Load control method and device for gas turbine generator set

Abstract

The invention relates to the technical field of power generation load control, and provides a load control method and device for a gas turbine generator set, and the method comprises the following steps: obtaining load control parameters of the gas turbine generator set in a current time period, the theoretical load of the gas turbine generator set in the current time period is obtained according to the load control parameters; the real load of the gas turbine generator set in the current time period is obtained; calculating the set load of the gas turbine generator set in the next time period according to the theoretical load and the real load of the gas turbine generator set in the current time period; and in the next time period, the set load serves as a reference value of a PID control system from the starting moment of the next time period, and the gas turbine generator set is controlled. The method can improve the accuracy of the load control target of the gas turbine generator set, and effectively reduces the lagging of the load control of the gas turbine generator set.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation load control, and in particular to a method and device for controlling the load of a gas turbine generator set. Background Art

[0002] Gas turbine power generation technology boasts advantages such as high efficiency, low emissions, and strong flexibility, and plays a vital role in the development of clean energy. A gas turbine primarily consists of a compressor, a combustion chamber, and a turbine. Its basic operating process is as follows: the compressor draws in air from the external atmosphere, which is then compressed and supercharged by an axial flow compressor, raising the air temperature accordingly. The compressed air is then pumped into the combustion chamber, where it mixes with the injected fuel and burns to produce high-temperature, high-pressure gas. The gas then enters the turbine, where it expands and generates work, driving the turbine to drive the compressor and the external load rotor at high speed, converting part of the chemical energy of the gas or liquid fuel into mechanical work, which is then output as electrical work.

[0003] Gas turbine generator sets respond slowly to load changes due to factors such as unit inertia and control system delays. Consequently, there is a significant lag in load control, making it difficult to achieve immediate load stabilization in load stability control schemes. Therefore, incorporating relevant prediction technologies to predict the expected load of a gas turbine generator set based on its control parameters can effectively compensate for this lag. However, gas turbines are not particularly sophisticated equipment, and the quality of their fuel and air is difficult to ensure constant. Therefore, the related art, which uses control parameters that have not yet occurred to predict the load results of a gas turbine generator set, clearly cannot guarantee accuracy. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method and device for controlling the load of a gas turbine generator set, which can improve the accuracy of the load control target of the gas turbine generator set and effectively reduce the lag of the load control of the gas turbine generator set.

[0005] The technical solution adopted in the present invention is as follows:

[0006] A method for controlling a load of a gas turbine generator set comprises the following steps: obtaining a load control parameter of the gas turbine generator set in a current time period, and obtaining a theoretical load of the gas turbine generator set in the current time period based on the load control parameter; obtaining an actual load of the gas turbine generator set in the current time period; calculating a set load of the gas turbine generator set in a next time period based on the theoretical load and the actual load of the gas turbine generator set in the current time period; and controlling the gas turbine generator set in the next time period, starting from the start time of the next time period, using the set load as a reference value of a PID (Proportional Integral Derivative) control system.

[0007] The load control parameters include one or more of fuel flow, air flow, and turbine blade angle.

[0008] The load control parameters are input into a deep learning prediction model to predict the theoretical load of the gas turbine generator set in the current period.

[0009] The set load of the gas turbine generator set in the next time period is calculated based on the theoretical load and actual load of the gas turbine generator set in the current time period, specifically including: calculating the theoretical credibility coefficient based on the difference between the actual load and the theoretical load of the gas turbine generator set in the current time period and the rated load of the gas turbine generator set; and calculating the set load of the gas turbine generator set in the next time period based on the theoretical load, actual load and the theoretical credibility coefficient of the gas turbine generator set in the current time period.

[0010] The theoretical credibility coefficient is calculated by the following formula:

[0011]

[0012] Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 They are respectively the real load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set.

[0013] The set load of the gas turbine generator set in the next period is calculated by the following formula:

[0014] L 设 =λL 理 +(1-λ)L 真

[0015] Among them, L 设 is the set load of the gas turbine generator set in the next period.

[0016] A load control device for a gas turbine generator set comprises: a first acquisition module, the first acquisition module being used to acquire the load control parameters of the gas turbine generator set in a current period, and to acquire the theoretical load of the gas turbine generator set in the current period based on the load control parameters; a second acquisition module, the second acquisition module being used to acquire the actual load of the gas turbine generator set in the current period; a calculation module, the calculation module being used to calculate the set load of the gas turbine generator set in the next period based on the theoretical load and the actual load of the gas turbine generator set in the current period; and a control module, the control module being used to control the gas turbine generator set in the next period, starting from the start time of the next period, using the set load as a reference value of a PID control system.

[0017] The load control parameters include one or more of fuel flow, air flow, and turbine blade angle.

[0018] The first acquisition module inputs the load control parameter into a deep learning prediction model to predict the theoretical load of the gas turbine generator set in the current period.

[0019] The calculation module is specifically used to: calculate the theoretical credibility coefficient based on the difference between the actual load and the theoretical load of the gas turbine generator set in the current period and the rated load of the gas turbine generator set; calculate the set load of the gas turbine generator set in the next period based on the theoretical load, actual load and the theoretical credibility coefficient of the gas turbine generator set in the current period.

[0020] The theoretical credibility coefficient is calculated by the following formula:

[0021]

[0022] Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 They are respectively the real load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set.

[0023] The set load of the gas turbine generator set in the next period is calculated by the following formula:

[0024] L 设 =λL 理 +(1-λ)L 真

[0025] Among them, L 设 is the set load of the gas turbine generator set in the next period.

[0026] Beneficial effects of the present invention:

[0027] The present invention obtains a theoretical load based on the load control parameters of the gas turbine generator set in the current period, and calculates a set load for the next period based on the theoretical load and the actual load of the gas turbine generator set in the current period, so as to perform control based on the set load in the next period. That is, the theoretical value of the current load is obtained based on the actual control parameters that have occurred currently, and the load control target at the next moment is predicted in combination with the actual value of the current load. Then, load control is performed based on the target starting from the next moment. As a result, the accuracy of the load control target of the gas turbine generator set can be improved, and the lag of the load control of the gas turbine generator set can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for controlling a gas turbine generator set load according to an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of a load control device for a gas turbine generator set according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, the gas turbine generator set load control method according to the embodiment of the present invention includes the following steps:

[0032] S1, obtaining a load control parameter of the gas turbine generator set in the current period, and obtaining a theoretical load of the gas turbine generator set in the current period according to the load control parameter.

[0033] The load control parameters in the embodiment of the present invention include all parameters that can be controlled in real time and affect the load of the gas turbine generator set through control, for example, they may include one or more of fuel flow, air flow, and turbine blade angle.

[0034] It should be understood that the load of the gas turbine generator set is correlated with the aforementioned load control parameters. In one embodiment of the present invention, a mathematical model encompassing the relationship between load and load control parameters can be pre-constructed. After the load control parameters are collected during the current period, the theoretical load of the gas turbine generator set during the current period can be calculated using this mathematical model. In another embodiment of the present invention, a deep learning prediction model can be pre-trained using a large amount of sample data. This deep learning prediction model uses the load control parameters as input and the load as output. After the load control parameters are collected during the current period, the load control parameters are input into the trained deep learning prediction model to predict the theoretical load of the gas turbine generator set during the current period.

[0035] S2, obtain the actual load of the gas turbine generator set in the current period.

[0036] The actual load of the gas turbine generator set during the current period can be obtained through detection. The detection time can be the same time point or time period as the collection of the load control parameters in step S1. However, it should be understood that there should be a certain time interval between the end time point of the actual load detection and the collection of the load control parameters and the start time of the next period to reserve time for the calculation and processing of step S3.

[0037] In an embodiment of the present invention, the division of time periods can be set according to the computing power conditions for executing the method of the embodiment of the present invention and the requirements for the quality of load control. Optionally, each time period can be any value between 10s and 30s.

[0038] S3, calculating a set load of the gas turbine generator set in the next period according to the theoretical load and the actual load of the gas turbine generator set in the current period.

[0039] Specifically, the theoretical credibility coefficient can be calculated based on the difference between the actual load and the theoretical load of the gas turbine generator set in the current period and the rated load of the gas turbine generator set, and the set load of the gas turbine generator set in the next period can be calculated based on the theoretical load, actual load and theoretical credibility coefficient of the gas turbine generator set in the current period.

[0040] Specifically, the theoretical credibility coefficient is calculated by the following formula:

[0041]

[0042] Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 They are respectively the actual load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set.

[0043] The set load of the gas turbine generator set in the next period is calculated by the following formula:

[0044] L 设 =λL 理 +(1-λ)L 真

[0045] Among them, L 设 The set load of the gas turbine generator set in the next period.

[0046] The essential meaning of the theoretical credibility coefficient λ in the embodiment of the present invention is that the closer the actual load of the gas turbine generator set in the current period is to the theoretical load, the more credible the theoretical load is, and the larger the theoretical credibility coefficient is, the greater the proportion of the theoretical load in the calculation formula for obtaining the set load in the next period.

[0047] S4, in the next period, starting from the start time of the next period, the gas turbine generator set is controlled using the set load as a reference value of the PID control system.

[0048] The PID control system in the embodiment of the present invention is used to control the load of the gas turbine generator set. In the next period, the PID control system uses the detected load as a feedback value and the set load calculated in step S3 as a reference value.

[0049] In summary, the load control method for a gas turbine generator set in an embodiment of the present invention obtains a theoretical load based on the load control parameters of the gas turbine generator set in the current time period, and calculates a set load for the next time period based on the theoretical load and the actual load of the gas turbine generator set in the current time period, so that control is performed based on the set load in the next time period. In other words, the theoretical value of the current load is obtained based on the actual control parameters that have currently occurred, and combined with the actual value of the current load, the load control target at the next moment is predicted, and load control is performed based on the target starting from the next moment. In this way, the accuracy of the load control target of the gas turbine generator set can be improved, and the lag of the load control of the gas turbine generator set can be effectively reduced.

[0050] Corresponding to the gas turbine generator set load control method of the above embodiment, the present invention further provides a gas turbine generator set load control device.

[0051] like Figure 2As shown, the load control device for a gas turbine generator set according to an embodiment of the present invention includes a first acquisition module 10, a second acquisition module 20, a calculation module 30, and a control module 40, wherein the first acquisition module 10 is used to obtain the load control parameters of the gas turbine generator set in the current period, and obtain the theoretical load of the gas turbine generator set in the current period based on the load control parameters; the second acquisition module 20 is used to obtain the actual load of the gas turbine generator set in the current period; the calculation module 30 is used to calculate the set load of the gas turbine generator set in the next period based on the theoretical load and the actual load of the gas turbine generator set in the current period; and the control module 40 is used to control the gas turbine generator set in the next period, starting from the start time of the next period, using the set load as a reference value of the PID control system.

[0052] The load control parameters in the embodiment of the present invention include all parameters that can be controlled in real time and affect the load of the gas turbine generator set through control, for example, they may include one or more of fuel flow, air flow, and turbine blade angle.

[0053] It should be understood that the load of the gas turbine generator set is correlated with the aforementioned load control parameters. In one embodiment of the present invention, a mathematical model containing the relationship between the load and the load control parameters can be pre-constructed. After the first acquisition module 10 collects the load control parameters in the current period, the mathematical model is used to calculate the theoretical load of the gas turbine generator set in the current period. In another embodiment of the present invention, a deep learning prediction model can be pre-trained using a large amount of sample data. The deep learning prediction model uses the load control parameters as input and the load as output. After the first acquisition module 10 collects the load control parameters in the current period, the load control parameters are input into the trained deep learning prediction model to predict the theoretical load of the gas turbine generator set in the current period.

[0054] The second acquisition module 20 can obtain the actual load of the gas turbine generator set during the current period through detection. The detection time can be the same time point or time period as the first acquisition module 10 collecting the load control parameters. However, it should be understood that there should be a certain time interval between the end time point of detecting the actual load and collecting the load control parameters and the start time of the next period to reserve processing time for the calculation module 30.

[0055] In an embodiment of the present invention, the division of time periods can be set according to the computing power conditions of the device of the embodiment of the present invention and the requirements for the quality of load control. Optionally, each time period can be any value between 10s and 30s.

[0056] The calculation module 30 can specifically calculate the theoretical credibility coefficient based on the difference between the actual load and the theoretical load of the gas turbine generator set in the current period and the rated load of the gas turbine generator set, and calculate the set load of the gas turbine generator set in the next period based on the theoretical load, actual load and theoretical credibility coefficient of the gas turbine generator set in the current period.

[0057] Specifically, the theoretical credibility coefficient is calculated by the following formula:

[0058]

[0059] Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 They are respectively the actual load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set.

[0060] The set load of the gas turbine generator set in the next period is calculated by the following formula:

[0061] L 设 =λL 理 +(1-λ)L 真

[0062] Among them, L 设 The set load of the gas turbine generator set in the next period.

[0063] The essential meaning of the theoretical credibility coefficient λ in the embodiment of the present invention is that the closer the actual load of the gas turbine generator set in the current period is to the theoretical load, the more credible the theoretical load is, and the larger the theoretical credibility coefficient is, the greater the proportion of the theoretical load in the calculation formula for obtaining the set load in the next period.

[0064] The PID control system in the embodiment of the present invention is used to control the load of the gas turbine generator set. In the next period, the PID control system uses the detected load as a feedback value and the set load calculated by the calculation module 30 as a reference value.

[0065] In summary, the load control device for a gas turbine generator set in an embodiment of the present invention obtains a theoretical load based on the load control parameters of the gas turbine generator set in the current period, and calculates a set load for the next period based on the theoretical load and the actual load of the gas turbine generator set in the current period, so as to perform control based on the set load in the next period. That is, the theoretical value of the current load is obtained based on the actual control parameters that have occurred, and combined with the actual value of the current load, the load control target at the next moment is predicted, and load control is performed based on the target starting from the next moment. In this way, the accuracy of the load control target of the gas turbine generator set can be improved, and the lag of the load control of the gas turbine generator set can be effectively reduced.

[0066] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. "Multiple" means two or more, unless otherwise specifically defined.

[0067] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0068] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0069] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0070] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising 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 be performed out of 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 pertain.

[0071] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (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 disc read-only memory (CDROM). Furthermore, 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 it in another suitable manner if necessary, and then storing it in a computer memory.

[0072] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0073] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0074] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0075] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for controlling a load of a gas turbine generator set, characterized in that: The following steps are involved: Obtaining a load control parameter of the gas turbine generator set in the current period, and obtaining a theoretical load of the gas turbine generator set in the current period according to the load control parameter; Obtaining the actual load of the gas turbine generator set in the current period; Calculating a set load of the gas turbine generator set in the next period according to the theoretical load and the actual load of the gas turbine generator set in the current period; In the next time period, starting from the start time of the next time period, the set load is used as a reference value of the PID control system to control the gas turbine generator set.

2. The gas turbine generator set load control method according to claim 1, characterized in that: The load control parameter includes one or more of a fuel flow rate, an air flow rate, and a turbine blade angle.

3. The gas turbine generator set load control method according to claim 1 or 2, characterized in that: The load control parameters are input into a deep learning prediction model to predict the theoretical load of the gas turbine generator set in the current period.

4. The gas turbine generator set load control method according to claim 1, characterized in that: Calculating the set load of the gas turbine generator set in the next period according to the theoretical load and the real load of the gas turbine generator set in the current period specifically includes: Calculating a theoretical credibility coefficient according to the difference between the actual load and the theoretical load of the gas turbine generator set in the current period and the rated load of the gas turbine generator set; The set load of the gas turbine generator set in the next time period is calculated according to the theoretical load, the actual load and the theoretical credibility coefficient of the gas turbine generator set in the current time period.

5. The gas turbine generator set load control method according to claim 4, characterized in that: The theoretical credibility coefficient is calculated by the following formula: Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 are respectively the actual load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set, The set load of the gas turbine generator set in the next period is calculated by the following formula: THE 设 =λL 理 +(1-λ)L 真 Among them, L 设 It is the set load of the gas turbine generator set in the next period.

6. A gas turbine generator set load control device, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire a load control parameter of the gas turbine generator set in a current period, and acquire a theoretical load of the gas turbine generator set in the current period according to the load control parameter; A second acquisition module, the second acquisition module is used to obtain the real load of the gas turbine generator set in the current period; A calculation module, the calculation module is used to calculate the set load of the gas turbine generator set in the next time period according to the theoretical load and the real load of the gas turbine generator set in the current time period; A control module is used to control the gas turbine generator set in the next time period starting from the start time of the next time period, using the set load as a reference value of the PID control system.

7. The gas turbine generator set load control device according to claim 6, characterized in that: The load control parameter includes one or more of a fuel flow rate, an air flow rate, and a turbine blade angle.

8. The gas turbine generator set load control device according to claim 6 or 7, characterized in that: The first acquisition module inputs the load control parameter into a deep learning prediction model to predict the theoretical load of the gas turbine generator set in the current period.

9. The gas turbine generator set load control device according to claim 6, characterized in that: The calculation module is specifically used for: Calculating a theoretical credibility coefficient according to the difference between the actual load and the theoretical load of the gas turbine generator set in the current period and the rated load of the gas turbine generator set; The set load of the gas turbine generator set in the next time period is calculated according to the theoretical load, the actual load and the theoretical credibility coefficient of the gas turbine generator set in the current time period.

10. The gas turbine generator set load control device according to claim 9, characterized in that: The theoretical credibility coefficient is calculated by the following formula: Among them, λ is the theoretical credibility coefficient, L 真 , L 理 , L 额 are respectively the actual load of the gas turbine generator set in the current period, the theoretical load of the gas turbine generator set in the current period, and the rated load of the gas turbine generator set, The set load of the gas turbine generator set in the next period is calculated by the following formula: THE 设 =λL 理 +(1-λ)L 真 Among them, L 设 It is the set load of the gas turbine generator set in the next period.

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