Intelligent heat dissipation control method for power distribution cabinet

Abstract

The present application relates to power distribution equipment heat management and intelligent control technical field, especially to a kind of intelligent heat dissipation control method for switchgear, three-dimensional temperature field distribution model is constructed by deploying thermal imaging and temperature and humidity sensor, according to the comparison of temperature instantaneous change rate and threshold value, self-adaptive trigger load extraction is carried out, and temperature change trend is forecasted based on load determination and ambient temperature;When the prediction result is rising trend and peak value exceeds threshold value, local hot spot area is identified, temperature deviation and heat accumulation coefficient are analyzed to determine whether it is local enhanced heat dissipation mode;In local enhanced heat dissipation mode, cooling rate is monitored, and standby heat dissipation component is started when it is lower than threshold value.The present application overcomes the problem that the key equipment of inverter and control system in photovoltaic power station and wind power plant has very high requirement on power supply continuity of switchgear, due to the severe fluctuation of ambient temperature, the internal precision components are frequently overheated and damaged, and the control efficiency is low.

Description

Technical Field

[0001] This invention relates to the field of thermal management and intelligent control technology for power distribution equipment, and in particular to an intelligent heat dissipation control method for power distribution cabinets. Background Technology

[0002] Currently, heat dissipation control in power distribution cabinets mainly adopts a temperature threshold linkage method. This involves installing temperature sensors inside the cabinet; when the detected temperature exceeds a preset threshold, the fan or air conditioner starts, and when it falls below the threshold, it stops. Some systems also support temperature trend display and alarm push functions. However, existing technologies still have the following shortcomings: First, existing solutions typically only set up a few temperature monitoring points inside the power distribution cabinet, failing to comprehensively perceive the temperature distribution differences in different areas within the cabinet and making it difficult to detect localized hotspots. Second, existing control methods use fixed threshold triggering, failing to consider the impact of ambient temperature changes and load fluctuations, leading to frequent starts in summer and delayed responses in winter, resulting in poor adaptability of heat dissipation control. Third, existing solutions are mostly passive response controls, only activating heat dissipation after the temperature exceeds the limit, lacking the ability to predict temperature change trends, resulting in significant lag in heat dissipation response. Fourth, existing solutions use a uniform start-stop control strategy for all heat dissipation equipment, failing to differentiate adjustments based on the actual temperature requirements of each area, leading to low heat dissipation efficiency and energy waste. Fifth, existing systems lack monitoring and evaluation of the operating status of the heat dissipation devices themselves, making it difficult to detect fan or air conditioner malfunctions in a timely manner, posing a risk of heat dissipation failure.

[0003] Chinese Patent Publication No. CN120674947A discloses an intelligent heat dissipation control method and system based on a three-dimensional simulation model. This method establishes a three-dimensional thermal simulation model of the target device, uses computational fluid dynamics simulation to obtain temperature distribution data of the device under different ambient temperatures and load conditions, constructs a temperature field distribution map based on the simulation data, and identifies hotspot areas. Then, it determines the globally optimal fan speed control strategy through simulation calculations, generates a mapping table of ambient temperature, load, and fan speed, and achieves intelligent adjustment of fan speed to achieve energy saving and noise reduction. The core of this method lies in obtaining the thermal characteristics of the device through offline simulation modeling and pre-determining the fan speed control strategy. It belongs to the technical path of simulation modeling + open-loop control. Its three-dimensional model is a static simulation model, does not involve online reconstruction and dynamic updating of real-time sensor data, and does not involve fault monitoring and backup compensation mechanisms for the heat dissipation device itself.

[0004] The limitations of existing technologies are that static simulation models cannot be dynamically updated with actual working conditions, resulting in a continuous decline in the effectiveness of control strategies; global unified speed regulation cannot identify local hot spots, resulting in both energy waste and insufficient heat dissipation; and the lack of heat dissipation device fault monitoring and backup compensation mechanisms poses a risk of heat dissipation failure. Summary of the Invention

[0005] To address this, the present invention provides an intelligent heat dissipation control method for power distribution cabinets, which overcomes the problems existing in the prior art: due to the extremely high requirements for power supply continuity of key equipment such as inverters and control systems in photovoltaic power plants and wind farms, but the outdoor ambient temperature fluctuates drastically, the existing fixed threshold heat dissipation technology is difficult to respond in time, resulting in frequent overheating and damage to internal precision components and low control efficiency.

[0006] To achieve the above objectives, the present invention provides an intelligent heat dissipation control method for a power distribution cabinet, comprising: By deploying thermal imaging sensors and multiple temperature and humidity sensors at corresponding monitoring points inside the target power distribution cabinet; Temperature, heat distribution data, and humidity are collected at various monitoring points inside the power distribution cabinet. A three-dimensional temperature field distribution model is then constructed based on the temperature, heat distribution data, and humidity. The construction process of the three-dimensional temperature field distribution model is as follows: the internal space of the target distribution cabinet is divided into several three-dimensional grid areas, each grid area corresponds to a monitoring point, the temperature value of each grid area is estimated by spatial interpolation algorithm, a three-dimensional temperature distribution matrix inside the distribution cabinet is generated, and the temperature correlation is corrected by combining humidity data. Obtain the threshold of the instantaneous temperature change rate within a predetermined time interval in the three-dimensional temperature field distribution model, and determine whether to extract the load of the distribution cabinet based on the current actual instantaneous temperature change rate of the distribution cabinet and the threshold of the instantaneous temperature change rate; If the instantaneous temperature change rate is greater than or equal to the instantaneous temperature change rate threshold, it is determined that there is an abnormal upward trend in the internal temperature of the distribution cabinet, and the load of the distribution cabinet is extracted. In response to extracting the load of the distribution cabinet, it is determined whether to extract the ambient temperature based on the load of the distribution cabinet and a predetermined load threshold of the distribution cabinet. If the load of the power distribution cabinet is greater than or equal to the load threshold of the power distribution cabinet, it is determined that the power distribution cabinet is currently in a high load operating state, and the ambient temperature is extracted. Based on the ambient temperature, combined with the current power distribution cabinet load and the historical temperature time series data in the three-dimensional temperature field distribution model, the temperature change trend within the future window is predicted by the pre-trained temperature prediction model. The temperature prediction model is a time-series prediction model based on long short-term memory network. It takes temperature data, ambient temperature data and power distribution cabinet load data within a predetermined time period in the past as input sequence and outputs the temperature prediction value of each monitoring node within a predetermined time window in the future. In response to the significant temperature change trend, local hot spots are identified at the corresponding monitoring points inside the distribution cabinet, and the temperature deviation and heat concentration coefficient of the corresponding areas are analyzed. The real-time temperature values ​​of each monitoring node in the three-dimensional temperature field distribution model are obtained, the average temperature value of all monitoring nodes is calculated, the real-time temperature value of each monitoring node is compared with the average temperature value, and the temperature deviation of each monitoring node is calculated. Based on the three-dimensional temperature field distribution model, the temperature gradient between each monitoring node and its adjacent monitoring nodes is calculated, and the heat accumulation coefficient of each monitoring node is calculated according to the temperature gradient. The temperature deviation of each monitoring node is compared with a predetermined deviation threshold, and the heat concentration coefficient of each monitoring node is compared with a predetermined concentration coefficient threshold. If the temperature deviation of a certain monitoring node is greater than or equal to the deviation threshold and the heat concentration coefficient of the monitoring node is greater than or equal to the concentration coefficient threshold, then the monitoring node is determined to be a local hot spot area. The global basic heat dissipation mode or the local enhanced heat dissipation mode is determined based on the temperature deviation and the heat concentration coefficient. If a local hotspot area exists, a local enhanced heat dissipation mode is determined for that local hotspot area; if there are no monitoring nodes with a temperature deviation greater than or equal to the deviation threshold and a heat accumulation coefficient greater than or equal to the accumulation coefficient threshold, a global basic heat dissipation mode is determined. The global basic heat dissipation mode controls all heat dissipation devices to operate at a preset baseline power. The local enhanced heat dissipation mode, based on the global basic heat dissipation mode, increases the operating power of the heat dissipation devices corresponding to the local hot spots and reduces the operating power of the heat dissipation devices corresponding to the non-hot spots. The cooling rate of the heat dissipation device is obtained in response to the results of the local enhanced heat dissipation mode. If the cooling rate is less than a predetermined cooling rate threshold, the backup heat dissipation component is activated, and the cooling rate is collected at the next predetermined time interval to determine whether to activate the alarm module.

[0007] The present invention also provides an intelligent heat dissipation control system for a power distribution cabinet, comprising: The system includes a three-dimensional temperature field distribution modeling module, a temperature change trend prediction module, a hotspot identification and analysis module, a heat dissipation mode determination module, a cooling rate monitoring module, and an alarm control module. The three-dimensional temperature field distribution modeling module includes thermal imaging sensors and multiple temperature and humidity sensors deployed at corresponding monitoring points inside the power distribution cabinet. These sensors are used to collect temperature, heat distribution data, and humidity data of each monitoring point area inside the power distribution cabinet, and to construct a three-dimensional temperature field distribution model based on the temperature, heat distribution data, and humidity data. The temperature change trend prediction module, which is connected to the three-dimensional temperature field distribution modeling module, is used to obtain the instantaneous temperature change rate threshold within a predetermined time interval in the three-dimensional temperature field distribution model, compare the current actual instantaneous temperature change rate of the distribution cabinet with the instantaneous temperature change rate threshold to determine whether to extract the distribution cabinet load, and when extracting the distribution cabinet load, determine whether to extract the ambient temperature based on the distribution cabinet load and the predetermined distribution cabinet load threshold, and predict the temperature change trend result within the future window based on the ambient temperature. The hotspot identification and analysis module is connected to the three-dimensional temperature field distribution modeling module and the temperature change trend prediction module, respectively. It is used to identify local hotspot areas of the corresponding monitoring points inside the distribution cabinet when the temperature change trend result is significant, and to analyze the temperature deviation and heat concentration coefficient of the local hotspot areas. A heat dissipation mode determination module, which is connected to the hot spot identification and analysis module, is used to determine the global basic heat dissipation mode or the local enhanced heat dissipation mode based on the temperature deviation and the heat concentration coefficient. A cooling rate monitoring module, which is connected to the heat dissipation mode determination module, is used to obtain the cooling rate of the heat dissipation device in a local enhanced heat dissipation mode, compare the cooling rate with a predetermined cooling rate threshold, and activate the backup heat dissipation component when the cooling rate is less than the cooling rate threshold. An alarm control module, connected to the cooling rate monitoring module, is used to collect the cooling rate at the next predetermined time interval after the backup heat dissipation component is activated. If the cooling rate is still less than the predetermined cooling rate threshold, the alarm module is activated.

[0008] Compared with existing technologies, this invention has the following advantages: This invention constructs a three-dimensional temperature field distribution model by deploying thermal imaging and temperature and humidity sensors. It adaptively triggers load extraction based on the comparison of the instantaneous temperature change rate with a threshold, and predicts temperature change trends based on load determination and ambient temperature. When the predicted result shows an upward trend and the peak exceeds the threshold, it identifies local hotspot areas, analyzes temperature deviation and heat accumulation coefficient to determine a global basic heat dissipation mode or a local enhanced heat dissipation mode. In the local enhanced heat dissipation mode, it monitors the cooling rate; when it falls below the threshold, it activates backup heat dissipation components and performs a secondary determination to trigger an alarm, thus achieving proactive detection and closed-loop management of the heat dissipation device's own faults. This invention overcomes the problems in existing technologies where inverters and control systems in photovoltaic power plants and wind farms have extremely high requirements for the continuity of power supply to the distribution cabinet, and outdoor ambient temperature fluctuates drastically. Existing fixed threshold heat dissipation technologies are unable to respond in time, leading to frequent overheating and damage to internal precision components and low control efficiency. This invention achieves an upgrade in heat dissipation for distribution cabinets from static simulation to dynamic perception, and from global uniformity to hierarchical differentiation, effectively improving heat dissipation efficiency and reliability.

[0009] In particular, this invention collects temperature, heat distribution and humidity data in real time by deploying thermal imaging sensors and temperature and humidity sensors inside the power distribution cabinet, and constructs a dynamic three-dimensional temperature field distribution model. This enables comprehensive real-time perception of the temperature distribution inside the power distribution cabinet, overcoming the defect that the static simulation model in the prior art cannot be dynamically updated with the operating conditions, and ensuring that the heat dissipation control strategy is always based on the current actual thermal state.

[0010] Furthermore, this invention uses the instantaneous rate of temperature change to determine adaptively trigger load extraction and ambient temperature extraction, and combines an LSTM temperature prediction model to predict future temperature change trends, thereby realizing a paradigm shift in heat dissipation control from passive response to active prediction and effectively eliminating heat dissipation response lag.

[0011] Furthermore, this invention identifies local hotspot areas through a three-dimensional temperature field distribution model, analyzes temperature deviation and heat accumulation coefficient, and generates a graded differentiated strategy for global basic heat dissipation or local enhanced heat dissipation based on the synergistic determination of the two, thereby achieving synergistic optimization of precise enhanced heat dissipation in hotspot areas and energy saving and consumption reduction in non-hotspot areas.

[0012] Furthermore, by monitoring the cooling rate of the heat dissipation device in real time, the present invention automatically activates the backup heat dissipation component and performs a secondary judgment to trigger an alarm when the heat dissipation efficiency is insufficient, thereby realizing closed-loop management of the heat dissipation device's own fault monitoring and backup compensation, effectively eliminating the risk of equipment overheating caused by heat dissipation failure. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the steps of an intelligent heat dissipation control method for a power distribution cabinet according to an embodiment of the present invention. Figure 2 This is a logic diagram for load extraction and trend prediction in an embodiment of the present invention; Figure 3 This is a logic diagram for determining the hotspot identification and hierarchical heat dissipation strategy in an embodiment of the present invention; Figure 4 This is a schematic diagram of the framework of an intelligent heat dissipation control system for a power distribution cabinet according to an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0015] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0016] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0017] Please see Figure 1 This is a flowchart illustrating the steps of an intelligent heat dissipation control method for a power distribution cabinet according to an embodiment of the present invention. The method described in the embodiment includes: Step S1: Deploy thermal imaging sensors and multiple temperature and humidity sensors at corresponding monitoring points inside the target power distribution cabinet, collect temperature, heat distribution data and humidity data of each monitoring point area inside the power distribution cabinet, use spatial interpolation algorithm to estimate temperature values ​​in areas where no sensors are installed, and construct a three-dimensional temperature field distribution model based on the temperature, heat distribution data and humidity data. Specifically, the internal space of the target distribution cabinet is divided into several three-dimensional grid regions, each corresponding to a temperature monitoring node. These temperature monitoring nodes correspond to the placement positions of the thermal imaging sensor and the temperature and humidity sensor. Based on the real-time temperature data collected by each monitoring node, a spatial interpolation algorithm is used to estimate the temperature values ​​of each grid region, generating a three-dimensional temperature distribution matrix inside the distribution cabinet. Based on the three-dimensional temperature distribution matrix and the thermal distribution data, a temperature correlation relationship is established between the monitoring points inside the distribution cabinet. This temperature correlation relationship is then corrected using the humidity data to obtain the three-dimensional temperature field distribution model.

[0018] In this embodiment, a low-voltage distribution cabinet in a power distribution room of an industrial park is used as an example. The distribution cabinet integrates a large number of electrical components such as circuit breakers, contactors, and busbars. A thermal imaging sensor is deployed at the top inside the distribution cabinet, and multiple temperature and humidity sensors are deployed at key locations on each layer of the cabinet—including near the busbars, the circuit breaker inlet and outlet terminals, and cable joints. Real-time temperature, heat distribution, and humidity data at each monitoring point are continuously collected by the sensors. The thermal imaging sensor acquires an infrared heat distribution image inside the distribution cabinet, and the temperature and humidity sensors acquire precise temperature and humidity values ​​at each monitoring point. By using a spatial interpolation algorithm to estimate the temperature in the grid area where no sensors are installed, a three-dimensional temperature distribution matrix is ​​generated. The temperature correlation is then corrected by combining the humidity data to construct a complete three-dimensional temperature field distribution model inside the distribution cabinet.

[0019] The embodiment uses thermal imaging sensors and temperature and humidity sensors to collect temperature, heat distribution and humidity data in collaboration. Combined with spatial interpolation algorithms, it estimates the temperature in areas without sensors and constructs a three-dimensional temperature field distribution model inside the power distribution cabinet. This realizes the upgrade from single-point monitoring to full-field perception, and provides a complete and real-time temperature field data foundation for heat dissipation control decisions. It overcomes the shortcomings of traditional single-point temperature monitoring that cannot reflect the uneven temperature distribution inside the cabinet.

[0020] Step S2: Obtain the temperature instantaneous change rate threshold within a predetermined time interval in the three-dimensional temperature field distribution model, and determine whether to extract the power distribution cabinet load based on the current actual temperature instantaneous change rate of the power distribution cabinet and the temperature instantaneous change rate threshold. Specifically, the instantaneous temperature change rate of each monitoring node in the three-dimensional temperature field distribution model within a predetermined time interval is obtained, and the maximum value of the instantaneous temperature change rate of each monitoring node is calculated as the instantaneous temperature change rate threshold. The current actual instantaneous temperature change rate of the distribution cabinet is compared with the instantaneous temperature change rate threshold. If the instantaneous temperature change rate is greater than or equal to the instantaneous temperature change rate threshold, it is determined that there is an abnormal upward trend in the internal temperature of the distribution cabinet, and the load of the distribution cabinet is extracted; if the instantaneous temperature change rate is less than the instantaneous temperature change rate threshold, it is determined that the internal temperature change of the distribution cabinet is within the normal range, and the load of the distribution cabinet is not extracted.

[0021] In this embodiment, the instantaneous temperature change rate threshold is calculated by acquiring the instantaneous temperature change rate of each monitoring node in the three-dimensional temperature field distribution model within a predetermined time window. If the instantaneous temperature change rate of a monitoring point exceeds this threshold, it can be determined that there is an abnormal upward trend in the area, and the load data of the distribution cabinet can be extracted. The current load current value is obtained by calling the current transformer data of the distribution cabinet.

[0022] The embodiment achieves intelligent prediction of heat dissipation demand by dynamically comparing the instantaneous rate of temperature change with a threshold. Load extraction is only triggered when there is an abnormal upward trend in temperature, which reduces meaningless data collection and computational resource consumption and improves the system's response efficiency.

[0023] Step S3: In response to extracting the distribution cabinet load, determine whether to extract the ambient temperature based on the distribution cabinet load and a predetermined distribution cabinet load threshold, and predict the temperature change trend within the future window based on the ambient temperature, the current distribution cabinet load, and the historical temperature time series data in the three-dimensional temperature field distribution model. Please see Figure 2 This is a logic diagram for load extraction and trend prediction determination in an embodiment of the present invention; the load extraction and trend prediction determination in the embodiment includes: Obtain the extracted power distribution cabinet load and compare the power distribution cabinet load with a predetermined power distribution cabinet load threshold. If the load of the power distribution cabinet is greater than or equal to the load threshold of the power distribution cabinet, it is determined that the power distribution cabinet is currently in a high load operation state, and the ambient temperature is extracted to predict the temperature change trend in combination with the ambient temperature. If the load of the power distribution cabinet is less than the load threshold of the power distribution cabinet, it is determined that the power distribution cabinet is currently in a low-load operating state, and it is determined not to extract the ambient temperature.

[0024] The ambient temperature, current distribution cabinet load, instantaneous temperature change rate, humidity, and historical temperature time series data from the three-dimensional temperature field distribution model are acquired and used as input feature parameters for the prediction model. The input feature parameters are then input into the pre-trained temperature prediction model, which outputs the predicted temperature values ​​for each monitoring node within a predetermined future time window. Based on the predicted temperature values, the temperature change trend inside the distribution cabinet within the predetermined future time window is determined, and the temperature change trend result is obtained.

[0025] In this embodiment, a high-load operating state is determined by extracting the current distribution cabinet load, which exceeds a predetermined distribution cabinet load threshold, and the ambient temperature is then determined. The current ambient temperature is collected by an ambient temperature sensor deployed outside the distribution cabinet. The ambient temperature, current distribution cabinet load, instantaneous temperature change rate, humidity data, and historical temperature time-series data are input into a pre-trained LSTM temperature prediction model. Based on the data sequence within a predetermined past time period, the model outputs the predicted temperature values ​​for each monitoring node within a predetermined future time window. The model prediction results show that if no additional heat dissipation measures are taken, the monitoring points near the busbar will exceed the preset safe temperature threshold within a predetermined time.

[0026] The embodiment adaptively triggers load extraction by comparing the instantaneous rate of temperature change with a threshold, and then decides whether to introduce ambient temperature for temperature trend prediction based on the load size. At the same time, it integrates multi-dimensional input features and uses an LSTM model to predict future temperature changes, realizing the transformation from passive response to active prediction. This provides a forward-looking decision basis for heat dissipation control and effectively eliminates the problem of heat dissipation response lag.

[0027] Step S4: In response to the temperature change trend result being an upward temperature trend and the predicted temperature peak exceeding a predetermined temperature threshold, identify the local hot spot area of ​​the corresponding monitoring point inside the distribution cabinet, analyze the temperature deviation and heat accumulation coefficient of the corresponding area, and determine the global basic heat dissipation mode or the local enhanced heat dissipation mode based on the temperature deviation and heat accumulation coefficient. Please see Figure 3 This is a logic diagram for hotspot identification and hierarchical heat dissipation strategy determination in an embodiment of the present invention; the hotspot identification and hierarchical heat dissipation strategy determination in the embodiment includes: Obtain the real-time temperature values ​​of each monitoring node in the three-dimensional temperature field distribution model, and calculate the average temperature value of all monitoring nodes; The real-time temperature values ​​of each monitoring node are compared with the average temperature value to calculate the temperature deviation of each monitoring node. Wherein, the temperature deviation is the ratio of the difference between the real-time temperature value of the monitoring node and the average temperature value to the average temperature value; Based on the three-dimensional temperature field distribution model, the temperature gradient between each monitoring node and its neighboring monitoring nodes is calculated, and the heat accumulation coefficient of each monitoring node is calculated according to the temperature gradient. The heat accumulation coefficient is used to characterize the degree of heat accumulation in the local area. The temperature deviation of each monitoring node is compared with a predetermined deviation threshold, and the heat accumulation coefficient of each monitoring node is compared with a predetermined accumulation coefficient threshold. If the temperature deviation of a certain monitoring node is greater than or equal to the deviation threshold, and the heat concentration coefficient of the monitoring node is greater than or equal to the concentration coefficient threshold, then the monitoring node is marked as a local hotspot area.

[0028] If the temperature deviation of a certain monitoring node is less than the deviation threshold or the heat concentration coefficient is less than the concentration coefficient threshold, then the monitoring node is determined to be a non-hotspot area. If local hotspots exist, a local enhanced cooling mode is selected; if no local hotspots exist, a global basic cooling mode is selected.

[0029] In this embodiment, the temperature trend results show that the monitoring points near the busbar are showing an upward trend, and the predicted peak temperature exceeds the safe temperature threshold. The system then identifies local hotspot areas: it calculates the average temperature value of all monitoring nodes, and the real-time temperature value of the monitoring point near the busbar exceeds the average temperature value, with its temperature deviation exceeding a predetermined deviation threshold. The system further calculates the temperature gradient between this monitoring point and adjacent monitoring nodes, and calculates the heat accumulation coefficient based on the temperature gradient. This heat accumulation coefficient exceeds a predetermined accumulation coefficient threshold, therefore, the monitoring point is marked as a local hotspot area. The system simultaneously checks other monitoring points and finds no nodes that simultaneously meet both thresholds, determining that only one local hotspot area exists, and thus deciding to execute the local enhanced heat dissipation mode.

[0030] The embodiment achieves accurate identification of local hotspot areas by using dual thresholds for temperature deviation and heat accumulation coefficient, and quantifies the severity of hotspots by combining multi-dimensional indicators, providing an accurate basis for determining differentiated heat dissipation strategies and reducing the one-sidedness and risk of misjudgment of a single indicator.

[0031] Step S5: In response to the result of the local enhanced heat dissipation mode, obtain the cooling rate of the heat dissipation device. If the cooling rate is less than a predetermined cooling rate threshold, determine to activate the backup heat dissipation component. Specifically, the real-time cooling rate of each monitoring point within the distribution cabinet under localized enhanced heat dissipation mode is obtained, where the cooling rate is the magnitude of temperature decrease at the monitoring point per unit time. The real-time cooling rate of each monitoring point is compared with a predetermined cooling rate threshold, which is determined based on the rated heat dissipation power of the heat dissipation device and the current heat load of the distribution cabinet. If the real-time cooling rate of at least one monitoring point is less than the predetermined cooling rate threshold, the current heat dissipation efficiency of the heat dissipation device is deemed insufficient, and the backup heat dissipation component is activated. If the real-time cooling rate of all monitoring points is greater than or equal to the predetermined cooling rate threshold, the current heat dissipation device is deemed to be operating normally, and the backup heat dissipation component is not activated.

[0032] In this embodiment, based on the determination result of step S4, a local enhanced heat dissipation mode is activated—the speed of the cooling fan corresponding to the area near the busbar is increased to a higher level, and the opening of the air conditioning vents corresponding to that area is correspondingly increased. After the local enhanced heat dissipation mode is activated and runs for a predetermined time, the system obtains the cooling rate of the monitoring points near the busbar and finds that it is less than the predetermined cooling rate threshold, determining that the current heat dissipation efficiency of the heat dissipation device is insufficient, and then determines to activate the backup heat dissipation component. The backup exhaust fan installed on the top of the distribution cabinet is activated to assist in heat dissipation.

[0033] The embodiment achieves dynamic monitoring of the actual heat dissipation efficiency of the heat dissipation device by acquiring the cooling rate in real time under the local enhanced heat dissipation mode and comparing it with a predetermined threshold. It can promptly detect problems of insufficient heat dissipation efficiency and automatically activate backup heat dissipation components for compensation, effectively reducing heat dissipation failures caused by performance degradation or malfunction of the heat dissipation device.

[0034] Step S6: Collect the cooling rate at the next predetermined time interval. If the cooling rate is still less than the predetermined cooling rate threshold, then determine to activate the alarm module.

[0035] Specifically, after activating the backup heat dissipation component, the real-time cooling rate of each monitoring point within the next predetermined time interval is collected, and the real-time cooling rate is compared again with a predetermined cooling rate threshold. If the real-time cooling rate is still less than the predetermined cooling rate threshold, it is determined that the heat dissipation compensation capability of the backup heat dissipation component is insufficient, and the alarm module is activated; if the real-time cooling rate is greater than or equal to the predetermined cooling rate threshold, it is determined that the backup heat dissipation component has restored effective heat dissipation, and the alarm module is not activated.

[0036] In this embodiment, after the backup exhaust fan has been running for a predetermined time, the cooling rate of the monitoring points near the busbar is collected again, and it is found that it is still less than the predetermined cooling rate threshold. The embodiment determines that the heat dissipation compensation capacity of the backup heat dissipation components is still insufficient, and the heat accumulation problem in the busbar area has not been effectively alleviated. The alarm module is activated, sending an alarm signal to the remote monitoring platform of the distribution cabinet, notifying maintenance personnel that there is an abnormal heat dissipation in the busbar area of ​​the distribution cabinet, and prompting them to check for potential faults such as whether the cooling fan is stuck or its performance has deteriorated, or whether the cooling effect of the air conditioning system has decreased.

[0037] The embodiment achieves closed-loop verification of heat dissipation effect by collecting and judging the cooling rate a second time after the backup heat dissipation component is started. When the backup heat dissipation compensation is still insufficient, an alarm is triggered in time to notify the operation and maintenance personnel to intervene and investigate, which effectively reduces the risk of equipment overheating due to heat dissipation failure.

[0038] Please see Figure 4 This is a schematic diagram of the framework of an intelligent heat dissipation control system for a power distribution cabinet according to an embodiment of the present invention. The system described in the embodiment includes: The three-dimensional temperature field distribution modeling module includes thermal imaging sensors and multiple temperature and humidity sensors deployed at corresponding monitoring points inside the power distribution cabinet. These sensors are used to collect temperature, heat distribution data, and humidity data of each monitoring point area inside the power distribution cabinet, and to construct a three-dimensional temperature field distribution model based on the temperature, heat distribution data, and humidity data. The temperature change trend prediction module, which is connected to the three-dimensional temperature field distribution modeling module, is used to obtain the instantaneous temperature change rate threshold within a predetermined time interval in the three-dimensional temperature field distribution model, compare the current actual instantaneous temperature change rate of the distribution cabinet with the instantaneous temperature change rate threshold to determine whether to extract the distribution cabinet load, and when extracting the distribution cabinet load, determine whether to extract the ambient temperature based on the distribution cabinet load and the predetermined distribution cabinet load threshold, and predict the temperature change trend result within the future window based on the ambient temperature. The hotspot identification and analysis module, which is connected to the three-dimensional temperature field distribution modeling module and the temperature change trend prediction module, is used to identify local hotspot areas of corresponding monitoring points inside the distribution cabinet when the temperature change trend result is significant, and to analyze the temperature deviation and heat concentration coefficient of the local hotspot areas. A heat dissipation mode determination module, which is connected to the hot spot identification and analysis module, is used to determine the global basic heat dissipation mode or the local enhanced heat dissipation mode based on the temperature deviation and the heat concentration coefficient. A cooling rate monitoring module, which is connected to the heat dissipation mode determination module, is used to obtain the cooling rate of the heat dissipation device in a local enhanced heat dissipation mode, compare the cooling rate with a predetermined cooling rate threshold, and activate the backup heat dissipation component when the cooling rate is less than the cooling rate threshold. An alarm control module, connected to the cooling rate monitoring module, is used to collect the cooling rate at the next predetermined time interval after the backup heat dissipation component is activated. If the cooling rate is still less than the predetermined cooling rate threshold, the alarm module is activated.

[0039] The modules are connected sequentially according to the data flow direction, with the output of the previous module serving as the input of the next module, together forming an intelligent heat dissipation control system for the power distribution cabinet.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent heat dissipation control in a power distribution cabinet, characterized in that, include: Deploy thermal imaging sensors and multiple temperature and humidity sensors at corresponding monitoring points inside the target power distribution cabinet; Collect temperature, heat distribution, and humidity data for each monitoring point area inside the power distribution cabinet; Based on the temperature, heat distribution data, and humidity, a spatial interpolation algorithm is used to estimate the temperature values ​​in areas without sensors to construct a three-dimensional temperature field distribution model. Obtain the threshold of the instantaneous temperature change rate within a predetermined time interval in the three-dimensional temperature field distribution model, and determine whether to extract the load of the distribution cabinet based on the current actual instantaneous temperature change rate of the distribution cabinet; In response to extracting the load of the distribution cabinet, it is determined whether to extract the ambient temperature based on the load of the distribution cabinet and a predetermined load threshold of the distribution cabinet. Based on the ambient temperature, the current power distribution cabinet load, and the historical temperature time series data in the three-dimensional temperature field distribution model, the predicted temperature change trend within the future window is obtained. In response to the temperature change trend results, local hot spots are identified at the corresponding monitoring points inside the distribution cabinet, and the temperature deviation and heat concentration coefficient of the corresponding areas are analyzed. The global basic heat dissipation mode or the local enhanced heat dissipation mode is determined based on the temperature deviation and the heat concentration coefficient. In response to the results of the local enhanced heat dissipation mode, the cooling rate of the heat dissipation device is obtained. If the cooling rate is less than a predetermined cooling rate threshold, the backup heat dissipation component is activated.

2. The intelligent heat dissipation control method for a power distribution cabinet according to claim 1, characterized in that, The process of constructing a three-dimensional temperature field distribution model includes: The internal space of the target power distribution cabinet is divided into several three-dimensional grid areas, each grid area corresponds to a temperature monitoring node, and the temperature monitoring node corresponds to the placement position of the thermal imaging sensor and the temperature and humidity sensor. Based on the real-time temperature data collected by each monitoring node, a spatial interpolation algorithm is used to estimate the temperature values ​​of each grid area, generating a three-dimensional temperature distribution matrix inside the distribution cabinet. Based on the three-dimensional temperature distribution matrix and the heat distribution data, the temperature correlation between each monitoring point inside the distribution cabinet is established, and the temperature correlation is corrected by combining the humidity data to obtain the three-dimensional temperature field distribution model.

3. The intelligent heat dissipation control method for a power distribution cabinet according to claim 2, characterized in that, The process of determining whether to extract the load from the distribution cabinet includes: The instantaneous temperature change rate of each monitoring node in the three-dimensional temperature field distribution model within a predetermined time interval is obtained, and the maximum value of the instantaneous temperature change rate of each monitoring node is calculated as the instantaneous temperature change rate threshold. The actual instantaneous temperature change rate of the current distribution cabinet is compared with the instantaneous temperature change rate threshold. If the instantaneous temperature change rate is greater than or equal to the instantaneous temperature change rate threshold, it is determined that there is an abnormal upward trend in the internal temperature of the distribution cabinet, and the load of the distribution cabinet is extracted.

4. The intelligent heat dissipation control method for a power distribution cabinet according to claim 3, characterized in that, The process of determining whether to extract ambient temperature includes: Obtain the extracted power distribution cabinet load and compare the power distribution cabinet load with a predetermined power distribution cabinet load threshold. If the load of the power distribution cabinet is greater than or equal to the load threshold of the power distribution cabinet, it is determined that the power distribution cabinet is currently in a high-load operating state, and the ambient temperature is extracted.

5. The intelligent heat dissipation control method for a power distribution cabinet according to claim 4, characterized in that, The process of predicting future temperature trends within a window includes: The ambient temperature, current power distribution cabinet load, instantaneous temperature change rate, humidity, and historical temperature time series data in the three-dimensional temperature field distribution model are acquired as input feature parameters for the prediction model. The input feature parameters are input into a pre-trained temperature prediction model, and the temperature prediction model outputs the temperature prediction values ​​of each monitoring node within a predetermined future time window. Based on the predicted temperature value, the trend of temperature change inside the distribution cabinet within a predetermined time window is determined, and the temperature change trend result is obtained.

6. The intelligent heat dissipation control method for a power distribution cabinet according to claim 5, characterized in that, The process of analyzing the temperature deviation and heat concentration coefficient of the corresponding region includes: Obtain the real-time temperature values ​​of each monitoring node in the three-dimensional temperature field distribution model, and calculate the average temperature value of all monitoring nodes; The real-time temperature value of each monitoring node is compared with the average temperature value, and the temperature deviation of each monitoring node is calculated. The temperature deviation is the ratio of the difference between the real-time temperature value and the average temperature value of the monitoring node to the average temperature value. Based on the three-dimensional temperature field distribution model, the temperature gradient between each monitoring node and its neighboring monitoring nodes is calculated, and the heat accumulation coefficient of each monitoring node is calculated according to the temperature gradient. The heat accumulation coefficient is used to characterize the degree of heat accumulation in the local area. The temperature deviation of each monitoring node is obtained and compared with a predetermined deviation threshold. At the same time, the heat accumulation coefficient of each monitoring node is obtained and compared with a predetermined accumulation coefficient threshold. If the temperature deviation of a certain monitoring node is greater than or equal to the deviation threshold, and the heat concentration coefficient of the monitoring node is greater than or equal to the concentration coefficient threshold, then the monitoring node is marked as a local hotspot area.

7. The intelligent heat dissipation control method for a power distribution cabinet according to claim 6, characterized in that, The process of determining the global basic cooling mode or the local enhanced cooling mode includes: The temperature deviation of each monitoring node is compared with a predetermined deviation threshold, and the heat accumulation coefficient of each monitoring node is compared with a predetermined accumulation coefficient threshold. If the temperature deviation of a certain monitoring node is greater than or equal to the deviation threshold, and the heat accumulation coefficient of the monitoring node is greater than or equal to the accumulation coefficient threshold, then the monitoring node is determined to be a local hot spot area, and a local enhanced heat dissipation mode is determined for the local hot spot area. If there are no monitoring nodes with a temperature deviation greater than or equal to the deviation threshold and a heat accumulation coefficient greater than or equal to the accumulation coefficient threshold, it is determined that there are no local hot spots inside the distribution cabinet, and the global basic heat dissipation mode is determined.

8. The intelligent heat dissipation control method for a power distribution cabinet according to claim 7, characterized in that, The process of determining when to activate the backup cooling system includes: The real-time cooling rate of each monitoring point in the power distribution cabinet under the local enhanced heat dissipation mode is obtained, wherein the cooling rate is the magnitude of the temperature drop of the monitoring point per unit time. The real-time cooling rate at each monitoring point is compared with a predetermined cooling rate threshold; the predetermined cooling rate threshold is determined based on the rated heat dissipation power of the heat dissipation device and the current heat load of the distribution cabinet. If the real-time cooling rate at at least one monitoring point is less than the predetermined cooling rate threshold, it is determined that the current heat dissipation efficiency of the heat dissipation device is insufficient, and the backup heat dissipation component is activated.

9. The intelligent heat dissipation control method for a power distribution cabinet according to claim 8, characterized in that, The process of determining when to activate the alarm module includes: After the backup heat dissipation component is activated, the real-time cooling rate of each monitoring point is collected within the next predetermined time interval, and the real-time cooling rate is compared with the predetermined cooling rate threshold again. If the real-time cooling rate is still less than the predetermined cooling rate threshold, it is determined that the heat dissipation compensation capability of the backup heat dissipation component is insufficient, and the alarm module is activated.

10. A power distribution cabinet intelligent heat dissipation control system applying the method according to any one of claims 1 to 9, characterized in that, include: The three-dimensional temperature field distribution modeling module includes thermal imaging sensors and multiple temperature and humidity sensors deployed at corresponding monitoring points inside the power distribution cabinet. These sensors are used to collect temperature, heat distribution data, and humidity data of each monitoring point area inside the power distribution cabinet, and to construct a three-dimensional temperature field distribution model based on the temperature, heat distribution data, and humidity data. The temperature change trend prediction module, which is connected to the three-dimensional temperature field distribution modeling module, is used to obtain the instantaneous temperature change rate threshold within a predetermined time interval in the three-dimensional temperature field distribution model, compare the current actual instantaneous temperature change rate of the distribution cabinet with the instantaneous temperature change rate threshold to determine whether to extract the distribution cabinet load, and when extracting the distribution cabinet load, determine whether to extract the ambient temperature based on the distribution cabinet load and the predetermined distribution cabinet load threshold, and predict the temperature change trend result within the future window based on the ambient temperature. The hotspot identification and analysis module, which is connected to the three-dimensional temperature field distribution modeling module and the temperature change trend prediction module, is used to identify local hotspot areas of corresponding monitoring points inside the distribution cabinet when the temperature change trend result is significant, and to analyze the temperature deviation and heat concentration coefficient of the local hotspot areas. A heat dissipation mode determination module, which is connected to the hot spot identification and analysis module, is used to determine the global basic heat dissipation mode or the local enhanced heat dissipation mode based on the temperature deviation and the heat concentration coefficient. A cooling rate monitoring module, which is connected to the heat dissipation mode determination module, is used to obtain the cooling rate of the heat dissipation device in a local enhanced heat dissipation mode, compare the cooling rate with a predetermined cooling rate threshold, and activate the backup heat dissipation component when the cooling rate is less than the cooling rate threshold. An alarm control module, connected to the cooling rate monitoring module, is used to collect the cooling rate at the next predetermined time interval after the backup heat dissipation component is activated. If the cooling rate is still less than the predetermined cooling rate threshold, the alarm module is activated.

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