Inverter heat dissipation methods, devices, equipment, media and products

Abstract

This application discloses a heat dissipation method, apparatus, device, medium, and product for an inverter. The method involves acquiring information about the actual operating conditions and the actual temperature of the inverter; searching for a target historical temperature rise curve corresponding to the actual operating conditions within a preset correspondence between historical operating conditions and historical temperature rise curves; extracting the inverter's temperature rise curve within a preset time period from the target historical temperature rise curve based on the inverter's actual temperature; correcting the temperature rise curve within the preset time period according to a preset smoothing coefficient to obtain the target temperature rise curve within the preset time period; and generating a fan speed adjustment command based on the target temperature rise curve. This speed adjustment command is used to adjust the fan speed to dissipate heat from the inverter. According to the embodiments of this application, the risk of overheating in energy storage inverters can be avoided, improving the safety and reliability of the charging and discharging process of energy storage inverters.

Description

Technical Field

[0001] This application belongs to the field of power electronics technology, and in particular relates to a heat dissipation method, device, equipment, medium and product for an inverter. Background Technology

[0002] During the charging and discharging process, energy storage inverters generate a large amount of heat due to the high-frequency switching of power devices (such as Insulated Gate Bipolar Transistors (IGBTs), Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs)) and the energy loss of inductors and capacitors.

[0003] Traditional cooling methods use fans with a fixed rotation speed, which always run at full speed regardless of the load, resulting in high energy consumption and noise. Some existing technologies use temperature sensors to collect the temperature of the heat-generating components of the energy storage inverter and then control the fan rotation based on the temperature. However, this only controls cooling based on the current temperature and suffers from response lag. Summary of the Invention

[0004] This application provides a heat dissipation method, apparatus, device, medium, and product for an inverter, which can transform the traditional post-event response into pre-event prevention, avoid the risk of overheating of the energy storage inverter, and improve the safety and reliability of the charging and discharging process of the energy storage inverter.

[0005] In a first aspect, embodiments of this application provide a heat dissipation method for an inverter, including: Obtain information on actual operating conditions and the actual temperature of the inverter; Find the target historical temperature rise curve that corresponds to the actual operating condition in the preset correspondence between historical operating conditions and historical temperature rise curves. Based on the actual temperature information of the inverter, the temperature rise curve of the inverter within a preset time period is extracted from the target historical temperature rise curve. The temperature rise curve within a preset time period is corrected according to a preset smoothing coefficient to obtain the target temperature rise curve within the preset time period. Based on the target temperature rise curve, a fan speed adjustment command is generated. The speed adjustment command is used to adjust the fan speed to dissipate heat from the inverter.

[0006] In one possible embodiment of the first aspect, the actual operating conditions include the actual type of operating conditions, the actual current of the load, and the actual temperature of the environment.

[0007] In one possible embodiment of the first aspect, the operating condition type includes a charging operating condition type and a discharging operating condition type; the inverter is electrically connected between the AC signal terminal and the DC signal terminal; When the actual operating condition is charging, the actual load current is the actual current at the DC signal terminal. When the actual operating condition is a discharge condition, the actual current of the load is the actual current at the AC signal terminal.

[0008] In one possible embodiment of the first aspect, the preset time period includes a first moment and a second moment, the first moment being earlier than the second moment; the temperature rise curve within the preset time period is corrected according to a preset smoothing coefficient to obtain a target temperature rise curve within the preset time period, including: In the temperature rise curve within a preset time period, obtain the first temperature corresponding to the first moment and determine the first temperature as the first reference temperature; Obtain the first predicted temperature corresponding to the first moment; The second predicted temperature at the second moment is determined based on the preset smoothing coefficient, the first reference temperature, and the first predicted temperature. Based on the first and second predicted temperatures, a target temperature rise curve is generated for a preset time period.

[0009] In one possible embodiment of the first aspect, determining the second predicted temperature at the second time point based on a preset smoothing coefficient, a first reference temperature, and a first predicted temperature includes: Multiply the preset smoothing coefficient by the first reference temperature to obtain the first product; Multiply the target difference by the first predicted temperature to obtain the second product, where the target difference is the difference between 1 and the smoothing coefficient; Adding the first product and the second product together yields the second predicted temperature at the second time point.

[0010] In one possible embodiment of the first aspect, it further includes: Obtain the actual temperature rise curve of the inverter within a preset time period; Calculate the error between the target temperature rise curve and the actual temperature rise curve within the preset time period; If the error is greater than or equal to a preset threshold, the preset smoothing coefficient is adjusted to obtain the adjusted smoothing coefficient. The error corresponding to the adjusted smoothing coefficient is less than the preset threshold.

[0011] In one possible embodiment of the first aspect, calculating the error between the target temperature rise curve and the actual temperature rise curve within a preset time period includes: Obtain the two temperature values ​​corresponding to the target temperature rise curve and the actual temperature rise curve at the same moment; Calculate the temperature difference between two temperature values ​​at the same time. The target mean value is obtained by summing the squares of each temperature difference and taking the average. The square root of the target mean is determined as the error between the target temperature rise curve and the actual temperature rise curve within the preset time period.

[0012] In one possible embodiment of the first aspect, it further includes: If a stalled or abnormally vibrating fan is detected, switch the fan to the standby fan.

[0013] Based on the same inventive concept, in a second aspect, embodiments of this application also provide a heat dissipation device for an inverter, comprising: The acquisition module is used to acquire information about the actual operating conditions and the actual temperature of the inverter. The query module is used to find the target historical temperature rise curve that corresponds to the actual operating condition from the preset correspondence between historical operating conditions and historical temperature rise curves. The interception module is used to extract the temperature rise curve of the inverter within a preset time period from the target historical temperature rise curve based on the actual temperature information of the inverter. The correction module is used to correct the temperature rise curve within a preset time period according to a preset smoothing coefficient, so as to obtain the target temperature rise curve within the preset time period. The generation module is used to generate fan speed adjustment commands based on the target temperature rise curve. The speed adjustment commands are used to adjust the fan speed to dissipate heat from the inverter.

[0014] Based on the same inventive concept, in a third aspect, embodiments of this application also provide a heat dissipation device for an inverter, the device including a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the first aspect, or the heat dissipation method of the inverter in any embodiment of the first aspect.

[0015] Based on the same inventive concept, in a fourth aspect, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the first aspect, or the heat dissipation method of the inverter in any embodiment of the first aspect.

[0016] Based on the same inventive concept, in a fifth aspect, embodiments of this application also provide a computer program product, wherein instructions in the computer program product, when executed by a processor of a device, enable the device to perform the heat dissipation method of the inverter in the first aspect or any embodiment of the first aspect.

[0017] The present application provides a method, apparatus, device, medium, and product for heat dissipation of an inverter. By acquiring information about the actual operating conditions and the actual temperature of the inverter, a target historical temperature rise curve corresponding to the actual operating conditions is found in a preset correspondence between historical operating conditions and historical temperature rise curves. Then, based on the actual temperature of the inverter, a portion of the target historical temperature rise curve is extracted as a predicted temperature rise curve for the inverter within a preset time period. Since this predicted temperature rise curve is entirely dependent on historical temperature rises, it needs to be corrected. This correction can be achieved using a preset smoothing coefficient to obtain the corrected target temperature rise curve. The target temperature rise curve represents the temperature change trend of the inverter within a preset time period (e.g., the next 5 minutes). Then, based on the target temperature rise curve, a fan speed adjustment command can be generated to adjust the fan speed for heat dissipation. The present application, by predicting the temperature rise trend, overcomes the lag limitation of related technologies that only perceive the current temperature, transforming traditional reactive response into proactive prevention. This avoids the overheating risk of energy storage inverters and improves the safety and reliability of the charging and discharging process of energy storage inverters. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings, in which the same or similar reference numerals denote the same or similar features, and the drawings are not drawn to scale.

[0019] Figure 1 This is a schematic flowchart of a heat dissipation method for an inverter provided in an embodiment of this application; Figure 2 This is a schematic diagram of the connection relationship of an inverter provided in an embodiment of this application; Figure 3 This is another schematic flowchart of the heat dissipation method for the inverter provided in the embodiments of this application; Figure 4 This is another schematic flowchart of the heat dissipation method for the inverter provided in the embodiments of this application; Figure 5 This is another schematic flowchart of the heat dissipation method for the inverter provided in the embodiments of this application; Figure 6 This is another schematic flowchart of the heat dissipation method for the inverter provided in the embodiments of this application; Figure 7 This is another schematic flowchart of the heat dissipation method for the inverter provided in the embodiments of this application; Figure 8 This is a schematic diagram of a heat dissipation device for an inverter provided in an embodiment of this application; Figure 9This is another schematic diagram of the heat dissipation device for the inverter provided in the embodiments of this application; Figure 10 This is a schematic diagram of a heat dissipation device for an inverter provided in an embodiment of this application. Detailed Implementation

[0020] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0023] Various modifications and variations can be made to this application without departing from its spirit or scope, which will be apparent to those skilled in the art. Therefore, this application is intended to cover modifications and variations falling within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the implementation methods provided in the embodiments of this application can be combined with each other without contradiction.

[0024] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first specifically explains the problems existing in the related technologies: During the charging and discharging process, energy storage inverters generate a large amount of heat due to the high-frequency switching of power devices (such as IGBTs, MOSFETs, etc.) and the energy loss of inductors and capacitors.

[0025] Some related heat dissipation methods use fans with fixed speeds. These fans continue to run at full speed even at low temperatures, wasting energy, reducing the machine's own conversion efficiency, and generating significant noise when running at high speeds, which affects the lifespan of components.

[0026] Some existing technologies use temperature sensors to collect the temperature of the heat-generating components of the energy storage inverter and then compare the temperature with a temperature threshold to control the fan speed. This technology can only control heat dissipation based on the current temperature, resulting in problems such as long temperature sampling period, full response of multiple units, inability to predict temperature rise trend, and response lag.

[0027] Based on this, the embodiments of this application provide a heat dissipation method, device, equipment, medium and program product for an inverter, which can predict the temperature rise trend, overcome the lag limitation of related technologies that only sense the current temperature, and transform the traditional post-event response into pre-event prevention, avoid the over-temperature risk of energy storage inverters, and improve the safety and reliability of the charging and discharging process of energy storage inverters.

[0028] The advantages of the embodiments of this application are as follows: 1) Improved energy efficiency: Saves 20% energy compared to traditional stationary fans; 2) Noise reduction: Reduces high-speed operation time, with an average reduction of 15 dB; 3) Extended lifespan: Avoids prolonged high-speed operation, improving safe lifespan; 4) Enhanced safety: Prevents overheating damage through predictive control.

[0029] The heat dissipation method of the inverter provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 This is a schematic flowchart of a heat dissipation method for an inverter provided in an embodiment of this application, as shown below. Figure 1 As shown, the method may include steps S110 to S150.

[0031] S110, obtains information on actual operating conditions and the actual temperature of the inverter; The actual operating conditions information may include the type of operating condition (such as charging / discharging), load current, and ambient temperature.

[0032] Information on the actual temperature of the inverter: This could be the maximum or average temperature of the heat-generating components within each inverter. For example, heat-generating components include at least one of the following in the inverter: switching transistors, inductors, capacitors, and copper busbars.

[0033] Specifically, information on the current actual operating conditions (such as operating condition type, load current, and ambient temperature) and the actual temperatures of key heat-generating components inside the inverter can be obtained. The information on the current actual operating conditions can be used to find the corresponding temperature rise curve among multiple historical temperature rise curves, which can serve as a reference for predicting future temperature rise trends. The information on the actual temperatures of the heat-generating components inside the inverter can be used to extract a segment of data from this temperature rise curve as a reference for predicting the temperature rise trend over a future period (such as the next 5 minutes).

[0034] S120: In the preset correspondence between historical operating conditions and historical temperature rise curves, find the target historical temperature rise curve that corresponds to the actual operating conditions.

[0035] The preset correspondence between historical operating conditions and historical temperature rise curves is a pre-generated correspondence. For example, the temperature rise curves of the inverter under various operating conditions can be collected in advance, and the correspondence between historical operating conditions and historical temperature rise curves can be generated.

[0036] Specifically, given the current actual operating conditions, the historical temperature rise curve corresponding to the current actual operating conditions can be found in the pre-generated correspondence between historical operating conditions and historical temperature rise curves as the target historical temperature rise curve. This target historical temperature rise curve can serve as a reference for predicting future temperature rise trends.

[0037] S130: Based on the actual temperature information of the inverter, extract the temperature rise curve of the inverter within a preset time period from the target historical temperature rise curve.

[0038] One preset time period constitutes one prediction cycle. The duration of the preset time period can be set according to actual needs, such as 5 minutes, 10 minutes, etc.

[0039] Specifically, after finding the target historical temperature rise curve corresponding to the current actual operating conditions, it is necessary to extract a segment as the temperature rise curve for the preset time period of prediction. Specifically, a segment of data can be extracted from the target historical temperature rise curve with the actual temperature of the inverter as the starting point as a reference for the temperature rise trend in the future period (such as the next 5 minutes).

[0040] For example, in the target historical temperature rise curve, the time corresponding to the actual temperature of the inverter is taken as the start time of the preset time period. Then, the start time and the preset time period are added together to obtain the end time of the preset time period. Then, in the target historical temperature rise curve, the temperature rise curve between the start time and the end time can be extracted and this segment of the temperature rise curve can be used as the temperature rise curve of the inverter in the preset time period (such as the next 5 minutes).

[0041] S140, the temperature rise curve within a preset time period is corrected according to the preset smoothing coefficient to obtain the target temperature rise curve within the preset time period.

[0042] The smoothing coefficient can be denoted as α, which is between 0 and 1.

[0043] Specifically, the temperature rise curve within a preset time period (e.g., 5 minutes) cannot be relied upon entirely to predict the temperature rise trend. It can only serve as a reference for the temperature rise trend in the future (e.g., the next 5 minutes). It needs to be adjusted / corrected to more accurately predict the temperature rise trend. Specifically, the temperature rise curve within the preset time period can be adjusted / corrected according to the set smoothing coefficient α to obtain the target temperature rise curve within the preset time period (e.g., 5 minutes).

[0044] In one example, different operating conditions correspond to different preset smoothing coefficients. It is necessary to pre-set smoothing coefficients for different operating conditions to determine which smoothing coefficient should be used in the actual operating condition to make the prediction more accurate. For example, if there are multiple candidate smoothing coefficients, the prediction error will differ depending on the candidate smoothing coefficient used in a certain operating condition. The candidate smoothing coefficient with the smallest prediction error can be selected as the smoothing coefficient corresponding to that operating condition. Similarly, a correspondence between operating conditions and smoothing coefficients can be generated in advance, allowing the smoothing coefficient corresponding to the current actual operating condition to be obtained from this correspondence for use in step S140.

[0045] S150 generates a fan speed adjustment command based on the target temperature rise curve. The speed adjustment command is used to adjust the fan speed to dissipate heat from the inverter.

[0046] Specifically, after predicting the target temperature rise curve within a preset time period (such as the next 5 minutes), the fan speed can be determined based on the temperature rise trend in the curve, and a corresponding speed adjustment command can be generated. The fan can be adjusted to a suitable speed through this speed adjustment command, which can not only dissipate heat from the inverter but also avoid energy waste.

[0047] For example, if the highest temperature in the target temperature rise curve within a preset time period (such as the next 5 minutes) is predicted, the correspondence between the fan speed adjustment can be found in Table 1.

[0048] Table 1 As shown in Table 1, if the predicted temperature will exceed 90℃ within 5 minutes, the fan speed is controlled at 70% in advance; if the predicted temperature will exceed 70℃ but not exceed 90℃ within 5 minutes, the fan speed is controlled at 50% in advance; if the predicted temperature will exceed 50℃ but not exceed 70℃ within 5 minutes, the fan speed is controlled at 20% in advance; if the predicted temperature remains basically unchanged within 5 minutes (e.g., below 50℃), the fan speed is controlled at 10% in advance. Therefore, by using temperature prediction algorithms and multi-level speed control strategies, energy efficiency can be improved and noise can be reduced.

[0049] According to the inverter heat dissipation method provided in this application embodiment, by acquiring information on the actual operating conditions and the actual temperature of the inverter, and then finding the target historical temperature rise curve corresponding to the actual operating conditions in the preset correspondence between historical operating conditions and historical temperature rise curves, a portion of the target historical temperature rise curve can be extracted as the predicted temperature rise curve of the inverter within a preset time period based on the actual temperature information of the inverter. Since the predicted temperature rise curve depends entirely on the historical temperature rise, it needs to be corrected. The curve can be corrected according to a preset smoothing coefficient to obtain the corrected target temperature rise curve. The target temperature rise curve is the temperature change trend of the inverter within a preset time period (such as the next 5 minutes). Then, based on the target temperature rise curve, a fan speed adjustment command can be generated. The fan speed can be adjusted through the speed adjustment command to dissipate heat from the inverter, thereby overcoming the lag limitation of related technologies that only sense the current temperature. It can transform the traditional post-event response into pre-event prevention, avoid the over-temperature risk of the energy storage inverter, and improve the safety and reliability of the charging and discharging process of the energy storage inverter.

[0050] In some embodiments, actual operating conditions include the actual type of operating condition (such as charging / discharging condition), the actual current of the load, and the actual ambient temperature.

[0051] The distinction between charging and discharging conditions is made because the operating states and energy conversion methods of the power devices inside the inverter differ significantly under these two conditions.

[0052] The actual load current is one of the key factors affecting inverter heating. The higher the current, the more heat the power devices generate during the turn-on and turn-off processes.

[0053] The actual ambient temperature has a significant impact on the heat dissipation of the inverter. In high-temperature environments, the efficiency of natural or fan-based heat dissipation by the inverter decreases, and heat is more likely to accumulate inside the inverter, leading to a higher temperature rise. In low-temperature environments, heat dissipation conditions are better, and the temperature rise is relatively lower.

[0054] The actual operating conditions in this application embodiment cover factors such as actual operating condition type (e.g., charging / discharging condition), actual load current, and actual ambient temperature. By accurately identifying the operating condition type, actual load current, and actual ambient temperature, the target historical temperature rise curve that matches the current actual operating condition can be found more precisely in the preset correspondence between historical operating conditions and historical temperature rise curves, laying the foundation for accurate prediction of temperature rise trends in the future.

[0055] In some embodiments, the operating condition type includes a charging operating condition type and a discharging operating condition type. For example... Figure 2 As shown, the inverter is electrically connected between the AC signal terminal and the DC signal terminal.

[0056] You can continue to see Figure 2 When the actual operating condition is charging, the actual load current is the actual current at the DC signal terminal.

[0057] You can continue to see Figure 2 When the actual operating condition is a discharge condition, the actual current of the load is the actual current of the AC signal terminal.

[0058] The embodiments of this application can be applied to different application scenarios (such as charging or discharging scenarios). For different operating conditions, the source of the actual load current is clearly given, which is conducive to accurately obtaining the actual load current value.

[0059] The following describes the specific process of correcting the temperature rise curve within a preset time period according to a preset smoothing coefficient in the heat dissipation method of the inverter provided in this application embodiment, so as to obtain the target temperature rise curve within the preset time period.

[0060] In some embodiments, such as Figure 3 As shown, the preset time period includes a first moment and a second moment, with the first moment being earlier than the second moment. The first moment can be denoted as moment t, and the second moment can be denoted as moment t+1. Step S140 corrects the temperature rise curve within the preset time period according to a preset smoothing coefficient to obtain the target temperature rise curve within the preset time period, and may include steps S141 to S144.

[0061] S141, in the temperature rise curve within the preset time period, obtain the first temperature corresponding to the first moment (time t), and determine the first temperature as the first reference temperature (the first reference temperature can be denoted as Tt).

[0062] S142, obtain the first predicted temperature corresponding to the first time (time t+1) (the first predicted temperature can be denoted as Xt).

[0063] S143, based on the preset smoothing coefficient α, the first reference temperature Tt and the first predicted temperature Xt, determine the second predicted temperature at the second time (the second predicted temperature can be denoted as Xt+1).

[0064] S144, based on the first predicted temperature Xt and the second predicted temperature Xt+1, can generate the target temperature rise curve within a preset time period.

[0065] Steps S141 to S144 involve determining the predicted temperature Xt+1 for the next moment based on the reference temperature Tt and predicted temperature Xt of the previous moment within a preset time period. When generating the final temperature rise curve (target temperature rise curve) for the preset time period, steps S141 to S144 need to be executed iteratively to determine the predicted temperature Xt+1 for the next moment based on the reference temperature Tt and predicted temperature Xt of the previous moment, until the predicted temperatures for each moment within the preset time period are obtained.

[0066] The smoothing coefficient α can be adjusted according to actual needs.

[0067] In this embodiment, the smoothing coefficient α can be adjusted according to actual needs to adapt to different working conditions, so that the prediction process can refer to the stability of historical temperature and combine the current prediction trend, thereby making the second predicted temperature more reasonable and accurate, and effectively improving the accuracy of temperature rise curve correction.

[0068] In some embodiments, such as Figure 4 As shown, step S143 determines the second predicted temperature Xt+1 at the second time based on the preset smoothing coefficient α, the first reference temperature Tt and the first predicted temperature Xt, and may include steps S1431 to S1433.

[0069] S1431, multiply the preset smoothing coefficient α and the first reference temperature Tt to obtain the first product.

[0070] S1432, multiply the target difference with the first predicted temperature Xt to obtain the second product, where the target difference is the difference between 1 and the smoothing coefficient α.

[0071] S1433, add the first product and the second product to obtain the second predicted temperature Xt+1 at the second time.

[0072] In one example, a large number of historical temperature rise curves under different historical operating conditions are obtained in advance, generating a correspondence between the operating conditions and the historical temperature rise curves. Within this correspondence, a target historical temperature rise curve corresponding to the current actual operating condition can be found. Then, a segment of the temperature rise curve is extracted from the target historical temperature rise curve as a reference for predicting the temperature rise trend over the next 5 minutes. This reference temperature rise curve is then adjusted / corrected using an Exponential Weighted Moving Average (EWMA) algorithm to predict the temperature change trend over the next 5 minutes.

[0073] For example, the formula for the Exponentially Weighted Moving Average (EWMA) algorithm is: Where Xt+1 is the predicted temperature at the second moment (i.e., the second predicted temperature), Tt is the temperature of the temperature rise curve at the first moment (i.e., the first reference temperature), Xt is the predicted temperature at the first moment (i.e., the first predicted temperature), and α is the smoothing coefficient (0 < α < 1).

[0074] For example, if the first moment is the start time within a preset time period, the value of the first predicted temperature can be equal to the value of the first reference temperature.

[0075] The embodiments of this application can dynamically adjust predictions based on historical temperature data (historical temperature rise curves) to optimize prediction accuracy.

[0076] It should be noted that the inventors discovered that a fixed α value cannot adapt to the complex and variable operating conditions of energy storage inverters (such as drastic fluctuations in load current and changes in ambient temperature). Therefore, an adaptive dynamic adjustment method based on historical prediction error feedback is proposed. The core of this method is to allow the algorithm to automatically select the most suitable α value based on the accuracy of recent predictions, ensuring that the prediction model remains in its optimal state. For example, the root mean square error (RMSE) can be used as a standard to measure prediction accuracy. RMSE can amplify the impact of larger errors, making it more suitable for the high requirements of predicting temperature peaks.

[0077] In some embodiments, such as Figure 5 As shown, the heat dissipation method for the inverter may also include steps S161 to S163.

[0078] S161, obtain the actual temperature rise curve of the inverter within a preset time period.

[0079] S162, calculate the error between the target temperature rise curve and the actual temperature rise curve within the preset time period.

[0080] S163, if the error is greater than or equal to a preset threshold, the preset smoothing coefficient α is adjusted to obtain the adjusted smoothing coefficient α', and the error corresponding to the adjusted smoothing coefficient α' is less than the preset threshold.

[0081] In one example, the prediction error corresponding to the preset smoothing coefficient α is greater than the preset threshold. In this case, the preset smoothing coefficient α needs to be adjusted to obtain the adjusted smoothing coefficient α'. The adjustment process is as follows: 1) Obtain the actual temperature rise curve of the inverter within a preset time period.

[0082] 2) For example, if there are multiple candidate smoothing coefficients, steps S110 to S140 are performed for each candidate smoothing coefficient to obtain the target temperature rise curve corresponding to each candidate smoothing coefficient.

[0083] 3) Calculate the prediction error between each target temperature rise curve and the actual temperature rise curve (e.g., the root mean square error of the two curves) to obtain multiple errors.

[0084] Among multiple errors, the candidate smoothing coefficient corresponding to the smallest error is selected as the adjusted smoothing coefficient α', and α' is used as the smoothing coefficient for the next prediction period.

[0085] This application embodiment obtains the actual temperature rise curve of the inverter within a preset time period and calculates the error between it and the predicted temperature rise curve within the preset time period as the prediction error. When the prediction error is greater than or equal to a preset threshold, the smoothing coefficient α is adjusted in time to obtain α'. This approach can dynamically correct the temperature rise prediction model and ensure that the system always performs temperature control based on the smoothing coefficient that best fits the current operating conditions, thus ensuring the stable operation of the inverter under complex operating conditions.

[0086] In some embodiments, such as Figure 6 As shown, step S162 calculates the error between the target temperature rise curve and the actual temperature rise curve within the preset time period, which may include steps S1621 to S1624.

[0087] S1621, obtain the two temperature values ​​corresponding to the target temperature rise curve and the actual temperature rise curve at the same moment.

[0088] S1622, calculate the temperature difference between two temperature values ​​at the same time.

[0089] S1623: Sum the squares of each temperature difference and take the average to obtain the target average value.

[0090] S1624, the square root of the target mean is determined as the error between the target temperature rise curve and the actual temperature rise curve within the preset time period.

[0091] For example, the formula for calculating the error between the target temperature rise curve and the actual temperature rise curve within the preset time period is as follows: Where n is the number of temperatures taken within the preset time period, and n can be set according to needs. The larger n is, the more accurate the error calculation. yi is the actual temperature in the actual temperature rise curve. The predicted temperature in the target temperature rise curve.

[0092] The prediction error in this embodiment is the root mean square error, which can comprehensively consider the error situation at all times and accurately reflect the degree of deviation between the prediction and the actual situation. When it is judged that the prediction error is too large, the smoothing coefficient is adjusted, which can effectively improve the accuracy of temperature rise prediction, thereby optimizing the inverter heat dissipation control and ensuring stable operation of the equipment.

[0093] In some embodiments, such as Figure 7 As shown, the inverter heat dissipation method may further include step S170.

[0094] S170 switches the fan to a standby fan if it detects that the fan is stalled or vibrating abnormally.

[0095] When a fault condition such as fan stall or abnormal vibration is detected in the embodiments of this application, the faulty fan can be quickly switched to a backup fan. In this way, the inverter heat dissipation can be effectively avoided due to fan failure, ensuring the continuous and stable operation of the inverter, reducing the risk of equipment damage caused by overheating, and extending the service life of the equipment.

[0096] In one example, such as Figure 8 As shown, the inverter's heat dissipation device includes a data acquisition module 10, a temperature prediction module 20, and an intelligent speed control module 30.

[0097] The data acquisition module 10 consists of temperature sensors 1~n, a signal conditioning circuit, a load current detection sensor, and a multi-channel ADC. For example, multiple temperature sensors (NTC thermistors) can be placed in key heat-generating parts of the inverter (IGBT modules, inductors, capacitors, copper busbars).

[0098] The temperature prediction module 20 includes a controller MCU. For example, the data acquisition module 10 transmits real-time collected inverter temperature data, ambient temperature data, and load current data to the MCU. Based on this, the MCU executes the above steps to predict the future temperature. If the predicted temperature will exceed 90°C within 5 minutes, the fan will be turned on to 70% speed in advance; if the predicted temperature will exceed 70°C within 5 minutes, the fan will be turned on to 50% speed in advance; if the predicted temperature will exceed 50°C within 5 minutes, the fan will be turned on to 20% speed in advance; and if the predicted temperature will not exceed 50°C within 5 minutes, the fan will be turned on to 10% speed in advance.

[0099] The intelligent speed control module 30 includes an FPGA, a drive circuit, and a fan. The controller MCU sends a speed adjustment command, the FPGA generates a corresponding duty cycle signal, and the drive circuit responds to the duty cycle signal to generate a drive signal for the fan, driving the fan to rotate. For example, a fan with speed control can be selected, dynamically adjusting the PWM duty cycle based on predicted temperature to control the fan speed.

[0100] For example, the inverter's cooling system has a fault protection function. For instance, a backup fan is designed to automatically switch to the backup fan when a fan stalls or abnormal vibrations are detected (as indicated by a feedback signal).

[0101] In this embodiment, the data acquisition module can comprehensively and accurately acquire data such as temperature and load current by arranging multiple temperature sensors at key heat-generating parts; the temperature prediction module uses the controller MCU to predict future temperatures and pre-sets different fan speeds based on different predicted temperatures to achieve proactive heat dissipation; the intelligent speed control module can dynamically adjust the PWM duty cycle to control the fan speed according to the predicted temperature, accurately matching the heat dissipation requirements; in addition, the device also has a fault protection function, with a backup fan that automatically switches when a fan stalls or abnormal vibrations are detected, ensuring the stable operation of the inverter in all aspects, effectively reducing the risk of overheating, and extending the service life of the equipment.

[0102] Based on the same inventive concept, embodiments of this application also provide a heat dissipation device for an inverter, such as... Figure 9 As shown, the device 900 may include an acquisition module 910, a query module 920, an interception module 930, a correction module 940, and a generation module 950: The acquisition module 910 is used to acquire information about the actual operating conditions and the actual temperature of the inverter. The query module 920 is used to find the target historical temperature rise curve corresponding to the actual operating condition in the preset correspondence between historical operating conditions and historical temperature rise curves. The interception module 930 is used to extract the temperature rise curve of the inverter within a preset time period from the target historical temperature rise curve based on the actual temperature information of the inverter. The correction module 940 is used to correct the temperature rise curve within a preset time period according to a preset smoothing coefficient, so as to obtain the target temperature rise curve within the preset time period. The generation module 950 is used to generate fan speed adjustment commands based on the target temperature rise curve. The speed adjustment commands are used to adjust the fan speed to dissipate heat from the inverter.

[0103] According to the inverter heat dissipation device provided in the embodiments of this application, by acquiring information on the actual operating conditions and the actual temperature of the inverter, and then finding the target historical temperature rise curve corresponding to the actual operating conditions in the preset correspondence between historical operating conditions and historical temperature rise curves, a portion of the curve can be extracted from the target historical temperature rise curve as the predicted temperature rise curve of the inverter within a preset time period based on the actual temperature information of the inverter. Since the predicted temperature rise curve depends entirely on the historical temperature rise, it needs to be corrected. The curve can be corrected according to a preset smoothing coefficient to obtain the corrected target temperature rise curve. The target temperature rise curve is the temperature change trend of the inverter within a preset time period (such as the next 5 minutes). Then, based on the target temperature rise curve, a fan speed adjustment command can be generated. The fan speed can be adjusted through the speed adjustment command to dissipate heat from the inverter, thereby overcoming the lag limitation of related technologies that only sense the current temperature. It can transform the traditional post-event response into pre-event prevention, avoid the over-temperature risk of the energy storage inverter, and improve the safety and reliability of the charging and discharging process of the energy storage inverter.

[0104] In some embodiments, actual operating conditions include the actual operating condition type, the actual load current, and the actual ambient temperature.

[0105] In some embodiments, the operating condition type includes a charging operating condition type and a discharging operating condition type; the inverter is electrically connected between the AC signal terminal and the DC signal terminal; When the actual operating condition is charging, the actual load current is the actual current at the DC signal terminal. When the actual operating condition is a discharge condition, the actual current of the load is the actual current at the AC signal terminal.

[0106] In some embodiments, the preset time period includes a first moment and a second moment, wherein the first moment is earlier than the second moment; the correction module is used to correct the temperature rise curve within the preset time period according to a preset smoothing coefficient to obtain a target temperature rise curve within the preset time period, specifically for: In the temperature rise curve within a preset time period, obtain the first temperature corresponding to the first moment and determine the first temperature as the first reference temperature; Obtain the first predicted temperature corresponding to the first moment; The second predicted temperature at the second moment is determined based on the preset smoothing coefficient, the first reference temperature, and the first predicted temperature. Based on the first and second predicted temperatures, a target temperature rise curve is generated for a preset time period.

[0107] In some embodiments, the correction module is used to determine the second predicted temperature at the second time point based on a preset smoothing coefficient, a first reference temperature, and a first predicted temperature, specifically for: Multiply the preset smoothing coefficient by the first reference temperature to obtain the first product; Multiply the target difference by the first predicted temperature to obtain the second product, where the target difference is the difference between 1 and the smoothing coefficient; Adding the first product and the second product together yields the second predicted temperature at the second time point.

[0108] In some embodiments, the device further includes a calculation module and a coefficient adjustment module: The acquisition module is also used to acquire the actual temperature rise curve of the inverter within a preset time period; The calculation module is used to calculate the error between the target temperature rise curve and the actual temperature rise curve within a preset time period. The coefficient adjustment module is used to adjust the preset smoothing coefficient when the error is greater than or equal to a preset threshold, so as to obtain the adjusted smoothing coefficient. The error corresponding to the adjusted smoothing coefficient is less than the preset threshold.

[0109] In some embodiments, the calculation module is used to calculate the error between the target temperature rise curve and the actual temperature rise curve within a preset time period, specifically for: Obtain the two temperature values ​​corresponding to the target temperature rise curve and the actual temperature rise curve at the same moment; Calculate the temperature difference between two temperature values ​​at the same time. The target mean value is obtained by summing the squares of each temperature difference and taking the average. The square root of the target mean is determined as the error between the target temperature rise curve and the actual temperature rise curve within the preset time period.

[0110] In some embodiments, the device further includes a switching module: The switching module is used to switch the fan to a standby fan when a stall or abnormal vibration is detected.

[0111] The various modules in the heat dissipation device of the inverter provided in this application embodiment can achieve... Figures 1 to 7 The functions of each step in the provided inverter heat dissipation method and its corresponding technical effects are described briefly and will not be elaborated here.

[0112] Figure 10 A schematic diagram of the hardware structure of the heat dissipation device for the inverter provided in an embodiment of this application is shown.

[0113] The heat dissipation device of the inverter may include a processor 1001 and a memory 1002 storing computer program instructions.

[0114] Specifically, the processor 1001 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0115] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to the inverter's heat dissipation device. In a particular embodiment, memory 1002 is a non-volatile solid-state memory.

[0116] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0117] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the inverter heat dissipation methods in the above embodiments.

[0118] In one example, the inverter's heat dissipation device may also include a communication interface 1003 and a bus 1004. For example... Figure 10 As shown, the processor 1001, memory 1002, and communication interface 1003 are connected through bus 1004 and complete communication with each other.

[0119] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0120] Bus 1004 includes hardware, software, or both, that couples the components of the inverter's thermal device together. For example, and not limited to, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X, PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA LocalBus, VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 1004 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnection is contemplated herein.

[0121] This device can execute the inverter heat dissipation method in this application embodiment based on each unit / component in the inverter's heat dissipation device, thereby achieving a combination Figures 1 to 7 The description describes the heat dissipation method for the inverter.

[0122] Furthermore, in conjunction with the inverter heat dissipation methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the inverter heat dissipation methods in the above embodiments.

[0123] This application also provides a computer program product in which the instructions, when executed by the processor of an electronic device, cause the electronic device to perform various processes that implement any of the above-described embodiments of the heat dissipation method for inverters.

[0124] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0125] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0126] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0127] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0128] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method of dissipating heat from an inverter, the method comprising: The method comprises the following steps: obtaining information of an actual working condition and information of an actual temperature of an inverter; in a preset corresponding relationship between historical working conditions and historical temperature rise curves, searching for a target historical temperature rise curve corresponding to the actual working condition; according to the information of the actual temperature of the inverter, intercepting a temperature rise curve of the inverter in a preset time period in the target historical temperature rise curve; according to a preset smoothing coefficient, correcting the temperature rise curve in the preset time period to obtain a target temperature rise curve in the preset time period; based on the target temperature rise curve, generating a rotating speed adjustment instruction of a fan, the rotating speed adjustment instruction being used to adjust the rotating speed of the fan to dissipate heat of the inverter.

2. The method of claim 1, wherein, The actual working condition comprises an actual working condition type, an actual current of a load and an actual temperature of an environment.

3. The method of claim 2, wherein, The working condition type comprises a charging working condition type and a discharging working condition type; the inverter is electrically connected between an alternating current signal end and a direct current signal end; in a case where the actual working condition type is the charging working condition type, the actual current of the load is an actual current of the direct current signal end; in a case where the actual working condition type is the discharging working condition type, the actual current of the load is an actual current of the alternating current signal end.

4. The method of claim 1, wherein, The preset time period comprises a first time and a second time, the first time being earlier than the second time; the preset smoothing coefficient is used to correct the temperature rise curve in the preset time period to obtain the target temperature rise curve in the preset time period, comprising: in the temperature rise curve in the preset time period, obtaining a first temperature corresponding to the first time, and determining the first temperature as a first reference temperature; obtaining a first predicted temperature corresponding to the first time; determining a second predicted temperature of the second time according to the preset smoothing coefficient, the first reference temperature and the first predicted temperature; based on the first predicted temperature and the second predicted temperature, generating the target temperature rise curve in the preset time period.

5. The method of claim 4, wherein, The preset smoothing coefficient is used to correct the temperature rise curve in the preset time period to obtain the target temperature rise curve in the preset time period, comprising: multiplying the preset smoothing coefficient and the first reference temperature to obtain a first product; multiplying a target difference value and the first predicted temperature to obtain a second product, wherein the target difference value is a difference value between 1 and the smoothing coefficient; adding the first product and the second product to obtain the second predicted temperature of the second time.

6. The method according to any one of claims 1 to 5, characterized in that, The method further comprises the following steps: obtaining an actual temperature rise curve of the inverter in the preset time period; calculating an error between the target temperature rise curve in the preset time period and the actual temperature rise curve in the preset time period; in a case where the error is greater than or equal to a preset threshold, adjusting the preset smoothing coefficient to obtain an adjusted smoothing coefficient, the error corresponding to the adjusted smoothing coefficient being less than the preset threshold.

7. The method of claim 6, wherein, The preset smoothing coefficient is used to correct the temperature rise curve in the preset time period to obtain the target temperature rise curve in the preset time period, comprising: obtaining two temperature values corresponding to a same time in the target temperature rise curve and the actual temperature rise curve; calculating a temperature difference value between the two temperature values corresponding to the same time; Square each of the temperature difference values and take the average to obtain a target average value; Determine the square root of the target average value as an error between a target temperature rise curve in the preset time period and an actual temperature rise curve in the preset time period.

8. The method of claim 1, wherein, Also includes: In the case of detecting that the fan exists locked rotor or abnormal vibration, switching the fan to a backup fan.

9. A heat dissipating device for an inverter, characterized by comprising: Includes: An acquisition module configured to acquire information of an actual working condition and information of an actual temperature of an inverter; A query module configured to find a target historical temperature rise curve corresponding to the actual working condition in a preset correspondence between historical working conditions and historical temperature rise curves; An intercepting module configured to intercept a temperature rise curve of the inverter in a preset time period from the target historical temperature rise curve according to the information of the actual temperature of the inverter; A correcting module configured to correct the temperature rise curve in the preset time period according to a preset smoothing coefficient to obtain a target temperature rise curve in the preset time period; A generating module configured to generate a speed regulation instruction of a fan based on the target temperature rise curve, the speed regulation instruction being used to regulate a speed of the fan to dissipate heat of the inverter.

10. A heat dissipating apparatus for an inverter, characterized by comprising: The device includes a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement the heat dissipation method of the inverter as claimed in any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the heat dissipation method of the inverter as claimed in any one of claims 1 to 8.

12. A computer program product, characterised in that, The instructions in the computer program product are executed by the processor of the device, so that the device can execute the heat dissipation method of the inverter as claimed in any one of claims 1 to 8.

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