Hydrogen blending adaptive control method and system for gas turbine pressure regulating station

Abstract

The application discloses a hydrogen mixing self-adaptive control method and system for a gas turbine pressure regulating station and belongs to the technical field of gas turbine power generation. The method comprises the following steps: determining a low calorific value requirement of mixed fuel based on real-time collected gas turbine load demand, solving an optimal hydrogen mixing ratio meeting the low calorific value requirement of the mixed fuel through a thermodynamic calculation model based on real-time collected low calorific values of natural gas and hydrogen, and outputting an optimal hydrogen mixing ratio target value after safety boundary constraint verification; and taking the optimal hydrogen mixing ratio target value as a set value, adopting a PID control algorithm to adjust the valve opening of the natural gas branch and adopting a feedforward-feedback compound control algorithm to adjust the valve opening of the hydrogen branch, so that the actual hydrogen mixing ratio tracks the target value. Through the cooperative matching of thermodynamic equilibrium solving and two-path differentiated control, the application realizes real-time and accurate matching of the hydrogen mixing ratio to the gas turbine load demand, and has the advantages of fast response, stable regulation, high safety and the like.

Description

Technical Field

[0001] This invention relates to the field of gas turbine power generation technology, specifically to a hydrogen-incorporated adaptive control method and system for a gas turbine pressure regulating station. Background Technology

[0002] Hydrogen blending in gas turbines is a crucial pathway to achieving low-carbon transformation in the power industry. Utilizing existing natural gas pipelines for hydrogen blending and transportation, and completing the mixing and regulation of natural gas and hydrogen within a pressure regulating station, is a key preliminary step in realizing hydrogen blending combustion. However, traditional gas turbine pressure regulating stations are primarily designed for pure natural gas and face the following technical challenges in hydrogen blending applications.

[0003] First, hydrogen and natural gas differ significantly in density, calorific value, and combustion characteristics, making a fixed hydrogen blending ratio unsuitable for adapting to dynamic changes in gas turbine load. When the load fluctuates, if the hydrogen blending ratio remains constant, the calorific value of the mixed fuel will deviate from the current requirements of the gas turbine, leading to unstable combustion or even flameout. Second, the valve response characteristics of natural gas and hydrogen pipelines differ, making it difficult to balance the regulation accuracy and response speed of both flow lines using a unified control strategy, easily resulting in regulation lag or overshoot. Furthermore, hydrogen is prone to leakage and explosiveness; existing safety protection measures and control processes are independent, leading to delayed protection actions under abnormal operating conditions and posing safety hazards. These issues restrict the increase of the hydrogen blending ratio and the safe and stable operation of the pressure regulating station. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a hydrogen blending adaptive control method and system for a gas turbine pressure regulating station. The optimal hydrogen blending ratio is dynamically solved through a thermodynamic calculation model and a model predictive control algorithm. A feedforward-feedback composite control strategy is adopted to coordinate the flow regulation of natural gas and hydrogen, thereby achieving accurate tracking and rapid response of the hydrogen blending ratio to the gas turbine load demand.

[0005] This invention is achieved through the following technical solution: In a first aspect, this application provides a hydrogen-incorporated adaptive control method for a gas turbine pressure regulating station, comprising the following steps: The lower heating value requirement of the mixed fuel is determined based on the real-time collected gas turbine load demand. Based on the real-time collected lower heating values ​​of natural gas and hydrogen, the optimal hydrogen blending ratio to meet the lower heating value requirement of the mixed fuel is solved by a thermodynamic calculation model. After verification by safety boundary constraints, the target value of the optimal hydrogen blending ratio is output. Using the target value of the optimal hydrogen blending ratio as a setpoint, the valve opening is adjusted by a PID control algorithm for the natural gas branch and by a feedforward-feedback composite control algorithm for the hydrogen branch, so that the actual hydrogen blending ratio tracks the target value.

[0006] Preferably, the thermodynamic calculation model is as follows: α=k·(LHV 需求 LHV NG ) / (LHV H2 LHV NG ) Where k is the gas turbine correction factor; LHV 需求 The lower heating value (LHV) of the fuel required by the gas turbine under current load. NG The lower heating value (LHV) of natural gas. H2 This is the lower heating value of hydrogen.

[0007] Preferably, the safety boundary constraint verification includes: The optimal hydrogen doping ratio obtained by the solution is compared with the preset upper limit of the hydrogen doping ratio. When the upper limit of the hydrogen doping ratio is exceeded, the hydrogen doping ratio is corrected to the upper limit of the hydrogen doping ratio.

[0008] Preferably, in the feedforward-feedback composite control of the hydrogen branch, the feedforward channel uses the hydrogen doping ratio prediction change curve generated by the model predictive control algorithm to calculate the advance compensation amount, and the feedback channel corrects the deviation between the real-time hydrogen flow rate and the target flow rate.

[0009] Preferably, the lower heating value of the natural gas and the lower heating value of the hydrogen are obtained by the following method: Prioritize extraction from online analytical instruments in real time. When online analytical instruments are unavailable, retrieve fuel characteristics from preset databases based on natural gas source type and hydrogen source type, respectively.

[0010] Preferably, during the adjustment process, the hydrogen concentration of the mixed fuel is monitored in real time by a hydrogen concentration analyzer at the outlet of the Venturi mixer, and the flow rate of the hydrogen branch is corrected when the measured value deviates from the target value.

[0011] Preferably, the model predictive control algorithm uses historical operating data and current operating condition change trends to generate a rolling prediction curve of the hydrogen doping ratio within a future preset time window, and the feedforward channel calculates the advance compensation amount of the hydrogen regulating valve opening based on the rate of change of the prediction curve at each moment.

[0012] Preferably, the gas turbine correction coefficient k is adaptively adjusted in real time based on the gas turbine exhaust temperature and the dynamic pressure of the combustion chamber, so that the optimal hydrogen blending ratio is dynamically corrected according to the actual operating state of the gas turbine.

[0013] Secondly, this application provides a hydrogen-blending adaptive control system for a gas turbine pressure regulating station, comprising: The data acquisition module is used to determine the lower heating value requirement of the mixed fuel based on the real-time collected gas turbine load demand. The hydrogen blending ratio calculation module is used to solve the optimal hydrogen blending ratio that meets the low heating value requirements of the mixed fuel by using a thermodynamic calculation model based on the real-time collected low heating values ​​of natural gas and hydrogen. After verification by safety boundary constraints, the module outputs the target value of the optimal hydrogen blending ratio. The flow regulation module is used to adjust the valve opening of the natural gas branch using a PID control algorithm and the hydrogen branch using a feedforward-feedback composite control algorithm, with the target value of the optimal hydrogen blending ratio as the set value, so that the actual hydrogen blending ratio tracks the target value.

[0014] Thirdly, this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the hydrogen blending adaptive control method for the gas turbine pressure regulating station as described above.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This application provides a hydrogen blending adaptive control method for a gas turbine pressure regulating station. The determination of the hydrogen blending ratio is established as an energy balance problem. This method takes the gas turbine load demand as input, first determines the required lower heating value of the mixed fuel, then combines the real-time acquired lower heating values ​​of natural gas and hydrogen to solve for the optimal hydrogen blending ratio, and outputs the target value after safety boundary verification. Thus, the hydrogen blending ratio is dynamically adjusted according to the load, solving the problem in the prior art where a fixed hydrogen blending ratio cannot adapt to load fluctuations. Regarding flow control, considering the differences in flow scale and response characteristics between natural gas and hydrogen, PID control and feedforward-feedback composite control are adopted respectively. The former ensures the stability of the main natural gas flow regulation, while the latter enables the hydrogen branch to quickly compensate for load changes and pressure disturbances. The combination of these two methods solves the problems of regulation lag and insufficient dynamic accuracy in the prior art. The safety boundary verification is embedded in the calculation process, ensuring that the hydrogen blending ratio is constrained and protected before output, avoiding the risk of separation between safety protection and regulation. By combining thermodynamic solutions, dual-path differentiated control, and built-in safety boundary verification, this method enables real-time matching of calorific value demand under load changes and hydrogen supply fluctuations, adjusting in a way that balances stability and speed, with safety constraints built into the decision-making process.

[0016] This application also proposes a hydrogen-blending adaptive control system for a gas turbine pressure regulating station, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned hydrogen-blending adaptive control method for gas turbine pressure regulating stations. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the hydrogen-adaptive control method for the gas turbine pressure regulating station of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] like Figure 1 As shown, a hydrogen-blending adaptive control method for a gas turbine pressure regulating station includes the following steps: Step 1: Perform airtightness testing, sensor calibration, valve testing, and communication verification according to preset safety standards and communication protocols, and output a system ready status signal; The specific implementation method of step 1 will be described in detail below.

[0022] S11: Inert gas is introduced into the natural gas and hydrogen pipelines, pressurized to a preset multiple of the working pressure, and held at the pressure for a preset time. The pressure drop at each connection point of the pipeline is monitored. When the pressure drop is less than the preset threshold, the pipeline is deemed to be airtight.

[0023] S12: Perform zero-point calibration and range verification on all sensors, including pressure transmitters, temperature sensors, flow meters, and hydrogen concentration analyzers. Calibrate the measurement errors of each sensor to a preset accuracy range.

[0024] S13: Perform a full-stroke operation test on all regulating valves and shut-off valves in the system, and record the action time and positioning accuracy of each valve. Verify whether the response time of the emergency shut-off device meets the safety design requirements.

[0025] In some embodiments, the pressure of the airtightness test is 1.5 times the working pressure, and the pressure holding time is 30 minutes.

[0026] In some embodiments, the sensor calibration error is controlled within ±0.3% of full scale. The communication delay does not exceed five milliseconds.

[0027] Through a systematic initialization and self-test process, the performance, measurement accuracy, and reliability of communication links of each hardware device are comprehensively verified before the system is officially put into operation. This ensures that all components are in normal working order before the system enters real-time control mode, eliminating control deviations or safety risks caused by hardware failures or communication anomalies at the source, and providing a reliable foundation for subsequent accurate data acquisition and safe operation.

[0028] Step 2: Based on the sensor network distributed at each key node of the pressure regulating station, the gas turbine load demand, fuel status and environmental parameters are continuously collected by setting a graded sampling frequency to obtain a multi-dimensional real-time operation dataset.

[0029] The specific implementation method of step 2 will be described in detail below.

[0030] S21: The gas turbine load demand signal is acquired in real time from the gas turbine control system via hardwiring or communication protocol at a first sampling frequency. This signal directly reflects the fuel calorific value demand of the gas turbine in the current and short timescale.

[0031] S22: The pressure, temperature, instantaneous flow rate, and composition information of natural gas and hydrogen are collected at a second sampling frequency through pressure transmitters, temperature sensors, flow meters, and online calorific value analyzers installed on natural gas and hydrogen pipelines.

[0032] S23: At the outlet of the Venturi mixer, the hydrogen concentration and calorific value of the mixed fuel gas are collected at a third sampling frequency. Simultaneously, parameters such as ambient temperature, atmospheric pressure, and humidity are collected at a fourth sampling frequency for compensation calculations of the sensor measurements.

[0033] S24: All collected data form a structured, multidimensional, real-time running dataset.

[0034] Preferably, the first sampling frequency is 100 Hz, the second sampling frequency is 50 Hz, the third sampling frequency is 20 Hz, and the fourth sampling frequency is 10 Hz.

[0035] The communication network can use industrial real-time Ethernet protocols such as PROFINET or EtherCAT to ensure deterministic data transmission and low latency.

[0036] By setting sampling frequencies in a tiered manner, targeted and efficient acquisition of process quantities at different time scales was achieved. High-frequency acquisition of gas turbine load signals ensured that the system could promptly detect rapid changes in combustion calorific value requirements; while medium- and low-frequency acquisition of fuel state and environmental parameters met the monitoring needs of relatively slow-changing processes. This tiered acquisition strategy optimized system resource utilization while ensuring data real-time performance and integrity. The collaborative acquisition of multi-dimensional data provided a comprehensive information foundation for subsequent calculations of the optimal hydrogen blending ratio.

[0037] Step 3: Based on the multidimensional real-time running dataset, and through thermodynamic calculation models and model predictive control algorithms, dynamically calculate the target value of the optimal hydrogen blending ratio that meets the calorific value requirements of the gas turbine load, adapts to hydrogen supply fluctuations, and takes into account safety constraints.

[0038] The specific implementation method of step 3 will be described in detail below.

[0039] S31: Obtain composition information and lower heating value of natural gas and hydrogen based on multidimensional real-time running dataset; There are two ways to obtain information on the composition and lower heating value of natural gas and hydrogen: The first method is to acquire information online in real time, extracting real-time composition information and real-time lower heating value of natural gas, as well as the purity and lower heating value of hydrogen from the collected real-time operational dataset. The second method is offline database query. When the online analysis instrument is faulty, under maintenance, or the data quality does not meet the preset reliability requirements, the preset natural gas fuel characteristic database is queried according to the type of natural gas source currently used to obtain the typical components and lower heating value corresponding to the gas source. Similarly, the preset hydrogen fuel characteristic database is queried according to the type of hydrogen source currently used to obtain the purity and lower heating value corresponding to the hydrogen source, which are used as alternative characteristic parameters in the calculation.

[0040] S32: Based on the real-time collected gas turbine load demand signal, the lower heating value of the mixed fuel required by the gas turbine under the current load is calculated using a load-calorific value demand mapping model. This load-calorific value demand mapping model is calibrated based on the performance curves provided by the gas turbine manufacturer and on-site commissioning data.

[0041] S33: Input the required lower heating values ​​of the blended fuel, natural gas, and hydrogen into the thermodynamic calculation model to calculate the theoretical hydrogen blending ratio that meets the calorific value requirements. Based on this, a gas turbine correction factor is introduced to correct the theoretical hydrogen blending ratio, thus obtaining the target value of the optimal hydrogen blending ratio.

[0042] The gas turbine correction coefficient is dynamically adjusted based on the actual operating efficiency, emission levels, and combustion stability requirements of the gas turbine.

[0043] The formula for calculating the optimal hydrogen doping ratio is: α=k·(LHV 需求 LHV NG ) / (LHV H2 LHV NG ) Where k is the gas turbine correction factor (0.8~1.2); LHV 需求 LHV is the lower heating value (in kJ / Nm³ or MJ / kg) of the fuel required by the gas turbine under current load. NG The lower heating value (LHV) of natural gas (unit: kJ / Nm³ or MJ / kg). H2 The lower heating value of hydrogen (unit: kJ / Nm³ or MJ / kg).

[0044] S34: Compare the optimal hydrogen doping ratio with the preset safe operating boundary. When the calculated hydrogen doping ratio exceeds the safe boundary, correct the hydrogen doping ratio to the boundary value to ensure that the target value of the hydrogen doping ratio is always within the safe operating envelope.

[0045] The safe operating boundaries include the upper limit of hydrogen blending ratio, the allowable range of pressure fluctuations, and the limit of hydrogen concentration change rate.

[0046] S35: Based on the model predictive control algorithm, it uses historical operating data and current operating condition trends to generate a predicted change curve of the hydrogen doping ratio within a future preset time window.

[0047] In some embodiments, the load-calorific value demand mapping model may also employ a machine learning-based regression model to obtain a more accurate mapping relationship through training on historical operating data.

[0048] In some embodiments, the gas turbine correction coefficient k can also be adaptively adjusted in real time based on stability indicators such as gas turbine exhaust temperature and combustion chamber dynamic pressure.

[0049] In some embodiments, the model predictive control algorithm may also employ a dynamic matrix control algorithm or a generalized predictive control algorithm.

[0050] By combining thermodynamic balance calculations with safety boundary constraints, multi-dimensional real-time operating data is transformed into a clear and actionable target value for the optimal hydrogen blending ratio. Its core principles are: First, accurate calculations based on the lower heating value of the fuel ensure that the calorific value of the blended fuel after hydrogen blending always precisely matches the gas turbine load demand, guaranteeing combustion stability from the outset. Second, the introduction of a gas turbine correction coefficient and online calorific value feedback enables the system to adapt to slow changes in gas turbine performance and fluctuations in fuel composition, avoiding the accumulation of deviations caused by rigid empirical formulas. Third, a safety boundary constraint verification mechanism provides a robust safety net for the calculation results, ensuring that optimization never exceeds the safety limit. Fourth, the generation of predictive curves provides forward-looking guidance for subsequent valve adjustments, upgrading the system from "passive tracking" to "active prediction," significantly improving response speed and stability under varying operating conditions.

[0051] Step 4: Based on the target value of the optimal hydrogen blending ratio and the predicted change curve, the opening commands of the regulating valves of the natural gas branch and the hydrogen branch are determined by the feedforward-feedback composite control algorithm, and the actuators are driven to move so that the actual hydrogen blending ratio can quickly and stably track the target value.

[0052] The specific implementation method of step 4 will be described in detail below.

[0053] S41: Based on the optimal hydrogen blending ratio target value and the total fuel demand of the gas turbine, the hydrogen blending ratio target value is decomposed into the natural gas target flow rate and the hydrogen target flow rate.

[0054] S42: The natural gas branch adopts a PID control algorithm to calculate the opening command of the natural gas regulating valve based on the deviation between the target flow rate and the real-time flow rate.

[0055] The hydrogen branch employs a feedforward-feedback composite control algorithm: the feedforward channel uses the hydrogen doping ratio to predict the change curve, and calculates and outputs the compensation amount for the hydrogen regulating valve opening based on the predicted trend of the curve at various future times; the feedback channel corrects the flow rate based on the deviation between the real-time hydrogen flow rate and the target hydrogen flow rate. The feedforward compensation amount and the feedback correction amount are superimposed to form the final opening command of the hydrogen regulating valve.

[0056] S43: Convert the calculated opening command into a corresponding electrical signal and send it to the electrical actuators of the natural gas regulating valve and the hydrogen regulating valve to drive the valve to operate and regulate the flow of each branch.

[0057] S44: The hydrogen concentration of the mixed fuel is monitored in real time by a hydrogen concentration analyzer at the outlet of the Venturi mixer. When there is a deviation between the measured value and the target value, the flow rate of the hydrogen branch is finely adjusted to ensure that the mixing uniformity deviation is within the preset range.

[0058] In some embodiments, the hydrogen branch may also employ fuzzy control algorithms or sliding mode control algorithms instead of feedforward-feedback composite control to adapt to stronger nonlinearity and uncertainty.

[0059] In some embodiments, the mixing uniformity closed-loop correction can also be performed by measuring the hydrogen concentration at multiple points on the outlet cross section of the Venturi mixer using a multi-sensor array to assess spatial uniformity.

[0060] By employing differentiated control strategies for the natural gas and hydrogen branches, both stability and speed of regulation are balanced. Natural gas, as the primary fuel, has a large flow rate, and PID control ensures stable regulation of the main flow. Hydrogen, as a blending component, has a relatively small flow rate but significantly impacts the blending ratio; a feedforward-feedback composite control achieves rapid response and precise tracking of blending ratio commands. The coordinated operation of the two branches effectively overcomes the asynchronous regulation issues caused by differences in valve characteristics and pipeline response delays, enabling the actual hydrogen blending ratio to transition to the target value without overshoot within a short time.

[0061] Step 5: Real-time collection of safety parameters such as hydrogen concentration, pipeline pressure, pressure change rate, and hydrogen concentration change rate, and comparison with preset multi-level thresholds to determine the risk level. When a parameter exceeds the first-level threshold, an early warning signal is issued and event information is recorded; when a parameter exceeds the second-level threshold, the hydrogen blending ratio is automatically reduced to a preset safe value; when a parameter exceeds the third-level threshold, the fast-cut-off valve of the hydrogen supply branch is immediately shut off, and the flow rate of the natural gas branch is adjusted to the value required for pure natural gas mode, completing a seamless switch. A safety event report is automatically generated after each protection action is triggered.

[0062] Step 6: Extract operational data and control records from the historical database, and remove data from abnormal periods to form an analysis dataset. Using a genetic algorithm or Bayesian optimization algorithm, optimize the PID control parameters and feedforward compensation coefficients offline, aiming to minimize the hydrogen doping ratio tracking error and the frequency of control valve actuation. Simultaneously, update the equipment health status assessment based on the runtime and number of actuations of key equipment.

[0063] Correspondingly, this application also provides a hydrogen-blending adaptive control system for a gas turbine pressure regulating station, including: The data acquisition module is used to determine the lower heating value requirement of the mixed fuel based on the real-time collected gas turbine load demand. The hydrogen blending ratio calculation module is used to solve the optimal hydrogen blending ratio that meets the low heating value requirements of the mixed fuel by using a thermodynamic calculation model based on the real-time collected low heating values ​​of natural gas and hydrogen. After verification by safety boundary constraints, the module outputs the target value of the optimal hydrogen blending ratio. The flow regulation module is used to adjust the valve opening of the natural gas branch using a PID control algorithm and the hydrogen branch using a feedforward-feedback composite control algorithm, with the target value of the optimal hydrogen blending ratio as the set value, so that the actual hydrogen blending ratio tracks the target value.

[0064] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0065] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0066] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the hydrogen-blending adaptive control method for a gas turbine pressure regulating station as described in any of the above embodiments.

[0067] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and wireless connection (including Wi-Fi, Bluetooth, Bluetooth Low Energy, and IEEE 802.11s-based communication technology).

[0068] This application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the hydrogen-blending adaptive control method for a gas turbine pressure regulating station as described in any of the above embodiments.

[0069] For descriptions of relevant parts of the hydrogen-blending adaptive control system, electronic equipment, and computer-readable storage medium of the gas turbine pressure regulating station provided in this application, please refer to the detailed description of the corresponding parts in the hydrogen-blending adaptive control method of the gas turbine pressure regulating station provided in this application, which will not be repeated here. Furthermore, parts of the technical solutions provided in this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0070] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A hydrogen-infused adaptive control method for a gas turbine pressure regulating station, characterized in that, Includes the following steps: The lower heating value requirement of the mixed fuel is determined based on the real-time collected gas turbine load demand. Based on the real-time collected lower heating values ​​of natural gas and hydrogen, the optimal hydrogen blending ratio to meet the lower heating value requirement of the mixed fuel is solved by a thermodynamic calculation model. After verification by safety boundary constraints, the target value of the optimal hydrogen blending ratio is output. Using the target value of the optimal hydrogen blending ratio as a setpoint, the valve opening is adjusted by a PID control algorithm for the natural gas branch and by a feedforward-feedback composite control algorithm for the hydrogen branch, so that the actual hydrogen blending ratio tracks the target value.

2. The hydrogen-blending adaptive control method for a gas turbine pressure regulating station according to claim 1, characterized in that, The thermodynamic calculation model α=k·(LHV 需求 LHV NG ) / (LHV H2 LHV NG ) Where k is the gas turbine correction factor; LHV 需求 The lower heating value (LHV) of the fuel required by the gas turbine under current load. NG The lower heating value (LHV) of natural gas. H2 This is the lower heating value of hydrogen.

3. The hydrogen-incorporation adaptive control method for a gas turbine pressure regulating station according to claim 1, characterized in that, The safety boundary constraint verification includes: The optimal hydrogen doping ratio obtained by the solution is compared with the preset upper limit of the hydrogen doping ratio. When the upper limit of the hydrogen doping ratio is exceeded, the hydrogen doping ratio is corrected to the upper limit of the hydrogen doping ratio.

4. The hydrogen-incorporation adaptive control method for a gas turbine pressure regulating station according to claim 1, characterized in that, In the feedforward-feedback composite control of the hydrogen branch, the feedforward channel uses the hydrogen doping ratio prediction change curve generated by the model predictive control algorithm to calculate the advance compensation amount, and the feedback channel corrects the deviation between the real-time hydrogen flow rate and the target flow rate.

5. The hydrogen-blending adaptive control method for a gas turbine pressure regulating station according to claim 1, characterized in that, The lower heating value of the natural gas and the lower heating value of the hydrogen are obtained as follows: Prioritize extraction from online analytical instruments in real time. When online analytical instruments are unavailable, retrieve fuel characteristics from preset databases based on natural gas source type and hydrogen source type, respectively.

6. The hydrogen-blending adaptive control method for a gas turbine pressure regulating station according to claim 1, characterized in that, During the adjustment process, the hydrogen concentration of the mixed fuel is monitored in real time by a hydrogen concentration analyzer at the outlet of the Venturi mixer. When there is a deviation between the measured value and the target value, the flow rate of the hydrogen branch is corrected.

7. The hydrogen-blending adaptive control method for a gas turbine pressure regulating station according to claim 4, characterized in that, The model predictive control algorithm uses historical operating data and current operating condition trends to generate a rolling prediction curve of the hydrogen doping ratio within a preset future time window. The feedforward channel calculates the advance compensation amount of the hydrogen regulating valve opening based on the rate of change of the prediction curve at each moment.

8. The hydrogen-blending adaptive control method for a gas turbine pressure regulating station according to claim 2, characterized in that, The gas turbine correction coefficient k is adaptively adjusted in real time based on the gas turbine exhaust temperature and the dynamic pressure of the combustion chamber, so that the optimal hydrogen blending ratio is dynamically corrected according to the actual operating state of the gas turbine.

9. A hydrogen-blending adaptive control system for a gas turbine pressure regulating station, characterized in that, include: The data acquisition module is used to determine the lower heating value requirement of the mixed fuel based on the real-time collected gas turbine load demand. The hydrogen blending ratio calculation module is used to solve the optimal hydrogen blending ratio that meets the low heating value requirements of the mixed fuel by using a thermodynamic calculation model based on the real-time collected low heating values ​​of natural gas and hydrogen. After verification by safety boundary constraints, the module outputs the target value of the optimal hydrogen blending ratio. The flow regulation module is used to adjust the valve opening of the natural gas branch using a PID control algorithm and the hydrogen branch using a feedforward-feedback composite control algorithm, with the target value of the optimal hydrogen blending ratio as the set value, so that the actual hydrogen blending ratio tracks the target value.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the hydrogen-blending adaptive control method for a gas turbine pressure regulating station as described in any one of claims 1 to 8.

Images