Hydrogen production power supply optimization system based on IGBT (Insulated Gate Bipolar Translator) module

Abstract

The invention discloses a hydrogen production power supply optimization system based on an IGBT (Insulated Gate Bipolar Translator) module, and relates to the technical field of power supply management in water electrolysis hydrogen production. The system monitors output power characteristics of solar energy and wind energy in real time through an energy source monitoring module, and the system can accurately obtain environmental state data; and the output power of the photovoltaic array and the wind driven generator is dynamically adjusted through the power adjusting module to ensure that the output power is matched with the actual power requirement of the electrolytic bath. The automatic adjusting mechanism can effectively cope with the fluctuation of renewable energy sources caused by factors such as weather and seasons, so that the running stability of the electrolytic cell is guaranteed. The power supply parameter optimization module automatically adjusts power supply parameters based on comparison between the power supply adaptation coefficient Y and a preset adaptation threshold value X so as to avoid overload or low-efficiency work caused by power supply inadaptation. The intelligent adjustment not only ensures the stable operation of the electrolytic cell, but also avoids the safety problems of power supply damage, overheating and the like, and prolongs the service life of a power supply system.

Description

Technical Field

[0001] The present invention relates to the technical field of power management in hydrogen production by electrolyzing water, and specifically to a hydrogen production power optimization system based on IGBT modules. Background Art

[0002] With the wide application of renewable energy, hydrogen production technology has gradually become an important energy conversion method. In modern hydrogen production processes, hydrogen production by electrolyzing water is the most mature and environmentally friendly method. Water is decomposed into hydrogen and oxygen by electrolyzing water, and an electrolytic cell is driven by electric energy to carry out the electrolysis reaction of water. However, when traditional power modules are combined with renewable energy, there are some problems, such as large energy fluctuations, poor power adaptability, inaccurate power regulation, etc. These problems seriously affect the stability and efficiency of the hydrogen production system by electrolyzing water.

[0003] Among them, solar energy and wind energy are common renewable energies, which are intermittent and fluctuating. This kind of fluctuation brings great challenges to the power supply for hydrogen production by electrolyzing water. The output power of solar energy and wind energy is greatly affected by environmental factors. With the changes of external conditions such as light intensity and wind speed, the actual power demand of the electrolytic cell also changes accordingly. If the power supply cannot adjust the power output in time, it may lead to insufficient power supply or overload of the electrolytic cell, affecting the hydrogen production efficiency or even damaging the equipment.

[0004] In addition, the stability, adaptability, and efficiency of the power module also directly affect the energy conversion efficiency in the hydrogen production process. In a power supply system using IGBT (Insulated Gate Bipolar Transistor) modules, traditional power modules usually cannot automatically adjust their output parameters according to the changes of renewable energy, especially there are limitations in the adjustment of key parameters such as power output, switching frequency, voltage, and current. Therefore, how to reasonably adjust the power supply parameters according to the actual power demand of the electrolytic cell to ensure the stability and reliability of the power supply has become an urgent problem to be solved in the hydrogen production process. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a hydrogen production power optimization system based on IGBT modules to solve the problems mentioned in the background art.

[0006] To achieve the above purposes, the present invention is realized through the following technical solutions: A hydrogen production power optimization system based on IGBT modules includes an energy source monitoring module, a power regulation module, an electrolytic cell operation data acquisition module, an environmental adaptation analysis module, and a power supply parameter optimization module;

[0007] The energy source monitoring module is used to monitor the environmental state data of renewable energy in real time, including the power output characteristics of solar energy and wind energy, generate a first data set, and integrate the electrical energy outputs of solar energy and wind energy into the total power input;

[0008] The power regulation module is used to dynamically regulate the output power of the solar photovoltaic array and the wind turbine according to the first data set, so that it matches the actual power demand of the electrolyzer;

[0009] The electrolyzer operation data acquisition module is used to monitor and record the electrolyzer operation data in real time and generate a second data set;

[0010] The environmental adaptability analysis module is used to extract the features of the first data set and the second data set to calculate and obtain the solar stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, and combine machine learning algorithms to calculate the power supply adaptability coefficient Y;

[0011] The power supply parameter optimization module is used to dynamically adjust the switching frequency, duty cycle, voltage range, and current limit parameters of the IGBT power supply based on the power supply adaptability coefficient Y and a preset adaptability threshold X to optimize the stability of the output electric energy.

[0012] Preferably, the first data set includes: the light radiation intensity C pv , the total light-receiving area A of the current photovoltaic array sg , the efficiency η of the photovoltaic array pv , the wind speed v w , the air density md, the efficiency η of the wind turbine wind , the swept area A of the wind turbine blades sl , the input voltage V of the electrolyzer electrolyzer , the input current I of the electrolyzer electrolyzer , the actual power demand P of the electrolyzer electrolyzer , the solar output power P solar and the wind energy output power P wind ;

[0013] The total power input includes obtaining the total power input P solar by summing the solar output power P wind and the wind energy output power P total .

[0014] Preferably, the power regulation module is used to dynamically adjust the power output of the photovoltaic array and the wind turbine according to the first data set and the actual power demand of the electrolyzer, so that it matches the actual power demand of the electrolyzer. The specific steps are as follows:

[0015] S11. Collect the real-time operation status data of the electrolyzer and calculate the actual power demand P of the electrolyzer electrolyzer :

[0016] P electrolyzer = V electrolyzer × I electrolyzer ;

[0017] Among them, V electrolyzer is the input voltage of the electrolytic cell, and I electrolyzer is the input current of the electrolytic cell;

[0018] S12. According to the actual power demand P electrolyzer of the electrolytic cell, extract the total power input P total , and calculate the power gap ΔP qk :

[0019] ΔP qk = P electrolyzer - P total ;

[0020] Among them, if ΔP qk > 0, it means that the power is insufficient and the power output needs to be increased;

[0021] If ΔP qk = 0, it means that the power is qualified and there is no need to adjust the power output of the photovoltaic array or the wind turbine, and continuous monitoring is carried out;

[0022] If ΔP qk < 0, it means that the power is excessive and the power output needs to be reduced;

[0023] S13. According to the power gap ΔP qk , select one of the ways to adjust the power output of the photovoltaic array or the wind turbine for adjustment, specifically:

[0024] S131. Adjust the operating point of the MPPT control algorithm to make the photovoltaic array output the maximum power under the current radiation intensity, and calculate the current maximum output reference value through the following formula

[0025]

[0026] Among them, η pv represents the efficiency of the photovoltaic array, A sg represents the total light-receiving area of the current photovoltaic array, C pv represents the light radiation intensity, and k mppt represents the dynamically adjusted MPPT gain coefficient;

[0027] S132. If ΔP qk > 0, it means that the power is insufficient. Based on the maximum output reference value and the power gap ΔP qk , calculate the area A additional of the photovoltaic array that needs to be started:

[0028]

[0029] Among them, the area A of the photovoltaic array to be started corresponding to the startup is additional of the photovoltaic array;

[0030] S133. If ΔP qk < 0, it indicates that the power is excessive. Based on the maximum output reference value and the power gap ΔP qk , calculate the area A of the photovoltaic array to be shut down reduce :

[0031]

[0032] Among them, the area A of the photovoltaic array to be shut down corresponding to the shutdown is reduce of the photovoltaic array.

[0033] Preferably, S13 further includes:

[0034] S134. According to the wind speed V w and the blade swept area A of the wind turbine sl , calculate the maximum power output reference value of the wind turbine at the current wind speed through the following formula

[0035]

[0036] Among them, md represents the air density, and η wind is the efficiency of the wind turbine, indicating the efficiency of converting wind energy into electrical energy, set in the range of 0.4 - 0.5, and v w is the wind speed;

[0037] S135. If ΔP qk > 0, it indicates that the power is insufficient. Based on the maximum power output reference value of the wind turbine at the current wind speed and the power gap ΔP qk , calculate the number N of wind turbines to be started wind_on :

[0038]

[0039] Among them, start the wind turbines corresponding to the number N of wind turbines to be started wind_on ;

[0040] S133. If ΔP qk < 0, it indicates that the power is excessive. Based on the maximum power output reference value of the wind turbine at the current wind speed and the power gap ΔP qk , calculate the number N of wind turbines to be shut down wind_off :

[0041]

[0042] Among them, close the number N of wind-solar generators that need to be closed wind_off of the wind turbines.

[0043] Preferably, the second data group includes: the electrode area A electrode , the current density Dmd, the electrolyte temperature T electrolyte , the electrolyte concentration D jnd and the hydrogen production H yield .

[0044] Preferably, the environmental adaptability analysis module includes a first green energy calculation unit, a second green energy calculation unit, and an electrolyzer analysis unit;

[0045] The first green energy calculation unit is used to calculate and obtain the solar energy stability factor S according to the first data group through the following formula:

[0046]

[0047] Among them, P solar,i represents the solar energy output power at the i-th moment, represents the average value of the solar energy output power, C pv,i represents the light radiation intensity at the i-th moment, with the unit of W / m 2 , normalized to [0,1], N represents the total number of sampling moments;

[0048] The second green energy calculation unit is used to calculate and obtain the wind energy stability factor Fw according to the first data group through the following formula:

[0049]

[0050] Among them, P wind,i represents the wind energy output power at the i-th moment, with the unit of kW, represents the average value of the wind energy output power, v w,i represents the wind speed at the i-th moment, with the unit of m / s, v cut_in represents the starting wind speed of the wind turbine, v rated represents the rated wind speed of the wind turbine.

[0051] Preferably, the electrolyzer analysis unit is used to analyze and obtain the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq according to the first data group and the second data group:

[0052] The electrolyzer power factor Pg, which describes the matching degree between the current power demand and the supply power of the electrolyzer, extracts the electrolyzer input voltage V electrolyzer and the electrolyzer input current I electrolyzer in the first data group, and combines the electrode area A in the second arrayelectrode And the current density Dmd, after dimensionless treatment, the electrolytic cell power factor Pg is calculated through the following formula:

[0053]

[0054] Extract the electrode area A in the second data set electrode And the hydrogen production H yield , after dimensionless treatment, the hydrogen production factor Hy is calculated through the following formula:

[0055]

[0056] Among them, n represents the number of electrons transferred by hydrogen, set to 2, F represents the Faraday constant,

[0057] Extract the electrolyte temperature T in the second data set electrolyte And the electrolyte concentration D jnd , after dimensionless treatment, the electrolyte equilibrium factor Leq is calculated through the following formula:

[0058]

[0059] Among them, ΔT represents the allowable fluctuation range value of the electrolyte temperature, T opt Represents the optimal operating temperature of the electrolyte, D jnd_opt Represents the optimal operating concentration of the electrolyte; ΔD jnd Represents the allowable fluctuation range of the electrolyte concentration, set to ±0.2 mol / L.

[0060] Preferably, the environmental adaptability analysis module further includes an IGBT power acquisition unit and a prediction unit;

[0061] The IGBT power acquisition unit is used to collect the operating parameters of the IGBT power module in real time. The operating parameters of the IGBT power module include the power input voltage IGBT v_in , the power output current IGBT I_out , the switching frequency IGBT F_s , the conduction voltage IGBT v_on And the IGBT operating temperature IGBT T ;

[0062] The prediction unit is used to construct an initial convolutional neural network model using a convolutional neural network, and train and test the initial convolutional neural network model with a first data set and a second data set, and use the trained initial convolutional neural network model as a power adaptation prediction model. At the same time, the intermediate layer output of the IGBT power device operating state is used as a feature vector to identify feature information, and the obtained IGBT power module operating parameters are used to train and test the power adaptation prediction model, and the trained power adaptation prediction model is used as data operation prediction;

[0063] After dimensionless processing of the solar stability factor S, wind energy stability factor Fw, electrolyzer power factor Pg, hydrogen production factor Hy, and electrolyte balance factor Leq, combined with the IGBT power module operating parameters, the power adaptation coefficient Y is calculated and obtained through the following related formulas:

[0064]

[0065] where a1, a2, a3, β1, β2, β3, β4, and β5 are weight values, Vin max represents the maximum input voltage of the power supply, I max represents the maximum output current of the power supply, Fs max represents the maximum switching frequency, Von max represents the maximum conduction voltage, T max represents the maximum operating temperature of the power supply.

[0066] Preferably, the power parameter optimization module is used to preset an adaptation threshold X, and compare the power adaptation coefficient Y with the adaptation threshold X to obtain the following evaluation results, including:

[0067] When the power adaptation coefficient Y > adaptation threshold X, it means that the power adaptability is qualified, the load is high, and the first strategy is generated;

[0068] When the power adaptation coefficient Y = adaptation threshold X, it means that the power adaptability is qualified, the current IGBT power parameters are maintained, no intervention and adjustment are required, and continuous monitoring is carried out;

[0069] When the power adaptation coefficient Y < adaptation threshold X, it means that the power adaptability is unqualified, the load is low, and the second strategy is generated.

[0070] Preferably, the first strategy includes: reducing the current switching frequency by 10%, improving the energy conversion efficiency, reducing the power duty cycle by 10% - 20% to increase the current output, increasing the input voltage and output voltage of the current IGBT power supply by 10% - 20%, and increasing the current limit by 5% - 10% to provide excess current output;

[0071] The second strategy includes: reducing the current switching frequency by 10%, reducing power loss, reducing the power duty cycle by 10%-20% to reduce the current output, reducing the input voltage and output voltage of the current IGBT power supply by 10%-20% to reduce resource waste, reducing the current limiting limit by 5%-10%, and avoiding overheating or damage of the IGBT power supply caused by excessive current.

[0072] The present invention provides an optimized system for a hydrogen production power supply based on an IGBT module, having the following beneficial effects:

[0073] (1) For the optimized system for a hydrogen production power supply based on an IGBT module, the energy source monitoring module monitors the power output characteristics of solar energy and wind energy in real time and integrates them into the total power input, ensuring that the power supply system can respond to external environmental changes in a timely manner and avoiding insufficient power supply or overload of the electrolyzer caused by the instability of renewable energy. The power regulation module dynamically adjusts the output power of the solar photovoltaic array and the wind turbine according to environmental data to accurately match the actual power demand of the electrolyzer, improving the efficiency and stability of the electrolytic water hydrogen production process.

[0074] (2) For the optimized system for a hydrogen production power supply based on an IGBT module, the environment adaptation analysis module extracts features from the characteristic data of solar energy and wind energy and the operating data of the electrolyzer, calculates and obtains important parameters such as the stability factor, the electrolyzer power factor, and the hydrogen production factor, and calculates the power supply adaptation coefficient Y in combination with a machine learning algorithm, providing a reliable basis for the optimized adjustment of the power supply. The power supply parameter optimization module automatically adjusts parameters such as the switching frequency, duty cycle, voltage range, and current limit of the IGBT power supply based on the power supply adaptation coefficient Y and in combination with a preset adaptation threshold X to ensure the stable output of the power supply and maximize the energy conversion efficiency of the system.

[0075] (3) The combination of hydrogen production and power generation is a key component in the field of renewable energy. By converting renewable electricity (such as wind power, photovoltaic power generation, etc.) into hydrogen for storage, it not only provides a solution for power load balancing, but also enhances energy flexibility and security, contributing to the green transformation of energy. By dynamically adjusting the power output of photovoltaic arrays or wind turbines, the system can quickly respond to environmental changes, precisely regulate power supply, maximize energy utilization efficiency, and avoid energy waste. In the case of insufficient power, the MPPT control algorithm is used to dynamically adjust the operating point according to the current radiation intensity to ensure that the photovoltaic array outputs the maximum power. By adjusting the area of the photovoltaic array, it is ensured that the system can increase the output of the photovoltaic array according to demand, thereby improving the response speed and regulation flexibility of the system. Based on the wind speed and the blade swept area of the wind turbine, the reference value of the maximum output power of the wind turbine at the current wind speed is calculated. When the power is insufficient, the system dynamically increases the number of wind turbines according to the power gap to meet the actual power demand of the electrolyzer; while when the power is excessive, the system reduces the power output by shutting down some wind turbines to avoid over-generation. Description of the Drawings

[0076] Figure 1 It is a schematic flow diagram of a hydrogen production power supply optimization system based on IGBT modules according to the present invention. Detailed Embodiments

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] Embodiment 1

[0079] Please refer to Figure 1 , the present invention provides a hydrogen production power supply optimization system based on IGBT modules, including an energy source monitoring module, a power regulation module, an electrolyzer operation data acquisition module, an environmental adaptation analysis module, and a power supply parameter optimization module;

[0080] The energy source monitoring module is used to monitor the environmental state data of renewable energy in real time, including the power output characteristics of solar energy and wind energy, generate a first data set, and integrate the electrical energy outputs of solar energy and wind energy into the total power input;

[0081] The power regulation module is used to dynamically adjust the output powers of the solar photovoltaic array and the wind turbine according to the first data set to make them match the actual power demand of the electrolyzer;

[0082] The electrolyzer operation data acquisition module is used to monitor and record the electrolyzer operation data in real time and generate a second data set;

[0083] The environmental adaptation analysis module is used to extract features from the first data set and the second data set to calculate and obtain the solar energy stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, and combine machine learning algorithms to calculate the power supply adaptation coefficient Y;

[0084] The power supply parameter optimization module is used to dynamically adjust the switching frequency, duty cycle, voltage range, and current limit parameters of the IGBT power supply based on the power supply adaptation coefficient Y and a preset adaptation threshold X to optimize the stability of the output electric energy.

[0085] In this embodiment, the energy source monitoring module monitors the power output characteristics of solar energy and wind energy in real time and integrates them into the total power input to ensure that the power supply system can respond to external environmental changes in a timely manner and avoid insufficient power supply or overload of the electrolyzer caused by the instability of renewable energy. The power regulation module dynamically adjusts the output power of the solar photovoltaic array and the wind turbine according to the environmental data to make it accurately match the actual power demand of the electrolyzer, improving the efficiency and stability of the electrolytic water hydrogen production process. The environmental adaptation analysis module extracts features from the solar energy and wind energy characteristic data and the electrolyzer operation data, calculates and obtains important parameters such as stability factors, electrolyzer power factors, and hydrogen production factors, and combines machine learning algorithms to calculate the power supply adaptation coefficient Y, providing a reliable basis for the optimization and adjustment of the power supply. The power supply parameter optimization module automatically adjusts parameters such as the switching frequency, duty cycle, voltage range, and current limit of the IGBT power supply based on the power supply adaptation coefficient Y and a preset adaptation threshold X to ensure the stable output of the power supply and maximize the energy conversion efficiency of the system.

[0086] Embodiment 2

[0087] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the first data set includes: the light radiation intensity C pv , the total light-receiving area A of the current photovoltaic array sg , the efficiency η of the photovoltaic array pv , the wind speed v w , the air density md, the efficiency η of the wind turbine wind , the swept area A of the wind turbine blades sl , the input voltage V of the electrolyzer electrolyzer , the input current I of the electrolyzer electrolyzer , the actual power demand P of the electrolyzer electrolyzer , the solar power output P solar and the wind power output P wind ;

[0088] The total power input includes obtaining the total power input \(P\) by summing the solar power output \(P\) solar and the wind power output \(P\). wind total

[0089] The light radiation intensity \(C\) pv is directly measured and obtained by a light sensor;

[0090] The total light-receiving area \(A\) of the current photovoltaic array sg is obtained by using a GPS sensor and a light sensor in combination, or directly inputting the parameters of the photovoltaic array configured by the system;

[0091] The efficiency \(\eta\) of the photovoltaic array pv is obtained by monitoring the output power with a current sensor;

[0092] The wind speed \(v\) w is directly measured and obtained by a wind speed sensor;

[0093] The air density \(md\) is measured and obtained by a barometric pressure sensor;

[0094] The efficiency \(\eta\) of the wind turbine wind is obtained by monitoring the output power of the wind turbine with a power sensor;

[0095] The swept area \(A\) of the wind turbine blades sl is obtained during system design and in combination with the blade size;

[0096] The input voltage \(V\) of the electrolyzer electrolyzer , the input current \(I\) of the electrolyzer electrolyzer , the actual power demand \(P\) of the electrolyzer electrolyzer , the solar power output \(P\) solar and the wind power output \(P\) wind are measured and obtained by a power sensor;

[0097] The total power input includes obtaining the total power input \(P\) by summing the solar power output \(P\) solar and the wind power output \(P\). wind total

[0098] Embodiment 3

[0099] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the power adjustment module is used to dynamically adjust the power outputs of the photovoltaic array and the wind turbine according to the first data set and the actual power demand of the electrolyzer, so that they match the actual power demand of the electrolyzer. The specific steps are as follows:

[0100] ​​​​S11. Collect the real-time operating status data of the electrolyzer and calculate the actual power demand P of the electrolyzer electrolyzer :

[0101] P electrolyzer =V electrolyzer ×I electrolyzer ;

[0102] Among them, V electrolyzer is the input voltage of the electrolyzer, and I electrolyzer is the input current of the electrolyzer;

[0103] S12. According to the actual power demand P of the electrolyzer electrolyzer , extract the total power input P total and calculate the power gap ΔP qk :

[0104] ΔP qk =P electrolyzer -P total ;

[0105] Among them, if ΔP qk >0, it means that the power is insufficient and the power output needs to be increased;

[0106] If ΔP qk =0, it means that the power is qualified and there is no need to adjust the power output of the photovoltaic array or wind turbine, and continuous monitoring is carried out;

[0107] If ΔP qk <0, it means that the power is excessive and the power output needs to be reduced;

[0108] S13. According to the power gap ΔP qk , select one of the ways to adjust the power output of the photovoltaic array or wind turbine for adjustment, specifically:

[0109] S131. Adjust the operating point of the MPPT control algorithm to make the photovoltaic array output the maximum power under the current radiation intensity, and calculate the current maximum output reference value through the following formula

[0110]

[0111] Among them, η pv represents the efficiency of the photovoltaic array, A sg represents the total light-receiving area of the current photovoltaic array, C pv represents the light radiation intensity, and k mppt represents the dynamically adjusted MPPT gain coefficient;

[0112] S132. If ΔP qk >0, it means that the power is insufficient. Based on the maximum output reference value and power gap ΔP qk to calculate the area A of the photovoltaic array to be started additional :

[0113]

[0114] wherein, turn on the photovoltaic array corresponding to the area A of the photovoltaic array to be started additional of the photovoltaic array;

[0115] S133. If ΔP qk < 0, it means power surplus. Based on the maximum output reference value and power gap ΔP qk to calculate the area A of the photovoltaic array to be turned off reduce :

[0116]

[0117] wherein, turn off the photovoltaic array corresponding to the area A of the photovoltaic array to be turned off reduce of the photovoltaic array.

[0118] S13 also includes:

[0119] S134. According to the wind speed V w and the blade swept area A of the wind turbine sl to calculate the maximum power output reference value of the wind turbine at the current wind speed through the following formula

[0120]

[0121] wherein, md represents the air density, η wind is the efficiency of the wind turbine, indicating the efficiency of converting wind energy into electrical energy, set within the range of 0.4 - 0.5, v w is the wind speed;

[0122] S135. If ΔP qk > 0, it means power shortage. Based on the maximum power output reference value of the wind turbine at the current wind speed and power gap ΔP qk to calculate the number N of wind turbines to be started wind_on :

[0123]

[0124] wherein, turn on the wind turbines corresponding to the number N of wind turbines to be started wind_on of the wind turbines;

[0125] S133. If ΔP qk< 0, indicating power surplus, based on the maximum power output reference value of the wind turbine at the current wind speed and the power gap ΔP qk , calculate the number N of wind turbines that need to be shut down wind_off :

[0126]

[0127] where N is the number of corresponding wind turbines that need to be shut down wind_off of the wind turbines

[0128] Example of a figure:

[0129]

[0130] In this embodiment, by collecting data such as the input voltage and current of the electrolyzer in real time, the actual power demand of the electrolyzer is accurately calculated to ensure that the power supply system can respond to power fluctuations and changes in a timely and precise manner. This refined adjustment ensures that the electrolyzer can obtain a stable power supply under different working conditions, avoiding problems of power shortage or overload and improving the overall stability of the system. The power gap calculation step (S12) can identify situations of power shortage, qualified power, or power surplus in real time and take different adjustment measures according to the results. By dynamically adjusting the power output of the photovoltaic array or wind turbines, the system can quickly respond to environmental changes, precisely adjust the power supply, maximize the energy utilization efficiency, and avoid energy waste. In the case of power shortage, the MPPT control algorithm is used to dynamically adjust the operating point according to the current radiation intensity to ensure that the photovoltaic array outputs the maximum power. By adjusting the area of the photovoltaic array, it is ensured that the system can increase the output of the photovoltaic array according to demand, thereby improving the response speed and adjustment flexibility of the system. Based on the wind speed and the blade swept area of the wind turbine, the maximum output power reference value of the wind turbine at the current wind speed is calculated. When the power is insufficient, the system dynamically increases the number of wind turbines according to the power gap to meet the actual power demand of the electrolyzer; while when the power is in surplus, the system reduces the power output by shutting down some wind turbines to avoid over-generation.

[0131] Example 4

[0132] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the second data set includes: the electrode area A electrode , the current density Dmd, the electrolyte temperature T electrolyte , the electrolyte concentration D jnd and the hydrogen production H yield .

[0133] The electrode area A electrodeObtained by measuring the electrode area with a laser rangefinder;

[0134] The current density Dmd is obtained by a current transformer in combination with the electrode area;

[0135] The temperature T of the electrolyte electrolyte Obtained by measuring with a temperature sensor;

[0136] The concentration D of the electrolyte jnd Obtained by measuring with a conductivity sensor or a concentration sensor;

[0137] The hydrogen production H yield Obtained by measuring with a gas flow sensor or a hydrogen sensor.

[0138] Example 5

[0139] This example is an explanatory note in Example 4. Please refer to Figure 1 , specifically, the environmental adaptability analysis module includes a first green energy calculation unit, a second green energy calculation unit, and an electrolytic cell analysis unit;

[0140] The first green energy calculation unit is used to calculate and obtain the solar energy stability factor S according to the first data set through the following formula:

[0141]

[0142] where P solar,i represents the solar energy output power at the i-th moment, represents the average value of the solar energy output power, C pv,i represents the light radiation intensity at the i-th moment, with the unit of W / m 2 , normalized to [0, 1], and N represents the total number of sampling moments;

[0143] The second green energy calculation unit is used to calculate and obtain the wind energy stability factor Fw according to the first data set through the following formula:

[0144]

[0145] where P wind,i represents the wind energy output power at the i-th moment, with the unit of kW, represents the average value of the wind energy output power, v w,i represents the wind speed at the i-th moment, with the unit of m / s, v cut_in represents the starting wind speed of the wind turbine, v rated represents the rated wind speed of the wind turbine.

[0146] In this embodiment, the first green energy calculation unit can quantify the volatility of solar energy output by calculating the solar energy stability factor S. This factor helps to identify the stability of solar power output in different time periods, thereby optimizing the power regulation strategy. By adjusting the operating point of the MPPT control algorithm, it can ensure that the photovoltaic array always maintains a high power output efficiency under different environmental conditions, thus ensuring stable power supply for the electrolyzer. The second green energy calculation unit calculates the wind energy stability factor Fw, and evaluates the stability of wind energy based on the real-time relationship between wind speed and wind energy output power, as well as the starting and rated wind speeds of the wind turbine. The calculation of this stability factor helps to judge the output fluctuation of the wind turbine, predict the power output of wind power generation in combination with the actual wind speed, and thus adjust the number of wind turbines turned on in a targeted manner to avoid over-reliance on excessive wind energy fluctuations.

[0147] Embodiment 6

[0148] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the electrolyzer analysis unit is used to analyze and obtain the electrolyzer power factor Pg, hydrogen production factor Hy, and electrolyte balance factor Leq according to the first data set and the second data set:

[0149] The electrolyzer power factor Pg describes the matching degree between the current power demand and supply power of the electrolyzer. The electrolyzer input voltage V in the first data set is extracted electrolyzer and the electrolyzer input current I electrolyzer . Combining the electrode area A in the second array electrode and the current density Dmd, after dimensionless processing, the electrolyzer power factor Pg is calculated through the following formula:

[0150]

[0151] The electrode area A in the second data set is extracted electrode and the hydrogen production H yield . After dimensionless processing, the hydrogen production factor Hy is calculated through the following formula:

[0152]

[0153] Among them, n represents the number of electrons transferred by hydrogen, set to 2, and F represents the Faraday constant.

[0154] The electrolyte temperature T in the second data set is extracted electrolyte and the electrolyte concentration D jnd . After dimensionless processing, the electrolyte balance factor Leq is calculated through the following formula:

[0155]

[0156] Among them, ΔT represents the allowable fluctuation range value of the electrolyte temperature, and T opt represents the optimal operating temperature of the electrolyte, and D jnd_opt represents the optimal operating concentration of the electrolyte; ΔD jnd represents the allowable fluctuation range of the electrolyte concentration, which is set to ±0.2 mol / L.

[0157] In this embodiment, the electrolyzer power factor Pg can reflect the power adaptability of the electrolyzer in real time by describing the matching degree between the current power demand of the electrolyzer and the supplied power. By combining the input voltage, current of the electrolyzer with the electrode area and current density data, this factor can provide an accurate power regulation basis for the system after being dimensionless processed. In the case of large fluctuations in renewable energy (such as solar and wind energy), the calculation of the electrolyzer power factor can help regulate the power supply module to provide just the right amount of electricity, avoiding over - supply or under - supply of power, thereby improving the hydrogen production efficiency. The hydrogen production factor Hy reflects the relationship between the hydrogen production of the electrolyzer and the input power and reaction conditions. By dimensionless processing parameters such as electrolyte temperature, current density, and electrode area, and combining the number of electrons transferred by hydrogen and the Faraday constant, the hydrogen production factor can be calculated. The calculation of this factor can evaluate the hydrogen generation efficiency in real time and provide a basis for adjusting the operating state of the electrolyzer. Optimizing the hydrogen production factor helps improve the economic benefits and sustainability of the hydrogen production process. The electrolyte balance factor Leq plays a key role in the operation of the electrolyzer. It ensures the balance of the electrolyte under optimal conditions by analyzing the fluctuation ranges of the electrolyte temperature and concentration. The fluctuations of the electrolyte temperature and concentration will directly affect the electrolysis efficiency and hydrogen production. The calculation of the electrolyte balance factor can help accurately adjust the temperature and concentration of the electrolyte to ensure that the electrolyzer operates under the best conditions. By appropriately controlling the fluctuation range of the electrolyte, the efficiency decline or equipment damage caused by electrolyte instability is avoided.

[0158] By combining the real - time data in the first and second data groups, the electrolyzer analysis unit can adaptively adjust the operating parameters of the electrolyzer under different operating states. This adaptive ability enables the electrolyzer to maintain the best operating state under different environmental conditions and load demands, maximize hydrogen production, and reduce energy waste or equipment failures caused by improper operation.

[0159] Example 7

[0160] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the environmental adaptability analysis module further includes an IGBT power acquisition unit and a prediction unit;

[0161] The IGBT power acquisition unit is used to collect the operation parameters of the IGBT power module in real time. The operation parameters of the IGBT power module include the power input voltage IGBT v_in 、the power output current IGBT I_out 、the switching frequency IGBT F_s 、the conduction voltage IGBT v_on and the IGBT operating temperature IGBT T ; These data help to identify the working state and potential risks of the power supply, ensuring that the power supply can operate stably under different working loads and environmental conditions. Through the collection and analysis of these data, the instability or fault risks of the power supply can be detected in a timely manner, reducing the occurrence of equipment failures.

[0162] The prediction unit is used to construct an initial convolutional neural network model using a convolutional neural network, and train and test the initial convolutional neural network model with a first data set and a second data set, and use the trained initial convolutional neural network model as a power adaptation prediction model. At the same time, the intermediate layer output of the IGBT power device operating state is used as a feature vector to identify feature information, and the power adaptation prediction model is trained and tested with the obtained operation parameters of the IGBT power module, and the trained power adaptation prediction model is used as data operation prediction;

[0163] After dimensionless processing of the solar stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, combined with the operation parameters of the IGBT power module, the power adaptation coefficient Y is calculated and obtained through the following related formulas:

[0164]

[0165] where a1, a2, a3, β1, β2, β3, β4, and β5 are weight values, Vin max represents the maximum input voltage of the power supply, I max represents the maximum output current of the power supply, Fs max represents the maximum switching frequency, Von max represents the maximum conduction voltage, T max represents the maximum operating temperature of the power supply. a1 + a2 + a3 = 1, β1 + β2 + β3 + β4 + β5 = 1.

[0166] In this embodiment, the prediction unit uses a convolutional neural network (CNN) to predict power adaptation. The powerful features of the convolutional neural network enable the system to learn and extract complex patterns from a large amount of data. During the training process, the system combines the rich information of the first data group and the second data group to train a more accurate power adaptation prediction model. Through this trained model, the system can quickly predict the power operation state, discover potential power adaptation problems in advance, and adjust the system operation in a timely manner to ensure the stability and continuity of energy input. The calculation of the power adaptation coefficient Y is based on the dimensionless processing of key factors such as the solar stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, and is calculated in combination with the key operating parameters of the IGBT power module (such as the maximum input voltage, output current, switching frequency, conduction voltage, and maximum operating temperature). This multi-dimensional comprehensive analysis makes the evaluation of the power adaptation coefficient more accurate and can flexibly adapt to the power requirements in different environments. By adjusting the relevant weight values, different energy modes and operating requirements can be flexibly adapted to optimize the overall efficiency of the power system.

[0167] The power adaptation prediction model can discover potential faults or anomalies in advance by monitoring and predicting the power adaptation situation in real time. Through the real-time analysis of the power module parameters, the system can predict the risks in power operation and reduce the possibility of sudden failures. In addition, the prediction model can provide immediate feedback to help maintenance personnel take corresponding measures to reduce the maintenance cost and downtime of the power system.

[0168] Embodiment 8

[0169] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the power parameter optimization module is used to preset the adaptation threshold X, and compare the power adaptation coefficient Y with the adaptation threshold X to obtain the following evaluation results, including:

[0170] When the power adaptation coefficient Y > the adaptation threshold X, it means that the power adaptability is qualified, the load is high, and the first strategy is generated;

[0171] When the power adaptation coefficient Y = the adaptation threshold X, it means that the power adaptability is qualified, the current IGBT power parameters are maintained, no intervention and adjustment are required, and continuous monitoring is carried out;

[0172] When the power adaptation coefficient Y < the adaptation threshold X, it means that the power adaptability is unqualified, the load is low, and the second strategy is generated.

[0173] The first strategy includes: reducing the current switching frequency by 10%, improving the energy conversion efficiency, reducing the power duty cycle by 10%-20% to increase the current output, increasing the input voltage and output voltage of the current IGBT power supply by 10%-20%, increasing the current limit by 5%-10%, and providing an excess current output;

[0174] The second strategy includes: reducing the current switching frequency by 10%, reducing the power loss, reducing the power duty cycle by 10%-20% to decrease the current output, reducing the input voltage and output voltage of the current IGBT power supply by 10%-20%, reducing resource waste, reducing the current limit by 5%-10%, and avoiding overheating or damage of the IGBT power supply caused by excessive current.

[0175] In this embodiment, the comparison between the power adaptation coefficient Y and the adaptation threshold X provides a real-time assessment of the power adaptability of the system. This assessment mechanism automatically generates different strategies according to the adaptation status of the power supply (qualified or unqualified). By comparing the power adaptation coefficient with the preset adaptation threshold, the system can dynamically judge the power adaptability and make corresponding adjustment decisions. This mechanism not only improves the intelligent level of power management but also effectively reduces the need for human intervention.

[0176] When the power adaptation coefficient Y is greater than the adaptation threshold X, it indicates that the power adaptability is qualified and the load is high. The system will generate the first strategy to further increase the current output by reducing the switching frequency and improving the energy conversion efficiency. This strategy can effectively meet the power demand under high load, avoid power overload, and ensure the stable operation of the system under high load conditions. Strategies such as increasing the current output, increasing the voltage, and increasing the current limit can provide excess power to avoid the situation where the power adaptability is unqualified due to high load.

[0177] When the power adaptation coefficient Y is equal to the adaptation threshold X, it indicates that the power adaptability is qualified. The system maintains the current power parameters without intervention and continues to monitor. This situation indicates that the power supply operates stably and does not require additional adjustment, reducing the number of system interventions, optimizing the energy usage efficiency, avoiding unnecessary adjustment operations, and ensuring the stable supply of energy.

[0178] When the power supply adaptation coefficient Y is less than the adaptation threshold X, it indicates that the power supply adaptability is unqualified and the load is low. The system will generate a second strategy to effectively reduce the current output by reducing the switching frequency, duty cycle, voltage, and current limit, preventing the power supply from overheating or being damaged. This strategy can reduce resource waste, lower power consumption, avoid failures caused by power overload, and protect the IGBT power module from damage. In the second strategy, by adjusting the operating state of the power supply (such as reducing the current limit, decreasing the current output, etc.), the system can prevent the power supply from overworking under low load, avoid power overheating and damage, and extend the service life of the power supply. This intelligent adjustment mechanism significantly improves the stability and safety of the power supply system.

[0179] The system effectively realizes the intelligent management of the power supply by automatically adjusting the operating parameters of the power supply to adapt to different load conditions, making the utilization of energy more efficient. Whether during the peak or trough of the load, the system can accurately control the power output, avoiding energy waste during overload or resource waste during low load, thus achieving efficient energy utilization.

[0180] The size of the threshold is set for easy comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0181] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As described above, this is only a preferred specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. A hydrogen production power supply optimization system based on IGBT modules, characterized in that, It includes an energy source monitoring module, a power regulation module, an electrolyzer operation data acquisition module, an environmental adaptability analysis module, and a power supply parameter optimization module; The energy source monitoring module is used to monitor the environmental state data of renewable energy in real time, including the power output characteristics of solar energy and wind energy, generate a first data set, and integrate the electrical energy outputs of solar energy and wind energy into the total power input; The power regulation module is used to dynamically adjust the output powers of the solar photovoltaic array and the wind turbine according to the first data set to make them match the actual power demand of the electrolyzer; The electrolyzer operation data acquisition module is used to monitor and record the electrolyzer operation data in real time and generate a second data set; The environmental adaptability analysis module is used to extract features from the first data set and the second data set to calculate and obtain the solar energy stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, and combine machine learning algorithms to calculate the power supply adaptability coefficient Y; The power supply parameter optimization module is used to preset an adaptation threshold X based on the power supply adaptability coefficient Y and dynamically adjust the switching frequency, duty cycle, voltage range, and current limit parameters of the IGBT power supply to optimize the stability of the output electrical energy.

2. The hydrogen production power supply optimization system based on IGBT module according to claim 1, wherein The first data set includes: light radiation intensity C pv , the total light-receiving area A of the current photovoltaic array sg , the efficiency η of the photovoltaic array pv , wind speed v w , air density md, the efficiency η of the wind turbine wind , the swept area A of the wind turbine blades sl , the input voltage V of the electrolyzer electrolyzer , the input current I of the electrolyzer electrolyzer , the actual power demand P of the electrolyzer electrolyzer , the solar output power P solar and the wind power output P wind ; The total power input includes the total power input \(P_{in}\) obtained by summing the solar power output \(P\) solar and the wind power output \(P\) wind . total .

3. The hydrogen production power supply optimization system based on IGBT module according to claim 1, characterized in that The power regulation module is used to dynamically adjust the power outputs of the photovoltaic array and the wind turbine according to the first data set and the actual power demand of the electrolyzer to make them match the actual power demand of the electrolyzer. The specific steps are as follows: S11. Collect the real-time operation status data of the electrolyzer and calculate the actual power demand P of the electrolyzer electrolyzer : P electrolyzer = V electrolyzer × I electrolyzer ; Among them, V electrolyzer is the input voltage of the electrolytic cell, and I electrolyzer is the input current of the electrolytic cell; S12. According to the actual power demand P of the electrolytic cell electrolyzer , extract the total power input P total , and calculate the power gap ΔP qk : ΔP qk = P electrolyzer - P total ; Among them, if ΔP qk > 0, it means that the power is insufficient and the power output needs to be increased; If ΔP qk = 0, it means the power is qualified and there is no need to adjust the power output of the photovoltaic array or the wind turbine, and continuous monitoring is carried out; If ΔP qk <0, it means that the power is in excess and the power output needs to be reduced; S13. According to the power gap ΔP qk , one of the ways of adjusting the power output of the photovoltaic array or the wind turbine is selected for adjustment, specifically: S131. Adjust the operating point of the MPPT control algorithm to make the photovoltaic array output the maximum power under the current radiation intensity, and calculate and obtain the current maximum output reference value through the following formula Among them, η pv represents the efficiency of the photovoltaic array, A sg represents the total light-receiving area of the current photovoltaic array, C pv represents the light irradiation intensity, k mppt represents the MPPT gain coefficient adjusted dynamically; S132. If ΔP qk > 0, it indicates insufficient power. Based on the maximum output reference value and the power gap ΔP qk , calculate the area A additional of the photovoltaic array that needs to be started: Among them, the area A of the photovoltaic array to be started corresponding to the start is additional photovoltaic array; S133. If ΔP qk < 0, it indicates that the power is in excess. Based on the maximum output reference value and the power gap ΔP qk , calculate the area A of the PV array that needs to be shut down reduce : Among them, close the area A of the photovoltaic array that needs to be closed reduce of the photovoltaic array 4. The hydrogen production power supply optimization system based on an IGBT module according to claim 3, characterized in that, The S13 also includes: S134. According to the wind speed V w and the blade swept area A of the wind turbine sl , calculate the reference value of the maximum power output of the wind turbine at the current wind speed through the following formula where md represents the air density, η wind is the efficiency of the wind turbine, representing the efficiency of converting wind energy into electrical energy, set within the range of 0.4 - 0.5, v w is the wind speed; S135. If ΔP qk > 0, it indicates insufficient power. Based on the maximum power output reference value of the wind turbine at the current wind speed and the power gap ΔP qk , calculate the number of wind turbines N wind_on to be started: Among them, the number N of wind turbines that need to be started corresponding to the start-up is wind_on wind turbines; S133. If ΔP qk < 0, it indicates that the power is excessive. Based on the maximum power output reference value of the wind turbine at the current wind speed and the power gap ΔP qk , calculate the number of wind turbines N wind_off to be shut down: Among them, close the number N of the wind-solar generators that need to be closed wind_off wind turbines 5. The hydrogen production power supply optimization system based on IGBT modules according to claim 1, characterized in that, The second data set includes: electrode area A electrode , current density Dmd, electrolyte temperature T electrolyte , electrolyte concentration D jnd and hydrogen production H yield .

6. The hydrogen production power supply optimization system based on an IGBT module according to claim 1, characterized in that, The environmental adaptability analysis module includes a first green energy calculation unit, a second green energy calculation unit, and an electrolyzer analysis unit; The first green energy calculation unit is used to calculate and obtain the solar energy stability factor S according to the first data set through the following formula: Among them, P solar,i represents the solar power output at the i-th moment, represents the average value of the solar power output, C pv,i represents the light radiation intensity at the i-th moment, with the unit of W / m 2 , normalized to [0, 1], and N represents the total number of sampling moments; The second green energy calculation unit is used to calculate and obtain the wind energy stability factor Fw according to the first data set and the second data set through the following formula: Among them, P wind,i represents the wind energy output power at the i-th moment, with the unit of kW, represents the average value of the wind energy output power, v w,i represents the wind speed at the i-th moment, with the unit of m / s, v cut_in represents the cut-in wind speed of the wind turbine, v rated represents the rated wind speed of the wind turbine.

7. The optimized hydrogen production power supply system based on IGBT modules according to claim 6, characterized in that, The electrolytic cell analysis unit is used to extract the electrolytic cell input voltage V in the first data set electrolyzer and the electrolytic cell input current I electrolyzer , combine the electrode area A in the second data set electrode and the current density Dmd. After dimensionless processing, the electrolytic cell power factor Pg is calculated through the following formula: Extract the electrode area A in the second data set electrode and the hydrogen production H yield , after dimensionless processing, obtain the hydrogen production factor Hy through the following formula: Where, n represents the number of electrons transferred by hydrogen, which is set to 2, and F represents the Faraday constant, Extract the electrolyte temperature T in the second data set electrolyte and the electrolyte concentration D jnd , after dimensionless processing, the electrolyte equilibrium factor Leq is calculated through the following formula: Among them, ΔT represents the allowable fluctuation range value of the electrolyte temperature, and T opt represents the optimal operating temperature of the electrolyte, and D jnd_opt represents the optimal operating concentration of the electrolyte; ΔD jnd represents the allowable fluctuation range of the electrolyte concentration, which is set to ±0.2 mol / L.

8. An optimized hydrogen production power supply system based on an IGBT module according to claim 7, characterized in that, The environmental adaptability analysis module also includes an IGBT power supply acquisition unit and a prediction unit; The IGBT power acquisition unit is used to collect the operation parameters of the IGBT power module in real time. The operation parameters of the IGBT power module include the power input voltage IGBT v_in , the power output current IGBT I_out , the switching frequency IGBT F_s , the conduction voltage IGBT v_on , and the IGBT operating temperature IGBT T ; The prediction unit is used to use a convolutional neural network to construct an initial convolutional neural network model, train and test the initial convolutional neural network model with the first data set and the second data set, and use the trained initial convolutional neural network model as a power supply adaptability prediction model. At the same time, the intermediate layer output of the IGBT power supply device operation state is used as a feature vector to identify feature information, and the power supply adaptability prediction model is trained and tested with the obtained IGBT power supply module operation parameters, and the trained power supply adaptability prediction model is used for data operation prediction; After dimensionless processing of the solar energy stability factor S, the wind energy stability factor Fw, the electrolyzer power factor Pg, the hydrogen production factor Hy, and the electrolyte balance factor Leq, combined with the IGBT power supply module operation parameters, the power supply adaptability coefficient Y is calculated and obtained through the following related formulas: where a1, a2, a3, β1, β2, β3, β4, and β5 are weighting factors, and Vin max represents the maximum input voltage of the power supply, I max represents the maximum output current of the power supply, Fs max represents the maximum switching frequency, Von max represents the maximum conduction voltage, T max represents the maximum operating temperature of the power supply.

9. The hydrogen production power supply optimization system based on IGBT modules according to claim 1, characterized in that The power supply parameter optimization module is used to preset an adaptation threshold X and compare the power supply adaptability coefficient Y with the adaptation threshold X to obtain the following evaluation results, including: When the power supply adaptation coefficient Y > adaptation threshold X, it indicates that the power supply adaptability is qualified, and the first strategy is generated; When the power supply adaptation coefficient Y = adaptation threshold X, it indicates that the power supply adaptability is qualified, the current IGBT power supply parameters are maintained, no intervention and adjustment are required, and continuous monitoring is carried out; When the power supply adaptation coefficient Y < adaptation threshold X, it indicates that the power supply adaptability is unqualified, and the second strategy is generated.

10. The hydrogen production power supply optimization system based on an IGBT module according to claim 9, wherein, The first strategy includes: reducing the current switching frequency by 10%, reducing the power supply duty cycle by 10% - 20%, increasing the input voltage and output voltage of the current IGBT power supply by 10% - 20%, and increasing the current limiting limit by 5% - 10%; The second strategy includes: reducing the current switching frequency by 10%, reducing the power supply duty cycle by 10% - 20%, reducing the input voltage and output voltage of the current IGBT power supply by 10% - 20%, and reducing the current limiting limit by 5% - 10%.

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