An inverter control method and system

Abstract

This invention relates to the technical field of inverter control, specifically to an inverter control method and system. The method includes the following steps: acquiring operation-related information, including environmental information related to inverter operation, internal inverter status information, and heat dissipation system operation information; evaluating the heat generation trend of power devices and the heat dissipation capacity of the heat dissipation system based on the operation-related information; and selecting and executing a heat dissipation scheme from a set of preset heat dissipation schemes based on the evaluation results of the heat generation trend of power devices and the heat dissipation capacity of the heat dissipation system. Through this technical solution, this application can achieve comprehensive perception of the inverter's operating status and, based on the dual evaluation of the heat generation trend of power devices and the heat dissipation capacity of the heat dissipation system, intelligently select and execute the optimal heat dissipation scheme, ensuring stable and efficient operation of the inverter in complex environments.

Description

Technical Field

[0001] This invention relates to the technical field of inverter control, and specifically to an inverter control method and system. Background Technology

[0002] Inverters, as core equipment for power conversion, play a crucial role in modern industry, data centers, and the renewable energy sector. They are primarily responsible for converting direct current (DC) power into alternating current (AC) power, providing stable and reliable power to various electrical devices.

[0003] In practical applications, inverters operate under complex and variable environmental conditions. For example, in large-scale photovoltaic power plants, inverters are typically deployed outdoors, directly exposed to the natural environment. During the day, high solar radiation intensity can cause a rapid increase in ambient temperature, leading to a sharp rise in the external temperature of the inverter. Simultaneously, the power generation of the photovoltaic array varies with the intensity and angle of sunlight, causing fluctuations in the inverter's output power and consequently affecting the heat generation rate of its internal power devices. In these scenarios, controlling the inverter's cooling system presents challenges. Traditional cooling control methods are often based on preset temperature thresholds or simple proportional-integral-derivative (PID) control algorithms. When the temperature of the power devices reaches a certain set value, the fan speed or coolant flow rate increases to enhance heat dissipation. However, this control approach may not achieve optimal cooling performance in scenarios with frequent changes in ambient temperature and load power. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned shortcomings by proposing an inverter control method and system.

[0005] The present invention adopts the following technical solution:

[0006] An inverter control method, the method comprising the following steps:

[0007] Obtain operation-related information, including environmental information related to inverter operation, internal status information of the inverter, and operating information of the heat dissipation system;

[0008] Based on operational information, assess the heat generation trend of power devices and evaluate the heat dissipation capability of the heat dissipation system;

[0009] Based on the evaluation results of the heat generation trend of the power device and the evaluation results of the heat dissipation capacity of the heat dissipation system, a heat dissipation scheme is selected and executed from multiple preset heat dissipation schemes.

[0010] When the power device has a high heat generation trend and the heat dissipation capacity of the heat dissipation system is low, a high-intensity heat dissipation scheme is selected and implemented, which includes increasing the fan speed.

[0011] When the heat generation trend of power devices is moderate and the heat dissipation capacity of the heat dissipation system is moderate, select and implement a medium-intensity heat dissipation scheme, which includes setting the fan to run at medium speed.

[0012] When the heat generation trend of power devices is low and the heat dissipation capacity of the heat dissipation system is strong, a low-intensity heat dissipation scheme is selected and implemented, which includes reducing the fan speed.

[0013] Through this technical solution, this application can achieve a comprehensive perception of the inverter's operating status, and based on a dual evaluation of the heat generation trend of power devices and the heat dissipation capacity of the heat dissipation system, intelligently select and execute the optimal heat dissipation scheme, thereby effectively solving the problems of lagging heat dissipation control and high energy consumption in the prior art, and ensuring that the inverter operates stably and efficiently in complex environments.

[0014] This application also discloses an inverter control system applied to the above-mentioned inverter control method, the system comprising:

[0015] The acquisition module acquires operation-related information, including environmental information, internal status information of the inverter, and working information of the heat dissipation system related to inverter operation.

[0016] The evaluation module assesses the heat generation trend of power devices and the heat dissipation capability of the heat dissipation system based on relevant operational information.

[0017] The processing module selects and executes a heat dissipation scheme from a number of preset heat dissipation schemes based on the evaluation results of the heat generation trend of the power device and the heat dissipation capacity of the heat dissipation system.

[0018] This application provides a system for implementing the aforementioned inverter control method. Through modular design, the functions of information acquisition, heat assessment, and scheme processing are clearly defined, thereby effectively supporting the implementation of the inverter control method and improving the system's integration and maintainability.

[0019] This application can effectively solve the problems of lagging heat dissipation control and high energy consumption in the prior art, ensuring that the inverter always maintains the optimal operating range in complex and ever-changing environments, extending the service life of the equipment, and improving the continuity and stability of the system.

[0020] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of an inverter control method according to the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of an inverter control system according to the present invention. Detailed Implementation

[0023] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0024] This embodiment provides an inverter control method and system, combined with Figure 1 and Figure 2 As shown.

[0025] refer to Figure 1 An inverter control method, the method comprising the following steps:

[0026] Obtain operation-related information, including environmental information related to inverter operation, internal status information of the inverter, and operating information of the heat dissipation system;

[0027] Based on operational information, assess the heat generation trend of power devices and evaluate the heat dissipation capability of the heat dissipation system;

[0028] Based on the evaluation results of the heat generation trend of the power device and the evaluation results of the heat dissipation capacity of the heat dissipation system, a heat dissipation scheme is selected and executed from multiple preset heat dissipation schemes.

[0029] When the power device has a high heat generation trend and the heat dissipation capacity of the heat dissipation system is low, a high-intensity heat dissipation scheme is selected and implemented, which includes increasing the fan speed.

[0030] When the heat generation trend of power devices is moderate and the heat dissipation capacity of the heat dissipation system is moderate, select and implement a medium-intensity heat dissipation scheme, which includes setting the fan to run at medium speed.

[0031] When the heat generation trend of power devices is low and the heat dissipation capacity of the heat dissipation system is strong, a low-intensity heat dissipation scheme is selected and implemented, which includes reducing the fan speed.

[0032] An inverter is a power conversion device responsible for converting direct current (DC) energy into alternating current (AC) energy. Power devices refer to the semiconductor devices inside the inverter that perform high-frequency switching operations, such as insulated-gate bipolar transistors (IGBTs) or metal-oxide-semiconductor field-effect transistors (MOSFETs), which generate a significant amount of heat during operation. A cooling system is a device used to dissipate the heat generated by the power devices; common types include air-cooled systems (using fans to force airflow) and liquid-cooled systems (using coolant circulation to remove heat). Operational information includes various data affecting inverter operation and heat dissipation, including environmental information (such as ambient temperature and solar radiation intensity), internal inverter status information (such as power device temperature and inverter output power), and cooling system operating information (such as fan speed and coolant flow rate). Heat generation trends refer to the tendency of power devices to generate heat over a future period. Heat dissipation capacity refers to the efficiency and capacity of the cooling system in dissipating heat in the current or future period. A heat dissipation scheme is a set of pre-defined strategies used to adjust the operating state of the cooling system to achieve specific heat dissipation goals, such as adjusting fan speed, coolant flow rate, or activating auxiliary cooling devices. The control method of this application is typically deployed in the control unit of the inverter, which acquires various operating data through a sensor network and executes evaluation and decision-making logic by the built-in processor.

[0033] The inverter control method of this application first requires acquiring operation-related information. This information can include environmental information related to inverter operation, internal inverter status information, and cooling system operation information. For example, environmental information can be acquired through external sensors, such as ambient temperature sensors and solar radiation intensity sensors. Internal inverter status information can be acquired through internal sensors, such as power device temperature sensors, current sensors, and voltage sensors, used to calculate the inverter's output power. Cooling system operation information can be acquired through sensors within the cooling system itself, such as fan speed sensors and coolant flow sensors. This information can be collected by the control unit in the form of analog or digital signals. As an optional implementation, operation-related information can also be acquired through data interaction with an external monitoring system; for example, the monitoring system can periodically send the latest environmental data and grid load data to the inverter control unit.

[0034] After obtaining relevant operational information, it is necessary to assess the heat generation trend of power devices and the heat dissipation capacity of the cooling system based on this information. The assessment of the heat generation trend of power devices can be calculated and analyzed based on the changing relationships between parameters such as inverter output power, power device temperature, and ambient temperature. For example, based on the changes in inverter output power and the rate of change in power device temperature, combined with the influence of ambient temperature on heat accumulation and dissipation, the temperature change trend of the power devices over a future period can be predicted, thus characterizing its heat generation trend. Alternatively, the heat generation behavior of power devices can be trend-analyzed based on historical operating data of the inverter under different operating conditions. By statistically analyzing the correlation changes of parameters such as output power, power device temperature, and ambient temperature in historical operating data, a correspondence between the current operating state and the heat generation changes of the power devices can be established, thereby predicting the heat generation trend of the power devices in the subsequent period after obtaining the current operational information. The assessment of the heat dissipation capacity of the cooling system can be based on a comprehensive judgment of parameters such as the operating state of the cooling system, ambient temperature, and power device temperature. For example, by acquiring parameters reflecting the operating intensity of the heat dissipation system, such as fan speed and coolant flow rate, and combining this with the temperature difference between the power device temperature and the ambient temperature, the heat transfer effect per unit time under the current heat dissipation conditions can be calculated, thereby evaluating the heat dissipation capacity of the heat dissipation system. Alternatively, by monitoring the inlet and outlet temperature difference of the cooling medium in real time, and combining this with the flow rate and thermal properties of the cooling medium, the actual heat removed by the heat dissipation system under the current operating conditions can be calculated, thus reflecting the actual heat dissipation capacity of the heat dissipation system.

[0035] After obtaining the assessment results of the heat generation trend of the power devices and the heat dissipation capacity of the cooling system, a cooling scheme needs to be selected and implemented from a number of preset cooling schemes. The preset cooling schemes can include various levels of heat dissipation intensity, such as low-intensity cooling (fan running at low speed), medium-intensity cooling (fan running at medium speed), and high-intensity cooling (fan running at high speed or auxiliary cooling devices activated). The logic for selecting a cooling scheme can be based on a decision matrix or a fuzzy control system. For example, if the assessment results show a high heat generation trend of the power devices and a low heat dissipation capacity of the cooling system, a high-intensity cooling scheme might be selected. Conversely, if the heat generation trend is low and the heat dissipation capacity is sufficient, a low-intensity cooling scheme might be selected to save energy. As an alternative implementation, an optimization algorithm can also be used to select the optimal cooling scheme. For example, an objective function can be defined that comprehensively considers factors such as the temperature of the power devices, the energy consumption of the cooling system, and the operating life of the inverter. Then, a search algorithm can be used to find the scheme that optimizes the objective function among the preset cooling schemes.

[0036] The inverter control method of this application acquires operational information and, based on this information, proactively assesses the heat generation trend of power devices and the heat dissipation capacity of the cooling system, thereby intelligently selecting and executing the optimal heat dissipation scheme. Specifically, the method first continuously collects various operational information, such as ambient temperature, inverter output power, power device temperature, and the operating status of the cooling system. This information is input into the control unit to build a comprehensive understanding of the inverter's thermal state. Subsequently, the control unit uses a built-in algorithm based on this real-time data to predict the heat that the power devices may generate in the future, i.e., assess their heat generation trend. Simultaneously, it also considers the current operating status of the cooling system and environmental conditions to assess the actual heat dissipation capacity of the cooling system. For example, when the ambient temperature rises and the inverter output power increases, the heat generation trend of the power devices is assessed as increasing; conversely, when the cooling system fan speed decreases or is blocked, its heat dissipation capacity is assessed as decreasing. Based on these two assessment results, the control unit dynamically selects the most suitable scheme from several preset heat dissipation schemes. For example, if it is predicted that the power devices are about to overheat and the heat dissipation capacity is insufficient, the system will immediately select and implement a solution to enhance heat dissipation, such as increasing the fan speed or activating auxiliary cooling. This proactive control strategy enables the inverter cooling system to actively adapt to changes in the external environment and load, rather than responding passively, thereby effectively preventing power devices from overheating, ensuring the stable and efficient operation of the inverter, and extending its service life.

[0037] Specifically, we will further explore and specify how to select and implement a heat dissipation scheme from multiple preset heat dissipation schemes based on the evaluation results of the heat generation trend of power devices and the heat dissipation capability evaluation results of the heat dissipation system.

[0038] Regarding the detailed assessment of heat generation trends and heat dissipation capacity, in this plan, the assessment of heat generation trends and heat dissipation capacity are the basis for selecting a heat dissipation solution. The following is a detailed description of each assessment process.

[0039] First, regarding the assessment of heat generation trends: Input parameters: power device temperature (monitored in real time by temperature sensors), inverter output power, ambient temperature, and power device operating status (e.g., switching frequency, load conditions). Assessment method: Relationship between temperature and power: The heat generated by a power device is directly proportional to its operating load. The heat generated by the power device can be estimated using the inverter's output power. For example, a higher inverter output power leads to a larger load on the power device, thus increasing the heat generation trend. Temperature change rate: The future temperature change trend can be predicted using the temperature change rate of the power device. A rapid temperature increase indicates a higher heat generation trend. Impact of ambient temperature: Ambient temperature directly affects the heat dissipation efficiency of the power device; therefore, the assessment value of heat generation needs to be adjusted based on the ambient temperature. High ambient temperatures reduce heat dissipation efficiency, potentially leading to a faster temperature rise. Reference to historical data: Machine learning or data analysis methods can be used to analyze historical data (such as past power output, temperature changes, etc.) to build a predictive model and estimate the heat generation trend of the power device over a future period. Output result: The assessment result of the heat generation trend of power devices is a dynamic heat generation rate, which represents the heat generated by power devices in the future within a certain period of time.

[0040] Secondly, regarding heat dissipation capacity assessment: Input parameters: operating status of the heat dissipation system (e.g., fan speed, coolant flow rate), temperature of power devices, and ambient temperature. Assessment methods: Fan speed and coolant flow rate: The capacity of the heat dissipation system is determined by real-time monitoring of fan speed and coolant flow rate. For example, if the fan speed is low or the coolant flow rate is insufficient, the heat dissipation capacity of the heat dissipation system is low. Temperature difference and heat conduction: The heat dissipation capacity of the heat dissipation system is related to the temperature difference between the power device temperature and the ambient temperature. When the temperature difference is large, the heat dissipation system can dissipate heat effectively, and vice versa. By real-time monitoring of the temperature difference in conjunction with fan speed and coolant flow rate, the current heat dissipation capacity can be assessed. Thermophysical properties of coolant: The flow rate, temperature, and density of the coolant all affect the performance of the heat dissipation system. By monitoring these parameters, the capacity of the heat dissipation system can be assessed more accurately. Historical performance benchmark: By using the ideal heat dissipation capacity (e.g., standard thermal resistance) at the beginning of system operation or after cleaning and maintenance as a benchmark, the actual heat dissipation capacity of the heat dissipation system is monitored to determine whether there is any blockage or malfunction. Output results: The heat dissipation capability assessment results of the heat dissipation system indicate the amount of heat that the heat dissipation system can currently dissipate and its efficiency, usually expressed in the form of heat transfer efficiency or thermal resistance value.

[0041] Finally, regarding the decision-making process for selecting a heat dissipation solution. After obtaining the assessment results of the heat generation trend of the power devices and the heat dissipation capacity assessment results of the heat dissipation system, an appropriate heat dissipation solution is selected. The detailed decision-making process is as follows.

[0042] Correspondence between evaluation results and heat dissipation solutions:

[0043] High load, high heat generation trend, low heat dissipation capacity: If the power device has a high heat generation trend and the heat dissipation capacity of the heat dissipation system is low, the system will choose a high-intensity heat dissipation solution, such as increasing the fan speed to the maximum; or starting auxiliary cooling devices, such as liquid cooling systems or external heat dissipation units.

[0044] Medium load, moderate heat generation trend, and appropriate heat dissipation capacity: If the heat generation trend of the power devices is moderate and the heat dissipation capacity of the cooling system is acceptable, the system can choose a medium-intensity heat dissipation scheme, such as setting the fan to run at medium speed and maintaining the existing coolant flow rate.

[0045] Low load, low heat generation trend, and good heat dissipation capacity: If the heat generation trend of the power device is low and the heat dissipation capacity of the cooling system is sufficient, the system will choose a low-intensity heat dissipation solution, such as: the fan running at low speed; maintaining the minimum coolant flow rate.

[0046] Decision Models and Algorithms:

[0047] When selecting a specific heat dissipation scheme, decision matrices, fuzzy control algorithms, or optimization algorithms can be used: Decision Matrices: These combine evaluation results with preset heat dissipation schemes to create a condition-based selection table. Whenever the system evaluates a certain state (e.g., high load, high temperature), it automatically selects a suitable heat dissipation scheme according to predefined rules in the table. Fuzzy Control Algorithms: Fuzzy control algorithms can handle uncertainty and fuzziness. In this case, the system can select an appropriate heat dissipation intensity based on a fuzzy set of heat generation trends and heat dissipation capacity evaluation results from power device data. For example, when the power device temperature is too high and the heat dissipation capacity is weak, the fuzzy controller can automatically adjust the fan speed and decide whether to activate additional heat dissipation systems such as liquid cooling. Optimization Algorithms: Genetic algorithms, particle swarm optimization algorithms, etc., can be used to comprehensively consider factors such as heat dissipation efficiency, energy consumption, and equipment lifespan to select the optimal scheme from multiple heat dissipation schemes. For example, an objective function can be set to comprehensively evaluate the impact of heat dissipation schemes on inverter heat control, energy efficiency, and system lifespan.

[0048] The following are some specific examples that illustrate how to select and implement a heat dissipation scheme based on the assessment results of the heat generation trend of power devices and the heat dissipation capacity assessment results of the heat dissipation system.

[0049] An operational example of heat generation trend assessment is presented, assuming the inverter is running and the following parameters are acquired in real time: power device temperature: 70°C; inverter output power: 10kW; ambient temperature: 30°C. Based on this data, the control unit assesses the heat generation trend: Temperature change rate: The power device temperature changes at a rate of 3°C / minute. Power device load: With an output power of 10kW, assuming a power device efficiency of 90%, the estimated heat generation rate is 10kW × 0.1 = 1kW (assuming a heat loss of 10%). Influence of ambient temperature: At an ambient temperature of 30°C, a temperature difference exceeding 25°C will affect heat dissipation efficiency. Based on these parameters, the system can predict the temperature change trend of the power devices over the next 5 minutes. Due to the high output power and large temperature change rate, the control unit assesses a high heat generation trend for the power devices.

[0050] Assessment results: The heat generation trend of power devices is high, and the temperature is predicted to continue to rise, possibly reaching 80°C within 10 minutes.

[0051] This is an operational example of evaluating the heat dissipation capacity of a cooling system. Assume the system has acquired the following data through its sensors: fan speed: 3000 rpm; coolant flow rate: 4 L / min; cooling system operating status: normal operation, but ambient temperature is high (30°C). When evaluating the cooling system's capacity, the following factors need to be considered: Fan speed: 3000 rpm corresponds to a medium airflow rate, providing general cooling effect. Coolant flow rate: 4 L / min is within the normal range and theoretically can effectively remove heat, but may be slightly insufficient in high-temperature environments. Considering the current heat generation trend of the power devices, the control unit assesses that the current cooling system's heat dissipation capacity is at a moderate level and is insufficient to cope with the rapidly rising temperature of the power devices.

[0052] Evaluation results: The heat dissipation capacity of the heat dissipation system is moderate, and it may not be able to adequately cool the power devices in a short period of time.

[0053] The system selects and executes specific operational examples of heat dissipation schemes, combining the evaluation results of the power device's heat generation trend (high) and the evaluation results of the heat dissipation capacity of the heat dissipation system (medium). The system then selects a scheme based on preset heat dissipation parameters: Low-intensity heat dissipation scheme: Fan runs at low speed, coolant flow rate is maintained at 4L / min. Medium-intensity heat dissipation scheme: Fan runs at medium speed, coolant flow rate is increased to 5L / min. High-intensity heat dissipation scheme: Fan runs at high speed, coolant flow rate is increased to 6L / min, and auxiliary cooling devices (e.g., liquid cooling system) are activated.

[0054] Decision-making process, regarding the assessment results: High heat generation trend: Power device temperatures are rising rapidly and are expected to reach critical temperatures soon. Moderate heat dissipation capacity: The current cooling system is insufficient to cope with the high heat generation trend.

[0055] Based on these evaluation results, the system automatically selects a high-intensity heat dissipation solution. At this point, the system performs the following operations: fan speed is increased to maximum (3500 rpm) to accelerate airflow; coolant flow rate is increased to 6 L / min to improve cooling efficiency; and auxiliary cooling device (liquid cooling system) is activated to accelerate heat transfer and dissipation.

[0056] Results: By increasing heat dissipation, the system effectively suppressed the temperature rise of the power devices. The control unit continuously monitors the temperature of the power devices to ensure that it remains within a safe range (e.g., below 80°C).

[0057] Regarding dynamic adjustments to specific operations, if the ambient temperature changes or the power device load fluctuates, the system will dynamically adjust the heat dissipation scheme based on the new data. Example: New Ambient Temperature: Assume the ambient temperature suddenly rises to 35°C. Increased Power Device Output Power: The power device load increases from 10kW to 15kW. The system will reassess: Power Device Heat Generation Trend: As the output power increases, the heat generation trend intensifies further. Heat Dissipation Capacity Assessment: Due to the increased ambient temperature, the efficiency of the heat dissipation system decreases, and the coolant flow rate and fan speed need to be further increased. Dynamically Adjusted Operation: Fan Speed ​​Increased to Maximum: The fan speed is further increased to 4000rpm. Coolant Flow Rate Increased to 7L / min: Enhanced liquid cooling effect. Activation of Backup Cooling Device: Activation of a second cooling device (e.g., adding an external cooling fan) as needed. After system execution, the power device temperature remains within a safe range, and the heat dissipation system effectively adapts to changes in ambient temperature.

[0058] Regarding the energy optimization example, during the selection of heat dissipation solutions, the system will prioritize low-energy-consumption options to avoid unnecessary energy waste. Example: Under low load conditions: If the inverter load drops to 5kW, the heat generation trend of the power devices decreases significantly. Heat dissipation capacity assessment: The heat dissipation system's capacity remains at a moderate level, but due to the low load, the system can select a low-energy-consumption solution. Optimized operation: Selecting a low-intensity heat dissipation solution: Fan speed reduced to 2000rpm, coolant flow rate maintained at 4L / min, ensuring minimal energy consumption while maintaining effective heat dissipation.

[0059] The above examples clearly illustrate how to select and implement a heat dissipation scheme based on the heat generation trends of power devices and the heat dissipation capacity assessment results of the cooling system. Each step, through specific data input and evaluation algorithms, ensures that the selection and execution of the heat dissipation scheme can respond promptly and effectively to changes in system status, thereby improving the inverter's operating efficiency and reliability while avoiding excessive energy consumption. This dynamic adjustment and real-time decision-making mechanism is the key advantage of this technical solution.

[0060] In summary, traditional inverter control methods typically employ a fixed cooling scheme when the performance of the heat dissipation system degrades, which cannot address issues of insufficient heat dissipation or excessive energy consumption. The solution proposed in this application dynamically selects a cooling scheme, intelligently choosing the most suitable cooling intensity based on real-time data. This ensures: Prevention of power device overheating: By promptly increasing the cooling intensity, the temperature of power devices is prevented from becoming too high, protecting the internal components of the inverter from damage. Optimization of energy consumption: When the heat generation trend of power devices is low, selecting a low-intensity cooling scheme helps save energy and improve system efficiency. Enhanced system responsiveness: It can dynamically adjust the cooling scheme to cope with factors such as load fluctuations and changes in ambient temperature, avoiding the lag problem in traditional methods.

[0061] This application further proposes the following steps for implementing the heat dissipation scheme:

[0062] Continuously acquire power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system;

[0063] Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices;

[0064] Calculate the dynamic effective thermal resistance between the power device and the ambient air using the power device temperature, ambient temperature, and the heat generated by the power device.

[0065] Obtain the standard thermal resistance value of the heat dissipation system under clean conditions;

[0066] When the heat dissipation system is in normal working condition, compare the dynamic effective thermal resistance with the standard thermal resistance value. If the dynamic effective thermal resistance is higher than the standard thermal resistance value and continues for a period of time, it is determined that there is physical blockage in the heat dissipation system or the actual heat dissipation capacity has decreased.

[0067] If it is determined that there is a physical blockage in the cooling system or that the actual cooling capacity has decreased, a cooling scheme to increase the cooling intensity will be forcibly implemented, and a maintenance alarm will be issued to the monitoring system. The cooling scheme to increase the cooling intensity includes increasing the fan speed and increasing the water pump flow.

[0068] Specifically, continuously acquiring data on power device temperature, ambient temperature, inverter output power, and the operating status of the cooling system refers to real-time monitoring and collection of these key operating parameters using various sensors integrated inside or outside the inverter. For example, power device temperature can be measured using thermistors or infrared sensors; ambient temperature can be obtained using ambient temperature sensors; inverter output power can be calculated using current transformers and voltage transformers; and the operating status of the cooling system can be monitored using fan speed sensors, water pump flow sensors, etc. Continuous acquisition of this data forms the basis for subsequent evaluation and judgment.

[0069] Estimating the heat generation of the power devices based on the inverter's output power and the device temperature can be understood as using a known power device loss model, combined with the current output power and device temperature, to calculate the instantaneous heat dissipation power of the power devices under the current operating conditions. This estimate reflects the rate of heat generation inside the power devices.

[0070] In practical applications, the dynamic effective thermal resistance between the power device and the ambient air is calculated using the power device temperature, ambient temperature, and the heat generated by the power device. This involves using thermodynamic principles to correlate the heat generated by the power device, the temperature difference between the device and the ambient temperature, and thus calculating a dynamic index characterizing the current heat dissipation efficiency. The higher this dynamic effective thermal resistance value, the worse the heat dissipation efficiency.

[0071] Furthermore, obtaining the standard thermal resistance value of the cooling system under clean conditions refers to the benchmark thermal performance index measured under standard test conditions during the initial operation of the inverter or after thorough cleaning and maintenance. This standard thermal resistance value represents the optimal heat dissipation capacity of the cooling system under ideal conditions.

[0072] When the cooling system is operating normally, the dynamic effective thermal resistance is compared with the standard thermal resistance value. If the dynamic effective thermal resistance is higher than the standard thermal resistance value and remains higher for a period of time, it is determined that the cooling system has a physical blockage or a decrease in actual heat dissipation capacity. This means that when the actual heat dissipation efficiency (reflected by the dynamic effective thermal resistance) is significantly lower than the efficiency under ideal conditions (reflected by the standard thermal resistance value), and this deviation is not an instantaneous fluctuation but a continuous phenomenon, the system considers that there is a problem with the heat dissipation capacity. This continuous judgment can avoid false alarms.

[0073] If the system detects physical blockage or a decrease in actual heat dissipation capacity, it will forcibly implement a heat dissipation strategy to enhance heat dissipation and send a maintenance alert to the monitoring system. This means that once a decline in the performance of the heat dissipation system is detected, the system will immediately take measures, such as increasing fan speed and water pump flow, to maximize heat dissipation capacity, while simultaneously notifying maintenance personnel to conduct inspections and maintenance to prevent component damage or system failure due to poor heat dissipation.

[0074] This application's solution continuously monitors the temperature of power devices, ambient temperature, inverter output power, and the operating status of the cooling system. Based on this data, it dynamically calculates the effective thermal resistance from the power devices to the ambient air, thereby reflecting the actual operating efficiency of the cooling system in real time. By comparing this dynamic effective thermal resistance with a preset standard thermal resistance value under clean conditions, it can accurately identify whether there is physical blockage or a decrease in the actual heat dissipation capacity of the cooling system. When a decrease in heat dissipation capacity is detected and persists for a period of time, the system no longer relies solely on the preset heat dissipation scheme selection logic, but forcibly executes a scheme to increase the heat dissipation intensity to cope with sudden deterioration in heat dissipation performance. At the same time, it issues a maintenance alarm to prompt manual intervention, thereby effectively avoiding the risk of inverter overheating caused by latent faults in the cooling system.

[0075] Through the above technical solution, this application overcomes the limitation of traditional inverter control methods in failing to respond promptly and effectively when the performance of the heat dissipation system deteriorates. By introducing a dynamic effective thermal resistance calculation and comparison mechanism, early and accurate judgment of physical blockage or actual heat dissipation capacity reduction in the heat dissipation system is achieved. This proactive fault diagnosis and mandatory heat dissipation enhancement measures significantly improve the reliability and safety of inverter operation, effectively extend the service life of power devices, provide timely maintenance warnings for operation and maintenance personnel, and reduce unplanned downtime and maintenance costs.

[0076] In some preferred embodiments, the implementation is as follows: The inverter controller continuously collects data from power device temperature sensors, ambient temperature sensors, inverter output power sensors, and cooling system fan speed sensors. Based on the inverter output power and power device temperature, the controller estimates the heat generated by the power devices using a loss-based method. Subsequently, using the power device temperature, ambient temperature, and estimated heat generation, the current dynamic effective thermal resistance is calculated. Assume that during the initial operation of the inverter, the standard thermal resistance of the cooling system in a clean state is measured to be 0.5 K / W. During normal inverter operation, the controller continuously compares the calculated dynamic effective thermal resistance with 0.5 K / W. If the dynamic effective thermal resistance is higher than 0.6 K / W (a preset threshold) for 10 consecutive minutes, the system determines that the cooling system may be clogged with dust or that fan performance has degraded. Once this determination is made, the controller immediately increases the cooling system fan speed to its maximum to forcibly increase heat dissipation intensity and simultaneously sends a maintenance alarm message, "Cooling system performance degraded, please check," to the remote monitoring system via the communication interface, so that maintenance personnel can intervene promptly.

[0077] This application further proposes steps for assessing the heat generation trends of power devices based on operational information, including:

[0078] Continuously acquire power device temperature, ambient temperature, inverter output power, and solar radiation intensity;

[0079] Perform consistency checks or rate of change analysis on solar radiation intensity to identify any anomalies in solar radiation intensity;

[0080] When there is an anomaly in solar radiation intensity, the weight of solar radiation intensity in the assessment of the heat generation trend of power devices is adjusted. The heat generation trend of power devices is assessed based on the rate of change of inverter output power, the rate of change of power device temperature, the rate of change of ambient temperature, and the solar radiation intensity after weight adjustment.

[0081] When the solar radiation intensity is normal, the heat generation trend of the power devices is assessed based on the rate of change of the inverter output power, the rate of change of the power device temperature, the rate of change of the ambient temperature, and the solar radiation intensity.

[0082] Specifically, continuously acquiring power device temperature, ambient temperature, inverter output power, and solar radiation intensity refers to real-time collection of these key operating parameters through corresponding sensors or internal monitoring systems. These parameters are core factors affecting the heat generation and dissipation of power devices. Among these, performing consistency checks or rate-of-change analysis on solar radiation intensity to identify any anomalies aims to ensure the reliability of the input data. Consistency checks may involve comparing current measurements with historical data or data from adjacent sensors to determine if they are within a reasonable range; rate-of-change analysis focuses on the magnitude of changes in solar radiation intensity over a short period to identify any drastic jumps or changes that do not conform to physical laws. When anomalies in solar radiation intensity are detected, adjusting the weight of solar radiation intensity in the assessment of the heat generation trend of power devices aims to reduce the negative impact of abnormal data on the assessment results. For example, depending on the severity of the anomaly, the weight of solar radiation intensity can be reduced, or even its impact can be temporarily ignored, relying more on other relatively stable parameters for assessment. The rate of change of inverter output power, the rate of change of power device temperature, the rate of change of ambient temperature, and solar radiation intensity (or the weighted solar radiation intensity) are key indicators used to comprehensively assess the heat generation trend of power devices. This rate information can reflect the dynamic changes in the system's thermal state, thus enabling more accurate prediction of future heat generation trends.

[0083] This application's solution effectively addresses the potential bias in power device heat generation trend assessment when environmental information is uncertain or anomalies exist by introducing an anomaly identification and weight adjustment mechanism for solar radiation intensity. Specifically, when solar radiation intensity data is identified as anomaly, its weight in the heat generation trend assessment is adjusted accordingly, thus avoiding misjudgments caused by abnormal data. Simultaneously, this solution incorporates the rate of change of inverter output power, power device temperature, and ambient temperature, enabling a more comprehensive and dynamic reflection of the actual situation in the heat generation trend assessment. Therefore, even when external environmental data is uncertain, more accurate and reliable power device heat generation trend assessment results can be obtained.

[0084] In some preferred embodiments, it is assumed that at a certain moment, the inverter control system continuously acquires power device temperature, ambient temperature, inverter output power, and solar radiation intensity. The system first performs a consistency check on the acquired solar radiation intensity data. For example, if the current solar radiation intensity value drops sharply from 1000 W / m² to 50 W / m² in a very short time, while other relevant environmental parameters (such as cloud cover, weather forecast) do not show such a drastic change, the system will determine that the solar radiation intensity data is abnormal, possibly due to a momentary sensor malfunction or partial shading. In this case, the system will adjust the weight of solar radiation intensity in the assessment of the power device's heat generation trend from a normal value (e.g., 0.3) to a lower value (e.g., 0.1), or even temporarily set its weight to zero, according to a preset strategy. Subsequently, the system will combine the rate of change of inverter output power, the rate of change of power device temperature, the rate of change of ambient temperature, and the weighted solar radiation intensity (or rely solely on other parameters) to comprehensively assess the heat generation trend of the power device. If the solar radiation intensity data is determined to be normal, its original weight will be used for evaluation. In this way, even when there are anomalies in solar radiation intensity data, the assessment of heat generation trends of power devices can still maintain high accuracy, thereby guiding the heat dissipation system to make correct decisions.

[0085] This application further proposes the following steps for assessing the heat generation trend of power devices by adjusting solar radiation intensity:

[0086] The reliability of solar radiation intensity is assessed, and the reliability assessment results are obtained. The reliability assessment results of solar radiation intensity are then smoothed over time to obtain the smoothed reliability assessment value.

[0087] Set the minimum time interval for weight adjustments and the limit on the magnitude of weight adjustments;

[0088] The weight of solar radiation intensity in the assessment of heat generation trends of power devices is calculated based on the smoothed reliability assessment value, the minimum time interval for weight adjustment, and the limit on the magnitude of weight adjustment.

[0089] Specifically, assessing the reliability of solar radiation intensity involves analyzing multiple dimensions of solar radiation intensity data, including quality, completeness, consistency, and deviation from historical patterns, to determine the reliability of current data and quantify its reliability level, thus deriving a reliability assessment result. For example, statistical methods or rule-based expert systems can be used for evaluation. Time-series smoothing of the reliability assessment results involves filtering the assessed reliability results to eliminate transient noise and short-term fluctuations, resulting in a more stable and representative smoothed reliability assessment value. This helps avoid overreaction or misjudgment caused by transient anomalies. Algorithms such as moving averages, exponential smoothing, or Kalman filtering can be used. In practical applications, setting a minimum time interval for weight adjustments specifies the shortest possible interval between two weight adjustments, aiming to prevent frequent weight changes and improve system stability. For example, this could be set to several minutes or hours. Setting a limit on the magnitude of weight adjustments specifies the maximum range of change for each weight adjustment, aiming to avoid drastic weight jumps and ensure the smoothness of the assessment process. For example, it can be set to a percentage of the current weight value. Thus, based on the smoothed reliability assessment value, the minimum time interval for weight adjustment, and the limit on the magnitude of weight adjustment, the weight of solar radiation intensity in the assessment of heat generation trends of power devices is calculated. This means that, taking into account the actual reliability of solar radiation data, the system's response speed to weight adjustment, and the stability requirements of the adjustment, a preset algorithm is used to dynamically determine the specific weight value of solar radiation intensity in the assessment of heat generation trends.

[0090] This application's solution effectively addresses the potential for instantaneous fluctuations and measurement errors in raw solar radiation data by introducing a reliability assessment of solar radiation intensity and time-series smoothing. Specifically, firstly, the reliability of solar radiation intensity is assessed to identify poor data quality or anomalies, providing a basis for subsequent weight adjustments. Secondly, time-series smoothing of the reliability assessment results filters out noise and short-term fluctuations, resulting in more stable and accurate reliability assessment values ​​and preventing misjudgments due to instantaneous anomalies. Furthermore, by setting minimum time intervals and amplitude limits for weight adjustments, the smoothness and controllability of the weight adjustment process are further ensured, preventing frequent or drastic changes in weights due to data fluctuations. It is precisely the synergistic effect of these mechanisms that makes the weight adjustment of solar radiation intensity in the assessment of heat generation trends in power devices more reasonable, stable, and accurate, thereby improving the overall accuracy of the heat generation trend assessment.

[0091] In some preferred embodiments, this application is implemented as follows: Assume the inverter control system continuously monitors solar radiation intensity. When the system detects an anomaly in solar radiation intensity (e.g., a significant deviation from historical data or data from adjacent sensors), a reliability assessment of the abnormal solar radiation intensity is initiated. Specifically, the reliability assessment module analyzes the completeness, consistency, and deviation from the prediction model of the current solar radiation intensity data. For example, if the data fluctuates drastically in a short period or differs significantly from the solar radiation data of other inverters in the same area, the reliability assessment result will be low. Assume the assessment result is a value between 0 and 1, where 1 represents complete reliability and 0 represents complete unreliability. Subsequently, the reliability assessment result is sent to a time series smoothing processor. For example, a 5-minute moving average filter is used to smooth the reliability assessment result to eliminate instantaneous noise and obtain a more stable smoothed reliability assessment value. Simultaneously, the system presets a minimum time interval of 10 minutes for weight adjustments, and the magnitude of each weight adjustment is limited to no more than ±5% of the current weight. Based on the smoothed reliability assessment value, the minimum time interval for weight adjustment, and the magnitude limit for weight adjustment, the system calculates the weight of solar radiation intensity in the assessment of heat generation trends of power devices. For example, if the smoothed reliability assessment value is high (close to 1), the weight may remain at a high level; if the assessment value is low, the weight will be adjusted accordingly. During the calculation process, the system checks whether more than 10 minutes have passed since the last weight adjustment and whether the difference between the calculated new weight and the current weight is within a ±5% magnitude limit. If it exceeds the limit, it will be truncated according to the magnitude limit. In this way, even if there are anomalies in the solar radiation intensity data, its weight in the assessment of heat generation trends can be dynamically, smoothly, and reliably adjusted, thereby ensuring the accuracy of the assessment of heat generation trends of power devices.

[0092] This application proposes an inverter control method, wherein the steps for implementing a heat dissipation scheme include:

[0093] Continuously acquire power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system;

[0094] Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices;

[0095] The dynamic heat transfer efficiency index of the heat dissipation system is calculated using the temperature of the power device, the ambient temperature, and the heat generated by the power device.

[0096] Obtain the historical performance baseline of the cooling system under different operating conditions;

[0097] When the heat dissipation system is in normal working condition, compare the dynamic heat transfer efficiency index with the historical performance baseline. If the dynamic heat transfer efficiency index deviates from the historical performance baseline continuously for a period of time, it is determined that there is performance degradation in the internal components of the heat dissipation system.

[0098] When it is determined that there is performance degradation in the internal components of the heat dissipation system, a heat dissipation scheme to enhance heat dissipation intensity is forcibly implemented, and a maintenance alarm is issued to the monitoring system. The heat dissipation scheme to enhance heat dissipation intensity includes increasing fan speed and increasing water pump flow.

[0099] Specifically, continuously acquiring power device temperature, ambient temperature, inverter output power, and the operating status of the cooling system refers to real-time collection of key parameters related to inverter operation and heat dissipation through various sensors installed inside and outside the inverter. Power device temperature can be measured by thermistors or infrared sensors, ambient temperature by ambient temperature sensors, inverter output power by current and voltage sensors, and the operating status of the cooling system can include parameters such as fan speed, water pump flow rate, and coolant temperature. The purpose is to provide the necessary data foundation for subsequent heat generation estimation and efficiency index calculation.

[0100] Estimating the heat generation of power devices based on inverter output power and power device temperature can be understood as using a pre-established power device loss model, combined with the current inverter output power and power device temperature, to calculate the instantaneous heat loss of the power devices at the current operating point, i.e., the heat generation. This model can be established based on experimental data or theoretical calculations, and its purpose is to quantify the heat source intensity of the power devices.

[0101] In practical applications, the dynamic heat transfer efficiency index of a heat dissipation system is calculated using the power device temperature, ambient temperature, and the heat generated by the power device. Specifically, this is achieved by calculating the ratio of the heat generated by the power device to the temperature difference between the power device and the ambient temperature, thus obtaining an index reflecting the current heat transfer efficiency of the heat dissipation system. For example, this index can be defined as heat generation divided by (power device temperature minus ambient temperature), or calculated using more complex heat transfer formulas. The purpose is to provide a quantitative parameter that reflects the real-time performance of the heat dissipation system.

[0102] Furthermore, obtaining the historical performance baseline of the cooling system under different operating conditions refers to the long-term monitoring and recording of the dynamic heat transfer efficiency index of the cooling system under various typical workloads, ambient temperatures, and cooling system operating modes during the initial operation of the inverter or after calibration, followed by statistical analysis. This establishes the normal range and average value of the index under different operating conditions. This baseline serves as a standard for measuring the performance of the cooling system, aiming to provide a reference for subsequent performance degradation assessment.

[0103] In this mechanism, under normal operating conditions, the dynamic heat transfer efficiency index is compared with a historical performance baseline. If the dynamic heat transfer efficiency index consistently deviates from the historical performance baseline for a period of time, it is determined that the internal components of the heat dissipation system are experiencing performance degradation. Specifically, "consistent deviation" means that the dynamic heat transfer efficiency index is consistently higher or lower than the normal fluctuation range of the historical performance baseline for an extended period, such as exceeding a preset threshold in multiple consecutive sampling periods. "Sustained for a period of time" means that this deviation needs to be maintained for more than a preset duration to avoid misjudging instantaneous fluctuations. The purpose of this judgment mechanism is to identify efficiency declines in internal components of the heat dissipation system (such as fans, water pumps, heat sinks, etc.) due to aging, wear, or minor blockages.

[0104] Therefore, when performance degradation is detected in internal components of the cooling system, a cooling solution to enhance heat dissipation is forcibly implemented, and a maintenance alert is sent to the monitoring system. This enhanced cooling solution may include, but is not limited to, increasing fan speed, increasing water pump flow, and activating backup cooling units to compensate for the loss of heat dissipation capacity due to component performance degradation. Simultaneously, a maintenance alert is sent to the monitoring system to remind maintenance personnel to promptly inspect and maintain the cooling system, preventing further deterioration of the problem.

[0105] This application's solution effectively addresses the problem of failing to detect performance degradation of internal components in the aforementioned basic solution by introducing a dynamic heat transfer efficiency index and a historical performance baseline. Specifically, by continuously monitoring the temperature of power devices, ambient temperature, inverter output power, and the operating status of the heat transfer system, the heat generation of the power devices can be accurately estimated. Based on this data, the calculated dynamic heat transfer efficiency index reflects the actual efficiency of the heat transfer system in transferring heat from the power devices to the ambient air in real time. When the performance of internal components in the heat transfer system degrades, even if the external environment or load changes little, their heat transfer efficiency will decrease accordingly, causing the dynamic heat transfer efficiency index to deviate from its historical performance baseline established under normal operating conditions. This deviation, especially when it occurs continuously and persists for a period of time, becomes a reliable indicator of internal component performance degradation. In this way, the system can identify potential problems in the early stages of performance degradation, rather than passively responding only when heat accumulation leads to overheating of power devices or system failure.

[0106] In some preferred embodiments, it is assumed that when an inverter is initially put into operation, there is a stable relationship between the fan speed and heat dissipation efficiency of its cooling system, and its dynamic heat transfer efficiency index is recorded and a historical performance baseline is established under different ambient temperatures and output power. For example, when the ambient temperature is 25°C and the inverter output power is 50kW, the average value of the dynamic heat transfer efficiency index of the cooling system is X, with a fluctuation range of ±Y. After several years of operation, due to fan bearing wear or blade dust accumulation, the actual fan speed may not reach the set value, or its airflow may decrease at the same speed, resulting in weakened heat dissipation capacity. At this time, even if the temperature of the power devices has not yet reached the dangerous threshold, the dynamic heat transfer efficiency index obtained through continuous calculation may be found to be consistently lower than the lower limit of the historical performance baseline XY for several hours. Based on this judgment of continuous deviation, the system will immediately identify that there is performance degradation in the internal components of the cooling system (i.e., the fan), forcibly increase the fan's operating power to improve heat dissipation intensity, and simultaneously issue a maintenance alarm to the monitoring system: "Fan performance degradation, inspection recommended." Once maintenance personnel receive the alert, they can perform targeted inspections and maintenance on the fans, thereby avoiding the risk of overheating of power devices that may result from a continuous decline in fan efficiency.

[0107] In some embodiments of this application, the step of obtaining the historical performance baseline of the heat dissipation system under different operating conditions can be specifically implemented as follows:

[0108] During the initial operation of the inverter, continuously monitor the temperature of the power devices, the ambient temperature, the inverter output power, and the operating status of the heat dissipation system.

[0109] Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices;

[0110] The dynamic heat transfer efficiency index of the heat dissipation system is calculated using the temperature of the power device, the ambient temperature, and the heat generated by the power device.

[0111] Based on the power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system, the current operating status of the heat dissipation system is determined.

[0112] In each defined working state, the dynamic heat transfer efficiency index is continuously measured and recorded, and the measurement data is statistically analyzed to establish a historical performance baseline for different working states, including the average value and fluctuation range, thereby forming a historical performance baseline of the heat dissipation system under different working states.

[0113] During the long-term operation of the inverter, baseline data corresponding to the operating state is selected from the historical performance baseline based on the current operating state of the heat dissipation system.

[0114] Specifically, the "initial operation phase of the inverter" refers to the initial stage after the inverter is installed and begins normal operation. This stage is generally considered to be a period when the system performance is stable and the components have not experienced significant wear or aging. Performance data established during this period can serve as a reliable benchmark. Continuously acquired data on power device temperature, ambient temperature, inverter output power, and the operating status of the cooling system are key input parameters for evaluating the performance of the cooling system. Among these, power device temperature directly reflects the heat load of the power devices; ambient temperature is an external factor affecting heat dissipation efficiency; inverter output power is directly related to the heat generated by the power devices; and the operating status of the cooling system includes operating parameters such as fan speed and water pump flow rate, used to characterize the actual operating conditions of the cooling system.

[0115] Furthermore, the heat generation of power devices can be estimated using parameters such as inverter output power and power device temperature, combined with a power device loss model. For example, the power loss can be calculated based on the inverter's efficiency curve and load conditions, thus yielding the heat generation. The dynamic heat transfer efficiency index of the cooling system is a quantitative indicator measuring the efficiency of the cooling system in transferring heat from power devices to ambient air. This index can be calculated based on the power device temperature, ambient temperature, and the heat generation of the power devices; for example, it can be defined as the ratio of heat generation to the difference between the power device temperature and the ambient temperature.

[0116] Furthermore, the operating status of the cooling system is affected by various factors, such as ambient temperature, inverter output power, and fan speed. By combining or segmenting these parameters, complex operating conditions can be divided into several discrete and representative operating states. For example, operating states can be divided into "high temperature and high power" and "low temperature and low power" based on ambient temperature, inverter output power, and fan speed. Dynamic heat transfer efficiency is continuously monitored under each defined operating state. Through long-term measurement and recording, a large amount of data can be accumulated. Statistical analysis of this data, such as calculating the average, standard deviation, maximum, and minimum values, can establish a historical performance baseline for that operating state. This baseline includes not only average performance but also normal fluctuation ranges, which helps distinguish between normal performance fluctuations and actual performance degradation. Once the historical performance baseline is established, the system will monitor the current operating state in real time during the long-term operation of the inverter and select the baseline data that best matches the current operating state from the established baseline data as a reference for subsequent performance comparisons and degradation assessments.

[0117] This application's solution systematically collects and analyzes key data such as power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system during the initial operation of the inverter. This allows for accurate estimation of the heat generated by the power devices and calculation of the dynamic heat transfer efficiency index of the heat dissipation system. By dividing complex operating conditions into multiple discrete operating states and continuously measuring, recording, and statistically analyzing the dynamic heat transfer efficiency index under each state, this application establishes a refined historical performance baseline that includes average values ​​and fluctuation ranges. This baseline reflects the inherent performance characteristics of the heat dissipation system under different normal operating conditions. During long-term inverter operation, by matching the current operating state with historical baseline data in real time, a reliable reference can be provided for real-time evaluation of the heat dissipation system performance, thereby enabling more accurate identification of performance degradation in the heat dissipation system.

[0118] This application further proposes steps for statistical analysis of measurement data, including:

[0119] Identify and remove outlier data points from the measurement data;

[0120] Time series smoothing is performed on the measurement data after removing outliers to obtain smoothed data.

[0121] Based on the smoothed data, the average value and fluctuation range are calculated, and a historical performance baseline containing the average value and fluctuation range is established for different operating conditions.

[0122] Specifically, identifying and removing outlier data points in measurement data refers to real-time or batch analysis of the raw measurement data collected during continuous measurement and recording of dynamic heat transfer efficiency indicators. This analysis detects and removes data points that do not conform to the normal data distribution or exhibit abnormal behavior. For example, these outlier data points may be caused by sensor malfunctions, transient interference, or data transmission errors. The aim is to ensure the data quality and accuracy of subsequent statistical analysis. Time series smoothing of the measurement data after outlier removal can be understood as applying appropriate algorithms to the remaining measurement data after removing outlier points to eliminate random noise and high-frequency fluctuations, thereby revealing potential trends and patterns in the data. The purpose is to make the data more stable, facilitating subsequent statistical calculations and improving the representativeness of historical performance baselines. In practical applications, based on smoothed data, the average value and fluctuation range are calculated to establish a historical performance baseline corresponding to different operating states. This involves statistically calculating the processed data for each defined operating state of the cooling system after anomaly removal and smoothing, to obtain the average value and fluctuation range of the dynamic heat transfer efficiency index under that operating state, such as standard deviation and confidence interval. The purpose is to form an accurate, reliable, and representative historical performance baseline for subsequent performance degradation assessment.

[0123] This application's solution effectively addresses the impact of noise and outliers in the original measurement data on the accuracy of statistical analysis by introducing outlier identification and removal, as well as time series smoothing. Specifically, identifying and removing outliers avoids data deviations caused by occasional errors or transient interference, ensuring that the data used for baseline establishment is authentic and valid. Subsequently, time series smoothing of the measurement data after outlier removal further filters out random noise and high-frequency fluctuations, making the data trend clearer and avoiding baseline instability caused by data jitter. It is precisely because of this data preprocessing that the subsequently calculated average values ​​and fluctuation ranges can more accurately reflect the true performance level of the cooling system under different operating conditions, thereby establishing a more reliable historical performance baseline.

[0124] In some preferred embodiments, it is assumed that the dynamic heat transfer efficiency index of the cooling system is continuously measured during the initial operation of the inverter. Under a specific operating condition, the raw measurement data may contain some instantaneous high or low values. These values ​​may be spikes caused by electromagnetic interference to the sensor for a short period, or due to brief errors during data transmission. For example, in a series of normal data points (e.g., 0.85, 0.86, 0.84), an abnormal data point (e.g., 1.20 or 0.50) suddenly appears, and this abnormal value lasts only a very short time. According to the scheme of this application, these abnormal data points are first identified and removed. For example, by setting an instantaneous fluctuation range and a minimum abnormal duration, if a data point jumps drastically within a short period and exceeds a preset threshold, it is marked as abnormal and removed from the dataset. After removing these abnormal data points, the remaining dataset may still contain some random noise, making the data curve less smooth. At this time, the measurement data after removing the abnormal data will undergo time series smoothing processing, such as using a moving average filter or exponential smoothing method, to eliminate this noise and make the data trend clearer and more stable. For example, the original data might fluctuate slightly around 0.85. After smoothing, the data will fluctuate more stably around the true value of 0.85. Finally, based on this data after anomaly removal and smoothing, the average value and fluctuation range of the dynamic heat transfer efficiency index under this operating condition are calculated, thus establishing an accurate and representative historical performance baseline. This baseline will more realistically reflect the normal performance of the cooling system under this operating condition, providing a reliable basis for subsequent performance degradation assessments.

[0125] This application further proposes steps for identifying and removing outlier data points in measurement data, including:

[0126] Set an initial data acquisition period, and monitor the measurement data in real time within the initial data acquisition period;

[0127] During the initial data acquisition period, if multiple consecutive measurement data points exceed the preset instantaneous fluctuation range and the duration of the exceedance is shorter than the preset minimum abnormal duration, the corresponding measurement data points will be identified as abnormal data points.

[0128] When a single measurement data point experiences a drastic change within a short period of time, and the change exceeds the preset change threshold, the corresponding measurement data point will be identified as an abnormal data point.

[0129] Once an outlier is identified, it will be removed from the measurement data.

[0130] Specifically, the "initial data acquisition period" refers to the time period used to collect raw performance data and establish an initial baseline during the initial operation of the inverter or the system calibration phase. This period is typically long enough to cover the operating performance of the cooling system under various typical operating conditions; for example, it can be set to several days to several weeks. During this period, the system monitors measurement data such as power device temperature, ambient temperature, inverter output power, and the operating status of the cooling system in real time to capture their dynamic changes. The "preset instantaneous fluctuation range" refers to the short-term, small-amplitude fluctuation range allowed for measurement data points under normal operating conditions. For example, this range can be determined based on historical experience data or statistical methods (such as standard deviation). When multiple consecutive measurement data points exceed this range, but the duration of the exceedance is "shorter than the preset minimum anomaly duration," it indicates that this may not be a systemic performance degradation, but rather a short-term anomaly caused by transient interference or sensor noise. For example, the minimum anomaly duration can be set to several seconds to several minutes to distinguish between transient disturbances and persistent problems. In practical applications, a "drastic jump in a single measurement data point within a short period" refers to a large, non-linear abrupt change in the data value within an extremely short time interval. This jump typically far exceeds the rate of change during normal operation. For example, the temperature of a power device might suddenly rise from 50°C to 100°C within seconds. In this case, the "jump amplitude" refers to the difference between the values ​​before and after the data point, while the "preset jump threshold" is the boundary for judging whether such a jump is abnormal. For example, it can be set to exceed a certain percentage change or absolute value change. Such drastic jumps often indicate sensor malfunction, data acquisition errors, or instantaneous system anomalies, rather than actual physical changes. When any of the above conditions are met, i.e., the measurement data point is identified as an abnormal data point, it will be removed from the measurement data. The purpose is to ensure that the data used for subsequent time series smoothing and the establishment of historical performance baselines are clean and reliable, thereby avoiding the negative impact of abnormal data on baseline accuracy.

[0131] This application's solution effectively addresses the issue of transient interference, sensor noise, or drastic data jumps affecting the accuracy of the historical performance baseline of a heat dissipation system by establishing clear rules for identifying abnormal data. Specifically, by introducing an initial data acquisition cycle for real-time monitoring, it ensures comprehensive capture of data characteristics from the initial stage of baseline establishment. Furthermore, by determining that multiple consecutive measurement data points exceed a preset transient fluctuation range for a duration shorter than a preset minimum abnormal duration, it effectively distinguishes between short-term, non-continuous fluctuations caused by environmental transients or sensor noise and genuine system performance changes, avoiding misjudging normal fluctuations as abnormalities. Simultaneously, by monitoring a single measurement data point for drastic jumps within a short period and comparing it to a preset jump threshold, it quickly identifies and eliminates transient extreme values ​​caused by sensor malfunctions or data acquisition errors. Failure to eliminate these extreme values ​​would severely distort the statistical analysis results. Therefore, this application's solution ensures that the data used to establish the historical performance baseline has undergone rigorous screening and purification, providing a more solid and reliable data foundation for subsequent assessments of heat dissipation system performance degradation.

[0132] In some preferred embodiments, it is assumed that the system sets an initial data acquisition cycle of two weeks during the initial operation of the inverter. During this period, the system continuously monitors the temperature of the power devices. To identify abnormal data points, the system presets an instantaneous fluctuation range of ±2°C, a minimum abnormal duration of 5 minutes, and a jump threshold of more than 15°C within 10 seconds.

[0133] Specifically, during the monitoring process, if the temperature of the power device rises from 60℃ to 63℃ at a certain moment and returns to 60℃ after 3 minutes, although it exceeds the instantaneous fluctuation range of ±2℃, the system will judge it as a normal instantaneous fluctuation or slight interference because the duration (3 minutes) is shorter than the preset minimum abnormal duration (5 minutes), and will not identify it as an abnormal data point.

[0134] However, if at another point in time the power device temperature rises from 60°C to 65°C within 1 minute and lasts for 6 minutes, the system will further analyze whether this is a genuine performance change, rather than simply rejecting it as an anomaly, because the duration of the out-of-range fluctuation (6 minutes) is longer than the minimum abnormal duration (5 minutes).

[0135] On the other hand, if the system detects that the temperature of the power device suddenly rises from 60°C to 80°C within just 5 seconds, a temperature change of 20°C, far exceeding the preset jump threshold of 15°C, the system will immediately identify this 80°C data point as an abnormal data point and remove it from the measurement data. Such a drastic jump usually indicates a sensor malfunction or data transmission error, rather than a real physical phenomenon.

[0136] Through the above mechanism, the system can effectively filter out invalid data caused by noise, transient interference or sensor failure, ensuring that the data used to establish historical performance baselines is clean and representative, thereby improving the accuracy of the baseline and the reliability of subsequent fault diagnosis.

[0137] This application further proposes the following steps for obtaining the smoothed data:

[0138] In each defined working state, frequency component analysis is performed on the measurement data after removing outliers to identify noise frequency components and nonlinear fluctuation patterns in the measurement data.

[0139] Based on the noise frequency components and nonlinear fluctuation patterns, multiple smoothing filters with different filtering characteristics or parameters are selected and combined to form an adaptive smoothing processing strategy.

[0140] An adaptive smoothing strategy is applied to the measurement data after outlier data is removed to obtain smoothed data.

[0141] Specifically, "frequency component analysis" refers to transforming measurement data from the time domain to the frequency domain using Fourier transform, wavelet analysis, or other spectral analysis techniques to reveal the distribution and intensity of different frequency components in the data. This analysis can identify specific "noise frequency components" caused by sensor noise, electromagnetic interference, mechanical vibration, etc., as well as "nonlinear fluctuation patterns" caused by system nonlinear response, environmental changes, etc. For example, it can identify high-frequency random noise, low-frequency drift, or periodic interference. "Selecting and combining multiple smoothing filters with different filtering characteristics or parameters" refers to dynamically selecting and configuring appropriate smoothing algorithms based on the characteristics of the identified noise frequency components and nonlinear fluctuation patterns. For example, for high-frequency random noise, a low-pass filter or moving average filter can be selected; for periodic interference at a specific frequency, a notch filter can be selected; for nonlinear fluctuations, Kalman filters, wavelet denoising, or nonlinear regression smoothing can be considered. These filters can be adjusted according to their cutoff frequency, order, window size, and other parameters, and can be combined in series or parallel to form an "adaptive smoothing processing strategy" that can be optimized for the current data characteristics. In practical applications, applying an adaptive smoothing strategy to measurement data after outlier removal means that the smoothing process is no longer static, but rather adjusted based on the real-time characteristics of the data. For example, the system can automatically adjust filter parameters based on frequency analysis results, or apply different filter combinations at different time periods to ensure that noise is effectively removed while preserving the true trend and useful information of the data to the greatest extent possible.

[0142] This application's solution addresses the limitations of traditional single smoothing methods when processing complex measurement data by introducing frequency component analysis and an adaptive smoothing strategy. Specifically, firstly, by performing frequency component analysis on the measurement data after removing outliers, various noise frequency components and nonlinear fluctuation patterns can be accurately identified. This in-depth analysis allows the system to gain a clearer understanding of the nature of interference sources, such as distinguishing between high-frequency random noise, periodic interference at specific frequencies, or fluctuations caused by nonlinear factors. Secondly, based on this detailed identification, the system no longer blindly applies a general smoothing algorithm but intelligently selects and combines multiple smoothing filters with different filtering characteristics or parameters. This means that filtering strategies can be designed or adjusted specifically; for example, low-pass filtering can be used for high-frequency noise, notch filtering for specific frequency interference, and more complex nonlinear smoothing algorithms for nonlinear fluctuations. This forms an adaptive smoothing strategy that can dynamically adjust according to data characteristics. Finally, applying this adaptive strategy to the measurement data can more accurately and effectively remove various noises and fluctuations, thereby obtaining more realistic and reliable smoothed data.

[0143] In some preferred embodiments, it is assumed that under a certain defined operating state, the dynamic heat transfer efficiency measurement data of the heat dissipation system after removing outlier data is analyzed. First, the time series data is analyzed for frequency components using Fast Fourier Transform. The analysis results show that there is significant 50Hz power frequency interference (noise frequency component) and some slow nonlinear fluctuation patterns caused by diurnal variations in ambient temperature. Based on this analysis, the system will construct an adaptive smoothing processing strategy. Specifically, this strategy may include: a digital notch filter with its center frequency set to 50Hz for accurately filtering out power frequency interference; an adaptive Kalman filter for handling nonlinear fluctuation patterns, which can dynamically adjust its gain and state estimation according to real-time changes in the data to better track the true trend of the data and suppress random noise; and a low-pass filter for further removing residual high-frequency random noise, whose cutoff frequency can be dynamically adjusted according to the signal-to-noise ratio of the data. These filters are combined to form a multi-stage, adaptive smoothing processing chain. When this adaptive smoothing strategy is applied to the raw measurement data, a notch filter is first used to remove power frequency interference, then a Kalman filter is used to process nonlinear fluctuations and random noise, and finally a low-pass filter is used for fine smoothing. This results in a highly smooth dynamic heat transfer efficiency index that accurately reflects the performance of the cooling system, providing strong support for establishing an accurate historical performance baseline.

[0144] refer to Figure 2 This application proposes an inverter control system applied to the aforementioned inverter control method. The system includes:

[0145] The acquisition module acquires operation-related information, including environmental information, internal status information of the inverter, and working information of the heat dissipation system related to inverter operation.

[0146] The evaluation module assesses the heat generation trend of power devices and the heat dissipation capability of the heat dissipation system based on relevant operational information.

[0147] The processing module selects and executes a heat dissipation scheme from a number of preset heat dissipation schemes based on the evaluation results of the heat generation trend of the power device and the heat dissipation capacity of the heat dissipation system.

[0148] Specifically, the acquisition module can be understood as a hardware or software component responsible for collecting various data required for inverter operation. For example, it may include various sensors (such as temperature sensors, current sensors, voltage sensors, etc.) to acquire environmental information (such as ambient temperature and humidity), internal inverter status information (such as power device temperature and output power), and cooling system operating information (such as fan speed and water pump flow rate). Furthermore, the acquisition module can interact with external monitoring systems or data storage units via communication interfaces to obtain more comprehensive operational information. Its purpose is to provide accurate and real-time foundational data for subsequent evaluation and processing.

[0149] The evaluation module can be understood as a logical unit that analyzes and judges the data collected by the acquisition module. Specifically, based on operational information and using preset algorithms, the evaluation module predictively assesses the heat generation trend of power devices and evaluates the current heat dissipation capacity of the cooling system in real time. For example, it can predict heat generation trends by establishing a heat generation model for the power devices and combining parameters such as inverter output power and power device temperature; simultaneously, it evaluates the current heat dissipation capacity by monitoring parameters such as fan speed and coolant temperature of the cooling system and combining the design parameters and historical performance data of the cooling system. Its purpose is to provide a basis for decision-making in selecting a heat dissipation solution.

[0150] In practical applications, the processing module is specifically a control unit that executes heat dissipation strategies based on the evaluation results of the assessment module. For example, the processing module can have a built-in decision engine. This engine selects the most suitable solution from multiple preset heat dissipation schemes (e.g., adjusting fan speed, changing coolant flow rate, activating auxiliary cooling devices, etc.) based on the heat generation trend evaluation results of the power devices and the heat dissipation capacity evaluation results of the heat dissipation system, and then instructs the heat dissipation system to execute it. The processing module can also communicate with the monitoring system, report operating status and execution results, and issue alarms when necessary. The purpose is to achieve intelligent and dynamic management of inverter heat dissipation.

[0151] This application's solution concretizes each step of the inverter control method into independent system modules, achieving refined management of inverter heat dissipation. The acquisition module is responsible for comprehensively and in real-time collecting all the data required for inverter operation, providing data support for subsequent decision-making. The evaluation module, based on this data, scientifically predicts and judges the heat generation of power devices and the heat dissipation capacity of the cooling system, thus overcoming the potential blindness or lag in traditional methods. The processing module intelligently selects and executes the optimal heat dissipation scheme based on the evaluation results, ensuring that the inverter remains within a safe operating temperature range under various conditions. This modular system design enables the inverter control method to operate efficiently and stably, effectively avoiding performance degradation or equipment damage caused by overheating.

[0152] Through the above technical solution, this application provides a specific physical implementation architecture that enables the abstract inverter control method to be executed efficiently and reliably. The system simplifies the implementation and maintenance of control logic through clear modular division, improving the system's scalability and robustness. The acquisition module ensures the comprehensiveness and real-time nature of the data, the evaluation module provides intelligent decision support, and the processing module guarantees the timely and effective execution of the heat dissipation scheme. This systematic implementation not only enhances the intelligence level of inverter heat dissipation control but also provides a solid foundation for the long-term stable operation and performance optimization of the inverter, thereby effectively reducing the risk of failure and maintenance costs caused by heat dissipation problems.

[0153] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An inverter control method, characterized in that, The method includes the following steps: Obtain operation-related information, including environmental information related to inverter operation, internal status information of the inverter, and operating information of the heat dissipation system; Based on operational information, assess the heat generation trend of power devices and evaluate the heat dissipation capability of the heat dissipation system; Based on the evaluation results of the heat generation trend of the power device and the evaluation results of the heat dissipation capacity of the heat dissipation system, a heat dissipation scheme is selected and executed from multiple preset heat dissipation schemes. When the power device has a high heat generation trend and the heat dissipation capacity of the heat dissipation system is low, a high-intensity heat dissipation scheme is selected and implemented, which includes increasing the fan speed. When the heat generation trend of power devices is moderate and the heat dissipation capacity of the heat dissipation system is moderate, select and implement a medium-intensity heat dissipation scheme, which includes setting the fan to run at medium speed. When the heat generation trend of power devices is low and the heat dissipation capacity of the heat dissipation system is strong, a low-intensity heat dissipation scheme is selected and implemented, which includes reducing the fan speed. Based on operational information, the steps to assess the heat generation trends of power devices include: Continuously acquire power device temperature, ambient temperature, inverter output power, and solar radiation intensity; Perform consistency checks or rate of change analysis on solar radiation intensity to identify any anomalies in solar radiation intensity; When there is an anomaly in solar radiation intensity, the weight of solar radiation intensity in the assessment of the heat generation trend of power devices is adjusted. The heat generation trend of power devices is assessed based on the rate of change of inverter output power, the rate of change of power device temperature, the rate of change of ambient temperature, and the solar radiation intensity after weight adjustment. When the solar radiation intensity is normal, the heat generation trend of the power devices is evaluated based on the rate of change of the inverter output power, the rate of change of the power device temperature, the rate of change of the ambient temperature, and the solar radiation intensity. The steps for adjusting the weight of solar radiation intensity in the assessment of heat generation trends in power devices include: The reliability of solar radiation intensity is assessed, and the reliability assessment results are obtained. The reliability assessment results of solar radiation intensity are then smoothed over time to obtain the smoothed reliability assessment value. Set the minimum time interval for weight adjustments and the limit on the magnitude of weight adjustments; The weight of solar radiation intensity in the assessment of heat generation trends of power devices is calculated based on the smoothed reliability assessment value, the minimum time interval for weight adjustment, and the limit on the magnitude of weight adjustment.

2. The inverter control method as described in claim 1, characterized in that, The steps for implementing a heat dissipation solution include: continuously acquiring the temperature of power devices, ambient temperature, inverter output power, and the operating status of the heat dissipation system; Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices; Calculate the dynamic effective thermal resistance between the power device and the ambient air using the power device temperature, ambient temperature, and the heat generated by the power device. Obtain the standard thermal resistance value of the heat dissipation system under clean conditions; When the heat dissipation system is in normal working condition, compare the dynamic effective thermal resistance with the standard thermal resistance value. If the dynamic effective thermal resistance is higher than the standard thermal resistance value and continues for a period of time, it is determined that there is physical blockage in the heat dissipation system or the actual heat dissipation capacity has decreased. If it is determined that there is a physical blockage in the cooling system or that the actual cooling capacity has decreased, a cooling scheme to increase the cooling intensity will be forcibly implemented, and a maintenance alarm will be issued to the monitoring system. The cooling scheme to increase the cooling intensity includes increasing the fan speed and increasing the water pump flow.

3. The inverter control method as described in claim 1, characterized in that, The steps for implementing a heat dissipation solution include: Continuously acquire power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system; Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices; The dynamic heat transfer efficiency index of the heat dissipation system is calculated using the temperature of the power device, the ambient temperature, and the heat generated by the power device. Obtain the historical performance baseline of the cooling system under different operating conditions; When the heat dissipation system is in normal working condition, compare the dynamic heat transfer efficiency index with the historical performance baseline. If the dynamic heat transfer efficiency index deviates from the historical performance baseline continuously for a period of time, it is determined that there is performance degradation in the internal components of the heat dissipation system. When it is determined that there is performance degradation in the internal components of the heat dissipation system, a heat dissipation scheme to enhance heat dissipation intensity is forcibly implemented, and a maintenance alarm is issued to the monitoring system. The heat dissipation scheme to enhance heat dissipation intensity includes increasing fan speed and increasing water pump flow.

4. The inverter control method as described in claim 3, characterized in that, The steps to obtain the historical performance baseline of the cooling system under different operating conditions include: During the initial operation of the inverter, continuously monitor the temperature of the power devices, the ambient temperature, the inverter output power, and the operating status of the heat dissipation system. Estimate the heat generated by the power devices based on the inverter output power and the temperature of the power devices; The dynamic heat transfer efficiency index of the heat dissipation system is calculated using the temperature of the power device, the ambient temperature, and the heat generated by the power device. Based on the power device temperature, ambient temperature, inverter output power, and the operating status of the heat dissipation system, the current operating status of the heat dissipation system is determined. In each defined working state, the dynamic heat transfer efficiency index is continuously measured and recorded, and the measurement data is statistically analyzed to establish a historical performance baseline for different working states, including the average value and fluctuation range, thereby forming a historical performance baseline of the heat dissipation system under different working states. During the long-term operation of the inverter, baseline data corresponding to the operating state is selected from the historical performance baseline based on the current operating state of the heat dissipation system.

5. The inverter control method as described in claim 4, characterized in that, The steps for statistical analysis of measurement data include: Identify and remove outlier data points from the measurement data; Time series smoothing is performed on the measurement data after removing outliers to obtain smoothed data. Based on the smoothed data, the average value and fluctuation range are calculated, and a historical performance baseline containing the average value and fluctuation range is established for different operating conditions.

6. The inverter control method as described in claim 5, characterized in that, The steps for identifying and removing outlier data points in measurement data include: Set an initial data acquisition period, and monitor the measurement data in real time within the initial data acquisition period; During the initial data acquisition period, if multiple consecutive measurement data points exceed the preset instantaneous fluctuation range and the duration of the exceedance is shorter than the preset minimum abnormal duration, the corresponding measurement data points will be identified as abnormal data points. When a single measurement data point experiences a drastic change within a short period of time, and the change exceeds the preset change threshold, the corresponding measurement data point will be identified as an abnormal data point. Once an outlier is identified, it will be removed from the measurement data.

7. The inverter control method as described in claim 5, characterized in that, The steps to obtain smoothed data include: In each defined working state, frequency component analysis is performed on the measurement data after removing outliers to identify noise frequency components and nonlinear fluctuation patterns in the measurement data. Based on the noise frequency components and nonlinear fluctuation patterns, multiple smoothing filters with different filtering characteristics or parameters are selected and combined to form an adaptive smoothing processing strategy. An adaptive smoothing strategy is applied to the measurement data after outlier data is removed to obtain smoothed data.

8. An inverter control system, employing the inverter control method as described in claim 1, characterized in that, The system includes: The acquisition module acquires operation-related information, including environmental information, internal status information of the inverter, and working information of the heat dissipation system related to inverter operation. The evaluation module assesses the heat generation trend of power devices and the heat dissipation capability of the heat dissipation system based on relevant operational information. The processing module selects and executes a heat dissipation scheme from a number of preset heat dissipation schemes based on the evaluation results of the heat generation trend of the power device and the evaluation results of the heat dissipation capacity of the heat dissipation system. Based on operational information, the steps to assess the heat generation trends of power devices include: Continuously acquire power device temperature, ambient temperature, inverter output power, and solar radiation intensity; Perform consistency checks or rate of change analysis on solar radiation intensity to identify any anomalies in solar radiation intensity; When there is an anomaly in solar radiation intensity, the weight of solar radiation intensity in the assessment of the heat generation trend of power devices is adjusted. The heat generation trend of power devices is assessed based on the rate of change of inverter output power, the rate of change of power device temperature, the rate of change of ambient temperature, and the solar radiation intensity after weight adjustment. When the solar radiation intensity is normal, the heat generation trend of the power devices is evaluated based on the rate of change of the inverter output power, the rate of change of the power device temperature, the rate of change of the ambient temperature, and the solar radiation intensity. The steps for adjusting the weight of solar radiation intensity in the assessment of heat generation trends in power devices include: The reliability of solar radiation intensity is assessed, and the reliability assessment results are obtained. The reliability assessment results of solar radiation intensity are then smoothed over time to obtain the smoothed reliability assessment value. Set the minimum time interval for weight adjustments and the limit on the magnitude of weight adjustments; The weight of solar radiation intensity in the assessment of heat generation trends of power devices is calculated based on the smoothed reliability assessment value, the minimum time interval for weight adjustment, and the limit on the magnitude of weight adjustment.

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