Self-adaptive fuel oil control system for preventing shaft sticking during high-working-condition shutdown of gas turbine

Abstract

The invention relates to the technical field of gas turbine control, and discloses a gas turbine high-working-condition shutdown anti-shaft-sticking self-adaptive fuel control system which comprises a temperature sensing module, a data storage module, a control processing module and a fuel execution module and is designed for dealing with the scene that a gas turbine needs to be restarted immediately after being accidentally shut down under a high working condition due to an external fault. The method aims at solving the problem of ignition failure or journal sticking risk caused by mismatching of fuel oil supply and the thermal state of equipment at the moment, and comprises the steps that when a system runs, the inlet air temperature and the combustion chamber environment temperature are collected in real time; inputting the parameters into a pre-established first mathematical model to obtain an optimal initial fuel oil supply quantity and executing ignition; and after successful ignition, fuel oil increment control is conducted according to the air inlet temperature and time and a pre-established second mathematical model, a data-driven mathematical model is used for replacing a fixed control curve, the fuel oil supply amount is made to adapt to the real-time thermal state, the restarting success rate is increased, the journal sticking accident is fundamentally eradicated, and operation safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of gas turbine control technology, specifically to an adaptive fuel control system for preventing shaft seizure during high-operating-condition shutdown of a gas turbine. Background Technology

[0002] As a core power source in marine propulsion, power plant generation, and industrial drive, the operational reliability of gas turbines directly impacts energy supply security and production continuity. However, in actual operation, gas turbines may be forced to shut down under high operating conditions due to grid fluctuations, mechanical failures, or sudden external disturbances (such as sudden load shedding). During such shutdowns, the unit is in an extreme thermal state—critical components such as rotors and bearings accumulate significant heat due to prolonged high-load operation, with internal temperatures far exceeding normal shutdown levels, resulting in significant thermal expansion. If a successful restart cannot be achieved within a short time, the unit will experience radial clearance reduction or even localized jamming during natural cooling due to differences in the contraction rates of various components, leading to a "shaft seizure" accident. This accident requires disassembly and repair, taking several days to weeks, causing not only huge economic losses but also seriously threatening power supply and shipping safety.

[0003] Immediate restarting after a shutdown under high operating conditions presents several technical challenges: First, the combustion chamber retains extremely high residual temperatures, creating thermal boundary conditions completely different from cold starts. Precise control of the initial fuel supply becomes crucial – insufficient fuel supply will result in ignition energy density below the combustible limit, leading to ignition failure; excessive fuel supply may cause detonation due to localized overheating, exacerbating thermal fatigue damage to high-temperature components such as turbine blades. Second, after successful ignition, the fuel supply needs to dynamically increase over time to offset the rapid cooling effect of high-temperature components, maintain stable combustion, and smoothly ramp up the engine speed to idle. Traditional start-up control strategies are mostly designed based on cold operating conditions, employing fixed time-fuel quantity mapping curves or relying on indirect parameters such as compressor outlet pressure and engine speed for open-loop control. These strategies are ill-suited to the dynamically changing thermodynamic environment after a shutdown under high operating conditions, and are prone to restart failure due to mismatch between fuel supply and actual thermal state, ultimately leading to the risk of bearing seizure.

[0004] While existing technologies (such as Chinese patents CN107905899B and CN111550284A) have made progress in optimizing the conventional startup process—for example, by improving combustion stability through segmented adjustment of fuel injection timing or by introducing wide-range environmental parameter compensation to enhance system robustness—their technical approaches still focus on general improvements in common scenarios, failing to delve into the unique needs of the specific scenario after a shutdown under high operating conditions. Specifically, existing technologies have not disclosed how to construct dynamic mathematical models based on key parameters that directly reflect the unit's thermal state, such as intake air temperature and combustion chamber ambient temperature, nor have they proposed a method for real-time calculation of fuel supply for this scenario. Furthermore, existing control logic often uses preset thresholds or empirical formulas, lacking the ability to deeply learn from historical successful startup data, thus failing to achieve precise matching between fuel supply and the unit's real-time thermal state.

[0005] In summary, existing technologies have significant shortcomings in controlling fuel supply under special thermal conditions after a shutdown under high operating conditions. There is an urgent need for a new control system that can sense key thermal parameters in real time, optimize based on historical data, and dynamically generate a fuel supply strategy that adapts to the current thermal environment. This would break through the bottleneck of traditional control methods and fundamentally solve the technical problems of bearing seizure risk and low restart success rate after a shutdown under high operating conditions. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides an adaptive fuel control system for preventing shaft seizure during high-operating-condition shutdown of gas turbines. This system has the advantages of real-time thermal state perception, dynamic fuel supply optimization, multi-dimensional operating condition adaptation, and intelligent self-learning iteration. It solves the problems of ignition failure, unstable startup process, and high risk of shaft seizure caused by mismatch between fuel supply and real-time thermal state after high-operating-condition shutdown.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: an adaptive fuel control system for preventing shaft seizure during high-operating-condition shutdown of a gas turbine, comprising a temperature sensing module, a data storage module, a control processing module, and a fuel execution module.

[0010] The temperature sensing module is used to measure the intake air temperature T. inlet and combustion chamber ambient temperature T comb ;

[0011] The data storage module is used to store the first mathematical model and the second mathematical model;

[0012] The control processing module receives data from the temperature sensing module and uses the model in the data storage module to perform calculations to obtain the initial fuel supply quantity Q.initial And the fuel increment ΔQ; and generate the corresponding control signal;

[0013] The fuel execution module is used to receive control signals and adjust the fuel supply.

[0014] Furthermore, a gas turbine high-condition shutdown anti-shaft seizure adaptive fuel control system includes the following steps:

[0015] Step 1: Parameter Acquisition: The intake air temperature T after the gas turbine shuts down under high operating conditions is acquired in real time using a temperature sensing module. inlet and combustion chamber ambient temperature T comb And a first mathematical model and a second mathematical model are pre-established in the data storage module;

[0016] Step 2, Model Calculation: The collected T... inlet and T comb Input the pre-established first mathematical model to calculate the initial fuel supply Q upon restart. initial ;

[0017] Step 3: Start Execution: The control processing module controls the fuel system to supply fuel at an initial quantity Q. initial Inject fuel into the combustion chamber and execute the ignition procedure;

[0018] Step 4: Dynamic Adjustment: Within the predetermined time window after successful ignition, adjust the current intake air temperature T. inlet At time t, the fuel execution module dynamically adjusts the fuel supply according to the pre-established second mathematical model.

[0019] Furthermore, the first mathematical model is established based on historical high-condition shutdown and successful restart data, representing T. inlet T comb With Q initial An algorithm for determining the correspondence between them.

[0020] Furthermore, the specific implementation of the first mathematical model is as follows: the functional equation obtained by fitting historical data through a multiple regression algorithm is as follows:

[0021] Q initial =a*T comb +b*T inlet +c

[0022] In the formula, a represents the incremental percentage of initial fuel demand when the combustion chamber temperature increases by one unit, b represents the incremental percentage of initial fuel demand when the intake air temperature increases by one unit, and c represents the baseline compensation value under combined operating conditions, covering other influencing factors not explicitly modeled. a, b, and c are all regression coefficients obtained by fitting historical data.

[0023] Furthermore, the specific implementation of the first mathematical model is as follows: the correspondence table of intake air temperature - combustion chamber temperature - initial fuel quantity is stored in the control unit of the control processing module, and it is used in conjunction with the interpolation algorithm.

[0024] Furthermore, the second mathematical model is an algorithm for calculating the fuel increment ΔQ based on historical successful start-up data, and its functional relationship is ΔQ=f(T inlet ,t), making the total fuel supply Q total =Q initial +ΔQ increases with time.

[0025] Furthermore, the functional relationship of the second mathematical model is ΔQ=f(T) inlet Specifically, t) means:

[0026] ΔQ=k*(T inlet / T ref )*t n

[0027] In the formula, k is the proportionality constant, and T ref For reference intake air temperature, n is a power exponent greater than 0.

[0028] Furthermore, the fuel execution module includes a fuel pump and an electronically controlled fuel valve, the electronically controlled fuel valve being adjusted for fuel flow by a control signal.

[0029] Furthermore, the control processing module includes a memory, a processor, and a computer program stored in the memory; when the processor executes the program, it can implement any step of the above method.

[0030] Furthermore, the data storage module includes a computer-readable storage medium on which a computer program is stored; when the program is executed by a processor, it can implement any step of the above method.

[0031] Compared with the prior art, the present invention provides an adaptive fuel control system for preventing shaft seizure during high-operating-condition shutdown of gas turbines, which has the following beneficial effects:

[0032] 1. This invention replaces the traditional fixed-mode fuel supply strategy by constructing a closed-loop adaptive control system of temperature sensing, model calculation, and fuel execution. This achieves precise dynamic matching between the fuel supply and the real-time thermal state of the gas turbine after high-operation shutdown, thereby eliminating the problem of start-up failure or thermal stress concentration caused by fuel incompatibility from the root, and ultimately improving the restart success rate and reliability of the fuel control system.

[0033] 2. This invention introduces a first mathematical model and a second mathematical model based on historical successful data to quantitatively correlate key temperature parameters with fuel demand, thereby achieving accurate calculation of initial fuel quantity and adaptive adjustment of fuel increment during startup. This enables a smooth and stable startup, like that of a skilled operator, without relying on operator experience, and ultimately effectively suppresses abnormal bearing wear, fundamentally preventing bearing seizure failure.

[0034] 3. This invention achieves the beneficial effect of making the entire anti-lock shaft control strategy highly flexible and scalable by implementing the control logic in the form of a computer program and storing it in a storage medium. When it is necessary to adapt to new models or optimize performance, only the software model parameters need to be updated without modifying the hardware, thereby greatly reducing the subsequent optimization and maintenance costs of the system and ultimately improving the universality and life cycle of the system technology of this invention. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the overall process steps of the method of the present invention;

[0036] Figure 2 This is a basic structural block diagram of the system of the present invention;

[0037] Figure 3 This is a diagram of the first mathematical model of the present invention (a three-dimensional surface diagram);

[0038] Figure 4 This is an incremental curve of the second mathematical model of the present invention at different intake temperatures. Detailed Implementation

[0039] The present invention will now be further described with reference to the accompanying drawings.

[0040] Please see Figure 1 - Figure 4 An adaptive fuel control system for preventing shaft seizure during high-operating-condition shutdown of a gas turbine includes a temperature sensing module, a data storage module, a control processing module, and a fuel actuation module.

[0041] The temperature sensing module is used to measure the intake air temperature T. inlet and combustion chamber ambient temperature T comb ;

[0042] The data storage module is used to store the first mathematical model and the second mathematical model;

[0043] The control processing module receives data from the temperature sensing module; it then calls the model in the data storage module to perform calculations and obtain the initial fuel supply quantity Q. initial And the fuel increment ΔQ; and generate the corresponding control signal;

[0044] The fuel actuator module is used to receive control signals and precisely adjust the fuel supply.

[0045] This system is designed to address scenarios where gas turbines need to be restarted immediately after an unexpected shutdown under high operating conditions due to external faults. It aims to solve the risk of ignition failure or shaft seizure caused by a mismatch between fuel supply and equipment thermal state. During system operation, the system collects intake air temperature and combustion chamber ambient temperature in real time. These parameters are input into a pre-established first mathematical model to map the optimal initial fuel supply and execute ignition. After successful ignition, fuel increment control is performed according to a pre-established second mathematical model based on intake air temperature and time. This invention replaces a fixed control curve with a data-driven mathematical model, allowing the fuel supply to adapt to real-time thermal state, significantly improving the restart success rate, fundamentally eliminating shaft seizure accidents, and ensuring operational safety.

[0046] The advantages are: through the coordinated work of the above modules, key temperature parameters after a gas turbine shutdown under high operating conditions can be sensed in real time, the fuel demand can be accurately calculated based on a mathematical model built on historical successful data, and the fuel supply can be dynamically adjusted through the fuel execution module, forming a closed-loop control of sensing-calculation-execution. Ultimately, this achieves the beneficial effects of preventing the gas turbine from seizing due to thermal imbalance, improving the restart success rate after a shutdown under high operating conditions, and ensuring the stable operation of the unit.

[0047] An adaptive fuel control system for preventing shaft seizure during high-operation-condition shutdown of a gas turbine includes the following steps:

[0048] Step 1: Parameter Acquisition: The intake air temperature T after the gas turbine shuts down under high operating conditions is acquired in real time through a temperature sensing module (with built-in thermocouple / resistance device). inlet and combustion chamber ambient temperature T comb And a first mathematical model and a second mathematical model are pre-established in the data storage module;

[0049] Step 2, Model Calculation: The collected T... inlet and T comb Input the pre-established first mathematical model to calculate the initial fuel supply Q upon restart. initial ;

[0050] Step 3: Start Execution: The control processing module controls the fuel system to supply fuel at an initial quantity Q. initial Inject fuel into the combustion chamber and execute the ignition procedure;

[0051] Step 4: Dynamic Adjustment: Within the predetermined time window after successful ignition, adjust the current intake air temperature T. inlet At time t, the fuel execution module dynamically adjusts the fuel supply according to the pre-established second mathematical model.

[0052] The advantages are: through the above steps, from real-time acquisition of key temperature parameters to model-based initial fuel quantity calculation, and then to dynamic fuel adjustment after ignition, a targeted restart control process is formed to ensure that the fuel supply is accurately matched with the real-time thermal state of the gas turbine. This avoids start-up failure due to insufficient fuel or thermal stress caused by excessive fuel during the start-up process, thus smoothly transitioning to the idling condition and ultimately achieving the beneficial effects of preventing bearing seizure and improving restart reliability.

[0053] Specifically, the first mathematical model is established based on historical high-condition shutdown and successful restart data, representing T. inlet T comb With Q initial An algorithm for determining the correspondence between them.

[0054] Specifically, the first mathematical model is implemented by fitting the historical data using a multiple regression algorithm to obtain the following functional equation:

[0055] Q initial =a*T comb +b*T inlet +c

[0056] In the formula, a represents the incremental percentage of initial fuel demand when the combustion chamber temperature increases by one unit, b represents the incremental percentage of initial fuel demand when the intake air temperature increases by one unit, and c represents the baseline compensation value under combined operating conditions, covering other influencing factors not explicitly modeled. a, b, and c are all regression coefficients obtained by fitting historical data.

[0057] The advantage is that by calculating the initial fuel supply Q... initial It can quantify the impact of combustion chamber temperature and intake air temperature on fuel demand. Combined with baseline compensation values ​​to cover potential influencing factors under complex operating conditions, it ensures that the initial fuel quantity is highly compatible with the thermal state of the gas turbine after shutdown, avoiding deviations caused by empirical fuel supply, and achieving the beneficial effects of ensuring ignition success rate and reducing thermal shock in the initial startup phase.

[0058] Specifically, the first mathematical model is implemented as follows: a table showing the correspondence between intake air temperature, combustion chamber temperature, and initial fuel quantity is stored in the control unit of the control processing module, and it is used in conjunction with an interpolation algorithm.

[0059] The advantages are: by combining the lookup table based on historical successful data with the interpolation algorithm, the fuel quantity data verified under actual working conditions can be directly called. At the same time, the interpolation algorithm fills the gaps between discrete data, which can not only ensure the reliability of fuel quantity calculation, but also improve the response speed of real-time control. Especially under complex and ever-changing working conditions, it can stably output accurate initial fuel quantity, thereby improving the system's adaptability and control accuracy.

[0060] Specifically, the second mathematical model is an algorithm based on historical successful start-up data for calculating the fuel increment ΔQ, with the functional relationship ΔQ=f(T) inlet ,t), making the total fuel supply Q total =Q initial +ΔQ increases with time.

[0061] Specifically, the functional relationship of the second mathematical model is ΔQ = f(T) inlet Specifically, t) means:

[0062] ΔQ=k*(T inlet / T ref )*t n

[0063] In the formula, k is the proportionality constant, and T ref For reference intake air temperature, n is a power exponent greater than 0.

[0064] The advantages are: by calculating the fuel increment ΔQ, the total fuel supply can be dynamically adjusted according to the real-time intake air temperature and start-up time, so that the fuel quantity increases smoothly over time, thereby avoiding drastic fluctuations in combustion chamber temperature caused by sudden changes in fuel supply, ensuring that the gas turbine smoothly transitions from the start-up state to the idling state, reducing abnormal wear of bearings caused by thermal deformation, and ultimately achieving the beneficial effects of preventing bearing seizure and extending the unit's life.

[0065] Specifically, the fuel execution module includes a fuel pump and an electronically controlled fuel valve, which is regulated by a control signal to precisely control the fuel flow.

[0066] The advantages are: by combining the power source provided by the fuel pump and the precise flow regulation of the electronic fuel valve, the system can quickly respond to the instructions of the control processing module, achieve precise control of the fuel supply, ensure that the calculated initial fuel quantity and dynamic increment are accurately executed, avoid control failure caused by fuel supply deviation, and achieve the beneficial effects of improving system control accuracy and ensuring regulation effect.

[0067] Specifically, the control processing module includes a memory, a processor, and a computer program stored in the memory; when the processor executes the program, it can implement any step of the above method.

[0068] Specifically, the data storage module includes a computer-readable storage medium on which a computer program is stored; when the program is executed by a processor, it can implement any step of the above method.

[0069] The advantages are: by combining the above-mentioned computer program with the storage medium, the mathematical model storage, temperature data processing, fuel quantity calculation and control logic can be automated to ensure the stability and consistency of the control process; at the same time, it is convenient to optimize the model parameters by updating the program later to improve the system's adaptability to different operating conditions, and ultimately achieve the beneficial effects of improving the system's intelligence level and reducing maintenance costs.

[0070] Example 1 (Based on a mathematical model using regression formulas, this example provides a specific implementation method)

[0071] S1. The first mathematical model was obtained by fitting historical successful startup data using a multiple linear regression algorithm. Its functional expression is as follows:

[0072] Q initial =a*T comb +b*T inlet +c

[0073] In the formula, a, b, and c are regression coefficients. These coefficients are obtained by fitting data from over 50 successful restarts after high-condition shutdowns of a certain type of gas turbine. For example, a = 1.5, b = 0.8, c = -50. Therefore, the specific expression of the model is:

[0074] Q initial =1.5*T comb +0.8*T inlet -50

[0075] (Unit: Q) initial For kg / h, T comb and T inlet All are in °C.

[0076] The second mathematical model defines the relationship between fuel increment and intake air temperature and time, and its functional expression is:

[0077] ΔQ=k*(T inlet / T ref )*t n

[0078] In the formula, k is the proportionality constant, and T ref Using the reference intake temperature (e.g., 25℃) and n as a power exponent (n>0), after optimization based on historical data, k=2.0 and n=1.2 are chosen. Therefore, the specific expression of this model is:

[0079] ΔQ=2.0*(T inlet / 25)*t 1.2

[0080] (Units: ΔQ is kg / h, t is s), total fuel quantity Q total =Qinitial +ΔQ.

[0081] Application process: A certain type of gas turbine underwent an emergency shutdown under high operating conditions, and the sensor measured T. inlet =30℃, T comb =300℃.

[0082] S2, The controller invokes the first mathematical model:

[0083] Q initial =1.5*300+0.8*30-50=450+24-50=424kg / h.

[0084] S3, fuel system supplied fuel at 424 kg / h, ignition successful.

[0085] S4. The controller calls the second mathematical model. At t = 2s, ΔQ = 2.0*(30 / 25)*(2). 1.2 ≈2.0*1.2*2.3≈5.52kg / h, Q total ≈429.5kg / h, the fuel quantity increases adaptively with time and intake air temperature.

[0086] Example 2 (Mathematical model based on lookup table and interpolation algorithm, in another specific implementation of the present invention)

[0087] The first mathematical model is implemented in the form of a two-dimensional lookup table, which is obtained by discretizing (T) data from historical data. comb T inlet Points and their corresponding success Q initial The values ​​are tabulated and stored in the controller. After the controller acquires the real-time temperature, it uses a bilinear interpolation algorithm to look up and calculate the accurate Q value in the table. initial value.

[0088] The second mathematical model uses the same function form as Example 1 and is also implemented in the form of a lookup table.

[0089] In summary, Examples 1 and 2 respectively demonstrate the implementation methods of mathematical models based on regression formulas and lookup tables. The feasibility of the invention is verified through specific parameter calculations and application processes: Both implementation methods can accurately calculate the initial fuel supply and dynamic increment based on the real-time collected intake air temperature and combustion chamber temperature, so that the fuel supply is adaptively matched with the thermal state of the gas turbine after high-condition shutdown, successfully achieving a smooth transition from startup to idling. This fully demonstrates that the invention can effectively prevent the bearing seizure problem after high-condition shutdown of the gas turbine, significantly improve the restart success rate, and has strong practicality and reliability.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A gas turbine high operating condition shutdown anti- shaft- lock adaptive fuel control system characterized by, The temperature sensing module, the data storage module, the control processing module and the fuel execution module are included. The temperature sensing module is used to measure the intake air temperature T inlet and the combustion chamber ambient temperature T comb ; The data storage module is used for storing the first mathematical model and the second mathematical model. The control processing module is used for receiving data of the temperature sensing module. Call the model in the data storage module to calculate and obtain an initial fuel supply amount Q initial and a fuel increment ΔQ; and generate a corresponding control signal; The fuel execution module is used for receiving the control signal and adjusting the fuel supply amount.

2. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 1, characterized in that, The method comprises the following steps: Step one, parameter collection: through the temperature sensing module to collect the inlet air temperature T inlet and the combustion chamber ambient temperature T comb of the gas turbine after high working condition shutdown in real time, and pre-establish a first mathematical model and a second mathematical model in the data storage module; Step two, model calculation: input the collected T inlet and T comb into the pre-established first mathematical model to calculate the initial fuel supply Q initial when the engine is restarted. Step three, start execution: the control processing module controls the fuel system to an initial fuel supply amount Q initial fuel injection into the combustion chamber and execution of the ignition procedure; Step four, dynamic adjustment: within a predetermined time window after the successful ignition, according to the current intake temperature T inlet and time t, the fuel execution module dynamically adjusts the fuel supply amount according to the pre-established second mathematical model.

3. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 2, characterized in that, The first mathematical model is an algorithm representing the corresponding relationship between T inlet , T comb and Q initial , which is established based on historical high operating condition shutdown and successful start data.

4. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 2, characterized in that, The specific implementation of the first mathematical model is a function equation obtained by fitting historical data through a multiple regression algorithm, and specifically is: Q initial = a * T comb + b * T inlet + c In the formula, a is the incremental proportion of the initial fuel demand when the combustion chamber temperature is increased by a unit amount, b is the incremental proportion of the initial fuel demand when the intake temperature is increased by a unit amount, and c is a baseline compensation value under a comprehensive working condition, covering other influencing factors that are not explicitly modeled, and a, b and c are regression coefficients obtained by fitting historical data.

5. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 2, characterized in that, The specific implementation of the first mathematical model is a corresponding relationship table of intake temperature-combustion chamber temperature-initial fuel amount stored in a control unit of the control processing module, and is used in combination with an interpolation algorithm.

6. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 2, characterized in that, The second mathematical model is an algorithm for calculating the fuel increment ΔQ, which is established based on historical successful start data, and the functional relationship is ΔQ = f(T inlet ,t), so that the total fuel supply Q total = Q initial + ΔQ increases over time.

7. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine engine as set forth in claim 6, characterized in that, The function relationship ΔQ = f(T inlet , t) of the second mathematical model is specifically: ΔQ = k * (T inlet / T ref )* t n In the formula, k is a proportional coefficient, T ref is the reference intake air temperature, and n is a power index greater than 0.

8. A high operating condition shutdown anti-pivot-adaptive fuel control system for a gas turbine as set forth in claim 1, characterized in that, The fuel execution module comprises a fuel pump and an electrically controlled fuel valve, and the electrically controlled fuel valve adjusts the fuel flow under the control of the control signal.

9. A high operating condition shutdown anti-stall adaptive fuel control system for a gas turbine engine as set forth in claim 1, characterized in that, The control processing module comprises a memory, a processor and a computer program stored in the memory; when the processor executes the program, each step of the method in any one of claims 2 can be realized.

10. A high operating condition shutdown anti-stall adaptive fuel control system for a gas turbine engine as set forth in claim 1, characterized in that, The data storage module comprises a computer readable storage medium, and the storage medium has a computer program stored thereon; when the program is executed by the processor, each step of the method in any one of claims 2 can be realized.

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