameter as input again.It will be in the multi-parameter Compensation device leak condition analysis insertion monitoring system based on ELM extreme learning machine, it is automated, intelligent, scientific real time on-line monitoring and leakage level are assessed, it can ensure the reliability of equipment and safety and save a large amount of manpower and material resources.
Description
Technical field
The invention belongs to heating tube leakage monitoring technical fields, and in particular to a kind of heating network well formula compensator leakage prison
Examining system further relates to the monitoring method of the system.
Background technique
Heat supply pipeline compensator is the component part of heating network, and heat supply pipeline compensator leakage monitoring mode belongs to heating power
The leak detection means of pipeline network leak monitoring mode, the two have versatility.It is complicated as operating condition and extremely huge
Heating system due to the influence of many factors, causes Pipeline damage leakage accident to take place frequently, usually root in the process of running
After pipeline network leak, the variation of system relevant parameter, which leaks it, to be monitored.Heating network leakage monitoring is a system work
Journey, it is necessary to carry out its leakage monitoring using multiple means and the method for multi-crossed disciplines.
Currently, domestic and foreign scholars are less to the research of heat supply pipeline compensator leakage monitoring method, mainly pass through reference
The leakage of the other judgements of type pipeline network leak monitoring method and positioning heat supply pipeline.The a part of compensator as heat distribution pipeline,
It drastically influences heat supply because the leakage accident that the damage of the factors such as material ageing, violation operation, external interference causes is in the great majority
The safe and stable operation of system.
Summary of the invention
The object of the present invention is to provide a kind of heating network well formula compensator leakage monitoring systems, solve existing compensator
The problem of leakage monitoring system monitoring effect difference.
It is a further object of the present invention to provide the monitoring methods of above-mentioned monitoring system.
The technical scheme adopted by the invention is that a kind of heating network well formula compensator leakage monitoring system, including master control
Unit, main control unit are connected with A/D conversion module by conducting wire, and A/D conversion module is connected separately with temperature by conducting wire and measures
Module, conductivity sensor and liquid level sensor, main control unit are connected with GPRS wireless module, GPRS wireless module by conducting wire
It is connect with remote monitoring center.
The features of the present invention also characterized in that
It further include power module, power module, including photovoltaic array, the photovoltaic array passes through conducting wire
It is connected with photovoltaic controller, photovoltaic controller is connect with battery, and battery is connected separately with the first power conversion by conducting wire
Device, second source converter and third supply convertor, second source converter are connected with voltage-stablizer by conducting wire, voltage-stablizer with
Main control unit connection.
Temperature-measuring module includes the first temperature sensor, second temperature sensor, third temperature sensor and the 4th temperature
Sensor is spent, and the first temperature sensor, second temperature sensor, third temperature sensor and the 4th temperature sensor are
PT100 platinum resistance thermometer sensor, formula temperature sensor.
Main control unit is STM32F103RBT6 single-chip microcontroller.
The model ATK-SIM800C of GPRS wireless module；The model WRT-136 of liquid level sensor.
Another technical solution of the present invention is a kind of heating network well formula compensator leakage monitoring method, specifically
Steps are as follows:
Step 1, according to the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature sensing
Device, conductivity sensor and liquid level sensor distinguish parameter T1, T2, T3, T4, G, H that analogue measurement arrives, and mend to heating network well formula
It repays device leakage situation and carries out simulation classification, respectively obtain normal condition, compared with the sample under Small leak state and larger leak condition
Matrix m*6；
When the temperature difference of T2 and T1, T3 and T1, T4 and T1 are respectively less than 20 DEG C, and G and H are 0, then heating network well formula
Compensator belongs to normal condition；Conversely, being then abnormal state；
In abnormal state, if H is less than or equal to 0.5, belong to compared with Small leak state；If H is greater than 0.5, belong to
In larger leak condition；
Step 2, each column of each sample matrix obtained after step 1 are normalized, make each sample
Each column parameter value x ' of this matrixiIt all falls in [0,1], as shown in formula (1)；
In formula (1), xmaxAnd xminThe maximum value and minimum value of each column parameter in respectively each sample matrix；xiTo return
One changes each column parameter value in preceding each sample matrix；x′iFor each column parameter value in each sample matrix after normalization；
Step 3, after step 2, sample matrix m*6 is spliced in such a way that row connects, forms the matrix of 3m*6, it
Afterwards, finally increase by a column in matrix, with distinguish three kinds of states data, with 1,2,3 successively representative simulation heating network well formula mend
The normal condition, smaller leak condition and larger leak condition for repaying device, obtain sample input matrix 3m*7；
Step 4, after step 3, heating network well formula compensator leakage condition predicting model is established, the specific steps are as follows:
Step 4.1, the sample input matrix obtained after through step 3 is as training sample and test sample, wherein training
Sample and the data amount check of test sample ratio are 8:2 as the input of ELM disaggregated model and establish heating network well formula compensator
Condition predicting model is leaked, as shown in formula (2):
In formula (2), ykFor the output valve of neuron；xjFor the input signal transmitted from other neurons；wkFor connection weight
Value, i.e. bonding strength；bkFor the threshold value of neuron；
In formula (2), the output matrix Φ of hidden layer, as shown in formula (3):
In formula (3), φ () is the activation primitive of neuron, and activation primitive is sig function；
Hidden layer number K progressively increases from 10 to 200, and progressively increases 10 every time；
Step 4.2, the connection matrix β between output layer and hidden layer is established, as shown in formula (4):
β=Φ+T=(ΦTΦ)-1ΦTT (4)；
In formula (4), T is the output matrix of training sample, ΦTΦ is unusual or nonsingular matrix；
Wherein, the output matrix T of training sample, as shown in formula (5):
In formula (5), M be output classification number, M=1,2 ... .., n；
Step 4.3, the learning error Z of extreme learning machine is calculated, hidden layer number K and activation when with learning error minimum
Function phi () is parameter, i.e. accuracy rate highest, and heating network well formula compensator leakage condition predicting model can be obtained；
Wherein, hidden layer number K and activation letter if learning error is identical, when taking hidden layer neuron number less
Number φ () is used as parameter；
The calculation formula of learning error Z, as shown in formula (6):
Z=| | Φ β-T | | (6)；
Step 5, after step 4, by the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature
The normalized that sensor, conductivity sensor and the collected sample data matrix of liquid level sensor carry out such as step 2 is spent, then
Using sample data matrix as the input of ELM disaggregated model, it is input to the heating network well formula compensator obtained after step 4 and lets out
It leaks in condition predicting model, which exports a status categories, and heating network well formula compensator leakage situation can be obtained.
The invention has the advantages that
The multisensor heat distribution pipe network well formula compensator leakage monitoring system is to compensator because of leakage monitoring caused by damaging
It works well, alarm signal can be issued in time when leakage accident occurs in it, be repaired in time for related personnel and important evidence is provided,
Reducing influences caused by leakage, has great importance to the normal operation for ensureing heating system.
Detailed description of the invention
Fig. 1 is a kind of frame diagram of heating network well formula compensator leakage monitoring system of the present invention；
Fig. 2 is heating network well formula compensator leakage condition predicting model measurement sample and reality output in the method for the present invention
Comparison diagram.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
A kind of heating network well formula compensator leakage monitoring system of the present invention, as shown in Figure 1, including main control unit 1, master control
Unit 1 is connected with A/D conversion module 2 by conducting wire, A/D conversion module 2 by conducting wire be connected separately with temperature-measuring module 3,
Conductivity sensor 4 and liquid level sensor 5, main control unit 1 are connected with GPRS wireless module 6, GPRS wireless module 6 by conducting wire
It is connect with remote monitoring center 7；
It further include power module, power module includes photovoltaic array, and photovoltaic array is connected by conducting wire
There is photovoltaic controller, photovoltaic controller is connect with battery, and the output voltage of photovoltaic controller is 12V, and battery passes through conducting wire
It is connected separately with the first supply convertor, second source converter and third supply convertor, the first supply convertor generates+
12V and -12V voltage, second source converter generation+5V and -5V voltage, third supply convertor generation+24V voltage, second
Supply convertor is connected with voltage-stablizer by conducting wire, and+5V can be depressured to+3.3V, and voltage-stablizer is connect with main control unit 1；Pressure stabilizing
The model ASM1117 of device；
Temperature-measuring module 3 includes the first temperature sensor, second temperature sensor, third temperature sensor and the 4th temperature
Sensor is spent, the first temperature sensor is mounted on right above well formula compensator, and second temperature sensor is mounted on well formula compensator
Underface, third temperature sensor and the 4th temperature sensor are separately mounted to 1 two sides of well formula compensator, conductivity sensor 4
It is mounted on immediately below well formula compensator in insulating layer, liquid level sensor 5 is mounted on well formula compensator bottom；
First supply convertor respectively with the first temperature sensor, second temperature sensor, third temperature sensor, the 4th
Temperature sensor is connected with conductivity sensor 4；
Second source converter respectively with the first temperature sensor, second temperature sensor, third temperature sensor, the 4th
Temperature sensor, liquid level sensor 5 are connected with A/D conversion module 2；
Third supply convertor is connected with liquid level sensor 5；
Wherein ,+12V the voltage of generation is supplied to temperature-measuring module 3 and conductivity sensor 4；
- 12V the voltage generated is supplied to temperature-measuring module 3；
+ 5V the voltage generated is supplied to temperature-measuring module 3, liquid level sensor 5 and A/D conversion module 2；
- 5V the voltage generated is supplied to A/D conversion module 2；
+ 24V the voltage generated is supplied to liquid level sensor 5；
The model of first supply convertor, second source converter and third supply convertor is respectively VRA1212YMD-
6WR3,VRA1205YMD-6WR3,VRA1224YMD-6WR3；First temperature sensor, second temperature sensor, third temperature pass
Sensor and the 4th temperature sensor are PT100 platinum resistance thermometer sensor, formula temperature sensor；
Main control unit 1 is STM32F103RBT6 single-chip microcontroller；
The model ATK-SIM800C of GPRS wireless module 6；
The model WRT-136 of liquid level sensor 5；
The parameter of acquisition is carried out analog-to-digital conversion by A/D conversion module 2, by 4 double integration A/D chip IC L7135CN and
ICL8069 chip composition, ICL8069 reference voltage source provide reference voltage for AD conversion chip ICL7135CN, pass through
The acquisition of each sensing data is realized in the control of STM32F103RBT6 single-chip microcontroller.
A kind of heating network well formula compensator leakage monitoring system of the present invention, concrete operating principle is:
By each sensor arrangement in the system on well formula compensator, the leakage monitoring to well formula compensator is realized；
Temperature-measuring module 3 is used to while detecting the temperature around well formula compensator, by any one being arranged on well formula compensator
Temperature is measured as reference, is compared with color temperature measured by other three temperature sensors, if there is some difference for the temperature difference,
Then can preliminary judgement well formula compensator occurred leakage accident, monitoring system issues compensator and leaks warning information；Start simultaneously
Liquid level sensor 5 and conductivity sensor 4 start conductance in production wells bottom liquid level and well formula compensator underface insulating layer and join
Number further confirms that well formula compensator is damaged if conductance G value is significantly raised or significant change also occurs for shaft bottom liquid level H
Leakage, and the parameters such as collected temperature, conductance, liquid level are sent to remote monitoring center by GPRS wireless network, then
Collected parameter is stored, handled and analyzed, the working condition of the monitored heat distribution pipe network compensator of Comprehensive Assessment, to not
Leakage status assessment is alarmed and damaged to normal operating condition.
Photovoltaic array will acquire solar energy daytime and be converted to electric energy, power for compensator leakage monitoring system, lead to
It crosses photovoltaic controller to protect circuit and battery is protected not damaged by overshoot, control output voltage is 12V, and by extra electricity
It is stored in battery, the 12V voltage that battery exports is converted to the receivable direct current of detection module by voltage conversion circuit
Pressure, night battery stand alone as compensator leak detection system by voltage conversion circuit and provide power supply.
A kind of heating network well formula compensator leakage monitoring method of the present invention, the specific steps are as follows:
Step 1, according to the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature sensing
Device, conductivity sensor and liquid level sensor distinguish parameter T1, T2, T3, T4, G, H that analogue measurement arrives, and mend to heating network well formula
It repays device leakage situation and carries out simulation classification, respectively obtain normal condition, compared with the sample under Small leak state and larger leak condition
Matrix m*6, in each sample matrix, the parameter value of same type is each matrix column, i.e. measured T1, T2, T3,
T4, G, H are each matrix column, and each matrix six arranges totally；
When the temperature difference of T2 and T1, T3 and T1, T4 and T1 are respectively less than 20 DEG C, and G and H are 0, then heating network well formula
Compensator belongs to normal condition；Conversely, being then abnormal state；
In abnormal state, if H is less than or equal to 0.5, belong to compared with Small leak state；If H is greater than 0.5, belong to
In larger leak condition；
Step 2, each column of each sample matrix obtained after step 1 are normalized, make each sample
Each column parameter value x ' of this matrixiIt all falls in [0,1], as shown in formula (1)；
In formula (1), xmaxAnd xminThe maximum value and minimum value of each column parameter, x in respectively each sample matrixiTo return
One changes each column parameter value in preceding each sample matrix, x 'iFor each column parameter value in each sample matrix after normalization；
Step 3, after step 2, sample matrix m*6 is spliced in such a way that row connects, forms the matrix of 3m*6, it
Afterwards, finally increase by a column in matrix, with distinguish three kinds of states data, with 1,2,3 successively representative simulation heating network well formula mend
The normal condition, smaller leak condition and larger leak condition for repaying device, obtain sample input matrix 3m*7；
Step 4, after step 3, heating network well formula compensator leakage condition predicting model is established, the specific steps are as follows:
Step 4.1, the sample input matrix obtained after through step 3 is as training sample and test sample, wherein training
Sample and the data amount check of test sample ratio are 8:2 as the input of ELM disaggregated model and establish heating network well formula compensator
Condition predicting model is leaked, as shown in formula (2):
In formula (2), yk is the output valve of neuron；xjFor the input signal transmitted from other neurons；wkFor connection weight
Value, i.e. bonding strength；bkFor the threshold value of neuron；
In formula (2), the output matrix Φ of hidden layer, as shown in formula (3):
In formula (3), φ () is the activation primitive of neuron, and activation primitive is sig function；
Hidden layer number K progressively increases from 10 to 200, and progressively increases 10 every time；
Step 4.2, the connection matrix β between output layer and hidden layer is established, as shown in formula (4):
β=Φ+T=(ΦTΦ)-1ΦTT (4)；
In formula (4), T is the output matrix of training sample, ΦTΦ is unusual or nonsingular matrix；
Wherein, the output matrix T of training sample, as shown in formula (5):
In formula (5), M be output classification number, M=1,2 ... .., n；
Step 4.3, the learning error Z of extreme learning machine is calculated, hidden layer number K and activation when with learning error minimum
Function phi () is parameter, i.e. accuracy rate highest, and heating network well formula compensator leakage condition predicting model can be obtained；
Wherein, hidden layer number K and activation letter if learning error is identical, when taking hidden layer neuron number less
Number φ () is used as parameter；
The calculation formula of learning error Z, as shown in formula (6):
Z=| | Φ β-T | | (6)；
Step 5, after step 4, by the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature
The normalized that sensor, conductivity sensor and the collected sample data matrix of liquid level sensor carry out such as step 2 is spent, then
Using sample data matrix as the input of ELM disaggregated model, it is input to the heating network well formula compensator obtained after step 4 and lets out
It leaks in condition predicting model, which exports a status categories, and heating network well formula compensator leakage situation can be obtained.
In method of the invention, hidden layer number K progressively increases 10 from initial value 10 every time, until 200, calculates model
It practises accuracy rate (accuracy rate=1- error), by comparing, the smallest neuron number of error is determined as final model and is implied
Layer number K takes the conduct parameter that hidden layer neuron number is small if there is identical accuracy rate, to reduce the complexity of model,
To ensure that the classification speed of model.
Method of the invention by the data of the test sample of acquisition be input to heating network well formula compensator leakage situation it is pre-
It surveys in model, and the reality output for the output matrix and test sample being calculated compares, as shown in Figure 2, it can be seen that
For disaggregated model in the experimental result of test set, horizontal axis represents test set data, and the longitudinal axis is affiliated 1,2,3 three types labels
(wherein, 1- normal condition；2- is compared with Small leak；3- is compared with gross leak), open circles indicate belonging to the script of test set data in figure
Classification, star line represent the calculated classification of algorithm.It can be seen that model prediction result with higher, to demonstrate base
In the validity of the heating network well formula compensator leakage condition predicting model of ELM algorithm.
Claims (7)
1. a kind of heating network well formula compensator leakage monitoring system, which is characterized in that including main control unit (1), the master control
Unit (1) is connected with A/D conversion module (2) by conducting wire, and the A/D conversion module (2) is connected separately with temperature by conducting wire
Measurement module (3), conductivity sensor (4) and liquid level sensor (5), the main control unit (1) are connected with GPRS by conducting wire
Wireless module (6), the GPRS wireless module (6) connect with remote monitoring center (7).
2. a kind of heating network well formula compensator leakage monitoring system according to claim 1, which is characterized in that further include
Power module, the power module, including photovoltaic array, the photovoltaic array are connected with photovoltaic by conducting wire
Controller, the photovoltaic controller are connect with battery, and battery is connected separately with the first supply convertor, second by conducting wire
Supply convertor and third supply convertor, the second source converter are connected with voltage-stablizer, the voltage-stablizer by conducting wire
It is connect with the main control unit (1).
3. a kind of heating network well formula compensator leakage monitoring system according to claim 1, which is characterized in that the temperature
Spending measurement module (3) includes the first temperature sensor, second temperature sensor, third temperature sensor and the 4th temperature sensing
Device, and first temperature sensor, second temperature sensor, third temperature sensor and the 4th temperature sensor are
PT100 platinum resistance thermometer sensor, formula temperature sensor.
4. a kind of heating network well formula compensator leakage monitoring system according to claim 1, which is characterized in that the master
Controlling unit (1) is STM32F103RBT6 single-chip microcontroller.
5. a kind of heating network well formula compensator leakage monitoring system according to claim 1, which is characterized in that described
The model ATK-SIM800C of GPRS wireless module (6)；The model WRT-136 of the liquid level sensor (5).
6. a kind of heating network well formula compensator leakage monitoring method, which is characterized in that specific step is as follows:
Step 1, according to the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature sensor, electricity
Derivative sensor and liquid level sensor distinguish parameter T1, T2, T3, T4, G, H that analogue measurement arrives, to heating network well formula compensator
Leakage situation carries out simulation classification, respectively obtains normal condition, compared with the sample matrix under Small leak state and larger leak condition
m*6；
When the temperature difference of T2 and T1, T3 and T1, T4 and T1 are respectively less than 20 DEG C, and G and H are 0, then heating network well formula compensates
Device belongs to normal condition；Conversely, being then abnormal state；
In abnormal state, if H is less than or equal to 0.5, belong to compared with Small leak state；If H be greater than 0.5, belong to compared with
Gross leak state；
Step 2, each column of each sample matrix obtained after step 1 are normalized, make each sample moment
Each column parameter value x ' of battle arrayiIt all falls in [0,1], as shown in formula (1)；
In formula (1), xmaxAnd xminThe maximum value and minimum value of each column parameter in respectively each sample matrix；xiFor normalization
Each column parameter value in preceding each sample matrix；x′iFor each column parameter value in each sample matrix after normalization；
Step 3, after step 2, sample matrix m*6 is spliced in such a way that row connects, forms the matrix of 3m*6, later,
Finally increase by a column in matrix, to distinguish the data of three kinds of states, with 1,2,3 successively representative simulation heating network well formula compensators
Normal condition, smaller leak condition and larger leak condition, obtain sample input matrix 3m*7；
Step 4, after step 3, heating network well formula compensator leakage condition predicting model is established；
Step 5, after step 4, the first temperature sensor, second temperature sensor, third temperature sensor, the 4th temperature are passed
Sensor, conductivity sensor and the collected sample data matrix of liquid level sensor carry out the normalized such as step 2, then by sample
Input of the notebook data matrix as ELM disaggregated model is input to the heating network well formula compensator leakage shape obtained after step 4
In condition prediction model, which exports a status categories, and heating network well formula compensator leakage situation can be obtained.
7. a kind of heating network well formula compensator leakage monitoring method according to claim 6, which is characterized in that the step
Rapid 4, the specific steps are as follows:
Step 4.1, the sample input matrix obtained after through step 3 is as training sample and test sample, wherein training sample
Data amount check ratio with test sample is 8:2, as the input of ELM disaggregated model, establishes the leakage of heating network well formula compensator
Condition predicting model, as shown in formula (2):
In formula (2), ykFor the output valve of neuron；xjFor the input signal transmitted from other neurons；wkFor connection weight, i.e.,
Bonding strength；bkFor the threshold value of neuron；
In formula (2), the output matrix Φ of hidden layer, as shown in formula (3):
In formula (3), φ () is the activation primitive of neuron, and activation primitive is sig function；
Hidden layer number K progressively increases from 10 to 200, and progressively increases 10 every time；
Step 4.2, the connection matrix β between output layer and hidden layer is established, as shown in formula (4):
β=Φ+T=(ΦTΦ)-1ΦTT (4)；
In formula (4), T is the output matrix of training sample, ΦTΦ is unusual or nonsingular matrix；
Wherein, the output matrix T of training sample, as shown in formula (5):
In formula (5), M be output classification number, M=1,2 ... .., n；
Step 4.3, the learning error Z of extreme learning machine is calculated, hidden layer number K and activation primitive when with learning error minimum
φ () is parameter, i.e. accuracy rate highest, and heating network well formula compensator leakage condition predicting model can be obtained；
Wherein, hidden layer number K and activation primitive φ if learning error is identical, when taking hidden layer neuron number less
() is used as parameter；
The calculation formula of learning error Z, as shown in formula (6):
Z=| | Φ β-T | | (6).
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| CN204962303U (en) * | 2015-07-02 | 2016-01-13 | 中国大唐集团科学技术研究院有限公司西北分公司 | Pipe network expansion joint of portable secret heat supply damages test probe |
| CN107917341A (en) * | 2016-10-09 | 2018-04-17 | 中国石油天然气股份有限公司 | Oil pipeline leakage detection method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110009874A (en) * | 2019-04-12 | 2019-07-12 | 泰州职业技术学院 | A kind of flammable gas alarm, combustible gas alarm method and system |
| CN114811443A (en) * | 2021-01-22 | 2022-07-29 | 北京科益虹源光电技术有限公司 | Waterway state monitoring system |
| CN114811443B (en) * | 2021-01-22 | 2024-04-16 | 北京科益虹源光电技术有限公司 | Waterway state monitoring system |
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