Cooling control method and system for power distribution cabinet

Abstract

The invention provides a cooling control method and system for a power distribution cabinet, and the method comprises the steps: monitoring the internal temperature through a temperature sensor at a preset frequency, and predicting the future temperature through the time sequence analysis in combination with historical data. Based on the prediction result and a preset energy consumption optimization algorithm, intelligently adjusting the working mode of the water-cooling and air-cooling assembly, including opening or closing operation; the PLC or the single-chip microcomputer controller receives the temperature data and the prediction result in real time and adjusts operation parameters of the cooling assembly according to a preset strategy; according to the method, efficient and energy-saving control over the temperature of the power distribution cabinet is achieved by monitoring and predicting the temperature in real time and intelligently regulating and controlling the cooling assembly; the whole process aims at optimizing energy consumption, ensuring that the power distribution cabinet operates at a proper temperature, and improving the overall efficiency of the system.

Description

Technical Field

[0001] The present application relates to the field of temperature control of power distribution cabinets, and in particular to a method and system for controlling cooling of power distribution cabinets. Background Art

[0002] As a vital equipment in the power system, the power distribution cabinet contains a large number of electrical components and terminals, which generate a lot of heat during operation. If the temperature inside the power distribution cabinet is too high, it will not only accelerate the aging of the electrical components and reduce their service life, but also may cause the insulation performance to deteriorate, increase the risk of short circuit and fire. Therefore, effective cooling of the power distribution cabinet is the key to ensuring the safe and stable operation of the power system.

[0003] Traditional cooling methods for power distribution cabinets usually use fixed air cooling or water cooling systems. Although the systems can reduce the temperature inside the power distribution cabinet to a certain extent, they often have problems such as high energy consumption and unstable cooling effect. Especially when the ambient temperature is high or the load inside the power distribution cabinet changes greatly, traditional cooling methods often cannot meet real-time and accurate cooling needs. Summary of the invention

[0004] The purpose of the present application is to overcome the defects in the above-mentioned prior art and provide a cooling control method and system for a power distribution cabinet.

[0005] The present application provides a method for controlling cooling of a power distribution cabinet, comprising:

[0006] Use temperature sensors to monitor the internal temperature data of the power distribution cabinet in real time at a preset sampling frequency;

[0007] Combining the temperature data with the historical temperature data, a time series analysis method is used to predict the temperature in the future;

[0008] According to the predicted temperature, combined with the preset energy consumption optimization algorithm, the working mode of the water cooling component and the air cooling component is adjusted, including turning on or off the water cooling component and the air cooling component respectively;

[0009] The temperature data and prediction results are received in real time through the PLC or single-chip microcomputer controller, and the operating parameters of the water cooling component and / or air cooling component are adjusted according to the preset control strategy to complete the cooling setting of the distribution cabinet.

[0010] The present application also provides a power distribution cabinet cooling control system, comprising:

[0011] High-precision temperature sensor arrays are arranged in different locations of the power distribution cabinet;

[0012] PLC or single-chip controller, used to receive temperature data, perform prediction and optimization calculations, and output control instructions;

[0013] An intelligent water cooling component, including a water pump and an intelligent temperature control valve for regulating water flow, and adjusting the cooling effect according to the control instructions;

[0014] The intelligent air cooling component includes a fan and an intelligent air duct, and controls the air flow distribution according to the control instruction. Optionally, it also includes an intelligent environment monitoring module for real-time monitoring of external environmental factors.

[0015] Optionally, an energy efficiency management module is also included for real-time monitoring of the energy efficiency ratio of the cooling system and the heat load changes of the electrical components inside the power distribution cabinet, and adjusting the control strategy according to the heat load changes to achieve more sophisticated energy consumption control.

[0016] Optionally, an emergency cooling mode module is also included, which is used to start the emergency cooling mode when the predicted temperature approaches or exceeds a preset high temperature threshold.

[0017] Optionally, an energy-saving mode module is also included, which is used to shut down or reduce the power of the cooling system when the predicted temperature is lower than a preset low temperature threshold.

[0018] Optionally, a user interaction interface module is also included for displaying temperature data, prediction results, control strategies and energy consumption information.

[0019] Optionally, a remote monitoring and control module is also included to implement remote access, monitoring and control functions, facilitating remote management and troubleshooting by operation and maintenance personnel.

[0020] Optionally, a backup power supply and backup cooling assembly are included.

[0021] Optionally, an expandable design module is also included, including reserved interfaces and expansion slots, so that users can add additional cooling components as needed.

[0022] The beneficial effects of this application are:

[0023] The present application provides a method for controlling the cooling of a power distribution cabinet, including: using a temperature sensor to monitor the internal temperature data of the power distribution cabinet in real time at a preset sampling frequency; combining the temperature data and historical temperature data, using a time series analysis method to predict the temperature in the future; adjusting the working mode of the water cooling component and the air cooling component according to the predicted temperature, combined with a preset energy consumption optimization algorithm, including turning the water cooling component and the air cooling component on or off respectively; receiving the temperature data and the prediction results in real time through a PLC or single-chip microcomputer controller, and adjusting the operating parameters of the water cooling component and / or the air cooling component according to a preset control strategy to complete the cooling and temperature reduction setting of the power distribution cabinet. The present application realizes efficient and energy-saving temperature control of the power distribution cabinet by real-time monitoring and predicting temperature data and intelligently adjusting the working mode of the cooling component. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the cooling and temperature reduction control method for the power distribution cabinet in this application;

[0025] Figure 2 This is a schematic diagram of the cooling control system of the power distribution cabinet in this application. DETAILED DESCRIPTION

[0026] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementing the present disclosure should not be limited by the embodiments set forth herein. On the contrary, the embodiments are provided to more thoroughly understand the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0027] like Figure 1 As shown, the present application provides a method for controlling cooling of a power distribution cabinet, the steps comprising:

[0028] S101. Use a temperature sensor to monitor the internal temperature data of the power distribution cabinet in real time at a preset sampling frequency.

[0029] Select an appropriate temperature sensor based on the size of the distribution cabinet, the internal layout, and the expected temperature monitoring accuracy. Temperature sensors include thermistors, thermocouples, digital temperature sensors (such as DS18B20), etc.

[0030] Install temperature sensors at key locations inside the power distribution cabinet (such as near heating elements, at vents, inside cabinet doors, etc.). The temperature sensors should be installed in locations that are not subject to external interference, such as direct sunlight, strong magnetic fields, etc., to ensure the accuracy of the measurement results.

[0031] According to the operating characteristics of the power distribution cabinet, the speed of temperature change, and the accuracy requirements of temperature control, the sampling frequency of the temperature sensor is set. The sampling frequency settings in this application include once per second, once every 5 seconds, and once per minute.

[0032] Connect the temperature sensor to the data acquisition system (such as PLC, single-chip controller, data recorder, etc.) Configure the communication parameters of the temperature sensor (such as baud rate, data format, etc.) according to the requirements of the data acquisition system.

[0033] Start the data acquisition system and begin to monitor the temperature data inside the power distribution cabinet in real time at a preset sampling frequency.

[0034] S102: combining the temperature data and historical temperature data, using a time series analysis method to predict the temperature in a future period of time.

[0035] The SARIMA model is a widely used time series forecasting method that can capture trends, seasonality, and random fluctuations in time series data.

[0036] Assume that the time series data set X = {x1, x2, ..., x n} and the corresponding temperature value Y = {y1, y2, ..., y n}.

[0037] Use the feature importance evaluation method to determine each historical temperature value x i For the current temperature value y t degree of impact.

[0038] The feature importance Ii can be calculated by a machine learning algorithm, where i represents the index of the feature (i.e., historical temperature values).

[0039] Ii=FeatureImportance(x i , Y)

[0040] Then, the important features are selected to build the SARIMA model, which may improve the prediction performance of the model.

[0041] Specifically, according to the feature importance ranking, the first k important features are selected to build the SARIMA model.

[0042] The SARIMA model is represented as:

[0043]

[0044] Among them, B is the lag operator, that is, By t =y t-1 , φ(B) is a non-seasonal autoregressive polynomial, Φ(B S) is the seasonal autoregressive polynomial, θ(B) is the non-seasonal moving average polynomial, Θ(B S ) is the seasonal moving average polynomial, d is the difference order used to make the time series stationary, S is the seasonal period, ∈ t is the error term, usually assumed to be white noise, β i is the external regressor x i , the coefficient of t, and k is the number of external regressors.

[0045] φ(B)=1-φ1B-φ2B 2 -…-φ p B p , where p is the non-seasonal autoregressive order.

[0046] Φ(B S )=1-Φ1B S -Φ2B 2S -…-Φ P B PS , where P is the seasonal autoregressive order.

[0047] θ(B)=1+θ1B+θ2B 2 +…+θ q B q , where q is the non-seasonal moving average order.

[0048] Θ(B S )=1+θ1B S +Θ2B 2S +…+Θ Q B QS , where Q is the seasonal moving average order.

[0049] Specifically, historical temperature data is collected, cleaned and normalized, and the data is checked for stationarity. If it is not stationary, differential processing is performed.

[0050] ACF and PACF graphs are drawn using time series data. The graphs are usually generated by statistical software (such as R, Python's statsmodels library, etc.).

[0051] In the PACF diagram, if the value after a certain lag order drops rapidly and approaches 0, and the subsequent values ​​no longer significantly exceed the confidence interval (usually set to ±2 / √N, where N is the sample size), the PACF diagram is considered to be truncated after the lag order. If the PACF diagram is truncated after a certain lag order k, p or P = k is preliminarily determined.

[0052] In the ACF graph, if the value after a certain lag order drops rapidly and approaches 0, and the subsequent values ​​no longer significantly exceed the confidence interval, the ACF graph is considered to be truncated after the lag order. If the ACF graph is truncated after a certain lag order k', it is preliminarily determined that q or Q = k'.

[0053] Because the truncation points of the ACF and PACF plots may not be very clear, or the data may be affected by other factors, it is often necessary to try different combinations of p, P, Q, and q.

[0054] For each combination of p, P, Q, and q, a SARIMA model is constructed and the goodness of fit of the model is calculated. Use information criteria (such as AIC, BIC) to compare the goodness of fit of different models. AIC and BIC are both standards for measuring the fit of the model, which take into account the complexity of the model and the goodness of fit of the data. Generally speaking, the smaller the AIC and BIC values, the better the fit of the model. The model with the smallest AIC or BIC value is selected as the best model, thereby determining the final p and q values.

[0055] The optimal values ​​of p, P, Q, and q were determined, and the parameters in the model were estimated using either the least squares method or the maximum likelihood estimation method.

[0056] Least Squares Method: Estimate parameters by minimizing the squared error between the predicted value and the actual value. That is, solve:

[0057]

[0058] Among them, θ opt is the optimal parameter vector that minimizes the sum of squared prediction errors.

[0059] A machine learning optimization model M(θ) is introduced, which predicts the forecast error based on historical data and preliminary estimated parameters:

[0060] MSE(θ)=M(θ)

[0061] but:

[0062]

[0063] The parameters are estimated by maximizing the conditional probability of the model parameters under given data, that is, solving the parameter value that maximizes the likelihood function L(φ, θ, ∈|y).

[0064] Parameters are estimated by minimizing the squared error between the predicted values ​​and the actual values. Parameters are estimated by maximizing the conditional probability of the model parameters given the data.

[0065] After determining the optimal p, P, Q, and q values ​​and estimating the parameters, the model needs to be validated.

[0066] The suitability of the model is verified by checking whether the residuals of the model meet the white noise assumption (that is, whether the residual series has no autocorrelation, no trend and constant variance).

[0067] Check whether the residuals of the model meet the white noise assumption. If the residuals are autocorrelated or heteroskedastic, the model needs to be modified.

[0068] Input the latest real-time temperature data into the trained SARIMA model. Run the model regularly according to the preset sampling frequency to predict the temperature in the future. The model will output the predicted temperature value in the future, which will serve as the basis for the subsequent adjustment of the working mode of the cooling component.

[0069] S103, adjusting the working modes of the water cooling component and the air cooling component according to the predicted temperature and in combination with a preset energy consumption optimization algorithm, including turning on or off the water cooling component and the air cooling component respectively;

[0070] Based on the preset energy consumption optimization algorithm, input the temperature forecast value for a period of time in the future (such as 24 hours), the current working status (on / off) of the water-cooled components and the air-cooled components, power, and real-time data such as the ambient temperature, the rated power, efficiency curve, and start / shutdown time of the water-cooled components and the air-cooled components.

[0071] For example: Based on the predicted temperature and ambient temperature, calculate the temperature difference at each time point. Based on the temperature difference and equipment parameters, calculate the required cooling capacity, the expression is as follows:

[0072]

[0073] Among them, T pred (t): predicted temperature at time t, T amb (t): ambient temperature at time t, ΔT(t): temperature difference at time t, i.e. Tpred(t)-Tamb(t), C req (t): cooling capacity required at time t, P dev : Rated power (or reference power) of the equipment, η dev (ΔT): Efficiency function of the device, indicating the efficiency at different temperature differences.

[0074] For each time point, a set of candidate solutions is generated, including different combinations of working modes of water-cooling components and air-cooling components (such as fully open, fully closed, partially open, etc.).

[0075] Calculate the energy consumption of each candidate and evaluate its impact on temperature control.

[0076] Use methods such as dynamic programming, heuristic algorithms (such as genetic algorithms, particle swarm optimization), or machine learning models (such as reinforcement learning) to select the solution with the lowest energy consumption and meeting the temperature control requirements from the candidate solutions.

[0077] Based on the optimally selected solution, a working mode adjustment plan for the water-cooling component and the air-cooling component is generated, including the timing and sequence of the on / off operations.

[0078] Adjust the working mode of water cooling components and air cooling components according to the execution plan. Monitor the ambient temperature, working status of cooling components and energy consumption data in real time. If there is a large deviation between the actual temperature and the predicted temperature, or the energy consumption exceeds the preset threshold, recalculate and adjust the execution plan.

[0079] S104: adjusting the operating parameters of the water cooling component and / or the air cooling component according to the respective opening or closing operations of the water cooling component and the air cooling component, and completing the cooling setting of the power distribution cabinet.

[0080] The water cooling component and the air cooling component are controlled by a PLC or a single-chip microcomputer controller to be turned on or off respectively to complete the cooling setting of the power distribution cabinet.

[0081] like Figure 2 As shown, the present application also provides a cooling and temperature control system for a distribution cabinet, which realizes precise control of the internal temperature of the distribution cabinet and optimized management of energy consumption through modules such as a high-precision temperature sensor array 201, a PLC or single-chip controller 202, an intelligent water cooling component 203, and an intelligent air cooling component 204, while combining intelligent environmental monitoring, energy efficiency management, emergency cooling mode, energy-saving mode, user interaction interface, remote monitoring and control, backup power supply and backup cooling components and other functions.

[0082] The high-precision temperature sensor array is arranged in the upper, middle, lower part of the distribution cabinet and near the key electrical components. Temperature sensors at different positions can capture the temperature gradient inside the distribution cabinet, thereby providing more detailed and accurate temperature data. The temperature sensor is located in the upper part of the distribution cabinet and monitors the higher temperature area formed by the rising hot air. The middle temperature sensor can capture the main temperature area inside the distribution cabinet and reflect the operating temperature of most electrical components in the distribution cabinet. The lower temperature sensor monitors the temperature changes after the cooling air enters the distribution cabinet, as well as the temperature status of the bottom of the distribution cabinet. Temperature sensors are arranged near key electrical components in the distribution cabinet (such as circuit breakers, contactors, transformers, etc.) to monitor the temperature of the important components in real time to ensure that they operate within a safe operating temperature range.

[0083] The collected temperature data is sent to the PLC or single-chip controller in real time through an efficient data communication interface. The interface includes but is not limited to RS485, CAN bus, Ethernet, etc. The communication protocol and interface standard ensure the reliability and stability of data transmission.

[0084] The PLC (Programmable Logic Controller) or MCU controller is the core processing unit of the system and is responsible for receiving temperature data from a high-precision temperature sensor array. After receiving the temperature data, the PLC or MCU controller will perform temperature prediction based on the preset algorithm and control strategy. This process involves time series analysis to achieve accurate prediction of the internal temperature of the power distribution cabinet in the future.

[0085] The controller further optimizes the control strategy of the cooling system, including adjusting the working mode and operating parameters of the water-cooling and air-cooling components, to ensure that the internal temperature of the power distribution cabinet is always kept within a safe and stable range, while optimizing energy consumption.

[0086] The intelligent water cooling assembly consists of a water pump that regulates the water flow and an intelligent temperature control valve. These two parts work together to accurately regulate the flow and temperature of the cooling water according to the instructions of the PLC (Programmable Logic Controller) or single-chip controller. The flow of cooling water is adjusted by changing the speed to adapt to the changing temperature inside the distribution cabinet. The valve opening is automatically adjusted to control the temperature of the cooling water according to the preset temperature set point and actual temperature feedback.

[0087] The PLC or single-chip controller receives the internal temperature data of the power distribution cabinet sent by the high-precision temperature sensor array, performs temperature prediction and optimization calculations, and then outputs control instructions to the water pump and temperature control valve to achieve precise regulation of cooling water flow and temperature.

[0088] The intelligent air cooling components include fans and intelligent air ducts. They adjust the fan speed and the opening and closing of the air duct according to the control instructions to optimize the air flow distribution and improve the cooling efficiency. The air flow rate is changed by changing the speed to adjust the temperature inside the distribution cabinet. According to the temperature distribution inside the distribution cabinet and the air flow requirements, the opening and closing of the air duct are automatically adjusted to optimize the air flow path.

[0089] The PLC or single-chip controller outputs control instructions to the fan and intelligent air duct according to the results of temperature prediction and optimization calculation, so as to achieve precise adjustment of the fan speed and the opening and closing of the air duct.

[0090] The intelligent environmental monitoring module monitors external environmental factors (such as temperature, humidity, wind speed, etc.) in real time and provides environmental data support for PLC or single-chip controller to optimize cooling strategy. Environmental data is collected through external sensor network and sent to PLC or single-chip controller for analysis and processing. External environmental parameters are provided for the formulation of cooling strategy to ensure that the cooling strategy can adapt to changes in the external environment.

[0091] The energy efficiency management module monitors the energy efficiency ratio of the cooling system and the heat load changes of the electrical components inside the power distribution cabinet in real time, and adjusts the control strategy according to the heat load changes to achieve energy consumption optimization. Through internal sensors and algorithms, the energy efficiency ratio and heat load changes of the cooling system are monitored and analyzed in real time, and the optimized control strategy is output. The energy efficiency of the cooling system is improved and the energy consumption of the power distribution cabinet is reduced.

[0092] When the predicted temperature approaches or exceeds the preset high temperature threshold, the emergency cooling mode module automatically starts the emergency cooling mode, increases the power of the cooling system, and ensures that the temperature inside the power distribution cabinet does not exceed the safe range. When the temperature prediction result approaches or exceeds the high temperature threshold, the PLC or single-chip controller automatically starts the emergency cooling mode, increases the speed of the water pump and the fan, etc., to improve the cooling effect. Prevent the temperature inside the power distribution cabinet from being too high and ensure the safe operation of the equipment.

[0093] When the predicted temperature is lower than the preset low temperature threshold, the energy-saving mode module automatically shuts down or reduces the power of the cooling system to achieve energy saving and consumption reduction. When the temperature prediction result is lower than the low temperature threshold, the PLC or single-chip controller automatically reduces or shuts down the power of the cooling system to reduce energy consumption. Reduce the energy consumption of the distribution cabinet and improve energy utilization efficiency.

[0094] The user interface module provides an intuitive display interface to display temperature data, prediction results, control strategies and energy consumption information, making it easier for users to understand the system status and make manual adjustments. The system status information is displayed through the touch screen or display, and manual adjustment functions are provided. This improves the operability and maintainability of the system.

[0095] The remote monitoring and control module realizes remote access, monitoring and control functions. The operation and maintenance personnel can perform fault diagnosis, parameter setting and policy adjustment through the remote management system of the network. The remote access and control functions are realized through network connection. The remote management capability and fault response speed of the system are improved.

[0096] When the main power supply or main cooling component fails, the backup power supply and backup cooling component will automatically switch to the backup power supply or enable the backup cooling component to ensure continuous and stable operation of the system. The fault switching function is realized through the power switching device and the backup cooling component. The reliability and stability of the system are improved.

[0097] The reserved interfaces and expansion slots of the scalability design module allow users to add additional cooling components or functional modules according to actual needs to improve the flexibility and scalability of the system. The reserved interfaces and expansion slots enable the expansion of the system. This meets the user's possible future needs and improves the flexibility and scalability of the system.

[0098] In this application, a high-precision temperature sensor array collects temperature data inside the power distribution cabinet in real time and sends it to a PLC or single-chip microcomputer controller. The PLC or single-chip microcomputer controller performs temperature prediction and optimization calculation based on the received temperature data to determine the optimal cooling strategy. The PLC or single-chip microcomputer controller outputs control instructions to the intelligent water cooling component and the intelligent air cooling component based on the optimized cooling strategy. The intelligent water cooling component and the intelligent air cooling component adjust the flow rate, temperature, speed of the fan, and opening and closing of the air duct according to the control instructions to achieve precise control of the temperature inside the power distribution cabinet. The energy efficiency management module monitors the energy efficiency ratio of the cooling system and the heat load changes of the electrical components inside the power distribution cabinet in real time, adjusts the control strategy according to the heat load changes, and achieves energy consumption optimization. When the predicted temperature approaches or exceeds the high temperature threshold, the emergency cooling mode is started; when the predicted temperature is lower than the low temperature threshold, the energy-saving mode is started. The user interaction interface module provides system status display and manual adjustment functions; the remote monitoring and control module realizes remote access, monitoring and control functions.

[0099] When the main power supply or main cooling component fails, it automatically switches to the backup power supply or enables the backup cooling component; users can add additional cooling components or functional modules through the scalable design module according to actual needs.

[0100] Although the present invention is disclosed as above by the embodiments, it is not intended to limit the protection scope of the present invention. Any changes and modifications made by any technician familiar with the technology without departing from the concept and scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for controlling cooling of a power distribution cabinet, characterized in that: include: Use temperature sensors to monitor the internal temperature data of the power distribution cabinet in real time at a preset sampling frequency; Combining the temperature data with the historical temperature data, a time series analysis method is used to predict the temperature in the future; According to the predicted temperature, combined with the preset energy consumption optimization algorithm, the working mode of the water cooling component and the air cooling component is adjusted, including turning on or off the water cooling component and the air cooling component respectively; The operating parameters of the water cooling component and / or the air cooling component are adjusted according to the opening or closing operations of the water cooling component and the air cooling component respectively, and the cooling and temperature reduction settings of the power distribution cabinet are completed.

2. A cooling control system for a power distribution cabinet, characterized in that: include: High-precision temperature sensor arrays are arranged in different locations of the power distribution cabinet; PLC or single-chip controller, used to receive temperature data, perform prediction and optimization calculations, and output control instructions; An intelligent water cooling component, including a water pump and an intelligent temperature control valve for regulating water flow, and adjusting the cooling effect according to the control instructions; The intelligent air cooling component includes a fan and an intelligent air duct, and controls the air flow distribution according to the control instructions.

3. According to claim 2, a power distribution cabinet cooling control system is characterized in that: It also includes an intelligent environmental monitoring module for real-time monitoring of external environmental factors.

4. According to claim 2, a power distribution cabinet cooling control system is characterized in that: It also includes an energy efficiency management module for real-time monitoring of the energy efficiency ratio of the cooling system and the heat load changes of the electrical components inside the distribution cabinet, and adjusting the control strategy according to the heat load changes to achieve more precise energy consumption control.

5. According to claim 2, a power distribution cabinet cooling control system is characterized in that: An emergency cooling mode module is also included, which is used to activate the emergency cooling mode when the predicted temperature approaches or exceeds a preset high temperature threshold.

6. A power distribution cabinet cooling control system according to claim 2, characterized in that: Also included is an energy-saving mode module for shutting down or reducing the power of the cooling system when the predicted temperature falls below a preset low temperature threshold.

7. A power distribution cabinet cooling control system according to claim 2, characterized in that: It also includes a user interface module for displaying temperature data, prediction results, control strategies and energy consumption information.

8. A power distribution cabinet cooling control system according to claim 2, characterized in that: It also includes a remote monitoring and control module for implementing remote access, monitoring and control functions, making it easier for operation and maintenance personnel to conduct remote management and troubleshooting.

9. A power distribution cabinet cooling control system according to claim 2, characterized in that: Includes backup power supply and backup cooling components.

10. A power distribution cabinet cooling control system according to claim 2, characterized in that: It also includes an expandable design module, including reserved interfaces and expansion slots, allowing users to add additional cooling components as needed.

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