Magnetic bearing rotor control system and method for charging station

Abstract

The invention discloses a magnetic bearing rotor control system and method for a charging station. The system comprises a displacement self-checking module, a pulsation suppression module, a control system, an electromagnetic adjustment module, a magnetic bearing rotor and a multi-stage protection module, the method comprises the following steps: the displacement self-checking module monitors rotor displacement and rotation angular velocity in real time by using an eddy current sensor, a laser displacement sensor and an optical encoder; the pulsation suppression module measures pulsation frequency and amplitude based on an MEMS sensor and a laser vibration meter; the control system fuses data through a central processing unit and generates a control signal by adopting a multi-objective optimization control strategy based on a particle swarm; an inverter in the pulsation suppression module receives the signal and then adjusts an electromagnetic field, and stable control over the rotor is achieved. And the multi-stage protection module provides overload, overcurrent, undervoltage and over-temperature protection, so that the safe operation of the system is ensured. According to the invention, the detection precision is improved through multi-sensor fusion, rapid response and energy consumption balance are realized in combination with an optimization control strategy, and the overall performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic bearings, and particularly to a magnetic bearing rotor control system and method for a charging station. Background Art

[0002] Due to advantages such as no friction, no lubrication requirement, and high-precision control, magnetic bearings are widely used in high-speed rotating equipment. However, during operation, the magnetic bearing rotor is prone to being affected by external disturbances and system instability, resulting in increased vibration, increased energy consumption, and even instability.

[0003] There are four key bottlenecks in the existing magnetic bearing rotor stability control technologies: First, the detection accuracy of traditional single sensors is insufficient, making it difficult to capture micron-level displacement changes under high-speed rotation in a timely manner; second, the single-objective PID control strategy cannot take into account multiple requirements such as displacement stability, vibration suppression, and energy consumption optimization; third, the adjustment of electromagnetic field parameters adopts a static preset mode and cannot dynamically adapt to complex working conditions such as sudden load changes; fourth, the protection mechanism has a slow response and lacks a hierarchical strategy.

[0004] In view of these problems, there is an urgent need to develop a new control method, focusing on breaking through key technologies such as multi-sensor fusion detection, multi-objective collaborative optimization based on intelligent algorithms, millisecond-level electromagnetic field dynamic adjustment, and hierarchical fast protection, so as to achieve high precision, high stability, and high energy efficiency of rotor operation in scenarios such as charging stations. Summary of the Invention

[0005] In response to the above technical problems, the present technical solution provides a magnetic bearing rotor control system and method for a charging station. Through the collaborative action of a displacement self-check module and a pulsation suppression module, high-precision rotor detection is achieved. The control system uses a central processor to fuse and optimize the collected data, generates a control signal based on a multi-objective optimization control strategy of particle swarm, and transmits it to an inverter in the pulsation suppression module. The inverter accurately adjusts the electromagnetic field intensity according to this signal and transmits the adjusted signal to the electromagnetic adjustment module, which acts on the magnetic bearing rotor to achieve stable control. In addition, the system integrates multi-level protection functions such as overload, overcurrent, undervoltage, and overtemperature to ensure safe operation and improve the response speed, anti-interference ability, and service life, effectively solving the above problems.

[0006] The present invention is achieved through the following technical solutions:

[0007] A magnetic bearing rotor control system for a charging station, comprising: a displacement self-check module, a pulsation suppression module, a control system, an electromagnetic regulation module, a magnetic bearing rotor, and a multi-level protection module; the displacement self-check module includes an eddy current sensor, a laser displacement sensor, and an optical encoder; the pulsation suppression module consists of a MEMS acceleration sensor network, a laser vibrometer, and an inverter; the control system takes a central processing unit CPU as the core; the multi-level protection module is composed of an overcurrent detection circuit, a voltage monitoring chip, a distributed temperature sensor, and a mechanical limit device, and works in coordination with each functional module of the system.

[0008] Further, the displacement self-check module monitors the radial displacement, axial displacement, and rotational angular velocity of the magnetic bearing rotor in real time through the eddy current sensor, laser displacement sensor, and optical encoder, providing accurate feedback for the electromagnetic regulation and pulsation suppression of the system.

[0009] Further, the pulsation suppression module uses a laser vibrometer to collect pulsations, measure transient characteristics, and provide high-resolution vibration amplitude and spectrum data, which is applicable to ultra-high-speed rotating systems; the MEMS sensors detect the frequency and amplitude of pulsations, capturing high-frequency, low-amplitude micro-pulsations during high-speed rotation with high sensitivity and low power consumption characteristics to achieve accurate monitoring.

[0010] Further, the inverter in the pulsation suppression module receives the control signal from the central processing unit, and by fusing the rotor displacement and vibration data of the displacement self-check module and the pulsation detection system in real time, uses the particle swarm multi-objective optimization algorithm to dynamically solve the optimal parameters of the electromagnetic field strength, frequency, and phase, and generates a high-frequency PWM signal based on space vector modulation and phase-locked loop technology to accurately adjust the three-phase voltage amplitude, frequency, and phase of the inverter output, synchronously optimizing the magnetic force distribution; by precisely controlling the electromagnetic regulation module, ensuring the stable suspension of the magnetic bearing rotor under different working conditions, reducing vibration, and improving the anti-interference ability; it can adjust the output in real time to keep the system in the best state during high-speed rotation or load changes, improving the operation efficiency and stability.

[0011] A magnetic bearing rotor control method for a charging station, comprising the steps of:

[0012] Step 1: Start the displacement self-check module, and use the eddy current sensor, laser displacement sensor, and optical encoder to monitor the radial displacement, axial displacement, and rotational angular velocity of the rotor in real time to ensure accurate feedback on the operating state of the system;

[0013] Step 2: Start the pulsation suppression module, and use the MEMS acceleration sensor network and laser vibrometer to accurately measure the pulsation frequency and amplitude when the rotor rotates at high speed; extract the pulsation characteristics through fast Fourier transform, analyze the vibration characteristics of the rotor, and transmit the data to the control system;

[0014] Step 3: The control system relies on the central processing unit to receive real-time data from the displacement self-check module and the pulsation suppression module, and adopts a multi-objective optimization control strategy based on particle swarm; The specific operation method includes:

[0015] Step 3.1: Initialize the optimization variables and set the initial values;

[0016] Step 3.2: Set four optimization objectives and ensure that the optimization variables meet the physical constraints of current, magnetic field strength, and voltage; Adopt the Pareto optimal strategy to seek a compromise solution, and combine multiple objectives into a single optimization function through the linear weighted sum method;

[0017] Step 3.3: Calculate the gradient using the multi-objective optimization gradient descent method based on particle swarm, and adjust the variables along the optimal direction; Update the control parameters iteratively until the convergence condition is met;

[0018] Step 3.4: Obtain the optimal control parameters to ensure the stable and efficient operation of the system;

[0019] Step 4: According to the optimal control parameters, the central processing unit generates a control signal, and the inverter receives the control signal to dynamically adjust the intensity, frequency, and phase of the electromagnetic field, optimizing the magnetic force distribution; By precisely controlling the electromagnetic regulation module, ensure that the magnetic bearing rotor is stably levitated under different working conditions, reduce vibration, and improve the anti-interference ability;

[0020] Step 5: The multi-level protection module monitors the system status in real time, including abnormal conditions such as overload, overcurrent, undervoltage, and overtemperature; When an abnormality is detected, the multi-level protection module works in coordination with the control center to adjust parameters or perform an emergency shutdown to prevent equipment damage or operation failures.

[0021] Further, the specific operation method of Step 3.1 is: Initialize the parameters, define the optimization variable x, and set the initial values:

[0022]

[0023] where I is the current control parameter; B is the magnetic field strength; P is the position of the electromagnetic coil; K p ,K i ,K d are the PID control gains; V is the output voltage of the inverter; f is the output frequency of the inverter.

[0024] Further, the specific operation method of Step 3.2 is:

[0025] Step 3.21: The optimization objectives involve multiple performance indicators. To achieve the best operating state of the system, four optimization objectives need to be established, respectively measuring displacement error, pulsation amplitude, energy consumption, and control response speed;

[0026] (1) The stability of the rotor is measured by its deviation from the ideal suspension point, and the goal is to minimize the displacement error:

[0027]

[0028] Where P i is the position of the electromagnetic coil at the i-th sampling moment; P ref is the desired suspension position; the controlled variable P 0 is the initial coil position, which determines the starting state of the system;

[0029] (2) The vibration amplitude of the rotor is monitored in real time by the pulsation suppression module. To reduce vibration and improve the stability of the suspension system, the following optimization formula is established:

[0030]

[0031] Where A j is the amplitude corresponding to the main pulsation frequency f j in the spectral analysis; M is the total number of vibration frequencies; the controlled variable B 0 is the initial magnetic field strength, which determines the magnitude and uniformity of the magnetic levitation force;

[0032] (3) The energy consumption of the magnetic levitation system mainly comes from the maintenance of the electromagnetic force. To reduce the total energy consumption during system operation and improve efficiency, the following optimization formula is established:

[0033]

[0034] Where P(t) is the instantaneous power of the system; V(t) is the output voltage provided by the inverter; I(t) is the current in the coil; the controlled variables V 0 I 0 are the initial voltage and current, which affect the initial power consumption level of the system;

[0035] (4) The delay of the control system affects the recovery ability of the rotor under external disturbances and can be modeled by the time constant τ:

[0036] f4(x) = τ(V, f)

[0037] Where τ is the time constant of the system, representing the dynamic response speed of the control system; V is the output voltage of the inverter, which affects the rapid adjustment ability of the magnetic levitation system; f is the output frequency of the inverter, which determines the response rate of the electromagnetic field change; the controlled variables V 0 is the initial voltage, which affects the power output ability of the control system; f 0 is the initial output frequency, which affects the response time of the system;

[0038] The final multi-objective optimization formula is as follows:

[0039]

[0040] Step 3.22: During the optimization process, to ensure the safety of the system operation, all optimization variables must satisfy certain physical constraints, including current, electromagnetic field strength, and voltage limits:

[0041] g1(x) = I max -I ≥ 0

[0042] g2(x) = B max -B ≥ 0

[0043]

[0044] where, I max is the maximum allowable current; B max is the maximum allowable magnetic induction intensity; U max is the maximum allowable voltage.

[0045] Furthermore, the specific operation method of Step 3.3 is as follows:

[0046] Step 3.31: Adopt the particle swarm optimization strategy to find the optimal solution: Without affecting a certain objective, minimize the displacement error, suppress the pulsation amplitude, reduce the energy consumption, and improve the control response speed; the formula is as follows:

[0047]

[0048] In the formula, x * is the optimal solution of the particle swarm, that is, based on the current solution, all objectives cannot be improved simultaneously;

[0049] Step 3.32: In order to find a suitable balance point during the optimization process, use the linear weighted sum method to combine multiple objectives into one optimization objective:

[0050] F(x) = λ1f1(x) + λ2f2(x) + λ3f3(x) + λ4f4(x)

[0051] In the formula, λ k is the weight parameter, satisfying the normalization condition:

[0052] Step 3.33: Since the objective function f k (x) has non-linear characteristics, use the multi-objective gradient descent method for optimization and calculate the gradient of each objective function:

[0053]

[0054] The optimization direction is determined by: That is, find a direction d to maximize the descent of the gradients of all objective functions;

[0055] Step 3.34: Update the particle velocity and position variables:

[0056]

[0057] where v i t is the velocity vector of particle iteration, v i t+1 is the velocity after particle iteration update, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r1, r2 are random numbers, p i best is the individual optimal position of the particle, g best is the global historical optimal position, x i t is the position vector of particle iteration, x i t+1 is the position vector after particle iteration update;

[0058] Step 3.35: Calculate the new objective value:

[0059]

[0060] where F(x t+1 ) is the new objective function value, representing the comprehensive optimization objective under the current control parameter x t+1 ; f k (x t +1 ) is the value of the k-th objective function, k = 1, 2, 3, 4 corresponding to different optimization objectives, namely displacement error, pulsation amplitude, energy consumption, and control response delay; x t+1 is the control parameter at the current iteration step t + 1, representing the adjusted variable value during the optimization process, such as current, magnetic field strength, voltage;

[0061] Step 3.36: Set the optimization termination condition:

[0062] ||x t+1 - x t || < ε

[0063] where x t+1 is the optimized variable value at the current step, representing the state of the system after the current iteration; x t is the optimized variable value at the previous step, representing the state of the system at the previous iteration; ||·|| is the norm of the vector, usually the Euclidean norm, calculating the distance between the current and previous parameters; ε is a small tolerance value, set as the condition for optimization termination; if the change between the control variables of two iterations is less than ε, the optimization process stops and the system is considered to have converged;

[0064] Step 3.37: If the termination condition is met, output the optimal control parameter x * ; Otherwise, return to Step 6 and continue iterating.

[0065] Furthermore, the multi-level protection module described in step 5 has overload, overcurrent, undervoltage, and overtemperature protection mechanisms, monitors the system status in real time, and works in coordination with the control center to adjust parameters or perform emergency shutdown when abnormalities are detected; responds quickly to abnormalities to prevent equipment damage or operational failures; and can ensure long-term stable and safe operation of the system, and improve reliability and service life;

[0066] The overload protection monitors the load current in real time through the current transformer. If the current exceeds the rated value by 120% and lasts for 500ms, the power of non-core loads is reduced first, and the main circuit is cut off if the overload continues;

[0067] The overcurrent protection adopts a high-speed electronic circuit breaker and a fuse in linkage. When the current exceeds the limit by 150% for 10ms, the circuit is instantly cut off and the redundant shunt path is activated to prevent the electromagnetic coil from overheating.

[0068] The undervoltage protection dynamically samples the bus voltage through the voltage monitoring chip. If the voltage is lower than 85% of the nominal value for 200ms, it automatically switches to the backup power supply and starts the dynamic voltage compensation module.

[0069] The over-temperature protection deploys distributed temperature sensors at key locations such as electromagnetic coils and inverters. When the temperature exceeds 90°C, forced air cooling is triggered, and when it reaches 105°C, the machine is shut down immediately and an alarm is sounded.

[0070] (III) Beneficial effects

[0071] The magnetic bearing rotor control system and method for a charging station proposed in the present invention have the following beneficial effects compared with the prior art:

[0072] (1) The multi-objective optimization control strategy based on the particle swarm in the present invention integrates advanced data technology and intelligent optimization algorithm. Different from the traditional fixed weight optimization, it dynamically adjusts the priority of displacement error, vibration suppression, energy consumption and response speed through real-time working condition analysis, accurately adjusts the control parameters, introduces the objective function gradient information into the standard PSO, accelerates the convergence to the Pareto frontier, enables the system to adapt to complex working conditions instantly, maintains excellent control accuracy and super robustness under extreme conditions, and significantly improves the overall performance.

[0073] (2) The inverter in the present invention constructs a non - linear mapping model of the electromagnetic field intensity based on the real - time collected rotor displacement and load current, updates the model parameters online, ensures that the steady - state error of the magnetic field intensity is <0.5%, calculates the optimal combination of electromagnetic field parameters in real - time according to the load demand, finds the Pareto optimal solution among energy consumption, stability, and response speed through the Lagrange multiplier method, adaptively adjusts the weight coefficient according to the working conditions, and dynamically adjusts the electromagnetic field intensity and frequency at the millisecond level to ensure that the rotor achieves extremely stable suspension control, while significantly reducing energy consumption and improving system efficiency.

[0074] (3) The present invention integrates sensor data such as current, voltage, and temperature, constructs a fault model through a Bayesian network, calculates the joint occurrence probability of overload, over - current, under - voltage, and over - temperature in real - time, monitors the system status all - weather, accurately predicts potential risks, eliminates sudden failures, and ensures the worry - free operation of the equipment with an extremely long service life. Description of the Drawings

[0075] Figure 1 It is the overall architecture block diagram of Embodiment 1 in the present invention.

[0076] Figure 2 It is the flow chart of the multi - objective optimization control strategy based on particle swarm in Embodiment 2 of the present invention. Detailed Embodiments

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Without departing from the design concept of the present invention, various modifications and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope of the present invention.

[0078] Embodiment 1:

[0079] As Figure 1 shown, a magnetic bearing rotor control system for a charging station includes: a displacement self - inspection module, a pulsation suppression module, a control system, an electromagnetic regulation module, a magnetic bearing rotor, and a multi - level protection module.

[0080] The displacement self - inspection module includes an eddy current sensor, a laser displacement sensor, and an optical encoder; the displacement self - inspection module monitors the radial and axial displacements and rotational angular velocity of the magnetic bearing rotor in real - time through the eddy current sensor, laser displacement sensor, and optical encoder, provides accurate feedback for the electromagnetic regulation and pulsation suppression of the system, and ensures the stable operation of the system.

[0081] The pulsation suppression module is composed of a MEMS acceleration sensor network, a laser vibrometer, and an inverter; the pulsation suppression module uses the laser vibrometer to collect pulsations, measure transient characteristics, and provide high-resolution vibration amplitude and spectrum data, which is applicable to ultra-high-speed rotating systems; the MEMS sensors detect the frequency and amplitude of pulsations, capture high-frequency and low-amplitude minute pulsations during high-speed rotation with the characteristics of high sensitivity and low power consumption, and achieve precise monitoring.

[0082] The inverter in the pulsation suppression module receives the control signal from the central processor, and by fusing in real-time the rotor displacement and vibration data of the displacement self-check module and the pulsation detection system, uses the particle swarm multi-objective optimization algorithm to dynamically solve the optimal parameters of the electromagnetic field strength, frequency, and phase, and generates a high-frequency PWM signal based on space vector modulation and phase-locked loop technology to precisely adjust the three-phase voltage amplitude, frequency, and phase of the inverter output, and synchronously optimize the magnetic force distribution; by precisely controlling the electromagnetic regulation module, it ensures that the magnetic bearing rotor is stably suspended under different working conditions, reduces vibration, and improves the anti-interference ability; it can adjust the output in real time to keep the system in the best state during high-speed rotation or load changes, and improve the operation efficiency and stability.

[0083] The control system takes the central processor CPU as the core;

[0084] The multi-level protection module consists of an overload current detection circuit, a voltage monitoring chip, a distributed temperature sensor, and a mechanical limit device, and works in coordination with each functional module of the system; the multi-level protection module integrates protection mechanisms such as overload, overcurrent, undervoltage, and overtemperature, monitors the system status in real time, and responds quickly in case of anomalies to prevent equipment damage or operation failures. Overload protection limits the system load to prevent exceeding the bearing capacity, overcurrent protection ensures stable current to avoid overheating of the electromagnetic coil, undervoltage protection prevents power fluctuations from affecting operation, and overtemperature protection monitors key components through temperature sensors to prevent performance degradation or failure caused by high temperature. This module can cooperate with the control center to adjust parameters or execute an emergency stop when detecting anomalies, ensuring the stable operation of the system and improving reliability and service life.

[0085] Embodiment 2:

[0086] As Figure 2 shown, a magnetic bearing rotor control method for a charging station includes the steps:

[0087] Step 1: Start the displacement self-check module, and use eddy current sensors, laser displacement sensors, and optical encoders to real-time monitor the radial displacement, axial displacement, and rotational angular velocity of the rotor to ensure accurate feedback of the system operation status.

[0088] Step 2: Activate the pulsation suppression module, and use the MEMS acceleration sensor network and the laser vibrometer to accurately measure the pulsation frequency and amplitude when the rotor rotates at high speed; extract the pulsation characteristics through fast Fourier transform, analyze the vibration characteristics of the rotor, and transmit the data to the control system.

[0089] Step 3: The control system relies on the central processing unit to receive real-time data from the displacement self-check module and the pulsation suppression module, and adopts a multi-objective optimization control strategy based on particle swarm; the specific operation methods include:

[0090] Step 3.1: Initialize the optimization variables and set the initial values; initialize the parameters, define the optimization variable x, and set the initial values:

[0091]

[0092] In the formula, I is the current control parameter; B is the magnetic field strength; P is the position of the electromagnetic coil; K p , K i , K d are the PID control gains; V is the output voltage of the inverter; f is the output frequency of the inverter.

[0093] Step 3.2: Set four optimization objectives and ensure that the optimization variables satisfy the physical constraints of current, magnetic field strength, and voltage; adopt the Pareto optimal strategy to seek a compromise solution, and combine multiple objectives into a single optimization function through the linear weighted sum method; the specific operation method is:

[0094] Step 3.21: The optimization objectives involve multiple performance indicators. To achieve the best operating state of the system, four optimization objectives need to be established to measure the displacement error, pulsation amplitude, energy consumption, and control response speed respectively.

[0095] (1) The stability of the rotor is measured by its deviation from the ideal suspension point, and the goal is to minimize the displacement error:

[0096]

[0097] In the formula, P i is the position of the electromagnetic coil at the i-th sampling moment; P ref is the desired suspension position; the controlled variable P 0 is the initial coil position, which determines the starting state of the system.

[0098] (2) The vibration amplitude of the rotor is monitored in real time by the pulsation suppression module. To reduce the vibration and improve the stability of the suspension system, the following optimization formula is established:

[0099]

[0100] In the formula, A jis the main pulsation frequency f in the spectrum analysis j the corresponding amplitude; M is the total number of vibration frequencies; the controlled variable B 0 the initial magnetic field strength, which determines the magnitude and uniformity of the magnetic levitation force.

[0101] (3) The energy consumption of the magnetic levitation system mainly comes from the maintenance of the electromagnetic force. In order to reduce the total energy consumption during system operation and improve efficiency, the following optimization formula is established:

[0102]

[0103] In the formula, P(t) is the instantaneous power of the system; V(t) is the output voltage provided by the inverter; I(t) is the current in the coil; the controlled variable V 0 I 0 the initial voltage and current, which affect the initial power consumption level of the system.

[0104] (4) The delay of the control system affects the rotor's recovery ability under external disturbances and can be modeled by the time constant τ:

[0105] f4(x) = τ(V, f)

[0106] In the formula, τ is the time constant of the system, representing the dynamic response speed of the control system; V is the output voltage of the inverter, which affects the rapid adjustment ability of the magnetic levitation system; f is the output frequency of the inverter, which determines the response rate of the electromagnetic field change; the controlled variable V 0 the initial voltage, which affects the power output ability of the control system; f 0 the initial output frequency, which affects the response time of the system.

[0107] The final multi-objective optimization formula is as follows:

[0108]

[0109] Step 3.22: During the optimization process, to ensure the safety of system operation, all optimization variables must satisfy certain physical constraints, including current, electromagnetic field strength, and voltage limits:

[0110] g1(x) = I max -I ≥ 0

[0111] g2(x) = B max -B ≥ 0

[0112]

[0113] Among them, I max is the maximum allowable current; B max is the maximum allowable magnetic induction intensity; U max is the maximum allowable voltage.

[0114] Step 3.3: Calculate the gradient using the multi-objective optimization gradient descent method based on particle swarm, and adjust the variables along the optimal direction; update the control parameters through iteration until the convergence condition is met; the specific operation method is as follows:

[0115] Step 3.31: Adopt the particle swarm optimization strategy to find the optimal solution: Without affecting a certain objective, optimize the minimization of displacement error, pulsation amplitude suppression, energy consumption, and control response speed; the formula is as follows:

[0116]

[0117] In the formula, x * is the optimal solution of the particle swarm, that is, based on the current solution, all objectives cannot be improved simultaneously.

[0118] Step 3.32: In order to find a suitable balance point during the optimization process, use the linear weighted sum method to combine multiple objectives into one optimization objective:

[0119] F(x) = λ1f1(x) + λ2f2(x) + λ3f3(x) + λ4f4(x)

[0120] In the formula, λ k is the weight parameter, satisfying the normalization condition:

[0121] Step 3.33: Since the objective function f k (x) has non-linear characteristics, use the multi-objective gradient descent method for optimization to calculate the gradient of each objective function:

[0122]

[0123] The optimization direction is determined by: That is, find a direction d to maximize the descent of the gradients of all objective functions.

[0124] Step 3.34: Update the particle velocity and position variables:

[0125]

[0126] In the formula, v i t is the velocity vector of particle iteration, v i t+1 is the velocity after particle iteration update, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r1, r2 are random numbers, p i best is the individual optimal position of the particle, g best is the global historical optimal position, x it is the position vector for particle iteration, x i t+1 is the position vector after particle iteration update.

[0127] Step 3.35: Calculate the new objective value:

[0128]

[0129] In the formula, F(x t+1 ) is the new objective function value, representing the comprehensive optimization objective under the current control parameter x t+1 ; f k (x t +1 ) is the value of the k-th objective function, where k = 1, 2, 3, 4 correspond to different optimization objectives, namely displacement error, pulsation amplitude, energy consumption, and control response delay; x t+1 is the control parameter at the current iteration step t + 1, representing the adjusted variable value during the optimization process, such as current, magnetic field strength, voltage.

[0130] Step 3.36: Set the optimization termination condition:

[0131] ||x t+1 - x t || < ε

[0132] In the formula, x t+1 is the optimized variable value at the current step, representing the state of the system after the current iteration; x t is the optimized variable value at the previous step, representing the state of the system at the previous iteration; ||·|| is the norm of the vector, usually the Euclidean norm, calculating the distance between the current and previous step parameters; ε is a small tolerance value, set as the condition for optimization termination. If the change between the control variables of two consecutive iterations is less than ε, the optimization process stops and the system is considered to have converged.

[0133] Step 3.37: If the termination condition is met, output the optimal control parameter x * ; otherwise, return to Step 6 to continue the iteration.

[0134] Step 3.4: Obtain the optimal control parameter to ensure the stable and efficient operation of the system.

[0135] Step 4: According to the optimal control parameter, the central processing unit generates a control signal, and the inverter receives the control signal to dynamically adjust the intensity, frequency, and phase of the electromagnetic field, optimizing the magnetic force distribution; by precisely controlling the electromagnetic regulation module, ensure that the magnetic bearing rotor is stably suspended under different working conditions, reduce vibration, and improve the anti-interference ability.

[0136] Step 5: The multi-level protection module monitors the system status in real time, including abnormal conditions such as overload, overcurrent, undervoltage, and overtemperature; when an abnormality is detected, the multi-level protection module works in coordination with the control center to adjust parameters or perform an emergency shutdown to prevent equipment damage or operating failures; the multi-level protection module has overload, overcurrent, undervoltage, and overtemperature protection mechanisms, monitors the system status in real time, works in coordination with the control center, adjusts parameters or performs an emergency shutdown when an abnormality is detected; responds quickly in case of an abnormality to prevent equipment damage or operating failures; can ensure the long-term stable and safe operation of the system, and improve reliability and service life.

[0137] The overload protection mentioned above monitors the load current in real time through a current transformer. If the current exceeds 120% of the rated value and lasts for 500 ms, the power of non-core loads will be preferentially reduced. If the overload continues, the main circuit will be cut off.

[0138] The overcurrent protection adopts the linkage of a high-speed electronic circuit breaker and a fuse. When the current exceeds the limit by 150% and lasts for 10 ms, the circuit will be instantly cut off and a redundant shunt path will be activated to avoid overheating of the electromagnetic coil.

[0139] The undervoltage protection dynamically samples the bus voltage through a voltage monitoring chip. If the voltage is lower than 85% of the nominal value and lasts for 200 ms, it will automatically switch to the backup power supply and activate the dynamic voltage compensation module.

[0140] The overtemperature protection deploys distributed temperature sensors at key positions such as electromagnetic coils and inverters. When the temperature exceeds 90 °C, forced air cooling will be triggered. When it reaches 105 °C, the machine will be immediately shut down and an alarm will be issued.

[0141] The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A magnetic bearing rotor control system for a charging station, characterized in that: include: Displacement self-check module, pulsation suppression module, control system, electromagnetic adjustment module, magnetic bearing rotor and multi-stage protection module; the displacement self-check module includes eddy current sensor, laser displacement sensor and optical encoder; the pulsation suppression module is composed of MEMS acceleration sensor network, laser vibrometer and inverter; the control system is based on central processing unit CPU; the multi-stage protection module is composed of overload current detection circuit, voltage monitoring chip, distributed temperature sensor and mechanical limit device, and works in coordination with various functional modules of the system.

2. A magnetic bearing rotor control system for a charging station according to claim 1, characterized in that: The displacement self-checking module monitors the radial and axial displacements and rotational angular velocity of the magnetic bearing rotor in real time through eddy current sensors, laser displacement sensors and optical encoders.

3. The magnetic bearing rotor control system for a charging station according to claim 1, characterized in that: The pulsation suppression module uses a laser vibrometer to collect pulsations and measure transient characteristics; the MEMS sensor detects the frequency and amplitude of the pulsations.

4. The magnetic bearing rotor control system for a charging station according to claim 1, characterized in that: The inverter in the pulsation suppression module receives the control signal of the central processor, and through real-time fusion of the rotor displacement and vibration data of the displacement self-detection module and the pulsation detection system, a particle swarm multi-objective optimization algorithm is used to dynamically solve the optimal parameters of the electromagnetic field intensity, frequency and phase, and a high-frequency PWM signal is generated based on space vector modulation and phase-locked loop technology to adjust the three-phase voltage amplitude, frequency and phase output by the inverter, and synchronously optimize the magnetic force distribution.

5. A magnetic bearing rotor control method for a charging station, characterized in that: Includes steps: Step 1: Start the displacement self-check module and use the eddy current sensor, laser displacement sensor and optical encoder to monitor the radial displacement, axial displacement and rotational angular velocity of the rotor in real time; Step 2: Start the pulsation suppression module and use the MEMS acceleration sensor network and laser vibrometer to measure the pulsation frequency and amplitude of the rotor when it rotates at high speed; extract the pulsation characteristics through fast Fourier transform, analyze the vibration characteristics of the rotor, and transmit the data to the control system; Step 3: The control system relies on the central processor to receive real-time data from the displacement self-check module and the pulsation suppression module, and uses a multi-objective optimization control strategy based on a particle swarm. The specific operation methods include: Step 3.1: Initialize optimization variables and set initial values; Step 3.2: Set four optimization objectives and ensure that the optimization variables meet the physical constraints of current, magnetic field strength and voltage; use the Pareto optimal strategy to seek a compromise solution and combine multiple objectives into a single optimization function through the linear weighted sum method; Step 3.3: Use the particle swarm-based multi-objective optimization gradient descent method to calculate the gradient and adjust the variables along the optimal direction; update the control parameters through iteration until the convergence condition is met; Step 3.4: Obtain optimal control parameters; Step 4: Based on the optimal control parameters, the central processor generates a control signal, and the inverter receives the control signal to dynamically adjust the intensity, frequency and phase of the electromagnetic field to optimize the magnetic force distribution; by controlling the electromagnetic regulation module, the magnetic bearing rotor is ensured to be stably suspended under different working conditions; Step 5: The multi-level protection module monitors the system status in real time, including abnormal conditions such as overload, overcurrent, undervoltage, and overtemperature; when an abnormality is detected, the multi-level protection module works in conjunction with the control center to adjust parameters or perform an emergency shutdown.

6. A magnetic bearing rotor control method for a charging station according to claim 5, characterized in that: The specific operation method of step 3.1 is: initializing parameters, defining optimization variables, and setting initial values: Where I is the current control parameter; B is the magnetic field strength; P is the position of the electromagnetic coil; K is the p , K i , K d is the PID control gain; V is the inverter output voltage; f is the inverter output frequency.

7. The magnetic bearing rotor control method for a charging station according to claim 5, characterized in that: The specific operation method of step 3.2 is: Step 3.21: Establish four optimization objectives to measure displacement error, pulsation amplitude, energy consumption and control response speed respectively; (1) The stability of the rotor is measured by its deviation from the ideal suspension point. The goal is to minimize the displacement error: Where P i is the position of the electromagnetic coil at the i-th sampling moment; P ref is the desired suspension position; controlled variable P 0 The initial coil position determines the system's initial state; (2) The vibration amplitude of the rotor is monitored in real time by the pulsation suppression module, and the following optimization formula is established: In the formula, A j is the main pulsation frequency f in spectrum analysis j The corresponding amplitude; M is the total number of vibration frequencies; the controlled variable B 0 The initial magnetic field strength determines the magnitude and uniformity of the magnetic levitation force; (3) The energy consumption of the magnetic levitation system mainly comes from the maintenance of electromagnetic force. The following optimization formula is established: Where P(t) is the instantaneous power of the system; V(t) is the output voltage provided by the inverter; I(t) is the current in the coil; the controlled variable V 0 I 0 Initial voltage and current affect the initial power consumption level of the system; (4) The delay of the control system affects the rotor's ability to recover from external disturbances and can be modeled by the time constant τ: f4(x)=τ(V,f) In the formula, τ is the time constant of the system, which indicates the dynamic response speed of the control system; V is the inverter output voltage, which affects the rapid adjustment ability of the magnetic suspension system; f is the inverter output frequency, which determines the response rate of the electromagnetic field change; the controlled variable V 0 Initial voltage, which affects the power output capacity of the control system; f 0 Initial output frequency, which affects the response time of the system; The final multi-objective optimization formula is as follows: Step 3.22: During the optimization process, all optimization variables must satisfy certain physical constraints, including current, electromagnetic field strength, and voltage limits: g1(x)=I max -I≥0 g2(x)=B max -B≥0 Among them, I max is the maximum allowable current; B max is the maximum allowable magnetic induction intensity; U max is the maximum allowable voltage.

8. The magnetic bearing rotor control method for a charging station according to claim 5, characterized in that: The specific operation method of step 3.3 is: Step 3.31: Use the particle swarm optimization strategy to find the optimal solution: optimize the displacement error minimization, pulsation amplitude suppression, energy consumption, and control response speed without affecting a certain goal; the formula is as follows: F(x * )≤F(x), In the formula, x * It is the particle swarm optimal solution, that is, based on the current solution, all objectives cannot be improved at the same time; Step 3.32: Combine multiple objectives into one optimization objective using the linear weighted sum method: F(x)=λ1f1(x)+λ2f2(x)+λ3f3(x)+λ4f4(x) In the formula, λ k is the weight parameter, satisfying the normalization condition: Step 3.33: Objective function f k (x) has nonlinear characteristics and is optimized using the multi-objective gradient descent method to calculate the gradient of each objective function: The optimization direction is: Decision, that is, to find a direction d that maximizes the gradient of all objective functions; Step 3.34: Update particle speed and position variables: In the formula, v i t is the velocity vector of the particle iteration, v i t+1 is the speed of the particle after iterative update, w is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r1, r2 are random numbers, p i best is the optimal position of individual particles, g best is the global historical optimal position, x i t is the position vector of particle iteration, x i t+1 is the position vector of the particle after iterative update; Step 3.35: Calculate the new target value: In the formula, F(x t+1 ) is the new objective function value, indicating the current control parameter x t+1 The comprehensive optimization goal under k (x t+1 ) is the value of the kth objective function, k = 1, 2, 3, 4 corresponds to different optimization objectives, namely displacement error, pulsation amplitude, energy consumption and control response delay; x t+1 is the control parameter of the current iteration step t+1, which represents the adjusted variable value during the optimization process, such as current, magnetic field strength, and voltage; Step 3.36: Set the optimization termination condition: ||x t+1 -x t ||<ò In the formula, x t+1 is the optimization variable value of the current step, indicating the state of the system after the current iteration; x t is the optimization variable value of the previous step, indicating the state of the system at the previous iteration; ||·|| is the norm of the vector, usually the Euclidean norm, which calculates the distance between the current and previous step parameters; ε is a small tolerance value, which is set as the condition for terminating the optimization. If the change between the control variables of two iterations is less than ε, the optimization process stops and the system is considered to have converged. Step 3.37: If the termination condition is met, output the optimal control parameter x * ; Otherwise, return to Step 6 and continue iterating.

9. The magnetic bearing rotor control method for a charging station according to claim 5, characterized in that: The multi-level protection module described in step 5 has overload, overcurrent, undervoltage, and overtemperature protection mechanisms, monitors the system status in real time, and works in conjunction with the control center to adjust parameters or perform emergency shutdown when an abnormality is detected; The overload protection monitors the load current in real time through the current transformer. If the current exceeds the rated value by 120% and lasts for 500ms, the power of non-core loads is reduced first, and the main circuit is cut off if the overload continues. The overcurrent protection adopts a high-speed electronic circuit breaker and a fuse in linkage. When the current exceeds the limit by 150% for 10ms, the circuit is instantly cut off and the redundant shunt path is activated to prevent the electromagnetic coil from overheating; The undervoltage protection dynamically samples the bus voltage through the voltage monitoring chip. If the voltage is lower than 85% of the nominal value for 200ms, it automatically switches to the backup power supply and starts the dynamic voltage compensation module. The over-temperature protection deploys distributed temperature sensors at key locations such as electromagnetic coils and inverters. When the temperature exceeds 90°C, forced air cooling is triggered, and when it reaches 105°C, the machine is shut down immediately and an alarm is sounded.

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