Combustion stability optimization control method and system for gas turbine

Abstract

The invention relates to the technical field of parameter optimization control, and provides a combustion stability optimization control method and system for a gas turbine, and the method comprises the following steps: S1, obtaining target values of a current environment parameter and a stability parameter of the gas turbine; s2, constructing a plurality of data combinations of fuel flow and air flow; s3, pre-estimating a corresponding stability parameter according to the current environment parameter and each data combination to obtain a plurality of pre-estimated values of the stability parameters; s4, selecting the estimated value with the minimum difference with the target value in the plurality of estimated values as the most accurate estimated value, and judging whether the difference between the most accurate estimated value and the target value is smaller than a preset threshold value, if so, executing the step S5, and otherwise, returning to the step S2; and S5, the fuel flow and the air flow of the gas turbine in the next time period are set according to the data combination corresponding to the most accurate estimated value. The combustion stability of the gas turbine can be effectively optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter optimization control, and in particular to a method and system for optimizing control of combustion stability of a gas turbine. Background Art

[0002] Gas turbine power generation technology has the advantages of high efficiency, low emissions, and strong flexibility, and occupies an important position in the development of clean energy. The gas turbine is mainly composed of a compressor, a combustion chamber, and a turbine. Its basic working process is as follows: the compressor inhales air from the external atmospheric environment, and is compressed step by step by the axial flow compressor to increase the pressure, and the air temperature is also increased accordingly; the compressed air is sent to the combustion chamber to mix with the injected fuel and burn to generate high-temperature and high-pressure gas; then it enters the turbine to expand and do work, driving the turbine to drive the compressor and the external load rotor to rotate at high speed, realizing the partial conversion of the chemical energy of the gas or liquid fuel into mechanical work, and outputting electrical work.

[0003] Among the various performances of gas turbines, combustion stability is an important factor affecting power generation efficiency, equipment stability and life span. Therefore, how to optimize the combustion stability of gas turbines is a technical problem that needs to be solved urgently. Summary of the invention

[0004] According to the research of the designers of the present invention, when the hardware equipment of the gas turbine is operating normally, the main factors affecting its combustion stability are environmental factors and fuel flow and air flow factors. In the current gas turbine combustion stability optimization control scheme, some schemes use the current or historical fuel flow and air flow as the basis for subsequent adjustment of the two flows. This generally defaults the current or historical combustion state of the gas turbine to be stable. If this is not the case, then the adjustment based on the current or historical flow data will continuously increase the combustion instability, which can easily seriously affect the normal operation of the equipment or even damage the equipment.

[0005] In view of this, the present invention proposes a gas turbine combustion stability optimization control method and system, which can effectively optimize the combustion stability of the gas turbine, thereby effectively ensuring the working efficiency and equipment stability of the gas turbine and extending the life of the gas turbine.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A method for optimizing and controlling combustion stability of a gas turbine comprises the following steps: S1, obtaining current environmental parameters and target values ​​of stability parameters of the gas turbine; S2, constructing a plurality of data combinations of fuel flow and air flow; S3, estimating corresponding stability parameters according to the current environmental parameters and each of the data combinations, and obtaining estimated values ​​of the plurality of stability parameters; S4, selecting an estimated value with the smallest difference from the target value among the plurality of estimated values ​​as the most accurate estimated value, and judging whether the difference between the most accurate estimated value and the target value is less than a preset threshold value, if so, executing step S5, otherwise returning to step S2; S5, setting the fuel flow and air flow of the gas turbine for the next time period with the data combination corresponding to the most accurate estimated value.

[0008] In the first round of steps S2 to S4, step S2 includes: obtaining multiple first-round values ​​of fuel flow rates according to the maximum value of the fuel flow rate, the minimum value of the fuel flow rate and the preset first-round first step length of the fuel flow rate; obtaining multiple first-round values ​​of air flow rates according to the maximum value of the air flow rate, the minimum value of the air flow rate and the preset first-round second step length of the air flow rate; combining the first-round values ​​of each fuel flow rate with the first-round values ​​of each air flow rate in pairs to obtain multiple data combinations.

[0009] In the i-th round of steps S2 to S4, step S2 includes: taking the data combination corresponding to the most accurate estimated value in the i-1th round as the benchmark combination, wherein i is an integer greater than 1; extending the fuel flow in the benchmark combination to both ends according to a preset sequence and the first step length of the i-th round to obtain multiple i-th round values ​​of the fuel flow, wherein the first step length of the i-th round is smaller than the first step length of the i-1th round, and the i-th round value of each fuel flow is within the range from the minimum value to the maximum value of the fuel flow; extending the air flow in the benchmark combination to both ends according to a preset sequence and the second step length of the i-th round to obtain multiple i-th round values ​​of the air flow, wherein the second step length of the i-th round is smaller than the second step length of the i-1th round, and the i-th round value of each air flow is within the range from the minimum value to the maximum value of the air flow; combining each i-th round value of the fuel flow with each i-th round value of the air flow in pairs to obtain multiple data combinations.

[0010] The preset sequence is a Lucas sequence. Starting from the first element of the Lucas sequence, n is taken. The fuel flow in the benchmark combination is extended to both ends by adding and subtracting n times the first step length of the i-th round to the fuel flow in the benchmark combination; starting from the first element of the Lucas sequence, n is taken. The air flow in the benchmark combination is extended to both ends by adding and subtracting n times the second step length of the i-th round to the air flow in the benchmark combination.

[0011] In step S3, the current environmental parameters and each of the data combinations are input into a pre-trained deep learning model to output an estimated value of the corresponding stability parameter.

[0012] The environmental parameters include ambient temperature, ambient humidity and air pressure.

[0013] The stability parameter of the gas turbine is a weighted value of one or at least two of the combustion chamber pressure, the turbine exhaust temperature or the rotation speed change rate.

[0014] A gas turbine combustion stability optimization control system comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the gas turbine combustion stability optimization control method is implemented.

[0015] Beneficial effects of the present invention:

[0016] The present invention sets a target value of a stability parameter of a gas turbine, estimates the stability parameter according to a constructed fuel and air flow combination, and determines whether the estimated value of the stability parameter is close to the target value. When the estimated value is close to the target value, the gas turbine is controlled with a corresponding fuel and air flow combination. This can effectively optimize the combustion stability of the gas turbine, thereby effectively ensuring the working efficiency and equipment stability of the gas turbine and extending the life of the gas turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of a method for optimizing the control of combustion stability of a gas turbine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the gas turbine combustion stability optimization control method according to the embodiment of the present invention comprises the following steps:

[0020] S1, obtaining the target values ​​of the current environmental parameters and the stability parameters of the gas turbine.

[0021] The control purpose of the gas turbine combustion stability optimization control method of the embodiment of the present invention is to make the gas turbine maintain combustion stability in multiple consecutive time periods. Steps S1 to S4 are executed in the current time period, and step S5 is executed in the next time period.

[0022] In one embodiment of the present invention, the environmental parameters include environmental temperature, environmental humidity, air pressure, etc. The current environmental parameters may be environmental parameters collected at any time point in the current period.

[0023] The stability parameter of the gas turbine may be a weighted value of one or at least two of the combustion chamber pressure, turbine exhaust temperature or speed change rate. That is, any one or more parameters that can reflect the combustion stability of the gas turbine can be used as the "stability parameter" of the embodiment of the present invention. The target value of the stability parameter of the gas turbine is pre-set, and in theory refers to the stability parameter expected in the next time period, but in fact the target value is constant in multiple consecutive time periods, so in the embodiment of the present invention, the target value only needs to be set once.

[0024] S2, constructing a plurality of fuel flow rate and air flow rate data combinations.

[0025] S3, estimating corresponding stability parameters according to current environmental parameters and each data combination, and obtaining estimated values ​​of multiple stability parameters.

[0026] S4, selecting the estimated value with the smallest difference from the target value among the multiple estimated values ​​as the most accurate estimated value, and determining whether the difference between the most accurate estimated value and the target value is less than a preset threshold. If yes, executing step S5, otherwise returning to step S2.

[0027] Depending on the judgment result of step S4, steps S2 to S4 have one or more execution rounds.

[0028] In an embodiment of the present invention, the difference between the most accurate estimated value and the target value refers to the absolute value of the difference between the two. If the difference is less than a preset threshold, it means that the two are relatively close, that is, the stability parameter estimated based on a data combination of a certain fuel flow rate and air flow rate is relatively close to the target. The preset threshold can be set according to the requirements for the optimization effect of the combustion stability optimization control, the size of the computing power for executing the method of the embodiment of the present invention, etc. It should be understood that the smaller the preset threshold, the better the optimization effect of the combustion stability optimization control, but the execution rounds of steps S2 to S4 may be more, and the computing power required to execute the method of the embodiment of the present invention is greater.

[0029] In the first round of steps S2 to S4, step S2 includes: obtaining multiple first-round values ​​of fuel flow rates according to the maximum value of the fuel flow rate, the minimum value of the fuel flow rate and the preset first-round first step length of the fuel flow rate; obtaining multiple first-round values ​​of air flow rates according to the maximum value of the air flow rate, the minimum value of the air flow rate and the preset first-round second step length of the air flow rate; combining the first-round values ​​of each fuel flow rate with the first-round values ​​of each air flow rate in pairs to obtain multiple data combinations.

[0030] In one embodiment of the present invention, the first step length of the first round can be obtained by dividing the difference between the maximum value and the minimum value of the fuel flow by a certain positive integer, and the second step length of the second round can be obtained by dividing the difference between the maximum value and the minimum value of the air flow by the positive integer. Starting from the minimum value of the fuel flow, the first step length of the first round is increased each time until the maximum value of the fuel flow, and multiple first round values ​​of the fuel flow can be obtained; starting from the minimum value of the air flow, the second step length of the first round is increased each time until the maximum value of the air flow, and multiple first round values ​​of the air flow can be obtained. For example, the rated range of the fuel flow of a gas turbine is 1000g / s to 5000g / s, and the preset first step length of the first round is 500g / s, then the first round values ​​of the multiple fuel flows obtained are 1000g / s, 1500g / s, 2000g / s, 2500g / s, ..., 5000g / s.

[0031] In the i-th round of steps S2 to S4, step S2 includes: taking the data combination corresponding to the most accurate estimated value in the i-1th round as the benchmark combination, wherein i is an integer greater than 1; extending the fuel flow in the benchmark combination to both ends according to a preset sequence and the first step length of the i-th round to obtain multiple i-th round values ​​of the fuel flow, wherein the first step length of the i-th round is smaller than the first step length of the i-1th round, and the i-th round value of each fuel flow is within the range from the minimum value to the maximum value of the fuel flow; extending the air flow in the benchmark combination to both ends according to a preset sequence and the second step length of the i-th round to obtain multiple i-th round values ​​of the air flow, wherein the second step length of the i-th round is smaller than the second step length of the i-1th round, and the i-th round value of each air flow is within the range from the minimum value to the maximum value of the air flow; combining each i-th round value of the fuel flow with each i-th round value of the air flow in pairs to obtain multiple data combinations.

[0032] In one embodiment of the present invention, the preset sequence is a Lucas sequence {1, 3, 4, 7, 11, 18, 29, 47, 76, 123, 199, 322, ...}, and starting from the first element of the Lucas sequence, taking n, the fuel flow in the benchmark combination is extended to both ends by adding and subtracting n times the first step length of the i-th round to the fuel flow in the benchmark combination; starting from the first element of the Lucas sequence, taking n, the air flow in the benchmark combination is extended to both ends by adding and subtracting n times the second step length of the i-th round to the air flow in the benchmark combination.

[0033] It should be understood that as the rounds proceed, the step size gradually decreases. Preferably, the first step size of the i-th round is 1 / 10 of the first step size of the i-1-th round; the second step size of the i-th round is 1 / 10 of the second step size of the i-1-th round.

[0034] For example, assuming that the fuel flow rate in the benchmark combination is 3000g / s, and the first step length of the i-th round is 50g / s, then the i-th round values ​​of multiple fuel flow rates are 3000g / s-50g / s, 3000g / s-3×50g / s, 3000g / s-4×50g / s, 3000g / s-7×50g / s, ... (extending to the minimum value); 3000g / s+50g / s, 3000g / s+3×50g / s, 3000g / s+4×50g / s, 3000g / s+7×50g / s, ... (extending to the maximum value).

[0035] By introducing the extension of the Lucas sequence, the desired data can be approached more quickly, that is, the data combination corresponding to the most accurate estimated value close to the target value can be obtained more quickly.

[0036] In one embodiment of the present invention, in step S3, the current environmental parameters and each data combination are input into a pre-trained deep learning model to output an estimated value of the corresponding stability parameter. The training of the deep learning model can use a large amount of historical data, that is, the environmental parameters and fuel flow and air flow data combinations in the historical data are used as input, and the corresponding stability parameters are used as output to train the deep learning model, so that the deep learning model has the ability to estimate the corresponding stability parameters according to the environmental parameters and data combinations.

[0037] When a most accurate estimated value is obtained in any execution round of steps S2 to S4 and the difference between the estimated value and the target value is less than a preset threshold, the cycle of steps S2 to S4 is terminated and step S5 is executed instead.

[0038] S5, setting the fuel flow and air flow of the gas turbine for the next period of time using the data combination corresponding to the most accurate estimated value.

[0039] The data combination used to estimate the most accurate estimated value is considered to be the optimal combination of fuel flow and air flow that can maintain stable combustion of the gas turbine. Therefore, the fuel flow and air flow of the gas turbine can be set to the values ​​in the data combination in the next period.

[0040] It should be understood that the length of each time period can be set according to the operation speed of executing the method of the embodiment of the present invention, the requirements for the optimization effect of the combustion stability optimization control, etc. For example, in a specific embodiment, each 1 minute can be set as a time period.

[0041] According to the gas turbine combustion stability optimization control method of the embodiment of the present invention, by setting the target value of the stability parameter of the gas turbine, estimating the stability parameter according to the constructed fuel and air flow combination, and judging whether the estimated value of the stability parameter is close to the target value, the gas turbine is controlled with the corresponding fuel and air flow combination when it is close to the target value, thereby effectively optimizing the combustion stability of the gas turbine, thereby effectively ensuring the working efficiency and equipment stability of the gas turbine and extending the life of the gas turbine.

[0042] Corresponding to the gas turbine combustion stability optimization control method of the above-mentioned embodiment, the present invention also proposes a gas turbine combustion stability optimization control system.

[0043] The gas turbine combustion stability optimization control system according to an embodiment of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the gas turbine combustion stability optimization control method according to any of the above embodiments is implemented.

[0044] According to the gas turbine combustion stability optimization control system of the embodiment of the present invention, by setting the target value of the stability parameter of the gas turbine, estimating the stability parameter according to the constructed fuel and air flow combination, and judging whether the estimated value of the stability parameter is close to the target value, the gas turbine is controlled with the corresponding fuel and air flow combination when it is close to the target value. In this way, the combustion stability of the gas turbine can be effectively optimized, thereby effectively ensuring the working efficiency and equipment stability of the gas turbine and extending the life of the gas turbine.

[0045] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0046] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0048] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0049] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0050] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0051] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0052] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0053] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0054] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing combustion stability of a gas turbine, characterized in that: The following steps are involved: S1, obtaining current environmental parameters and target values ​​of stability parameters of the gas turbine; S2, constructing a plurality of fuel flow and air flow data combinations; S3, estimating corresponding stability parameters according to the current environmental parameters and each of the data combinations to obtain estimated values ​​of multiple stability parameters; S4, selecting the estimated value with the smallest difference from the target value among the multiple estimated values ​​as the most accurate estimated value, and determining whether the difference between the most accurate estimated value and the target value is less than a preset threshold, if so, executing step S5, otherwise returning to step S2; S5, setting the fuel flow and air flow of the gas turbine in the next time period according to the data combination corresponding to the most accurate estimated value.

2. The method for optimizing combustion stability of a gas turbine according to claim 1, characterized in that: In the first round of steps S2 to S4, step S2 includes: According to the maximum value of the fuel flow rate, the minimum value of the fuel flow rate and the preset first step length of the first round of the fuel flow rate, the first round values ​​of the plurality of fuel flow rates are obtained; According to the maximum value of the air flow, the minimum value of the air flow and the preset first round second step length of the air flow, a plurality of first round values ​​of the air flow are obtained; The first round values ​​of each fuel flow rate and the first round values ​​of each air flow rate are combined in pairs to obtain multiple data combinations.

3. The method for optimizing combustion stability of a gas turbine according to claim 2, characterized in that: In the i-th round of steps S2 to S4, step S2 includes: The data combination corresponding to the most accurate estimated value in the i-1th round is used as the benchmark combination, where i is an integer greater than 1; Extending the fuel flow in the reference combination to both ends according to a preset sequence and the first step length of the i-th round to obtain a plurality of i-th round values ​​of the fuel flow, wherein the first step length of the i-th round is smaller than the first step length of the i-1-th round, and the i-th round value of each fuel flow is within the range from the minimum value to the maximum value of the fuel flow; The air flow in the reference combination is extended to both ends in a preset sequence and an i-th round second step length to obtain a plurality of i-th round values ​​of the air flow, wherein the i-th round second step length is smaller than the i-1-th round second step length, and the i-th round value of each air flow is within the range from the minimum value to the maximum value of the air flow; The i-th round values ​​of each fuel flow rate and the i-th round values ​​of each air flow rate are combined in pairs to obtain multiple data combinations.

4. The method for optimizing combustion stability control of a gas turbine according to claim 3, characterized in that: The preset sequence is a Lucas sequence, starting from the first element of the Lucas sequence, taking n, and extending the fuel flow in the benchmark combination to both ends by adding and subtracting n times the first step length of the i-th round respectively; Starting from the first element of the Lucas sequence, n are taken, and the air flow in the benchmark combination is extended to both ends by adding and subtracting n times the second step length of the i-th round respectively.

5. The method for optimizing combustion stability control of a gas turbine according to claim 1, characterized in that: In step S3, the current environmental parameters and each of the data combinations are input into a pre-trained deep learning model to output an estimated value of the corresponding stability parameter.

6. The method for optimizing combustion stability of a gas turbine according to claim 1, characterized in that: The environmental parameters include ambient temperature, ambient humidity and air pressure.

7. The method for optimizing combustion stability control of a gas turbine according to claim 1, characterized in that: The stability parameter of the gas turbine is a weighted value of one or at least two of the combustion chamber pressure, the turbine exhaust temperature or the rotation speed change rate.

8. A gas turbine combustion stability optimization control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the gas turbine combustion stability optimization control method according to any one of claims 1 to 7 is implemented.

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