Gas turbine state recovery control method based on state evaluation

Abstract

The invention relates to the technical field of gas turbines, in particular to a gas turbine state recovery control method based on state evaluation, and the method comprises the steps: S1, collecting external environment parameters and monitoring parameters of a gas turbine; s2, synchronously establishing a gas turbine simulation twinborn model through parameters of a gas turbine physical entity; s3, based on a chromatographic analysis method, quantizing the operation state of the physical entity of the gas turbine by numerical values from three levels of parameter level, component level and equipment level; s4, operating the gas turbine simulation twinborn model, inputting external environment parameters and related monitoring parameters, setting a target function according to the monitoring parameters, and adjusting internal performance parameters of the gas turbine simulation twinborn model through an optimization algorithm to achieve synchronization with a gas turbine physical entity; and S5, operation state recovery control is conducted, input control parameters of the gas turbine are selected, and when the operation state of the gas turbine is abnormal, the optimal input control quantity is solved through an optimization algorithm, so that recovery of the operation state of the gas turbine is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas turbines, and more particularly to a gas turbine state recovery control method based on state assessment. Background Art

[0002] Gas turbines are widely used in power generation, aviation, and industrial drive, and their performance and reliability are crucial to the overall benefits of the system. Gas turbines may suffer performance degradation or failure due to various factors during long-term operation. Traditional maintenance and control methods mainly rely on regular inspections and empirical judgments, which cannot reflect the actual operating status of gas turbines in real time, resulting in high maintenance costs, slow fault response, and low operating efficiency.

[0003] In order to improve the operating efficiency and reliability of gas turbines, a recovery control method based on real-time status evaluation is urgently needed. Through real-time evaluation of the various components and overall status of the gas turbine, comprehensive consideration of the external environment, operating conditions and historical data, the input control parameters are selected and optimized, and real-time monitoring and intelligent control of the gas turbine are realized to ensure that the optimal operating state of the gas turbine is restored and maintained under the premise of economic efficiency, easy operation and environmental compliance. Summary of the invention

[0004] The purpose of the present invention is to provide a gas turbine state recovery control method based on state assessment, which realizes real-time monitoring and intelligent control of the gas turbine through real-time assessment of each component and the overall state of the gas turbine, so as to ensure that the optimal operating state of the gas turbine is restored and maintained under the premise of economic efficiency, simple operation and environmental compliance.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A gas turbine state recovery control method based on state assessment, the method comprising the following steps:

[0007] Step S1: collecting external environment parameters and monitoring parameters of the gas turbine;

[0008] Step S2: Synchronously establishing a gas turbine simulation twin model through parameters of the gas turbine physical entity;

[0009] Step S3: Based on the tomographic analysis method, the operating state of the physical entity of the gas turbine is numerically quantified from three levels: parameter level, component level, and equipment level, to evaluate the operating state of the gas turbine;

[0010] Step S4: running the gas turbine simulation twin model, inputting external environment parameters and related monitoring parameters, setting the objective function with the monitoring parameters, and adjusting the internal performance parameters of the gas turbine simulation twin model through the optimization algorithm to achieve synchronization with the physical entity of the gas turbine;

[0011] Step S5: operating state recovery control, selecting the input control parameters of the gas turbine, and when the operating state of the gas turbine is abnormal, solving the optimal input control amount through the optimization algorithm to achieve the recovery of the operating state of the gas turbine;

[0012] The external environmental parameters of the gas turbine in step S1 include: ambient temperature, ambient pressure and rotation speed, etc.; the monitoring parameters include: compressor inlet temperature, compressor inlet pressure, turbine outlet pressure, turbine outlet temperature and fuel flow, etc.;

[0013] The method of simulating the twin model of the gas turbine in step S2 is to establish a gas turbine simulation model using the volume inertia method, and to achieve gas turbine performance degradation and fault simulation by introducing performance degradation mechanisms and fault factors;

[0014] The parameter-level status evaluation in step S3 includes three parts. The first part establishes a monitoring parameter benchmark model and calculates the normal value of the monitoring parameter under different states by regression prediction method; the second part calculates the deviation between the normal value and the actual value and obtains the threshold interval of the monitoring parameter by probability density statistics method; the third part converts the result of parameter evaluation into a number between 0 and 1 by membership function. The selection of membership function refers to trapezoidal and triangular membership functions. The specific calculation formula is as follows, and the parameter-level status evaluation result is calculated;

[0015]

[0016] Among them, a and e values ​​are the lower fault threshold and upper fault threshold of the parameter respectively; b and e values ​​are the lower abnormal threshold and upper abnormal threshold respectively; c is the reference value obtained by modeling the reference value of the parameter;

[0017] In step S3, the component-level status assessment is performed by using a radar chart method, that is, the polygon area represents the current status of the component, and the status assessment result of each component is calculated;

[0018] Suppose there is a polygon, and the distance from each vertex to the center point is r1, r2, ... r n , and the angle of each vertex is 2Π / n radians; the radar chart is calculated as follows:

[0019]

[0020]

[0021] Where n is the number of vertices, that is, the number of monitoring parameters contained in each component; r nis the distance from each vertex to the center point, i.e., the evaluation result of each monitoring parameter; S1 is the polygonal area of ​​the current component; Smax is the polygonal area of ​​the component when the theoretical state is the best; Acomp is the evaluation result of each component state;

[0022] In the step S3, the device-level status evaluation integrates the status evaluation results of each component through the Bayesian network to evaluate the overall status of the device. For each node Xi in the Bayesian network, its joint probability density distribution can be expressed as:

[0023]

[0024] Where Xi represents the i-th node, which includes the gas turbine and all its components to be evaluated; Pa(Xi) represents the parent node set of Xi;

[0025] The internal performance parameters in step S4 refer to the internal calculation parameters of the gas turbine simulation twin model, and the internal calculation parameters of the gas turbine simulation twin model include flow correction factor, total pressure recovery coefficient correction factor and efficiency correction factor, etc.;

[0026] In step S4, the objective function is set with the monitoring parameters, and the internal performance parameters of the gas turbine simulation twin model are adjusted by the optimization algorithm to achieve synchronization with the physical entity of the gas turbine, which specifically includes:

[0027] The gas turbine collects monitoring parameters through sensors, including speed, power, exhaust temperature and compressor outlet pressure; the calculation error c of each target monitoring parameter i , using the model's simulation calculation value y i ′ and the actual gas turbine operating parameter value y i It is expressed as:

[0028]

[0029] The gas turbine digital twin process is described as an optimization problem of solving a set of component performance parameters to minimize the calculation error of the target monitoring parameters. The objective function is defined as a function of the calculation error of each selected target monitoring parameter:

[0030]

[0031] x={x1,x2,…,x i ,…,x n}

[0032] stx k,min ≤x≤x k,max k=1,2,3,…,n

[0033] Where: m is the number of target monitoring parameters; wi is the weighting coefficient of each target monitoring parameter, n is the number of performance parameters; x i is the performance parameter; x k,max and x k,min are the upper and lower limits of the corresponding performance parameter correction factors respectively;

[0034] The optimization algorithm specifically includes: adopting a particle swarm optimization algorithm based on particle restricted areas and Levy flight, balancing the traditional PSO update and Levy flight update through a probability mechanism, setting a particle restricted area to redistribute particles, and initially redistributing particles located in the restricted area to the position of the previous particle, and particles entering the restricted area during the iteration process retain the position of the previous iteration;

[0035] The input control parameters of the gas turbine are selected in step S4, specifically including: the state recovery control of the gas turbine needs to comprehensively consider the state assessment results of each component, environmental conditions, operating conditions, historical data, economy, operability and environmental protection; by selecting appropriate input control parameters, it is ensured that the gas turbine resumes normal operation under the premise of economic efficiency, simple operation and environmental compliance;

[0036] In step S5, when the operating state of the gas turbine is abnormal, an optimal input control amount is obtained by solving the optimization algorithm to restore the operating state of the gas turbine;

[0037] Specifically, it includes: adjusting the input control amount to maximize the gas turbine operation status evaluation result, and the specific calculation is as follows:

[0038] Obj=min{1-Assessment[gasturbin(ΔIN1,ΔIN2,...,ΔIN n )]}

[0039] Wherein, gasturbine is the gas turbine twin model, the input is the gas turbine input control quantity, the output is the gas turbine real-time monitoring parameter, Assessment is the operation status assessment model, the input is the gas turbine monitoring parameter, and the output is the current gas turbine status assessment result; ΔIN n is the input control quantity.

[0040] The beneficial effects of the present invention are:

[0041] The gas turbine simulation twin model established by the volumetric inertia method introduces performance degradation mechanism and fault factors to realize the simulation of gas turbine performance degradation and faults. The internal performance parameters of the gas turbine simulation twin model are adjusted by the optimization algorithm to synchronize them with the physical entity of the gas turbine, realizing the combination of virtual and real, and enhancing the practicality and reliability of the simulation model. The twin mechanism fully considers the direct reflection of the performance parameters on the operating status of the gas turbine; the status evaluation is carried out at three levels: parameter level, component level and equipment level, going deeper and deeper, comprehensively covering all aspects of the gas turbine to ensure the comprehensiveness and reliability of the evaluation results; the gas turbine control recovery parameters are clarified, and when the operating status of the gas turbine is abnormal, the input control quantity is used as the independent variable and the status evaluation result is used as the dependent variable. The optimal input control quantity is solved by the optimization algorithm, and the target optimization of the gas turbine operating status is carried out. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0043] Figure 1 It is a schematic diagram of the steps of a gas turbine state recovery control method based on state assessment of the present invention;

[0044] Figure 2 It is a flow chart of a gas turbine state recovery control method based on state assessment of the present invention;

[0045] Figure 3 It is a schematic diagram of the gas turbine state recovery result of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below with reference to the accompanying drawings.

[0047] like Figures 1 to 3 As shown, in order to achieve the technical effect of "realizing real-time monitoring and intelligent control of the gas turbine through real-time evaluation of each component and the overall state of the gas turbine, ensuring that it can restore and maintain the optimal operating state of the gas turbine under the premise of economic efficiency, simple operation and environmental compliance", the steps and functions of the gas turbine state recovery control method based on state evaluation are described in detail below;

[0048] like Figure 1 As shown, a gas turbine state recovery control method based on state assessment comprises the following steps:

[0049] Step S1: data collection, collecting gas turbine external environment parameters and monitoring parameters;

[0050] In the specific implementation, the external environment parameters and monitoring parameters of the gas turbine are dynamically collected through the gas turbine control system acquisition unit and the sensors installed on the gas turbine;

[0051] Taking a certain type of three-shaft gas turbine as an example, the collected external environmental parameters and monitoring parameters mainly include: ambient temperature, ambient pressure, low-pressure compressor inlet temperature, low-pressure compressor inlet pressure, low-pressure compressor outlet temperature, low-pressure compressor outlet pressure, high-pressure compressor outlet temperature, high-pressure compressor outlet pressure, low-pressure turbine outlet temperature, low-pressure turbine outlet temperature, power turbine outlet temperature, power turbine outlet pressure, low-pressure turbine speed, high-pressure turbine speed, power turbine speed, fuel flow, throttle opening, etc.;

[0052] Step S2: establishing a simulation model, and synchronously establishing a gas turbine simulation twin model through the parameters of the gas turbine physical entity;

[0053] In a specific embodiment, a gas turbine simulation model is established using the volume inertia method, taking a certain type of three-shaft gas turbine gas path components as an example, including a low-pressure compressor, a high-pressure compressor, a combustion chamber, a high-pressure turbine, a low-pressure turbine, a power turbine, etc.;

[0054] Step S3: operating state evaluation, based on the tomographic analysis method, the operating state of the gas turbine physical entity is numerically quantified from three levels: parameter level, component level, and equipment level, to evaluate the operating state of the gas turbine;

[0055] In a specific embodiment, a monitoring parameter benchmark model is established through a long short-term memory network, and the input parameters of the benchmark model are ambient temperature, ambient pressure, and calculated torque. The normal values ​​of the monitoring parameters under different states are calculated through a regression prediction method based on LSTM;

[0056] In the specific example, the parameters are normalized by the maximum and minimum method. i,j is the original data, is the normalized value of the i-th parameter j. and are the maximum and minimum values ​​of parameter j. Data preprocessing normalizes the data to [0,1] to reduce the impact of working conditions and environment.

[0057]

[0058] In a specific embodiment, historical data of the input window size is used for prediction. Assuming the window length is n, the two-dimensional input dimension of the three input parameters constructed is (3, n), and the corresponding output dimension is (1, p), that is, n step-length historical data is used to predict the trend after p step-lengths. Among them, n = 3, p = 1, input into the LSTM model, train the LSTM benchmark prediction model, and realize the prediction of the future 1 step-length benchmark value.

[0059] In the specific embodiment, the benchmark model consists of 2 LSTM layers and 1 fully connected layer. The low-pressure turbine exhaust temperature time series features are extracted in the LSTM hidden layer, and then the regression from the features to the predicted value is realized through the fully connected layer. The tanh activation function, L2 regularization (coefficient 0.001), Adam optimizer (learning rate 0.01), mse loss function, and the number of training iterations are 300.

[0060] In a specific embodiment, the threshold interval of the monitoring parameter is calculated by the kernel density estimation method, and the selection of the threshold interval can be set by the confidence interval, wherein the upper and lower limit abnormal thresholds are 95% confidence intervals, and the upper and lower limit fault thresholds are 99% confidence intervals;

[0061] In a specific embodiment, the result of parameter evaluation is converted into a number between 0 and 1 through a membership function. The selection of the membership function refers to the trapezoidal and triangular membership functions. The specific calculation formula is as follows, and the parameter-level state evaluation result is calculated.

[0062]

[0063] Among them, a and e values ​​are the lower fault threshold and upper fault threshold of the parameter respectively; b and e values ​​are the lower abnormal threshold and upper abnormal threshold respectively; c is the reference value obtained by modeling the reference value of the parameter.

[0064] In a specific embodiment, the gas turbine includes 4 components, which are represented by 4 vertices in the radar diagram. The distance from each vertex to the center point is r1, r2, r3, and r4, respectively, and the angle of each vertex is 90 degrees. The radar diagram is calculated as follows.

[0065]

[0066] In a specific embodiment, the gas turbine is divided into four components: gas circuit, fuel system, lubricating oil system, and cooling system. The Bayesian network contains a total of 5 nodes, and its joint probability density distribution can be expressed as:

[0067] P(T,G,O,F,C)=P(T|G,O,F,C)·P(G)·P(O)·P(F)·P(C)

[0068] Among them, T stands for gas turbine, G, O, F, and C stand for the four components of gas circuit, fuel system, lubricating oil system, and cooling system respectively;

[0069] Step S4: running the gas turbine simulation twin model, inputting external environment parameters and related monitoring parameters, setting the objective function with the monitoring parameters, and adjusting the internal performance parameters of the gas turbine simulation twin model through the optimization algorithm to achieve synchronization with the physical entity of the gas turbine;

[0070] In a specific embodiment, taking the gas path components as an example, the internal performance parameters include: low-pressure compressor flow correction factor, low-pressure compressor efficiency correction factor, high-pressure compressor flow correction factor, high-pressure compressor efficiency correction factor, combustion chamber total pressure recovery coefficient correction factor, high-pressure turbine flow correction factor, high-pressure turbine efficiency correction factor, low-pressure turbine flow correction factor, low-pressure turbine efficiency correction factor, power turbine flow correction factor, and power turbine efficiency correction factor.

[0071] In a specific embodiment, the optimization algorithm has a particle swarm size N of 30, an iteration number iter of 500, a search space dimension dim of 10, an inertia weight w of 0.7, a cognitive coefficient c1 of 1.2, a social coefficient c2 of 1.5, and a Levy flight step length RL of 0.15*levy(N,dim,1.5), where 1.5 is the β parameter of the Levy distribution.

[0072] Step S5: operating state recovery control, selecting the input control parameters of the gas turbine, and when the operating state of the gas turbine is abnormal, solving the optimal input control amount through the optimization algorithm to achieve the recovery of the operating state of the gas turbine;

[0073] Specifically, it includes: adjusting the input control amount to maximize the gas turbine operation status evaluation result, and the specific calculation is as follows:

[0074] Obj=min{1-Assessment[gasturbin(ΔIN1,ΔIN2,...,ΔIN n )]}

[0075] Wherein, gasturbine is the gas turbine twin model, the input is the gas turbine input control quantity, the output is the gas turbine real-time monitoring parameter, Assessment is the operation status assessment model, the input is the gas turbine monitoring parameter, and the output is the current gas turbine status assessment result; ΔIN n is the input control quantity;

[0076] In a specific embodiment, the control speed and the intake air flow rate are selected as the input control variables.

[0077] In a specific embodiment, since the speed range of the gas turbine power turbine is between 3000r / min and 3600r / min, the upper and lower limits of the gas turbine control speed parameter change are determined to be [-600, 600], in r / min. Due to the limitation of the adjustable guide vane, the intake flow rate change should be controlled within 10%. Under the 0.95 working condition, the intake flow rate is about 80kg / s, and the upper and lower limits of the intake flow rate parameter change are determined to be [-6, 6], in kg / s.

[0078] In the specific embodiment, the recovery states that can be achieved under different faults are also different. Among them, the recovery effect of fault 5 is the best, and the health degree is restored from 0.5846 in the initial state to 0.9795. The recovery effect of fault 15 is the worst, and the health degree is restored from 0.4602 in the initial state to 0.4921, which can only slightly improve the performance of the gas turbine. Overall, the recovery results under different states are improved.

[0079] In a specific embodiment, the optimized input control amount is shown in the following table:

[0080] Table 5.3 Fault state input control quantity

[0081]

[0082] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0083] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A gas turbine state recovery control method based on state assessment, characterized in that: The method comprises the following steps: Step S1: collecting external environment parameters and monitoring parameters of the gas turbine; Step S2: Synchronously establishing a gas turbine simulation twin model through parameters of the gas turbine physical entity; Step S3: Based on the tomographic analysis method, the operating state of the gas turbine physical entity is numerically quantified from three levels: parameter level, component level, and equipment level, to evaluate the operating state of the gas turbine; Step S4: running the gas turbine simulation twin model, inputting external environment parameters and related monitoring parameters, setting the objective function with the monitoring parameters, and adjusting the internal performance parameters of the gas turbine simulation twin model through the optimization algorithm to achieve synchronization with the physical entity of the gas turbine; Step S5: operating state recovery control, selecting the input control parameters of the gas turbine, and when the operating state of the gas turbine is abnormal, solving the optimal input control amount through the optimization algorithm to achieve the recovery of the operating state of the gas turbine.

2. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The external environmental parameters of the gas turbine in step S1 include: ambient temperature, ambient pressure and rotation speed; the monitoring parameters include: compressor inlet temperature, compressor inlet pressure, turbine outlet pressure, turbine outlet temperature and fuel flow.

3. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The method for simulating the twin model of the gas turbine in step S2 is to establish a gas turbine simulation model using the volume inertia method, and to achieve gas turbine performance degradation and fault simulation by introducing performance degradation mechanisms and fault factors.

4. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The parameter level status assessment in step S3 comprises three parts: the first part is to establish a monitoring parameter benchmark model and calculate the normal values ​​of the monitoring parameters under different states by regression prediction method; The second part calculates the deviation between the normal value and the actual value, and obtains the threshold interval of the monitoring parameter by probability density statistics; The third part calculates the parameter-level state assessment results through the membership function.

5. The gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: In step S3, the component-level status assessment is performed by using a radar chart method, that is, the polygon area represents the current status of the component, and the status assessment result of each component is calculated; Suppose there is a polygon, and the distance from each vertex to the center point is r1, r2, ... r n , and the angle of each vertex is 2Π / n radians; the radar chart is calculated as follows: Where n is the number of vertices, that is, the number of monitoring parameters contained in each component; r n is the distance from each vertex to the center point, that is, the evaluation result of each monitoring parameter; S1 is the polygonal area of ​​the current component; Smax is the polygonal area of ​​the component when the theoretical state is the best; Acomp is the evaluation result of each component state.

6. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The device-level status assessment in step S3 integrates the status assessment results of each component through a Bayesian network to perform an overall status assessment of the device.

7. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The internal performance parameters in step S4 refer to the internal calculation parameters of the gas turbine simulation twin model, and the internal calculation parameters of the gas turbine simulation twin model include a flow correction factor, a total pressure recovery coefficient correction factor and an efficiency correction factor.

8. The gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: In step S4, the objective function is set with the monitoring parameters, and the internal performance parameters of the gas turbine simulation twin model are adjusted by the optimization algorithm to achieve synchronization with the physical entity of the gas turbine, which specifically includes: The gas turbine collects monitoring parameters through sensors, including speed, power, exhaust temperature and compressor outlet pressure; the calculation error c of each target monitoring parameter i , using the model's simulation calculation value y i ′ and the actual gas turbine operating parameter value y i It is expressed as: The gas turbine digital twin process is described as an optimization problem of solving a set of component performance parameters to minimize the calculation error of the target monitoring parameters. The objective function is defined as a function of the calculation error of each selected target monitoring parameter: x={x1,x2,…,x i ,…,x n } s.t.x k,min ≤x≤x k,max k=1,2,3,…,n Where: m is the number of target monitoring parameters; w i is the weighting coefficient of each target monitoring parameter, n is the number of performance parameters; x i is the performance parameter; x k,max and x k,min are the upper and lower limits of the corresponding performance parameter correction factors respectively.

9. The gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: The input control parameters of the gas turbine are selected in step S4, specifically including: the state recovery control of the gas turbine needs to comprehensively consider the state evaluation results of each component, environmental conditions, operating conditions, historical data, economy, operability and environmental protection.

10. A gas turbine state recovery control method based on state assessment according to claim 1, characterized in that: In step S5, when the operating state of the gas turbine is abnormal, an optimal input control amount is obtained by solving the optimization algorithm to restore the operating state of the gas turbine; Specifically, it includes: adjusting the input control amount to maximize the gas turbine operation status evaluation result, and the specific calculation is as follows: Obj=min{1-Assessment[gasturbin(ΔIN1,ΔIN2,...,ΔIN n )]} Wherein, gasturbine is the gas turbine twin model, the input is the gas turbine input control quantity, the output is the gas turbine real-time monitoring parameter, Assessment is the operation status assessment model, the input is the gas turbine monitoring parameter, and the output is the current gas turbine status assessment result; ΔIN n is the input control quantity.

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