A heat dissipation cooling method, device and equipment for an intelligent bus duct

Abstract

The present application relates to the technical field of bus duct, in particular to a heat dissipation and cooling method, device and equipment of intelligent bus duct. The method dynamically corrects the real-time joule heat power of the conductor by comprehensively considering the skin effect of harmonic and the influence of resistance temperature drift; the natural convection and forced cooling parameters are identified online according to the working condition; the residual error between the measured temperature rise rate and the model theoretical prediction value is used to generate a fault factor; a multi-objective function including insulation aging and mechanical stress cost is constructed, and the weight ratio of the two costs is dynamically adjusted by using the fault response weight, and the control instruction of the cooling fan is solved. The present application quantifies the real heat generation inside the conductor, considers the comprehensive management of insulation life and mechanical connection in heat dissipation control through online identification of heat dissipation environment change, realizes closed-loop heat dissipation and cooling control, effectively improves the accuracy and reliability of bus duct thermal management, and prolongs the comprehensive service life of the equipment.

Description

Technical Field

[0001] This invention relates to the field of busbar technology, and specifically to a heat dissipation and cooling method, apparatus and equipment for an intelligent busbar. Background Technology

[0002] Busbar trunking, as the core trunking equipment of low-voltage power transmission and distribution systems, is widely used in high-rise buildings and industrial fields. Its internal conductors are compactly arranged, relying primarily on a metal casing for heat dissipation. However, with the widespread use of nonlinear loads such as frequency converters in power grids, the skin effect and proximity effect caused by a large number of high-order harmonic currents lead to a significant increase in the AC resistance of the conductors. Furthermore, the resistivity of the metal conductor drifts with increasing temperature, causing traditional methods for estimating heat generation based on the effective value of current and resistance at room temperature to become severely distorted.

[0003] Due to the large heat capacity of busbar trunking, the outer casing temperature response is lag-dependent, and during long-term operation, thermal expansion and contraction can easily increase the contact resistance of connectors, resulting in hidden localized overheating. Common temperature control methods often cannot distinguish between normal load heating and contact fault heating, and a single, forceful cooling strategy can easily generate alternating thermal stress at the connection points. Localized overheating caused by early contact faults is often masked by normal load heating, making it difficult to identify in time and potentially leading to safety accidents. Summary of the Invention

[0004] To address the shortcomings of existing technologies, such as inaccurate heat generation estimation, delayed thermal response, inability to adapt to the effects of harmonics and temperature changes on conductor heating, lack of online identification capability for changes in the heat dissipation environment, and difficulty in early identification of hidden faults caused by poor connector contact, the present invention aims to provide an intelligent busbar cooling method, apparatus, and equipment. The specific technical solution adopted is as follows:

[0005] This invention provides a heat dissipation and cooling method for an intelligent busbar trunking system, the method comprising:

[0006] The operating current and casing temperature of the busbar trunking are obtained; based on the operating current and casing temperature, the influence of harmonic effects and resistance temperature drift is analyzed to obtain the real-time Joule thermal power; the casing heating rate and the temperature difference outside the casing are determined based on the casing temperature change.

[0007] Based on real-time Joule thermal power, shell heating rate and shell temperature difference, the natural convection thermal response coefficient is updated under natural heat dissipation conditions, and the forced cooling gain coefficient is obtained by combining airflow transport hysteresis characteristics under forced air cooling conditions.

[0008] The theoretically predicted temperature rise is calculated using the natural convection thermal response coefficient and the forced cooling gain coefficient; the actual observed temperature rise of the shell is compared with the theoretically predicted temperature rise, the residual factor is extracted, and the fault response weight is generated.

[0009] An operating cost function incorporating insulation aging costs and mechanical stress costs is constructed. The weight ratio of insulation aging costs and mechanical stress costs is dynamically adjusted according to the fault response weight. The sensitivity gradient is analyzed using the forced cooling gain coefficient, and the control commands for the cooling fan are solved.

[0010] Furthermore, the method for obtaining the real-time Joule thermal power includes:

[0011] Harmonic analysis is performed on the operating current to obtain each harmonic component; the square of each harmonic component is multiplied by a preset AC resistance gain coefficient and then summed to obtain the equivalent heating current.

[0012] The temperature coefficient of resistance of the conductor material is obtained, and the reference resistance of the conductor is corrected by using the shell temperature to obtain the corrected resistance. The real-time Joule thermal power is obtained by combining the corrected resistance with the equivalent heating current.

[0013] Furthermore, the method for determining the natural convection thermal response coefficient includes:

[0014] Under natural heat dissipation conditions, the heat dissipation response is obtained by combining the shell heating rate and the temperature difference outside the shell; the heating response is obtained by the ratio of the heat dissipation response at a given time to the real-time Joule thermal power.

[0015] Using a preset forgetting coefficient, the natural convection thermal response coefficient and the temperature rise response of the previous moment are weighted and combined to obtain the natural convection thermal response coefficient of the current moment.

[0016] If the natural heat dissipation condition is not met, the natural convection thermal response coefficient of the previous moment shall be used as the natural convection thermal response coefficient of the current moment.

[0017] Furthermore, the method for determining the forced cooling gain coefficient includes:

[0018] Under forced air cooling conditions, the fan duty cycle at a preset lag period before the current moment is obtained. The cooling drive index is obtained by combining the fan duty cycle with the temperature difference outside the casing. The product of the real-time Joule heat power at the current moment and the natural convection heat response coefficient is used as the theoretical temperature rise rate.

[0019] The forced cooling gain coefficient is obtained by analyzing the rate deviation between the theoretical temperature rise rate and the shell temperature rise rate, and by using the ratio of the rate deviation to the cooling drive index.

[0020] Furthermore, the method for obtaining the theoretically predicted temperature rise includes:

[0021] The cooling influencing factor is determined by combining the fan duty cycle, forced cooling gain coefficient and shell temperature difference at the preset lag period before the current moment;

[0022] The product of the real-time Joule thermal power and the natural convection thermal response coefficient is used as the theoretical temperature rise rate; the difference between the theoretical temperature rise rate and the cooling influence factor is used as the theoretical predicted temperature rise.

[0023] Furthermore, the method for obtaining the fault response weights includes:

[0024] The abnormal deviation component is obtained by comparing the current shell heating rate with the theoretically predicted temperature rise; the residual factor at the current moment is obtained based on the abnormal deviation component and the theoretical temperature rise rate.

[0025] The current fault response weight is determined based on the degree to which the residual factor exceeds the preset fault threshold at the current moment.

[0026] Furthermore, the construction of the operating cost function includes:

[0027] The insulation aging cost term is positively correlated with the predicted shell temperature value in a nonlinear manner; the mechanical stress cost term is positively correlated with the change in the temperature rise rate; the operating cost function is a weighted sum of the insulation aging cost term, the mechanical stress cost term, and the fan energy consumption term.

[0028] Furthermore, the control commands for solving the cooling fan include:

[0029] Based on the fault response weights, the weight of insulation aging cost is increased and the weight of mechanical stress cost is decreased; the gradient direction of the operating cost function relative to the fan control command is analyzed using the forced cooling gain coefficient; and the current fan duty cycle is iteratively updated based on the gradient direction and the preset step size.

[0030] The present invention also provides a heat dissipation and cooling device for an intelligent busbar trunking, the device comprising:

[0031] The heat source sensing module is used to acquire the operating current and shell temperature of the busbar trunking; based on the operating current and shell temperature, it analyzes the influence of harmonic effects and resistance temperature drift to obtain real-time Joule heat power; and determines the shell heating rate and the temperature difference outside the shell based on the shell temperature change.

[0032] The environmental change identification module is used to update the natural convection thermal response coefficient under natural heat dissipation conditions based on real-time Joule thermal power, shell heating rate and shell temperature difference, and to obtain the forced cooling gain coefficient by combining airflow transport hysteresis characteristics under forced air cooling conditions.

[0033] The fault response assessment module is used to calculate the theoretically predicted temperature rise using the natural convection thermal response coefficient and the forced cooling gain coefficient; it compares the actual observed temperature of the casing with the theoretically predicted temperature rise, extracts the residual factor, and generates fault response weights.

[0034] The multi-objective adaptive control module is used to construct an operating cost function that includes insulation aging cost and mechanical stress cost. It dynamically adjusts the weight ratio of insulation aging cost and mechanical stress cost according to the fault response weight, analyzes the sensitivity gradient using the forced cooling gain coefficient, and solves the control command of the cooling fan.

[0035] The present invention also provides a heat dissipation and cooling device for an intelligent busbar trunking, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the heat dissipation and cooling method for an intelligent busbar trunking as described above.

[0036] The present invention has the following beneficial effects:

[0037] This invention dynamically corrects the real-time Joule heat power of the conductor by comprehensively considering the harmonic skin effect and the resistance temperature drift, improving the accuracy of heat source sensing and providing accurate feedforward input for subsequent thermal management. Secondly, by identifying natural convection and forced cooling parameters online under different operating conditions, the system can adapt to changes in the environment and airflow characteristics, improving the robustness and control accuracy of the heat dissipation system under various operating conditions. Furthermore, by using the residual between the measured temperature rise rate and the theoretical prediction value of the model to generate a fault factor, it can effectively isolate weak abnormal heating caused by poor connector contact from high background load heating, achieving non-intrusive early warning of early hidden faults. By constructing a multi-objective function that includes the costs of insulation aging and mechanical stress, and dynamically adjusting the weight ratio of the two costs using fault response weights, the control strategy can prioritize stable temperature rise to protect mechanical connections when the equipment is normal, and prioritize rapid cooling to protect insulation during fault warnings, thereby intelligently balancing and extending the overall service life of the equipment. This invention quantifies the actual internal heat generation of conductors and, by identifying changes in the heat dissipation environment online, achieves comprehensive management of insulation life and mechanical connections in heat dissipation control, realizing closed-loop heat dissipation and cooling control. This effectively improves the accuracy and reliability of busbar thermal management and extends the overall service life of the equipment. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating a heat dissipation and cooling method for an intelligent busbar trunking according to an embodiment of the present invention;

[0040] Figure 2This is a schematic diagram of the structure of a heat dissipation and cooling device for an intelligent busbar trunking according to an embodiment of the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a heat dissipation and cooling method, apparatus, and device for an intelligent busbar trunking according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] The following description, in conjunction with the accompanying drawings, details a specific solution for a heat dissipation and cooling method, apparatus, and equipment for an intelligent busbar trunking provided by the present invention.

[0044] This application provides a heat dissipation and cooling method for an intelligent busbar trunking. Please refer to [link / reference]. Figure 1 The diagram illustrates a flow chart of a heat dissipation and cooling method for an intelligent busbar trunking according to an embodiment of the present invention. The method includes the following steps:

[0045] S1: Obtain the operating current and casing temperature of the busbar trunking; based on the operating current and casing temperature, analyze the influence of harmonic effects and resistance temperature drift to obtain real-time Joule thermal power; determine the casing heating rate and the temperature difference outside the casing based on the casing temperature change.

[0046] Considering the compact arrangement of conductors inside the busbar trunking, the fact that heat dissipation mainly relies on the metal casing, and the susceptibility to harmonics and temperature changes caused by nonlinear loads during operation, resulting in a complex heating state, in this embodiment of the invention, a current transformer and signal conditioning circuit installed at the input end of the busbar trunking are used to collect operating current data at a fixed sampling period, which is set to 1 second in this embodiment. At the same time, the temperature of the casing is collected in sequence by a temperature sensor attached to the surface of the busbar trunking casing.

[0047] It should be noted that preprocessing of the monitored data may include data standardization and time-scale normalization to facilitate unified data analysis and remove the influence of units. It should also be noted that data preprocessing is a technique well-known to those skilled in the art, and the sampling frequency can be adjusted by the implementer, without further elaboration or limitation here.

[0048] To accurately quantify the true heat generation power of the busbar trunking and avoid estimation errors caused by traditional calculations based solely on the effective value of the current, the effects of harmonic effects on resistance gain and temperature rise on resistance drift are considered. The influence of harmonic effects and resistance temperature drift is analyzed to obtain the real-time Joule heat power. In this embodiment of the invention, harmonic analysis is performed on the operating current to obtain the harmonic components. Specifically, the operating current signal is decomposed using Fast Fourier Transform (FFT) to extract the fundamental frequency and the odd harmonic components of the 3rd, 5th, and up to the preset highest order (e.g., 21st), while ignoring the less influential even harmonics to simplify the calculation.

[0049] The equivalent heating current is obtained by multiplying the squares of each harmonic component by a preset AC resistance gain coefficient and then summing them. This equivalent heating current integrates the heating contributions of different harmonics, reflecting the actual equivalent heating state of the conductor under harmonic effects. The preset AC resistance gain coefficient is the ratio of the conductor's AC resistance to its DC resistance at different harmonic frequencies, characterizing the degree of resistance gain caused by the skin effect and proximity effect. The product represents the additional heating weight of each harmonic due to resistance gain; therefore, the summation reflects the combined effect of all harmonics on the total heating.

[0050] This allows us to obtain the temperature coefficient of resistance of the conductor material, a linear constant representing the change in resistivity with temperature (e.g., 0.00393 for copper busbars, 0.00403 for aluminum busbars), reflecting the effect of temperature on conductor resistance. The conductor's reference resistance is then corrected using the casing temperature to obtain the corrected resistance. Considering the physical characteristic that the resistance of metallic conductors increases significantly with increasing temperature, 20°C is used as the reference temperature. The temperature drift of the resistance is quantified by the difference between the casing temperature and the reference temperature. As an example, the expression for the corrected resistance is: In the formula, Represented as the first Correction resistor at any time, Represented as the first The reference resistance of a conductor at a constant reference temperature. It is expressed as the temperature coefficient of resistance. Represented as the first The difference between the casing temperature and the reference temperature at any given time.

[0051] Finally, by combining the corrected resistance and the equivalent heating current, the real-time Joule thermal power is obtained, which reflects the actual heat generation intensity of the busbar under the current harmonic environment and temperature conditions. In this embodiment of the invention, the product of the corrected resistance and the equivalent heating current is used as the real-time Joule thermal power, while compensating for the influence of harmonics and temperature on the resistance, ensuring the accuracy of the heating power.

[0052] This provides key thermal state feedback data for subsequent thermal model parameter identification, acquiring parameters characterizing the thermal response trend of the busbar trunking, including the outer shell heating rate and the outer shell temperature difference. In this embodiment of the invention, considering that temperature signals are susceptible to instantaneous jumps caused by electromagnetic interference, and that direct differential methods easily amplify noise, the outer shell heating rate is obtained through a mean difference algorithm before and after a window. Specifically, within the analysis window before each moment, the outer shell heating rate at each moment is obtained based on the degree of increase in outer shell temperature before and after the analysis window. The analysis window is a sliding time window with a length of 10 seconds, dividing the analysis window into two equal periods. The difference between the average outer shell temperature in the later period and the average outer shell temperature in the earlier period is taken as the heating value. The ratio of the heating value to the duration of the period is taken as the outer shell heating rate, reflecting the speed trend of busbar trunking temperature change and effectively smoothing out noise interference.

[0053] The ambient temperature is then obtained synchronously by temperature sensors deployed around the busbar trunking. The difference between the outer casing temperature and the ambient temperature at each moment is taken as the outer casing temperature difference at each moment. The larger the temperature difference, the easier it is for heat to dissipate from the outer casing to the environment, providing a core basis for subsequent heat dissipation parameter identification.

[0054] S2: Based on real-time Joule thermal power, shell heating rate and shell temperature difference, the natural convection thermal response coefficient is updated under natural heat dissipation conditions, and the forced cooling gain coefficient is obtained by combining airflow transport hysteresis characteristics under forced air cooling conditions.

[0055] The actual heat dissipation capacity of busbar trunking is easily affected by dynamic factors such as installation method, degree of dust accumulation on air inlet filter and environmental airflow conditions. Fixed thermal parameter models cannot adapt to complex working conditions. Therefore, by identifying working conditions online, the environmental thermal characteristics and fan cooling efficiency can be accurately captured, providing reliable model support for subsequent fault diagnosis and adaptive control.

[0056] First, the current operating conditions are determined to meet the natural heat dissipation identification conditions by analyzing the fan's operating status and real-time Joule thermal power. In this embodiment of the invention, the fan duty cycle is a control signal characterizing the fan's operating intensity, reflecting the magnitude of the fan's output air volume. A preset fan start-up threshold is set, such as 5%. When the fan duty cycle is lower than the preset fan start-up threshold, the fan has virtually no cooling output. Simultaneously, the real-time Joule thermal power is required to be greater than the minimum effective identification power, such as 20% of the rated power, to ensure sufficient thermal drive signal to guarantee identification accuracy. At this point, the natural heat dissipation condition is determined to be met.

[0057] Under natural heat dissipation conditions, the heat dissipation response is obtained by combining the shell heating rate and the external temperature difference. In this embodiment of the invention, the external temperature difference is multiplied by a preset basic natural heat dissipation constant, and the sum of the product and the shell heating rate is taken as the heat dissipation response. The preset basic natural heat dissipation constant is a fixed value determined through type testing, characterizing the inherent rate of heat dissipation from the busbar casing to the environment. The product reflects the basic radiative heat dissipation intensity corresponding to the external temperature difference. Therefore, the heat dissipation response obtained by combining the shell heating rate comprehensively reflects the overall thermal response state of the system under natural heat dissipation conditions.

[0058] Furthermore, the temperature rise response is obtained by the ratio of the heat dissipation response at a given time to the real-time Joule heat power. Since the heat dissipation response is the total heat change of the system per unit time, and the real-time Joule heat power is the heat input per unit time, the temperature rise effect corresponding to a unit of heat output can be quantified by the ratio of the two, thus obtaining the temperature rise response, which reflects the basic characteristics of natural heat dissipation under the current environment.

[0059] A preset forgetting coefficient is used to adjust the rate and smoothness of parameter updates. The value ranges from 0.01 to 0.05; smaller values ​​make the parameters more stable, while larger values ​​improve the sensitivity of the parameters to environmental changes. Implementers can adjust this according to the implementation scenario. In this embodiment, the natural convection thermal response coefficient and the temperature rise response are weighted and combined to obtain the natural convection thermal response coefficient at the current moment. Specifically, the temperature rise response is weighted using the preset forgetting coefficient, and the natural convection thermal response coefficient at the previous moment is weighted using the difference between a constant 1 and the preset forgetting coefficient. The weighted sum is used as the natural convection thermal response coefficient at the current moment. This weighting method retains the effective information of historical parameters while dynamically incorporating the thermal characteristics of the current operating conditions, ensuring the stability and timeliness of the natural convection thermal response coefficient.

[0060] If the current operating conditions do not meet the natural heat dissipation requirements, it indicates that the fan has started or the heat generation intensity is insufficient, and the conditions for identifying natural heat dissipation parameters are not met. The natural convection thermal response coefficient from the previous moment is then used as the natural convection thermal response coefficient for the current moment. Specifically, if there is a prolonged period where the natural heat dissipation requirements are not met, it indicates that the system has been in a forced air-cooled state for an extended period. The historically identified natural convection thermal response coefficients may drift due to environmental changes, thus affecting the accuracy of subsequent calculations. In this embodiment of the invention, the preset long-term duration is 24 hours. When the duration of the non-compliance with natural heat dissipation requirements exceeds the preset long-term duration, the current natural convection thermal response coefficient is controlled to linearly approximate the factory default value. This is done by gradually reverting the natural convection thermal response coefficient to the factory default value in small steps, ensuring that the model parameters are always within a reasonable range and avoiding control failure due to parameter drift.

[0061] The forced cooling effect brought by the fan is considered to be an incremental supplement to natural heat dissipation. This incremental effect can be quantified to obtain the actual cooling efficiency of the fan, thus providing a basis for precise control of fan operation. In this embodiment of the invention, the forced air cooling condition can be determined based on the fan duty cycle. When the fan duty cycle is not lower than a preset start-up threshold, it indicates that the fan has stably output cooling airflow and meets the conditions for forced cooling parameter identification, thus determining that the forced air cooling condition is satisfied.

[0062] In this embodiment of the invention, under forced air cooling conditions, the fan duty cycle at a preset lag period prior to the current moment is obtained. Since there is a physical time delay in the transmission of cooling airflow from the fan outlet to the temperature sensor location, directly matching the current fan command with the real-time temperature response would result in a causal misalignment. Therefore, considering the airflow transmission hysteresis characteristic, a preset lag period is established. The preset lag period can range from 10 to 30 seconds, calibrated based on the duct length and wind speed, and is not limited here. The historical fan duty cycle corresponding to the lag period is used for calculation. Combining the fan duty cycle with the external temperature difference, a cooling drive index is obtained, reflecting the synergistic effect of fan operating intensity and heat dissipation driving force, characterizing the effective input level of forced cooling.

[0063] Furthermore, the product of the real-time Joule heat power and the natural convection thermal response coefficient is used as the theoretical temperature rise rate, characterizing the expected temperature rise rate corresponding to the current heat generation intensity under natural heat dissipation conditions only. The deviation between the theoretical temperature rise rate and the shell temperature rise rate is analyzed to reflect the additional cooling effect brought by forced air cooling. The forced cooling gain coefficient is obtained by the ratio of the rate deviation to the cooling drive index, characterizing the ability of a unit cooling drive index to suppress the temperature rise rate. The larger the forced cooling gain coefficient, the stronger the cooling efficiency of the fan under the current operating conditions. As an example, the expression for the forced cooling gain coefficient is: In the formula, Represented as the first Forced cooling gain coefficient at time, Represented as the first Real-time Joule thermal power at any given moment Represented as the first The natural convection thermal response coefficient at time t. Represented as the first The rate of shell heating at any given time, Represented as the first Cooling-driven metrics at any time Represented as a preset minimum regularization constant, in this embodiment of the invention, it can be 10. -6 This is to prevent calculation errors caused by a denominator of zero.

[0064] If the current operating conditions do not meet the forced air cooling conditions, it means that the fan has not started and there is no additional forced cooling effect. In this case, there is no need to identify the forced cooling gain coefficient, and the forced cooling gain coefficient of the previous moment is used as the current forced cooling gain coefficient.

[0065] This completes the online identification of the natural convection thermal response coefficient and the forced cooling gain coefficient.

[0066] S3: Calculate the theoretically predicted temperature rise using the natural convection thermal response coefficient and the forced cooling gain coefficient; compare the actual observed temperature of the outer shell with the theoretically predicted temperature rise, extract the residual factor, and generate the fault response weight.

[0067] After analyzing the actual thermal response, the temperature rise trend of the bus trunking under normal operating conditions can be predicted by constructing a thermal model. By comparing it with the actual thermal response, abnormal heat generation components can be separated, thereby identifying hidden faults such as poor connector contact and performing fault response analysis. This avoids early faults being masked by normal load heat generation. Therefore, the temperature rise is first predicted by combining the identified thermal parameters with historical control commands.

[0068] In this embodiment of the invention, the cooling influence factor is determined by combining the fan duty cycle, forced cooling gain coefficient, and shell temperature difference at a preset lag period before the current time. In a specific embodiment of the invention, the historical fan duty cycle, current forced cooling gain coefficient, and current shell temperature difference at the time corresponding to the preset lag period are multiplied to obtain the cooling influence factor, which reflects the suppression effect of historical fan operation on temperature rise under the current operating conditions and characterizes the correction magnitude of forced cooling on the normal temperature rise trend.

[0069] Furthermore, the product of the real-time Joule heat power and the natural convection thermal response coefficient is used as the theoretical temperature rise rate, reflecting the rate of temperature change that the busbar should exhibit under the current heat generation intensity, considering only natural heat dissipation. The difference between the theoretical temperature rise rate and the cooling influence factor is used as the theoretical predicted temperature rise. By comprehensively considering the triple effects of load heat generation, natural heat dissipation benchmark, and forced cooling suppression, a mathematical reconstruction of the normal thermal behavior of the busbar is achieved, characterizing the true temperature rise trend under fault-free conditions.

[0070] Further analysis and comparison can be made between the actual observed temperature of the casing and the theoretically predicted temperature rise to quantify the degree of unexplained abnormal heating and reflect whether there are additional heat sources caused by poor contact in the connector. In this embodiment of the invention, the abnormal deviation component is obtained by the deviation between the current casing heating rate and the theoretically predicted temperature rise. Specifically, the absolute value of the difference between the current casing heating rate and the theoretically predicted temperature rise is smoothed by a moving average to obtain the abnormal deviation component. This filters out instantaneous noise interference and truly reflects the temperature rise deviation caused by abnormal heat sources. When the abnormal deviation component approaches zero, it indicates that the system thermal response conforms to normal laws and there is no obvious fault. When the abnormal deviation component increases significantly, there may be faults such as poor contact.

[0071] Based on the abnormal deviation component and the theoretical temperature rise rate, the residual factor at the current moment is obtained. Specifically, the theoretical temperature rise rate and the sum of the reference power constant are used as the denominator, and the abnormal deviation component is used as the numerator to obtain the residual factor. The residual factor represents the proportion of abnormal heating to normal heating. The reference power constant is a preset fixed value, such as 0.1, which is used to avoid numerical divergence caused by the denominator being too small under low load conditions and to ensure the stability of the residual factor calculation. No restrictions are imposed here.

[0072] Finally, based on the degree to which the residual factor exceeds the preset fault threshold at the current moment, the current fault response weight is determined. This fault response weight is used to dynamically adjust the priority of subsequent control strategies, achieving a more precise match between the fault state and the control logic. In one specific embodiment of this invention, the preset fault threshold is set to 0.2. This preset fault threshold can be calibrated through numerous experiments to distinguish between measurement noise and significant faults. The square of the ratio of the current residual factor to the preset fault threshold is used as a candidate weight value. This squared relationship makes the weight highly sensitive to fault symptoms; when the residual factor slightly exceeds the threshold, the weight rises rapidly, reflecting the progression of fault severity. When the candidate weight value is less than the preset saturation value, the candidate weight value is used as the fault response weight; otherwise, it indicates a significant contact hazard, and the preset saturation value is used as the fault response weight.

[0073] In this embodiment of the invention, the preset saturation value is set to 1, which ensures that the weight always transitions smoothly within a reasonable range of 0 to 1.

[0074] S4: Construct an operating cost function that includes insulation aging cost and mechanical stress cost. Dynamically adjust the weight ratio of insulation aging cost and mechanical stress cost according to the fault response weight. Analyze the sensitivity gradient using the forced cooling gain coefficient and solve for the control command of the cooling fan.

[0075] Since the heat dissipation control of the bus trunking needs to simultaneously resolve the contradiction between "high temperature damage to insulation" and "excessive temperature change rate damage to mechanical connections", and also needs to take into account the energy consumption of the fan operation, it is necessary to balance insulation protection, mechanical reliability and energy saving requirements to carry out adaptive control, and consider the sensitivity feedback of the forced cooling gain coefficient to the fan control command to ensure control accuracy.

[0076] In this embodiment of the invention, the insulation aging cost term is non-linearly positively correlated with the predicted casing temperature. Based on the Arrhenius principle, the higher the temperature, the faster the insulation dielectric lifespan is depleted. This non-linear relationship accurately characterizes the accelerated aging effect of high temperature on insulation. In one specific embodiment of the invention, the predicted casing temperature is the expected temperature after executing the fan control command, derived through a thermal model. As an example, the expression for the insulation aging cost term is: In the formula, This is represented as the cost of insulation aging. This is expressed as the predicted casing temperature. This represents the rated operating temperature of the busbar trunking. It is expressed as the thermal sensitivity constant of the conductor material, such as 10℃. It is represented as an exponential function with the natural constant as the base.

[0077] The mechanical stress cost term is positively correlated with the change in the temperature rise rate. The greater the fluctuation in the temperature rise rate, the more significant the difference in thermal expansion between the conductor and components such as bolts, resulting in stronger alternating thermal stress. Long-term cycling can easily lead to wear on the contact surface and loosening of bolts. In one specific embodiment of this invention, the change in the temperature rise rate is the difference between the predicted temperature rise rate of the outer casing and the current actual temperature rise rate of the outer casing, obtained by comparing the prediction from the thermal model with real-time monitoring data. As an example, the expression for the mechanical stress cost term is: In the formula, Represented as mechanical stress cost term, This is expressed as the predicted rate of temperature rise in the outer casing. This represents the actual rate of temperature rise of the outer casing.

[0078] The operating cost function is then a weighted sum of the insulation aging cost, mechanical stress cost, and fan energy consumption. The fan energy consumption reflects the energy consumption of the cooling fan. The higher the fan duty cycle, the greater the energy consumption. The product of the cube of the fan duty cycle and the preset energy consumption weight can be used as the fan energy consumption, which is consistent with the direct proportional relationship between fan energy consumption and the cube of the rotational speed.

[0079] Since insulation protection is prioritized during fault conditions and mechanical connection protection is prioritized during normal conditions, the weighting ratio of insulation aging cost and mechanical stress cost is dynamically adjusted through fault response weighting. In this embodiment of the invention, the baseline insulation weight can be 0.3, and the baseline mechanical weight can be 0.6. Under normal operating conditions, the weight of mechanical stress cost is higher, prioritizing temperature stability to protect the connector. Based on the fault response weighting, the weight of insulation aging cost is increased while the weight of mechanical stress cost is decreased. Specifically, obtaining the weight of insulation aging cost includes: multiplying the fault response weight by a preset fault protection gain coefficient as the insulation adjustment coefficient. The preset fault protection gain coefficient can be 5, used to amplify the insulation protection priority during faults. Furthermore, the product of the baseline insulation weight and the insulation adjustment coefficient is used as the insulation adjustment degree, and the sum of the insulation adjustment degree and the baseline insulation weight is used as the weight of insulation aging cost. A larger weight indicates a more severe fault, and a higher insulation protection weight.

[0080] The weighting of mechanical stress cost is obtained by multiplying the baseline mechanical weight and the fault response weight as the mechanical adjustment degree, and using the difference between the baseline mechanical weight and the mechanical adjustment degree as the weight of mechanical stress cost. The larger the weight, the more severe the fault, and the lower the mechanical stress penalty weight, allowing for a larger temperature change rate to cool down quickly.

[0081] To accurately determine the optimal fan control command, the gradient direction of the operating cost function relative to the fan control command is analyzed using the forced cooling gain coefficient. Specifically, the forced cooling gain coefficient is used as a sensitivity operator to characterize the impact of a unit change in fan control command on the temperature rise rate. The partial derivatives of each cost term with respect to the fan duty cycle are calculated using the chain rule. The weighted summation of these partial derivatives yields the comprehensive gradient of the operating cost function. The weights can be adjusted by the implementer and are not restricted here. The output gradient direction includes both a numerical value and a direction; a positive direction indicates that the fan duty cycle needs to be increased, and a negative direction indicates that the fan duty cycle needs to be decreased.

[0082] Finally, based on the gradient direction and a preset step size, the current turbine duty cycle is iteratively updated. Specifically, the preset step size, such as 0.5 to 2.0, is used to adjust the speed of command updates. The negative of the product of the gradient direction and the preset step size is used as the original control increment. To avoid the superposition of thermal stress caused by drastic fluctuations in turbine speed, the original control increment is also limited, meaning the maximum change in a single step does not exceed the maximum allowable value, such as 5%. If the original control increment exceeds the maximum allowable value, it is taken as the maximum allowable value to obtain the final control increment. The current turbine duty cycle is added to the final control increment to obtain the turbine duty cycle at the next time step.

[0083] Thus, the solution for the cooling fan control command was completed, achieving a multi-objective balance between insulation protection, mechanical reliability, and energy saving, ensuring that the system can achieve optimal heat dissipation control under different conditions.

[0084] In summary, this invention dynamically corrects the real-time Joule heat power of the conductor by comprehensively considering the harmonic skin effect and the resistance temperature drift, improving the accuracy of heat source sensing and providing accurate feedforward input for subsequent thermal management. Secondly, by identifying natural convection and forced cooling parameters online under different operating conditions, the system can adapt to changes in the environment and airflow characteristics, improving the robustness and control accuracy of the heat dissipation system under various operating conditions. Furthermore, by using the residual between the measured temperature rise rate and the theoretical prediction value of the model to generate a fault factor, it can effectively isolate the weak abnormal heating caused by poor connector contact from the high background load heating, achieving non-intrusive early warning of early hidden faults. By constructing a multi-objective function that includes the costs of insulation aging and mechanical stress, and using fault response weights to dynamically adjust the weight ratio of the two costs, the control strategy can prioritize ensuring stable temperature rise to protect mechanical connections when the equipment is normal, and prioritize rapid cooling to protect insulation when a fault warning occurs, thereby intelligently balancing and extending the overall service life of the equipment. This invention quantifies the actual internal heat generation of conductors and, by identifying changes in the heat dissipation environment online, achieves comprehensive management of insulation life and mechanical connections in heat dissipation control, realizing closed-loop heat dissipation and cooling control. This effectively improves the accuracy and reliability of busbar thermal management and extends the overall service life of the equipment.

[0085] This application also provides a heat dissipation and cooling device for an intelligent busbar trunking; please refer to [link / reference]. Figure 2 The diagram shows a schematic of the structure of a heat dissipation and cooling device for an intelligent busbar trunking according to an embodiment of the present invention. The device includes: a heat source sensing module 201, an environmental change identification module 202, a fault response evaluation module 203, and a multi-objective adaptive control module 204.

[0086] The heat source sensing module 201 is used to acquire the operating current and shell temperature of the bus trunking; based on the operating current and shell temperature, it analyzes the influence of harmonic effects and resistance temperature drift to obtain real-time Joule heat power; and determines the shell heating rate and the temperature difference outside the shell based on the shell temperature change.

[0087] The environmental change identification module 202 is used to update the natural convection thermal response coefficient under natural heat dissipation conditions based on real-time Joule thermal power, shell heating rate and shell temperature difference, and to obtain the forced cooling gain coefficient by combining airflow transmission hysteresis characteristics under forced air cooling conditions.

[0088] The fault response assessment module 203 is used to calculate the theoretically predicted temperature rise using the natural convection thermal response coefficient and the forced cooling gain coefficient; compare the actual observed temperature of the shell with the theoretically predicted temperature rise, extract the residual factor, and generate the fault response weight.

[0089] The multi-objective adaptive control module 204 is used to construct an operating cost function that includes insulation aging cost and mechanical stress cost. It dynamically adjusts the weight ratio of insulation aging cost and mechanical stress cost according to the fault response weight, analyzes the sensitivity gradient using the forced cooling gain coefficient, and solves the control command of the cooling fan.

[0090] It should be noted that the apparatus provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the heat dissipation and cooling device for an intelligent busbar trunking and the heat dissipation and cooling method for an intelligent busbar trunking provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0091] This application embodiment also provides a heat dissipation and cooling device for an intelligent busbar trunking. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the heat dissipation and cooling method for an intelligent busbar trunking as described above.

[0092] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A heat dissipation cooling method for an intelligent bus duct, characterized in that, The method includes: The operating current and casing temperature of the busbar trunking are obtained; based on the operating current and casing temperature, the influence of harmonic effects and resistance temperature drift is analyzed to obtain the real-time Joule thermal power; the casing heating rate and the temperature difference outside the casing are determined based on the casing temperature change. Based on real-time Joule thermal power, shell heating rate and shell temperature difference, the natural convection thermal response coefficient is updated under natural heat dissipation conditions, and the forced cooling gain coefficient is obtained by combining airflow transport hysteresis characteristics under forced air cooling conditions. The theoretically predicted temperature rise is calculated using the natural convection thermal response coefficient and the forced cooling gain coefficient; the actual observed temperature rise of the shell is compared with the theoretically predicted temperature rise, the residual factor is extracted, and the fault response weight is generated. A cost function that includes insulation aging cost and mechanical stress cost is constructed. The weight ratio of insulation aging cost and mechanical stress cost is dynamically adjusted according to the fault response weight. The sensitivity gradient is analyzed using the forced cooling gain coefficient, and the control command of the cooling fan is solved. The method for determining the natural convection thermal response coefficient includes: Under the condition of satisfying natural heat dissipation, the heat dissipation response is obtained by combining the shell heating rate and the temperature difference outside the shell; the heating response is obtained by the ratio of the heat dissipation response at a given time to the real-time Joule heat power; and the natural convection heat response coefficient at the current time is obtained by weighting and combining the natural convection heat response coefficient and the heating response coefficient at the previous time using a preset forgetting coefficient. If the natural heat dissipation condition is not met, the natural convection thermal response coefficient of the previous moment shall be used as the natural convection thermal response coefficient of the current moment. The method for determining the forced cooling gain coefficient includes: Under forced air cooling conditions, the fan duty cycle at a preset lag period before the current moment is obtained. The cooling drive index is obtained by combining the fan duty cycle with the temperature difference outside the casing. The product of the real-time Joule heat power at the current moment and the natural convection heat response coefficient is used as the theoretical temperature rise rate. The forced cooling gain coefficient is obtained by analyzing the rate deviation between the theoretical temperature rise rate and the shell temperature rise rate, and by using the ratio of the rate deviation to the cooling drive index.

2. The heat dissipation and cooling method for an intelligent busbar trunking according to claim 1, characterized in that, The method for obtaining real-time Joule thermal power includes: Harmonic analysis is performed on the operating current to obtain each harmonic component; the square of each harmonic component is multiplied by a preset AC resistance gain coefficient and then summed to obtain the equivalent heating current. The temperature coefficient of resistance of the conductor material is obtained, and the reference resistance of the conductor is corrected by using the shell temperature to obtain the corrected resistance. The real-time Joule thermal power is obtained by combining the corrected resistance with the equivalent heating current.

3. The heat dissipation and cooling method for an intelligent busbar trunking according to claim 1, characterized in that, The methods for obtaining the theoretically predicted temperature rise include: The cooling influencing factor is determined by combining the fan duty cycle, forced cooling gain coefficient and shell temperature difference at the preset lag period before the current moment; The product of the real-time Joule thermal power and the natural convection thermal response coefficient is used as the theoretical temperature rise rate; the difference between the theoretical temperature rise rate and the cooling influence factor is used as the theoretical predicted temperature rise.

4. The heat dissipation and cooling method for an intelligent busbar trunking according to claim 3, characterized in that, The method for obtaining the fault response weight includes: The abnormal deviation component is obtained by comparing the current shell heating rate with the theoretically predicted temperature rise; the residual factor at the current moment is obtained based on the abnormal deviation component and the theoretical temperature rise rate. The current fault response weight is determined based on the degree to which the residual factor exceeds the preset fault threshold at the current moment.

5. The heat dissipation and cooling method for an intelligent busbar trunking according to claim 1, characterized in that, The construction of the running cost function includes: The insulation aging cost term is positively correlated with the predicted shell temperature value in a nonlinear manner; the mechanical stress cost term is positively correlated with the change in the temperature rise rate; the operating cost function is a weighted sum of the insulation aging cost term, the mechanical stress cost term, and the fan energy consumption term.

6. The heat dissipation and cooling method for an intelligent busbar trunking according to claim 1, characterized in that, The control commands for solving the cooling fan include: Based on the fault response weight, the weight of insulation aging cost is increased and the weight of mechanical stress cost is decreased; the gradient direction of the operating cost function relative to the fan control command is analyzed using the forced cooling gain coefficient; based on the gradient direction and preset step size, the current fan duty cycle is iteratively updated.

7. A heat dissipation and cooling device for an intelligent busbar trunking, characterized in that, The device includes: The heat source sensing module is used to acquire the operating current and shell temperature of the busbar trunking; based on the operating current and shell temperature, it analyzes the influence of harmonic effects and resistance temperature drift to obtain real-time Joule heat power; and determines the shell heating rate and the temperature difference outside the shell based on the shell temperature change. The environmental change identification module is used to update the natural convection thermal response coefficient under natural heat dissipation conditions based on real-time Joule thermal power, shell heating rate and shell temperature difference, and to obtain the forced cooling gain coefficient by combining airflow transport hysteresis characteristics under forced air cooling conditions. The method for determining the natural convection thermal response coefficient includes: Under the condition of satisfying natural heat dissipation, the heat dissipation response is obtained by combining the shell heating rate and the temperature difference outside the shell; the heating response is obtained by the ratio of the heat dissipation response at a given time to the real-time Joule heat power; and the natural convection heat response coefficient at the current time is obtained by weighting and combining the natural convection heat response coefficient and the heating response coefficient at the previous time using a preset forgetting coefficient. If the natural heat dissipation condition is not met, the natural convection thermal response coefficient of the previous moment shall be used as the natural convection thermal response coefficient of the current moment. The method for determining the forced cooling gain coefficient includes: Under forced air cooling conditions, the fan duty cycle at a preset lag period before the current moment is obtained. The cooling drive index is obtained by combining the fan duty cycle with the temperature difference outside the casing. The product of the real-time Joule heat power at the current moment and the natural convection heat response coefficient is used as the theoretical temperature rise rate. The forced cooling gain coefficient is obtained by analyzing the rate deviation between the theoretical temperature rise rate and the shell temperature rise rate and by using the ratio of the rate deviation to the cooling drive index. The fault response assessment module is used to calculate the theoretically predicted temperature rise using the natural convection thermal response coefficient and the forced cooling gain coefficient; it compares the actual observed temperature of the casing with the theoretically predicted temperature rise, extracts the residual factor, and generates fault response weights. The multi-objective adaptive control module is used to construct an operating cost function that includes insulation aging cost and mechanical stress cost. It dynamically adjusts the weight ratio of insulation aging cost and mechanical stress cost according to the fault response weight, analyzes the sensitivity gradient using the forced cooling gain coefficient, and solves the control command of the cooling fan.

8. A heat dissipation and cooling device for an intelligent busbar trunking, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the heat dissipation and cooling method for an intelligent busbar trunking as described in any one of claims 1 to 6.

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