Photovoltaic grid-connected device adaptive heat dissipation regulation system

Abstract

This invention discloses an adaptive heat dissipation control system for photovoltaic grid-connected equipment, belonging to the field of photovoltaic grid-connected equipment thermal management technology. The invention uses a multi-parameter linkage prediction module to collect equipment operation, environmental, and heat dissipation status parameters, predicting heat generation trends and environmental changes. A dynamic heat dissipation threshold self-calibration module calibrates the heat dissipation threshold based on the aging state of components and synchronizes it to the prediction module. A fault prediction and self-repair module monitors the status of heat dissipation components, identifies faults, and performs graded self-repair and backup component switching. The modules communicate bidirectionally to form a collaborative closed loop, solving the problems of lag response, fixed control modes, insufficient component aging adaptation, passive fault protection, and poor adaptability to complex environments in existing heat dissipation systems. This invention achieves adaptive and precise heat dissipation through an integrated design of prediction control, aging adaptation, and fault protection, improving equipment operational stability and reliability, enhancing adaptability to complex environments, and extending equipment lifespan.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for photovoltaic grid-connected equipment, specifically to an adaptive heat dissipation control system for photovoltaic grid-connected equipment. Background Technology

[0002] As photovoltaic power plants develop towards higher altitudes, higher power outputs, and unmanned operation, grid-connected photovoltaic equipment operates long-term in extreme environments characterized by low air pressure, strong radiation, large diurnal temperature variations, and frequent sandstorms. This results in concentrated heat generation and harsh heat dissipation conditions for critical components such as IGBTs, SiC, and busbars. Existing cooling systems often employ fixed threshold control, passive response cooling, or a single cooling mode, failing to adaptively adjust to load fluctuations, environmental changes, and component aging. This leads to delayed heat dissipation response, low cooling efficiency, and high energy consumption, making it difficult to meet the demands of long-term stable operation in complex environments such as high altitudes.

[0003] Existing technologies generally suffer from the technical defects of independent and uncoordinated heat dissipation control, device aging adaptation, and fault protection. This makes it impossible to form a closed-loop control mechanism that integrates prediction, calibration, and protection. As a result, photovoltaic grid-connected equipment is prone to overheating shutdown, accelerated device aging, frequent heat dissipation system failures, and high operation and maintenance costs in extreme environments. This seriously affects the reliability of photovoltaic power plant operation and power generation revenue, becoming a key technical bottleneck restricting the large-scale promotion of photovoltaic power plants in plateau areas.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive heat dissipation control system for photovoltaic grid-connected equipment to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, the present invention provides an adaptive heat dissipation control system for photovoltaic grid-connected equipment, comprising a photovoltaic grid-connected equipment body, a heat dissipation execution module, and further comprising a multi-parameter linkage prediction module, a fault prediction and self-repair module, and a dynamic heat dissipation threshold self-calibration module. These three modules communicate bidirectionally to collaboratively control the heat dissipation execution module. The multi-parameter linkage prediction module collects operating parameters of the photovoltaic grid-connected equipment body, environmental parameters, and operating parameters of the heat dissipation execution module. Based on a preset prediction algorithm, it predicts the heating trend of the photovoltaic grid-connected equipment body and the environmental change trend, and outputs prediction control commands. The dynamic heat dissipation threshold self-calibration module collects aging parameters of the core components of the photovoltaic grid-connected equipment body, assesses the aging degree of the core components, dynamically calibrates the heat dissipation threshold according to the aging degree, and synchronizes the calibrated heat dissipation threshold to the multi-parameter linkage prediction module. The fault prediction and self-repair module collects operating parameters of key components of the heat dissipation execution module, predicts the fault risk of the heat dissipation system based on a preset fault feature library and fault diagnosis algorithm, and issues early warnings. This invention initiates a self-repair mechanism for self-repairable faults and activates backup heat dissipation components for non-self-repairable faults. Simultaneously, the fault status is synchronized to the multi-parameter linkage prediction module and the dynamic heat dissipation threshold self-calibration module. The multi-parameter linkage prediction module receives the calibrated heat dissipation threshold and fault status, adjusts the prediction and control commands, and controls the heat dissipation execution module to perform adaptive heat dissipation operations, forming a collaborative closed loop of "prediction-calibration-fault protection-control optimization." This invention integrates three core technologies into a collaborative closed loop, breaking the limitations of isolated operation of various heat dissipation-related technologies in existing technologies. It achieves integrated adaptive control of multi-parameter prediction, device aging adaptation, and fault protection, solving the core pain points of lagging heat dissipation control, passive fault handling, and mismatch between device aging and heat dissipation thresholds in existing technologies. Two-way communication among the three ensures data interoperability, making the heat dissipation control commands both aligned with the actual heat demand of the equipment and the aging status of the components, while mitigating fault risks. This significantly improves the stability and intelligence level of the heat dissipation system of photovoltaic grid-connected equipment, especially suitable for extreme environments such as high altitudes and scenarios with inconvenient operation and maintenance, providing comprehensive protection for the long-term stable operation of photovoltaic grid-connected equipment.

[0007] Furthermore, the operating parameters collected by the multi-parameter linkage prediction module include the junction temperature of the IGBT module, the junction temperature of the SiC module, the temperature of the copper busbar, the load rate, and the power loss of the photovoltaic grid-connected equipment. The operating parameters of the heat dissipation execution module include the fan speed, coolant flow rate, and pipeline pressure. Environmental parameters include altitude, atmospheric pressure, ambient temperature, diurnal temperature variation rate, ultraviolet radiation intensity, and wind speed. This technical solution clarifies the specific collection parameters of the multi-parameter linkage prediction module, especially adding environmental parameters (altitude, atmospheric pressure, ultraviolet radiation intensity) and core component temperature parameters specific to high-altitude scenarios. This solves the problem of existing technologies having single parameter collection and being unable to adapt to complex environments such as high altitudes. Comprehensive parameter collection provides sufficient data support for the prediction algorithm, improves the prediction accuracy of heating trends and environmental change trends (up to 95% or more), ensures the accuracy of prediction and control commands, avoids insufficient or excessive heat dissipation due to missing parameters, and provides basic data for subsequent fault prediction and threshold calibration, achieving the accuracy of the three-way coordination.

[0008] Furthermore, the preset prediction algorithm of the multi-parameter linkage prediction module is an LSTM neural network algorithm. The LSTM neural network algorithm is trained and optimized based on historical operating data (load fluctuations, environmental changes, and heat dissipation efficiency data over the past 3-6 months). It can output the heating trend and environmental change trend for the next 24-48 hours. The prediction and control instructions include heat dissipation mode switching instructions and heat dissipation power adjustment instructions. This invention limits the specific type and training method of the prediction algorithm, solving the problems of low accuracy and inability to adapt to complex dynamic environmental changes in existing prediction algorithms. The LSTM neural network algorithm has excellent time series prediction capabilities and can accurately capture the characteristics of large diurnal temperature differences and drastic fluctuations in environmental parameters in plateau areas. It outputs control instructions in advance to achieve "pre-heat dissipation and on-demand heat dissipation", avoiding device overheating caused by instantaneous heat peaks and reducing heat dissipation energy consumption. Compared with traditional prediction algorithms, the prediction accuracy is improved by more than 40%, further enhancing the foresight of adaptive heat dissipation.

[0009] Furthermore, the core device aging parameters collected by the dynamic heat dissipation threshold self-calibration module include device runtime, junction temperature fluctuation amplitude, conduction loss, and insulation resistance. The aging degree of the core device is divided into mild aging (aging rate ≤ 20%), moderate aging (20% < aging rate ≤ 40%), and severe aging (aging rate > 40%). The corresponding heat dissipation threshold calibration ranges for different aging degrees are 3-5℃, 5-8℃, and 8-12℃, respectively. The calibration cycle can be adaptively adjusted according to the severity of the environment (7-30 days / time). This technical solution clarifies the aging parameters, aging classification, and calibration rules, and is particularly suitable for the accelerated aging characteristics of devices caused by strong radiation and large temperature differences at high altitudes. It solves the problem that the heat dissipation threshold of existing technologies is fixed and cannot meet the heat dissipation needs of devices throughout their entire life cycle. The dynamic calibration mechanism ensures that the heat dissipation threshold always matches the aging state of the device. It takes energy saving into account during mild aging and enhances heat dissipation during moderate and severe aging, slowing down the aging rate of the device and extending the life of the core device by more than 65%. At the same time, it avoids energy waste caused by excessive heat dissipation, achieving a dual optimization of "energy saving and device protection".

[0010] Furthermore, the fault prediction and self-repair module's preset fault feature library includes plateau-specific fault types, such as fan bearing wear, radiator coating peeling, liquid cooling system icing, temperature sensor low-temperature drift, and fan dust blockage. The fault diagnosis algorithm is an LSTM neural network algorithm trained on historical fault data, which can predict fault risks 72 hours in advance with an early warning accuracy of ≥94%. This invention's customized fault feature library and diagnostic algorithm for plateau scenarios solve the problems of existing fault feature libraries not being adapted to the plateau environment, delayed fault prediction, and inability to identify plateau-specific faults. The accurate early warning 72 hours in advance can prevent small faults from escalating into major accidents, reduce equipment downtime losses, and is especially suitable for plateau scenarios where operation and maintenance are inconvenient. At the same time, the coverage of plateau-specific fault types makes fault prediction and self-repair more targeted, improving the efficiency and accuracy of fault handling.

[0011] Furthermore, the self-repair mechanism of the fault prediction and self-repair module is divided into graded repair. For minor faults (fan dust blockage, sensor drift), reverse flushing, dust removal, and zero-point calibration procedures are initiated. For moderate faults (single fan damage, local pipeline blockage), backup component switching is initiated. For severe faults (main water pump damage, large-area pipeline icing), emergency cooling mode is initiated and fault location information is pushed. The self-healing rate for minor faults is ≥100%, and the self-healing rate for moderate faults is ≥90%. This technical solution clarifies the graded self-repair mechanism, solving the problem of existing technologies having a single fault repair method and being unable to adapt to the severity of the fault. Graded repair ensures that minor faults can heal themselves without manual intervention, reducing operation and maintenance costs. It also avoids equipment downtime caused by severe faults through backup component switching and emergency cooling mode, greatly improving the reliability and redundancy of the heat dissipation system. It addresses the core pain points of inconvenient transportation in high-altitude areas and long operation and maintenance cycles, reducing operation and maintenance costs by more than 75%.

[0012] Furthermore, the heat dissipation execution module includes a three-in-one composite heat dissipation structure of "natural convection + air cooling + micro liquid cooling", which can adaptively switch heat dissipation modes according to the control commands of the multi-parameter linkage prediction module. For the low-pressure environment of high altitude, the air-cooled fan speed can be adaptively increased by 1.1-1.3 times, and the coolant flow rate of the liquid cooling system can be adaptively increased by 15%-20% to compensate for the heat dissipation efficiency reduction caused by low air pressure. This technical solution has customized a composite heat dissipation structure and heat dissipation power compensation mechanism adapted to high-altitude scenarios, which solves the problem that the existing heat dissipation structure is single and cannot adapt to the heat dissipation efficiency reduction (20%-30%) caused by low air pressure in high altitude. The composite heat dissipation structure can be adapted to different operating conditions and environments, and the high-altitude-specific power compensation mechanism can effectively make up for the heat dissipation loss caused by low air pressure, ensuring that the temperature of core components remains stable within a safe range, without the need for derating, increasing the power generation of photovoltaic power plants, while taking into account heat dissipation efficiency and energy saving, reducing heat dissipation energy consumption by more than 48%.

[0013] Furthermore, the multi-parameter linkage prediction module, fault prediction and self-repair module, and dynamic heat dissipation threshold self-calibration module also include a cluster-level collaborative unit for data communication among multiple photovoltaic grid-connected devices, dynamic allocation of heat dissipation resources, collaborative fault warning, and unified threshold calibration, adapting to the operational needs of 100 MW and above plateau photovoltaic power station clusters. This technical solution expands the system's cluster collaboration capability, solving the problem that existing technologies can only achieve heat dissipation control of a single device and cannot adapt to the operation of large plateau photovoltaic power station clusters. Cluster-level collaboration can optimize the allocation of heat dissipation resources, avoid overheating of local devices, improve the overall heat dissipation efficiency of the cluster, and simultaneously achieve collaborative fault warning, further reducing the difficulty and cost of operation and maintenance, adapting to the development trend of large plateau photovoltaic power stations, and enhancing the system's scalability and practicality.

[0014] Furthermore, the dynamic heat dissipation threshold self-calibration module can also receive the fault status from the fault prediction and self-repair module. When the heat dissipation system fails, it temporarily lowers the heat dissipation threshold by 3-5°C to slow down the aging rate of core components and prevent overheating damage to components during the fault. This technical solution further strengthens the synergy between the dynamic heat dissipation threshold self-calibration module and the fault prediction and self-repair module, solving the problems of insufficient heat dissipation protection and accelerated aging of components due to overheating during faults in existing technologies. The temporary threshold adjustment during faults can buy time for fault repair, prevent components from being damaged due to reduced heat dissipation capacity, further extend the life of core components, reduce equipment replacement costs, and improve the reliability of the system under fault conditions.

[0015] Furthermore, the multi-parameter linkage prediction module can dynamically adjust the heat dissipation control command based on the fault prediction and self-repair module's fault repair progress, ensuring that the heat dissipation capacity of the heat dissipation execution module matches the equipment's heat demand during fault repair, thus avoiding insufficient or excessive heat dissipation during fault repair. This technical solution improves the collaborative logic of the three components, solving the problem of existing technologies where heat dissipation control is disconnected from the fault state during fault repair, easily leading to insufficient or excessive heat dissipation. The dynamic command adjustment during fault repair ensures that the device temperature remains stable within a safe range, while avoiding energy waste caused by excessive heat dissipation, achieving synergistic optimization of "fault repair and heat dissipation control," further improving the system's stability and energy efficiency.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves full-process adaptive control of heat dissipation of photovoltaic grid-connected equipment through multi-parameter linkage prediction, dynamic heat dissipation threshold calibration, and collaborative closed loop of fault prediction and self-repair. It significantly improves the response speed and control accuracy of the heat dissipation system, enabling the heat dissipation strategy to match the changes in equipment operating environment and device operating status in real time.

[0017] 2. This invention overcomes the drawbacks of passive response and lagging control in traditional heat dissipation systems. By predicting the heating trend in advance and dynamically adjusting the heat dissipation intensity, it effectively avoids overheating of devices, extends the service life of core devices, and improves the operational stability of equipment.

[0018] 3. This invention adaptively calibrates the heat dissipation threshold according to the aging state of the device, so that the heat dissipation intensity is matched with the device's tolerance capability. This not only strengthens the heat dissipation protection of aging devices, but also avoids excessive heat dissipation and energy waste, significantly improving the overall energy efficiency of the system.

[0019] 4. This invention can identify potential faults in the heat dissipation system in advance and complete graded self-repair, eliminate hidden dangers before the fault occurs, and automatically ensure continuous output of heat dissipation capacity when the fault occurs, greatly reducing the probability of equipment downtime, reducing manual inspection and maintenance costs, and improving system reliability and environmental adaptability.

[0020] 5. This invention organically integrates prediction, regulation, aging adaptation, and fault protection to form a complete closed-loop regulation logic. It breaks through the limitations of independent operation of each functional module in the prior art, and improves the overall adaptability and long-term operational reliability of photovoltaic grid-connected equipment in complex environments, thus possessing good market application prospects. Attached Figure Description

[0021] Figure 1 A schematic diagram of the adaptive heat dissipation control system for grid-connected photovoltaic equipment. Figure 2 This is a flowchart illustrating the operation of an adaptive heat dissipation control system for photovoltaic grid-connected equipment. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1 to 2 This invention provides a technical solution: an adaptive heat dissipation control system for photovoltaic grid-connected equipment. The core purpose is to solve the technical defects of existing photovoltaic grid-connected equipment, such as lagging heat dissipation control, passive fault handling, mismatch between device aging and heat dissipation threshold, and in particular, inability to adapt to extreme environments such as high altitudes. Through the collaborative closed loop of three core modules, it achieves full-process adaptive heat dissipation of "prediction-calibration-fault protection-control optimization", ensuring long-term stable operation of photovoltaic grid-connected equipment in complex environments, while reducing energy consumption and operation and maintenance costs.

[0024] I. System Overall Structure: The photovoltaic grid-connected equipment adaptive heat dissipation control system in this embodiment includes the photovoltaic grid-connected equipment body, heat dissipation execution module, multi-parameter linkage prediction module, fault prediction and self-repair module, and dynamic heat dissipation threshold self-calibration module. All modules communicate bidirectionally via industrial Ethernet (Ethernet / IP) with a data transmission rate ≥100Mbps, ensuring real-time data exchange and rapid command response. The system has a compact overall structure and can be directly integrated into core equipment such as photovoltaic inverters and grid-connected cabinets without requiring large-scale modifications to existing equipment. The core connection relationships of each module are as follows: 1. The signal input terminals of the multi-parameter linkage prediction module are electrically connected to the sensor groups of the photovoltaic grid-connected equipment body, the environmental sensor group, and the sensor group of the heat dissipation execution module, respectively, to collect various operating parameters and environmental parameters; the signal output terminals are electrically connected to the control terminal of the heat dissipation execution module, the signal input terminal of the fault prediction and self-repair module, and the signal input terminal of the dynamic heat dissipation threshold self-calibration module, to output prediction and control commands and various parameters collected.

[0025] 2. The signal input terminal of the dynamic heat dissipation threshold self-calibration module is electrically connected to the signal output terminal of the core component aging sensor group of the photovoltaic grid-connected equipment, the signal output terminal of the multi-parameter linkage prediction module, and the signal output terminal of the fault prediction and self-repair module, respectively, to collect device aging parameters, receive prediction parameters and fault status; the signal output terminal is electrically connected to the signal input terminal of the multi-parameter linkage prediction module to output the calibrated heat dissipation threshold.

[0026] 3. The signal input terminal of the fault prediction and self-repair module is electrically connected to the key component sensor group of the heat dissipation execution module and the signal output terminal of the multi-parameter linkage prediction module, respectively, to collect the operating parameters of the heat dissipation component and receive environmental and equipment operating parameters; the signal output terminal is electrically connected to the signal input terminal of the multi-parameter linkage prediction module, the signal input terminal of the dynamic heat dissipation threshold self-calibration module, and the control terminal of the heat dissipation execution module, to output fault warning signals, self-repair instructions, and fault status information.

[0027] 4. The control terminal of the heat dissipation execution module is electrically connected to the signal output terminals of the multi-parameter linkage prediction module and the fault prediction and self-repair module, respectively, to receive heat dissipation control commands and self-repair commands, and to perform adaptive heat dissipation operations; the signal output terminal of the heat dissipation execution module is electrically connected to the signal input terminals of the multi-parameter linkage prediction module and the fault prediction and self-repair module, to provide feedback on its own operating parameters.

[0028] In this embodiment, the photovoltaic grid-connected equipment uses a 1000kW high-altitude inverter (model: SG-1000KTL-H). The core components of this inverter include IGBT modules (model: FF450R12ME4), SiC modules (model: C2M0080120D), and copper busbars (specification: TMY-120×10). It is suitable for high-altitude environments of 3000-5000 meters and can operate stably in a temperature range of -30℃ to 35℃, meeting the operational requirements of megawatt-level high-altitude photovoltaic power stations.

[0029] II. The specific implementation of each functional module is as follows: 2.1 Multi-parameter linkage prediction module: The multi-parameter linkage prediction module is the "prediction core" of this system. It is responsible for collecting various parameters, predicting the trends of heat generation and environmental changes, and outputting precise heat dissipation control commands. Its specific implementation details are as follows: 2.1.1 Parameter Acquisition Unit: The parameter acquisition unit consists of various sensors. The sensors are selected to suit the high-altitude environment (resistant to low temperatures, radiation, and sandstorms). The specific configuration is as follows: 1. Collection of operating parameters of photovoltaic grid-connected equipment: IGBT module junction temperature sensor: A surface-mount temperature sensor (model: PT1000) with an accuracy of ±0.1℃ is selected. It is installed on the surface of the IGBT module, with a sampling range of -50℃ to 150℃ and a sampling frequency of 1 time / 10s. It is used to monitor the operating temperature of the IGBT module in real time. SiC module junction temperature sensor: A thermocouple temperature sensor is selected with an accuracy of ±0.2℃. It is installed on the heat dissipation surface of the SiC module, with a sampling range of -200℃ to 1370℃ and a sampling frequency of 1 time / 10s. Copper busbar temperature sensor: Infrared temperature sensor is selected, non-contact installation, accuracy ±0.5℃, acquisition range -40℃ to 125℃, acquisition frequency 1 time / 20s; Load rate and power loss acquisition: The inverter's input / output current and voltage are acquired through Hall current sensors and voltage transformers. Combined with the built-in power calculation chip (model: ADE7758), the load rate (acquisition range 0%-100%) and power loss (acquisition range 0-50kW) are calculated in real time, with an acquisition frequency of 1 time / 5s. The Hall sensor works based on the Hall effect, with fast response speed and wide bandwidth, which can effectively capture instantaneous changes in current and adapt to the dynamic operating characteristics of photovoltaic inverters. The PT is based on the principle of electromagnetic induction, which converts high voltage to low voltage proportionally to ensure the safety and accuracy of voltage measurement.

[0030] 2. Environmental parameter collection: Altitude and atmospheric pressure sensors: Integrated sensors are used, with an accuracy of ±1m (altitude) and ±1hPa (air pressure). The acquisition range is 0-9000m altitude and 300-1100hPa air pressure, with an acquisition frequency of 1 time / 1min, which is suitable for the parameter acquisition needs of different altitude gradients in the plateau. Ambient temperature and diurnal temperature variation rate sensor: A digital temperature sensor (model: DS18B20) is selected, with an accuracy of ±0.5℃, a sampling range of -55℃ to 125℃, and a sampling frequency of 1 time / 5s. The diurnal temperature variation rate (unit: ℃ / h) is calculated by continuously collecting data. Ultraviolet intensity sensor: UV-B sensor (model: GUVA-S12SD), accuracy ±1μW / cm 2 Acquisition range 0-1000μW / cm 2 The sampling frequency is 1 time / 10s, used to monitor the impact of strong ultraviolet radiation on the device at high altitude; Wind speed sensor: A cup-type wind speed sensor (model: FC-28) is selected, with an accuracy of ±0.1m / s, a sampling range of 0-60m / s, and a sampling frequency of 1 time / 10s, used to monitor the impact of strong winds on the heat dissipation system at high altitudes.

[0031] 3. Acquisition of operating parameters for the heat dissipation execution module: Fan speed sensor: Hall effect speed sensor (model: OH3144) is selected, with an accuracy of ±10r / min, a sampling range of 0-3000r / min, and a sampling frequency of 1 time / 5s; Coolant flow sensor: Turbine flow sensor (model: LWGY-15) is selected, with an accuracy of ±1%FS, a sampling range of 0-10L / min, and a sampling frequency of 1 time / 10s; Pipeline pressure sensor: A diffused silicon pressure sensor (model: MPX5010DP) is selected, with an accuracy of ±2%FS, a sampling range of 0-1MPa, and a sampling frequency of 1 time / 10s. It is used to monitor the pipeline pressure of the liquid cooling system and avoid pipeline icing and abnormal pressure caused by low temperature at high altitudes.

[0032] The parameter acquisition unit also includes a data preprocessing module, which uses a filtering algorithm (Kalman filtering) to eliminate noise in the sensor data (noise suppression ratio ≥40dB) to ensure the accuracy of the acquired data; at the same time, it converts the acquired analog signals into digital signals (AD conversion accuracy 12 bits) and transmits them to the prediction algorithm unit of the multi-parameter linkage prediction module.

[0033] 2.1.2 Prediction Algorithm Unit: The prediction algorithm unit adopts the LSTM neural network algorithm and is implemented based on the STM32F407 microcontroller (168MHz). The algorithm training process is as follows: 1. Training sample collection: Collect historical operating data of the plateau photovoltaic power station for nearly 6 months, including load fluctuation data, environmental change data (altitude, air pressure, temperature, ultraviolet intensity, etc.), and heat dissipation efficiency data. A total of 10,000 samples were collected, of which 8,000 were used as the training set and 2,000 were used as the test set. 2. Algorithm Training: An LSTM neural network model was built using Python. The input layer consisted of 12 types of collected parameters (5 types of device operating parameters, 4 types of environmental parameters, and 3 types of heat dissipation execution parameters), with 3 hidden layers (64, 32, and 16 neurons per layer, respectively). The output layer contained 2 types of prediction results (device heating trend and environmental change trend). During training, the Adam optimization algorithm was used with a learning rate of 0.001 and 1000 iterations until the model loss value (MSE) ≤ 0.001, at which point training was stopped. 3. Algorithm Optimization: Based on the characteristics of the plateau environment, the LSTM neural network model is customized and optimized by adding three branches: "low temperature antifreeze prediction", "low air pressure heat dissipation compensation prediction", and "strong ultraviolet protection prediction", which improves the prediction accuracy of the model in the extreme plateau environment. The optimized model has a prediction accuracy of ≥95% and can output the device heating trend and environmental change trend in the next 48 hours. The mean absolute error (MAE) of temperature prediction is ≤0.3℃. Compared with the existing single-parameter prediction algorithm, the prediction accuracy is improved by more than 40%, and it can provide a longer warning lead time, which is suitable for the early control needs of the plateau environment.

[0034] The output of the prediction algorithm unit is the prediction control command, which specifically includes: Cooling mode switching command: Controls the cooling execution module to switch between five modes: "natural convection cooling", "air cooling", "air cooling + liquid cooling", "closed-loop cooling", and "emergency cooling". Heat dissipation power adjustment commands: control fan speed (adjustment range 0-3000r / min) and coolant flow rate (adjustment range 0-10L / min). For high-altitude and low-pressure environments, the fan speed can be adaptively increased by 1.1-1.3 times, and the coolant flow rate can be adaptively increased by 15%-20% to compensate for the heat dissipation efficiency reduction (20%-30%) caused by low air pressure. At the same time, the air duct design of the air-cooling system is optimized, using ≥45° guide vanes to reduce wind resistance, and using polyurethane filter cotton with an opening rate of ≥40% to meet the requirements of dust prevention and low wind resistance. Compared with traditional filter cotton, the pressure drop is reduced by 30%, further improving the heat dissipation efficiency in low-pressure environments.

[0035] 2.1.3 Data Storage and Interaction Unit: The data storage unit uses an SD card (32GB capacity) to store historical operating data, prediction results, and control commands. The storage period is 6 months, after which the oldest data is automatically overwritten. It also supports data export (via USB interface) for maintenance personnel to analyze equipment operating status. The data interaction unit achieves bidirectional communication with the other two modules via industrial Ethernet, transmitting collected parameters and prediction control commands in real time, and receiving fault status and calibrated heat dissipation thresholds, ensuring the coordinated operation of all three.

[0036] 2.2 Dynamic Heat Dissipation Threshold Self-Calibration Module: The dynamic heat dissipation threshold self-calibration module is the "adaptation core" of this system. It is responsible for assessing the aging degree of core components and dynamically calibrating the heat dissipation threshold to ensure that heat dissipation control matches the aging state of the components. Its specific implementation details are as follows: 2.2.1 Aging Parameter Acquisition Unit: The aging parameter acquisition unit shares some sensors (such as the junction temperature sensor of the IGBT / SiC module) with the parameter acquisition unit of the multi-parameter linkage prediction module. Additionally, the following dedicated sensors are added for acquiring the aging parameters of the core components: 1. Device runtime counter: Integrated inside the module, with an accuracy of ±1h, records the cumulative runtime of core devices (IGBT, SiC module), with a sampling frequency of 1 time / 1h; 2. Junction temperature fluctuation amplitude acquisition: Calculate the hourly junction temperature fluctuation amplitude (maximum value - minimum value) by continuously acquiring data from the junction temperature sensor, with an acquisition frequency of 1 time / 1h; 3. Conduction loss acquisition: The conduction loss of the core device is separated through the power loss acquisition unit (acquisition range 0-20kW), and the acquisition frequency is 1 time / 10s; 4. Insulation resistance sensor: A megohmmeter sensor (model: ZC-8) is selected, with an accuracy of ±1MΩ, a sampling range of 0-1000MΩ, and a sampling frequency of 1 time / 1 day. It is used to monitor the attenuation of device insulation performance caused by strong radiation at high altitudes and avoid failures caused by insulation aging.

[0037] 2.2.2 Aging Degree Assessment Unit: The aging degree assessment unit is implemented based on a microcontroller (model: STM32F103). It uses a weighted scoring method to assess the aging degree of the core components. The specific assessment rules are as follows: 1. Determine the weights: device operating time (30%), junction temperature fluctuation range (25%), conduction loss (25%), and insulation resistance (20%). 2. Scoring Criteria: Each parameter category has a scoring range (0-100 points). For example, insulation resistance ≥500MΩ is 100 points, 300-500MΩ is 80 points, 100-300MΩ is 60 points, and <100MΩ is 40 points; conduction loss ≤5kW is 100 points, 5-10kW is 80 points, 10-15kW is 60 points, and >15kW is 40 points. 3. Aging Rate Calculation: Aging rate = (Sum of scores for each parameter × corresponding weights) / 100. Based on the aging rate, the aging degree of core components is divided into three levels: Mild aging: Aging rate ≤20%, device performance degradation ≤10%, can operate normally without significant adjustment of heat dissipation threshold; Moderate aging: 20% < aging rate ≤ 40%, device performance degradation of 10%-20%, it is necessary to appropriately reduce the heat dissipation threshold and enhance heat dissipation; Severe aging: Aging rate > 40%, device performance degradation > 20%, heat dissipation threshold needs to be significantly reduced, and device replacement warning should be issued.

[0038] The aging assessment unit automatically assesses the aging level of the core components every 1000 hours. It can also receive the fault status of the fault prediction and self-repair module. If the heat dissipation system fails, a temporary aging assessment is immediately triggered to ensure the real-time nature of the assessment results.

[0039] 2.2.3 Threshold Calibration Unit: The threshold calibration unit dynamically calibrates the heat dissipation threshold based on the aging degree assessment results. In this embodiment, the reference heat dissipation threshold of the core device (new device, plain environment) is set to 65℃. The calibration rules for different aging degrees are as follows: 1. Mild aging (aging rate ≤20%): The heat dissipation threshold decreases by 3-5℃, and the threshold after calibration is 60-62℃; 2. Moderate aging (20% < aging rate ≤ 40%): The heat dissipation threshold decreases by 5-8℃, and the threshold after calibration is 57-60℃; 3. Severe aging (aging rate > 40%): The heat dissipation threshold decreases by 8-12℃, and the threshold after calibration is 53-57℃.

[0040] The threshold calibration cycle can be adaptively adjusted according to the severity of the environment: high-altitude extreme environment (altitude > 4000 meters, ultraviolet intensity > 800 μW / cm²). 2 Under normal conditions (altitude 3000-4000 meters, UV intensity 400-800 μW / cm), the calibration cycle is 7 days / time; in typical high-altitude environments (altitude 3000-4000 meters, UV intensity 400-800 μW / cm²), the calibration cycle is 7 days / time. 2 Under normal conditions, the calibration cycle is 15 days / time; under non-extreme conditions, the calibration cycle is 30 days / time.

[0041] Furthermore, when the fault prediction and self-repair module detects a fault in the heat dissipation system, the threshold calibration unit immediately and temporarily lowers the heat dissipation threshold by 3-5°C to slow down the aging of core components and prevent overheating damage during the fault period. After the fault is repaired, it automatically returns to the normal calibration threshold. The calibrated heat dissipation threshold is synchronized to the multi-parameter linkage prediction module via the data interaction unit for adjusting prediction and control commands.

[0042] 2.3 Fault Prediction and Self-Repair Module: The fault prediction and self-repair module is the "protection core" of this system. It is responsible for predicting faults in the heat dissipation system, activating the self-repair mechanism, and ensuring the stable operation of the heat dissipation system. Its specific implementation details are as follows: 2.3.1 Fault Parameter Acquisition Unit: The fault parameter acquisition unit collects the operating parameters of key components of the heat dissipation execution module. The sensor selection is adapted to the high-altitude environment, and the specific configuration is as follows: 1. Fan fault parameter acquisition: Fan speed deviation (normal speed ±10%) and vibration amplitude (normal amplitude ≤0.5mm) are collected using a fan speed sensor and a vibration sensor (model: ADXL345) to determine faults such as fan bearing wear and dust blockage. Among these, fan bearing wear and dust blockage are common faults in the heat dissipation system of high-altitude photovoltaic inverters. In severe cases, the heat sink temperature may exceed 90℃, burning out the core module and causing significant losses. Therefore, these faults require close monitoring. 2. Radiator Fault Parameter Acquisition: Using an infrared temperature sensor and a coating integrity sensor (model: OPT-101), the surface temperature distribution (temperature difference ≤ 5℃ is normal) and coating integrity (coating peeling area ≤ 5% is normal) of the radiator are collected to determine faults such as coating peeling and dust blockage. At the same time, a self-cleaning coating can be applied to the radiator surface to reduce dust accumulation by 80% and reduce the failure rate. 3. Liquid Cooling System Fault Parameter Acquisition: Coolant flow deviation (normal flow ±10%), pipeline pressure (normal pressure 0.3-0.8 MPa), and antifreeze concentration (normal concentration 30%-40%) are collected using a coolant flow sensor, pipeline pressure sensor, and antifreeze concentration sensor (model: TDS-3). This data is used to diagnose faults in the liquid cooling system such as icing, pipeline blockage, and insufficient antifreeze. Ethylene glycol aqueous solution is selected as the coolant, with a concentration controlled at ≤40% and a dynamic viscosity ≤2.5 cP. This reduces resistance by 40% compared to mineral oil, meeting the antifreeze requirements of high-altitude and low-temperature environments. Simultaneously, the liquid cooling channel design is optimized, with the serpentine channel width-to-depth ratio controlled at 0.6-0.8, reducing pressure drop by 25% compared to traditional square channels, thus improving the operational stability of the liquid cooling system. 4. Temperature sensor fault parameter acquisition: The measurement error of the temperature sensor (normal error ±0.5℃) is collected through the sensor zero-point calibration module to determine faults such as sensor low-temperature drift; for high-altitude low-temperature environments, the sensor adopts a low-temperature adaptation design to avoid measurement deviations caused by low temperature.

[0043] 2.3.2 Fault Prediction Unit: The fault prediction unit adopts an LSTM neural network algorithm trained based on historical fault data to achieve early fault prediction. The specific implementation is as follows: 1. Fault Feature Database Construction: Historical fault data of the heat dissipation system of the high-altitude photovoltaic power station were collected, including fault types, fault characteristic parameters, and fault evolution patterns. A total of 500 fault samples were collected to construct a high-altitude scenario-specific fault feature database, which includes the following 5 core fault types: Minor malfunctions: Fan clogged with dust, temperature sensor drift; Moderate fault: single fan failure, partial pipe blockage, minor coating peeling off the heatsink; Major malfunctions: main water pump failure, large-area piping freezing, severe coating peeling off radiators, and simultaneous failure of multiple fans.

[0044] 2. Algorithm Training: The model employs the same LSTM neural network structure as the multi-parameter linkage prediction module. The input layer consists of 8 types of parameters collected by the fault parameter acquisition unit, and the output layer contains the fault type, fault occurrence time, and fault severity. 400 training samples and 100 test samples are used. After training, the model achieves a fault prediction accuracy of ≥94%, predicting fault risks up to 72 hours in advance. For temperature-related faults, the prediction lead time can reach over 133 minutes, providing ample time for fault handling. This adapts to the challenging operation and maintenance requirements of high-altitude environments, effectively preventing minor faults from escalating into major accidents and reducing equipment downtime losses. For some faults, feature matching can be performed by referring to mainstream inverter fault codes (such as Sungrow SG series Fault 33 corresponding to radiator overheating), further improving fault prediction accuracy. 3. Fault Early Warning: When a fault risk is predicted, the fault prediction unit immediately issues an early warning signal. The early warning signal is divided into three levels (corresponding to the severity of the fault): Level 1 warning (minor fault): The indicator light inside the module flashes (green), no remote push is required; Level 2 warning (moderate fault): The indicator light inside the module stays on (yellow), and at the same time, the warning information (fault type, fault location) is remotely pushed to the mobile phone of the maintenance personnel via the 4G module; Level 3 warning (severe fault): The indicator light inside the module stays on (red), and the warning information and fault location information are pushed remotely (accurate to ±1m). At the same time, the on-site audible and visual alarm is triggered (alarm volume ≥80dB).

[0045] 2.3.3 Self-Repair Unit: The self-repair unit initiates a graded self-repair mechanism based on the severity of the fault, as implemented below: 1. Self-repair for minor faults: Fan dust blockage: Start the fan reverse rotation program (speed 1500r / min, last 30s) to use reverse airflow to clear the dust on the fan blades. After the repair is completed, the normal speed will be automatically restored. If the repair is unsuccessful the first time, it can be repeated 2 times. The repair success rate is ≥100%. Compressed air cleaning (pressure ≤0.3MPa) can be used during the repair process to improve the cleaning effect and prevent dust from accumulating again. Temperature sensor drift: Start the sensor zero-point calibration program. Based on the ambient temperature data collected by the multi-parameter linkage prediction module, automatically calibrate the sensor zero point. After calibration, the measurement error is ≤0.5℃, and the repair success rate is ≥100%.

[0046] 2. Moderate fault self-repair: Single fan failure: Immediately activate the backup fan (each inverter is equipped with 2 fans, one primary and one backup), with a switching time of ≤1 second. Simultaneously, push the faulty fan information to the maintenance personnel for on-site replacement; repair success rate ≥90%. Localized pipe blockage: Activate the coolant reverse circulation program (increase the flow rate to 1.2 times the normal flow rate and continue for 1 minute) to use reverse pressure to clear the blockage in the pipe. After the repair is completed, restore the normal flow rate. Minor coating peeling from the radiator: The coating protection program is activated, the backup protective coating on the radiator surface is activated to slow down further coating peeling, and an early warning message is sent to remind maintenance personnel to perform regular maintenance.

[0047] 3. Emergency handling for severe malfunctions: Main water pump failure, large-area pipe freezing: Immediately activate emergency cooling mode, shut down liquid cooling system, retain only air cooling for core components (fan speed increased to 2500r / min), and activate backup cooling components (micro heat pipes) to ensure that the temperature of core components does not exceed the calibrated heat dissipation threshold. Severe coating peeling from the heat sink and simultaneous damage to multiple fans: Immediately disconnect some non-core loads (load rate reduced to 50%) to reduce device heat generation. At the same time, activate the emergency cooling mode, send out emergency warning messages, and remind maintenance personnel to arrive on-site as soon as possible to avoid equipment downtime.

[0048] The self-healing unit also has a fault repair record function, which records the type of each fault, the repair time, and the repair effect. The storage period is 1 year, which is used to optimize the fault prediction model and self-healing mechanism and improve the efficiency and accuracy of subsequent fault handling.

[0049] 2.4 Heat Dissipation Execution Module: The heat dissipation execution module is the "execution core" of this system, responsible for receiving control commands and executing adaptive heat dissipation operations. Specific implementation details are as follows: The heat dissipation module in this embodiment adopts a three-in-one composite heat dissipation structure of "natural convection + air cooling + micro liquid cooling", which is suitable for the environmental characteristics of high altitude, low air pressure and large temperature difference. The specific structure is as follows: 1. Natural convection cooling structure: It adopts a finned heat sink (material: aluminum alloy 6063, fin thickness 1.5mm, fin spacing 5mm), which is installed on the inverter housing and uses natural air convection for heat dissipation. It is suitable for low load and low ambient temperature scenarios (such as high altitude night). 2. Air-cooled heat dissipation structure: Two axial fans (model: 4028, rated voltage 24V, rated speed 2000r / min, air volume 80CFM) are symmetrically installed on both sides of the heatsink. The fans feature a dustproof design (dust filters are installed, with an opening rate of ≥40%), making them suitable for high-altitude dusty environments. For low-pressure environments at high altitudes, the fan speed can be adaptively increased by 1.1-1.3 times (2200-2600r / min) to compensate for the decrease in heat dissipation efficiency. At the same time, the fan layout is optimized, using diagonal staggered installation to eliminate airflow interference, increasing the system air volume by 12% and further improving the heat dissipation effect. 3. Miniature Liquid Cooling Structure: Utilizing a miniature heat pipe (model: PHP-6×100, thermal conductivity ≥400W / m·K) + coolant circulation system, the heat pipe is mounted on the surface of the IGBT and SiC modules. The coolant circulation system includes a water pump (model: DC40-12, rated flow rate 5L / min), a coolant tank (5L capacity), and piping (material: stainless steel, diameter 10mm). The coolant is an ethylene glycol aqueous solution (concentration 30%-40%), which does not freeze at -30℃, making it suitable for high-altitude, low-pressure environments. The coolant flow rate can be adaptively adjusted for high-altitude, low-pressure environments. Increased flow rate by 15%-20% (5.75-6 L / min) to improve heat dissipation efficiency; optimized liquid cooling channel topology using a tree-like branched structure (1 inlet, 4 outlets) to replace the single path, achieving a flow velocity uniformity of 95%, avoiding local high-pressure areas, and reducing system impedance; simultaneously, the microchannel cold plate is manufactured using etching + diffusion welding process, with a surface roughness Ra≤0.8μm, reducing frictional resistance and further improving liquid cooling efficiency; in addition, paraffin-based phase change material (phase change enthalpy ≥180J / g) can be filled inside the liquid cooling plate to smooth transient heat loads, reduce pump power fluctuations, and improve system operational stability. 4. Sealed heat dissipation structure: When the dust concentration is detected to be too high (>500μg / m³), 3 In case of strong winds (wind speed > 15m / s), the air-cooled duct will automatically close and a closed-loop heat dissipation mode will be activated. Heat dissipation will be achieved through the built-in heat exchanger to avoid damage to the heat dissipation system caused by sand and strong winds. The heat exchanger uses high-efficiency heat exchange materials to ensure heat dissipation efficiency in a closed state, while reducing dust entry and lowering the failure rate.

[0050] The control terminal of the heat dissipation execution module adopts a PLC controller (model: S7-1200), which receives heat dissipation control commands from the multi-parameter linkage prediction module and self-repair commands from the fault prediction and self-repair module. It controls the switching of heat dissipation mode, adjustment of fan speed, and adjustment of coolant flow rate, with a response time of ≤1s, ensuring the timeliness of heat dissipation operation.

[0051] III. Overall System Workflow: The adaptive heat dissipation control system for photovoltaic grid-connected equipment in this embodiment follows a collaborative closed loop of "data acquisition → predictive control → threshold calibration → fault protection → iterative optimization," requiring no manual intervention throughout the entire process. The specific steps are as follows: 1. Step 1: Data Acquisition Startup. After the system is powered on, the multi-parameter linkage prediction module, the dynamic heat dissipation threshold self-calibration module, and the fault prediction and self-repair module start simultaneously. Each parameter acquisition unit begins to collect various parameters (equipment operating parameters, environmental parameters, heat dissipation execution parameters, device aging parameters, and fault parameters). After data preprocessing, the data is transmitted to the core unit of each module. Among them, the voltage and current data collected by the PT, CT, and Hall sensors are processed and synchronously transmitted to the multi-parameter linkage prediction module to provide basic data support for the prediction algorithm and ensure the accuracy of the prediction results. 2. Step 2: Multi-parameter linkage prediction. The prediction algorithm unit of the multi-parameter linkage prediction module, based on the collected multi-dimensional parameters and historical operating data, predicts the device heating trend (such as the midday high temperature peak and the low temperature antifreeze requirement at night) and environmental change trend (such as strong wind and sandstorm weather) in the next 48 hours, and initially outputs heat dissipation control instructions (heat dissipation mode, fan speed, coolant flow rate). 3. Step 3: Dynamic Threshold Calibration. The aging assessment unit of the dynamic heat dissipation threshold self-calibration module evaluates the aging degree of the core components based on the collected device aging parameters. The threshold calibration unit dynamically calibrates the heat dissipation threshold according to the aging degree and synchronizes the calibrated heat dissipation threshold to the multi-parameter linkage prediction module. If the fault prediction and self-repair module detects a fault in the heat dissipation system at this time, it synchronously transmits the fault status to the threshold calibration unit, and the threshold calibration unit temporarily lowers the heat dissipation threshold by 3-5℃. 4. Step 4: Predictive Command Adjustment. The multi-parameter linkage predictive module receives the calibrated heat dissipation threshold and fault status, and adjusts the initial output heat dissipation control command to ensure that the control command not only meets the heat dissipation requirements of the device but also adapts to the aging state of the device, while avoiding fault risks. For example, when the core device is moderately aged and the environment is high-altitude, high-temperature, and low-pressure, the mode is adjusted to "air cooling + liquid cooling coordinated heat dissipation", the fan speed is increased to 2300 r / min, the coolant flow rate is increased to 5.8 L / min, and the heat dissipation threshold is calibrated to 58℃. 5. Step 5: Heat Dissipation Execution and Fault Protection. The heat dissipation execution module receives the adjusted heat dissipation control instructions and executes the corresponding heat dissipation operations. Simultaneously, the fault prediction and self-repair module monitors the operating parameters of the heat dissipation system in real time, predicts fault risks, and immediately issues an early warning signal if a fault is detected, and initiates a graded self-repair mechanism based on the severity of the fault. During fault repair, the multi-parameter linkage prediction module dynamically adjusts the heat dissipation control instructions according to the fault repair progress to ensure that the heat dissipation capacity matches the device's heat generation requirements. For example, if the fan is clogged with dust, a reverse rotation dust removal program is initiated, and the multi-parameter linkage prediction module temporarily increases the speed of the backup fan to 2000 r / min to ensure stable device temperature. 6. Step 6: Iterative Optimization. After fault repair is completed, the fault prediction and self-repair module synchronizes the repair results to the other two modules; the multi-parameter linkage prediction module adjusts the prediction control instructions based on the restored heat dissipation capacity of the heat dissipation components, restoring normal optimization logic; the dynamic heat dissipation threshold self-calibration module reassesses the aging degree of the device based on the temperature change and aging rate of the device during the fault, and adjusts the heat dissipation threshold; at the same time, the multi-parameter prediction module synchronously feeds back the operating data during the fault and the threshold self-calibration module feeds back the device aging assessment data to the fault database of the fault prediction module, updating fault characteristics and repair strategies, and improving the accuracy of subsequent fault prediction and self-repair; the three form a collaborative closed loop to continuously optimize the heat dissipation control effect; 7. Step 7: Cluster Collaboration (Optional). When the system is applied to a 100 MW-level plateau photovoltaic power station cluster, the three main modules of multiple devices achieve data interconnection through industrial Ethernet. The multi-parameter linkage prediction module shares the heat generation and environmental data of each device in the cluster to predict the overall heat generation trend of the cluster; the fault prediction and self-repair module shares the heat dissipation fault information of each device in the cluster to achieve collaborative fault early warning; the threshold self-calibration module shares the device aging data in the cluster to achieve cluster-level device aging adaptation and ensure that all devices in the cluster are in the optimal heat dissipation state; at the same time, cluster collaboration optimizes the allocation of heat dissipation resources, avoids local device overheating, improves the overall heat dissipation efficiency of the cluster, and reduces operation and maintenance costs.

[0052] IV. Specific Implementation Example: A High-Altitude Photovoltaic Power Station Scenario: The following uses a high-altitude photovoltaic power station (a 100-megawatt-class photovoltaic power station) at an altitude of 3500 meters as an example to describe in detail the specific application process and effects of this system: 4.1 Scene Parameters: 1. Environmental parameters: Altitude 3500 meters, atmospheric pressure 650 hPa, diurnal temperature range 25℃ (nighttime low -20℃, daytime high 5℃), UV intensity 700 μW / cm² 2 The average wind speed is 5 m / s, with occasional strong winds (wind speed > 15 m / s) and sandstorms. 2. Equipment parameters: A 1000kW high-altitude inverter (SG-1000KTL-H) is selected. The core components are IGBT modules (FF450R12ME4) and SiC modules (C2M0080120D). The load rate is maintained at 80%-90% for a long time. The core components have been running for 12,000 hours and are in a moderate aging state (aging rate 30%). 3. System Configuration: The adaptive heat dissipation control system of the present invention adopts a composite structure of "natural convection + air cooling + micro liquid cooling" for the heat dissipation execution module. The three modules achieve bidirectional communication through industrial Ethernet with a data transmission rate of 100Mbps.

[0053] 4.2 Application Process: 1. Data Acquisition: The parameters acquired by the multi-parameter linkage prediction module are: IGBT module junction temperature 55℃, SiC module junction temperature 52℃, copper bus temperature 48℃, load rate 85%, power loss 40kW; environmental parameters: altitude 3500m, atmospheric pressure 650hPa, ambient temperature 2℃, diurnal temperature variation rate 10℃ / h, ultraviolet radiation intensity 700μW / cm². 2 1. Wind speed: 5 m / s; 2. Heat dissipation parameters: fan speed 2000 r / min, coolant flow rate 5 L / min, pipe pressure 0.5 MPa; 3. Aging parameters collected by the dynamic heat dissipation threshold self-calibration module: running time 12000 h, junction temperature fluctuation 8 ℃ / h, conduction loss 12 kW, insulation resistance 400 MΩ; 4. Fault parameters collected by the fault prediction and self-repair module: fan speed deviation 5%, radiator surface temperature difference 3 ℃, coolant flow rate deviation 3%, antifreeze concentration 35%, no obvious fault; 2. Prediction and Calibration: Based on the above parameters, the multi-parameter linkage prediction module predicts that the daytime ambient temperature will rise to 5℃, the load rate will remain at 85%, and the device junction temperature will rise to 62℃ within the next 24 hours; the dynamic heat dissipation threshold self-calibration module evaluates the core device as moderately aged (aging rate 30%), calibrates the heat dissipation threshold from 65℃ to 58℃, and synchronizes it to the multi-parameter linkage prediction module. 3. Control and Execution: The multi-parameter linkage prediction module adjusts the heat dissipation control command and controls the heat dissipation execution module to switch to the "air cooling + liquid cooling coordinated heat dissipation" mode, increasing the fan speed to 2300r / min (an increase of 15% to compensate for the heat dissipation attenuation under low air pressure) and the coolant flow rate to 5.8L / min (an increase of 16%). 4. Fault Protection: During operation, if the fault prediction and self-repair module detects that the fan speed deviation has increased to 12% (out of normal range), it will predict that the fan is slightly blocked by sand and dust (minor fault), and immediately issue a level one warning and start the fan reverse rotation dust removal program (speed 1500r / min, lasting 30s). After the repair is completed, the fan speed deviation will return to 4%, and the heat dissipation module will resume normal operation. 5. Cyclic Optimization: After the fault is repaired, the fault prediction and self-repair module synchronizes the repair results to the other two major modules. The multi-parameter linkage prediction module adjusts the prediction command, and the fan speed is restored to 2300r / min. The dynamic heat dissipation threshold self-calibration module re-evaluates the aging degree of the device, which is still moderate aging, and maintains the heat dissipation threshold of 58℃. At the same time, the fault data is updated to the fault feature library to optimize the accuracy of subsequent fault prediction.

[0054] 4.3 Beneficial Effects: This system has the following advantages in this plateau photovoltaic power station: 1. Improved equipment operational stability: The inverter downtime rate due to heat dissipation issues has been reduced from 8% to below 0.6%, and the annual stable operation rate of the equipment has been increased to 98.5%, solving the core contradiction of low-temperature antifreeze and high-temperature heat dissipation in high-altitude environments; in a nighttime low-temperature environment of -20℃, the liquid cooling system did not freeze, and the temperature of core components remained stable below 50℃, avoiding performance degradation of components due to low-temperature cold start and high-temperature operation cycles; 2. Reduced heat dissipation energy consumption: The energy consumption of the heat dissipation system has been reduced from 8kW / unit to 4.16kW / unit, a reduction of 48%. Each inverter can save approximately 36,000 kWh of electricity per year, improving the power generation revenue of the photovoltaic power station. Through impedance optimization of the air-cooling and liquid-cooling systems, energy consumption has been further reduced and heat dissipation efficiency has been improved. 3. Extended device lifespan: The aging rate of core components (IGBT, SiC module) is slowed down, and the expected lifespan is extended from the original 10 years to 16.5 years, an increase of more than 65%, reducing the high equipment replacement and transportation costs in high-altitude areas; through dynamic threshold calibration, the device is prevented from aging faster due to overheating, while reducing energy waste caused by excessive heat dissipation. 4. Reduced operation and maintenance costs: The self-healing rate of minor faults in the heat dissipation system reaches 100%, and the self-healing rate of moderate faults reaches 90%. The inspection cycle of operation and maintenance personnel has been extended from the original 1 month to 3 months, reducing operation and maintenance costs by more than 75% and solving the pain point of inconvenient operation and maintenance in high-altitude areas. The fault prediction and self-repair mechanism effectively prevents minor faults from evolving into major accidents, reduces power generation losses caused by equipment downtime, and reduces downtime losses annually. 5. Excellent adaptability to high altitudes: The system can adapt to the environmental characteristics of low air pressure, large temperature difference and strong radiation in high-altitude areas without manual parameter adjustment. It can operate stably in high-altitude areas with an altitude of 3,000-5,000 meters, adapting to the needs of photovoltaic power stations with different altitude gradients. At the same time, the closed heat dissipation mode effectively resists the damage of sand and strong wind to the heat dissipation system, and the corrosion rate and failure rate of heat dissipation components are greatly reduced.

[0055] V. Compared with existing technologies, the system of this invention has significant improvements in equipment operation stability, heat dissipation and energy saving, device lifespan, operation and maintenance costs, and fault handling capabilities, especially in extreme high-altitude environments where its advantages are even more pronounced. Furthermore, the LSTM prediction algorithm used in this invention improves prediction accuracy by more than 40% compared to existing single-parameter prediction algorithms and provides a longer warning lead time, further enhancing system reliability. The composite heat dissipation structure and impedance optimization design effectively compensate for the heat dissipation efficiency reduction caused by low air pressure at high altitudes, ensuring the stability of the heat dissipation effect.

[0056] VI. It should be noted here that: to further improve the performance of this system, the following optional optimization schemes can be adopted, all of which fall within the protection scope of this invention: 1. Add phase change energy storage materials (such as paraffin-based phase change materials) to the heat dissipation execution module to smooth out instantaneous heat peaks and further improve heat dissipation stability, especially suitable for scenarios with frequent load fluctuations; the phase change material is filled in the liquid cooling plate with a phase change enthalpy ≥180J / g, which can effectively smooth out transient heat loads, reduce pump power fluctuations, and improve system operation stability; 2. An AI adaptive learning algorithm is introduced into the multi-parameter linkage prediction module. Based on long-term equipment operation data, the prediction model parameters are automatically optimized to further improve the prediction accuracy (up to 98%). It can be combined with the CNN-LSTM hybrid model to further improve the prediction accuracy of parameters such as temperature and load, so that MAE≤0.2℃, and adapt to more complex plateau environment changes. 3. The fault prediction and self-repair module adds remote control functionality, allowing maintenance personnel to remotely control the self-repair program via mobile phone, further reducing maintenance difficulty; at the same time, it can combine mainstream fault codes of photovoltaic inverters to optimize fault diagnosis and repair strategies, improving the pertinence and efficiency of fault handling. 4. Using nanofluids (such as Al2O3 nanofluids) as coolants can improve the heat dissipation efficiency of liquid cooling systems and further reduce heat dissipation energy consumption. Nanofluids can reduce the cell temperature of photovoltaic / photothermal systems to 43°C, while improving the average daily thermal efficiency and electrical efficiency by 15% and 9%, respectively, further optimizing the heat dissipation and power generation performance of the system. 5. Introduce edge computing technology into cluster collaboration to enable local processing of data within the cluster, reduce data transmission latency, improve the response speed of cluster collaborative control, and adapt to the operational needs of gigawatt-level plateau photovoltaic power stations.

[0057] In summary, the adaptive heat dissipation control system for photovoltaic grid-connected equipment of the present invention achieves full-process adaptive heat dissipation control of photovoltaic grid-connected equipment through the collaborative closed loop of multi-parameter linkage prediction module, fault prediction and self-repair module, and dynamic heat dissipation threshold self-calibration module. It effectively solves the core technical defects of photovoltaic grid-connected equipment in extreme environments such as high altitude, including heat dissipation efficiency degradation, frequent failures, rapid device aging, and inconvenient operation and maintenance.

[0058] This detailed implementation describes the hardware selection, software algorithms, parameter settings, and workflow of each module. Combined with the specific application scenario of a high-altitude photovoltaic power station, it fully presents the system's implementation process. The modules do not operate in isolation but form an organic whole of "prediction-calibration-fault protection-control optimization." The multi-parameter linkage prediction module provides accurate trend support, the dynamic heat dissipation threshold self-calibration module achieves adaptation throughout the device's lifecycle, and the fault prediction and self-repair module ensures stable system deployment. These three modules promote and complement each other, breaking the limitations of isolated operation of heat dissipation control, fault protection, and device aging adaptation in existing technologies, resulting in a synergistic and efficient technical effect.

[0059] Compared to existing photovoltaic grid-connected equipment heat dissipation technologies, the core innovation of this system lies in its targeted solution to the comprehensive pain points of extreme environments such as high altitudes. Through plateau-specific parameter acquisition, algorithm optimization, heat dissipation structure design, and fault feature database construction, it achieves full-dimensional adaptiveness in terms of environment, device, and fault compatibility. It can cope with complex environmental changes such as low air pressure, large temperature differences, strong radiation, and sandstorms without manual intervention. This system can reduce the downtime rate of photovoltaic grid-connected equipment by more than 92%, reduce heat dissipation energy consumption by more than 48%, extend the lifespan of core components by more than 65%, and reduce operation and maintenance costs by more than 75%. It significantly improves the operational stability, economy, and reliability of photovoltaic grid-connected equipment, and is particularly suitable for photovoltaic power station applications in complex scenarios such as plateaus, deserts, and coastal areas.

[0060] The technical solution of this invention is not only applicable to 1000kW-class high-altitude inverters, but can also be adapted to different power levels of photovoltaic grid-connected equipment (such as 500kW and 2000kW inverters) by adjusting hardware selection, parameter settings, and algorithm models according to actual needs. Furthermore, it can be expanded through cluster collaboration to meet the operational requirements of large-scale photovoltaic power plant clusters of hundreds of megawatts and above, demonstrating broad applicability and scalability. In addition, the optional optimization schemes disclosed in this specific embodiment can further improve system performance, providing a clear direction for subsequent technology upgrades and further expanding the scope of protection and application scenarios of this invention.

[0061] The adaptive heat dissipation control system for photovoltaic grid-connected equipment of the present invention effectively overcomes the core defects of existing technologies through reasonable module design, collaborative logical architecture, and precise scenario adaptation. It provides an intelligent, collaborative, and scenario-based heat dissipation solution for photovoltaic grid-connected equipment, which not only has outstanding substantive features and significant progress, but also has strong engineering practicality and industry promotion value. It can provide strong technical support for the efficient development of photovoltaic clean energy, especially for the construction of photovoltaic power plants in extreme environments such as plateaus, and promote the development of photovoltaic grid-connected equipment heat dissipation technology towards intelligence, energy saving, and long-term effectiveness.

Claims

1. A photovoltaic grid-connected equipment adaptive heat dissipation control system, comprising a photovoltaic grid-connected equipment body and a heat dissipation execution module, characterized in that: It also includes a multi-parameter linkage prediction module, a fault prediction and self-repair module, and a dynamic heat dissipation threshold self-calibration module. The three modules communicate bidirectionally and coordinately control the heat dissipation execution module. The multi-parameter linkage prediction module is used to collect the operating parameters of the photovoltaic grid-connected equipment, environmental parameters, and operating parameters of the heat dissipation execution module. Based on the preset prediction algorithm, it predicts the heating trend of the photovoltaic grid-connected equipment and the environmental change trend, and outputs the prediction control command. The dynamic heat dissipation threshold self-calibration module is used to collect aging parameters of the core components of the photovoltaic grid-connected equipment, assess the aging degree of the core components, dynamically calibrate the heat dissipation threshold according to the aging degree, and synchronize the calibrated heat dissipation threshold to the multi-parameter linkage prediction module. The fault prediction and self-repair module is used to collect the operating parameters of key components of the heat dissipation execution module. Based on the preset fault feature library and fault diagnosis algorithm, it predicts the fault risk of the heat dissipation system and issues an early warning. For self-repairable faults, it activates the self-repair mechanism. For non-self-repairable faults, it activates the backup heat dissipation component. At the same time, it synchronizes the fault status to the multi-parameter linkage prediction module and the dynamic heat dissipation threshold self-calibration module. The multi-parameter linkage prediction module receives the calibrated heat dissipation threshold and fault status, adjusts the prediction and control instructions, and controls the heat dissipation execution module to perform adaptive heat dissipation operations.

2. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The multi-parameter linkage prediction module collects operating parameters including the junction temperature of the IGBT module, the junction temperature of the SiC module, the temperature of the copper bus, the load rate, and the power loss of the photovoltaic grid-connected equipment. The operating parameters of the heat dissipation execution module include fan speed, coolant flow rate, and pipeline pressure. The environmental parameters include altitude, atmospheric pressure, ambient temperature, diurnal temperature variation rate, ultraviolet intensity, and wind speed.

3. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The preset prediction algorithm of the multi-parameter linkage prediction module is the LSTM neural network algorithm. The LSTM neural network algorithm is trained and optimized based on historical operating data and can output the heating trend and environmental change trend in the next 24-48 hours. The prediction and control instructions include heat dissipation mode switching instructions and heat dissipation power adjustment instructions.

4. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The core device aging parameters collected by the dynamic heat dissipation threshold self-calibration module include device operating time, junction temperature fluctuation range, conduction loss, and insulation resistance. The aging degree of the core device is divided into mild aging, moderate aging, and severe aging. The corresponding heat dissipation threshold calibration ranges for different aging degrees are 3-5℃, 5-8℃, and 8-12℃, and the calibration cycle can be adaptively adjusted according to the severity of the environment.

5. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The fault prediction and self-repair module has a preset fault feature library containing fault types specific to high-altitude scenarios, including fan bearing wear, radiator coating peeling, liquid cooling system icing, temperature sensor low-temperature drift, and fan dust blockage. The fault diagnosis algorithm is an LSTM neural network algorithm trained based on historical fault data, which can predict fault risks 72 hours in advance.

6. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The self-repair mechanism of the fault prediction and self-repair module is divided into graded repair. For minor faults, reverse flushing, dust removal, and zero-point calibration procedures are initiated. For moderate faults, backup component switching is initiated. For severe faults, emergency heat dissipation mode is initiated and fault location information is pushed.

7. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The heat dissipation execution module includes a three-in-one composite heat dissipation structure of "natural convection + air cooling + micro liquid cooling". It can adaptively switch the heat dissipation mode according to the control instructions of the multi-parameter linkage prediction module. For high-altitude low-pressure environments, the air cooling fan speed can be adaptively increased by 1.1-1.3 times, and the coolant flow rate of the liquid cooling system can be adaptively increased by 15%-20% to compensate for the heat dissipation efficiency reduction caused by low air pressure.

8. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The multi-parameter linkage prediction module, fault prediction and self-repair module, and dynamic heat dissipation threshold self-calibration module also include a cluster-level collaborative unit, which is used for data communication between multiple photovoltaic grid-connected devices, dynamic allocation of heat dissipation resources, collaborative fault early warning, and unified threshold calibration, adapting to the operation requirements of 100-megawatt and above plateau photovoltaic power station clusters.

9. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The dynamic heat dissipation threshold self-calibration module can also receive the fault status of the fault prediction and self-repair module. When the heat dissipation system fails, the heat dissipation threshold is temporarily reduced by 3-5℃.

10. The adaptive heat dissipation control system for photovoltaic grid-connected equipment as described in claim 1, characterized in that: The multi-parameter linkage prediction module can dynamically adjust the heat dissipation control command according to the fault prediction and the fault repair progress of the self-repair module, so as to ensure that the heat dissipation capacity of the heat dissipation execution module matches the heat dissipation requirements of the equipment during the fault repair period, and avoid insufficient or excessive heat dissipation during the fault repair period.

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