Method and system for optimizing closing resistance performance of circuit breaker based on multi-field coupling filter bank

Abstract

The invention discloses a multi-field coupling filter bank circuit breaker closing resistor performance optimization method and system, and the method comprises the steps: simulating the flow path and electric field intensity distribution of a current in a resistor through COMSOL, and carrying out the interaction analysis of the electric field distribution and thermal field distribution in real time; based on a feedback mechanism, a current path and a thermal management design are dynamically adjusted according to mechanical deformation, and the adaptive capacity of the resistor to mechanical stress in the heating expansion process is optimized; an alloy material is selected, and a nano coating technology is utilized to carry out surface optimization treatment on the closing resistor; the change of a current path is monitored in real time, the structure of the resistor is adjusted according to the temperature change, and the self-adaptive adjustment of the system when the current load and the voltage change is realized by utilizing a machine learning algorithm; and according to the current load and the temperature, the liquid cooling flow and the air cooling wind speed are adjusted in real time, and the working state of the cooling system is dynamically controlled. According to the invention, through a thermal-electric-mechanical coupling optimization method, the equipment is dynamically adjusted, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat dissipation of power equipment, and more specifically, particularly relates to a performance optimization method and system for closing resistors of 800 kV filters based on multi-field coupling. Background Art

[0002] With the gradual increase in the power density of power electronic devices, the heat dissipation problem has become a key factor restricting the performance and lifespan of the devices. Especially in devices with high voltage levels such as 800 kV, the change in current load will cause significant fluctuations in the device temperature, and traditional heat dissipation methods often cannot respond to these changes in real time, resulting in too high or too low temperatures, thus affecting the stability and working efficiency of the system. Existing cooling technologies, especially liquid cooling and air cooling systems, although can alleviate the problem of device overheating to a certain extent, still have the following problems:

[0003] Static control: Most cooling systems rely on statically set cooling flow rates and wind speeds and do not adjust according to the dynamic changes in device temperature or load. Traditional cooling systems fail to achieve real-time temperature monitoring and automatic adjustment, and adverse effects may have already occurred to the device when the temperature is too high or too low.

[0004] Lack of precise regulation: Lack of an intelligent regulation mechanism based on the feedback of temperature sensors, and the cooling flow rate and wind speed cannot be adjusted in real time according to temperature changes.

[0005] In the prior art, the coupled analysis of multiple physical fields such as thermal fields, electric fields, and mechanical deformations is relatively lacking. In some applications, the change in current load will not only cause temperature changes but may also lead to structural deformations of the device. This thermo-electro-mechanical coupling effect has a very significant impact on the cooling system during actual operation. However, existing systems fail to fully utilize these coupling effects for dynamic optimization. Summary of the Invention

[0006] In view of the above or existing problems of the performance optimization method and system for closing resistors of 800 kV filters based on multi-field coupling, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a performance optimization method for closing resistors of a filter bank circuit breaker based on multi-field coupling, including: using COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, and performing interactive analysis of the electric field distribution and the thermal field distribution in real time. When the electric field changes, automatically adjust the thermal field simulation;

[0009] Based on the feedback mechanism, dynamically adjust the current path and thermal management design according to mechanical deformation, optimize the resistance material and shape using dynamic simulation of thermo-mechanical coupling, and optimize the adaptability of the resistance to mechanical stress during the thermal expansion process;

[0010] Select alloy materials with high electrical conductivity and low thermal expansion coefficient, and use nano-coating technology to optimize the surface of the closing resistor;

[0011] Adjust the thermal field distribution in real time according to the change of the electric field, and adjust the structure of the resistor according to the change of temperature. Use machine learning algorithms to dynamically optimize the model through historical operation data to achieve the adaptive adjustment of the system when the current load and voltage change;

[0012] Regulate the liquid cooling flow rate and air cooling wind speed in real time according to the current load and temperature. Introduce thermal sensors into the heat dissipation system of the resistor, and dynamically control the working state of the cooling system according to the real-time temperature data.

[0013] As a preferred scheme of the method for optimizing the performance of the 800 kV filter closing resistor based on multi-field coupling according to the present invention, wherein: using COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, and performing interactive analysis of the electric field distribution and the thermal field distribution in real time. When the electric field changes, automatically adjust the thermal field simulation, including:

[0014] Set the geometric shape of the resistor, the physical properties of the material, and the boundary conditions of the voltage source or current source, obtain the electric field distribution by solving the electric potential equation, and output the electric field distribution map to display the electric field intensity in different regions;

[0015] When the current passes through the resistor, power loss is generated and converted into heat. The power loss density is:

[0016] where J is the current density and E is the electric field intensity;

[0017] Use the heat conduction equation to simulate the heat conduction:

[0018]

[0019] where T is the temperature, k is the thermal conductivity, and Q is the heat source generated by the current;

[0020] Use COMSOL to calculate the electric field distribution, obtain the electric field intensity and current density, and use the electric field distribution as the heat source input to calculate the temperature change at each point.

[0021] As a preferred embodiment of the method for optimizing the closing resistor performance of an 800 kV filter based on multi-field coupling according to the present invention, the following steps are included: based on the feedback mechanism, the current path and thermal management design are dynamically adjusted according to mechanical deformation, and the resistor material and shape are optimized by dynamic simulation of thermo-mechanical coupling, including:

[0022] Set the thermal stress boundary conditions according to the coefficient of thermal expansion α, and use the formulas for thermal expansion and thermal stress to calculate the deformation:

[0023] where E is the elastic modulus, α is the coefficient of thermal expansion, and ΔT is the temperature change;

[0024] In each iteration, the changes in the electric field, thermal field, and mechanical field are calculated through simulation. The thermal stress and thermal expansion change the material shape, and mechanical deformation occurs in the region where the temperature rises, thus forming a thermo-mechanical coupling feedback loop.

[0025] As a preferred embodiment of the method for optimizing the closing resistor performance of a circuit breaker for a filter bank based on multi-field coupling according to the present invention, the following steps are included: select an alloy material with high electrical conductivity and low coefficient of thermal expansion, and use the nano-coating technology to optimize the surface of the closing resistor, including:

[0026] Select beryllium copper alloy as the base material of the closing resistor, and combine it with a nano-silver coating to further improve its surface conductivity; use the chemical vapor deposition method to uniformly cover the surface of the beryllium copper alloy with the nano-silver coating, and ensure the thickness and uniformity of the coating by controlling the deposition rate and temperature.

[0027] As a preferred embodiment of the method for optimizing the closing resistor performance of an 800 kV filter based on multi-field coupling according to the present invention, the following steps are included: when current flows through the resistor, the change in the current path is monitored in real time, the thermal field distribution is adjusted in real time according to the change in the electric field, and the structure of the resistor is adjusted according to the change in temperature, including:

[0028] When the temperature changes, the structure of the resistor is dynamically adjusted to maintain its performance. The temperature change causes thermal expansion of the material, which in turn affects the shape, size, and electrical conductivity of the resistor. The volume expansion of the material is expressed by the following formula:

[0029] where ΔV is the volume change, V0 is the original volume, α is the linear coefficient of thermal expansion of the material,

[0030] ΔT is the temperature change;

[0031] Calculate the deformation of the resistor under temperature changes in real time based on the temperature field data and material properties such as elastic modulus and coefficient of thermal expansion; dynamically adjust the geometry of the resistor according to the stress and deformation data monitored in real time. The structural adjustment includes adjusting the thermal conductivity by increasing or decreasing the material thickness to control the temperature distribution.

[0032] Reduce stress concentration by optimizing the surface curvature or contact area, and monitor the real-time temperature and deformation data through sensors to automatically initiate structural adjustment when the temperature reaches a predetermined threshold.

[0033] As a preferred embodiment of the method for optimizing the closing resistor performance of the multi-field coupling 800 kV filter according to the present invention, wherein: using a machine learning algorithm to dynamically optimize the model through historical operation data to achieve adaptive adjustment of the system when the current load and voltage change, including:

[0034] There is a linear relationship between the output voltage, current and input feature X(t), and the model is:

[0035] where, β0, β1, …, β n are the weights of the model, and ϵ(t) is the error term;

[0036] When the current load or voltage changes, use the trained machine learning model to predict the device temperature and resistance changes of the output based on the real-time input current load and voltage; adjust the control strategy according to the prediction results. When the load is too high, adjust the current magnitude or voltage to avoid device damage;

[0037] Use a genetic algorithm to optimize the regulation strategy of voltage and current. Each step in the optimization generates a new solution and gradually approaches the optimal solution:

[0038] where, f old is the current solution, and f new is the new solution after mutation.

[0039] As a preferred embodiment of the method for optimizing the closing resistor performance of the multi-field coupling filter bank circuit breaker according to the present invention, wherein: adjust the liquid cooling flow rate and air cooling wind speed in real time according to the current load and temperature, introduce a thermal sensor in the resistor heat dissipation system, and dynamically control the working state of the cooling system according to the real-time temperature data, including:

[0040] If the resistor temperature exceeds the set threshold, the cooling intensity needs to be increased; if the temperature is lower than the preset safety range, the cooling intensity is reduced;

[0041] The relationship between the wind speed and the resistor temperature and ambient temperature is expressed by the formula:

[0042] Among them, k2 is a constant. The greater the temperature difference and the higher the wind speed, so as to dissipate heat more effectively.

[0043] By real-time monitoring the resistance and the temperature of the cooling system, continuously adjusting the control strategy. If the increase in liquid cooling flow rate causes the temperature to drop rapidly, the control system will lower the liquid cooling flow rate to avoid overcooling, and at the same time appropriately lower the air cooling wind speed to save energy; if the temperature continues to rise, the system will increase the liquid cooling flow rate and increase the air cooling wind speed to prevent the resistance from being damaged due to overheating.

[0044] A performance optimization system for the closing resistance of a multi-field coupling filter bank circuit breaker, including: a multi-field coupling modeling module, which is used to use COMSOL to simulate the current flow path and electric field strength distribution in the resistance, and interactively analyze the electric field distribution and the thermal field distribution in real time. When the electric field changes, automatically adjust the thermal field simulation;

[0045] A multi-field simulation module, which is used to dynamically adjust the current path and thermal management design based on the feedback mechanism according to mechanical deformation, and optimize the resistance material and shape by using dynamic simulation of thermal-mechanical coupling to optimize the adaptability of the resistance to mechanical stress during the thermal expansion process;

[0046] A surface treatment optimization module, which is used to select alloy materials with high electrical conductivity and low thermal expansion coefficient, and use nano-coating technology to optimize the surface treatment of the closing resistance;

[0047] An optimization adjustment module, which is used to real-time monitor the change of the current path when the current flows through the resistance, adjust the thermal field distribution in real time according to the change of the electric field, and adjust the structure of the resistance according to the change of the temperature. Using machine learning algorithms, dynamically optimize the model through historical operation data to achieve the adaptive adjustment of the system when the current load and voltage change;

[0048] A heat dissipation and cooling design module, which is used to adjust the air cooling wind speed in real time according to the current load and temperature, introduce a thermal sensor in the heat dissipation system of the resistance, and dynamically control the working state of the cooling system according to the real-time temperature data.

[0049] A computing device, the computing device includes:

[0050] At least one processor, a memory and an input / output unit;

[0051] Among them, the memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the steps of the method for optimizing the performance of the closing resistance of the multi-field coupling filter bank circuit breaker.

[0052] A computer-readable storage medium includes instructions that, when run on a computer, cause the computer to perform the steps of a method for optimizing the closing resistance performance of a multi-field coupling filter bank circuit breaker.

[0053] The beneficial effects of the present invention are as follows: By monitoring the current load and temperature changes in real time and combining with the automatic adjustment of the cooling system, the present invention avoids the decline or damage of equipment performance caused by overheating or overcooling. Dynamically adjusts the working state of the cooling system according to real-time temperature data. The thermo-electro-mechanical coupling optimization method combines the comprehensive analysis of current load, temperature changes and mechanical structure deformation, and can make real-time adjustments for the dynamic changes of the equipment in a high-voltage environment, effectively improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a flowchart of a method for optimizing the closing resistance performance of a multi-field coupling filter bank circuit breaker provided by an embodiment of the present invention.

[0056] Figure 2 It is a schematic structural diagram of a system for optimizing the closing resistance performance of a multi-field coupling filter bank circuit breaker provided by an embodiment of the present invention.

[0057] Figure 3 Schematically shows a structural diagram of a medium according to an embodiment of the present invention.

[0058] Figure 4 Schematically shows a structural diagram of a computing device according to an embodiment of the present invention.

[0059] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification.

[0061] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0062] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0063] Embodiment

[0064] The following refers to Figure 1 , Figure 1 is a flowchart for optimizing the closing resistor performance of a multi-field coupled filter bank circuit breaker provided in an embodiment of the present invention. It should be noted that the implementation manner of the present invention can be applied to any applicable scenario.

[0065] Figure 1 The flow of the method for optimizing the closing resistor performance of a multi-field coupled filter bank circuit breaker provided in an embodiment of the present invention shown in the figure includes:

[0066] S1: Use COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, and perform interactive analysis of the electric field distribution and thermal field distribution in real time. When the electric field changes, automatically adjust the thermal field simulation.

[0067] Preferably, set the geometric shape of the resistor, the physical properties of the material, the voltage source or current source boundary conditions, and obtain the electric field distribution by solving the electric potential equation, output the electric field distribution diagram, and display the electric field intensity in different regions;

[0068] When the current passes through the resistor, power loss is generated and converted into heat. The power loss density is:

[0069] where J is the current density and E is the electric field intensity;

[0070] Adopt the heat conduction equation to simulate the heat conduction:

[0071]

[0072] where T is the temperature, k is the thermal conductivity, and Q is the heat source generated by the current;

[0073] Use COMSOL to calculate the electric field distribution, obtain the electric field intensity and current density, and use the electric field distribution as the heat source input to calculate the temperature change at each point.

[0074] Furthermore, assume that the designed closing resistor is a cuboid with dimensions of length L = 10 cm, width W = 5 cm, and height H = 2 cm. The material of the resistor is selected as copper, which has the following physical properties: electrical conductivity σ is 5.8×10 7 S / m, thermal conductivity k is 398 W / (m⋅K), specific heat capacity cp is 385 J / (kg⋅K), and the density ρ is 8.96 g / cm 3 , and the initial temperature T0 = 25 °C;

[0075] Set the voltage source boundary conditions at the two end faces of the resistor, which are respectively:

[0076] V1 = 800 kV (input end);

[0077] V2 = 0 V (output end);

[0078] Use the electric field distribution boundary conditions to calculate the electric field distribution according to the above settings;

[0079] In this embodiment, use the above data to calculate the current density: J = 5.8×10 7 S / m × 1.6 V / m = 9.28×10 7 A / m2;

[0080] When the current passes through the resistor, power loss is generated and converted into heat. The power loss density is given by the following formula:

[0081] Q = J⋅E = 9.28×10 7 A / m2 × 1.6 V / m = 1.4848×10 8 W / m3;

[0082] When the current passes through the resistor, the current density causes power loss, which is converted into heat and causes the resistor temperature to rise; the maximum temperature T max is 95 °C and appears in the central region of the resistor. The temperature gradually decreases along the surface and edges of the resistor, and the lowest temperature T min is 45 °C and appears at both ends of the resistor;

[0083] Since the conductivity is a function of temperature, assume that the change of conductivity with temperature follows a linear relationship:

[0084] where, σ0 = 5.8×10 7 S / m is the initial conductivity, α = 0.0038 / °C is the temperature coefficient of copper,

[0085] T0 = 25 °C;

[0086] Assume that the resistor temperature rises to 95 °C, then the change in conductivity is:

[0087] σ(95 °C) = 5.8×107 S / m × (1 + 0.0038×(95 - 25)) = 5.8×107 S / m × 1.190

[0088] Obtained: σ(95°C) = 6.9×10 7 S / m.

[0089] S2: Based on the feedback mechanism, dynamically adjust the current path and thermal management design according to mechanical deformation, and optimize the resistance material and shape using thermo-mechanical coupling dynamic simulation to optimize the adaptability of the resistance to mechanical stress during thermal expansion.

[0090] Preferably, set the thermal stress boundary conditions according to the coefficient of thermal expansion α, and use the thermal expansion and thermal stress formulas to calculate the deformation:

[0091] where E is the elastic modulus, α is the coefficient of thermal expansion, and ΔT is the temperature change;

[0092] In each iteration, calculate the changes in the electric field, thermal field, and mechanical field through simulation. The thermal stress and thermal expansion change the material shape, and mechanical deformation occurs in the region where the temperature rises, thus forming a thermo-mechanical coupling feedback loop.

[0093] Furthermore, use COMSOL to solve the electric field distribution. Assume that the input voltage of the resistor is 800 kV, then the distribution of the electric field strength E in the resistor is as follows:

[0094] The electric field strength E is distributed along the length direction in the resistor, gradually decreasing from 800 kV at the input end to 0 V at the output end;

[0095] Calculate the power loss density Q according to the current density and electric field, and then solve the temperature distribution through the heat conduction equation. It is calculated that the maximum temperature of the resistor is Tmax = 95°C, and the minimum temperature is Tmin = 45°C;

[0096] Using the temperature change ΔT = 95°C - 25°C = 70°C and the coefficient of thermal expansion of copper α = 16×10 -6 / °C, calculate the thermal stress according to the thermal stress formula: σ thermal = 120 GPa × 16×10 -6 / °C × 70°C = 134.4 MPa

[0097] Assume that the elastic modulus of copper is E = 120 GPa, calculate the deformation of the material using the thermal stress, and the calculated maximum deformation amount is δmax = 0.02 mm;

[0098] During each simulation calculation process, the shape of the resistor will change slightly due to thermal expansion, and the new geometry in turn affects the electric field distribution and temperature field; take these new geometries as inputs and update the thermal and mechanical fields in the next iteration until the system reaches a stable state;

[0099] Through multiple iterative calculations, the following results are finally obtained:

[0100] The temperature distribution of the resistor tends to be stable, with the maximum temperature Tmax = 90 °C and the minimum temperature Tmin = 40 °C. The thermal stress stabilizes at about 130 MPa, and the material deformation is small, ensuring the structural stability of the device.

[0101] S3: Select alloy materials with high electrical conductivity and low thermal expansion coefficient, and use nano - coating technology to optimize the surface of the closing resistor.

[0102] Preferably, beryllium - copper alloy is selected as the base material of the closing resistor, combined with a nano - silver coating to further improve its surface conductivity; the nano - silver coating (in some specific embodiments, considering the high cost of the silver coating, a copper plating can also be used instead) is uniformly coated on the surface of the beryllium - copper alloy by chemical vapor deposition. By controlling the deposition rate and temperature, the thickness and uniformity of the coating are ensured.

[0103] S4: When current flows through the resistor, the change of the current path is monitored in real - time, the thermal field distribution is adjusted in real - time according to the electric - field change, and the structure of the resistor is adjusted according to the temperature change. Using machine - learning algorithms, the model is dynamically optimized through historical operation data to achieve the adaptive adjustment of the system when the current load and voltage change.

[0104] Preferably, when the temperature changes, the structure of the resistor is dynamically adjusted to maintain its performance. The temperature change causes the thermal expansion of the material, which in turn affects the shape, size, and electrical conductivity of the resistor. The volume expansion of the material is expressed by the following formula:

[0105] where ΔV is the volume change, V0 is the original volume, α is the linear thermal expansion coefficient of the material,

[0106] ΔT is the temperature change;

[0107] According to the temperature - field data and material properties such as elastic modulus and thermal expansion coefficient, the deformation of the resistor under temperature change is calculated in real - time; according to the stress and deformation data monitored in real - time, the geometric shape of the resistor is dynamically adjusted. The structural adjustment includes adjusting the thermal conductivity by increasing or decreasing the material thickness to control the temperature distribution,

[0108] By optimizing the surface curvature or contact area, stress concentration is reduced, and real - time temperature and deformation data are monitored by sensors to automatically start structural adjustment when the temperature reaches a predetermined threshold.

[0109] Preferably, there is a linear relationship between the output voltage and current of the system and the input feature X(t), and the model is:

[0110] Among them, β0, β1, …, β n are the weights of the model, and ϵ(t) is the error term;

[0111] When the current load or voltage changes, use the trained machine learning model to predict the changes in the device temperature and resistance output based on the real-time input current load and voltage; adjust the control strategy according to the prediction results. When the load is too high, adjust the current magnitude or voltage to avoid device damage;

[0112] Use the genetic algorithm to optimize the regulation strategies of voltage and current. Each step in the optimization generates a new solution and gradually approaches the optimal solution:

[0113] Among them, f old is the current solution, and f new is the new solution after mutation.

[0114] Furthermore, use sensors to monitor the temperature change and deformation of the resistor in real time. According to the measured temperature data, calculate the thermal stress and deformation by combining the elastic modulus, thermal expansion coefficient, and volume change of the resistor. The specific steps are as follows:

[0115] Assume that the initial temperature of the resistor is T0 = 25 °C. After working for a certain period of time, the temperature rises to Tmax = 90 °C; the temperature change amount ΔT = Tmax - T0 = 90 °C - 25 °C = 65 °C;

[0116] Assume that the original volume of the resistor is V0 = 100 cm 3 , and calculate the volume change according to the thermal expansion formula:

[0117] ΔV = V0 ⋅ α ⋅ ΔT = 100 cm3 × 16 × 10 -6 / °C × 65 °C = 0.104 cm3. Therefore, the volume of the resistor will increase by 0.104 cm 3 ;

[0118] Calculate the thermal stress according to the thermal expansion formula and the elastic modulus:

[0119] σthermal = E ⋅ α ⋅ ΔT = 120 GPa × 16 × 10 -6 / °C × 65 °C = 125.28 MPa. It is calculated that the thermal stress of the resistor is 125.28 MPa;

[0120] Calculate the deformation amount of the resistor due to thermal expansion according to the thermal stress formula:

[0121] δthermal = 0.00104 mm

[0122] It is calculated that the deformation of the resistor is 0.00104 mm;

[0123] When the temperature of the resistor reaches a predetermined threshold, the temperature and deformation data monitored by the sensor will trigger an automatic structure adjustment mechanism. The specific adjustment methods are as follows:

[0124] If the temperature of the resistor is too high, the heat dissipation performance can be improved by increasing the thickness of the material. This adjustment can increase the thermal conductivity, reduce the excessive rise of the local temperature, and avoid excessive thermal stress;

[0125] If the resistor is overheated and has a large deformation, the impact of volume expansion can be reduced by reducing the thickness;

[0126] Increase the curvature on the surface of the resistor or improve the contact area to reduce the stress concentration phenomenon. Especially in the area with a large current density, the optimization of the surface curvature helps to relieve the excessive local stress;

[0127] According to the temperature field data, dynamically adjust the flow rate of the cooling system (such as liquid cooling or air cooling system) to ensure uniform temperature distribution and avoid the emergence of overheated areas.

[0128] S5: Adjust the liquid cooling flow rate and air cooling wind speed in real time according to the current load and temperature. Introduce thermal sensors and automatic regulating valves in the heat dissipation system of the resistor, and dynamically control the working state of the cooling system according to the real-time temperature data.

[0129] Preferably, if the resistor temperature exceeds the set threshold, the cooling intensity needs to be increased; if the temperature is lower than the preset safety range, the cooling intensity is reduced;

[0130] Adjust the cooling system through an automatic regulating valve. The cooling system can include a liquid cooling system and / or an air cooling system, where

[0131] The flow rate of the coolant, and the relationship between the set flow rate and the temperature is expressed by the following formula:

[0132] where k1 is a constant. If the resistor temperature T resistor rises, the liquid cooling flow rate Q liquid will increase to improve the cooling effect;

[0133] The relationship between the wind speed and the resistor temperature and the ambient temperature is expressed by the formula:

[0134] where k2 is a constant. The greater the temperature difference, the higher the wind speed for more effective heat dissipation.

[0135] By continuously monitoring the resistance and the temperature of the cooling system and adjusting the control strategy in real time, if the increase in liquid cooling flow rate causes the temperature to drop rapidly, the control system will reduce the liquid cooling flow rate to avoid overcooling, and at the same time, appropriately reduce the air cooling wind speed to save energy; if the temperature continues to rise, the system will increase the liquid cooling flow rate and the air cooling wind speed to prevent the resistance from being damaged due to overheating.

[0136] Furthermore, the system will continuously monitor the temperature T of the resistance resistor , the ambient temperature T ambient and the status of the cooling system. Based on these temperature data, the system automatically adjusts the liquid cooling flow rate and the wind speed to maintain the equipment temperature within a safe range.

[0137] When the resistance temperature T resistor exceeds the set safety threshold, the liquid cooling flow rate will increase; when the temperature drops within the safe range, the liquid cooling flow rate will decrease;

[0138] The wind speed changes according to the temperature difference between the resistance temperature and the ambient temperature. When the temperature difference is large, the wind speed will increase; when the temperature difference is small, the wind speed will decrease;

[0139] When T resistor >T safe , the liquid cooling flow rate Q liquid increases, according to the formula Q liquid =k1⋅(T resistor -T safe );

[0140] The air cooling wind speed Vair will also increase, according to the formula Vair=k2⋅(Tresistor-Tambient);

[0141] If the excessive liquid cooling flow rate causes the temperature to drop rapidly, the system will adjust the liquid cooling flow rate to prevent overcooling;

[0142] When T resistor <T safe , the liquid cooling flow rate Q liquid decreases, and the wind speed V air decreases;

[0143] If the temperature continues to drop and the liquid cooling flow rate is too small, the system will adjust the liquid cooling flow rate again to ensure that the equipment is not overcooled and is in the best operating state;

[0144] Assume that the safety temperature threshold of the resistor is set to T safe =75 °C, and the resistor temperature T resistor =80 °C, the ambient temperature T ambient =25 °C, constants k1 = 0.5, k2 = 1.2; according to the formula, first calculate the liquid cooling flow rate and the wind speed:

[0145] Q liquid = 0.5⋅(80 °C - 75 °C) = 0.5⋅5 = 2.5 L / min, Vair = 1.2⋅(80 °C - 25 °C) = 1.2⋅55 = 66 m / s; At this time, the system will automatically increase the liquid cooling flow rate and wind speed to accelerate cooling according to the resistance temperature being higher than the safety threshold. The liquid cooling flow rate increases to 2.5 L / min, and the wind speed increases to 66 m / s.

[0146] If the temperature continues to rise, the system may further increase the cooling intensity, or if the temperature starts to drop rapidly, the system will reduce the cooling intensity to ensure that the temperature is stable within the safe range.

[0147] After introducing the method of the exemplary embodiment of the present invention, next, with reference to Figure 2 the system for optimizing the closing resistor performance of an 800 kV filter based on multi-field coupling of the exemplary embodiment of the present invention will be described. This system includes:

[0148] A multi-field coupling modeling module for using COMSOL to simulate the current flow path and electric field strength distribution in the resistor, and performing interactive analysis of the electric field distribution and thermal field distribution in real time. When the electric field changes, the thermal field simulation is automatically adjusted.

[0149] A multi-field simulation module for dynamically adjusting the current path and thermal management design based on the feedback mechanism according to mechanical deformation, and optimizing the resistor material and shape using dynamic simulation of thermal-mechanical coupling to optimize the adaptability of the resistor to mechanical stress during thermal expansion.

[0150] A surface treatment optimization module for selecting alloy materials with high electrical conductivity and low thermal expansion coefficient and performing surface optimization treatment of the closing resistor using nano-coating technology.

[0151] An optimization adjustment module for real-time monitoring of changes in the current path when current flows through the resistor, real-time adjustment of the thermal field distribution according to the electric field change, and adjustment of the resistor structure according to the temperature change. Using machine learning algorithms, the model is dynamically optimized through historical operation data to achieve adaptive adjustment of the system when the current load and voltage change.

[0152] A heat dissipation cooling design module for adjusting the liquid cooling flow rate and air cooling wind speed in real time according to the current load and temperature, introducing thermal sensors and automatic control valves in the resistor's heat dissipation system, and dynamically controlling the working state of the cooling system according to real-time temperature data.

[0153] After introducing the method and device of the exemplary embodiment of the present invention, next, with reference to Figure 3 the computer-readable storage medium of the exemplary embodiment of the present invention will be described. Please refer to Figure 3, which shows that the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., program product) is stored. When the computer program is run by a processor, it will implement the steps recorded in the above method embodiments. For example, use COMSOL to simulate the flow path of current in a resistor and the distribution of electric field strength, and perform interactive analysis of the electric field distribution and the thermal field distribution in real time. When the electric field changes, automatically adjust the thermal field simulation; based on a feedback mechanism, dynamically adjust the current path and thermal management design according to mechanical deformation, and use thermo-mechanical coupled dynamic simulation to optimize the resistor material and shape, and optimize the adaptability of the resistor to mechanical stress during thermal expansion;

[0154] Select an alloy material with high electrical conductivity and low thermal expansion coefficient, and use nano-coating technology to optimize the surface of the closing resistor; when current flows through the resistor, monitor the change of the current path in real time, adjust the thermal field distribution in real time according to the change of the electric field, and adjust the structure of the resistor according to the change of temperature. Use machine learning algorithms to dynamically optimize the model through historical operation data to achieve adaptive adjustment of the system when the current load and voltage change; adjust the liquid cooling flow rate and air cooling wind speed in real time according to the current load and temperature, introduce thermal sensors and automatic control valves in the heat dissipation system of the resistor, and dynamically control the working state of the cooling system according to real-time temperature data; the specific implementation methods of each step will not be repeated here.

[0155] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other optical and magnetic storage media, which will not be elaborated here one by one.

[0156] After introducing the methods, devices and media of the exemplary embodiments of the present invention, next, refer to Figure 4 A computing device for optimizing the performance of the closing resistor of an 800 kV filter based on multi-field coupling according to the exemplary embodiment of the present invention.

[0157] Figure 4 A block diagram of an exemplary computing device 40 suitable for implementing the embodiments of the present invention is shown. The computing device 40 may be a computer system or a server. Figure 4 The shown computing device 40 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0158] As Figure 4As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 that couples the different system components including the system memory 402 and the processing unit 401.

[0159] Computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computing device 40, including volatile and non-volatile media, removable and non-removable media.

[0160] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 can be used for reading and writing to a non-removable, non-volatile magnetic medium ( Figure 4 not shown in the figure and commonly referred to as a "hard disk drive"). Although not shown in Figure 4 the figure, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 through one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present invention.

[0161] A program / utility 4025 having a set (at least one) of program modules 4024 can be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, and an implementation of a network environment may be included in each or some combination of these examples. Program modules 4024 generally execute the functions and / or methods in the embodiments described in the present invention.

[0162] Computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, a pointing device, a display, etc.). Such communication can be through an input / output (I / O) interface 405. Also, computing device 40 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 406. As Figure 4 shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401, etc.) through bus 403. It should be understood that althoughFigure 4 which is not shown in the figure, and other hardware and / or software modules may be used in combination with the computing device 40.

[0163] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it uses COMSOL to simulate the flow path of current in a resistor and the distribution of electric field strength, and performs interactive analysis of the electric field distribution and the thermal field distribution in real time. When the electric field changes, it automatically adjusts the thermal field simulation; based on a feedback mechanism, it dynamically adjusts the current path and thermal management design according to mechanical deformation, and uses thermo-mechanical coupled dynamic simulation to optimize the resistor material and shape, and optimize the adaptability of the resistor to mechanical stress during thermal expansion; it selects alloy materials with high electrical conductivity and low thermal expansion coefficient, and uses nano-coating technology to optimize the surface of the closing resistor; when current flows through the resistor, it monitors the change of the current path in real time, adjusts the thermal field distribution according to the change of the electric field, and adjusts the structure of the resistor according to the change of temperature. It uses machine learning algorithms to dynamically optimize the model through historical operation data to achieve adaptive adjustment of the system when the current load and voltage change; it adjusts the liquid cooling flow rate and air cooling wind speed according to the current load and temperature in real time, introduces thermal sensors and automatic control valves in the heat dissipation system of the resistor, and dynamically controls the working state of the cooling system according to real-time temperature data.

[0164] The specific implementation manners of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the synchronous escape wiring device based on multi-commodity flow are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of the two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0165] In the description of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0166] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0167] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0168] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0170] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0171] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0172] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the shown operations must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

Claims

1. A method for optimizing the closing resistor performance of a multi-field coupling filter circuit breaker, characterized in that Including: Step (1): Use COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, interactively analyze the electric field distribution and thermal field distribution in real time, and automatically adjust the thermal field simulation when the electric field changes; Step (2): Based on the feedback mechanism, dynamically adjust the current path and thermal management design according to mechanical deformation, and use thermo-mechanical coupled dynamic simulation to optimize the resistor material and shape, and optimize the adaptability of the resistor to mechanical stress during the thermal expansion process; Step (3): Select an alloy material with high electrical conductivity and low thermal expansion coefficient, and use nano-coating technology to optimize the surface of the closing resistor; Step (4): Adjust the thermal field distribution in real time according to the electric field change, use machine learning algorithms, and dynamically optimize the model through historical operation data to achieve the adaptive adjustment of the system when the current load and voltage change; Step (5): Adjust the air-cooling wind speed in real time according to the current load and temperature, introduce thermal sensors in the resistor's heat dissipation system, and dynamically control the working state of the cooling system according to real-time temperature data.

2. The method for optimizing the closing resistor performance of a multi-field coupling filter bank circuit breaker according to claim 1, wherein The use of COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, interactively analyze the electric field distribution and thermal field distribution in real time, and automatically adjust the thermal field simulation when the electric field changes, includes: Set the geometric shape of the resistor, the physical properties of the material, and the voltage source or current source boundary conditions, obtain the electric field distribution by solving the electric potential equation, output the electric field distribution map, and display the electric field intensity in different regions; Power loss occurs when current passes through a resistor, which is converted into heat. The power loss density is: , where J is the current density and E is the electric field intensity; Use the heat conduction equation to simulate the heat conduction: ， where T is the temperature, k is the thermal conductivity, and Q is the heat source generated by the current; Use COMSOL to calculate the electric field distribution, obtain the electric field intensity and current density, and use the electric field distribution as the heat source input to calculate the temperature change at each point.

3. The method for optimizing the closing resistor performance of a multi-field coupling filter bank circuit breaker according to claim 1, wherein The based on the feedback mechanism, dynamically adjust the current path and thermal management design according to mechanical deformation, and use thermo-mechanical coupled dynamic simulation to optimize the resistor material and shape, includes: Set the thermal stress boundary conditions according to the thermal expansion coefficient α, and use the thermal expansion and thermal stress formulas to calculate the deformation: ， where E is the elastic modulus, α is the thermal expansion coefficient, and ΔT is the temperature change; In each iteration, calculate the changes in the electric field, thermal field, and mechanical field through simulation. The thermal stress and thermal expansion change the material shape, and the mechanical deformation occurs in the area where the temperature rises, thus forming a thermo-mechanical coupled feedback loop.

4. The method for optimizing the closing resistor performance of a multi-field coupling filter bank circuit breaker according to claim 1, wherein The selection of an alloy material with high electrical conductivity and low thermal expansion coefficient, and the use of nano-coating technology to optimize the surface of the closing resistor, includes: Select beryllium copper alloy as the base material of the closing resistor, combine it with a nano-silver coating to further improve its surface conductivity; use chemical vapor deposition method to evenly cover the surface of the beryllium copper alloy with the nano-silver coating, and ensure the thickness and uniformity of the coating by controlling the deposition rate and temperature.

5. The method for optimizing the closing resistor performance of a multi-field coupling filter circuit breaker according to claim 1, characterized in that When the current flows in the resistor, monitor the change of the current path in real time, adjust the thermal field distribution in real time according to the electric field change, and adjust the structure of the resistor according to the temperature change, includes: When the temperature changes, the structure of the resistor is dynamically adjusted to maintain its performance. The temperature change causes thermal expansion of the material, which in turn affects the shape, size, and conductivity of the resistor. The volume expansion of the material is expressed by the following formula: ， where ΔV is the volume change, V0 is the original volume, and α is the linear thermal expansion coefficient of the material. ΔT is the temperature change. Based on the temperature field data and material properties such as elastic modulus and thermal expansion coefficient, the deformation of the resistor under temperature change is calculated in real time. According to the stress and deformation data monitored in real time, the geometric shape of the resistor is dynamically adjusted. The structural adjustment includes adjusting the thermal conductivity by increasing or decreasing the material thickness to control the temperature distribution. By optimizing the surface curvature or contact area, stress concentration is reduced. The real-time temperature and deformation data are monitored by sensors, and the structural adjustment is automatically initiated when the temperature reaches a predetermined threshold.

6. The method for optimizing the closing resistor performance of a multi-field coupling filter bank circuit breaker according to claim 1, characterized in that, Using machine learning algorithms to dynamically optimize the model through historical operation data to achieve adaptive adjustment of the system when the current load and voltage change, including: There is a linear relationship between the output voltage, current, and input feature X(t). The model is: ， Among them, β0, β1, …, β n are the weights of the model, and ϵ(t) is the error term; When the current load or voltage changes, the trained machine learning model is used to predict the device temperature and resistance change of the output based on the real-time input current load and voltage. According to the prediction results, the control strategy is adjusted. When the load is too high, the current magnitude or voltage is adjusted to avoid device damage. Using genetic algorithms to optimize the regulation strategy of voltage and current. Each step in the optimization generates a new solution, gradually approaching the optimal solution: ， Among them, f old is the current solution, and f new is the new solution after mutation.

7. The method for optimizing the closing resistor performance of a multi-field coupling filter bank circuit breaker according to claim 1, wherein According to the current load and temperature, the air-cooling wind speed is adjusted in real time. A thermal sensor is introduced into the heat dissipation system of the resistor, and the working state of the cooling system is dynamically controlled according to the real-time temperature data, including: If the resistor temperature exceeds the set threshold, the cooling intensity needs to be increased; if the temperature is lower than the preset safe range, the cooling intensity is reduced. The relationship between the wind speed, resistor temperature, and ambient temperature is expressed by the formula: ， where k2 is a constant. The greater the temperature difference, the higher the wind speed for more effective heat dissipation. By continuously monitoring the temperature of the resistor and the cooling system in real time, the control strategy is adjusted. The air-cooling wind speed is appropriately reduced to save energy. If the temperature continues to rise, the system will increase the air-cooling wind speed to prevent the resistor from being damaged due to overheating.

8. A performance optimization system for the closing resistor of a multi-field coupling filter bank circuit breaker, characterized in that, Including: A multi-field coupling modeling module for using COMSOL to simulate the current flow path and electric field intensity distribution in the resistor, and interactively analyzing the electric field distribution and thermal field distribution in real time. When the electric field changes, the thermal field simulation is automatically adjusted. A multi-field simulation module for dynamically adjusting the current path and thermal management design based on the feedback mechanism according to mechanical deformation, and optimizing the resistor material and shape using thermo-mechanical coupling dynamic simulation to optimize the adaptability of the resistor to mechanical stress during thermal expansion. A surface treatment optimization module for selecting alloy materials with high electrical conductivity and low thermal expansion coefficient and using nano-coating technology to optimize the surface treatment of the closing resistor. Optimization and adjustment module, which is used to monitor the change of the current path in real time when current flows through the resistor, adjust the thermal field distribution in real time according to the electric field change, adjust the structure of the resistor according to the temperature change, and use machine learning algorithms to dynamically optimize the model through historical operation data to achieve the adaptive adjustment of the system when the current load and voltage change; Heat dissipation and cooling design module, which is used to adjust the air-cooling wind speed in real time according to the current load and temperature, introduce thermal sensors into the heat dissipation system of the resistor, and dynamically control the working state of the cooling system according to the real-time temperature data.

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