A method and system for intelligent operation control of switchgear

Abstract

This application provides an intelligent operation control method and system for switchgear, applied in the field of smart grid technology. The method constructs a water vapor gradient distribution field and surface dielectric parameters within the switchgear, identifies the time-varying mechanical characteristics of the mechanism's springs, and dynamically couples electric field calculations with nonlinear mechanical motion to form a simultaneous calculation model. Based on this, it integrates voltage synchronization windows, quantified insulation, and mechanical constraints to online optimize and generate the optimal drive current waveform and execute closing. Finally, it uses closing process data to calibrate key parameters in real time. The system includes functional modules that execute the above processes. This application achieves adaptive optimization control for closing based on specific environments and equipment states, effectively improving the safety, reliability, and intelligence level of operation.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a method and system for intelligent operation control of switchgear. Background Technology

[0002] Switchgear is the core power distribution equipment of the power grid, and the reliability of its closing operation is crucial for the safe and stable operation of the smart grid. Traditional control relies on fixed programs or simple interlocks, resulting in low levels of intelligence and making it difficult to meet the requirements of smart grids for equipment status self-sensing and self-healing.

[0003] Existing methods typically test insulation after tripping and close the circuit near the zero-crossing point to suppress inrush current. However, treating insulation, mechanical, and electrical control as independent or sequential processes fails to address the intertwined challenges of environmental changes, mechanical aging, and motor coupling. Current technologies largely rely on alarms from individual temperature and humidity sensors within the cabinet or periodic withstand voltage tests, which are significantly inadequate. Due to differences in thermal field and ventilation within the cabinet, there is a vertical gradient in moisture concentration. Single-point measurements cannot reflect the true microenvironment of critical components such as busbars. There is a lack of quantitative conversion models from ambient humidity to surface dielectric properties, making it impossible to assess the non-uniform distribution of water film formed on rough surfaces. Consequently, it is difficult to achieve accurate and forward-looking insulation condition prediction and integrated decision-making.

[0004] Existing closing mechanisms mostly employ spring mechanisms with preset energy or constant-speed motors, resulting in fixed control curves. However, key components such as energy storage springs experience fatigue during operation, leading to decreased stiffness and changes in force-displacement hysteresis characteristics. This causes drift in closing dynamic parameters, a time-varying characteristic that remains unresolved in the dynamic equipment management system of smart grids. Existing systems generally lack the ability to identify such time-varying and nonlinear states online. The dynamic electrodynamic forces generated by the interaction between contact movement and electric field distribution during closing are ignored in traditional models. This model, which fails to reflect the true coupled dynamics and equipment health status, runs counter to the digital twin and data-driven decision-making pursued by smart grids, making it difficult to guarantee the closing quality and mechanical lifespan of long-term operation. Summary of the Invention

[0005] The embodiments of this application provide a method and system for intelligent operation control of switchgear, which realizes the online generation of a globally optimal operation command that simultaneously satisfies multiple constraints of insulation, mechanical and electrical, based on the specific environment and equipment status at each closing operation. This achieves an adaptive balance of safety, reliability and efficiency. To achieve the above objectives, this application adopts the following technical solution: A method for intelligent operation control of a switchgear, the method comprising: Collect temperature and humidity data inside the switch cabinet and microscopic morphology data of the busbar surface to construct a vertical gradient distribution field of water vapor inside the cabinet; Based on the gradient distribution field and micromorphological data, the equivalent water film thickness distribution on the surface of the busbar is calculated, and the corresponding local dielectric constant shift parameter is determined. Collect historical operation and load data of the operating mechanism, calculate mechanical fatigue state parameters, and identify the current stiffness parameters and hysteresis characteristic parameters of the energy storage spring based on the mechanical fatigue state parameters. Based on the gradient distribution field and dielectric constant shift parameter, the electric field process of the non-uniform medium is calculated; based on the stiffness parameter and hysteresis characteristic parameter, the motion process of the mechanism with nonlinear hysteresis is calculated; the electric field process calculation and the motion process calculation are coupled to form a joint calculation relationship. Obtain the voltage phase information of the target circuit and determine the voltage synchronization tolerance window for the closing operation; The tolerance window is used as a timing constraint, the electric field process intensity is kept below the critical breakdown field strength as an insulation constraint, and the contact pressure is kept stable as a mechanical constraint. All of these are incorporated into the simultaneous calculation relationship. Trajectory optimization is performed in the solution space that satisfies all constraints to generate the drive current waveform of the operating mechanism and execute the closing operation. A curve was established by collecting the mechanical vibration spectrum during the closing process and the contact resistance after closing. The mechanical vibration spectrum is compared with the predicted spectrum calculated based on the motion process to generate first deviation data, and the hysteresis characteristic parameters are calibrated according to the first deviation data. The contact resistance curve is compared with the expected curve to generate second deviation data, and the dielectric constant offset parameter is calibrated based on the second deviation data. The calibrated hysteresis characteristic parameters and dielectric constant offset parameters are stored as input parameters for constructing the combined calculation relationship during the closing operation.

[0006] In some possible implementations, the acquisition of temperature and humidity data inside the switchgear and microscopic morphology data of the busbar surface to construct a vertical gradient distribution field of water vapor inside the cabinet includes: Temperature and humidity sensor groups are installed in the high, middle and low areas of the switch cabinet interior space; Collect temperature and relative humidity readings from each sensor group; Substitute the temperature and relative humidity readings into the saturated vapor pressure calculation formula to obtain the absolute vapor concentration value at each monitoring point. The absolute water vapor concentration values ​​of all monitoring points are processed using a spatial interpolation algorithm to generate three-dimensional spatial distribution data of water vapor concentration. Extract the concentration change profile along the vertical direction from the three-dimensional water vapor concentration spatial distribution data, and use it as the water vapor vertical gradient distribution field.

[0007] In some possible implementations, calculating the equivalent water film thickness distribution on the surface of the busbar based on the gradient distribution field and micromorphological data includes: Obtain the surface roughness profile information from the micro-morphology data of the busbar surface; Based on the spatial position of each micro-groove in the surface roughness profile information, the corresponding absolute water vapor concentration value is determined from the water vapor vertical gradient distribution field. Based on the principle of capillary condensation and using the determined absolute water vapor concentration values ​​corresponding to each groove, the critical thickness of condensate in each micro-groove is calculated. The critical condensate thickness of all grooves is summarized to form the equivalent water film thickness distribution on the surface of the busbar.

[0008] In some possible implementations, the coupling of the electric field process calculation with the motion process calculation to form a simultaneous calculation relationship includes: Set a uniform time step sequence; Within each time step, the motion process of the actuator, including nonlinear hysteresis, is calculated, and the spatial displacement and velocity of the contact under the step are output. Using the spatial displacement as the input condition for electric field calculation, the geometric configuration of the contact gap is determined. Based on the geometric configuration and the dielectric constant offset parameter, the non-uniform dielectric electric field process is calculated to obtain the current electric field intensity distribution. The current electric field intensity distribution is applied to the contact surface, and the equivalent electrodynamic force is calculated; The equivalent electrodynamic force is used as a mechanical load input and fed back into the motion process calculation for the next time step. The process is iterative on the time step sequence, with data from the electric field process and the motion process being input to each other to form a simultaneous calculation relationship.

[0009] In some possible implementations, maintaining the electric field intensity below the critical breakdown field strength as an insulation constraint and maintaining stable contact pressure as a mechanical constraint includes: The insulation constraint condition is quantified as follows: in the time step sequence of the simultaneous calculation relationship, the calculated maximum electric field strength shall not exceed the preset critical breakdown field strength threshold. The mechanical constraint condition is quantified as follows: within a set time period after the contact closure event occurs, the fluctuation amplitude of the instantaneous contact pressure between the contacts shall not exceed the preset upper limit threshold of the allowable fluctuation. The trajectory optimization process uses the driving current waveform as the optimization variable.

[0010] In some possible implementations, obtaining the voltage phase information of the target circuit and determining the voltage synchronization tolerance window for the closing operation includes: Obtain the voltage waveform of the target circuit; The fundamental phase angle is extracted from the voltage waveform using the zero-crossing detection method. Obtain the voltage phase angle of adjacent circuits that are electrically coupled to the target circuit; Calculate the difference between the fundamental phase angle of the target circuit and the voltage phase angle of each adjacent circuit; Based on the preset allowable closing inrush current threshold, the allowable closing phase angle range is calculated by reverse calculation for each of the differences; The intersection of all calculated allowable closing phase angle ranges is used to obtain the voltage synchronization tolerance window.

[0011] In some possible implementations, the process of solving the simultaneous computational relations includes: During the iteration of the time step sequence, the contact movement velocity and electric field distribution calculated in two adjacent iterations are obtained; When the rate of change of the contact movement speed is less than the first convergence threshold, and the rate of change of the electric field strength in the key region of the electric field distribution is less than the second convergence threshold, it is determined that the simultaneous calculation relationship has reached convergence within the current time step, and the calculation of the next time step begins.

[0012] In some possible implementations, the step of comparing the mechanical vibration spectrum with a predicted spectrum calculated based on the motion process to generate first deviation data, and calibrating the hysteresis characteristic parameters based on the first deviation data, includes: Feature extraction is performed on the collected mechanical vibration spectrum to obtain a set of measured amplitudes consisting of the amplitudes of the dominant frequency and the key harmonics; From the predicted spectrum calculated based on the motion process, extract the predicted amplitude set corresponding to the frequency points in the measured amplitude set; An amplitude deviation sequence is formed by calculating the amplitude difference between the measured amplitude set and the predicted amplitude set at each corresponding frequency point. The amplitude deviation sequence is input into the parameter update algorithm, which calculates the adjustment amount for the hysteresis characteristic parameter based on the amplitude deviation sequence, and applies the adjustment amount to complete the calibration update of the hysteresis characteristic parameter.

[0013] In some possible implementations, the step of comparing the established contact resistance curve with the expected curve to generate second deviation data, and calibrating the dielectric constant offset parameter based on the second deviation data, includes: Based on the contact resistance, a curve is established, the steady-state resistance value after the contact stabilizes is extracted, and the fall time required for the resistance value to drop from the initial state to the steady-state resistance value is extracted. Read the expected steady-state resistance value and expected fall time corresponding to the steady-state resistance value and fall time extracted above from the expected curve; Calculate the difference between the steady-state resistance value and the expected steady-state resistance value, and calculate the ratio of the fall time to the expected fall time; Based on the difference and the ratio, the surface energy parameters used in calculating the equivalent water film thickness distribution are corrected in reverse by using a pre-established functional relationship. The local dielectric constant offset parameter is updated based on the corrected surface energy parameter.

[0014] A switchgear intelligent operation control system, the system comprising: The data acquisition module is used to collect temperature and humidity data inside the switch cabinet and microscopic morphology data of the busbar surface, as well as historical operation and load data of the operating mechanism. The distribution field construction and parameter calculation module is used to construct the vertical gradient distribution field of water vapor inside the cabinet based on the collected temperature and humidity data and micro-morphology data, and calculate the equivalent water film thickness distribution on the surface of the busbar based on the gradient distribution field and micro-morphology data, and determine the corresponding local dielectric constant shift parameter. The mechanical state identification module is used to calculate mechanical fatigue state parameters based on the historical operation and load data, and to identify the current stiffness parameters and hysteresis characteristic parameters of the energy storage spring based on the mechanical fatigue state parameters. The coupled calculation module is used to calculate the electric field process of the non-uniform medium based on the gradient distribution field and the dielectric constant offset parameter, and to calculate the motion process of the mechanism with nonlinear hysteresis based on the stiffness parameter and hysteresis characteristic parameter. The electric field process calculation and the motion process calculation are coupled to form a joint calculation relationship. The synchronization window determination module is used to obtain the voltage phase information of the target circuit and determine the voltage synchronization tolerance window for the closing operation. The constraint insertion and optimization module is used to use the tolerance window as a timing constraint, the electric field process intensity to be maintained below the critical breakdown field strength as an insulation constraint, and the contact pressure to be stable as a mechanical constraint, and to insert them into the simultaneous calculation relationship. The module then performs trajectory optimization in the solution space that satisfies all the constraints to generate the drive current waveform of the operating mechanism. The operation execution module is used to perform the closing operation based on the generated drive current waveform; The calibration module is used to collect the mechanical vibration spectrum during the closing process and the contact resistance curve after closing. It compares the mechanical vibration spectrum with the predicted spectrum calculated based on the motion process to generate first deviation data and calibrates the hysteresis characteristic parameter according to the first deviation data. It compares the contact resistance curve with the expected curve to generate second deviation data and calibrates the dielectric constant offset parameter according to the second deviation data. The storage module is used to store the calibrated hysteresis characteristic parameters and dielectric constant offset parameters as input parameters for constructing the combined calculation relationship in subsequent closing operations.

[0015] As can be seen from the above technical solution, this application has the following beneficial effects: 1. This system constructs a dynamically coupled digital twin model integrating environmental, electric, and mechanical fields, and introduces an online parameter calibration mechanism based on operational feedback. This achieves a paradigm shift from open-loop program execution to closed-loop model prediction and optimization. It can generate customized optimal drive commands for each closing task, taking into account specific environmental conditions, equipment health status, and real-time grid operating conditions. This method places the three core constraints of insulation safety, mechanical reliability, and electrical synchronization within a high-fidelity multiphysics simulation optimization framework for collaborative solution. This avoids suboptimal decisions or wasted safety margins that may result from traditional sequential judgments, thus improving the overall quality and intelligence level of the closing operation.

[0016] 2. This system quantitatively calculates local dielectric parameters based on the water vapor gradient field inside the cabinet and the surface morphology of the busbar, enabling insulation assessment to accurately reflect the non-uniform electric field under complex conditions such as condensation and contamination. This allows for the calculation of safe and efficient operating strategies even in humid environments. By utilizing historical data to identify mechanical hysteresis characteristics and online calibration of key parameters based on the closing vibration spectrum and contact resistance curve, the control model can continuously track equipment aging and performance drift, becoming more accurate with use and effectively combating uncertainties and time-varying characteristics. This provides a solid technical foundation for the lifelong reliable operation and predictive maintenance of switchgear. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 A first flowchart provided for an embodiment of this application; Figure 2 A second flowchart provided for embodiments of this application; Figure 3 A third flowchart provided for embodiments of this application; Figure 4 The fourth flowchart provided for the embodiments of this application. Detailed Implementation

[0019] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are for distinguishing different objects, not for specifying a particular order.

[0020] In the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0021] Research has revealed that the operation and control strategies of switchgear, a core node in smart grids, are mostly discrete or sequential: for example, first checking if the insulation meets requirements, then determining if the mechanism is ready, and finally selecting the phase for closing. This method artificially separates the coupled physical processes, failing to address the three intertwined challenges that smart grid devices must overcome under complex operating conditions: dynamic changes in surface dielectric properties due to environmental humidity, nonlinear drift in motion characteristics due to mechanical fatigue, and the altered electric field distribution and electrodynamic reaction generated by the closing process itself. The control decision-making model is static and isolated, mismatched with the dynamically coupled actual physical processes, resulting in insufficient control margin or suboptimal decisions, making it difficult to support the requirements of smart grids for equipment self-sensing, self-healing operation, and refined control.

[0022] To address the aforementioned problems, this application provides an intelligent operation control method and system for switchgear: To solve the above problems, such as Figures 1-4 As shown in the figure, this embodiment elaborates on the detailed implementation process of the intelligent operation control method for switchgear.

[0023] Step S101: Collect temperature and humidity data inside the switch cabinet and microscopic morphology data of the busbar surface to construct the vertical gradient distribution field of water vapor inside the cabinet.

[0024] Implementation Details and Principles: Inside the switchgear, due to heat sources such as conductor heating, the breathing effect, and air convection, the moisture concentration is not uniformly distributed, typically exhibiting a vertical gradient from bottom to top. This gradient directly affects the micro-condensation conditions on the busbar surface at different heights. To achieve accurate sensing, temperature and humidity sensor groups are installed in the high-level area near the top busbar compartment, the middle of the circuit breaker compartment in the mid-level area, and the low-level cable compartment or bottom of the switchgear interior. Each group may include multiple sensors to improve the reliability of local measurements. Temperature and relative humidity readings are collected at each point.

[0025] Glossary and Data Processing: Absolute water vapor concentration is a physical quantity characterizing the mass of water vapor per unit volume of air, and it reflects condensation potential more directly than relative humidity. The measured temperature and relative humidity are converted into absolute water vapor concentration values ​​for each monitoring point using the saturated vapor pressure calculation formula. Subsequently, based on spatial interpolation algorithms, such as Kriging interpolation or inverse distance weighting, three-dimensional spatial distribution data of water vapor concentration covering the entire switchgear area of ​​concern, especially the busbar passage and break area, is generated using the concentration values ​​from discrete monitoring points. From this three-dimensional data, a concentration change profile passing vertically through key parts of the busbar is extracted; this profile is defined as the vertical gradient distribution field of water vapor within the switchgear; it provides a humidity benchmark for assessing the microenvironment at each point on the busbar surface.

[0026] Beneficial effects: By constructing a gradient distribution field, this application is the first in the field of switchgear control to improve the perception of environmental humidity from single-point alarm to spatial field assessment. This provides an indispensable input condition for subsequent accurate calculation of the water film condition on the surface of the busbar at different locations. If the gradient is not considered and the humidity is assumed to be uniform, it will lead to the misjudgment of high-dry areas as humid areas or the underestimation of the risk of low-humid areas, thus resulting in inaccurate insulation assessment.

[0027] Step S102: Based on the gradient distribution field and micromorphological data, calculate the equivalent water film thickness distribution on the surface of the busbar and determine the corresponding local dielectric constant shift parameter.

[0028] Implementation details and principles: The surface of the busbar and contacts is rough at the microscale, with a large number of irregular grooves. According to the principle of capillary condensation, when the water vapor in the air does not reach macroscopic saturation, it can condense in these microscopic grooves to form a liquid film. The smaller the radius of curvature of the groove, that is, the sharper and narrower it is, the lower the concentration of ambient water vapor required for condensation. The formation of the surface water film is the result of the combined effect of ambient humidity and surface morphology.

[0029] Definitions and Calculations: The process involves acquiring microscopic morphology data of the busbar surface, pre-measured or estimated online using equipment such as surface profilometers or scanning electron microscopes. Surface roughness profile information is extracted, and key micro-grooves and their geometric features, such as opening width and depth, are identified. For each micro-groove in the profile information, the absolute water vapor concentration at that location is obtained by interpolation from the constructed water vapor vertical gradient distribution field based on its three-dimensional spatial coordinates. Combining the equivalent radius of curvature of the groove and the surface energy parameters of the busbar material, a capillary condensation theory model is applied to calculate the critical thickness of condensate within the groove under current environmental conditions. By traversing all grooves and statistically integrating the data, the equivalent water film thickness distribution of the entire busbar surface is formed. This distribution characterizes the spatial non-uniformity of the water film.

[0030] Glossary and Parameter Mapping: The local dielectric constant offset parameter is a comprehensive parameter used to quantify the impact of a water film on the macroscopic electrical properties of an insulating medium. The dielectric constant of pure air is approximately 1, while that of water is approximately 80. When a water film adheres to a surface, the equivalent dielectric constant of that region increases. Through a pre-established physical model or empirical mapping relationship, the calculated equivalent water film thickness near a certain point is converted into the local dielectric constant offset parameter used when calculating the electric field at that point. For example, this parameter can be defined as a multiplier factor greater than 1 to correct the dielectric properties of that local region in the electric field simulation software.

[0031] Beneficial effects: This step establishes a quantitative physical bridge from macroscopic environmental humidity and microscopic surface morphology to the spatial distribution of dielectric properties, enabling electric field calculations to accurately reflect the non-uniformity of the insulating medium caused by environmental condensation, rather than assuming that all insulating media are dry and uniform air. This gives subsequent insulation strength assessments, such as the determination of critical breakdown field strength, unprecedented accuracy and scenario adaptability, especially when dealing with complex conditions such as condensation and pollution.

[0032] Step S103: Collect historical operation and load data of the operating mechanism, calculate mechanical fatigue state parameters, and identify the current stiffness parameters and hysteresis characteristic parameters of the energy storage spring based on the mechanical fatigue state parameters.

[0033] Implementation details and principles: During long-term opening and closing operations, the energy storage springs of the operating mechanism, such as the closing springs, will experience material fatigue. This is macroscopically manifested as a decrease in spring stiffness and a hysteresis phenomenon in the force-displacement curve during loading and unloading cycles, i.e., the loading path and unloading path do not coincide, forming a closed loop. This hysteresis characteristic is a manifestation of mechanical energy dissipation and nonlinear friction, and its shape changes with the degree of fatigue. The degradation of spring performance directly leads to key dynamic indicators such as closing speed and contact pressure deviating from the design values.

[0034] Data and Identification: Continuously collect and store historical operation data, including the timestamp, type, and historical load data for each opening and closing, as well as the effective value of the circuit current within a specific time window before and after the operation, indirectly reflecting the stress of the mechanism on the load size; mechanical fatigue state parameters are an intermediate variable that comprehensively characterizes the degree of cumulative damage, and can be constructed based on the number of operations, load-weighted cumulative values, etc. This application adopts a nonlinear system identification algorithm, using historical operation sequences and load data as input, to fit the dynamic behavior of the mechanism; the direct output of this identification process is the current stiffness parameter reflecting the current state of the spring and the hysteresis characteristic parameters describing its nonlinear friction and energy dissipation characteristics, such as the shape control parameters and yield force parameters in the hysteresis model.

[0035] Beneficial effects: By identifying parameters online, this application ensures that key mechanical parameters in the motion process calculation model are no longer fixed design values, but rather health status values ​​that are updated in real time throughout the equipment's lifecycle. This solves the problem of inaccurate model predictions caused by mechanical wear and fatigue. For example, a decrease in spring stiffness can lead to insufficient closing velocity under the same energy storage conditions, while changes in hysteresis characteristics can affect the force impact at the moment of contact. Identifying these parameters in advance provides a precise basis for subsequent optimization of closing energy.

[0036] Step S104: Based on the gradient distribution field and dielectric constant offset parameter, perform electric field process calculation for non-uniform medium; based on the stiffness parameter and hysteresis characteristic parameter, perform motion process calculation for mechanism with nonlinear hysteresis; couple the electric field process calculation and motion process calculation to form a joint calculation relationship.

[0037] Implementation details and principles: This is the core step in achieving multiphysics co-simulation in this application. The two calculation processes are not performed independently, but are tightly coupled.

[0038] Calculation of electric field process in non-uniform dielectric: Using numerical calculation methods such as the finite element method, an electric field model including stationary contact, moving contact and surrounding insulating dielectric is established. The key innovation is that the dielectric properties are no longer uniform. The determined local dielectric constant offset parameter is assigned as a spatial function to the corresponding region in the model, such as the thin layer on the surface of the busbar, thereby constructing an electric field model of non-uniform dielectric. The spatial position of the contact is dynamically input.

[0039] Calculation of motion process of mechanism with nonlinear hysteresis: Using multibody dynamics simulation method, a mechanical model of the operating mechanism, including motor, connecting rod, spring, contact, etc., is established; the identified current stiffness parameters and hysteresis characteristic parameters are assigned to the mechanical model of the energy storage spring so that the model can accurately reflect the nonlinearity and memory characteristics of the spring. The model takes the driving current or torque as input and outputs the displacement, velocity, acceleration of the contact and the force on each component.

[0040] Coupling and simultaneous computational relationships: Define a unified, high-resolution time-step sequence, such as at the microsecond level. Within each time step: Motion calculation first: Based on the current driving current and the mechanical state at the previous moment, the spatial displacement and velocity of the last contact in the current step are calculated; Electric field calculation update: Input the new spatial displacement of the contact into the electric field model, update the geometric configuration of the calculation region, and then solve the Poisson equation to obtain the electric field intensity distribution of the entire space at the current moment, especially the field intensity of the contact gap and the busbar surface; Electrodynamic feedback: Based on the obtained electric field distribution, calculate the Maxwell stress tensor acting on the surfaces of the moving and stationary contacts, and integrate it to obtain the equivalent electrodynamic force acting on the contacts. This force may be proportional to the square of the voltage between the contacts and is affected by the medium. Load closed loop: The calculated equivalent electrodynamic force is used as an external load and fed back to the motion process calculation model for the next time step, affecting the dynamic response of the mechanism.

[0041] Beneficial effects: This combined computational relationship constructs a high-fidelity digital twin. Its effect lies in its ability to dynamically simulate the entire closing process, where the mechanism's motion changes the electric field, and the electric field change generates electrodynamic force that reacts on the mechanism—a real physical coupling process. Traditional methods either ignore the electrodynamic force or use empirical formulas to estimate a constant value, failing to reflect its dynamic changes and the influence of the medium during the contact approach process. The coupled simulation of this application can accurately predict whether the electric field strength at any given moment approaches the breakdown threshold, as well as the impact of electrodynamic force on the closing stability, providing a reliable virtual test platform for finding the optimal motion trajectory that satisfies multiple constraints.

[0042] Step S105: Obtain the voltage phase information of the target circuit and determine the voltage synchronization tolerance window for the closing operation.

[0043] Implementation details and principles: To avoid excessive inrush current or surge current at the moment of closing, the control contacts need to close near the zero crossing point of the voltage waveform. However, if there are multiple power sources or parallel circuits in the system, the phase difference with adjacent circuits must also be considered to prevent circulating current.

[0044] Specific calculations: Continuously monitor the voltage waveform of the target circuit, extract its fundamental phase angle using zero-crossing detection or Fourier analysis, and obtain the voltage phase angles of adjacent circuits with electrical connections, such as parallel buses or the other side of a transformer; calculate the phase difference between the target circuit and each adjacent circuit. Based on the system impedance and the preset allowable closing inrush current threshold, deduce the allowable closing phase angle range for each phase difference using circuit theory. For example, within a window of several degrees before and after the zero-crossing point of the target circuit voltage, take the intersection of all these allowable ranges to obtain the final voltage synchronization tolerance window. This window is a time interval corresponding to the voltage phase angle of the target circuit.

[0045] Beneficial effects: This step refines and systematizes electrical synchronization requirements beyond simple zero-crossing closing. Its effect lies in its comprehensive consideration of the electrical states of both the current circuit and related circuits. The defined tolerance window ensures a globally optimal time constraint for the electrical impact during closing, rather than a locally optimal one. This avoids unnecessary electrodynamic shocks to the equipment caused by neglecting circulating currents.

[0046] Step S106: The tolerance window is used as a timing constraint, the electric field process intensity is kept below the critical breakdown field strength as an insulation constraint, and the contact pressure is kept stable as a mechanical constraint. These are then combined and placed into the simultaneous calculation relationship.

[0047] Implementation details and principles: The constructed simultaneous calculation relationship can simulate the closing process under different drive current waveforms, transforming engineering requirements into mathematical constraints for this simulation optimization problem.

[0048] Timing constraint: The time of completion of the optimized closing operation must fall within the voltage synchronization tolerance window determined in step S105, usually marked by stable contact of the contacts.

[0049] Insulation constraint: The maximum spatial electric field strength calculated within the time step sequence of the entire closing process simulated by the simultaneous calculation relationship must not exceed the critical breakdown field strength threshold of the medium at any given time, typically at the point of minimum contact gap or the sharpest point on the busbar surface. This threshold can be preset using theoretical or experimental data such as material properties and Paschen curves.

[0050] Mechanical constraints: After a contact closure event occurs, within a set period of time, such as from contact to complete rest, the calculated change in instantaneous contact pressure between the contacts must be stable, and its fluctuation should be less than a preset upper limit threshold for allowable fluctuation, in order to avoid violent mechanical collisions and bounces.

[0051] Beneficial effects: The key to achieving intelligent decision-making in this application is to accurately map the three major engineering requirements (timeliness, insulation, and stability) into the boundary conditions of the simulation optimization problem. Its effect is that it transforms the complex, multi-objective engineering decision-making problem into a mathematical optimization problem with clear constraints under a strict physical model, making it possible to automatically find the global optimal solution by computer.

[0052] Step S107: Perform trajectory optimization in the solution space that satisfies all constraints, generate the drive current waveform of the operating mechanism, and execute the closing operation.

[0053] Implementation Details and Principles: The optimization variable for trajectory optimization is the drive current waveform or motor torque waveform, which can be parameterized as current values ​​at a series of key time points. Optimization algorithms, such as model predictive control frameworks combined with sequential quadratic programming or genetic algorithms, search the parameter space of the drive current waveform. For each set of candidate current waveform parameters, a coupled simulation of the entire process from the open position to the closed position is performed within the constructed simultaneous calculation relationship, and it is checked whether all set constraints are met. Among the candidate solutions that satisfy all constraints, the optimization algorithm further searches for the optimal solution based on additional objectives, such as minimum energy consumption and minimum time, and finally outputs the drive current waveform corresponding to the optimal solution. The control system then drives the motor or electromagnet of the operating mechanism according to this waveform to perform the closing operation.

[0054] Beneficial effects: This is the final stage of decision generation and execution; its fundamental effect lies in the fact that the driving instructions it generates are not fixed or empirical, but rather optimal instructions tailored to the current environment, equipment status, and system electrical conditions. For example, in a humid environment, to meet stricter insulation constraints, the algorithm may proactively generate a current waveform with a slightly slower speed and smoother acceleration to reduce the rate of change of the electric field; when a spring is fatigued, to achieve the same final velocity, the algorithm may generate a current waveform with higher energy for compensation. This achieves a leap from "open-loop execution" to closed-loop pre-optimization.

[0055] Steps S108 to S110: Online calibration and parameter update.

[0056] Implementation details and principles: After this optimization operation is performed, the system enters the learning and calibration phase.

[0057] S108: Data Acquisition: High-speed acquisition of the mechanical vibration spectrum during the closing process and the contact resistance curve between the contacts after closing.

[0058] S109: Calibrate the hysteresis parameter: Compare the measured mechanical vibration spectrum with the vibration spectrum predicted by the motion process calculation model in step S104 under the optimal current waveform drive. The difference in the spectrum mainly reflects the inaccuracy of the model in characterizing nonlinear factors such as friction and collision damping. By using a parameter update algorithm, adjust the hysteresis parameter in the model to make the model's predicted spectrum approximate the measured spectrum, thus completing the calibration of this parameter.

[0059] S110: Calibrate Dielectric Constant Offset Parameters: Establish a curve of the measured contact resistance, i.e., the process of resistance decreasing to a steady state over time, and compare it with the expected curve, based on the prediction of an ideal clean and dry surface model. The rate of resistance establishment and the steady-state value are very sensitive to surface micro-contamination and thin films, such as water films and oxide films. Through the pre-established functional relationship, the actual surface energy parameters or degree of contamination can be inferred from the curve differences, thereby correcting the surface energy parameters on which the equivalent water film thickness depends. Based on the corrected surface energy parameters, the equivalent water film thickness distribution and local dielectric constant offset parameters are recalculated, completing the calibration of these parameters.

[0060] Beneficial Effects: The online calibration mechanism is the technical guarantee for achieving the adaptive capability of becoming more accurate with use in this application. Its core effect lies in using the vibration and resistance curves containing rich physical information generated by each operation as a free real data source to continuously correct the most uncertain and drift-prone parameters (mechanical hysteresis and surface condition) in the model. This makes the digital twin model used for optimization decisions more closely resemble the actual health condition of the equipment in the next operation, forming an enhanced closed loop that effectively combats the uncertainties brought about by equipment aging and environmental changes.

[0061] Example 2 like Figures 1-4 As shown, specifically: Inside the switchgear of this embodiment, due to the heating of the conductors, heat accumulates upwards, forming a temperature gradient with higher temperatures at the top and lower temperatures at the bottom. Meanwhile, cold air sinks, potentially causing the bottom to be even colder. According to the Clapeyron Clausius equation, temperature directly affects saturated vapor pressure, resulting in a complex vertical distribution of absolute humidity. The top may have lower relative humidity due to higher temperatures, but its absolute humidity may not necessarily be low; the bottom may be closer to the dew point due to lower temperatures.

[0062] Specific effects: By deploying sensors in high, medium, and low zones and constructing a three-dimensional distribution field, it is possible to accurately capture the internal microclimate formed by thermodynamic processes within the cabinet, which differs from the humidity of the external environment. This is especially important for switchgear installed indoors with large temperature differences or high self-heating. It avoids misjudgment of overall humidity caused by placing a single sensor in a non-representative location, such as only installing it in the middle of the cabinet door, and provides a realistic environmental background for insulation assessment. This effect is particularly crucial for protecting vertically arranged busbar systems, especially for tall armored switchgear.

[0063] After long-term operation, the busbars and contact surfaces in switchgear will develop specific microstructures due to electro-corrosion, oxidation, and dust accumulation. The distribution, depth, and sharpness of the grooves on a newly polished surface are drastically different from those on a surface after ten years of operation.

[0064] By combining microscopic morphology data, its specific effect lies in enabling water film thickness calculation to remember the historical surface conditions of individual equipment. For older cabinets with rough surfaces and heavy contamination, the algorithm calculates a thicker and more continuous water film, thus providing a more conservative and safer estimate of dielectric constant shift. For new cabinets with smooth surfaces, the assessment risk is lower. This achieves personalized and precise insulation assessment, solving the problem that different equipment have different insulation risks under the same humidity, yet cannot be treated differently.

[0065] In vacuum switchgear or SF6 switchgear, the dielectric characteristics of the contact gaps differ from those in air switchgear, and the calculation formulas and parameters for electrical circuits also differ. However, the physical nature of the coupling remains unchanged.

[0066] Specific effects: This claim clarifies a unified time step and closed-loop data exchange for displacement, electric field, electrodynamic force, and load, providing a general multiphysics coupling simulation framework for different types of switchgear; different types of switchgear can be adapted simply by replacing the dielectric constitutive model and power calculation formula in the electric field calculation; this modular coupling method enhances the portability and universality of the technical solution of this application.

[0067] In optimization algorithms, constraints must be expressed as mathematical inequalities to be processed. Maintaining the following, or stationarity, are verbal descriptions and cannot be directly computed.

[0068] Specific effects: By quantifying constraints into comparisons with preset thresholds, it successfully translates ambiguous engineering language into mathematical language that optimization algorithms can recognize and execute. The maximum electric field strength not exceeding the threshold can be directly judged at each simulation time step; the pressure fluctuation amplitude not exceeding the threshold can be judged by calculating the standard deviation or peak value on a data sequence after synapse occurrence. This translation step is a crucial prerequisite for achieving automatic optimization, enabling the computer to rigorously check constraint satisfaction as if checking a formula.

[0069] In complex distribution networks or substations, the circuits of switchgear operation are often not isolated. For example, when closing a standby incoming line, the phase relationship with the voltage on the other side of the bus tie switch that is in operation must be considered.

[0070] Specific effect: By calculating the phase difference with all adjacent circuits and taking the intersection, its specific effect is that it achieves system-level optimization of closing synchronization, rather than device-level optimization. This ensures that the closing operation has minimal impact not only on the switchgear itself but also on the entire related local power grid system, preventing unexpected circulating current or power flow impacts caused by closing, and improving the overall safety and stability of power grid operation.

[0071] Coupled simulations may require multiple iterations within each time step to reach equilibrium, meaning that motion and electric field feedback each other until the change becomes negligible; unrestricted iterations will result in excessively long computation times, failing to meet the requirements of real-time optimization.

[0072] Specific effects: By setting dual convergence thresholds based on the rate of change of velocity and the rate of change of electric field strength, a smart balance is achieved between computational accuracy and efficiency. When the change in physical quantities becomes negligible, the iteration of the current time step is terminated early, and the process quickly moves to the next time step, greatly accelerating the simulation speed of the entire closing process. This makes it possible to simulate and evaluate a large number of candidate current waveforms within a limited time, ensuring the practicality of the optimization algorithm.

[0073] The vibration spectrum during the closing process includes a rich array of frequency components, corresponding to the dynamic behavior of different components of the mechanism, such as linkage impact, contact collision, and spring chatter. Hysteresis characteristics directly affect the damping and energy dissipation of these collisions.

[0074] Specific effects: By using vibration spectrum for calibration, it finds an observation signal that is extremely sensitive to mechanical nonlinear parameters and easy to measure online; the amplitude and frequency distribution of the vibration spectrum are sensitive to changes in hysteresis model parameters; through comparison and adjustment, it can efficiently match the abstract hysteresis parameters in the model with the sound fingerprint emitted by the actual mechanism, with high calibration accuracy and no need to disassemble the equipment for offline testing.

[0075] The contact resistance of the contactor decreases from initial contact to final stability, reflecting the physical process of the surface film being broken by mechanical pressure and the gradual establishment of contact on the clean metal surface; the presence of water film and oxide film will significantly affect the speed of this process and the final steady-state resistance value.

[0076] Specific effects: By using contact resistance to establish a curve for calibration, it transforms the traditional electrical contact quality indicator of contact resistance into a detection tool for inverting the state of the surface dielectric film; by analyzing the characteristics of the resistance curve, it is possible to indirectly infer the surface film that causes the dielectric constant shift, including not only water film but also the overall effect of other contaminant films, thereby achieving a more comprehensive calibration of the dielectric constant shift parameter, covering other surface contamination factors besides water film.

[0077] Example 3 Specifically: The intelligent operation control system of the switchgear can be integrated into the intelligent control unit of the switchgear body, or it can be deployed on the edge computing device of the station control layer. The data flow and control flow of each module cooperate closely to form a complete intelligent body for perception, decision-making, execution and learning.

[0078] The data acquisition module consists of a multi-zone temperature and humidity sensor array arranged within the cabinet, a micro-vision or laser profile sensor for acquiring the surface condition of the busbar, a voltage transformer for acquiring three-phase voltage, a current transformer for acquiring load current, a vibration acceleration sensor mounted on the operating mechanism, and a micro-resistance test circuit for measuring contact resistance. This module is responsible for synchronously or on-demand acquisition of all raw data, and for performing preliminary filtering and formatting.

[0079] The distribution field construction and parameter calculation module, as well as the mechanical state identification module, are typically run by a high-performance computing core in an embedded processor or industrial control computer. This core loads software libraries containing algorithms for saturated vapor pressure calculation, spatial interpolation, capillary condensation models, and nonlinear system identification. It receives raw data, performs the described calculation and identification tasks offline or online, and outputs key model parameters such as gradient distribution field, dielectric constant offset parameters, stiffness parameters, and hysteresis characteristic parameters.

[0080] The coupling calculation module and constraint insertion and optimization module are the system's decision-making brain, requiring the highest computing power. They can be deployed on local computing units with GPU acceleration or access more powerful cloud computing resources via high-speed networks. This module has built-in or can utilize simplified engines from commercial multiphysics coupling simulation software or self-developed solvers, as well as optimization algorithm libraries. It receives real-time parameters from the parameter calculation and identification module, constructs a specific simultaneous calculation model for this closing task, incorporates timing constraints and other quantitative constraints provided by the synchronization window determination module, performs trajectory optimization calculations, and finally outputs the optimal drive current waveform. The constraint insertion and optimization module embodies intelligence, transforming engineering problems into mathematical problems and solving them.

[0081] Synchronization window determination module: Implemented by a high-speed analog signal acquisition chip and a digital signal processor, it analyzes the voltage waveform in real time, quickly calculates the phase and tolerance window, and provides key time boundaries for optimization.

[0082] Operation execution module: Typically a high-performance motor driver or power electronic switching circuit. It receives digital current waveform commands generated by the optimization module and, through closed-loop control, such as a current loop, precisely drives the servo motor or electromagnet of the operating mechanism to complete the closing action.

[0083] Calibration and storage modules: The calibration module starts after operation, processes vibration and resistance data, runs parameter update algorithms, and fine-tunes the model parameters. The storage module is used to persistently save calibrated parameters, historical operation records, optimized waveforms, etc., forming a unique digital archive for the switchgear, which can be retrieved for the next operation, thus accumulating experience.

[0084] This application, through the aforementioned method and system, achieves for the first time a closed-loop intelligent optimization control based on a high-fidelity multi-physics coupling model and online parameter calibration in the field of switchgear closing operation. It fundamentally solves the control challenges arising from the strong coupling of environmental, mechanical, and electrical factors, transforming the closing operation from the execution of a fixed program into a globally optimal customized action calculated for each specific condition. This fundamentally improves the reliability, safety, and intelligence level of switchgear operation.

[0085] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent operation control of a switchgear, characterized in that, The method includes: Collect temperature and humidity data inside the switch cabinet and microscopic morphology data of the busbar surface to construct a vertical gradient distribution field of water vapor inside the cabinet; Based on the gradient distribution field and micromorphological data, the equivalent water film thickness distribution on the surface of the busbar is calculated, and the corresponding local dielectric constant shift parameter is determined. Collect historical operation and load data of the operating mechanism, calculate mechanical fatigue state parameters, and identify the current stiffness parameters and hysteresis characteristic parameters of the energy storage spring based on the mechanical fatigue state parameters. Based on the gradient distribution field and dielectric constant shift parameter, the electric field process of the non-uniform medium is calculated; based on the stiffness parameter and hysteresis characteristic parameter, the motion process of the mechanism with nonlinear hysteresis is calculated; the electric field process calculation and the motion process calculation are coupled to form a joint calculation relationship. Obtain the voltage phase information of the target circuit and determine the voltage synchronization tolerance window for the closing operation; The tolerance window is used as a timing constraint, the electric field process intensity is kept below the critical breakdown field strength as an insulation constraint, and the contact pressure is kept stable as a mechanical constraint. All of these are incorporated into the simultaneous calculation relationship. Trajectory optimization is performed in the solution space that satisfies all constraints to generate the drive current waveform of the operating mechanism and execute the closing operation. A curve was established by collecting the mechanical vibration spectrum during the closing process and the contact resistance after closing. The mechanical vibration spectrum is compared with the predicted spectrum calculated based on the motion process to generate first deviation data, and the hysteresis characteristic parameters are calibrated according to the first deviation data. The contact resistance curve is compared with the expected curve to generate second deviation data, and the dielectric constant offset parameter is calibrated based on the second deviation data. The calibrated hysteresis characteristic parameters and dielectric constant offset parameters are stored as input parameters for constructing the combined calculation relationship during the closing operation.

2. The method according to claim 1, characterized in that, The method of collecting temperature and humidity data inside the switchgear and microscopic morphology data of the busbar surface to construct a vertical gradient distribution field of water vapor inside the switchgear includes: Temperature and humidity sensor groups are installed in the high, middle and low areas of the switch cabinet interior space; Collect temperature and relative humidity readings from each sensor group; Substitute the temperature and relative humidity readings into the saturated vapor pressure calculation formula to obtain the absolute vapor concentration value at each monitoring point. The absolute water vapor concentration values ​​of all monitoring points are processed using a spatial interpolation algorithm to generate three-dimensional spatial distribution data of water vapor concentration. Extract the concentration change profile along the vertical direction from the three-dimensional water vapor concentration spatial distribution data, and use it as the water vapor vertical gradient distribution field.

3. The method according to claim 2, characterized in that, The calculation of the equivalent water film thickness distribution on the surface of the busbar based on the gradient distribution field and micromorphological data includes: Obtain the surface roughness profile information from the micro-morphology data of the busbar surface; Based on the spatial position of each micro-groove in the surface roughness profile information, the corresponding absolute water vapor concentration value is determined from the water vapor vertical gradient distribution field. Based on the principle of capillary condensation and using the determined absolute water vapor concentration values ​​corresponding to each groove, the critical thickness of condensate in each micro-groove is calculated. The critical condensate thickness of all grooves is summarized to form the equivalent water film thickness distribution on the surface of the busbar.

4. The method according to claim 1, characterized in that, The coupling of the electric field process calculation with the motion process calculation to form a joint calculation relationship includes: Set a uniform time step sequence; Within each time step, the motion process of the actuator, including nonlinear hysteresis, is calculated, and the spatial displacement and velocity of the contact under the step are output. Using the spatial displacement as the input condition for electric field calculation, the geometric configuration of the contact gap is determined. Based on the geometric configuration and the dielectric constant offset parameter, the non-uniform dielectric electric field process is calculated to obtain the current electric field intensity distribution. The current electric field intensity distribution is applied to the contact surface, and the equivalent electrodynamic force is calculated; The equivalent electrodynamic force is used as a mechanical load input and fed back into the motion process calculation for the next time step. The process is iterative on the time step sequence, with data from the electric field process and the motion process being input to each other to form a simultaneous calculation relationship.

5. The method according to claim 1, characterized in that, Maintaining the electric field intensity below the critical breakdown field strength as an insulation constraint condition and using stable contact pressure as a mechanical constraint condition includes: The insulation constraint condition is quantified as follows: in the time step sequence of the simultaneous calculation relationship, the calculated maximum electric field strength shall not exceed the preset critical breakdown field strength threshold. The mechanical constraint condition is quantified as follows: within a set time period after the contact closure event occurs, the fluctuation amplitude of the instantaneous contact pressure between the contacts shall not exceed the preset upper limit threshold of the allowable fluctuation. The trajectory optimization process uses the driving current waveform as the optimization variable.

6. The method according to claim 1, characterized in that, The step of acquiring the voltage phase information of the target circuit and determining the voltage synchronization tolerance window for the closing operation includes: Obtain the voltage waveform of the target circuit; The fundamental phase angle is extracted from the voltage waveform using the zero-crossing detection method. Obtain the voltage phase angle of adjacent circuits that are electrically coupled to the target circuit; Calculate the difference between the fundamental phase angle of the target circuit and the voltage phase angle of each adjacent circuit; Based on the preset allowable closing inrush current threshold, the allowable closing phase angle range is calculated by reverse calculation for each of the differences; The intersection of all calculated allowable closing phase angle ranges is used to obtain the voltage synchronization tolerance window.

7. The method according to claim 1, characterized in that, The process of solving the simultaneous computational relationships includes: During the iteration of the time step sequence, the contact movement velocity and electric field distribution calculated in two adjacent iterations are obtained; When the rate of change of the contact movement speed is less than the first convergence threshold, and the rate of change of the electric field strength in the key region of the electric field distribution is less than the second convergence threshold, it is determined that the simultaneous calculation relationship has reached convergence within the current time step, and the calculation of the next time step begins.

8. The method according to claim 1, characterized in that, The step of comparing the mechanical vibration spectrum with the predicted spectrum calculated based on the motion process to generate first deviation data, and calibrating the hysteresis characteristic parameters based on the first deviation data, includes: Feature extraction is performed on the collected mechanical vibration spectrum to obtain a set of measured amplitudes consisting of the amplitudes of the dominant frequency and the key harmonics; From the predicted spectrum calculated based on the motion process, extract the predicted amplitude set corresponding to the frequency points in the measured amplitude set; An amplitude deviation sequence is formed by calculating the amplitude difference between the measured amplitude set and the predicted amplitude set at each corresponding frequency point. The amplitude deviation sequence is input into the parameter update algorithm, which calculates the adjustment amount for the hysteresis characteristic parameter based on the amplitude deviation sequence, and applies the adjustment amount to complete the calibration update of the hysteresis characteristic parameter.

9. The method according to claim 3, characterized in that, The step of comparing the established contact resistance curve with the expected curve to generate second deviation data, and calibrating the dielectric constant offset parameter based on the second deviation data, includes: Based on the contact resistance, a curve is established, the steady-state resistance value after the contact stabilizes is extracted, and the fall time required for the resistance value to drop from the initial state to the steady-state resistance value is extracted. Read the expected steady-state resistance value and expected fall time corresponding to the steady-state resistance value and fall time extracted above from the expected curve; Calculate the difference between the steady-state resistance value and the expected steady-state resistance value, and calculate the ratio of the fall time to the expected fall time; Based on the difference and the ratio, the surface energy parameters used in calculating the equivalent water film thickness distribution are corrected in reverse by using a pre-established functional relationship. The local dielectric constant offset parameter is updated based on the corrected surface energy parameter.

10. An intelligent operation control system for switchgear, characterized in that, The system includes: The data acquisition module is used to collect temperature and humidity data inside the switch cabinet and microscopic morphology data of the busbar surface, as well as historical operation and load data of the operating mechanism. The distribution field construction and parameter calculation module is used to construct the vertical gradient distribution field of water vapor inside the cabinet based on the collected temperature and humidity data and micro-morphology data, and calculate the equivalent water film thickness distribution on the surface of the busbar based on the gradient distribution field and micro-morphology data, and determine the corresponding local dielectric constant shift parameter. The mechanical state identification module is used to calculate mechanical fatigue state parameters based on the historical operation and load data, and to identify the current stiffness parameters and hysteresis characteristic parameters of the energy storage spring based on the mechanical fatigue state parameters. The coupled calculation module is used to calculate the electric field process of the non-uniform medium based on the gradient distribution field and the dielectric constant offset parameter, and to calculate the motion process of the mechanism with nonlinear hysteresis based on the stiffness parameter and hysteresis characteristic parameter. The electric field process calculation and the motion process calculation are coupled to form a joint calculation relationship. The synchronization window determination module is used to obtain the voltage phase information of the target circuit and determine the voltage synchronization tolerance window for the closing operation. The constraint insertion and optimization module is used to use the tolerance window as a timing constraint, the electric field process intensity to be maintained below the critical breakdown field strength as an insulation constraint, and the contact pressure to be stable as a mechanical constraint, and to insert them into the simultaneous calculation relationship. The module then performs trajectory optimization in the solution space that satisfies all the constraints to generate the drive current waveform of the operating mechanism. The operation execution module is used to perform the closing operation based on the generated drive current waveform; The calibration module is used to collect the mechanical vibration spectrum during the closing process and the contact resistance curve after closing. It compares the mechanical vibration spectrum with the predicted spectrum calculated based on the motion process to generate first deviation data and calibrates the hysteresis characteristic parameter according to the first deviation data. It compares the contact resistance curve with the expected curve to generate second deviation data and calibrates the dielectric constant offset parameter according to the second deviation data. The storage module is used to store the calibrated hysteresis characteristic parameters and dielectric constant offset parameters as input parameters for constructing the combined calculation relationship in subsequent closing operations.

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