Optimization method for parameters of power frequency electro-hydraulic control system of steam turbine

Abstract

The invention provides a turbine power frequency electro-hydraulic control system parameter optimization method, and relates to the technical field of system parameter optimizing.The turbine power frequency electro-hydraulic control system parameter optimization method comprises the steps that a prediction model is trained and adjusted through a historical rotating speed curve and a power adjustment amount, and the predicted power adjustment amount is obtained according to a real-time rotating speed curve; the method comprises the following steps: acquiring a flow opening formula and steam energy according to a relationship between the opening of a valve and the flow, analyzing the steam energy formula in MATLAB to acquire a corresponding relationship between an opening interval and a selected interval, and constructing an opening score to acquire the most accurate opening interval of the valve opening for steam energy control; the change index is constructed according to the deviation between the actual power adjustment amount and the predicted power adjustment amount, the interval between the pressure needed by secondary frequency modulation and the opening degree of the valve is judged according to the relation between the change index and the index threshold value, accurate control over the steam turbine rotor is achieved, and therefore the control performance of the power frequency electro-hydraulic system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of system parameter optimization, and particularly to an optimization method for parameters of a power-frequency electro-hydraulic control system of a steam turbine. Background Art

[0002] With the continuous increase in power demand and the improvement of the proportion of renewable energy, the situation of frequency deviation in the power grid occurs from time to time. When the power grid has a frequency deviation, the generator sets will actively perform primary frequency modulation. Then, the AGC system will issue instructions to each unit according to the specific deviation situation for secondary frequency modulation, requiring the power generation status of the units to make corresponding changes, so as to adjust the frequency deviation in the power grid. As an important power generation equipment in thermal power units, the power generation of a steam turbine is directly affected by the rotational speed of the rotor in the steam turbine unit. And the rotational speed of the rotor is controlled by the steam flow rate to realize the conversion of steam energy and power generation. During the secondary frequency modulation process, the AGC system will directly take over the power-frequency electro-hydraulic system to adjust the power generation of the steam turbine. Then, the power-frequency electro-hydraulic system controls the pressure in the steam chamber and the valves to adjust the rotational speed in the steam turbine unit. The control accuracy of the pressure and valves for the flow rate directly affects the performance of the power-frequency electro-hydraulic system for the rotational speed regulation ability, thus affecting the secondary frequency modulation process.

[0003] In the prior art, the optimization method for parameters of the power-frequency electro-hydraulic control system of large steam turbines in thermal power plants is disclosed in CN103401256A. The influence of the power-frequency electro-hydraulic control system of the steam turbine generator set on the stability of the power system is combined with the primary frequency modulation performance of the unit and the requirements of the system dynamic stability margin. The parameters of the power-frequency electro-hydraulic control system are obtained through an intelligent optimization algorithm.

[0004] According to the disclosed document, the optimization of the PID control parameters during the primary frequency modulation process of the steam turbine unit is realized. The PID control parameters belong to the parameter optimization of the power-frequency electro-hydraulic system from the data level. However, the secondary frequency modulation process is not optimized, and the two key physical control parameters, namely the relevant pressure parameters and valve openings during the frequency modulation process, are not considered. It is impossible to avoid the problem of inaccurate control caused by the inherent characteristics of the hardware during the frequency modulation process, and the parameters of the power-frequency electro-hydraulic system are not optimized from the physical level.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimization method for parameters of a power-frequency electro-hydraulic control system of a steam turbine to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system, the specific steps comprising: The historical speed curve and power adjustment of the steam turbine are obtained, and timestamps are added. The speed curve is segmented with the timestamps as nodes. The speed curve segment between two adjacent timestamps is a sample. The speed curve segment is used as a training set, and the corresponding power adjustment amount is used as a label to train the convolutional neural network model to obtain the speed regulation prediction model. Taking the timestamp of the last power adjustment as the starting point, obtaining the real-time turbine speed curve, and taking the timestamp of the current power adjustment as the end point, forming the current speed curve, inputting the current speed curve into the adjustment prediction model, and obtaining the predicted power adjustment; Obtain the conversion coefficient between steam energy and steam turbine power and obtain the energy adjustment amount according to the power adjustment amount, obtain the flow opening formula according to the relationship between the valve opening and the flow, and obtain the steam energy according to the flow opening formula and the steam pressure data; The formula of steam energy is analyzed in MATLAB, and the selection interval is constructed according to the steam energy, and the valve opening interval under different pressure conditions is obtained. The opening score is constructed according to the linear representation degree of the opening interval and the selection interval, and the minimum opening score is obtained. The pressure corresponding to the minimum opening score is calibrated as the control pressure; The actual power adjustment is obtained, and a change index is constructed according to the difference between the actual power adjustment and the predicted power adjustment, an index threshold is set, and the pressure and valve opening range required for secondary frequency modulation are determined according to the relationship between the change index and the index threshold.

[0008] Furthermore, a historical speed curve of the steam turbine is obtained, where the horizontal axis of the speed curve is time and the vertical axis is speed. A historical power adjustment amount is obtained, where the power adjustment amount is the power change amount in the secondary frequency modulation process specified by the AGC system. A timestamp is added to the power adjustment amount, where the timestamp is the time when the AGC system instruction is received. The speed curve is segmented using the timestamp of the power adjustment amount as a node, and the speed curve segment between two adjacent timestamps is a sample. The segmented multiple speed curve segments are used as a training set, and the power adjustment amount corresponding to each speed curve segment is used as a label to input into a convolutional neural network model for training, and the trained model is calibrated as an adjustment prediction model.

[0009] Furthermore, the speed curve of the steam turbine is obtained in real time with the timestamp of the last power adjustment as the starting point. When the steam turbine triggers a frequency modulation, the time of obtaining the AGC system instruction is taken as the end point to form this speed curve. The speed curve is input into the adjustment prediction model to output the predicted power adjustment.

[0010] Furthermore, the conversion coefficient of steam energy into turbine power is obtained from the merchant, and the energy adjustment amount of steam in the turbine chamber per unit time is obtained according to the power adjustment amount, and the formula is as follows:

[0011] in, is the energy adjustment amount, is the conversion factor, To predict the power adjustment amount; The valve model used in this steam turbine was tested in the laboratory to obtain multiple sets of data. The valve opening was used as the independent variable, and the flow rate per unit time corresponding to the opening was used as the dependent variable to obtain the flow opening formula. The formula is as follows:

[0012] in, For flow, is the opening, is the relationship function between flow rate and opening; The steam energy input into the turbine chamber per unit time is obtained based on the following formula:

[0013] in, is the steam energy, is the steam pressure, The steam pressure is expressed as The entropy value at that time is obtained through the steam table.

[0014] Furthermore, the formula of steam energy is analyzed in MATLAB, and a surface diagram is constructed with steam pressure and opening as independent variables and steam energy as dependent variable. The selection interval is constructed based on the current steam energy. The selection interval is the adjustment range of steam energy. The range of the selection interval is ,in is the current steam energy, is the energy adjustment amount; Get the opening range under different steam pressure conditions in the selected range respectively. The logic of getting the opening range is: For steam pressure , the corresponding entropy value is obtained through the steam table , and then obtain the steam pressure The opening range below , the formula based on is:

[0015]

[0016] in, is the lower limit of the opening range, is the upper limit of the opening range, It is the inverse function of the relationship between flow rate and opening.

[0017] Furthermore, the opening score under different pressures is constructed according to the linear representation degree of the opening range and the selection range, and the formula is as follows:

[0018] in, For the pressure is The opening score at is the steam energy, is the opening, Because pressure is The minimum value in the opening range when Because pressure is The maximum value in the opening interval, is the coefficient; The pressure at which the opening score is the largest is selected and calibrated as the control pressure.

[0019] Furthermore, the pressure of the steam chamber of the steam turbine is increased to the control pressure, and the actual power adjustment is obtained according to the AGC system. The change index is formed according to the actual power change and the predicted power change, and the formula is as follows:

[0020] in, is the change index, To predict the power adjustment, is the actual power adjustment, is a constant.

[0021] Furthermore, an index threshold is set. When the change index does not exceed the index threshold, secondary frequency modulation is performed in the current control pressure and valve opening range. When the change index exceeds the index threshold, the actual power adjustment amount replaces the predicted power adjustment amount, the steam energy is regenerated, and the opening range and control pressure are reacquired in MATLAB, and secondary frequency modulation is performed according to the newly acquired control pressure and opening range.

[0022] Furthermore, the unit time lengths of the steam energy, the predicted power adjustment amount, and the actual power adjustment amount in the method are the same.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the predicted power adjustment amount by training the speed regulation prediction model, and constructs the corresponding formulas among steam energy, valve opening and pressure, obtains the relationship between the steam energy selection interval and the valve opening interval in MATLAB, selects the opening interval in which the valve opening and steam energy present the most linear relationship through the opening score, realizes the precise adjustment of steam energy, improves the precise control of the turbine rotor speed, and improves the control performance of the power frequency electro-hydraulic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0026] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0027] Example:

[0028] See also Figure 1 , the present invention provides a technical solution: A method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system, the specific steps comprising: Step 1: Obtain the historical speed curve and power adjustment of the steam turbine, add timestamps, and segment the speed curve with timestamps as nodes. The speed curve segment between two adjacent timestamps is a sample. The speed curve segment is used as a training set, and the corresponding power adjustment amount is used as a label to train the convolutional neural network model to obtain the speed regulation prediction model; The step 1 includes the following contents: A historical speed curve of the steam turbine is obtained, where the horizontal axis of the speed curve is time and the vertical axis is speed. A historical power adjustment amount is obtained, where the power adjustment amount is the power change amount in the secondary frequency modulation process specified by the AGC system. A timestamp is added to the power adjustment amount, where the timestamp is the time when the AGC system instruction is received. The speed curve is segmented using the timestamp of the power adjustment amount as a node, and the speed curve segment between two adjacent timestamps is a sample. The segmented multiple speed curve segments are used as a training set, and the power adjustment amount corresponding to each speed curve segment is used as a label to input the convolutional neural network model for training, and the trained model is calibrated as an adjustment prediction model.

[0029] The convolutional neural network model is set to have a convolution kernel size of 3x3, a number of convolution kernels of 64, a number of fully connected layers of 500, and a learning rate of 0.001.

[0030] The speed curve of the secondary frequency modulation event usually shows a single trend in a short period of time. This pattern has significant temporal locality rather than global long-term dependence, which is suitable for the application scenarios of the convolutional neural network model. The 3x3 convolution kernel is the most common convolution kernel size in the convolutional neural network, which can effectively capture the local change pattern. Compared with larger convolution kernels, the 3x3 convolution kernel can reduce the amount of calculation. The number of 64 convolution kernels can avoid redundancy while ensuring the model capacity. 500 neurons provide sufficient nonlinear fitting capabilities to encode complex speed-power relationships while avoiding gradient vanishing or computational redundancy caused by extreme sparsity. 0.001 is a common initial learning rate for adaptive optimizers such as Adam, which is suitable for models of medium complexity. The prediction task of the power adjustment amount requires a fine weight update step to avoid ignoring subtle timing pattern differences due to excessive learning rates, and avoid slow convergence due to low learning rates.

[0031] The convolutional neural network model is trained by segmenting the timestamps of the historical speed curve and the power adjustment amount, and the training data and labels are strictly aligned with the AGC instruction timestamp as the node to avoid data aliasing at different frequency modulation stages, thereby improving the model's recognition accuracy for secondary frequency modulation events. This step provides a highly robust speed regulation prediction model for subsequent real-time predictions.

[0032] Step 2: Taking the timestamp of the last power adjustment as the starting point, obtain the real-time turbine speed curve, and taking the timestamp of the current power adjustment as the end point to form the current speed curve, input the current speed curve into the adjustment prediction model, and obtain the predicted power adjustment; The step 2 includes the following contents: The speed curve of the steam turbine is obtained in real time, starting from the timestamp of the last power adjustment. When the steam turbine triggers a frequency modulation, the speed curve is formed with the time of obtaining the AGC system instruction as the end point. The speed curve is input into the adjustment prediction model to output the predicted power adjustment.

[0033] Step 3: Obtain the conversion coefficient between steam energy and steam turbine power and obtain the energy adjustment amount according to the power adjustment amount, obtain the flow opening formula according to the relationship between the valve opening and the flow rate, and obtain the steam energy according to the flow opening formula and the steam pressure data; The step 3 includes the following contents: The conversion coefficient of steam energy to turbine power is obtained from the merchant, and the energy adjustment of steam in the turbine chamber per unit time is obtained according to the power adjustment. The formula is as follows:

[0034] in, is the energy adjustment amount, is the conversion factor, To predict the power adjustment amount; The energy adjustment of the steam turbine chamber is constructed by the conversion coefficient. The performance of different steam turbine chambers is different. The higher the conversion coefficient, the smaller the energy adjustment, and the lower the conversion coefficient, the higher the energy adjustment.

[0035] The valve model used in this steam turbine was tested in the laboratory to obtain multiple sets of data. The valve opening was used as the independent variable, and the flow rate per unit time corresponding to the opening was used as the dependent variable to obtain the flow opening formula. The formula is as follows:

[0036] in, For flow, is the opening, is the relationship function between flow rate and opening; As a preferred embodiment, starting with the valve opening of 0%, each increase of 2% is a test point to obtain the flow rate per unit time at different openings, select the polynomial model as the basis of the flow opening formula, and use the least squares method to solve each coefficient. 80% of the sample set is used as the training set, and the remaining 20% ​​is used as the verification set. The flow opening formula is obtained when the maximum relative error is set to less than 2%.

[0037] The steam energy input into the turbine chamber per unit time is obtained based on the following formula:

[0038] in, is the steam energy, is the steam pressure, The steam pressure is expressed as The entropy value at that time is obtained through the steam table.

[0039] The relationship between steam energy and opening is constructed through a thermodynamic model. The higher the entropy value, the higher the steam energy passing through at the same opening.

[0040] The quantitative relationship between power adjustment and energy adjustment is established through the conversion coefficient, and the abstract power demand is converted into operable valve opening and pressure parameters by combining the flow opening formula and steam energy formula calibrated in the laboratory. This step avoids the dependence on complex thermodynamic equations through energy-power decoupling and valve characteristic quantification, and at the same time constructs an energy closed-loop model to ensure real-time matching of steam energy supply and dynamic conditions (such as pressure fluctuations), providing a physically implementable parameter basis for subsequent optimization.

[0041] Step 4: Analyze the formula of steam energy in MATLAB, construct a selection interval based on steam energy, and obtain the valve opening interval under different pressure conditions. Construct an opening score based on the linear representation degree of the opening interval and the selection interval, obtain the minimum opening score, and calibrate the pressure corresponding to the minimum opening score as the control pressure; The step 4 includes the following contents: Step 401: Analyze the formula of steam energy in MATLAB, construct a surface graph with steam pressure and opening as independent variables and steam energy as dependent variable, and construct a selection interval based on the current steam energy. The selection interval is the adjustment range of steam energy. The range of the selection interval is ,in is the current steam energy, is the energy adjustment amount; Get the opening range under different steam pressure conditions in the selected range respectively. The logic of getting the opening range is: For steam pressure , the corresponding entropy value is obtained through the steam table , and then obtain the steam pressure The opening range below , the formula based on is:

[0042]

[0043] in, is the lower limit of the opening range, is the upper limit of the opening range, It is the inverse function of the relationship between flow rate and opening.

[0044] In MATLAB, a surface graph of steam energy, pressure and opening is constructed, and the selection interval is defined. At the same time, the opening interval is expanded to 80%-120% of the definition domain. By visualizing the global distribution of steam energy supply, the physical boundary of steam energy adjustment can be clarified to avoid local optimization traps. The expansion of the opening interval reserves judgment space for the inconsistency between the actual power adjustment and the predicted power adjustment in the later stage, enhances the tolerance of the system, and ensures the robustness of parameter optimization.

[0045] Step 402: Constructing the opening scores under different pressures according to the linear representation degree of the opening interval and the selection interval, based on the following formula:

[0046] in, For the pressure is The opening score at is the steam energy, is the opening, Because pressure is The minimum value in the opening range when Because pressure is The maximum value in the opening interval, is the coefficient; The specific value of needs to be determined according to the specific valve system and pressure conditions, and the opening range can be obtained through experimental data or simulation calculation. The law of change of The value of makes the openness score range .

[0047] Among them, the first partial derivative of steam energy with respect to the opening reflects the rate of change of steam energy relative to the opening, and judges the degree to which the relationship between steam energy and the opening tends to be linear. The second partial derivative of steam energy with respect to the opening reflects the change of the rate of change of steam energy relative to the opening, and judges the linear slope of steam energy relative to the opening. The smaller the linear slope, the less the steam energy increases when the valve opening increases by the same amount, reflecting the more precise the valve's control over steam energy. The final result is a larger opening score, and the negative value caused by the second partial derivative is avoided by the absolute value. The smaller the area formed by the curve of the second partial derivative and the horizontal axis in the definition domain interval, whether the second partial derivative is positive or negative, the larger the opening score will be. The linear slope in the opening interval is accumulated in the form of integration, and the degree of control accuracy is judged from the entire opening interval.

[0048] The pressure at which the opening score is the largest is selected and calibrated as the control pressure.

[0049] By obtaining the optimal control pressure based on the most accurate opening range, the curvature change of the energy-opening curve can be quantified, and the area with high linearity can be given priority to avoid control oscillation or instability caused by nonlinear areas, thereby achieving the optimal balance between regulation efficiency and system stability.

[0050] Step 5: Obtain the actual power adjustment, and construct a change index based on the difference between the actual power adjustment and the predicted power adjustment, set the index threshold, and determine the pressure and valve opening range required for secondary frequency modulation based on the relationship between the change index and the index threshold.

[0051] The step 5 includes the following contents: Step 501: Raise the pressure of the steam chamber of the steam turbine to the control pressure, obtain the actual power adjustment amount according to the AGC system, and form a change index according to the actual power change amount and the predicted power change amount, based on the following formula:

[0052] in, is the change index, To predict the power adjustment, is the actual power adjustment, is a constant.

[0053] It is the baseline value of the variation index, which is set empirically based on historical operating conditions and combined with the index threshold to achieve the sensitivity of feedback control.

[0054] By constructing a variation index based on the difference between the predicted and actual power adjustments, the sensitivity of the prediction deviation can be amplified through an exponential function. When the actual power adjustment exceeds the predicted power adjustment, the variation index will increase. The higher the actual power adjustment exceeds the predicted power adjustment, the faster the variation index increases, providing a quantitative guarantee for whether the prediction results can meet the actual conditions.

[0055] Step 502: Set an index threshold. When the change index does not exceed the index threshold, perform secondary frequency modulation in the current control pressure and valve opening range. When the change index exceeds the index threshold, replace the predicted power adjustment with the actual power adjustment, regenerate steam energy, and reacquire the opening range and control pressure in MATLAB, and perform secondary frequency modulation according to the newly acquired control pressure and opening range.

[0056] Set the index threshold for feedback control - within the threshold, the current parameters are used to perform secondary frequency modulation. When the threshold is exceeded, steam energy is regenerated and the control pressure and opening range are optimized. Dual-mode adaptive control can be achieved: maintain efficient frequency modulation under steady-state conditions, and quickly correct parameters under transient or abnormal conditions, forming a "prediction-execution-feedback" closed loop, significantly improving the system's adaptability and fault tolerance to sudden disturbances, and ultimately ensuring global frequency modulation accuracy and stability. In this embodiment, the unit time lengths of the steam energy, the predicted power adjustment amount, and the actual power adjustment amount in this method are the same.

[0057] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0058] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0059] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system, characterized in that: The specific steps include: The historical speed curve and power adjustment of the steam turbine are obtained, and timestamps are added. The speed curve is segmented with the timestamps as nodes. The speed curve segment between two adjacent timestamps is a sample. The speed curve segment is used as a training set, and the corresponding power adjustment amount is used as a label to train the convolutional neural network model to obtain the speed regulation prediction model. Taking the timestamp of the last power adjustment as the starting point, obtaining the real-time turbine speed curve, and taking the timestamp of the current power adjustment as the end point, forming the current speed curve, inputting the current speed curve into the adjustment prediction model, and obtaining the predicted power adjustment; Obtain the conversion coefficient between steam energy and steam turbine power and obtain the energy adjustment amount according to the power adjustment amount, obtain the flow opening formula according to the relationship between the valve opening and the flow, and obtain the steam energy according to the flow opening formula and the steam pressure data; The formula of steam energy is analyzed in MATLAB, and the selection interval is constructed according to the steam energy, and the valve opening interval under different steam pressure conditions is obtained. The opening score is constructed according to the linear representation degree of the opening interval and the selection interval, and the minimum opening score is obtained. The pressure corresponding to the minimum opening score is calibrated as the control pressure; The actual power adjustment is obtained, and a change index is constructed according to the difference between the actual power adjustment and the predicted power adjustment, an index threshold is set, and the pressure and valve opening range required for secondary frequency modulation are determined according to the relationship between the change index and the index threshold.

2. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 1, characterized in that: A historical speed curve of the steam turbine is obtained, where the horizontal axis of the speed curve is time and the vertical axis is speed. A historical power adjustment amount is obtained, where the power adjustment amount is the power change amount in the secondary frequency modulation process specified by the AGC system. A timestamp is added to the power adjustment amount, where the timestamp is the time when the AGC system instruction is received. The speed curve is segmented using the timestamp of the power adjustment amount as a node, and the speed curve segment between two adjacent timestamps is a sample. The segmented multiple speed curve segments are used as a training set, and the power adjustment amount corresponding to each speed curve segment is used as a label to input the convolutional neural network model for training, and the trained model is calibrated as an adjustment prediction model.

3. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 2, characterized in that: The speed curve of the steam turbine is obtained in real time, starting from the timestamp of the last power adjustment. When the steam turbine triggers a frequency modulation, the speed curve is formed with the time of obtaining the AGC system instruction as the end point. The speed curve is input into the adjustment prediction model to output the predicted power adjustment.

4. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 3 is characterized in that: The conversion coefficient of steam energy to turbine power is obtained from the merchant, and the energy adjustment of steam in the turbine chamber per unit time is obtained according to the power adjustment. The formula is as follows: ， in, is the energy adjustment amount, is the conversion factor, To predict the power adjustment amount; The valve model used in this steam turbine was tested in the laboratory to obtain multiple sets of data. The valve opening was used as the independent variable, and the flow rate per unit time corresponding to the opening was used as the dependent variable to obtain the flow opening formula. The formula is as follows: ， in, For flow, is the opening, is the relationship function between flow rate and opening; The steam energy input into the turbine chamber per unit time is obtained based on the following formula: ， in, is the steam energy, is the steam pressure, The steam pressure is expressed as The entropy value at that time is obtained through the steam table.

5. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 4, characterized in that: The formula of steam energy is analyzed in MATLAB, and a surface graph is constructed with steam pressure and opening as independent variables and steam energy as dependent variable. The selection interval is constructed based on the current steam energy. The selection interval is the adjustment range of steam energy. The range of the selection interval is ,in is the current steam energy, is the energy adjustment amount; The opening ranges under different steam pressure conditions in the selected range are obtained respectively. The logic for obtaining the opening range is: For steam pressure , the corresponding entropy value is obtained through the steam table , and then obtain the steam pressure The opening range below , the formula based on is: ， ， in, is the lower limit of the opening range, is the upper limit of the opening range, It is the inverse function of the relationship between flow rate and opening.

6. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 5, characterized in that: The opening score under different pressures is constructed according to the linear representation degree of the opening range and the selection range. The formula is as follows: ， in, For the pressure is The opening score at is the steam energy, is the opening, Because pressure is The minimum value in the opening range when Because pressure is The maximum value in the opening interval, is the coefficient; The pressure at which the opening score is the largest is selected and calibrated as the control pressure.

7. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 6, characterized in that: The pressure of the steam chamber of the steam turbine is raised to the control pressure, and the actual power adjustment is obtained according to the AGC system. The change index is formed according to the actual power change and the predicted power change. The formula is as follows: ， in, is the change index, To predict the power adjustment, is the actual power adjustment, is a constant.

8. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 7, characterized in that: An index threshold is set. When the change index does not exceed the index threshold, secondary frequency modulation is performed in the current control pressure and valve opening range. When the change index exceeds the index threshold, the actual power adjustment amount replaces the predicted power adjustment amount, the steam energy is regenerated, and the opening range and control pressure are reacquired in MATLAB, and secondary frequency modulation is performed according to the newly acquired control pressure and opening range.

9. The method for optimizing parameters of a steam turbine power frequency electro-hydraulic control system according to claim 8, characterized in that: The unit time lengths of the steam energy, the predicted power adjustment amount, and the actual power adjustment amount in the method are the same.

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