A method for detecting a gas pipeline leakage fault

Abstract

The present application relates to the field of gas pipeline leakage fault detection, and more particularly to a gas pipeline leakage fault detection method, which comprises: establishing a thermodynamic model of the gas pipeline; establishing a state space equation based on the thermodynamic model of the gas pipeline; establishing a robust fault detector based on the state space equation; collecting measurement data of the gas pipeline as actual measurement values to be detected; generating input conditions of the thermodynamic model of the gas pipeline based on environmental conditions and operating conditions in the measurement data, and calculating model prediction values output by the thermodynamic model of the gas pipeline; the robust fault detector calculates a residual error according to the actual measurement values to be detected and the model prediction values, and determines whether the gas pipeline has a leakage according to the residual error; and the leakage distribution of the gas pipeline is generated by comprehensively detecting the leakage of the gas pipeline at multiple places. The gas pipeline leakage detection method can obtain the leakage of the gas pipeline.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of gas pipeline leakage fault detection, and in particular to a gas pipeline leakage fault detection method. BACKGROUND

[0002] In a steel industrial park, gas is an indispensable energy source to drive furnaces, machines, and various steelmaking processes. Gas pipelines play a crucial role in ensuring the safe transportation of gas. However, due to factors such as pipeline aging and corrosion, these pipelines face potential leakage and failure risks. Such leakage and failure not only threaten safety, but also can disrupt production processes, resulting in significant financial losses.

[0003] In order to timely detect the leakage fault of the gas pipeline, the current method is to manually check, but this method is inefficient and costly. Due to the limited nature of human resources, combined with the complexity and widespread distribution of gas pipeline networks, manual inspection becomes an impractical and resource-intensive task. Therefore, there is an urgent need to provide a method for automatically detecting gas pipeline leakage faults to improve the efficiency of leakage fault detection at a lower cost, thereby improving the safety of gas pipelines. SUMMARY

[0004] The technical problem to be solved by the present application is to overcome the deficiencies in the prior art and provide a gas pipeline leakage fault detection method.

[0005] The present application is achieved by the following technical solutions:

[0006] A gas pipeline leakage fault detection method, the gas pipeline leakage fault detection method comprising the following steps:

[0007] Step 1, establishing a thermodynamic model of the gas pipeline;

[0008] Step 2, establishing a state space equation based on the thermodynamic model of the gas pipeline;

[0009] Step 3, establishing a robust fault detector based on the state space equation, and setting H- / H∞ performance indicators in the robust fault detector;

[0010] Step 4, collecting measurement data of the gas pipeline, and preprocessing the measurement data as actual measurement values to be detected;

[0011] Step 5, generating input conditions for the gas pipeline thermodynamic model based on the environmental conditions and operating conditions in the measurement data, and calculating model predicted values from the gas pipeline thermodynamic model output;

[0012] Step 6: The robust fault detector calculates the residual based on the actual measured value and the model prediction value, and determines whether the gas pipeline is leaking based on the residual.

[0013] Step 7: Based on the comprehensive leak detection data from multiple locations in the gas pipeline, generate a leak distribution report for the gas pipeline.

[0014] Preferably, the state-space equation is:

[0015]

[0016] Where E = [In0], M = [A0], h = [CFs], ξ is the unknown input such as system disturbance and modeling uncertainty, g is the nonlinear vector in the thermodynamic model of the gas pipeline, and B, D and F a Given a known constant matrix of appropriate dimension, u is the input vector, and f a The problem is an actuator failure in the system.

[0017] Preferably, the robust fault detector is:

[0018]

[0019] Where z represents the detector state. Let y be the reconstructed value. and The states of the system are respectively and actuator failure f a The reconstructed values ​​are N, L, and G, which are the observer gain matrices to be designed.

[0020] Preferably, the H∞ performance index is γ>0, and

[0021] Preferably, "collecting measurement data from gas pipelines and preprocessing it to obtain the actual measurement value to be tested" includes the following steps:

[0022] Noise reduction processing is performed on the measurement data;

[0023] The measurement data is harmonized based on the data harmonization principle of the Gaussian correction criterion.

[0024] Preferably, "data harmonization of measurement data based on the Gaussian correction criterion" includes the following steps:

[0025] Step 41: Under a certain operating condition, collect the gas path measurement parameters of a certain period of stable operation of the gas pipeline of the current object, and use them as the gas path measurement parameters to be diagnosed offline after noise reduction.

[0026] Step 42: When adjusting the gas path measurement parameters, the actual gas path component characteristics may deviate from the theoretical characteristics. Here, the flow characteristic index and efficiency characteristic index of the gas path component are introduced as "virtual" measurement parameters (the value is 1 when the component is healthy) and adjusted together. Since the performance and health status of the gas path component are unknown, its "virtual" measurement parameters are all set to 1, and its uncertainty is set to 1%.

[0027] Step 43: Based on the data harmonization principle of the Gaussian correction criterion, the gas path measurement data to be diagnosed offline and the "virtual" measurement parameters under this working condition are harmonized.

[0028] The beneficial effects of this invention are as follows: This gas pipeline leak detection method first establishes a thermodynamic model of the gas pipeline, and then establishes a state-space equation based on the thermodynamic model. Subsequently, a robust fault detector is established. The robust fault detector can calculate the residual between the actual measured values ​​of the gas pipeline and the model prediction values ​​of the thermodynamic model, and determine whether a leak has occurred in the gas pipeline based on the residual, thereby obtaining the leakage status of the gas pipeline. Furthermore, an H- / H∞ performance index is set in the robust fault detector to suppress the influence of unknown inputs on fault reconstruction. The gain matrix of the proposed fault diagnosis observer is solved using linear matrix inequalities, thus facilitating the design of the observer. In summary, this invention can determine the leakage status of gas pipelines at a relatively low cost. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the preferred embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] This invention provides a method for detecting gas pipeline leaks, the method comprising the following steps:

[0031] Step 1: Establish a thermodynamic model of the gas pipeline.

[0032] Step 2: Establish the state-space equations based on the thermodynamic model of the gas pipeline. The state-space equations are typically in the form of first-order differential equations, as shown below:

[0033]

[0034] in, Let f be the rate of change of the state variables, x be the state vector, u be the input vector, y be the output vector, ξ be the unknown inputs such as system disturbances and modeling uncertainties, and f be the output vector. a with f Srespectively, are the actuator fault and sensor fault of the system, g is a nonlinear vector in the system and satisfies Lipschitz condition, A, B, C, D, F a , F S are known constant matrices with appropriate dimensions.

[0035] Further, the system is rewritten in a singular system form. Specifically, let E = [In 0], M = [A 0], H = [CFs].

[0036] Since is a full column rank matrix, the inverse matrix exists. Let Thus, SE + TH = In + r.

[0037] Let For simplicity, the time variable t of all vectors is omitted in the following text, thus the system can be written as

[0038]

[0039] Step 3: Establish a robust fault detector based on the state space equation, and set H- / H∞ performance index in the robust fault detector.

[0040]

[0041] wherein: z is the state of the detector, is the reconstructed value of y, and are the reconstructed values of the state and the actuator fault f a of the system, respectively, and N, L and G are the observer gain matrices to be designed.

[0042] Further, let Let then

[0043] In addition,

[0044] Let F = L - NT, N = SM - FH, then

[0045]

[0046] Let and then wherein,

[0047] set up Q = [F T G T ] T ,but

[0048] To suppress the influence of unknown input d on fault reconstruction, the H∞ performance index γ>0 is designed such that... because and Therefore, when the above equation holds, robust reconfiguration for actuator and sensor faults can be achieved. As shown in the equation, a smaller γ value indicates that the unknown input has a smaller impact on fault reconfiguration. The following section will solve an optimization problem to obtain the minimum value of γ, while simultaneously ensuring that the error dynamic system is robustly asymptotically stable.

[0049] Step 4: Collect measurement data from the gas pipeline and preprocess it to obtain the actual measurement values ​​to be tested. Preprocessing includes noise reduction of the measurement data and data harmonization based on the Gaussian correction criterion. Data harmonization based on the Gaussian correction criterion includes steps 41 to 43:

[0050] Step 41: Under a certain operating condition, collect the gas path measurement parameters of a certain period of stable operation of the gas pipeline of the current object, and use them as gas path measurement parameters to be diagnosed offline after noise reduction.

[0051] Step 42: When adjusting the gas path measurement parameters, the actual gas path component characteristics may deviate from the theoretical characteristics. Here, the flow characteristic index and efficiency characteristic index of the gas path component are introduced as "virtual" measurement parameters (the value is 1 when the component is healthy) and adjusted together. Since the performance and health status of the gas path component are unknown, its "virtual" measurement parameters are all set to 1, and its uncertainty is set to 1%.

[0052] Step 43: Based on the data harmonization principle of the Gaussian correction criterion, the gas path measurement data to be diagnosed offline and the "virtual" measurement parameters under this working condition are harmonized.

[0053] Step 5: Based on the environmental and operational conditions in the above measurement data, generate the input conditions for the gas pipeline thermodynamic model, and output the model prediction value.

[0054] Step 6: The robust fault detector calculates the residuals based on the actual measured values ​​and model predictions, and determines whether a gas pipeline leak has occurred based on the residuals.

[0055] Step 7: Integrate the multiple leak detection conditions of the gas pipeline to generate the leakage distribution of the gas pipeline.

[0056] The stability of the robust fault detector is explained as follows.

[0057] Considering the gas pipeline thermodynamic model and the robust fault detector, if there exists a positive definite matrix P and a matrix Y such that the following linear matrix inequality optimization problem has a solution, then the error dynamic system is robust asymptotically stable.

[0058]

[0059] wherein * represents the symmetric term of the symmetric matrix, and

[0060]

[0061]

[0062] Define the Lyapunov functional Taking the derivative of V along the system can obtain

[0063]

[0064] In addition, it can be calculated that

[0065]

[0066] Therefore

[0067] Set Then

[0068] Set A sufficient condition for J<0 is Therefore

[0069]

[0070] Set Then Therefore, from Γ<0, it can be obtained that wherein

[0071] Further, by the Schur complement theorem, it can be known that Γ<0 is equivalent to

[0072]

[0073] Set Y=PQ, and it can be calculated that Thus becomes

[0074]

[0075] The above formula is a linear matrix inequality. If there is a minimum γ that makes the above formula true, then Further, J < 0, so it can be known that

[0076] Therefore, the matrix [F T G T ] = P -1 Y, so the matrices F and G are obtained, and N = SM - FH. Then L = F + NT is obtained, so the design of the robust fault detector is completed. In addition, when solving the linear matrix inequality, the matrix obtained by solving the minimum problem can be too large, so a larger γ than the minimum value γ min may be set, and the gain matrix of the observer is obtained by solving the feasible solution of the linear matrix inequality.

[0077] The above only describes the preferred embodiments of the present application, and it should be noted that those of ordinary skill in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered within the protection scope of the present application.

Claims

1. A method of detecting a leak fault in a gas pipeline, characterized in that, The method comprises the following steps: Step 1, establishing a thermodynamic model of the gas pipeline; Step 2, establishing a state space equation based on the thermodynamic model of the gas pipeline; Step 3, establishing a robust fault detector based on the state space equation, and setting H- / H∞ performance indexes in the robust fault detector; Step 4, collecting measurement data of the gas pipeline, and taking the preprocessed measurement data as actual measurement values to be detected; Step 5, generating input conditions of the thermodynamic model of the gas pipeline based on environmental conditions and operating conditions in the measurement data, and calculating model predicted values by the thermodynamic model of the gas pipeline; Step 6, calculating residuals by the robust fault detector according to the actual measurement values to be detected and the model predicted values, and determining whether the gas pipeline leaks according to the residuals; Step 7, generating a leakage distribution of the gas pipeline by comprehensively detecting multiple leakages of the gas pipeline; The state space equation is: where E = [Ino], M = [Ao], H = [Cfs], ξ is unknown input such as system disturbance and modeling uncertainty, g is a nonlinear vector in the thermodynamic model of the gas pipeline, B, D and F a are known constant matrices of appropriate dimensions, u is the input vector, f a is the actuator fault of the system; The robust fault detector is: where A is the detector state, is the reconstructed value of y, and are the state ζ and actuator fault f of the system, respectively a is the reconstructed value of y, N, L and G are the observer gain matrices to be designed. The H performance index is γ > 0, and 2. The method of claim 1, wherein, "Collecting measurement data of the gas pipeline, and taking the preprocessed measurement data as actual measurement values to be detected” comprises the following steps: Performing noise reduction processing on the measurement data; Performing data reconciliation on the measurement data based on the data reconciliation principle of the Gauss correction criterion.

3. A method of detecting a leak fault in a gas pipeline according to claim 2, characterized in that, "Performing data reconciliation on the measurement data based on the data reconciliation principle of the Gauss correction criterion” comprises the following steps: Step 41, under a certain operating condition, collecting gas path measurement parameters of a certain period of stable operation of the current object gas pipeline, and taking the noise-reduced gas path measurement parameters as gas path measurement parameters to be offline diagnosed; Step 42, when reconciling the gas path measurement parameters, the actual gas path component characteristics may also deviate from the theoretical characteristics to a certain extent, and here the flow characteristic index and efficiency characteristic index of the gas path component are introduced as "virtual” measurement parameters, and when the component is healthy, the values are 1, and are reconciled together, since the performance and health status of the unknown gas path component is unknown, the "virtual” measurement parameters are taken as 1, and the uncertainty is taken as 1%; Step 43, performing data reconciliation on the gas path measurement data to be offline diagnosed and the "virtual” measurement parameters under the operating condition based on the data reconciliation principle of the Gauss correction criterion.

Images