Optimal effective vacuum control method and system for condenser based on big data processing

Abstract

The invention discloses a condenser optimal effective vacuum control method and system based on big data processing, and the method comprises the steps: obtaining a relation curve between condenser vacuum and different circulating water inlet temperatures, circulating water quantity and steam turbine dead steam quantity in design parameters of a steam turbine condenser, and a relation curve between the circulating water quantity and the power consumption of a circulating water pump; constructing an optimal vacuum prediction model; the steam turbine dead steam flow and the steam turbine condenser circulating water inlet temperature are obtained and input into the optimal vacuum prediction model, and the optimal vacuum and the corresponding optimal circulating water pump frequency are obtained through prediction. The method has the advantages of simple operation, high prediction precision and the like.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of steam turbine generator sets, and particularly relates to a method and system for controlling the optimal effective vacuum of a condenser based on big data processing. Background Art

[0002] The vacuum of a condenser is one of the most important indicators affecting the economy of a steam turbine generator set. For every 1 kPa change in vacuum, the power supply coal consumption rate of a 300 - 600 MW unit is affected by approximately 2.5 g / kWh. Reducing the cooling circulating water volume can reduce power consumption, but this will deteriorate the vacuum of the steam turbine and increase heat loss; when the steam parameters and flow rate remain unchanged, increasing the vacuum will increase the effective enthalpy drop of the steam in the steam turbine, and accordingly increase the output power of the generator. However, when increasing the vacuum value, more cooling circulating water volume needs to be supplied to the condenser, thus increasing the power consumption of the circulating water pump; if the cooling circulating water volume is excessively increased, it is easy to make the vacuum degree too high, resulting in the energy consumed by increasing the cooling circulating water volume being higher than the energy generated by the unit, thus obtaining the opposite effect. Therefore, an optimal cooling circulating water volume and an optimal effective vacuum should be determined. The optimal effective vacuum refers to the vacuum when the difference between the increase in the power of the steam turbine due to the increase in the condenser vacuum and the additional power consumed by the circulating water pump is the maximum. Traditionally, the optimal effective vacuum is determined by experimental methods.

[0003] In the prior art, the main principle of the invention patent application 202110602875.9 is to obtain multiple groups of data under different seasons and working conditions through experimental methods, obtain the relationship formula between the steam turbine load and the optimal effective vacuum through Excel fitting, and then rely on the theoretical calculation formulas of the exhaust steam temperature, circulating water temperature, and flow rate to combine and obtain the final control result. This method requires long - term experiments in spring, summer, autumn, and winter to obtain reliable conclusions, and it is only applicable to one steam turbine generator set. Different experiments need to be carried out for each different power plant, and the generalization ability is poor.

[0004] The invention patent application 201710339532.1 obtains the relationship between the incremental power of the steam turbine and the operating frequency of the circulating water pump through the collection of operation data, and continues to update the nodes of the historical database prediction model in real - time after completing the data model to ensure the effectiveness of the data model. However, this invention completely relies on the statistics of the filtered incremental power of the steam turbine. The change in the frequency of the circulating water pump has a relatively small impact on the power of the steam turbine. Under normal operating conditions, the incremental power affected by the change in the circulating water volume only accounts for 0.4% or even less of the total power of the steam turbine. It is difficult to exclude the power micro - changes caused by other factors and only consider the incremental power caused by the circulating water, so the data credibility is not high. Summary of the Invention

[0005] Aiming at the technical problems existing in the prior art, the present invention provides a condenser optimal effective vacuum control method and system based on big data processing with high prediction accuracy.

[0006] To solve the above technical problems, the technical solution proposed by the present invention is as follows:

[0007] A condenser optimal effective vacuum control method based on big data processing, comprising the steps of:

[0008] Obtain the relationship curves between the condenser vacuum and different circulating water inlet temperatures, circulating water volumes, and turbine exhaust steam volumes in the design parameters of the steam turbine condenser, as well as the relationship curve between the circulating water volume and the circulating water pump power consumption, and construct an optimal vacuum prediction model;

[0009] Obtain the turbine exhaust steam flow rate and the circulating water inlet temperature of the steam turbine condenser, and input them into the optimal vacuum prediction model to predict the optimal vacuum and the corresponding optimal circulating water pump frequency.

[0010] Preferably, the specific steps for obtaining the turbine exhaust steam volume are as follows:

[0011] Obtain the steam condensation amount of the low-pressure heater;

[0012] Based on the steam condensation amount of the low-pressure heater, obtain the turbine exhaust steam flow rate.

[0013] Preferably, the specific steps for obtaining the steam condensation amount of the low-pressure heater are as follows:

[0014] Calculate the condensation amount on the steam side according to the temperature difference and flow rate of the turbine condensate at the inlet and outlet of the low-pressure heater.

[0015] Preferably, the specific steps for obtaining the turbine exhaust steam flow rate based on the steam condensation amount of the low-pressure heater are as follows:

[0016] Subtract the extraction steam flow rates of each stage of the steam turbine from the steam inlet flow rate of the steam turbine to obtain the turbine exhaust steam flow rate. The specific calculation formula is: D’ = D - D1 - D2 - D3...;

[0017] Where D’: turbine exhaust steam flow rate, t / h; D: total steam inlet volume of the steam turbine, t / h; D1, D2, D3...: extraction steam flow rates of each stage of the steam turbine, t / h.

[0018] Preferably, the specific steps for obtaining the turbine exhaust steam flow rate based on the steam condensation amount of the low-pressure heater are as follows:

[0019] Subtract the other water entering the condenser and the condensate water going to other systems from the turbine condensate water flow rate to obtain the turbine exhaust steam flow rate. The calculation formula is: D’ = D0 - D3 - D 进 -D 出 ;

[0020] Where D’: the exhaust steam flow rate of the steam turbine, t / h; D0: the total condensate water volume at the outlet of the condensate pump of the steam turbine condenser, t / h; D3: the steam volume from the steam turbine to the low-pressure heater, t / h; D 进 : the other water inflow volume of the steam turbine condenser, t / h; D 出 : the other water outflow volume of the steam turbine condenser, t / h.

[0021] Preferably, the specific process of constructing the optimal vacuum prediction model is as follows:

[0022] Among them, the relationship between the circulating water pump frequency and the exhaust steam temperature is used as the main model, and the two models of the circulating water temperature and the exhaust steam flow rate of the steam turbine are used as the correction models; the exhaust steam temperature corresponding to the circulating water pump frequency in the main model is corrected according to the correction models, and the sources of the two correction models are fitted through actual operation data.

[0023] Preferably, the correction method is: after obtaining the exhaust steam temperature through the abscissa in the main model, respectively correct it to the required temperature according to the slope change of the model according to the actual temperature through the circulating water temperature model, and correct it to the exhaust steam temperature corresponding to the required exhaust steam volume according to the slope change of the model according to the exhaust steam volume through the steam turbine exhaust steam volume model.

[0024] Preferably, the specific process of constructing the optimal vacuum prediction model is as follows:

[0025] Perform three-dimensional fitting on the relationship between the exhaust steam temperature of the steam turbine or the vacuum of the condenser and the circulating water pump frequency and the circulating water temperature as the main model; correct it to the exhaust steam temperature corresponding to the required exhaust steam volume according to the slope change of the model according to the exhaust steam volume through the steam turbine exhaust steam volume model.

[0026] Preferably, the specific steps for predicting the optimal vacuum and the corresponding optimal circulating water pump frequency are as follows:

[0027] Calculate the difference in the power of the steam turbine caused by the difference in the exhaust steam temperature or vacuum corresponding to a certain working condition and the exhaust steam temperature or vacuum corresponding to the reference benchmark working condition;

[0028] Subtract the difference in the power consumption of the circulating water pump from the difference in the power of the steam turbine between the corresponding working condition and the reference benchmark working condition, that is, obtain the net income at different frequencies. The highest point of the net income at different exhaust steam flow rates is the optimal operating frequency of the circulating water pump, and the corresponding is the optimal effective vacuum.

[0029] The present invention also discloses a condenser optimal effective vacuum control system based on big data processing, including a memory and a processor connected to each other. A computer program is stored on the memory, and the computer program executes the steps of the above-mentioned method when being run by the processor.

[0030] Compared with the prior art, the advantages of the present invention are as follows:

[0031] Based on the initial set data model, the present invention updates the database and the data model according to real-time data, and eliminates the interference of other complex factors affecting the steam turbine power on the collected data, so as to obtain a data model related to the best effective vacuum and the circulating water flow under different working conditions, which is used for the final control of the best effective vacuum of the condenser and optimizes the control of the circulating water pump speed.

[0032] For the exhaust steam volume of the steam turbine that is difficult to measure through meters (the flow rates of low-parameter steam such as the exhaust steam volume of the steam turbine and the extraction steam flow of the three-stage extraction are generally not equipped with flow monitoring), the difference between the condensate flow rate of the condenser and the extracted steam volume consumed by calculating the low-pressure heaters is used to inversely deduce the exhaust steam volume of the steam turbine, so that there is a reliable basis for the exhaust steam volume of the steam turbine and no special metering device is required. Finally, it is combined with the exhaust steam temperature to calculate the change in the power of the steam turbine, and it can be applied to systems lacking flow monitoring.

[0033] The present invention adopts a mathematical model based on the design vacuum parameters of the steam turbine and the condenser as the initial basis, which can be directly used, and then continuously learns and optimizes the mathematical model according to actual data; that is, in the way of combining initial theory with practice, the present invention can be put into use at the initial stage, without going through a long test process, but continuously improving during normal operation to improve the accuracy and reliability of the system.

[0034] The present invention uses the total frequency of the system circulating water pumps as the frequency value, so that the basis of the system can be the operation of a single variable-frequency circulating water pump, or the operation mode of one industrial-frequency pump + one variable-frequency pump, or even more pumps, and can take into account the start and stop of the industrial-frequency pumps, achieving not only the control of the circulating water pump frequency, but also the control of the number of circulating water pumps started.

[0035] The present invention sets up different levels of a main model and a correction model, models the most important adjustment parameters through the main model, and corrects the results through the correction model, with a wide range of application. Description of the Drawings

[0036] Figure 1 It is a curve graph of the variable-frequency pump frequency - exhaust steam temperature in the present invention.

[0037] Figure 2 It is a curve graph between the circulating water temperature and the exhaust steam volume and the exhaust steam temperature in the present invention; among them, (a) is the curve graph of the circulating water temperature - exhaust steam temperature; (b) is the curve graph of the exhaust steam volume - exhaust steam temperature.

[0038] Figure 3 It is a schematic diagram of the main model in the present invention.

[0039] Figure 4 It is a curve graph of the relationship between the total frequency of the circulating water pumps and the total power consumption in the present invention.

[0040] Figure 5 This is the curve graph of the total frequency - net increased power grid connection power of the circulating water pump in the present invention.

[0041] Figure 6 This is the flow chart of the optimal effective vacuum control method of the condenser of the present invention in the embodiment. Detailed implementation manners

[0042] The present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0043] As Figure 6 shown, the optimal effective vacuum control method of the condenser based on big data processing provided by the embodiment of the present invention includes the steps:

[0044] S1. Collect all data affecting the power of the steam turbine, including the total amount of main steam inlet of the steam turbine, the extraction steam flow rate of each stage, the total flow rate at the outlet of the condensate pump, the inlet and outlet water temperatures on the water side of the low-pressure heater, the extraction steam temperature and pressure at the inlet of the steam side of the low-pressure heater, other water inflows such as the make-up water amount of demineralized water in the condenser, other water outflows of the condenser, the temperature of the circulating water at the inlet of the condenser, and the temperature and pressure of the exhaust steam of the steam turbine.

[0045] S2. Calculate the steam condensation amount of the low-pressure heater for the extraction steam without flow measurement, that is, the low-pressure extraction steam flow rate of the steam turbine. Specifically, according to the temperature difference and flow rate of the condensate water of the steam turbine at the inlet and outlet of the low-pressure heater, calculate the condensation amount on the steam side (similarly, for multi-stage heating, it can also be calculated separately by this method). For example:

[0046]

[0047] Where D3: the steam amount from the steam turbine to the low-pressure heater (the extraction steam amount of the third extraction), t / h; t1, t2: the inlet and outlet temperatures of the water side (total condensate water) of the low-pressure heater of the steam turbine, °C; D0: the total condensate water amount at the outlet of the condensate pump of the steam turbine condenser, t / h; h3: the latent heat of vaporization corresponding to the steam from the steam turbine to the low-pressure heater (the third extraction steam), kj / kg; 4.2: the specific heat capacity of the condensate water, kj / kg*°C.

[0048] S3. Calculate the steam amount that does work affected by the vacuum value of the condenser, that is, the exhaust steam flow rate of the steam turbine. Specifically, it can be obtained by the following two methods:

[0049] Method 1: Subtract the extraction steam amounts of each stage of the steam turbine (obtained in step S2) from the steam inlet amount of the steam turbine to obtain the exhaust steam flow rate of the steam turbine. The specific calculation formula is:

[0050] D’ = D - D1 - D2 - D3...;

[0051] where D’: the flow rate of turbine exhaust steam, t / h; D: the total inlet flow rate of main steam of the turbine, t / h; D1, D2, D3...: the extraction steam flow rates of each stage of the turbine, t / h.

[0052] Method 2: Subtract the other water entering the condenser (including condensate water from low-pressure heaters, make-up water of demineralized water in the condenser, etc.) and the water from the condensate water to other systems from the condensate water flow rate of the turbine to obtain the flow rate of turbine exhaust steam. The specific calculation formula is:

[0053] D’ = D0 - D3 - D 进 -D 出 ;

[0054] where D’: the flow rate of turbine exhaust steam, t / h; D0: the total condensate water volume at the outlet of the condensate water pump of the turbine condenser, t / h; D3: the steam volume from the turbine to the low-pressure heater (the extraction steam volume of the third stage), t / h; D_in: the other inlet water volume of the turbine condenser (such as make-up water of demineralized water), t / h; D_out: the other outlet water volume of the turbine condenser, t / h.

[0055] S4. Use the one-to-one correspondence curve between the exhaust steam temperature (or condenser vacuum) of the turbine in the design parameters of the turbine condenser and different circulating water inlet temperatures, circulating water pump frequencies, and turbine exhaust steam volumes as the mathematical model for predicting the results in the initial stage.

[0056] Specifically, it can be obtained through the following two methods:

[0057] Method 1: Use the relationship between the circulating water pump frequency and the exhaust steam temperature as the main model, and the two models of circulating water temperature and turbine exhaust steam flow rate as the correction models;

[0058] For example: Under certain water temperature and certain turbine exhaust steam flow rate, the main model of the circulating water pump frequency and the exhaust steam temperature, as Figure 1 shown;

[0059] If you want to obtain the corresponding exhaust steam temperature under different water temperatures and different exhaust steam flow rates, then correct the exhaust steam temperature corresponding to the circulating water pump frequency in the main model according to the correction models. The sources of the two correction models are also fitted through actual operation data;

[0060] The correction method is: As Figure 2 shown, after obtaining the exhaust steam temperature through the abscissa in the main model, it is the exhaust steam temperature of the turbine under the condition of 26°C / 122 t / h. Then, correct it to the required temperature according to the slope change of the model through the circulating water temperature model according to the actual temperature, and correct it to the exhaust steam temperature corresponding to the required exhaust steam volume according to the slope change of the model through the turbine exhaust steam volume model.

[0061] Method 2: Use software such as MATLAB for data processing to directly obtain the relationships among the other three parameters at a certain circulating water temperature.

[0062] Specifically, use software such as MATlab to perform three-dimensional fitting on the relationships among the exhaust steam temperature (or condenser vacuum) of the steam turbine, the frequency of the circulating water pump, and the circulating water temperature as the main model to find the exhaust steam temperature of the steam turbine under different working conditions; then use the steam turbine exhaust steam flow as the correction model through the above Method 1, as Figure 3 shown.

[0063] S5. Calculate the difference in the power of the steam turbine caused by the difference between the exhaust steam temperature (or vacuum) corresponding to a certain working condition and the exhaust steam temperature (or vacuum) corresponding to the reference benchmark working condition;

[0064] Example:

[0065] Where ΔP: the change in the power of the steam turbine, KW; h’: the enthalpy value of the exhaust steam corresponding to the exhaust steam temperature under a certain working condition, kj / kg; h0: the enthalpy value of the exhaust steam corresponding to the exhaust steam temperature under the reference benchmark working condition, kj / kg; 0.9: the stage efficiency of the steam turbine, a constant; 3.6: the unit conversion rate; D’: the exhaust steam flow of the steam turbine, t / h;

[0066] S6. The power consumption model curve of the circulating water pump. After the installation of the circulating water system is completed, the power consumption model of the circulating water pump is already determined. According to the number and frequency of the circulating water pumps in operation, calculate according to the electricity meter or the running current to obtain the power consumption model of the circulating water pump at the corresponding frequency. Here, the sum of the values of the frequencies of multiple industrial frequency + variable frequency pumps is used as the frequency value of the system. For example, a system of 85HZ represents an operation mode of one industrial frequency pump at 50HZ + one variable frequency pump at 35HZ. A system of 45HZ represents an operation mode of a single variable frequency pump at 45HZ. Thus, an example of the power consumption model of the circulating water pump is obtained as Figure 4 shown: In terms of system safety, the variable frequency circulating water pump is required to have a minimum output requirement. The example variable frequency operation limit is 34 - 50HZ.

[0067] S7. Finally, subtract the difference in the power consumption of the circulating water pump from the difference in the power of the steam turbine between the corresponding working condition and the benchmark working condition to obtain the net profit at different frequencies. The highest point of the net profit at different exhaust steam flows is the optimal operating frequency of the circulating water pump, which corresponds to the optimal effective vacuum, as Figure 5 shown.

[0068] Based on the initial set data model, the present invention updates the database and the data model according to real-time data, and eliminates the interference of other complex factors affecting the steam turbine power on the collected data, so as to obtain a data model related to the best effective vacuum and the circulating water flow under different working conditions, which is used for the final control of the best effective vacuum of the condenser and optimizes the control of the circulating water pump speed.

[0069] For the exhaust steam volume of the steam turbine that is difficult to measure through meters (the flow of low-parameter steam such as the exhaust steam volume of the steam turbine and the extraction flow of the third extraction is generally not equipped with flow monitoring), the difference between the condensate flow of the condenser and the extracted steam volume consumed by calculating the low-pressure heater is used to inversely deduce the exhaust steam volume of the steam turbine, so that there is a reliable basis for the exhaust steam volume of the steam turbine and no special metering device is required. Finally, it is combined with the exhaust steam temperature to calculate the change in the power of the steam turbine, and it can be applied to systems lacking flow monitoring.

[0070] Since the exhaust steam volume of the steam turbine is the corresponding steam part of the load change caused by the change in the vacuum of the steam turbine, the change in the exhaust steam temperature of the steam turbine and the exhaust steam volume of the steam turbine are used to calculate the change value of the power of the steam turbine, instead of collecting the incremental power data of the steam turbine; among them, the change in the power of the steam turbine calculated theoretically by the exhaust steam temperature can not only calculate sensitively and accurately, but also avoid the interference of the power change caused by other factors on the model. Compared with directly statistically comparing the incremental power of the steam turbine, it has the advantages of high accuracy and sensitive response.

[0071] The present invention adopts a mathematical model based on the design vacuum parameters of the steam turbine and the condenser as the initial basis, which can be directly used, and then continuously learns and optimizes the mathematical model according to the actual data; that is, in the way of combining the initial theory with the actual situation, the present invention can be put into use at the initial stage, without going through a long test process, but continuously improving and perfecting during normal operation to improve the accuracy and reliability of the system.

[0072] The present invention uses the total frequency of the system circulating water pump as the frequency value, so that the basis of the system can be the operation of a single variable-frequency circulating water pump, or the operation mode of one industrial-frequency pump + one variable-frequency pump, or even more pumps, and can take into account the start and stop of the industrial-frequency pump, so that not only the frequency of the circulating water pump is controlled, but also the start number of the circulating water pump is controlled.

[0073] The present invention sets up different levels of the main model and the correction model, models the most important adjustment parameters through the main model, and corrects the results through the correction model, with a wide range of application.

[0074] The present invention abandons the intermediate process quantity of the circulating water flow and directly corresponds to the power consumption of the circulating water system, streamlining the process, but the actual model is more accurate instead.

[0075] The present invention also discloses a condenser optimal effective vacuum control system based on big data processing, which includes a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method described above. The control system of the present invention corresponds to the above control method and also has the advantages described in the above control method.

[0076] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, several improvements and retouches made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A method for controlling the optimal effective vacuum of a condenser based on big data processing, characterized in that Including the steps of: Obtaining the relationship curves between the condenser vacuum and different circulating water inlet temperatures, circulating water volumes, and turbine exhaust steam volumes in the design parameters of the steam turbine condenser, as well as the relationship curve between the circulating water volume and the circulating water pump power consumption, and constructing an optimal vacuum prediction model; Obtaining the turbine exhaust steam flow rate and the circulating water inlet temperature of the steam turbine condenser, and inputting them into the optimal vacuum prediction model to predict the optimal vacuum and the corresponding optimal circulating water pump frequency.

2. The optimal effective vacuum control method for a condenser based on big data processing according to claim 1, wherein The specific steps for obtaining the turbine exhaust steam volume are: Obtaining the steam condensation amount of the low-pressure heater; Based on the steam condensation amount of the low-pressure heater, obtaining the turbine exhaust steam flow rate.

3. The optimal effective vacuum control method for a condenser based on big data processing according to claim 2, characterized in that, The specific steps for obtaining the steam condensation amount of the low-pressure heater are: Calculating the condensation amount on the steam side according to the temperature difference and flow rate of the steam turbine condensate at the inlet and outlet of the low-pressure heater.

4. The method for controlling the optimal effective vacuum of a condenser based on big data processing according to claim 2 or 3, characterized in that The specific steps for obtaining the turbine exhaust steam flow rate based on the steam condensation amount of the low-pressure heater are: Subtracting the extraction steam flow rates at all stages of the steam turbine from the steam inlet flow rate of the steam turbine to obtain the turbine exhaust steam flow rate. The specific calculation formula is: D’ = D - D1 - D2 - D3...; Where D’: turbine exhaust steam flow rate, t / h; D: total main steam inlet flow rate of the steam turbine, t / h; D1, D2, D3...: extraction steam flow rates at all stages of the steam turbine, t / h.

5. The method for controlling the optimal effective vacuum of a condenser based on big data processing according to claim 2 or 3, characterized in that, The specific steps for obtaining the turbine exhaust steam flow rate based on the steam condensation amount of the low-pressure heater are: The exhaust steam flow of the turbine is obtained by subtracting other water entering the condenser and the water condensed to other systems from the turbine condensate flow. The calculation formula is: D'=D0-D3-D 进 -D 出 ; where D’: exhaust steam flow rate of steam turbine, t / h; D0: total condensate water volume at the outlet of condensate pumps of steam turbine condenser, t / h; D3: steam volume from steam turbine to low-pressure heater, t / h; D 进 : other water inflow volume of steam turbine condenser, t / h; D 出 : other water outflow volume of steam turbine condenser, t / h.

6. The optimal effective vacuum control method for a condenser based on big data processing according to claim 1 or 2 or 3, characterized in that The specific process of constructing the optimal vacuum prediction model is: Among them, the relationship between the circulating water pump frequency and the exhaust steam temperature is used as the main model, and the two models of the circulating water temperature and the turbine exhaust steam flow rate are used as correction models; the exhaust steam temperature corresponding to the circulating water pump frequency in the main model is corrected according to the correction models, and the sources of the two correction models are fitted through actual operation data.

7. The method for controlling the optimal effective vacuum of a condenser based on big data processing according to claim 6, characterized in that, The correction method is: after obtaining the exhaust steam temperature through the abscissa in the main model, respectively correct it to the required temperature according to the slope change of the model by the circulating water temperature model according to the actual temperature, and correct it to the exhaust steam temperature corresponding to the required exhaust steam volume according to the slope change of the model by the turbine exhaust steam volume model.

8. The optimal effective vacuum control method for a condenser based on big data processing according to claim 1 or 2 or 3, characterized in that, The specific process of constructing the optimal vacuum prediction model is: Performing three-dimensional fitting on the relationship between the turbine exhaust steam temperature or the condenser vacuum and the circulating water pump frequency and the circulating water temperature as the main model; correcting it to the exhaust steam temperature corresponding to the required exhaust steam volume according to the slope change of the model by the turbine exhaust steam volume model.

9. The method for controlling the optimal effective vacuum of a condenser based on big data processing according to claim 1 or 2 or 3, characterized in that, The specific steps for predicting the optimal vacuum and the corresponding optimal circulating water pump frequency are: Calculating the difference in the power of the steam turbine caused by the difference in the exhaust steam temperature or vacuum corresponding to a certain working condition and the exhaust steam temperature or vacuum corresponding to the reference benchmark working condition; Subtracting the difference in the power consumption of the circulating water pump from the difference in the power of the steam turbine between the corresponding working condition and the reference benchmark working condition, that is, obtaining the net income at different frequencies. The highest point of the net income at different exhaust steam flow rates is the optimal operating frequency of the circulating water pump, and the corresponding is the optimal effective vacuum.

10. A condenser optimal effective vacuum control system based on big data processing, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, and is characterized in that The computer program, when run by a processor, executes the steps of the method according to any one of claims 1-9.

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