High-efficiency energy-saving transformer for wind power photovoltaic base booster station

Abstract

The invention discloses an efficient energy-saving transformer for a booster station of a wind power photovoltaic base. According to the efficient energy-saving transformer, a control module is arranged, and a cooling system can automatically adjust the rotating speed and the working mode of a cooling fan according to real-time temperature data of a transformer body and the external environment. The temperature sensor and the load sensor monitor the working state in the transformer in real time and transmit data to the control module, and the control module judges whether the fan speed needs to be started or adjusted according to a set temperature control algorithm. The control module optimizes the cooling strategy by analyzing the relationship between the temperature and the load, and ensures that the transformer is always kept within the optimal working temperature range. Besides, the control module closely cooperates with other equipment in the heat dissipation box, so that response can be made in time when the load of the transformer changes or the temperature fluctuates, the energy consumption is effectively reduced, excessive heat dissipation is reduced, and the overall operation efficiency of the system is improved.

Description

Technical Field

[0001] The invention belongs to the field of transformers, and in particular, relates to a high-efficiency energy-saving transformer for a booster station of a wind power photovoltaic base. Background Art

[0002] With the rapid development of renewable energy such as wind power and photovoltaics, the scale of wind power and photovoltaic bases is constantly expanding, and the requirements for power transmission and conversion equipment are also increasing. Especially in the booster station of wind power and photovoltaic bases, the transformer is one of the core equipment, and its operating efficiency is directly related to the stability, reliability and energy efficiency of the entire power system. Therefore, how to improve the operating efficiency of the transformer and reduce energy consumption has become the focus of current technical research.

[0003] In the booster station of wind power and photovoltaic bases, transformers bear the heavy task of large-scale power conversion. The changes in their operating load and ambient temperature make them prone to overheating, especially in remote areas or high temperature environments. In order to prevent overheating and ensure the normal operation of the transformer, it is usually necessary to rely on heat dissipation devices for effective temperature control. However, traditional heat dissipation devices, especially the working mode of fans, often do not have a good control strategy. As a result, as the load and temperature change, the heat dissipation equipment needs to run at full capacity to reduce the temperature of the transformer, resulting in additional energy consumption, which affects the overall energy efficiency of the system.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a high-efficiency energy-saving transformer for a wind power photovoltaic base booster station, thereby solving the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] A high-efficiency energy-saving transformer for a booster station of a wind power photovoltaic base comprises: a heat sink, a transformer body is installed inside the heat sink, a slot is provided on one side of the heat sink, a connection frame is fixedly connected to one side of the slot, a plurality of ventilation holes are provided on the top of the connection frame, and shielding mechanisms are provided on the top and bottom of the connection frame;

[0008] A plurality of heat dissipation mechanisms are connected to the interior of the heat dissipation box, a plurality of first temperature sensors are arranged inside the heat dissipation box, a load sensor is arranged above the transformer body, a second temperature sensor for detecting the external environment is arranged above the heat dissipation box, a display screen connected to the signals between the first temperature sensor and the second temperature sensor is arranged outside the heat dissipation box, an installation box is arranged on the outer wall of the heat dissipation box, and a control module for signal connection between the heat dissipation mechanisms is arranged inside the installation box.

[0009] Optionally, the control module includes:

[0010] A data processing unit for determining whether to start the heat dissipation structure to cool the transformer according to the transformer temperature; and

[0011] A control unit controls the opening and closing of the heat dissipation mechanism and the shielding mechanism according to the results of the data processing unit.

[0012] Optionally, the heat dissipation mechanism includes a plurality of heat dissipation fans connected to the side walls of the heat dissipation box, and the air outlets of the heat dissipation fans face the transformer body.

[0013] Optionally, the shielding mechanism includes two mounting blocks fixedly connected above the connecting frame, a rotating shaft is rotatably connected between the two mounting blocks via a torsion spring, a cover plate is fixedly connected to the circumferential side of the rotating shaft, an electric push rod is fixedly connected to one side of the inner wall of the connecting frame, a push plate is fixedly connected to the upper end of the electric push rod, and a straight groove for the push plate to slide is provided above the connecting frame, wherein the push plate will retract into the space of the connecting frame when the fan is turned off, and after the fan is started, the push plate will push the two cover plates to expand, so that the ventilation holes 4 are exposed to the outside, thereby realizing air exchange.

[0014] Optionally, the processing unit performs the following steps when receiving a signal sent by the temperature sensor:

[0015] S1: Based on the temperature data obtained from the first temperature sensor, use the formula Calculate the thermal stability limit power value P of the power machinery equipment circuit t , where I max is the maximum current value of the thermal stability of the power machinery equipment line, I * is the normal current value of the line when the ambient temperature is constant, K is the ambient temperature correction coefficient, U is the voltage of the power machinery equipment line, Power factor calculated for power flow

[0016] S2: Use the power flow calculation method to calculate the transformer's active thermal power change P by combining the transformer's voltage and current values. i , whose expression is: Among them, δ sr is the thermal sensitivity coefficient, x i is the rated temperature coefficient of the circuit at the ith temperature sensing point;

[0017] S3: When the power transformer has thermal stability phenomenon, the formula Calculation line l k The active power value increases by ΔP i Next, calculate the effect of thermal stability on transformer temperature λ i , whose expression is: Where η is the transient variation coefficient of thermal stability of the power transformer;

[0018] S4: Combine the influence of static power transformer temperature with the thermal stability state estimation of power transformer based on edge computing to obtain the thermal stability control coefficient F i , whose expression is: in, is the thermal stability state of the transformer estimated based on historical data and models, H′ is the Jacobian matrix, and R is the parameter matrix of power, power, circuit stability, etc.;

[0019] S5: According to the preset thermal stability control threshold ∈, the thermal stability control coefficient F is analyzed by comparison i and thresholds to decide whether to start the cooling system or shut down the equipment for maintenance. The control rules are as follows:

[0020] Optionally, during the real-time operation of the transformer, the temperature T of the transformer needs to be obtained through a first temperature sensor. in (t), which will be different from the preset target temperature T set (t) and calculate the temperature difference ΔT(t) = T in (t)-T set (t):

[0021] The temperature adjustment feedback Q is calculated based on the difference ΔT(t) between the real-time temperature and the target temperature. i (t), thereby adjusting the heat dissipation strategy of the transformer, which is expressed as: Q i (t) = Q i (t-1)+α·(T in (t)-T set (t)), where Q i (t) is the temperature adjustment feedback, which reflects the impact of temperature change on the cooling strategy. α is the learning factor, which controls the adjustment step size.

[0022] Optionally, in the process of adjusting the fan speed, it is necessary to use the historical temperature data of the transformer to establish an accurate mapping relationship between temperature and fan speed to establish a temperature-fan speed mapping model. The steps are as follows:

[0023] Obtain the temperature T in the historical data from the first temperature sensor and the second temperature sensor in and ambient temperature T env , transformer load L(t) data, and aggregate them into a training data set;

[0024] At the same time, the fan speed data N corresponding to each time point is collected f(t), including the fan speed values ​​at different temperatures, and the preliminary relationship between the three is found through linear regression analysis, and its expression is: N f (t) = a1·T in (t)+a2T·T env (t)+a3·L(T)+b, where a1, a2, a3 are regression coefficients and is the intercept;

[0025] Through the training data set, SVR can find an excellent nonlinear function to describe the complex relationship between temperature and fan speed. The trained SVR model can be expressed as: Among them, L max is the maximum load of the transformer, β1, β2, β3 are the adjustment coefficients of the model, which are obtained by optimizing the training data.

[0026] Optionally, the steps for obtaining the fan speed using the temperature-fan speed mapping model are as follows:

[0027] The current transformer temperature T is collected by the first temperature sensor, the second temperature sensor and the load sensor in (t) and ambient temperature T env (t), transformer load L(t), and then the current transformer temperature T in (t) and ambient temperature T env (t) and transformer load L(t) are input into the trained temperature-fan speed mapping model. At this time, the temperature-fan speed mapping model will calculate the corresponding fan speed N according to the relationship between temperature and ambient temperature. f (t).

[0028] Optionally, based on the initial fan speed N f (t), the weighted least squares method is used for further adjustment, and the steps are as follows:

[0029] Get the current fan speed Then calculate the current fan speed With initial fan speed N f (t) The error between

[0030] Based on the weighted least squares method, the weighted sum of squared errors is calculated to minimize the objective function J(ξ) to optimize the fan speed, which is expressed as: Among them, w i is the weight of each data point;

[0031] Using the weighted least squares optimization results, the fan speed is updated to Ensure that the error between the fan speed and the target fan speed is small, and the expression is: in, is the fan speed adjustment value obtained by weighted least squares optimization. Precisely adjust cooling system response to ensure transformer temperature remains within ideal range during operation.

[0032] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0033] 1. By optimizing the heat dissipation mechanism and intelligent control system, efficient transformer temperature regulation and heat dissipation management are achieved. Multiple temperature sensors and load sensors are set in the heat dissipation box to monitor the operating status of the transformer in real time, accurately collect temperature and load data, and ensure intelligent adjustment of the cooling system. The design of the notch and connection frame makes air circulation more efficient, which helps to improve heat dissipation performance.

[0034] 2. By setting up the control module, the cooling system can automatically adjust the speed and working mode of the cooling fan according to the real-time temperature data of the transformer body and the external environment. The temperature sensor and load sensor monitor the working status inside the transformer in real time and transmit the data to the control module. The control module determines whether to turn on or adjust the fan speed according to the set temperature control algorithm. The control module optimizes the cooling strategy by analyzing the relationship between temperature and load to ensure that the transformer always remains within the optimal operating temperature range.

[0035] 3. The control module of the present invention works closely with other devices in the heat dissipation box, and can respond in time when the transformer load changes or the temperature fluctuates, effectively reducing energy consumption and excessive heat dissipation, and improving the overall operating efficiency of the system. Through this intelligent control method, the heat dissipation system not only improves the heat dissipation effect of the transformer, but also reduces energy consumption and prolongs the service life of the equipment.

[0036] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0038] In the figure:

[0039] Figure 1 It is a schematic diagram of the three-dimensional structure of the transformer body located in the heat dissipation box;

[0040] Figure 2 It is a schematic diagram of the three-dimensional structure inside the heat dissipation box;

[0041] Figure 3It is a schematic diagram of the three-dimensional structure of the connection frame;

[0042] Figure 4 This is the system block diagram of the control module;

[0043] Figure 5 for Figure 1 Schematic diagram of the structure at point A in the middle.

[0044] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0045] 1. Heat dissipation box; 2. Transformer body; 3. Connection frame; 4. Ventilation hole; 5. First temperature sensor; 6. Load sensor; 7. Second temperature sensor; 8. Display screen; 9. Cooling fan; 10. Rotating shaft; 11. Cover plate; 12. Electric push rod; 13. Push plate; 14. Installation box.

[0046] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0047] The present invention will now be described in further detail with reference to the accompanying drawings.

[0048] See also Figure 1-5 As shown, in this embodiment, a high-efficiency energy-saving transformer for a booster station of a wind power photovoltaic base is provided, comprising a heat dissipation box 1, a transformer body 2 is installed inside the heat dissipation box 1, a notch is provided on one side of the heat dissipation box 1, a connection frame 3 is fixedly connected to one side of the notch, a plurality of ventilation holes 4 are provided through the top of the connection frame 3, and shielding mechanisms are provided above and below the connection frame 3;

[0049] A plurality of heat dissipation mechanisms are connected to the interior of the heat dissipation box 1, a plurality of first temperature sensors 5 are arranged inside the heat dissipation box 1, a load sensor 6 is arranged above the transformer body 2, a second temperature sensor 7 for detecting the external environment is arranged above the heat dissipation box 1, a display screen 8 connected to the signals of the first temperature sensor 5 and the second temperature sensor 7 is arranged outside the heat dissipation box 1, an installation box 14 is arranged on the outer wall of the heat dissipation box 1, and a control module for signal connection between the heat dissipation mechanisms is arranged inside the installation box 14.

[0050] By setting up a control module, the heat dissipation system can automatically adjust the speed and working mode of the heat dissipation fan 9 according to the real-time temperature data of the transformer body 2 and the external environment. The temperature sensor and load sensor 6 monitor the working status inside the transformer in real time, and transmit the data to the control module. The control module determines whether it is necessary to turn on or adjust the fan speed according to the set temperature control algorithm. The control module optimizes the cooling strategy by analyzing the relationship between temperature and load to ensure that the transformer always remains within the optimal operating temperature range. In addition, the close cooperation between the control module and other equipment in the heat dissipation box 1 can respond in time when the transformer load changes or the temperature fluctuates, effectively reducing energy consumption and excessive heat dissipation, and improving the overall operation efficiency of the system. Through this intelligent control method, the heat dissipation system not only improves the heat dissipation effect of the transformer, but also reduces energy consumption and extends the service life of the equipment.

[0051] In this embodiment, the control module includes:

[0052] A data processing unit for determining whether to start the heat dissipation structure to cool the transformer according to the transformer temperature; and

[0053] A control unit controls the opening and closing of the heat dissipation mechanism and the shielding mechanism according to the results of the data processing unit.

[0054] In this embodiment, the heat dissipation mechanism includes a plurality of heat dissipation fans 9 connected to the side wall of the heat dissipation box 1, and the air outlets of the heat dissipation fans 9 face the transformer body.

[0055] In this embodiment, the shielding mechanism includes two mounting blocks fixedly connected above the connecting frame 3, a rotating shaft 10 is rotatably connected between the two mounting blocks through a torsion spring, a cover plate 11 is fixedly connected to the circumferential side of the rotating shaft 10, an electric push rod 12 is fixedly connected to one side of the inner wall of the connecting frame 3, a push plate 13 is fixedly connected to the upper end of the electric push rod 12, and a straight groove for the push plate 13 to slide is opened above the connecting frame 3, wherein the push plate 13 will retract into the space of the connecting frame 3 when the fan is turned off, and after the fan is started, the push plate 13 will push the two cover plates 11 to unfold, so that the ventilation holes 4 are exposed to the outside, thereby realizing air exchange.

[0056] In this embodiment, the processing unit performs the following steps when receiving the signal sent by the temperature sensor:

[0057] S1: Based on the obtained temperature data, use the formula Calculate the thermal stability limit power value P of the power machinery equipment circuit t , where I max is the maximum current value of the thermal stability of the power machinery equipment line, I * is the normal current value of the line when the ambient temperature is constant, K is the ambient temperature correction coefficient, U is the voltage of the power machinery equipment line, Power factor calculated for power flow

[0058] S2: Use the power flow calculation method to calculate the transformer's active thermal power change P by combining the transformer's voltage and current values. i , whose expression is: Among them, δ sr is the thermal sensitivity coefficient, x i is the rated temperature coefficient of the circuit at the ith temperature sensing point;

[0059] S3: When the power transformer has thermal stability phenomenon, the formula Calculation line l k The active power value increases by ΔP i Next, calculate the effect of thermal stability on transformer temperature λ i , whose expression is: Where η is the transient variation coefficient of thermal stability of the power transformer;

[0060] S4: Combine the influence of static power transformer temperature with the thermal stability state estimation of power transformer based on edge computing to obtain the thermal stability control coefficient F i , whose expression is: in, It is the thermal stability state of the transformer estimated based on historical data and model, H6 is the Jacobian matrix, and R is the parameter matrix of power, power, circuit stability, etc.;

[0061] S5: According to the preset thermal stability control threshold ∈, the thermal stability control coefficient F is analyzed by comparison i and thresholds to decide whether to start the cooling system or shut down the equipment for maintenance. The control rules are as follows:

[0062] In this embodiment, during the real-time operation of the transformer, the temperature T of the transformer needs to be obtained through a temperature sensor. in (t), which will be different from the preset target temperature T set (t) and calculate the temperature difference ΔT(t) = T in (t)-T set (t):

[0063] The temperature adjustment feedback Q is calculated based on the difference ΔT(t) between the real-time temperature and the target temperature. i (t), thereby adjusting the heat dissipation strategy of the transformer, which is expressed as: Q i (t) = Q i (t-1)+α·(T in (t)-T set (t)), where Q i(t) is the temperature adjustment feedback, which reflects the impact of temperature changes on the cooling strategy, and α is the learning factor, which controls the adjustment step size. By acquiring the temperature data of the transformer in real time and dynamically comparing it with the preset target temperature, the temperature difference can be accurately calculated, thereby realizing the temperature adjustment feedback mechanism. This feedback mechanism enables the cooling strategy to be automatically optimized according to the current operating status of the transformer, ensuring that the transformer always maintains the optimal operating temperature under different load and environmental conditions. By introducing a learning factor to control the adjustment step size, the impact of temperature changes on the heat dissipation strategy can be effectively adjusted, so that the cooling system can avoid excessive heat dissipation or energy waste while ensuring the heat dissipation effect.

[0064] In this embodiment, in the process of adjusting the fan speed, it is necessary to use the historical temperature data of the transformer to establish an accurate mapping relationship between temperature and fan speed and establish a temperature-fan speed mapping model. The steps are as follows:

[0065] Get the temperature T from the transformer in historical data in and ambient temperature T env , transformer load L(t) data, and aggregate them into a training data set;

[0066] At the same time, the fan speed data N corresponding to each time point is collected f (t), including the fan speed values ​​at different temperatures, and the preliminary relationship between the three is found through linear regression analysis, and its expression is: N f (t) = a1·T in (t)+a2T·T env (t)+a3·L(T)+b, where a1, a2, a3 are regression coefficients and b is the intercept;

[0067] Through the training data set, SVR can find an excellent nonlinear function to describe the complex relationship between temperature and fan speed. The trained SVR model can be expressed as: Among them, L max is the maximum load of the transformer, β1, β2, β3 are the adjustment coefficients of the model, which are obtained by optimizing the training data.

[0068] In this embodiment, the steps of obtaining the fan speed using the temperature-fan speed mapping model are as follows:

[0069] The current transformer temperature T is collected by the temperature sensor in (t) and ambient temperature T env (t), transformer load L(t)

[0070] The current transformer temperature T in (t) and ambient temperature T env(t) and transformer load L(t) are input into the trained temperature-fan speed mapping model. At this time, the temperature-fan speed mapping model will calculate the corresponding fan speed N according to the relationship between temperature and ambient temperature. f (t). By making full use of the historical temperature, load and ambient temperature data of the transformer, an accurate temperature-fan speed mapping model can be established to achieve accurate adjustment of the fan speed. The relationship between temperature and fan speed is initially captured through linear regression analysis, and then the support vector regression SVR algorithm is used to further optimize and fit the nonlinear function to ensure that the model can efficiently reflect the impact of temperature changes on fan speed. The SVR model can not only accurately predict the fan speed required under different working conditions, but also can adaptively adjust in a changing working environment, improve the cooling efficiency of the transformer, reduce energy consumption, and extend the service life of the equipment.

[0071] In this embodiment, according to the obtained preliminary fan speed N f (t), the weighted least squares method WLS is used for further adjustment, and the steps are as follows:

[0072] Get the current fan speed Then calculate the current fan speed With initial fan speed N f (t) The error between

[0073] Based on the weighted least squares method WLS, the weighted error sum of squares is calculated to minimize the objective function J(ξ) to optimize the fan speed, which is expressed as Among them, w i is the weight of each data point. The error of different data points may contribute differently to the objective function. The weight w i Used to control the influence of each data point in the total error. Generally, data points with larger weights receive more attention during the optimization process.

[0074] J(ξ) is the value of the objective function, which represents the total error of the weighted least squares optimization. The goal is to minimize this error so as to minimize the error between the calculated fan speed and the target fan speed. is the sum symbol, which means summing all data points from i=1 to i=n, ​​where n is the total number of data points. The expression is: The square of the error for each data point represents the square of the deviation between the actual fan speed and the target fan speed. The purpose of squaring is to make the error measure independent of direction and avoid negative and positive errors canceling each other out.

[0075] Using the weighted least squares optimization results, the fan speed is updated to Ensure that the error between the fan speed and the target fan speed is small, and the expression is: in, is the fan speed adjustment value obtained by weighted least squares optimization. Precisely adjust the cooling system response to ensure that the transformer temperature is maintained within the ideal range during operation. Increase, increase the fan speed, if Reduce to lower the fan speed.

[0076] Experimental analysis:

[0077] During the experiment, in order to collect power data of electric mechanical equipment under different states, thermal stability faults were carried out in the form of high voltage transmission, and high-intensity voltage (10-20 kV) was connected to the equipment. In order to ensure that the experiment can verify the effectiveness of the method in this paper, the transformer temperature control system (reference [1]) was used as a comparison method, and energy consumption was used as a performance indicator to analyze the control effects of the two methods and use the same product. At the same time, 24 fans were arranged on both sides of the inner wall of the heat dissipation box 1. The transformer initial temperature was 25°C as the standard, and the transformer cooling target temperature was 10°C as the end standard for the experiment to ensure the fairness of the experiment.

[0078] The energy consumption of 24 fans when achieving cooling effect is taken as an indicator. The smaller the value of this indicator, the lower the energy consumption of the thermal stability control method. The experimental process still uses the transformer temperature control system as a comparison method. The experimental results of the two methods are shown in Table 1.

[0079] Method Type Fan Total Energy Consumption kWh

[0080] Transformer temperature control system 3.2

[0081] The present invention 2.8

[0082] The fan energy consumption of the thermal stability control method is generally lower than that of the transformer temperature control system. For example, at an initial temperature of 25°C, the energy consumption of the thermal stability control method is 2.8kWh, while that of the transformer temperature control system is 3.2kWh, and the temperature change of 10°C after cooling is the same. Although there is a difference in fan energy consumption, the thermal stability control method is comparable to the transformer temperature control system in terms of temperature change. In all experimental cases, the temperature changes of the two are consistent. For example, at an initial temperature of 25°C, the temperature change of both is 10°C, and both can reach the cooling target temperature of 15°C.

[0083] By monitoring the oil temperature and ambient temperature of the transformer in real time and dynamically adjusting the fan speed based on this information, the fan speed is no longer fixed, but can be flexibly adjusted according to the actual cooling needs of the transformer. This means that when the temperature is low, the fan speed can be appropriately reduced to reduce unnecessary energy consumption; when the temperature is high, the fan speed is increased to ensure sufficient heat dissipation. In addition, optimization algorithms such as weighted least squares (WLS) are used to adjust the actual fan speed based on the relationship between historical temperature data and fan speed. By continuously optimizing the matching relationship between the fan speed and load, the system can reduce the burden on the fan and avoid redundant operation according to the optimal control strategy.

[0084] Comparative Literature:

[0085] [1] Tan Jiawei, Zhang Xianmei, Liu Dandan, et al. Design of temperature control system for dry-type transformer[J]. Modern Information Technology, 2022, 6(12): 169 171,177.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0088] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-efficiency energy-saving transformer for a wind power photovoltaic base booster station, characterized in that: include: A heat sink (1), a transformer body (2) is arranged inside the heat sink (1), a slot is provided on one side of the heat sink (1), a connection frame (3) is fixedly connected to one side of the slot, a plurality of ventilation holes (4) are provided through the top of the connection frame (3), and shielding mechanisms are provided above and below the connection frame (3); A plurality of heat dissipation mechanisms are connected to the inside of the heat dissipation box (1), a plurality of first temperature sensors (5) are arranged inside the heat dissipation box (1), a load sensor (6) is arranged above the transformer body (2), a second temperature sensor (7) for detecting the external environment is arranged above the heat dissipation box (1), a display screen (8) connected to the first temperature sensors (5) and the second temperature sensors (7) is arranged outside the heat dissipation box (1), an installation box (14) is arranged on the outer wall of the heat dissipation box (1), and a control module for signal connection between the heat dissipation mechanisms is arranged inside the installation box (14).

2. A high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 1, characterized in that: The control module includes: A data processing unit for determining whether to start the heat dissipation structure to cool the transformer according to the transformer temperature; and A control unit controls the opening and closing of the heat dissipation mechanism and the shielding mechanism according to the results of the data processing unit.

3. The high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 1, characterized in that: The heat dissipation mechanism comprises a plurality of heat dissipation fans (9) connected to the side wall of the heat dissipation box (1), and the air outlets of the heat dissipation fans (9) face the transformer body.

4. The high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 1, characterized in that: The shielding mechanism comprises two mounting blocks fixedly connected above a connecting frame (3); a rotating shaft (10) is rotatably connected between the two mounting blocks via a torsion spring; a cover plate (11) is fixedly connected to the peripheral side of the rotating shaft (10); an electric push rod (12) is fixedly connected to one side of the inner wall of the connecting frame (3); a push plate (13) is fixedly connected to the upper end of the electric push rod (12); and a straight groove for the push plate (13) to slide is provided above the connecting frame (3).

5. The high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 2, characterized in that: The processing unit performs the following steps when receiving the signal sent by the temperature sensor: S1: Based on the temperature data obtained from the first temperature sensor (5), the formula Calculate the thermal stability limit power value P of the power machinery equipment circuit t , where I max is the maximum current value of the thermal stability of the power machinery equipment line, I * is the normal current value of the line when the ambient temperature is constant, K is the ambient temperature correction coefficient, U is the voltage of the power machinery equipment line, Power factor calculated for power flow S2: Use the power flow calculation method to calculate the transformer's active thermal power change P by combining the transformer's voltage and current values. i , whose expression is: Among them, δ sr is the thermal sensitivity coefficient, x i is the rated temperature coefficient of the circuit at the ith temperature sensing point; S3: When the power transformer has thermal stability phenomenon, the formula Calculation line l k The active power value increases by ΔP i Next, calculate the effect of thermal stability on transformer temperature λ i , whose expression is: Where η is the transient variation coefficient of thermal stability of the power transformer; S4: Combine the influence of static power transformer temperature with the thermal stability state estimation of power transformer based on edge computing to obtain the thermal stability control coefficient F i , whose expression is: in, is the thermal stability state of the transformer estimated based on historical data and models, H′ is the Jacobian matrix, and R is the parameter matrix of power, power, circuit stability, etc.; S5: According to the preset thermal stability control threshold ∈, the thermal stability control coefficient F is analyzed by comparison i and thresholds to decide whether to start the cooling system or shut down the equipment for maintenance. The control rules are as follows:

6. A high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 5, characterized in that: During the real-time operation of the transformer, it is also necessary to obtain the temperature T of the transformer through the first temperature sensor (5). in (t), which will be different from the preset target temperature T set (t) and calculate the temperature difference ΔT(t) = T in (t)-T set (t): The temperature adjustment feedback Q is calculated based on the difference ΔT(t) between the real-time temperature and the target temperature. i (t), thereby adjusting the heat dissipation strategy of the transformer, which is expressed as: Q i (t) = Q i (t-1)+α·(T in (t)-T set (t)), where Q i (t) is the temperature adjustment feedback, which reflects the impact of temperature change on the cooling strategy. α is the learning factor, which controls the adjustment step size.

7. A high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 6, characterized in that: In the process of adjusting the fan speed, it is necessary to use the historical temperature data of the transformer to establish an accurate mapping relationship between temperature and fan speed and establish a temperature-fan speed mapping model. The steps are as follows: The temperature T in the historical data is obtained from the first temperature sensor (5) and the second temperature sensor (7). in and ambient temperature T env , transformer load L(t) data, and aggregate them into a training data set; At the same time, the fan speed data N corresponding to each time point is collected f (t), including the fan speed values ​​at different temperatures, and the preliminary relationship between the three is found through linear regression analysis, and its expression is: N f (t) = a1·T in (t)+a2T·T env (t)+a3·L(T)+b, where a1, a2, a3 are regression coefficients and b is the intercept; Through the training data set, SVR can find an excellent nonlinear function to describe the complex relationship between temperature and fan speed. The trained SVR model can be expressed as: Among them, L max is the maximum load of the transformer, β1, β2, β3 are the adjustment coefficients of the model, which are obtained by optimizing the training data.

8. The high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 7, characterized in that: The steps to obtain fan speed using the temperature-fan speed mapping model are: The current transformer temperature T is collected by the first temperature sensor (5), the second temperature sensor (7) and the load sensor (6). in (t) and ambient temperature T env (t), transformer load L(t), and then the current transformer temperature T in (t) and ambient temperature T env (t) and transformer load L(t) are input into the trained temperature-fan speed mapping model. At this time, the temperature-fan speed mapping model will calculate the corresponding fan speed N according to the relationship between temperature and ambient temperature. f (t).

9. The high-efficiency energy-saving transformer for a wind power photovoltaic base booster station according to claim 8, characterized in that: According to the initial fan speed N f (t), and further adjustment is performed using the weighted least squares (WLS) method, the steps of which are: Get the current fan speed Then calculate the current fan speed With initial fan speed N f (t) The error between Based on the weighted least squares method (WLS), the weighted sum of squared errors is calculated to minimize the objective function J(ξ) to optimize the fan speed, which is expressed as Among them, w i is the weight of each data point; Using the weighted least squares optimization results, the fan speed is updated to Ensure that the error between the fan speed and the target fan speed is small, and the expression is: in, is the fan speed adjustment value obtained by weighted least squares optimization. Precisely adjust cooling system response to ensure transformer temperature remains within ideal range during operation.

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