CN113551744A - Ultrasonic flowmeter performance online monitoring method and system - Google Patents
Ultrasonic flowmeter performance online monitoring method and system Download PDFInfo
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Abstract
The invention relates to an ultrasonic flowmeter performance on-line monitoring method and a system, which are characterized by comprising the following steps: acquiring the pressure and the temperature at two ends of a pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time; respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located in real time; the running state of the ultrasonic flowmeter is judged according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow of the pipe section where the ultrasonic flowmeter is located in real time.
Description
Technical Field
The invention relates to an ultrasonic flowmeter performance on-line monitoring method and system, and belongs to the field of ultrasonic flow.
Background
Ultrasonic flow meters were introduced in the 90 s of the 20 th century, based on the principle of sound wave propagation velocity, were rapidly popularized with the advantages of high accuracy, high range ratio, zero pressure loss, no movable parts, simple maintenance, long service life, wide range of measurement, capability of bidirectional measurement and capability of measuring pulsating flow. Although the ultrasonic flowmeter has incomparable advantages in high-pressure and high-flow natural gas flow metering, the ultrasonic flowmeter belongs to a speed type flowmeter and has a wide range, so that the ultrasonic flowmeter is also influenced by factors such as unstable flow state, physical parameters, noise, dirt, measurement accuracy of a matched instrument and the like caused by installation conditions. For the above problems, the "JJG 1030-2007" ultrasonic flow meter certification protocol specifies: if the ultrasonic flowmeter has a self-diagnosis function and passes the inspection in use, the verification period can be prolonged from 2 years to 6 years, and the provision can save a large amount of inspection cost. The regulations also stipulate an important method for testing the ultrasonic flowmeter in use, namely, sound velocity calculation is carried out on natural gas according to AGA8 natural gas and hydrocarbon gas compression factor and AGA10 natural gas sound velocity, and the actual sound velocity measured by each sound channel is compared to judge the metering performance of the flowmeter.
The CBM system (Condition Based Monitoring, ultrasonic flow meter remote diagnosis system) is a system that is Based on field measurement system devices, and transmits information of each device to a Monitoring center in a unified manner, thereby completing real-time, remote Monitoring and diagnosis functions of the field measurement related devices. The CBM system checks the ultrasonic flow meter, and analyzes basic diagnosis indexes of flow velocity, flow velocity profile coefficient, sound channel sound velocity, sound velocity deviation, sound channel signal quality, sound channel gain, sound channel receiving rate, sound channel signal to noise ratio and the like of the data recording type ultrasonic flow meter through the acquisition of results of each metering branch type ultrasonic flow meter and a chromatographic analyzer and the operation of a temperature/pressure transmitter and various diagnosis information, and generates a diagnosis report when high-level diagnosis indexes (vortex angle, pulsating flow, cross flow, fluid flow distribution symmetry and the like of each channel) are abnormal, and gives a warning. The functions of self-diagnosis, sound velocity test and the like of the ultrasonic flowmeter are allowed to be used in gas indexes so as to ensure the working stability and reliability of the ultrasonic flowmeter and provide guarantee for normal and effective working of natural gas metering. Therefore, the operation state and the function of the trade transfer metering system are confirmed, and the time for delivery can be prolonged after the system is provided.
However, there are also systems in which a student performs a tracking analysis of a flow meter during operation, tracks a verification result of an actual flow rate which passes a verification several years in succession, verifies a flow meter in use, and performs a signal inspection sound velocity check. The out-of-tolerance of the detected sound speed is probably caused by errors of the calculated sound speed caused by incorrect errors of medium temperature, pressure and gas composition data acquisition, and other deviations of the measured sound speed are not excluded, so that the instability of the CBM system is caused. Meanwhile, for the CBM system, there are problems that many additional devices are required to support.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an online monitoring method and system for performance of an ultrasonic flow meter, which have high stability and do not need to be supported by additional equipment.
In order to achieve the purpose, the invention adopts the following technical scheme: an ultrasonic flowmeter performance on-line monitoring method comprises the following steps:
acquiring the pressure and the temperature at two ends of a pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time;
respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located in real time;
and judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
Further, the determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by using the pre-established hydraulics mechanism model and the BP neural network model according to the pressure and the temperature at the two ends of the pipe section where the ultrasonic flowmeter is located in real time respectively comprises:
acquiring historical field operation data of a pipe section where the ultrasonic flowmeter is located;
determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the acquired historical field operation data by adopting a pre-established hydraulics mechanism model;
and determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a trained BP neural network model according to the acquired historical field operation data.
Further, the determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by using the pre-established hydraulics mechanism model according to the acquired historical field operation data includes:
determining the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions by adopting a hydraulics mechanism model according to the acquired historical field operation data;
and obtaining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions.
Further, the determining the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions by adopting a hydraulics mechanism model according to the acquired historical field operation data comprises the following steps:
establishing a continuity equation, a motion equation and an energy equation corresponding to the natural gas flow according to a mass conservation law, a momentum conservation law and an energy conservation law;
taking a point acquired by the SCADA system as a quasi-steady-state point, assuming that the natural gas performs isothermal flow in a pipeline in the verification station and assuming that the pipeline in the verification station is a horizontal pipeline;
determining the friction coefficient lambda of the pipe section where the ultrasonic flowmeter is located under different working conditions:
wherein Z is a compression factor of natural gas under a pipeline condition; t is the verification temperature C0Is a unit conversion factor; pQThe starting point pressure of the gas pipeline; pZCalculating the end point pressure of the section for the gas transmission pipe; d is the inner diameter of the gas transmission pipe; delta*Is the relative density of natural gas; and L is the length of the gas transmission pipe calculation section.
Further, the determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by using the trained BP neural network model according to the acquired historical field operation data includes:
extracting the pressure and the temperature at two ends of the pipeline under different working conditions in historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training a BP neural network model;
and inputting the pressure and the temperature of the two ends of the pipe section where the real-time ultrasonic flowmeter is located, which are acquired in real time, into the trained BP neural network model to obtain the flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
Further, the extracting of the pressure and the temperature at the two ends of the pipeline under different working conditions in the historical field operation data of the pipe section where the ultrasonic flowmeter is located and the training of the BP neural network model include:
establishing a BP neural network model;
correcting the established BP neural network model by adopting a genetic algorithm to obtain a corrected BP neural network model;
and extracting the pressure and the temperature at two ends of the pipeline under different working conditions in the historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training the corrected BP neural network model to obtain the trained BP neural network model.
Further, the determining criterion for determining the operating state of the ultrasonic flowmeter according to the flow rate of the pipe section where the ultrasonic flowmeter is located obtained in real time and the determined flow rate of the pipe section where the ultrasonic flowmeter is located in real time includes:
judging the running state of the ultrasonic flowmeter according to the relative deviation between the flow of the pipe section where the ultrasonic flowmeter is located and the flow of the pipe section where the ultrasonic flowmeter is located;
comparing the pressure of front and rear straight pipe sections of the ultrasonic flowmeter with the total flow of the pipe section of the ultrasonic flowmeter in a preset period of time calculated in real time by adopting a hydraulics mechanism model and the total flow of the pipe section of the ultrasonic flowmeter in the period of time calculated by adopting the measured value of the ultrasonic flowmeter by adopting an established BP neural network model, and judging the running state of the ultrasonic flowmeter;
calculating the relative deviation between the actual average flow of each time window and the average flow passing through the ultrasonic flowmeter in the time interval in a time window form, and judging the running state of the ultrasonic flowmeter;
and clustering the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a clustering analysis algorithm, and judging the running state of the ultrasonic flowmeter.
In another aspect, an online performance monitoring system for an ultrasonic flow meter is provided, including:
the data acquisition module is used for acquiring the pressure and the temperature at two ends of a pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time;
the flow determining module is used for respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located obtained in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model;
and the running state judging module is used for judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
In another aspect, a processing device is provided, which includes computer program instructions, wherein the computer program instructions are used to implement the steps corresponding to the above-mentioned online performance monitoring method for an ultrasonic flow meter when executed by the processing device.
In another aspect, a computer readable storage medium is provided, where computer program instructions are stored on the computer readable storage medium, where the computer program instructions are executed by a processor to implement the steps corresponding to the above-mentioned online performance monitoring method for an ultrasonic flow meter.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention can determine the performance of the ultrasonic flowmeter in real time only by relying on the existing online data without additionally arranging checking equipment, does not need to carry out field operation, can realize remote online analysis of the system, has simple operation and high stability, is suitable for flowmeters with various principles, and can unify the checking methods.
2. The invention provides a numerical value reflecting the self capacity of the ultrasonic flowmeter through simulation calculation, and can prolong the online working time of the ultrasonic flowmeter.
3. The invention monitors the performance of the ultrasonic flowmeter by combining the hydraulics mechanism model and the BP neural network model modified by the genetic algorithm with the clustering algorithm, and monitors the performance of the ultrasonic flowmeter by comparing the calculated value of the BP neural network model modified by the genetic algorithm, the calculated value of the hydraulics mechanism model and the established judgment standard with the measured value of the on-site ultrasonic flowmeter, thereby being widely applied to the field of ultrasonic flow.
Drawings
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a topology structure of a BP neural network according to an embodiment of the present invention, where an Input Layer is an Input Layer, a Hidden Layer is a Hidden Layer, and an out Layer is an output Layer;
FIG. 3 is a partial sectional view of an ultrasonic flow meter in a field provided by an embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a recommended value of the friction coefficient according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating the relative deviation of the friction coefficient after the recommended value is obtained according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating an accumulated value of traffic for a period of time between a calculated traffic and an actual traffic according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the average flow rate of the actual and calculated time windows provided by an embodiment of the present invention;
FIG. 8 is a diagram illustrating the optimization results of a genetic algorithm provided by an embodiment of the present invention;
fig. 9 is a schematic diagram of a fitting result of the BP neural network model according to an embodiment of the present invention, where Training is Training Data, validity is verification Data, Test is Test Data, All is Data, Target on the horizontal axis is a Target value, Output on the vertical axis is an Output value, Data is Data, and Fit is a fitting straight line;
FIG. 10 is a schematic diagram of the prediction output of the BP neural network model according to an embodiment of the present invention;
FIG. 11 is a diagram illustrating percentage prediction errors of a BP neural network model according to an embodiment of the present invention;
FIG. 12 is a schematic diagram illustrating the recognition of abnormal operating conditions by the neural network output according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of an online monitoring of an operation status of a neural network according to an embodiment of the present invention;
fig. 14 is a diagram illustrating a result of cluster analysis according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
According to the hydraulic analysis method, when some data are available, the flow can be calculated according to the pressure, the temperature, the compression factor, the pipe diameter, the pipe length and the relative density of the pipe-conveyed natural gas of the front and rear straight pipe sections of the ultrasonic flowmeter in a hydraulic mechanism mode, wherein for a fixed ultrasonic flowmeter, the compression factor, the relative density, the pipe diameter, the pipe length and the temperature of the pipe-conveyed natural gas are invariables, and the pressure change is obvious to the flow influence, so that the metering performance of the ultrasonic flowmeter can be detected without adding extra equipment on site from the perspective of combining hydraulics with a BP neural network. The embodiment of the invention provides an ultrasonic flowmeter performance on-line monitoring method and system, wherein a hydraulics mechanism model and a genetic algorithm modified BP neural network model are adopted to monitor the performance of an ultrasonic flowmeter, and a K-means clustering algorithm is adopted to perform auxiliary evaluation on the performance change of the ultrasonic flowmeter.
Example 1
As shown in fig. 1, the present embodiment provides an online performance monitoring method for an ultrasonic flow meter, including the following steps:
1) the method comprises the steps of obtaining the pipe diameter and the wall thickness of a pipe section where the ultrasonic flowmeter is located and historical field operation data collected by an SCADA system, wherein the historical field operation data comprise the pressure, the temperature and the flow passing through the ultrasonic flowmeter before and after the ultrasonic flowmeter straight pipe section.
2) By adopting a hydraulics mechanism model, the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions is inversely calculated according to the acquired historical field operation data to form a historical database of the friction coefficient of the pipeline, which specifically comprises the following steps:
2.1) establishing a continuity equation, a motion equation and an energy equation corresponding to the natural gas flow according to the mass conservation law, the momentum conservation law and the energy conservation law so as to describe the interrelation among the flow rate, the pressure, the temperature, the density and the elevation equivalent of the natural gas:
wherein rho is the density of the natural gas; t is time; u. ofxIs the speed in the x-axis direction; u. ofyIs the speed in the y-axis direction; u. ofzIs the velocity in the z-axis direction; d is the inner diameter of the pipeline; f is the friction coefficient; theta is an included angle between the speed direction and the displacement direction; t is the verification temperature; p is the pressure of the natural gas; h is the enthalpy of the system; dsIs elevation; g is the acceleration of gravity; dQ is the amount of heat exchange per mass flow of gas over dx.
2.2) in order to conveniently establish an equivalent pipeline friction coefficient calculation model, the following assumptions are made:
taking the points acquired by the SCADA system (data acquisition and monitoring control system) as quasi-steady-state points, namely: at this moment, the natural gas stably flows in the pipeline in the verification station, and the mass flow of the natural gas is constant and does not change along with the change of time.
Secondly, assuming that the natural gas flows isothermally in the pipeline in the verification station, and adopting the verification temperature T, the influence of the energy equation can be not considered.
Third, assuming that the pipes in the verification station are horizontal pipes, i.e. ds＝0。
2.3) because the volume flow under the standard condition is often adopted in the actual work, for the convenience of use, the mass flow M is converted into the volume flow under the engineering standard condition, the local resistance coefficient is considered, and the gas state equation of the following formula (2) is substituted into the equation set (1), so that the equation set (1) can be simplified into the following hydraulic mechanism model (3):
P＝ρZRT (2)
according to the hydraulic mechanism model (3), the friction coefficient lambda of the ultrasonic flowmeter and the front and rear straight pipe sections can be obtained through inverse calculation:
in the formula, R is the gas long-distance transportation of air; z is the compressibility factor of natural gas under pipeline conditions (average pressure and average temperature); t is the verification temperature, and T is 273+ Tpj，tpjThe average temperature of the gas transmission pipe is shown in unit of; q is the volume flow of the gas transmission pipe under the engineering standard condition, and the unit is m3/s；C0Is a unit conversion factor; pQIs the starting pressure of the gas pipeline with the unit of Pa；PZCalculating the end point pressure of the gas delivery pipe with the unit of Pa(ii) a D is the inner diameter of the gas transmission pipe; lambda is the hydraulic friction coefficient; delta*Is the relative density of natural gas; and L is the length of the calculation section of the gas transmission pipe, and the unit is m.
3) And acquiring the pressure and the temperature at two ends of the pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time.
4) And obtaining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions.
5) Extracting the pressure and the temperature at two ends of the pipeline under different working conditions in historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training a BP neural network model, wherein the method specifically comprises the following steps:
5.1) establishing a BP neural network model.
The BP neural network model is a multilayer feedforward network trained according to an error inverse propagation algorithm, is one of the most widely applied neural network models at present, and has the basic principle that through training of sample data, a network weight value and a threshold value are continuously corrected, so that an error function is reduced along the direction of negative gradient to approach an expected value. The BP neural network is able to learn a large number of input-output pattern mappings without prior disclosure of mathematical equations describing such mappings. For the present invention, the BP neural network model topology is shown in fig. 2. The BP algorithm consists of two processes, forward propagation of the data stream and backward propagation of the error signal. When the neuron is transmitted in the forward direction, the transmission direction is from the input layer to the hidden layer and then to the output layer, and the state of each layer of neuron only affects the next layer of neuron.
5.1.1) As shown in FIG. 2, the input layer of the BP neural network model is set to include n nodes, the hidden layer includes l nodes, and the output layer includes m nodes. The weight between the input layer and the hidden layer is wikThe weight between the hidden layer and the output layer is wkjThe transfer function of the hidden layer is f1The transfer function of the output layer is f2Then the output h of the layer node is impliedkComprises the following steps:
output y of the output layer nodejComprises the following steps:
thus, the BP neural network model completes the approximate mapping of the n-dimensional vector to the m-dimensional vector, and turns to the back propagation process of the error signal if the expected output cannot be obtained at the output layer.
5.2) correcting the established BP neural network model by adopting a genetic algorithm to obtain a corrected BP neural network model:
5.2.1) has p learning samples, noted X1、X2、…、Xq、…、XpWherein
for the input sample data, the q learning sample XqInputting the data into a BP neural network model to obtain a group of output Yq，
Wherein,
is the output sample data.
5.2.2) obtaining the error E of the qth learning sample by adopting a square error functionq：
In the formula,
is the desired output.
For p learning samples, the global error E is:
5.2.3) adjusting the weight w by adopting an accumulative error BP algorithmkjSo that the global error E becomes small, namely:
in the formula,. DELTA.wkjFor the adjusted weight between the hidden layer and the output layer: eta is the learning rate. Defining an error signal deltayjComprises the following steps:
wherein:
in the formula, SjIs the net input to node j; y isjIs an output layer matrix; f. of2' is the first derivative of net ingress and egress.
The weights Δ w between the adjusted hidden layer and the output layer can be obtained from the above equations (10) to (12)kjComprises the following steps:
5.2.4) weight w between input layer and hidden layerikThe adjustment process of (3) is similar to the weight w between the hidden layer and the output layer in step 5.2.3) abovekjThe adjusted weight Δ w between the input layer and the hidden layerikComprises the following steps:
in the formula, SkIs the net input to node k; x is the number ofiIs an input layer matrix; f. of1' is the first derivative of the net input.
5.2.5) executing an error function gradient descending strategy in a weight vector space by alternately performing a forward propagation process and a backward propagation process, and dynamically iterating a group of weight vectors to enable an error function of the BP neural network model to reach a minimum value, thereby finishing the information extraction and memory process.
And 5.3) extracting the pressure and the temperature at two ends of the pipeline under different working conditions in the historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training the corrected BP neural network model to obtain the trained BP neural network model.
6) And inputting the pressure and the temperature of the two ends of the pipe section where the real-time ultrasonic flowmeter is located, which are acquired in real time, into the trained BP neural network model to obtain the flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
7) Judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located, the flow obtained in the step 3) and the flow obtained in the step 6), wherein the judgment standard is as follows:
directly comparing the flow of the pipe section where the ultrasonic flowmeter is located obtained in real time with the flow obtained in the step 3) and the flow obtained in the step 6), and judging the running state of the ultrasonic flowmeter through relative deviation, namely judging whether the performance of the ultrasonic flowmeter is deviated or not.
Secondly, comparing the pressure of the front and rear straight pipe sections of the ultrasonic flowmeter with the total flow of the pipe section of the ultrasonic flowmeter in a preset period of time calculated in real time by adopting a formula (3) by adopting the established BP neural network model and the total flow of the pipe section of the ultrasonic flowmeter in the period of time calculated by adopting the measured value of the ultrasonic flowmeter, and judging the running state of the ultrasonic flowmeter by adopting relative deviation.
And thirdly, calculating the relative deviation between the actual average flow of each time window and the average flow passing through the ultrasonic flowmeter in the time interval through the form of the time window, and judging the running state of the ultrasonic flowmeter.
And fourthly, clustering the flow obtained in the step 3) and the flow obtained in the step 6) by adopting a clustering analysis algorithm, and judging the running state of the ultrasonic flowmeter.
In the step 7), the specific process of the adopted clustering analysis algorithm is as follows:
A) selecting the number k of the categories of the clusters, and selecting k central points, wherein the sample point set is A ═ a1,a2,...,an}。
B) Finding out a certain central point closest to each sample point, dividing the sample points closest to the same central point into a class, and finishing one-time cluster analysis.
C) Judging whether the data before and after clustering are the same, if so, terminating the algorithm; otherwise, go to step D).
D) Respectively calculating the central point of each type of divided sample points, setting the calculated central point as a new central point of the type of sample points, and entering the step B), wherein the central point cjComprises the following steps:
in the formula, MjIs a sample in a set M of k samples of the same class, and M ═ M1,M2,M3,...,Mn}。
In the formula, SSE is the sum of squares of errors.
The online monitoring method for the performance of the ultrasonic flowmeter is described in detail by the following specific embodiments:
firstly, SPS hydraulic simulation software is adopted to establish a natural gas distribution station simulation model, and a section of data for simulating performance faults generated by the ultrasonic flowmeter is generated by changing the friction coefficient of the ultrasonic flowmeter.
The results of the evaluation criteria established by obtaining the flow through the pipe section where the ultrasonic flowmeter is located in real time in a hydraulic mechanism model are shown in fig. 4 to 7. The results of the evaluation criteria established by the neural network model are shown in fig. 8 to 13, in which the selected inputs are the pressure and temperature at both ends of the ultrasonic flow meter, and the output is the operating condition flow of the ultrasonic flow meter. And then processing the generated data through a k-means function of Matlab software, setting a clustering mode as Euclidean distance clustering, wherein the repetition frequency is five times, and the clustering analysis of the Matlab clustering is shown in FIG. 14. It can be seen that after the performance of the ultrasonic flowmeter is simulated to change, for the clustering of the historical flow of the ultrasonic flowmeter, two types of boundary points appear at the 500 th sample point. Meanwhile, by adding a deduced horizontal gas pipe flow calculation formula and a BP neural network model modified by a genetic algorithm, it can be seen that no matter the flow is calculated under the current working condition, the relative deviation between the current calculated flow and the actual flow, the accumulated value of the flow within a period of time or the average flow between the actual flow and the calculated time window can reflect the change of the metering performance of the ultrasonic flowmeter. Under the condition of not increasing field devices, the effective detection of the performance of the ultrasonic flowmeter is realized.
Example 2
The embodiment provides an ultrasonic flowmeter performance on-line monitoring system, including:
and the data acquisition module is used for acquiring the pressure and the temperature at two ends of the pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time.
And the flow determining module is used for respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located in real time.
And the running state judging module is used for judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
Example 3
The present embodiment provides a processing device corresponding to the method for monitoring performance of an ultrasonic flow meter on line provided in embodiment 1, where the processing device may be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the method in embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be run on the processing device, and the processing device executes the online performance monitoring method of the ultrasonic flowmeter provided by the embodiment 1 when running the computer program.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 4
The present embodiment provides a computer program product corresponding to the online performance monitoring method of the ultrasonic flow meter provided in this embodiment 1, and the computer program product may include a computer readable storage medium on which computer readable program instructions for executing the online performance monitoring method of the ultrasonic flow meter described in this embodiment 1 are loaded.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. An ultrasonic flowmeter performance on-line monitoring method is characterized by comprising the following steps:
acquiring the pressure and the temperature at two ends of a pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time;
respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located in real time;
and judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
2. The method for monitoring the performance of the ultrasonic flowmeter in an online manner as claimed in claim 1, wherein the step of respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting the pre-established hydraulics mechanism model and the BP neural network model according to the pressure and the temperature at two ends of the pipe section where the ultrasonic flowmeter is located in real time comprises the following steps:
acquiring historical field operation data of a pipe section where the ultrasonic flowmeter is located;
determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the acquired historical field operation data by adopting a pre-established hydraulics mechanism model;
and determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a trained BP neural network model according to the acquired historical field operation data.
3. The method for on-line monitoring of the performance of the ultrasonic flowmeter as claimed in claim 2, wherein the step of determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time by using the pre-established hydraulic mechanism model according to the acquired historical field operation data comprises the steps of:
determining the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions by adopting a hydraulics mechanism model according to the acquired historical field operation data;
and obtaining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the friction coefficient of the pipe section where the ultrasonic flowmeter is located under different working conditions.
4. The method for monitoring the performance of the ultrasonic flowmeter in an online manner as claimed in claim 3, wherein the step of determining the friction coefficient of the pipe section of the ultrasonic flowmeter under different working conditions by adopting a hydraulic mechanism model and according to the acquired historical field operation data comprises the following steps:
establishing a continuity equation, a motion equation and an energy equation corresponding to the natural gas flow according to a mass conservation law, a momentum conservation law and an energy conservation law;
taking a point acquired by the SCADA system as a quasi-steady-state point, assuming that the natural gas performs isothermal flow in a pipeline in the verification station and assuming that the pipeline in the verification station is a horizontal pipeline;
determining the friction coefficient lambda of the pipe section where the ultrasonic flowmeter is located under different working conditions:
wherein Z is a compression factor of natural gas under a pipeline condition; t is the verification temperature C0Is a unit conversion factor; pQThe starting point pressure of the gas pipeline; pZCalculating the end point pressure of the section for the gas transmission pipe; d is the inner diameter of the gas transmission pipe; delta*Is the relative density of natural gas; and L is the length of the gas transmission pipe calculation section.
5. The method for online monitoring of the performance of the ultrasonic flowmeter as claimed in claim 2, wherein the determining the flow through the pipe section of the ultrasonic flowmeter in real time by using the trained BP neural network model according to the acquired historical field operation data comprises:
extracting the pressure and the temperature at two ends of the pipeline under different working conditions in historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training a BP neural network model;
and inputting the pressure and the temperature of the two ends of the pipe section where the real-time ultrasonic flowmeter is located, which are acquired in real time, into the trained BP neural network model to obtain the flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
6. The method for monitoring the performance of the ultrasonic flowmeter in the online manner as claimed in claim 5, wherein the step of extracting the pressure and the temperature at the two ends of the pipeline under different working conditions from historical field operation data of the pipeline section where the ultrasonic flowmeter is located and training the BP neural network model comprises the steps of:
establishing a BP neural network model;
correcting the established BP neural network model by adopting a genetic algorithm to obtain a corrected BP neural network model;
and extracting the pressure and the temperature at two ends of the pipeline under different working conditions in the historical field operation data of the pipe section where the ultrasonic flowmeter is located, and training the corrected BP neural network model to obtain the trained BP neural network model.
7. The method for monitoring the performance of the ultrasonic flowmeter in the on-line manner as claimed in claim 1, wherein the determining criteria for determining the operating state of the ultrasonic flowmeter according to the real-time acquired flow rate of the pipe section where the ultrasonic flowmeter is located and the determined real-time flow rate passing through the pipe section where the ultrasonic flowmeter is located comprises:
judging the running state of the ultrasonic flowmeter according to the relative deviation between the flow of the pipe section where the ultrasonic flowmeter is located and the flow of the pipe section where the ultrasonic flowmeter is located;
comparing the pressure of front and rear straight pipe sections of the ultrasonic flowmeter with the total flow of the pipe section of the ultrasonic flowmeter in a preset period of time calculated in real time by adopting a hydraulics mechanism model and the total flow of the pipe section of the ultrasonic flowmeter in the period of time calculated by adopting the measured value of the ultrasonic flowmeter by adopting an established BP neural network model, and judging the running state of the ultrasonic flowmeter;
calculating the relative deviation between the actual average flow of each time window and the average flow passing through the ultrasonic flowmeter in the time interval in a time window form, and judging the running state of the ultrasonic flowmeter;
and clustering the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time by adopting a clustering analysis algorithm, and judging the running state of the ultrasonic flowmeter.
8. An ultrasonic flow meter performance on-line monitoring system, comprising:
the data acquisition module is used for acquiring the pressure and the temperature at two ends of a pipe section where the ultrasonic flowmeter is located and the flow measured by the ultrasonic flowmeter in real time;
the flow determining module is used for respectively determining the flow passing through the pipe section where the ultrasonic flowmeter is located in real time according to the pressure and the temperature of two ends of the pipe section where the ultrasonic flowmeter is located obtained in real time by adopting a pre-established hydraulics mechanism model and a BP neural network model;
and the running state judging module is used for judging the running state of the ultrasonic flowmeter according to the flow of the pipe section where the ultrasonic flowmeter is located acquired in real time and the determined flow passing through the pipe section where the ultrasonic flowmeter is located in real time.
9. A processing device, characterized by comprising computer program instructions, wherein the computer program instructions, when executed by the processing device, are adapted to implement the steps corresponding to the method for online monitoring of the performance of an ultrasonic flow meter according to any of claims 1-7.
10. A computer readable storage medium, characterized in that computer program instructions are stored thereon, wherein the computer program instructions, when executed by a processor, are adapted to implement the corresponding steps of the method for online monitoring of ultrasonic flow meter performance according to any of claims 1-7.
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