Transformer Intelligent Cooling Control System

Abstract

The present invention discloses an intelligent cooling control system for transformers, which relates to the technical field of intelligent cooling control and includes: a data acquisition module, a predicted oil temperature module, a function fitting module, and an operation adjustment module; the data acquisition module is used to acquire data that causes the temperature of the transformer to change, marked as real-time measurement data, and the predicted oil temperature module is used to acquire the predicted oil temperature; the function fitting module is used to acquire a relationship function between the divided wind speed, the divided oil flow rate, and the cooling temperature, marked as a temperature fitting function; the operation adjustment module is used to obtain the optimal wind speed and the optimal oil flow rate based on the predicted oil temperature and the temperature fitting function, and adjust the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate. The present invention is used to solve the problems that the existing intelligent cooling control technology performs cooling adjustment according to the real-time temperature, resulting in cooling delay and excessive adjustment frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent cooling control, and particularly to an intelligent cooling control system for transformers. Background Art

[0002] When a transformer is operating, heat is often generated. If the temperature of the transformer is too high, it will affect the normal use and lifespan of the transformer. Therefore, it is necessary to cool the transformer accordingly.

[0003] Existing transformer cooling methods include air cooling, forced oil circulation air cooling, and forced oil circulation water cooling, etc. In the existing technology of forced oil circulation air cooling, corresponding adjustments are made according to the real-time temperature of the transformer. For example, the cooling effect is enhanced only when the temperature rises. Since the unpredictable temperature change amount cannot be adjusted in advance, such a cooling method has a delay and too many adjustment frequencies. For example, in the patent application with the application publication number CN209447649U, an intelligent cooling control system for a forced oil air-cooled transformer is disclosed. This solution adjusts the cooling according to the real-time temperature of the transformer. In the existing technology, the intelligent cooling control technology adjusts the cooling according to the real-time temperature, resulting in a delay in cooling and too many adjustment frequencies. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the existing technology to some extent. By obtaining real-time measurement data, obtaining the predicted oil temperature, obtaining the temperature fitting function between the divided wind speed, divided oil flow rate and cooling temperature, and based on the predicted oil temperature and the temperature fitting function, obtaining the optimal wind speed and optimal oil flow rate, and adjusting the forced oil circulation air cooling equipment to operate at the optimal wind speed and optimal oil flow rate, so as to solve the problem that the existing intelligent cooling control technology adjusts the cooling according to the real-time temperature, resulting in a delay in cooling and too many adjustment frequencies.

[0005] To achieve the above object, the present application provides an intelligent cooling control system for transformers, including: a data acquisition module, a predicted oil temperature module, a function fitting module, and an adjusted operation module;

[0006] The data acquisition module is used to acquire the data that causes the temperature change of the transformer, marked as real-time measurement data. The real-time measurement data includes: real-time load current, real-time oil temperature, and real-time ambient temperature;

[0007] The predicted oil temperature module is used to acquire the predicted oil temperature;

[0008] The function fitting module is used to acquire the relationship function between the divided wind speed, divided oil flow rate and cooling temperature, marked as the temperature fitting function;

[0009] The adjustment operation module is used to obtain the optimal wind speed and the optimal oil flow rate based on the predicted oil temperature and the temperature fitting function, and adjust the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate.

[0010] Further, the predicted oil temperature module is configured with a histogram drawing strategy, and the histogram drawing strategy includes:

[0011] Under the condition of real-time measurement data and the forced oil circulation air-cooling equipment being turned off, obtain the first quantity of historical oil temperature change amounts after the first time;

[0012] Obtain the range of the historical oil temperature change amounts, and mark it as the historical oil temperature change range;

[0013] Evenly divide the historical oil temperature change range into a ranges, and mark them as temperature division intervals; count the frequency of the historical oil temperature change amounts in each division interval, and mark it as the temperature interval frequency;

[0014] Taking the historical oil temperature change amount as the X-axis, the temperature interval frequency as the Y-axis, and the temperature division interval as the interval of the histogram, draw a histogram of the temperature change amount.

[0015] Further, the predicted oil temperature module is configured with a strategy for obtaining an optimized temperature change amount histogram, and the strategy for obtaining an optimized temperature change amount histogram includes:

[0016] Starting from the leftmost temperature division interval of the temperature change amount histogram, judge whether the temperature interval frequency is greater than the frequency threshold in turn from left to right. If it is less than or equal to, delete the part of the temperature interval frequency being judged in the temperature change amount histogram. If it is greater, stop the judgment;

[0017] Starting from the rightmost temperature division interval of the temperature change amount histogram, judge whether the temperature interval frequency is greater than the frequency threshold in turn from right to left. If it is less than or equal to, delete the part of the temperature interval frequency being judged in the temperature change amount histogram. If it is greater, stop the judgment;

[0018] Mark the temperature change amount histogram after the judgment as the optimized temperature change amount histogram.

[0019] Further, the predicted oil temperature module is configured with a strategy for obtaining the historical oil temperature mean value, and the strategy for obtaining the historical oil temperature mean value includes:

[0020] Obtain the minimum value and the maximum value of the abscissa of the optimized temperature change amount histogram, and mark them as the temperature minimum value and the temperature maximum value respectively;

[0021] Calculate the mean value of the historical oil temperature change amounts between the temperature minimum value and the temperature maximum value, and mark it as the historical oil temperature change amount mean value.

[0022] Further, the predicted oil temperature module is configured with a strategy for obtaining the predicted oil temperature, and the strategy for obtaining the predicted oil temperature includes:

[0023] Judge whether the mean value of the historical oil temperature change value is positive. If so, calculate the sum of the historical oil temperature change mean and the real-time oil temperature and mark it as the predicted oil temperature.

[0024] Further, the function fitting module is configured with a strategy for obtaining the operating data combination, and the strategy for obtaining the operating data combination includes:

[0025] Obtain the wind speed range and the oil flow rate range;

[0026] Divide the wind speed range into n equal intervals, obtain the wind speed at each division node, and mark it as the divided wind speed;

[0027] Divide the oil flow rate range into m equal intervals, obtain the oil flow rate at each division node, and mark it as the divided oil flow rate;

[0028] Combine a divided wind speed and a divided oil flow rate to obtain an operating data combination;

[0029] Obtain all possible operating data combinations.

[0030] Further, the function fitting module is configured with a strategy for drawing the cooling temperature scatter plot, and the strategy for drawing the cooling temperature scatter plot includes:

[0031] Under the conditions of zero real-time load current, predicted oil temperature and real-time ambient temperature, operate the forced oil circulation air-cooled equipment under the operating data combination, and obtain the second quantity of oil temperature after the first time, and mark it as the cooling oil temperature;

[0032] Establish a three-dimensional coordinate system with the divided wind speed as the X-axis data, the divided oil flow rate as the Y-axis data, and the cooling oil temperature as the Z-axis, and mark it as the cooling temperature coordinate system;

[0033] Plot the divided wind speed, divided oil flow rate and cooling temperature as data points in the cooling temperature coordinate system to obtain the cooling temperature scatter plot.

[0034] Further, the function fitting module is configured with a function fitting strategy, and the function fitting strategy includes:

[0035] Preset the cooling temperature fitting function as: Z = b1*X + b1*Y + b3; where b1, b2 and b3 are the coefficients of the cooling temperature fitting function, X is the divided wind speed, Y is the divided oil flow rate, and Z is the cooling temperature;

[0036] Fit the cooling temperature scatter plot to obtain the cooling temperature fitting function.

[0037] Further, the adjustment operation module is configured with an adjustment operation strategy, and the adjustment operation strategy includes:

[0038] Substitute the predicted oil temperature into the cooling temperature fitting function to obtain the divided wind speed and the divided oil flow rate, which are respectively marked as the optimal wind speed and the optimal oil flow rate;

[0039] Adjust the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate.

[0040] Advantages of the present invention: By obtaining real-time measurement data, obtaining the predicted oil temperature, obtaining the temperature fitting function between the divided wind speed, the divided oil flow rate and the cooling temperature, and based on the predicted oil temperature and the temperature fitting function, obtaining the optimal wind speed and the optimal oil flow rate, and adjusting the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate, the advantages are that the future temperature change can be predicted according to the real-time data, and the adjustment can be made according to the predicted temperature, solving the problems of cooling with delay and excessive adjustment frequency;

[0041] Advantages of the present invention: By obtaining the optimal wind speed and the optimal oil flow rate based on the predicted oil temperature and the temperature fitting function, and adjusting the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate, the advantages are that the optimal wind speed and the optimal oil flow rate are selected, reducing the waste of resources. Description of the Drawings

[0042] Figure 1 is the principle block diagram of the system of the present invention;

[0043] Figure 2 is the schematic diagram of the histogram of the temperature change amount of the present invention;

[0044] Figure 3 is the schematic diagram of the scatter plot of the cooling temperature of the present invention. Detailed Embodiments

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Embodiment 1, please refer to Figure 1 As shown, the present application provides a transformer intelligent cooling control system, including a data acquisition module, a predicted oil temperature module, a function fitting module, and an adjustment operation module;

[0047] The data acquisition module is used to obtain the data that causes the temperature change of the transformer, marked as real-time measurement data, and the real-time measurement data includes: real-time load current, real-time oil temperature, and real-time ambient temperature;

[0048] The predicted oil temperature module is used to obtain the predicted oil temperature;

[0049] The predicted oil temperature module is configured with a histogram drawing strategy, and the histogram drawing strategy includes:

[0050] Under the conditions of real-time measurement data and the shutdown of the forced oil circulation air-cooled equipment, obtain the historical oil temperature change amount of the first quantity after the first time; The forced oil circulation air cooling forces the transformer oil to circulate inside the oil tank through the submerged oil pump, transfers the heat to the external cooler, and then accelerates the air flow through the fan to dissipate the heat into the environment. Therefore, the oil flow rate and speed magnitude are controllable units; Under this condition setting, the heating situation of the transformer can be obtained;

[0051] Obtain the range of the historical oil temperature change amount, marked as the historical oil temperature change range;

[0052] Evenly divide the historical oil temperature change range into a ranges, marked as temperature division intervals; Count the frequency of the historical oil temperature change amount in each division interval, marked as the temperature interval frequency;

[0053] Using the historical oil temperature change amount as the X-axis, the temperature interval frequency as the Y-axis, and the temperature division interval as the interval of the histogram, draw the temperature change amount histogram;

[0054] In practical applications, please refer to Figure 2 As shown, obtain the historical oil temperature change range from 0.5 degrees Celsius to 3 degrees Celsius; Evenly divide the historical oil temperature change range into 5 temperature division intervals; Count the frequency of the historical oil temperature change amount in each division interval. For example, the interval frequency from 0.5 degrees Celsius to 1 degree Celsius is 5600. Using the historical oil temperature change amount as the X-axis, the temperature interval frequency as the Y-axis, and the temperature division interval as the interval of the histogram, draw the temperature change amount histogram.

[0055] The predicted oil temperature module is configured with a strategy for obtaining an optimized temperature change amount histogram, and the strategy for obtaining an optimized temperature change amount histogram includes:

[0056] Start from the leftmost temperature division interval of the temperature change amount histogram and judge whether the temperature interval frequency is greater than the frequency threshold in sequence from right to left. If it is less than or equal to, delete the part of the temperature interval frequency being judged in the temperature change amount histogram. If it is greater than, stop the judgment; The frequency threshold is set according to the first quantity and a. The frequency threshold setting is often small. Usually, the data with a quantity less than the frequency threshold is less;

[0057] Start from the rightmost temperature division interval of the temperature change amount histogram and judge whether the temperature interval frequency is greater than the frequency threshold in sequence from left to right. If it is less than or equal to, delete the part of the temperature interval frequency being judged in the temperature change amount histogram. If it is greater than, stop the judgment;

[0058] Mark the histogram of the temperature change amount after judgment as the optimized histogram of the temperature change amount;

[0059] In practical applications, please refer to Figure 2 As shown, if the first quantity is 16000, a is 5, the frequency threshold can be set to 100. The frequency of the temperature interval in the leftmost temperature division interval is 98. Since 98 is less than the frequency threshold 100, delete the part with the temperature interval frequency of 98 in the histogram of the temperature change amount, and continue to judge to the right. Since 5996 is greater than 100, stop the judgment; the frequency of the temperature interval in the leftmost temperature division interval is 68. Since 68 is less than the frequency threshold 100, delete the part with the temperature interval frequency of 68 in the histogram of the temperature change amount, and continue to judge to the right. Since 2102 is greater than 100, stop the judgment.

[0060] The predicted oil temperature module is configured with a strategy for obtaining the historical average oil temperature. The strategy for obtaining the historical average oil temperature includes:

[0061] Obtain the minimum value and the maximum value of the abscissa of the optimized histogram of the temperature change amount, and mark them as the minimum temperature and the maximum temperature respectively;

[0062] Calculate the average value of the historical oil temperature change amount between the minimum temperature and the maximum temperature, and mark it as the average value of the historical oil temperature change amount; calculate the average value of the real-time oil temperature change amount after deletion to make the historical average oil temperature more accurate;

[0063] In practical applications, please refer to Figure 2 As shown, the minimum temperature and the maximum temperature are 1.0 °C and 2.5 °C respectively. Calculate the average value of the historical oil temperature change amount between 1.0 °C and 2.5 °C as 1.66 °C, and retain two decimal places for the calculation result;

[0064] The predicted oil temperature module is configured with a strategy for obtaining the predicted oil temperature. The strategy for obtaining the predicted oil temperature includes:

[0065] Judge whether the average value of the historical oil temperature change value is positive. If so, calculate the sum of the historical average oil temperature change and the real-time oil temperature, and mark it as the predicted oil temperature; if the average value of the historical oil temperature change value is positive, it proves that the temperature is rising, so the cooling needs to be turned on. If the average value of the historical oil temperature change value is negative, it proves that the temperature is falling, so the cooling does not need to be turned on;

[0066] In practical applications, 1.66 °C is positive. Calculate the sum of the historical average oil temperature change and the real-time oil temperature as: 1.66 + 50 = 51.66 °C, then the predicted oil temperature is 51.66 °C;

[0067] The function fitting module is used to obtain the relationship function between the divided wind speed, the divided oil flow rate and the cooling temperature, and mark it as the temperature fitting function;

[0068] The function fitting module is configured with a strategy for obtaining operating data combinations, and the strategy for obtaining operating data combinations includes:

[0069] Obtain the wind speed range and the oil flow rate range;

[0070] Divide the wind speed range into n equal intervals, obtain the wind speed at each division node, and label it as the divided wind speed;

[0071] Divide the oil flow rate range into m equal intervals, obtain the oil flow rate at each division node, and label it as the divided oil flow rate;

[0072] Combine a divided wind speed and a divided oil flow rate to obtain an operating data combination;

[0073] Obtain all possible operating data combinations;

[0074] In practical applications, obtain a wind speed range of 0 - 8 m / s and an oil flow rate range of 0 - 4 m / s; divide the wind speed range into 9 equal intervals, obtain the wind speed at each division node, and label it as the divided wind speed, where the divided wind speeds are 0 m / s, 1 m / s,..., 8 m / s; divide the oil flow rate range into 5 equal intervals, obtain the oil flow rate at each division node, and label it as the divided oil flow rate, where the divided oil flow rates are 0 m / s, 1 m / s,..., 4 m / s; obtain 40 operating data combinations, such as the divided wind speed of 1 m / s and the divided oil flow rate of 1 m / s.

[0075] The function fitting module is configured with a strategy for plotting a scatter diagram of cooling temperature, and the strategy for plotting a scatter diagram of cooling temperature includes:

[0076] Under the conditions of a real - time load current of 0, a predicted oil temperature, and a real - time ambient temperature, operate the forced - oil - circulation air - cooled equipment with the operating data combinations, and obtain a second quantity of oil temperatures after the first time, and label them as the cooling oil temperatures; the cooling effect of the cooling device can be obtained under such conditions.

[0077] Establish a three - dimensional coordinate system with the divided wind speed as the X - axis data, the divided oil flow rate as the Y - axis data, and the cooling oil temperature as the Z - axis, and label it as the cooling temperature coordinate system;

[0078] Plot the divided wind speed, the divided oil flow rate, and the cooling temperature as data points in the cooling temperature coordinate system to obtain a scatter diagram of cooling temperature; label the data points in the scatter diagram of cooling temperature as cooling temperature scatter points;

[0079] The function fitting module is configured with a function fitting strategy, and the function fitting strategy includes:

[0080] The preset cooling temperature fitting function is: Z = b1*X + b2*Y + b3; where b1, b2, and b3 are the coefficients of the cooling temperature fitting function, X is the divided wind speed, Y is the divided oil speed, and Z is the cooling temperature;

[0081] The cooling temperature scatter plot is fitted to obtain the cooling temperature fitting function;

[0082] In practical applications, please refer to Figure 3 As shown, the cooling temperature scatter plot is fitted to obtain the cooling temperature fitting function as Z = 1.72*X + 4*Y + 50;

[0083] The adjustment operation module is used to obtain the optimal wind speed and the optimal oil flow rate based on the predicted oil temperature and the temperature fitting function, and adjust the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate;

[0084] The adjustment operation module is configured with an adjustment operation strategy, and the adjustment operation strategy includes:

[0085] Substitute the predicted oil temperature into the cooling temperature fitting function to obtain the divided wind speed and the divided oil speed, which are respectively marked as the optimal wind speed and the optimal oil flow rate;

[0086] Adjust the forced oil circulation air-cooling equipment to operate at the optimal wind speed and the optimal oil flow rate;

[0087] In practical applications, please refer to Figure 3 As shown, for the predicted oil temperature of 51.66 degrees Celsius, substitute Z = 51.66 into Z = 1.72*X + 4*Y + 50, then obtain multiple sets of values of X and Y, and select the values of X and Y that are both close to the middle of the wind speed range and the oil flow rate range, such as X = 0.5 m / s and Y = 0.2 m / s, and adjust the forced oil circulation air-cooling equipment to operate at a wind speed of 0.5 m / s and an oil speed of 0.2 m / s.

[0088] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media that contain computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the processes Figure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0089] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

Claims

1. Transformer intelligent cooling control system, characterized in that: include: Data acquisition module, oil temperature prediction module, function fitting module and adjustment operation module; The data acquisition module is used to acquire data that causes the transformer temperature to change, which is marked as real-time measurement data. The real-time measurement data includes: real-time load current, real-time oil temperature and real-time ambient temperature; The predicted oil temperature module is used to obtain the predicted oil temperature; The function fitting module is used to obtain the relationship function between the divided wind speed, the divided oil flow rate and the cooling temperature, which is marked as the temperature fitting function; The operation adjustment module is used to obtain the optimal wind speed and the optimal oil flow rate based on the predicted oil temperature and the temperature fitting function, and adjust the forced oil circulation air cooling device to operate at the optimal wind speed and the optimal oil flow rate; The function fitting module is configured with a combination strategy for obtaining operation data, and the combination strategy for obtaining operation data includes: Get the wind speed range and oil flow rate range; Divide the wind speed range into n equal intervals, obtain the wind speed of each split node, and mark it as the split wind speed; Divide the oil flow rate range into m equal intervals, obtain the oil flow rate of each split node, and mark it as the split oil rate; Combining a divided wind speed and a divided oil speed to obtain an operating data combination; Get all possible combinations of running data; The function fitting module is configured with a cooling temperature scatter plotting strategy, and the cooling temperature scatter plotting strategy includes: Under the conditions of real-time load current being 0, predicted oil temperature and real-time ambient temperature, the forced oil circulation air cooling device is operated under the operation data combination, and the oil temperature of the second quantity is obtained after the first time, which is marked as the cooling oil temperature; A three-dimensional coordinate system is established with the wind speed as the X-axis data, the oil speed as the Y-axis data, and the cooling oil temperature as the Z-axis, and is marked as the cooling temperature coordinate system; The divided wind speed, the divided oil speed and the cooling temperature are plotted as data points in the cooling temperature coordinate system to obtain a cooling temperature scatter plot; The function fitting module is configured with a function fitting strategy, and the function fitting strategy includes: The preset cooling temperature fitting function is: Z=b1*X+b1*Y+b3; b1, b2 and b3 are cooling temperature fitting function coefficients, X is the divided wind speed, Y is the divided oil speed, and Z is the cooling temperature; The cooling temperature scatter plot is fitted to obtain the cooling temperature fitting function.

2. The transformer intelligent cooling control system according to claim 1, characterized in that: The oil temperature prediction module is configured with a histogram drawing strategy, and the histogram drawing strategy includes: Under the condition that the real-time measurement data and the forced oil circulation air cooling device are turned off, a first amount of historical oil temperature changes after a first period of time is obtained; Get the range of historical oil temperature changes, marked as historical oil temperature change range; The historical oil temperature variation range is evenly divided into a ranges, which are marked as temperature division intervals; the frequency of the historical oil temperature variation in each division interval is counted, which is marked as the temperature interval frequency; With the historical oil temperature change as the X-axis, the temperature interval frequency as the Y-axis, and the temperature division interval as the histogram interval, a temperature change histogram is drawn.

3. The transformer intelligent cooling control system according to claim 2, characterized in that: The oil temperature prediction module is configured with a strategy for obtaining an optimized temperature change histogram, and the strategy for obtaining an optimized temperature change histogram includes: Starting from the temperature division interval on the leftmost side of the temperature change histogram, determine whether the temperature interval frequency is greater than the frequency threshold. If it is less than or equal to the frequency threshold, delete the temperature interval frequency part being determined in the temperature change histogram. If it is greater than the frequency threshold, stop determining. Starting from the rightmost temperature division interval of the temperature change histogram, determine whether the temperature interval frequency is greater than the frequency threshold. If it is less than or equal to the frequency threshold, delete the temperature interval frequency part being determined in the temperature change histogram. If it is greater than the frequency threshold, stop determining. The determined temperature variation histogram is marked as an optimized temperature variation histogram.

4. The transformer intelligent cooling control system according to claim 3, characterized in that: The oil temperature prediction module is configured with a strategy for obtaining the historical oil temperature average value, and the strategy for obtaining the historical oil temperature average value includes: Obtain the minimum value and the maximum value of the abscissa of the optimized temperature variation histogram, and mark them as the minimum temperature value and the maximum temperature value respectively; The average of the historical oil temperature changes between the minimum temperature and the maximum temperature is obtained, and is marked as the average of the historical oil temperature changes.

5. The transformer intelligent cooling control system according to claim 4, characterized in that: The oil temperature prediction module is configured with a strategy for obtaining the predicted oil temperature, and the strategy for obtaining the predicted oil temperature includes: Determine whether the mean of the historical oil temperature change values ​​is a positive number. If so, calculate the sum of the mean of the historical oil temperature change and the real-time oil temperature and mark it as the predicted oil temperature.

6. The transformer intelligent cooling control system according to claim 5, characterized in that: The adjustment operation module is configured with an adjustment operation strategy, and the adjustment operation strategy includes: Substitute the predicted oil temperature into the cooling temperature fitting function to obtain the divided wind speed and divided oil speed, which are marked as the optimal wind speed and the optimal oil flow rate respectively; Adjust the forced oil circulation air cooling equipment to operate at the optimal air speed and optimal oil flow rate.

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