A method and device for estimating primary frequency modulation margin of a thermal power unit

Abstract

The application belongs to the field of electric power, and discloses a method and device for estimating primary frequency modulation margin of a thermal power unit, which comprises the following steps: obtaining real-time operation parameters of the thermal power unit, wherein the real-time operation parameters at least include unit rated load, main steam pressure and current main steam valve opening degree; matching corresponding valve flow characteristic curves based on the type of the steam turbine, and inversely calculating the current valve position flow instruction from the current main steam valve opening degree; determining a corresponding margin calculation model according to the load interval in which the thermal power unit currently locates, and calculating the estimated values of the up and down frequency modulation margins according to the determined margin calculation model; the margin calculation model is used to calculate the estimated values of the up and down frequency modulation margins based on the main steam pressure, the current valve position flow instruction, preset maximum / minimum valve position flow instruction, experience coefficient and correction coefficient corresponding to the type of the boiler.

Description

Technical Field

[0001] This invention belongs to the field of power, and in particular relates to a method and apparatus for predicting the primary frequency regulation margin of thermal power units. Background Technology

[0002] With the continuous increase in the penetration rate of new energy sources such as wind power and photovoltaics, and the increasing risk of UHVDC blocking faults, the power grid's need for dynamic sensing and precise allocation of primary frequency regulation resources is becoming increasingly urgent.

[0003] In related technologies, the frequency regulation capability assessment of thermal power units mostly relies on offline tests, which cannot reflect the adjustable space under the actual operating conditions of the unit in real time. This leads to a mismatch between dispatch instructions and actual capabilities, which affects both frequency security and operational economy. Summary of the Invention

[0004] In view of this, the present invention discloses a method and apparatus for predicting the primary frequency control margin of thermal power units, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: According to a first aspect of the present invention, a method for predicting the primary frequency control margin of a thermal power unit is proposed, comprising: Acquire real-time operating parameters of the thermal power unit, including at least the unit's rated load, main steam pressure, and current main steam regulating valve opening; Based on the turbine type matching corresponding valve flow characteristic curve, the current main steam valve opening is back-calculated into the current valve position flow command; The corresponding margin calculation model is determined based on the current load range of the thermal power unit, and the estimated values ​​of the upper and lower frequency regulation margins are calculated based on the determined margin calculation model. The margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, empirical coefficients, and correction coefficients corresponding to the boiler type.

[0006] According to a second aspect of the present invention, a device for predicting the primary frequency regulation margin of a thermal power unit is provided, comprising: Acquisition Unit: Acquires real-time operating parameters of the thermal power unit, including at least the unit's rated load, main steam pressure, and current main steam regulating valve opening; Matching unit: Matches the corresponding regulating valve flow characteristic curve based on the turbine type, and back-calculates the current main steam regulating valve opening into the current valve position flow command; Calculation unit: Determines the corresponding margin calculation model based on the current load range of the thermal power unit, and calculates the estimated values ​​of the upper and lower frequency regulation margins based on the determined margin calculation model; the margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, empirical coefficients, and correction coefficients corresponding to the boiler type.

[0007] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0008] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0009] As can be seen from the above technical solutions, the beneficial effects of the method and device for estimating the primary frequency regulation margin of thermal power units disclosed in this invention are as follows: On the one hand, by introducing the turbine flow characteristic curve and the boiler type correction coefficient, the estimation can cover the mainstream thermal power equipment types, thereby realizing the estimation of the primary frequency regulation margin of thermal power units; on the other hand, by distinguishing between the normal load section and the deep adjustment load section, the design differentiates the calculation logic, adapts to the full operating characteristics of the unit, and thus improves the estimation accuracy. Attached Figure Description

[0010] Figure 1 This is a flowchart of an exemplary embodiment of a method for estimating the primary frequency control margin of a thermal power unit; Figure 2 This is a schematic diagram illustrating the principle of real-time prediction of primary frequency control margin of a thermal power unit, provided in an exemplary embodiment. Figure 3 This is a schematic diagram of the flow characteristic curves of the regulating valves of SAIC and Dongfang Electric units and the corresponding typical power plant primary frequency regulation margin prediction model, provided in an exemplary embodiment. Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 5 This is a block diagram of a primary frequency control margin prediction device for a thermal power unit, provided in an exemplary embodiment. Detailed Implementation

[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0012] It should be noted that the steps of the corresponding methods in other embodiments are not necessarily performed in the order shown and described in this invention. In some other embodiments, the methods may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0013] With the continuous increase in the penetration rate of new energy sources such as wind power and photovoltaics, and the increasing risk of UHVDC blocking faults, the power grid's need for dynamic sensing and precise allocation of primary frequency regulation resources is becoming increasingly urgent.

[0014] In related technologies, the frequency regulation capability assessment of thermal power units mostly relies on offline tests, which cannot reflect the adjustable space under the actual operating conditions of the unit in real time. This leads to a mismatch between dispatch instructions and actual capabilities, which affects both frequency security and operational economy.

[0015] To address the shortcomings in related technologies, this invention proposes a method and apparatus for predicting the primary frequency control margin of thermal power units.

[0016] Figure 1 This is a flowchart illustrating an exemplary embodiment of a method for estimating the primary frequency control margin of a thermal power unit. For example... Figure 1 As shown, the method may include the following steps: Step 101: Obtain the real-time operating parameters of the thermal power unit. The real-time operating parameters include at least the unit's rated load, main steam pressure, and current main steam regulating valve opening. Step 102: Match the corresponding regulating valve flow characteristic curve based on the turbine type, and back-calculate the current main steam regulating valve opening into the current valve position flow command; Step 103: Determine the corresponding margin calculation model based on the current load range of the thermal power unit, and calculate the estimated values ​​of the upper and lower frequency regulation margins based on the determined margin calculation model; the margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, empirical coefficients, and correction coefficients corresponding to the boiler type.

[0017] Figure 2 This is a schematic diagram illustrating the principle of real-time prediction of primary frequency control margin of a thermal power unit, as provided in an exemplary embodiment. Figure 2 As shown, Part A is the flow command calculated from the turbine control valve opening; Part B is the primary frequency control margin calculation model; and Part C is the real-time estimated value output by the model.

[0018] In this embodiment, on the one hand, by introducing the turbine flow characteristic curve and the boiler type correction coefficient, the prediction can cover the mainstream thermal power equipment types, thereby realizing the prediction of the primary frequency regulation margin of thermal power units; on the other hand, differentiated calculation logic is designed to distinguish between normal load section and deep adjustment load section, adapting to the full operating characteristics of the unit, thereby improving the prediction accuracy.

[0019] In one embodiment, the formula for calculating the up-frequency regulation forecast in the margin calculation model corresponding to the normal load period is as follows: P1=min(6%Pe,0.15× ×(Maximum valve position flow command) (Current valve position flow command) × K 修正 ); Where Pe is the rated load of the unit, K 修正 This is the correction factor corresponding to the boiler type.

[0020] When estimating the frequency regulation, the flow command is first calculated by back-calculating the current main steam valve opening from the flow characteristic curve. The actual maximum flow command is set, and the deviation between the maximum flow command and the current flow command is calculated. The current pressure is square rooted, and the flow command deviation is multiplied by the square root of the pressure. An empirical coefficient of 0.15 is set and multiplied by the previous result. Then, a correction coefficient is added, and finally, the result is divided by 6% of the rated load to obtain the real-time output value of the frequency regulation prediction.

[0021] The formula for calculating the down-regulation prediction in the margin calculation model corresponding to the normal load period is as follows: P2= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ).

[0022] When predicting the frequency reduction, firstly, the flow command is calculated by back-calculating the current main steam valve opening through the flow characteristic curve. Then, a minimum flow command is set, and the deviation between the current flow command and the minimum flow command is calculated. Next, the square root of the current pressure is taken, and the flow command deviation is multiplied by the square root of the pressure. An empirical coefficient of 0.15 is set and multiplied by the previous result. Then, a correction coefficient is applied, and finally, the smaller value of 6% of the rated load is taken as the negative value to obtain the real-time output value of the frequency reduction prediction.

[0023] Different types of steam turbines require valve flow characteristic curves to obtain the flow command corresponding to the current opening. The mainstream steam turbine flow characteristic curves are those of Shanghai Steam Turbine, Dongfang Steam Turbine, and Harbin Steam Turbine. The maximum flow command and minimum flow command are set according to different types of steam turbines. The correction coefficient K is set according to different boiler types and field test data. The mainstream boiler types are those of Shanghai Boiler, Harbin Boiler, Dongfang Boiler, and Beijing Bawei Boiler Factory. The correction coefficient K is set in combination with field test data.

[0024] The formula for calculating the up-frequency modulation forecast in the margin calculation model corresponding to the deep-load section is as follows: P3 = min(6%Pe, 0.15 × ×(min[100, (current valve opening value + estimated opening value)] corresponding to the flow command) (Current valve position flow command) × K 修正 ).

[0025] The maximum valve position flow command for the deep adjustment load section is determined in the following way: the current valve position opening is summed with the preset estimated opening value, and the smaller value is taken from 100. Then, the smaller opening value is substituted into the matching valve flow characteristic curve to calculate the corresponding flow command.

[0026] When estimating the frequency regulation, firstly, the flow command is calculated by back-calculating the current main steam valve opening from the flow characteristic curve. Then, the current opening is added to the estimated opening value and the result is divided by 100 and the smaller value is taken. Next, the smaller opening is used to back-calculate the maximum flow command from the flow characteristic curve. The deviation between the maximum flow command and the current flow command is calculated. The current pressure is square-rooted, and the flow command deviation is multiplied by the square root of the pressure. An empirical coefficient of 0.15 is set and multiplied by the previous result. Then, a correction coefficient is applied, and finally, the result is divided by 6% of the rated load and the smaller value is taken to obtain the real-time output value of the frequency regulation prediction.

[0027] The formula for calculating the down-regulation frequency forecast in the margin calculation model corresponding to the deep-adjustment load segment is as follows: P4= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ).

[0028] When predicting the frequency reduction, firstly, the flow command is calculated by back-calculating the current main steam valve opening through the flow characteristic curve. Then, a minimum flow command is set, and the deviation between the current flow command and the minimum flow command is calculated. Next, the square root of the current pressure is taken, and the flow command deviation is multiplied by the square root of the pressure. An empirical coefficient of 0.15 is set and multiplied by the previous result. Then, a correction coefficient is applied, and finally, the smaller value of 6% of the rated load is taken as the negative value to obtain the real-time output value of the frequency reduction prediction.

[0029] Different types of steam turbines require valve flow characteristic curves to obtain the flow command corresponding to the current opening. The mainstream steam turbine flow characteristic curves are those of Shanghai Steam Turbine, Dongfang Steam Turbine, and Harbin Steam Turbine. The minimum flow command is set according to the different types of steam turbines. When setting the maximum flow command, the opening value is estimated first, and then the current opening value is added to calculate the maximum flow command. The correction coefficient K is set according to different boiler types and field test data. The mainstream boiler types are Shanghai Boiler, Harbin Boiler, Dongfang Boiler, and Beijing Bawei Boiler Factory. The correction coefficient K is set in combination with the field test data.

[0030] The maximum valve position flow command and minimum valve position flow command in the normal load section are pre-calibrated based on the matching turbine control valve flow characteristic curve. The maximum valve position flow command corresponds to the flow command under the condition of full valve opening, and the minimum valve position flow command corresponds to the flow command under the condition of minimum adjustable valve opening.

[0031] In one embodiment, the turbine types include three categories: SAIC, Dongfang Electric, and Harbin Electric. Each type of turbine corresponds to an independent control valve flow characteristic curve, which is used to realize the forward mapping and reverse conversion between control valve opening and valve position flow command.

[0032] In one embodiment, the method further includes: after completing the parameter configuration, inputting the unit load, main steam pressure, and valve opening under historical test conditions into the prediction model; if the output margin prediction value and the actual test margin error of the corresponding test condition are within the allowable range, then the model parameter adaptation is determined to be effective.

[0033] In one embodiment, the aforementioned method is applied to the unit's DCS system to collect main steam pressure and valve opening signals in real time at millisecond intervals, and dynamically output the primary frequency regulation margin that varies with operating conditions.

[0034] Taking the primary frequency regulation of SAIC's 1000MW supercritical unit as an example: At a load range of 75%Pe, the unit load is 750MW, the unit pressure is 22.3MPa, the unit valve opening is 31%, and the corresponding flow command is 87t / h. Setting the maximum flow rate to 100 and the correction factor to 6.5, we obtain the frequency regulation margin prediction model above 50%Pe on the left, as follows: Figure 3 The formula for predicting frequency regulation in the load segment above 50% Pe in Part B is shown. After substituting the actual field data, the resulting frequency regulation margin is basically consistent with the field test data.

[0035] At a load range of 40% Pe, the unit load is 400MW, the unit pressure is 13MPa, the unit control valve opening is 24.1%, and the corresponding flow command is 78t / h. The estimated valve position is set to 35%, the maximum flow rate is the flow rate corresponding to (current valve position + 35), and the correction factor is set to 6.5. This yields the frequency control margin prediction model below 50% Pe on the left side, as follows: Figure 3The formula for predicting frequency regulation in the load segment below 50% Pe in Part B is shown. After substituting the actual field data, the resulting frequency regulation margin is basically consistent with the field test data.

[0036] Taking the primary frequency regulation of Dongfang Electric's 1000MW supercritical unit as an example: At a load range of 75%Pe, the unit load is 750MW, the unit pressure is 23.8MPa, the unit control valve opening is 35%, and the corresponding flow command is 90.7t / h. Setting the maximum flow rate to 100 and the correction factor to 7, we obtain the frequency regulation margin prediction model above 50%Pe on the left, as follows: Figure 3 The formula for predicting frequency regulation in the load segment above 50% Pe in section D is shown. After substituting the actual field data, the resulting frequency regulation margin is basically consistent with the field test data.

[0037] At a load of 40% Pe, the unit load is 400MW, the unit pressure is 13.6MPa, the unit control valve opening is 31%, and the corresponding flow command is 77.5t / h. Setting the estimated valve position to 5%, the maximum flow rate is the flow rate corresponding to (current valve position + 5), and the correction factor to 7, we obtain the frequency control margin prediction model below 50% Pe on the left, as follows: Figure 3 The formula for predicting frequency regulation in the load segment below 50% Pe in section D is shown. After substituting the actual field data, the resulting frequency regulation margin is basically consistent with the field test data.

[0038] Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 4 At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, memory 408, and non-volatile memory 410, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into memory 408 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0039] Please refer to Figure 5 A primary frequency control margin prediction device for thermal power units can be applied to, for example... Figure 5 The device shown, in order to implement the technical solution of the present invention, includes: The acquisition unit 501 is used to acquire the real-time operating parameters of the thermal power unit, which include at least the unit's rated load, main steam pressure, and current main steam regulating valve opening. Matching unit 502 is used to match the corresponding regulating valve flow characteristic curve based on the turbine type, and to back-calculate the current main steam regulating valve opening into the current valve position flow command; The calculation unit 503 is used to determine the corresponding margin calculation model according to the current load range of the thermal power unit, and to calculate the estimated values ​​of the upper and lower frequency regulation margins according to the determined margin calculation model. The margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, the empirical coefficient, and the correction coefficient corresponding to the boiler type.

[0040] Optionally, the formula for calculating the up-frequency regulation forecast in the margin calculation model corresponding to the normal load period is as follows: P1=min(6%Pe,0.15× ×(Maximum valve position flow command) (Current valve position flow command) × K 修正 ); Where Pe is the rated load of the unit, F is the main steam pressure, and K is the rated load of the unit. 修正 The correction factor is the one corresponding to the boiler type. The formula for calculating the down-regulation prediction in the margin calculation model corresponding to the normal load period is as follows: P2= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ); The formula for calculating the up-frequency modulation forecast in the margin calculation model corresponding to the deep-load section is as follows: P3 = min(6%Pe, 0.15 × ×(min[100, (current valve opening value + estimated opening value)] corresponding to the flow command) (Current valve position flow command) × K 修正 ); The formula for calculating the down-regulation frequency forecast in the margin calculation model corresponding to the deep-adjustment load segment is as follows: P4= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ).

[0041] Furthermore, the maximum valve position flow command and the minimum valve position flow command of the normal load section are pre-calibrated based on the matching turbine control valve flow characteristic curve. The maximum valve position flow command corresponds to the flow command under the condition of full valve opening, and the minimum valve position flow command corresponds to the flow command under the condition of minimum adjustable valve opening.

[0042] Furthermore, the maximum valve position flow command of the deep adjustment load section is determined in the following way: the current valve position opening is summed with the preset estimated opening value, and the smaller value is taken from 100. Then, the smaller opening value is substituted into the matching valve flow characteristic curve to back-calculate the corresponding flow command.

[0043] Optionally, the turbine types include three categories: SAIC, Dongfang Electric, and Harbin Electric. Each type of turbine corresponds to an independent valve flow characteristic curve, which is used to realize the forward mapping and reverse conversion of valve opening and valve position flow command.

[0044] Optional, also includes: Input unit 504 is used to input the unit load, main steam pressure and valve opening under historical test conditions into the prediction model after the parameter configuration is completed. The decision unit 505 is used to determine that the model parameter adaptation is effective if the error between the output margin estimate and the actual test margin of the corresponding working condition is within the allowable range.

[0045] Optionally, the method is applied to the unit's DCS system to collect the main steam pressure and valve opening signals in real time at millisecond intervals, and dynamically output the primary frequency regulation margin that varies with operating conditions.

[0046] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0047] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0048] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0049] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0050] For any other form of computer-readable medium (or computer-readable storage medium) as described above, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0051] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.

[0052] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0054] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0055] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0056] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A method for predicting the primary frequency control margin of a thermal power unit, characterized in that, include: Acquire real-time operating parameters of the thermal power unit, including at least the unit's rated load, main steam pressure, and current main steam regulating valve opening; Based on the turbine type matching corresponding valve flow characteristic curve, the current main steam valve opening is back-calculated into the current valve position flow command; The corresponding margin calculation model is determined based on the current load range of the thermal power unit, and the estimated values ​​of the upper and lower frequency regulation margins are calculated based on the determined margin calculation model. The margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, empirical coefficients, and correction coefficients corresponding to the boiler type.

2. The method according to claim 1, characterized in that, The formula for calculating the up-frequency regulation forecast in the margin calculation model corresponding to the normal load period is as follows: P1=min(6%Pe,0.15× ×(Maximum valve position flow command) (Current valve position flow command) × K 修正 ); Where Pe is the rated load of the unit, F is the main steam pressure, and K is the rated load of the unit. 修正 This is the correction factor corresponding to the boiler type; The formula for calculating the down-regulation prediction in the margin calculation model corresponding to the normal load period is as follows: P2= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ); The formula for calculating the up-frequency modulation forecast in the margin calculation model corresponding to the deep-load section is as follows: P3 = min(6%Pe, 0.15 × ×(min[100, (current valve opening value + estimated opening value)] corresponding to the flow command) (Current valve position flow command) × K 修正 ); The formula for calculating the down-regulation frequency forecast in the margin calculation model corresponding to the deep-adjustment load segment is as follows: P4= min(6%Pe, 0.15×) ×(Current valve position flow command) Minimum valve position flow command) × K 修正 ).

3. The method according to claim 2, characterized in that, The maximum valve position flow command and minimum valve position flow command for the normal load section are pre-calibrated based on the matched turbine control valve flow characteristic curve. The maximum valve position flow command corresponds to the flow command under the condition of full valve opening, and the minimum valve position flow command corresponds to the flow command under the condition of minimum adjustable valve opening.

4. The method according to claim 3, characterized in that, The maximum valve position flow command for the deep adjustment load section is determined in the following way: the current valve position opening is summed with the preset estimated opening value, and the smaller value is taken from 100. Then, the smaller opening value is substituted into the matching valve flow characteristic curve to calculate the corresponding flow command.

5. The method according to claim 1, characterized in that, The turbine types include three categories: SAIC, Dongfang Electric, and Harbin Electric. Each type of turbine corresponds to an independent control valve flow characteristic curve, which is used to realize the forward mapping and reverse conversion between control valve opening and valve position flow command.

6. The method according to claim 1, characterized in that, Also includes: After completing the parameter configuration, input the unit load, main steam pressure, and valve opening under historical test conditions into the prediction model; If the error between the output margin estimate and the actual experimental margin under the corresponding working condition is within the allowable range, then the model parameters are deemed to be effectively adapted.

7. The method according to claim 1, characterized in that, The method is applied to the unit's DCS system to collect main steam pressure and valve opening signals in real time at millisecond intervals, and dynamically output the primary frequency regulation margin that varies with operating conditions.

8. A device for predicting the primary frequency control margin of a thermal power unit, characterized in that, include: Acquisition Unit: Acquires real-time operating parameters of the thermal power unit, including at least the unit's rated load, main steam pressure, and current main steam regulating valve opening; Matching unit: Matches the corresponding regulating valve flow characteristic curve based on the turbine type, and back-calculates the current main steam regulating valve opening into the current valve position flow command; Calculation unit: Determines the corresponding margin calculation model based on the current load range of the thermal power unit, and calculates the estimated values ​​of the upper and lower frequency regulation margins based on the determined margin calculation model; the margin calculation model calculates the estimated values ​​of the upper and lower frequency regulation margins based on the main steam pressure, the current valve position flow command, the preset maximum / minimum valve position flow command, empirical coefficients, and correction coefficients corresponding to the boiler type.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

Images