CN113503749B - Intelligent water temperature control method for indirect air cooling system - Google Patents
Intelligent water temperature control method for indirect air cooling system Download PDFInfo
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- CN113503749B CN113503749B CN202110796389.5A CN202110796389A CN113503749B CN 113503749 B CN113503749 B CN 113503749B CN 202110796389 A CN202110796389 A CN 202110796389A CN 113503749 B CN113503749 B CN 113503749B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28B—STEAM OR VAPOUR CONDENSERS
- F28B1/00—Condensers in which the steam or vapour is separate from the cooling medium by walls, e.g. surface condenser
- F28B1/02—Condensers in which the steam or vapour is separate from the cooling medium by walls, e.g. surface condenser using water or other liquid as the cooling medium
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28B—STEAM OR VAPOUR CONDENSERS
- F28B11/00—Controlling arrangements with features specially adapted for condensers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28B—STEAM OR VAPOUR CONDENSERS
- F28B9/00—Auxiliary systems, arrangements, or devices
- F28B9/04—Auxiliary systems, arrangements, or devices for feeding, collecting, and storing cooling water or other cooling liquid
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28D—HEAT-EXCHANGE APPARATUS, NOT PROVIDED FOR IN ANOTHER SUBCLASS, IN WHICH THE HEAT-EXCHANGE MEDIA DO NOT COME INTO DIRECT CONTACT
- F28D1/00—Heat-exchange apparatus having stationary conduit assemblies for one heat-exchange medium only, the media being in contact with different sides of the conduit wall, in which the other heat-exchange medium is a large body of fluid, e.g. domestic or motor car radiators
- F28D1/02—Heat-exchange apparatus having stationary conduit assemblies for one heat-exchange medium only, the media being in contact with different sides of the conduit wall, in which the other heat-exchange medium is a large body of fluid, e.g. domestic or motor car radiators with heat-exchange conduits immersed in the body of fluid
- F28D1/0233—Heat-exchange apparatus having stationary conduit assemblies for one heat-exchange medium only, the media being in contact with different sides of the conduit wall, in which the other heat-exchange medium is a large body of fluid, e.g. domestic or motor car radiators with heat-exchange conduits immersed in the body of fluid with air flow channels
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F28—HEAT EXCHANGE IN GENERAL
- F28F—DETAILS OF HEAT-EXCHANGE AND HEAT-TRANSFER APPARATUS, OF GENERAL APPLICATION
- F28F27/00—Control arrangements or safety devices specially adapted for heat-exchange or heat-transfer apparatus
- F28F27/02—Control arrangements or safety devices specially adapted for heat-exchange or heat-transfer apparatus for controlling the distribution of heat-exchange media between different channels
Abstract
The invention discloses an intelligent water temperature control method for an indirect air cooling system, which relates to the technical field of automatic control and comprises the following steps: acquiring DSC data of a power plant and temperature data of a radiator system and carrying out regularization treatment; setting the maximum opening limit of the shutter and the target cold water main pipe temperature of each sector according to the working condition in winter; predicting the opening variation of each actuator in the next period; determining whether the opening degree of the actuator needs to be updated, recording the difference value between the predicted opening degree and the actual opening degree when the updating is needed, and eliminating the abnormal predicted opening degree through the maximum opening degree limitation; respectively calculating the number of actuators and the control sequence of the opening to be updated in each sector in the next period; and the DCS control platform issues a control instruction to the indirect air cooling system. The method realizes independent control of the opening of the sector actuator, and can select the control number and sequence to maximize the work of the steam turbine in winter, thereby achieving the purposes of reducing the back pressure and increasing the energy type power generation utilization rate.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to an intelligent water temperature control method for an indirect air cooling system.
Background
In a modern power plant, steam generated by a steam turbine to do work needs to be cooled by a cooling system, and a mode of taking heat away by taking air as a medium for cooling is called air cooling for short, and the cooling system is also called an air cooling system. The air cooling system has the advantages of almost no water consumption and is very commonly applied in northern areas of China. The control of the traditional cooling system generally adopts a PID control mode, and a control strategy for eliminating the error is generated by the error between the actual behavior of a control target and the actual behavior of a controlled object, which is the essence of the PID control technology.
In winter environment, the heat exchanger is prevented from freezing by increasing the operation temperature, but the consumed coal is too large, and under the control of PID, an actuator for controlling air flow in an air cooling system is basically switched on and off in a whole sector, so that the most economical operation back pressure is missed.
Disclosure of Invention
The inventor provides an intelligent water temperature control method for an indirect air cooling system aiming at the problems and technical requirements, the water outlet temperature of a cold water main pipe is controlled to be +/-0.5 ℃ of the target cold water main pipe temperature of each sector, the work of a steam turbine in winter is maximized as far as possible, and the back pressure is reduced.
The technical scheme of the invention is as follows:
an intelligent water temperature control method for an indirect air cooling system is disclosed, wherein the indirect air cooling system comprises a DCS control platform, a steam turbine, a condenser, a circulating water pump and valve, an air cooling tower and a radiator system, exhaust steam of the steam turbine enters the condenser, the condenser is connected with the air cooling tower through the circulating water pump by a circulating water inlet pipeline, and the air cooling tower is connected with the condenser by a circulating water outlet pipeline to form a circulating loop; the radiator system comprises shutters and cooling triangles arranged on the periphery of the air cooling tower and an actuator for controlling the opening degree of the shutters, the DCS control platform is connected with the actuator, the shutters are arranged on the air inlet side of the cooling triangles, every two cooling triangles share one shutter, and the cooling triangles are divided into a plurality of sectors;
the method comprises the following steps:
acquiring power plant DSC data and temperature data of a radiator system and carrying out regularization treatment, wherein the power plant DSC data comprises the actual cold water main pipe temperature of each sector;
setting the maximum opening limit of the shutter and the target cold water main pipe temperature of each sector according to the working condition in winter;
predicting the opening variation of each actuator in the next period according to the DSC data of the power plant, the temperature data of the radiator system and the target cold water main pipe temperature of each sector;
acquiring the temperature change trend of a cold water main pipe of each sector in the latest continuous set period, the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature and the temperature distribution, and determining whether the opening of the actuator needs to be updated;
when the opening degree needs to be updated, taking the maximum opening degree limit as an override condition, recording the difference value between the predicted opening degree and the actual opening degree, and eliminating the abnormal predicted opening degree through the override condition, wherein the abnormal predicted opening degree comprises the opening degree with too small opening degree and too large opening degree jitter;
respectively calculating the number of actuators and the control sequence of the opening of each sector to be updated in the next period according to the difference value between the actual cold water main pipe temperature of each sector and the target cold water main pipe temperature and the temperature distribution and sequencing of the cooling triangles;
the DCS control platform issues control instructions to the indirect air cooling system, wherein the control instructions comprise the number and the control sequence of actuators of which the opening is required to be updated in each sector and the final opening of each actuator;
and when the actual cold water main pipe temperature reaches the error water temperature range allowed by the target cold water main pipe temperature, the actuator keeps the opening change of the previous period, otherwise, the step of acquiring the DSC data of the power plant and the temperature data of the radiator system and performing regularization processing is executed again.
The technical scheme is that all sectors are matched with one unit, a water inlet and a water outlet of a cold water main pipe of each sector and a circulating water outlet are provided with temperature sensors, a temperature measuring point of a cooling triangle of each sector is provided with a grating array sensor, and a DCS control platform is also connected with the temperature sensors and the grating array sensors;
predicting the opening variation of each actuator in the next period according to the DSC data of the power plant, the temperature data of the radiator system and the target cold water main pipe temperature of each sector, and comprising the following steps:
judging whether each sector has a freezing risk or not according to the temperature data of the radiator system;
if so, closing the opening of the corresponding shutter; otherwise, inputting the DSC data of the power plant and the temperature data of the radiator system into a polynomial model to calculate the temperature of a cold water main pipe of each sector in the next period;
inputting the temperature of a cold water main pipe of each sector in the next period, power plant DSC data and temperature data of a radiator system into an XGboost model to calculate the opening variation of each actuator in each sector in the next period;
the DSC data of the power plant also comprises the water inlet temperature of a cold water main pipe, power generation load, backpressure, environmental temperature, wind speed, the temperature of circulating water discharged from the tower, the opening information of an upper period, the number of sectors used by a unit, the number of model switching sectors and the opening information of other actuators in the same sector; the temperature data of the radiator system comprises a sector temperature average (the temperature average of all cooling triangles in the sector), a cooling triangle temperature average, a sector temperature standard deviation and a cooling triangle temperature standard deviation.
The further technical scheme is that the number of actuators of each sector in the next period, the opening of which needs to be updated, and the control sequence are respectively calculated according to the difference value between the actual cold water main pipe temperature of each sector and the target cold water main pipe temperature and the temperature distribution and the sequencing of a cooling triangle, and the method comprises the following steps:
collecting client data and dividing the client data into a sample set and a test set, wherein the client data comprises a standard deviation of temperatures of a cooling triangle and a sector and a difference value of an actual cold water main pipe temperature and a target cold water main pipe temperature;
taking the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature as a water temperature difference value, and taking the temperature standard difference value of the cooling triangle and the sector as a uniformity difference value;
under the same coordinate system, fitting two first safety curves according to a sample set by taking the absolute value of the water temperature difference as an x axis and the uniformity difference as a y axis; fitting a second safety curve according to the sample set by taking the uniformity difference value as an x axis and the water temperature difference value as a y axis; taking the water temperature difference as an x axis, and taking x =0.5 as a third safety straight line;
the first safety curve, the second safety curve and the third safety straight line are connected, and the intersection area is solved to serve as a safety area;
determining the number of the same sector in the safety area in the test set as a safety number, and taking the number of actuators needing to update the opening in the sector as the sum minus the safety number as a derived value;
sequencing actuators needing opening updating according to the fact that the cooling triangles are high in mean temperature and small in variance, and sequentially opening the opening of the actuators; and conversely, sequencing the actuators of which the opening needs to be updated according to the low mean temperature and the large variance of the cooling triangle, and closing the openings of the actuators in sequence.
The further technical scheme is that the polynomial model has the expression as follows:
wherein, a 0 -a m Assigning a coefficient, x, to each entered parameter's weight i (i =1,2, \8230;, n) represents plant DSC data and temperature data of the radiator system, respectively, n represents the number of parameters, ε i Representing the bias constant.
The further technical scheme is that the XGboost model has the expression as follows:
wherein Obj represents the predicted opening variation, G represents the first derivative of the square error of the predicted value and the actual value of the model, H is the taylor formula expansion term, T represents the tree node of the model, γ represents the minimum limit value for learning the splitting of each tree, and λ is the blocking coefficient for limiting the derivative error interval step.
The method further comprises the following step of subtracting the safety number from the total number of the actuators needing to update the opening degree in the sector, wherein the method comprises the following steps:
under the same coordinate system, a fourth curve which takes the water temperature difference as an x axis and the number of actuators as a y axis is fitted according to artificial experience, and the expression is as follows: y =5.678 × log e T error L +3.8868, wherein T error Representing the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature;
and substituting the test set data into the fourth curve to obtain the number of actuators with the opening needing to be updated as a reference value, comparing the reference value with the derivation value, and taking the value with a smaller value as the final number of actuators with the opening needing to be updated.
The further technical scheme is that the expression of the first safety curve is as follows: x = -0.22 (T) sq_std -T each_sq_std ) 2 +1.1*|T sq_std -T each_sq_std |+1.428
The expression of the second safety curve is: y =1.11 (T) error ) 2 -|T error |-4.452
Wherein, T sq_std Denotes the temperature standard deviation, T, of the sector each_sq_std Denotes the temperature standard deviation, T, of the cooling triangle error The difference value of the actual cold water main pipe temperature and the target cold water main pipe temperature is represented.
The indirect air cooling system further comprises an edge server arranged locally, wherein a model for predicting the opening variation of each actuator in the next period is stored in the edge server, and the edge server is in communication connection with the DCS control platform;
after the step of executing the step of issuing the control instruction to the indirect air cooling system by the DCS control platform, the method further comprises the following steps:
uploading the number and control sequence of actuators of which the opening needs to be updated in each sector and the final opening variation of each actuator to an edge server, recording and arranging the actuators into a log form, and storing historical power plant DSC data and temperature data of a radiator system in a database;
after a certain period of control is carried out, high-quality data after test operation is extracted to serve as a training sample for updating the model, and the updated model is downloaded to the local.
The beneficial technical effects of the invention are as follows:
aiming at working conditions in winter, anti-freezing detection and protection are carried out on a cooling triangle before opening degree prediction, a polynomial model and an XGboost model which are fitted with a plurality of units are combined for integrated learning, independent regulation and control of the opening degree of a sector actuator are achieved, a traditional control method that the actuators in the same sector are simultaneously opened and closed is broken through, fine adjustment and stabilization of the water temperature are achieved within an error range of +/-0.5 ℃ of a target value, the most economical output increment value opening degree variation is successfully learned, opening degree control is carried out after the water temperature is stabilized, excessive adjustment of the opening degree due to inertia is prevented, after the opening degree is determined to be updated, abnormal opening degree is eliminated by taking maximum opening degree limitation as an override condition, and water temperature excessive control is prevented; fitting three safety curves and a straight line through data provided by a client to determine a safety region, and sequentially controlling actuators needing opening updating in the same sector according to a specified control sequence of the actuators; and training the model in real time during the trial operation to obtain an optimal parameter model, so as to achieve the purposes of reducing the backpressure and increasing the energy type power generation utilization rate.
Drawings
Fig. 1 is a schematic diagram of an indirect air cooling system provided in the present application.
Fig. 2 is an overall flowchart of the intelligent water temperature control method provided by the present application.
Fig. 3 is a schematic flowchart of predicting the opening degree variation of the next cycle according to the present application.
Fig. 4 is a fitting graph of the number of actuators for updating the opening degree provided in the present application.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
As shown in fig. 1, an indirect air cooling system includes a DCS control platform (not shown in the figure), a turbine 1, a condenser 2, a circulating water pump 3 and a valve, an air cooling tower 4, a radiator system, and a local edge server (not shown in the figure), wherein exhaust steam of the turbine 1 enters the condenser 2, the condenser 2 is connected to the air cooling tower 4 through the circulating water pump 3 via a circulating water inlet pipeline 51, and the air cooling tower 4 is connected to the condenser 2 via a circulating water outlet pipeline 52 to form a circulation loop.
The radiator system completes the control of the temperature of circulating water and ensures reasonable temperature of the circulating water discharged from the tower, and comprises a shutter 6 and a cooling triangle which are arranged on the periphery of an air cooling tower 4 and an actuator (not shown in the figure) for controlling the opening degree of the shutter, wherein the shutter is arranged on the air inlet side of the cooling triangle, the cooling triangle comprises two vertically arranged radiating fins, every two cooling triangles share one shutter, the cooling triangle is divided into a plurality of sectors, and all the sectors are matched with one unit.
Temperature sensors are arranged at a water inlet and a water outlet of the cold water main pipe of each sector and a circulating water outlet, and a grating array sensor is arranged at a temperature measuring point of a cooling triangle of each sector; the edge server stores a model (namely a polynomial model and an XGboost model) for predicting the opening variation of each actuator in the next period.
The DCS control platform is respectively connected with the actuator, the temperature sensor, the grating array sensor and the edge server and used for acquiring DSC data of a power plant and temperature data of a radiator system.
As shown in fig. 2, an intelligent water temperature control method for an indirect air cooling system includes the following steps:
step 1: and acquiring DSC data of the power plant and temperature data of the radiator system in one period and carrying out regularization processing.
The power plant DSC data comprises the inlet water temperature of a cold water main pipe of each sector, the actual temperature of the cold water main pipe (namely the actual outlet water temperature of cooling water of the sector), the power generation load, the backpressure, the ambient temperature, the wind speed, the outlet tower temperature of circulating water, the opening information of the upper period, the number of sectors used by a unit, the number of model switching sectors and the opening information of other actuators in the same sector.
The temperature data of the radiator system comprises a sector temperature average (namely, the temperature average of all cooling triangles in the sector), a cooling triangle temperature average, a sector temperature standard deviation and a cooling triangle temperature standard deviation.
Alternatively, one period is generally set to 30s.
Step 2: and (3) setting the maximum opening limit of the shutter and the target cold water main pipe temperature of each sector according to the winter working condition, and recording the actual cold water main pipe temperature in the step 1.
And step 3: and predicting the opening variation of each actuator in the next period according to the DSC data of the power plant, the temperature data of the radiator system and the target cold water main pipe temperature of each sector.
As shown in fig. 3, the method specifically includes the following steps:
step 31: and judging whether each sector has a freezing risk according to the temperature data of the radiator system, if so, reducing the opening of the corresponding shutter 6, and judging the freezing risk again, otherwise, entering the step 32.
Step 32: and inputting the DSC data of the power plant and the temperature data of the radiator system into a polynomial model to calculate the temperature of a cold water main pipe of each sector in the next period.
The polynomial model is expressed as:
wherein, a 0 -a m Assigning a coefficient, x, to the weight of each parameter i (i =1,2, \8230;, n) represents the plant DSC data and the temperature data of the radiator system, respectively, n represents the number of entries, ∈ i The bias constants are expressed, and in the model training process, the bias constants are different because the training sample sets are different.
Step 33: and inputting the temperature of the cold water main pipe of each sector in the next period, the DSC data of the power plant and the temperature data of the radiator system into an XGboost model to calculate the opening variation of each actuator in each sector in the next period.
The XGboost model has the expression:
wherein Obj represents the predicted opening variation, G represents the first derivative of the square error of the predicted value and the actual value of the model, H is the taylor formula expansion term, T represents the tree node of the model, γ represents the minimum limit value for learning the splitting of each tree, and λ is the blocking coefficient for limiting the derivative error interval step.
And 4, step 4: and acquiring the temperature change trend of the cold water main pipe of each sector in the latest continuous set period, the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature and the temperature distribution, and determining whether the opening of the actuator needs to be updated.
And when the actual temperature of the cold water main pipe does not reach the target temperature of the cold water main pipe, the cold water main pipe is in a rising or falling state, the actuator is kept still, the opening degree of the actuator is updated until the water temperature is kept stable and the heat dissipation capacity reaches a limit value, the opening degree is prevented from being excessively adjusted due to inertia, and then the step 5 of updating the opening degree of the actuator is executed according to a temperature protection logic.
Alternatively, the last consecutive set period may be set to the last three consecutive periods.
And 5: and recording the difference between the predicted opening (obtained by predicting the opening variation) and the actual opening in the current period by taking the maximum opening limit as an override condition, and eliminating the abnormal predicted opening by the override condition, wherein the abnormal predicted opening comprises the openings with too small opening and too large opening jitter, so as to prevent the water temperature from being over-controlled.
And 6: respectively calculating the number of actuators and a control sequence of the opening of each sector in the next period, which need to be updated, according to the difference value between the actual cold water main pipe temperature of each sector and the target cold water main pipe temperature and the temperature distribution and the sequencing of the cooling triangles, and specifically comprising the following steps:
step 61: and collecting client data and dividing the client data into a sample set and a test set, wherein the client data comprises the standard deviation of the temperature of the cooling triangle and the sector, and the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature.
Step 62: and taking the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature as a water temperature difference value, and taking the temperature standard difference value between the cooling triangle and the sector as a uniformity difference value.
And step 63: as shown in fig. 4, in the same coordinate system, two first safety curves 71 are fitted according to a sample set with the absolute value of the water temperature difference as the x-axis and the uniformity difference as the y-axis, where the expression is:
x＝-0.22*(T sq_std -T each_sq_std ) 2 +1.1*|T sq_std -T each_sq_std |+1.428
and fitting a second safety curve 72 according to the sample set by taking the uniformity difference value as an x axis and the water temperature difference value as a y axis, wherein the expression is as follows: y =1.11 (T) error ) 2 -|T error |-4.452
Wherein, T sq_std Denotes the temperature standard deviation, T, of the sector each_sq_std Denotes the temperature standard deviation, T, of the cooling triangle error The difference between the actual cold water main pipe temperature and the target cold water main pipe temperature is represented.
With the water temperature difference as the x-axis, x =0.5 is taken as the third safety line 73.
Step 64: the first safety curve 71, the second safety curve 72 and the third safety line 73 are connected, and the intersecting area is solved as a safety area (i.e., a shaded area in the figure).
Step 65: and determining the number of the same sector in the test set falling into the safety area as a safety number, and subtracting the safety number from the total number of the actuators needing to update the opening in the sector to obtain a derived value.
And step 66: fitting a fourth curve 74 with the water temperature difference as an x axis and the number of actuators as a y axis according to artificial experience, wherein the expression is as follows: y =5.678 | log e T error L +3.8868, wherein, T error The difference value of the actual cold water main pipe temperature and the target cold water main pipe temperature is represented.
Step 67: and substituting the test set data into the fourth curve 74 to obtain the number of actuators with the opening needing to be updated as a reference value, comparing the reference value with the derivation value, and taking the value with the smaller value as the final number of actuators with the opening needing to be updated.
Step 68: sequencing actuators needing opening updating according to the fact that the cooling triangles are high in mean temperature and small in variance, and sequentially opening the opening of the actuators; and conversely, sequencing the actuators of which the opening needs to be updated according to the low mean temperature and the large variance of the cooling triangle, and closing the openings of the actuators in sequence.
And 7: and the DCS control platform issues a control instruction to the indirect air cooling system, wherein the control instruction comprises the number and the control sequence of the actuators with the opening needing to be updated in each sector and the final opening of each actuator (namely the sum of the predicted opening variation and the opening of the current period).
And 8: uploading the number and control sequence of actuators of which the opening needs to be updated in each sector and the final opening variation of each actuator to an edge server, recording and arranging the actuators into a log form, and storing historical power plant DSC data and temperature data of a radiator system in a database.
And after a certain period of control, extracting high-quality data after test operation as a training sample for updating the model, and downloading the updated model to the local.
When the actual cold water main pipe temperature reaches the error water temperature range allowed by the target cold water main pipe temperature (namely the target cold water main pipe temperature is +/-0.5 ℃), the opening variation is not predicted any more, the actuator still keeps the opening variation of the previous period, and otherwise, the step 1 is executed again.
The method achieves independent regulation and control of the opening of the actuator of the sector, breaks through the traditional control method that the actuator of the same sector is simultaneously opened and closed, achieves fine adjustment and stabilization of the water temperature in the error range of the target value +/-0.5 ℃, successfully learns the most economical output increment value opening variation, trains the model in real time during trial operation to obtain the optimal parameter model, and achieves the purposes of reducing the backpressure and increasing the energy type power generation utilization rate.
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.
Claims (8)
1. An intelligent water temperature control method for an indirect air cooling system is characterized in that the indirect air cooling system comprises a DCS control platform, a steam turbine, a condenser, a circulating water pump, a valve, an air cooling tower and a radiator system, wherein exhaust steam of the steam turbine enters the condenser, the condenser is connected with the air cooling tower through a circulating water inlet pipeline through the circulating water pump, and the air cooling tower is connected with the condenser through a circulating water outlet pipeline to form a circulating loop; the radiator system comprises a shutter and a cooling triangle which are arranged on the periphery of the air cooling tower, and an actuator for controlling the opening degree of the shutter, the DCS control platform is connected with the actuator, the shutter is arranged on the air inlet side of the cooling triangle, every two cooling triangles share one shutter, and the cooling triangle is divided into a plurality of sectors;
the method comprises the following steps:
acquiring power plant DSC data and temperature data of a radiator system and carrying out regularization treatment, wherein the power plant DSC data comprises the actual cold water main pipe temperature of each sector;
setting the maximum opening limit of the shutter and the target cold water main pipe temperature of each sector according to the working condition in winter;
predicting the opening variation of each actuator in the next period according to the power plant DSC data, the temperature data of the radiator system and the target cold water main pipe temperature of each sector;
acquiring the temperature change trend of the cold water main pipe of each sector in the latest continuous set period, the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature and the temperature distribution, and determining whether the opening of the actuator needs to be updated;
when the opening degree needs to be updated, recording the difference value between the predicted opening degree and the actual opening degree by taking the maximum opening degree limit as an override condition, and eliminating abnormal predicted opening degree through the override condition, wherein the abnormal predicted opening degree comprises opening degrees with too small opening degree and too large opening degree jitter;
respectively calculating the number of actuators and a control sequence of the opening of each sector to be updated in the next period according to the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature of each sector and the temperature distribution and the sequencing of the cooling triangles;
the DCS control platform issues control instructions to the indirect air cooling system, wherein the control instructions comprise the number and the control sequence of actuators with opening degrees needing to be updated in each sector and the final opening degree of each actuator;
and when the actual cold water main pipe temperature reaches an error water temperature range allowed by the target cold water main pipe temperature, the actuator keeps the opening change of the previous period, and otherwise, the step of acquiring the DSC data of the power plant and the temperature data of the radiator system and performing regularization processing is executed again.
2. The intelligent water temperature control method according to claim 1, wherein a unit is collocated with all sectors, a temperature sensor is arranged at a water inlet and a water outlet of a cold water main pipe and a circulating water outlet of each sector, a grating array sensor is arranged at a temperature measuring point of a cooling triangle of each sector, and the DCS control platform is further connected with the temperature sensor and the grating array sensor;
the predicting of the opening variation of each actuator in the next period according to the power plant DSC data, the temperature data of the radiator system and the target cold water main pipe temperature of each sector comprises the following steps:
judging whether each sector has a freezing risk or not according to the temperature data of the radiator system;
if so, closing the opening of the corresponding shutter; otherwise, inputting the power plant DSC data and the temperature data of the radiator system into a polynomial model to calculate the temperature of a cold water main pipe of each sector in the next period;
inputting the temperature of the cold water main pipe of each sector in the next period, power plant DSC data and temperature data of a radiator system into an XGboost model to calculate the opening variation of each actuator in each sector in the next period;
the power plant DSC data also comprises the water inlet temperature of a cold water main pipe, power generation load, backpressure, environmental temperature, wind speed, the temperature of circulating water flowing out of the tower, opening information of an upper period, the number of sectors used by a unit, the number of model switching sectors and the opening information of other actuators in the same sector; the temperature data of the radiator system comprises a sector temperature equalization, a cooling triangle temperature equalization, a sector temperature standard deviation and a cooling triangle temperature standard deviation, wherein the sector temperature equalization refers to the temperature equalization of all cooling triangles in a sector.
3. The intelligent water temperature control method according to claim 1, wherein the calculating of the number of actuators and the control sequence of the actuators with the opening degree needing to be updated in each sector in the next period according to the difference between the actual cold water main temperature and the target cold water main temperature of each sector and the temperature distribution and the sequencing of the cooling triangles respectively comprises:
collecting client data and dividing the client data into a sample set and a test set, wherein the client data comprises a standard deviation of temperature of a cooling triangle and a sector, and a difference value between actual cold water main pipe temperature and target cold water main pipe temperature;
taking the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature as a water temperature difference value, and taking the temperature standard difference value between the cooling triangle and the sector as a uniformity difference value;
in the same coordinate system, fitting two first safety curves according to the sample set by taking the absolute value of the water temperature difference as an x axis and the uniformity difference as a y axis; fitting a second safety curve according to the sample set by taking the uniformity difference value as an x axis and the water temperature difference value as a y axis; taking the water temperature difference as an x axis, and taking x =0.5 as a third safety straight line;
the first safety curve, the second safety curve and the third safety straight line are connected in a simultaneous mode, and an intersecting area is solved to serve as a safety area;
determining the number of the same sector in the safety area in the test set as a safety number, and subtracting the safety number from the total number of actuators needing to update the opening in the sector to obtain a derived value;
sequencing actuators needing opening updating according to the fact that the cooling triangles are high in mean temperature and small in variance, and sequentially opening the opening of the actuators; and conversely, sequencing the actuators of which the opening needs to be updated according to the low mean temperature and the large variance of the cooling triangle, and closing the openings of the actuators in sequence.
4. An intelligent water temperature control method according to claim 2, wherein the polynomial model has the expression:
wherein, a 0 -a m Assigning a coefficient, x, to the weight of each parameter i (i =1,2, \8230;, n) represents the power plant DSC data and the temperature data of the radiator system, respectively, n represents the number of entries, ∈ i Indicating the bias constant.
5. An intelligent water temperature control method according to claim 2, wherein the XGBoost model has the expression:
wherein Obj represents the predicted opening variation, G represents the first derivative of the square error of the predicted value and the actual value of the model, H is the taylor formula expansion term, T represents the tree node of the model, γ represents the minimum limit value for learning the splitting of each tree, and λ is the blocking coefficient for limiting the error interval step distance of the derivative.
6. The intelligent water temperature control method according to claim 3, wherein after the step of subtracting a safety number from a total number of actuators requiring opening updating in the sector, the method further comprises:
under the same coordinate system, a fourth curve which takes the water temperature difference as an x axis and the number of actuators as a y axis is fitted according to artificial experience, and the expression is as follows: y =5.678 | logeT error L +3.8868, wherein T error Representing the difference value between the actual cold water main pipe temperature and the target cold water main pipe temperature;
and substituting the test set data into the fourth curve to obtain the number of actuators with the opening needing to be updated as a reference value, comparing the reference value with a derived value, and taking a value with a smaller value as the final number of actuators with the opening needing to be updated.
7. An intelligent water temperature control method according to claim 3, wherein the expression of the first safety curve is: x = -0.22 (T) sq_std- T each_sq_std ) 2 +1.1*|T sq_std -T each_sq_std |+1.428
The expression of the second safety curve is: y =1.11 (T) error ) 2 -|T error |-4.452
Wherein, T sq_std Denotes the temperature standard deviation, T, of the sector each_sq_std Denotes the temperature standard deviation, T, of the cooling triangle error The difference value of the actual cold water main pipe temperature and the target cold water main pipe temperature is represented.
8. The intelligent water temperature control method according to any one of claims 1 to 7, wherein the indirect air cooling system further comprises a local edge server, the edge server stores a model for predicting the opening variation of each actuator in the next period, and the edge server is in communication connection with the DCS control platform;
after the step of executing the step of issuing the control instruction to the indirect air cooling system by the DCS control platform, the method further comprises the following steps:
uploading the number and control sequence of the actuators with the opening to be updated in each sector and the final opening variation of each actuator to the edge server, recording and arranging the actuators into a log form, and storing historical power plant DSC data and temperature data of a radiator system in a database;
after a certain period of control is carried out, high-quality data after test operation is extracted to serve as a training sample for updating the model, and the updated model is downloaded to the local.
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