Wide-voltage self-adaptive closed-loop vector swimming pool pump anti-interference method

Abstract

The invention provides a wide-voltage self-adaptive closed-loop vector swimming pool pump anti-interference method, and relates to the technical field of motor control and power electronics, and the method comprises the steps: collecting three-phase power supply voltage fluctuation characteristic parameters and motor operation state parameters in real time; performing amplitude compensation and phase synchronization correction on the voltage parameter based on the dynamic voltage compensation factor; a closed-loop vector anti-interference control model is constructed, and model parameters are adjusted in real time through a vector immunity coefficient; dynamically correcting control output by adopting a multivariable feedback regulation mechanism; executing an anti-interference instruction and synchronously updating PWM modulation parameters, and verifying a control effect in combination with hardware-in-the-loop simulation; and parameter self-learning optimization is realized through a genetic algorithm. The dynamic anti-interference capability under power grid voltage fluctuation and harmonic interference is improved, intelligent thermal management is realized through a temperature-voltage-current multi-dimensional coupling model, full-life-cycle self-adaptive optimization of the system is guaranteed, and the problems of compensation lag, insufficient harmonic suppression and poor long-term stability are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical fields of motor control and power electronics, and particularly to an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump. Background Art

[0002] Traditional motor control methods for pool pumps have significant drawbacks when dealing with complex power grid environments. Existing technologies are difficult to effectively handle the coupling effect of wide-range voltage fluctuations and harmonic interference. Conventional voltage compensation strategies adopt a fixed-parameter mode and cannot be dynamically adjusted according to the real-time voltage amplitude deviation, phase shift, and harmonic distortion degree, resulting in a lagging or excessive compensation response during voltage sudden changes, causing motor torque pulsation and even unstable operation. At the same time, harmonic suppression means are limited to hardware filtering and static harmonic compensation, lacking the correlation analysis between harmonic components and the motor operating state, and unable to achieve dynamic harmonic separation and targeted suppression, resulting in an aggravated distortion of the stator current waveform.

[0003] Existing control strategies have obvious shortcomings in thermal management and dynamic response. Traditional temperature protection mechanisms adopt a fixed-threshold design, unable to sense the instantaneous temperature rise change caused by voltage fluctuations, with a lagging early warning of the risk of winding overheating, and not deeply embedding temperature parameters into the control algorithm. Under high-temperature conditions, the rated control intensity is still mechanically executed, leading to a vicious cycle of heat accumulation. In terms of dynamic response performance, traditional PID control is difficult to adapt to rapid voltage fluctuations due to fixed parameters, with a significant lag in the adjustment process and a rotational speed recovery time exceeding a reasonable range. The vector control algorithm lacks the dynamic coupling modeling of voltage disturbance and current vector, and the anti-interference ability continuously decays with the operation duration.

[0004] In addition, existing technologies lack the ability to adapt to working conditions and ensure long-term stability. Control parameters rely on laboratory calibration values and cannot adapt to differences in on-site environments such as different power grid qualities and pipeline characteristics, and are even more difficult to cope with the problem of reduced control accuracy caused by motor parameter drift and component aging. During long-term operation, the system performance gradually deteriorates with environmental changes, and frequent shutdown maintenance is required to calibrate parameters, severely restricting the reliability of the equipment and increasing the operation and maintenance costs. The above problems jointly restrict the stable operation of pool pumps under complex working conditions, and there is an urgent need for a technological breakthrough through multi-parameter dynamic coupling compensation and intelligent closed-loop control. Summary of the Invention

[0005] To solve the technical problems in the prior art, such as insufficient dynamic coupling compensation for voltage fluctuations and harmonic interference, current distortion caused by the lack of a dynamic separation mechanism for harmonic suppression, lagging temperature protection mechanism and lack of deep integration with closed-loop control, lagging dynamic response caused by fixed PID parameters, attenuation of anti-interference ability due to the lack of dynamic coupling relationship between voltage disturbance and current vector in vector control modeling, and lack of working condition adaptability and long-term stability due to the dependence of control parameters on static calibration, the present invention provides an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump.

[0006] The technical solution provided by the present invention is as follows:

[0007] An anti-interference method for a wide-voltage adaptive closed-loop vector pool pump provided by the present invention includes:

[0008] S1. Real-time collect the voltage fluctuation characteristic parameters at the three-phase power supply input end, including the effective voltage value V rms , the voltage harmonic distortion rate THD v and the voltage phase offset angle θ off . At the same time, monitor the motor operating state parameters, including the rotor angular velocity ω, the stator current vector amplitude I s and the stator winding temperature T coil ;

[0009] S2. Adaptive correction of the collected voltage parameters according to the dynamic voltage compensation factor DVC. The dynamic voltage compensation factor DVC is dynamically generated by the coupling relationship between the voltage fluctuation characteristic parameters and the motor operating state. The correction process includes amplitude compensation and phase synchronization compensation for the effective voltage value;

[0010] S3. Construct an anti-interference control model based on the closed-loop vector control algorithm, and adjust the control model parameters in real time through the vector anti-interference coefficient VRI. The vector anti-interference coefficient VRI is calculated from the interaction between the corrected voltage parameters and the motor operating parameters;

[0011] S4. Adopt a multivariable feedback regulation mechanism to dynamically correct the output of the control model, including current loop feedforward compensation and speed loop lag compensation, where the compensation amount is non-linearly adjusted according to the product of the dynamically calculated dynamic voltage compensation factor DVC and the vector anti-interference coefficient VRI;

[0012] S5. Execute the anti-interference control instruction, synchronously update the duty cycle and frequency of the PWM modulation wave, and verify the control effect through hardware-in-the-loop simulation. When the system instability risk is detected, immediately start the adaptive protection strategy based on the dynamic voltage compensation factor DVC.

[0013] Further, the specific content of S2 includes:

[0014] The calculation method of the dynamic voltage compensation factor DVC (Dynamic Voltage Compensation factor) is as follows:

[0015]

[0016] where k adj is the voltage stability adjustment coefficient obtained based on the Adaptive Kalman Filtering Algorithm, and its calculation process includes:

[0017] S201. Establish a six-dimensional state space model including voltage fluctuation characteristic parameters;

[0018] S202. Online update the noise covariance matrix using the variable forgetting factor recursive least squares method;

[0019] S203. Realize the real-time estimation of state variables through the orthogonal projection algorithm;

[0020] ΔV rms is the absolute value of the deviation between the measured effective voltage value and the rated voltage V nom , and α is the voltage amplitude compensation weight coefficient:

[0021]

[0022] β is the harmonic suppression weight coefficient:

[0023]

[0024] THD max is the maximum allowable voltage harmonic distortion rate threshold of the system, T ref is the reference temperature of the stator winding, I rated is the rated current of the motor, ω max is the maximum allowable speed, θ max is the maximum phase offset angle.

[0025] Furthermore, the specific content of S3 includes:

[0026] The calculation of the vector resistance index VRI (Vector Resistance Index) adopts a hybrid strategy of the Fuzzy PID (Fuzzy PID Control Algorithm) control algorithm and model predictive control, specifically including:

[0027] S301. Construct a three-dimensional fuzzy inference system including 12 fuzzy rules, and the input variables are the corrected voltage effective value deviation ΔV' rms , the current vector phase difference Δθ is and the rotational speed volatility

[0028] S302. Adopt an improved weighted average defuzzification method, where the weight coefficient is adaptively adjusted according to the motor running time t op as follows:

[0029]

[0030] where τ is the time constant and T amb is the ambient temperature;

[0031] S303. Take the difference between the fuzzy output and the model predictive control output as the input of the PID controller, and update the proportional coefficient K p , integral time T i and differential time T d online through a recursive parameter tuning algorithm with a forgetting factor;

[0032] S304. The final vector resistance interference coefficient VRI is calculated by the following formula:

[0033]

[0034] where s is the complex frequency variable, γ is the phase-voltage coupling coefficient,

[0035] Furthermore, the S2 further includes:

[0036] The amplitude compensation of the effective voltage value adopts a dynamic segmented compensation strategy, and the specific implementation method is:

[0037] When ΔV rms ≤0.1V nom , V comp = V rms ×(1 + 0.5×DVC);

[0038] When 0.1V nom <ΔV rms ≤0.2V nom ,

[0039] When ΔV rms >0.2V nom , V comp = V rms ×[1 + 1.5×DVC - 0.1×(DVC - DVC th ) 2 ;

[0040] where V compis the effective value of the compensated voltage, DVC max is the maximum value of the dynamic voltage compensation factor T max is the allowable maximum winding temperature, DVC th is the compensation threshold

[0041] Further, the S3 further includes:

[0042] The prediction horizon length N of the model predictive control p is dynamically adjusted according to the vector disturbance rejection coefficient VRI calculated in real time:

[0043]

[0044] where floor() is the floor function, VRI max is the maximum value of the vector disturbance rejection coefficient, VRI max = 10×(1 - 0.1×THD v ), τ N is the time domain adjustment time constant The adjustment period of the prediction horizon length is synchronized with the electrical period of the motor, and the prediction model parameters are updated once per electrical period.

[0045] Further, the S4 further includes:

[0046] The calculation of the current loop feedforward compensation amount adopts a dynamic impedance matching algorithm, specifically including:

[0047] The feedforward compensation amount ΔI ff is determined by the following formula:

[0048]

[0049] where R s is the nominal value of the stator resistance, δ is the temperature-impedance coupling coefficient K coup is the current change rate coupling gain is the real-time differential value of the stator current vector amplitude, and the application timing of the feedforward compensation amount ΔI ff is synchronized with the voltage phase offset angle θ off to ensure that the compensation starts at the voltage zero crossing.

[0050] Further, the S4 further includes:

[0051] The rotational speed loop lag compensation adopts an adaptive compensator designed based on Lyapunov Stability Theory, and the compensation amount Δω comp is calculated by the following formula:

[0052]

[0053] where η is the compensation intensity coefficient, DVC min is the minimum value of the dynamic voltage compensation factor, ω ref is the rotational speed reference value, and the compensation amount Δω comp is injected into the rotational speed loop controller after being processed by a second-order low-pass filter. The cut-off frequency f c of the filter is dynamically adjusted according to the motor load rate:

[0054]

[0055] where f base is the reference cut-off frequency.

[0056] Furthermore, the S5 further includes:

[0057] The adaptive protection strategy includes a dual-trigger mechanism for over-temperature protection and voltage dip protection:

[0058] S501. The over-temperature protection threshold T protect is dynamically corrected according to the dynamic voltage compensation factor DVC:

[0059]

[0060] When T coil ≥T protect immediately reduce the motor power to the safe value P safe =P rated ×(1 - 0.7×DVC);

[0061] S502. The trigger condition for voltage dip protection is that both of the following conditions are met:

[0062] and THD v >0.15×THD max

[0063] At this time, start the emergency vector reconstruction mode and force the VRI to be locked as and switch to open-loop control until the voltage recovers.

[0064] Furthermore, the S5 further includes:

[0065] The hardware-in-the-loop simulation verification is carried out by jointly testing a real-time digital simulator (RTDS) and a physical controller, specifically including:

[0066] S5a. Construct a digital twin containing a thermodynamic model of the motor winding and a hydrodynamic load model, where the thermal resistance R of the thermodynamic model thremal is calculated as:

[0067]

[0068] where, R0 is the reference thermal resistance value;

[0069] S5b. The simulation step size Δt sim is adaptively adjusted according to the prediction time domain length N p as follows:

[0070]

[0071] where, T electrical is the electrical period of the motor;

[0072] S5c. By comparing the error rate ε between the simulation output and the actual sensor data, the control model parameters are corrected online:

[0073] When ε > 5%, according to ΔK p = -0.1×ε×VRI, the PID parameters are updated.

[0074] Furthermore, it also includes a self-learning optimization module for system parameters, and the specific implementation method is:

[0075] S601. Run the genetic algorithm (GA) optimization program once every 24 hours, and the optimization variables include the reference coefficients of α, β, γ, and η;

[0076] S602. The fitness function F is designed as:

[0077]

[0078] where, THDv avg is the daily average voltage harmonic distortion rate, and T coil,max is the daily maximum winding temperature;

[0079] S603. Adopt the elitist retention strategy and the adaptive mutation rate, and the mutation rate μ gen is dynamically adjusted according to the system stability index S:

[0080]

[0081] where S = 1 - (number of faulty words / total running time) × 100, and S max is the maximum stability index designed for the system;

[0082] S604. The optimization results are encrypted by a digital certificate and stored in a non-volatile memory, and take effect when powered on next time.

[0083] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0084] (1) In the present invention, through the synergistic effect of the dynamic voltage compensation factor and the vector disturbance rejection coefficient, a multi-parameter coupling compensation mechanism is constructed, which can sense the comprehensive influence of voltage fluctuation amplitude, phase offset and harmonic distortion in real time, and dynamically adjust the compensation strategy. The piecewise non-linear compensation algorithm effectively suppresses over-compensation and under-compensation phenomena. Combined with the closed-loop cooperative control of the voltage-current vector, it ensures that the motor maintains a stable torque output within a wide voltage fluctuation range. The combined application of the harmonic component dynamic separation technology and the phase synchronization correction greatly reduces the current waveform distortion rate, improves the immunity of the system to power grid harmonic pollution, and ensures the stable water pump flow;

[0085] (2) In the present invention, based on the multi-dimensional coupling model of temperature-voltage-current, the present invention deeply integrates the winding temperature parameter into the control loop, realizes the dynamic adjustment of the protection threshold with the working conditions, breaks through the lag defect of the traditional fixed threshold protection. The adaptive compensator designed by the Lyapunov stability theory, combined with the dynamic cut-off frequency adjustment of the second-order low-pass filter, significantly improves the control robustness under high-temperature working conditions. The hybrid strategy of fuzzy PID and model predictive control optimizes the control parameters to match the voltage fluctuation rate in real time through the prediction time domain length adaptive mechanism, shortens the speed recovery time, and effectively suppresses the instability risk caused by load mutation;

[0086] (3) In the present invention, the parameter self-learning module integrated with the genetic algorithm dynamically optimizes the control parameters through a multi-objective fitness function, breaks through the dependence of the traditional static calibration mode on the on-site environment, significantly improves the adaptability to different installation scenarios. The hardware-in-the-loop simulation system realizes the real-time verification of the control strategy and parameter iteration through the digital twin model. Combined with the optimized data stored in encrypted form, it ensures the self-correction ability of parameter drift during long-term operation. The synergistic effect of the dynamic anti-interference model and the self-learning mechanism enables the system to maintain stable performance throughout the life cycle, reduces the frequency of manual maintenance, and extends the service life of the equipment. Description of the Drawings

[0087] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0088] Figure 1 It is a schematic flow chart of an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump provided by an embodiment of the present invention;

[0089] Figure 2 It is a schematic main control flow chart of an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump provided by an embodiment of the present invention;

[0090] Figure 3 It is a schematic protection and optimization flow chart of an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump provided by an embodiment of the present invention. Specific embodiments

[0091] The following will describe the technical solutions in the present invention in conjunction with the accompanying drawings.

[0092] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0093] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0094] In the embodiments of the present invention, sometimes subscripts such as W1 may be miswritten as non-subscript forms such as W1. When the difference is not emphasized, the meanings they express are the same.

[0095] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0096] Refer to the attached drawings of the specification Figure 1, showing a schematic flow chart of an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump provided by an embodiment of the present invention.

[0097] An embodiment of the present invention provides an anti-interference method for a wide-voltage adaptive closed-loop vector pool pump. The processing flow may include the following steps:

[0098] S1. Real-time collect the voltage fluctuation characteristic parameters at the three-phase power supply input end, including the effective voltage value V rms , the voltage harmonic distortion rate THD v and the voltage phase offset angle θ off , and at the same time monitor the motor operating state parameters, including the rotor angular velocity ω, the stator current vector amplitude I s and the stator winding temperature T coil ;

[0099] S2. Adaptive correction of the collected voltage parameters according to the dynamic voltage compensation factor DVC. The dynamic voltage compensation factor DVC is dynamically generated by the coupling relationship between the voltage fluctuation characteristic parameters and the motor operating state. The correction process includes amplitude compensation and phase synchronization compensation for the effective voltage value;

[0100] S3. Construct an anti-interference control model based on the closed-loop vector control algorithm, and adjust the control model parameters in real time through the vector immunity coefficient VRI. The vector immunity coefficient VRI is calculated from the interaction between the corrected voltage parameters and the motor operating parameters;

[0101] S4. Adopt a multivariable feedback regulation mechanism to dynamically correct the output of the control model, including current-loop feedforward compensation and speed-loop lag compensation, where the compensation amount is non-linearly adjusted according to the product of the dynamically calculated dynamic voltage compensation factor DVC and the vector immunity coefficient VRI;

[0102] S5. Execute the anti-interference control instruction, synchronously update the duty cycle and frequency of the PWM modulation wave, and verify the control effect through hardware-in-the-loop simulation. When the system instability risk is detected, immediately start the adaptive protection strategy based on the dynamic voltage compensation factor DVC.

[0103] As Figure 2As shown, the main control process starts with the real-time acquisition of the effective values of the three-phase voltage, the harmonic distortion rate, and the phase offset angle, and synchronously obtains the angular velocity of the motor rotor, the amplitude of the stator current vector, and the winding temperature parameters. The calculation of the dynamic voltage compensation factor relies on a multi-dimensional state space model that includes voltage fluctuation characteristics and the operating state of the motor. The noise covariance matrix is updated online through the variable forgetting factor recursive least squares method, and the orthogonal projection algorithm is used to solve the voltage stability adjustment coefficient. The voltage compensation stage is divided into three intervals according to the ratio of the measured effective value deviation to the rated voltage: linear compensation is performed when the deviation is less than or equal to 10%, a compensation strategy with a sine correction term is adopted when the deviation is between 10% and 20%, and quadratic function compensation is enabled when the deviation exceeds 20% to suppress overshoot. The phase synchronization link injects the phase offset angle deviation into the feedback loop through a phase-locked loop to achieve dynamic alignment of the carrier signal. The generation of the vector immunity coefficient integrates three-dimensional fuzzy rule base reasoning, model predictive control trajectory planning, and dynamic calculation of the phase-voltage coupling coefficient. The input variables of the fuzzy rule base include the corrected voltage deviation, the current phase difference, and the rotational speed volatility. The multi-variable feedback regulation module processes the current loop feed-forward compensation and the speed loop lag compensation in parallel. The feed-forward compensation amount integrates the temperature-impedance coupling factor to reflect the influence of winding temperature rise on the stator resistance. The lag compensation amount is designed based on the Lyapunov stability theory to enhance the damping characteristics of the system. The generation link of the adaptive PWM modulation wave adjusts the duty cycle and frequency parameters according to the real-time anti-interference requirements. Finally, through the hardware-in-the-loop simulation system, the output of the digital twin model is compared with the actual sensor data. When the error rate exceeds the limit, the online tuning of the proportional-integral parameters is triggered to form a closed-loop control verification system.

[0104] As Figure 3As shown, the protection and optimization process starts with real-time monitoring of the deviation between the winding temperature and the effective value of the voltage. When the temperature exceeds the dynamic protection threshold, the system calculates the hierarchical power reduction curve based on the compensation factor and the square term of the load current, and smoothly reduces the output power to the safe operating range. When the voltage sag protection detects that the effective value deviation exceeds the limit and the harmonic distortion rate exceeds the standard, it immediately locks the emergency correction value of the immunity coefficient and switches to the open-loop control mode to avoid system collapse. The hardware-in-the-loop simulation module constructs a digital twin model that includes the coupling of thermodynamic conduction, electromagnetic characteristics, and mechanical load. The simulation step size is dynamically compressed according to the immunity coefficient and the electrical cycle of the motor, and is reduced to the microsecond level during severe disturbances to improve the calculation accuracy. When the simulation error exceeds the limit, the proportional-integral-derivative parameters are automatically corrected. The parameter self-learning optimization module starts the genetic algorithm program at a fixed time every day. It constructs a multi-objective fitness function through the integral of the speed tracking error, the daily average harmonic distortion rate, and the highest winding temperature, and uses the elitist retention strategy and the adaptive mutation rate mechanism to optimize the reference value of the control parameters. The optimization results are encrypted and stored in the non-volatile memory. The protection strategy and the optimization mechanism run in parallel through independent threads, which not only ensures the response speed of real-time control but also realizes the long-term stability maintenance, and finally forms a multi-level collaborative system covering instantaneous protection, medium-term parameter adjustment, and long-term performance optimization.

[0105] First, the three-phase input voltage signals are collected in real time by a high-precision voltage sensor at a sampling rate of 10 kHz, and the effective value of the voltage V rms and the total harmonic distortion rate THD v are calculated using the fast Fourier transform. Among them, the calculation of THD v is based on the ratio of the square root of the sum of the squares of the fundamental component V1 and the harmonic components from the 2nd to the 50th order. The specific formula is:

[0106]

[0107] This calculation can quantify the degree of grid voltage distortion. At the same time, the voltage phase offset angle θ off is extracted through an enhanced phase-locked loop and high-frequency noise is filtered out. In the motor operating state monitoring section, the three-phase stator currents are transformed by Clarke-Park transformation to obtain the d-q axis current components, and then the current vector amplitude is calculated. The speed encoder signal is used to analyze the rotor angular velocity ω by the M method, and the temperature sensor transmits the winding temperature T 2 to the main controller in real time through the I coil C bus. The calculation of the dynamic voltage compensation factor DVC integrates the coupling effects of voltage stability, temperature, and load. Its expression is:

[0108]

[0109] where k adjOnline update is performed through a six-dimensional state space model and the recursive least squares method with a variable forgetting factor. The design enables the automatic reduction of the compensation intensity under high-temperature conditions to avoid overheating. When the rotational speed approaches the limit or the phase shift is large, the harmonic suppression weight is enhanced. The voltage amplitude compensation adopts a dynamic segmented strategy. When ΔV rms exceeds 0.2V nom quadratic function compensation for V comp is executed, where V rms = V th × [1 + 1.5DVC - 0.1(DVC - DVC 2 ) off . This formula limits the overcompensation risk through the quadratic term. At the same time, the phase-locked loop injects the θ

[0110] The core of the closed-loop control generates the vector resistance interference coefficient VRI through a hybrid strategy of fuzzy PID and model predictive control. In its calculation formula , the fuzzy inference system activates 12 expert rules according to the three input variables of ΔV′ rms , Δθ is and . For example, when the voltage deviation and the phase difference increase simultaneously, an exponential increase in the proportional coefficient K p is triggered. The prediction time domain length N p of the model predictive control is dynamically adjusted according to to ensure that the control granularity matches the system response speed. The phase-voltage coupling coefficient is designed to strengthen the phase compensation effect when the load current increases, and the dynamic regulation is enhanced through a sine function near the rated speed. In the multivariable feedback regulation, the feedforward compensation amount of the current loop suppresses transient disturbances through a dual mechanism of impedance matching and current change rate compensation, where is constructed to make the compensation amount more sensitive to temperature changes when VRI is high. The lag compensation amount of the speed loop is designed based on the Lyapunov stability theory and is injected into the control loop after being smoothed by a second-order low-pass filter. The cut-off frequency of the filter is adjusted in real time to adaptively match the filtering characteristics to the load conditions.

[0111] In the adaptive protection strategy, the over-temperature protection threshold limits the influence range of DVC through the hyperbolic tangent function. When T coil ≥ T protect the power is stepped down to P safe = Prated ×(1 - 0.7×DVC), this design establishes a dynamic balance between the voltage compensation intensity and the temperature rise risk. The voltage sag protection is at and THD v > 0.15×THD max when it forcibly locks VRI to and switches to open-loop control to avoid system instability. Hardware-in-the-loop simulation constructs a digital twin containing a thermal-electro-mechanical coupling model through a real-time digital simulator. The cubic term of the thermal resistance in the thermodynamic model accurately simulates the large-current non-linear temperature rise. The simulation step size is dynamically compressed according to the immunity coefficient, and the step size is shortened to 1 μs during severe voltage fluctuations to improve the simulation accuracy. When the error rate ε exceeds 5%, the PID parameter online tuning is triggered, and the correction amount ΔK p = -0.1×ε×VRI and is written into the controller through the CAN bus to achieve closed-loop optimization.

[0112] The parameter self-learning optimization module starts the genetic algorithm every day at midnight. The settings of the population size of 50 and 100 iterations balance the calculation efficiency and the optimization effect. The fitness function comprehensively evaluates the speed tracking, harmonic suppression, and temperature control performance. The elitist retention strategy retains the top 10% of individuals, and the mutation rate is dynamically adjusted according to the system stability index S. The optimization results are encrypted and stored by AES-128 and take effect after restart. In a typical application scenario, when it is detected that the voltage drops from 220V to 180V suddenly and THD v rises to 12%, the system completes the calculation of DVC = 1.35 within 20 ms, and raises V comp to 198V through secondary compensation. VRI = 8.2 drives the feedforward compensation ΔI ff = 2.8A to inject current into the current loop. The Lyapunov compensator outputs Δω comp = -60 rpm to suppress the speed fluctuation. The real-time simulation verifies that the error rate ε = 4% does not trigger protection, and the complete control cycle is completed within 50 μs. This implementation method realizes the technical effects of the speed deviation being less than ±0.5%, the harmonic suppression rate being increased by 45%, and the winding temperature rise being reduced by 18% within the voltage fluctuation range of ±30% through dynamic parameter coupling, multi-algorithm collaboration, and digital twin verification. The system instability recovery time is shortened to within 80 ms.

[0113] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0114] (1) In the present invention, through the synergistic effect of the dynamic voltage compensation factor and the vector disturbance rejection coefficient, a multi-parameter coupled compensation mechanism is constructed, which can real-time sense the comprehensive influence of voltage fluctuation amplitude, phase shift, and harmonic distortion, and dynamically adjust the compensation strategy. The piecewise non-linear compensation algorithm effectively suppresses over-compensation and under-compensation phenomena. Combining the closed-loop collaborative control of voltage-current vectors ensures that the motor maintains stable torque output within a wide voltage fluctuation range. The combined application of the harmonic component dynamic separation technology and phase synchronization correction significantly reduces the current waveform distortion rate, improves the immunity of the system to grid harmonic pollution, and guarantees the stable water pump flow rate;

[0115] (2) In the present invention, based on the multi-dimensional coupling model of temperature-voltage-current, the present invention deeply integrates the winding temperature parameter into the control loop to achieve dynamic adjustment of the protection threshold according to the working conditions, breaking through the lag defect of traditional fixed-threshold protection. The adaptive compensator designed through the Lyapunov stability theory, combined with the dynamic cut-off frequency adjustment of the second-order low-pass filter, significantly improves the control robustness under high-temperature working conditions. The hybrid strategy of fuzzy PID and model predictive control optimizes the control parameters to match the voltage fluctuation rate in real time through the prediction time domain length adaptive mechanism, shortens the speed recovery time, and effectively suppresses the instability risk caused by load mutation;

[0116] (3) In the present invention, the parameter self-learning module integrated with the genetic algorithm dynamically optimizes the control parameters through the multi-objective fitness function, breaking through the dependence of the traditional static calibration mode on the on-site environment, and significantly improving the adaptability to different installation scenarios. The hardware-in-the-loop simulation system realizes the real-time verification of the control strategy and parameter iteration through the digital twin model. Combining the optimized data stored in encrypted form ensures the self-correction ability of parameter drift during long-term operation. The synergistic effect of the dynamic anti-interference model and the self-learning mechanism enables the system to maintain stable performance throughout the life cycle, reduces the frequency of manual maintenance, and extends the service life of the equipment.

[0117] The above content is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.

[0118] The following points need to be explained:

[0119] (1) The drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0120] (2) For clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is enlarged or reduced, that is, these drawings are not drawn to actual scale. It can be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be intervening elements.

[0121] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0122] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method, characterized in that: include: S1, real-time acquisition of voltage fluctuation characteristic parameters at the three-phase power input end, including voltage RMS V rms , Voltage harmonic distortion rate THD v And the voltage phase shift angle θ off , while monitoring the motor operating state parameters, including the rotor angular velocity ω, the stator current vector amplitude I s and stator winding temperature T coil ; S2, adaptively correcting the collected voltage parameters according to a dynamic voltage compensation factor DVC, wherein the dynamic voltage compensation factor DVC is dynamically generated by the coupling relationship between the voltage fluctuation characteristic parameter and the motor operation state, and the correction process includes amplitude compensation and phase synchronization compensation for the voltage effective value; S3, constructing an anti-interference control model based on a closed-loop vector control algorithm, and adjusting the control model parameters in real time through a vector anti-interference coefficient VRI, wherein the vector anti-interference coefficient VRI is calculated by the interaction between the corrected voltage parameters and the motor operating parameters; S4. Dynamically correct the output of the control model using a multivariable feedback adjustment mechanism, including current loop feedforward compensation and speed loop lag compensation, where the compensation amount is nonlinearly adjusted based on the product of the real-time calculated dynamic voltage compensation factor DVC and the vector immunity coefficient VRI; S5. Execute anti-interference control instructions, synchronously update the duty cycle and frequency of the PWM modulation wave, and verify the control effect through hardware-in-the-loop simulation. When the risk of system instability is detected, immediately start the adaptive protection strategy based on the dynamic voltage compensation factor DVC.

2. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 1, characterized in that: The S2 specifically includes: The calculation method of the dynamic voltage compensation factor DVC is: Among them, k adj is the voltage stability adjustment coefficient obtained based on the adaptive Kalman filter algorithm, and its calculation process includes: S201, establishing a six-dimensional state space model including voltage fluctuation characteristic parameters; S202, using a variable forgetting factor recursive least squares method to update the noise covariance matrix online; S203, achieving real-time estimation of state variables through an orthogonal projection algorithm; ΔV rms is the measured voltage effective value and the rated voltage V nom The absolute value of the deviation, α is the voltage amplitude compensation weight coefficient, β is the harmonic suppression weight coefficient, THD max It is the maximum voltage harmonic distortion rate threshold allowed by the system.

3. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 1, characterized in that: The S3 specifically includes: The calculation of the vector immunity coefficient VRI adopts a hybrid strategy of fuzzy PID control algorithm and model predictive control, specifically including: S301, construct a three-dimensional fuzzy inference system containing 12 fuzzy rules, and the input variable is the corrected voltage effective value deviation ΔV′ rms , current vector phase difference Δθ is and speed fluctuation rate S302, using an improved weighted average defuzzification method, where the weight coefficient is based on the motor running time t op Make adaptive adjustments; S303: Using the difference between the fuzzy output and the model predictive control output as the input of the PID controller, and updating the proportional coefficient K online through a recursive parameter tuning algorithm with a forgetting factor. p , integration time T i and the differential time T d ; S304, the final vector immunity coefficient VRI is calculated by the following formula: Where s is the complex frequency variable, γ is the phase-voltage coupling coefficient, 4. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 2, characterized in that: The S2 further comprises: The amplitude compensation of the voltage effective value adopts a dynamic segmented compensation strategy, which is specifically implemented as follows: When ΔV rms ≤0.1V nom When V comp =V rms ×(1+0.5×DVC); When 0.1V nom <ΔV rms ≤0.2V nom hour, When ΔV rms >0.2V nom When V comp =V rms ×[1+1.5×DVC-0.1×(DVC-DVC th ) 2 ]; Among them, V comp DVC is the effective value of the voltage after compensation. max is the maximum value of the dynamic voltage compensation factor, T max To allow the maximum winding temperature, DVC th is the compensation threshold, 5. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 3, characterized in that: The S3 further comprises: The prediction time domain length N of the model predictive control p The vector immunity coefficient VRI is dynamically adjusted according to the real-time calculation, the adjustment period of the prediction time domain length is synchronized with the motor electrical cycle, and the prediction model parameters are updated once every electrical cycle.

6. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 4, characterized in that: The S4 further comprises: The calculation of the current loop feedforward compensation amount adopts a dynamic impedance matching algorithm, which specifically includes: Feedforward compensation ΔI ff Determined by the following formula: Among them, R s is the nominal value of stator resistance, δ is the temperature-impedance coupling coefficient, δ=0.05× K coup is the current change rate coupling gain, is the real-time differential value of the stator current vector amplitude, the feedforward compensation value ΔI ff The application timing and voltage phase shift angle θ off Synchronization ensures that the compensation action starts at the voltage zero crossing point.

7. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 3, characterized in that: The S4 further comprises: The speed loop lag compensation adopts an adaptive compensator designed based on Lyapunov stability theory, and the compensation amount Δω comp Calculated by the following formula: Where η is the compensation strength coefficient, DVC min is the minimum value of the dynamic voltage compensation factor, ω ref is the speed reference value, the compensation value Δω comp After being processed by a second-order low-pass filter, it is injected into the speed loop controller. The filter cutoff frequency f c Dynamic adjustment according to motor load rate: Among them, f base is the reference cut-off frequency.

8. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 1, characterized in that: The S5 further comprises: The adaptive protection strategy includes a dual trigger mechanism of over-temperature protection and voltage sag protection: S501, over temperature protection threshold T protect Dynamic correction based on the dynamic voltage compensation factor DVC: When T coil ≥T protect When the motor power is reduced to the safe value P safe =P rated ×(1-0.7×DVC); S502: Voltage sag protection triggering conditions are met at the same time: And THD v >0.15×THD max At this time, the emergency vector reconstruction mode is started, forcing the VRI to be locked to And switch to open loop control until the voltage recovers.

9. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 5, characterized in that: The S5 further comprises: The hardware-in-the-loop simulation verification uses a real-time digital simulator and a physical controller for joint testing, specifically including: S5a, construct a digital twin including the motor winding thermodynamic model and the fluid dynamics load model; S5b, simulation step length Δts im According to the predicted time domain length N p Adaptive adjustment; S5c, by comparing the error rate ε between the simulation output and the actual sensor data, the control model parameters are corrected online: When ε>5%, according to ΔK p = -0.1 × ε × VRI, Update PID parameters.

10. A wide voltage adaptive closed-loop vector swimming pool pump anti-interference method according to claim 1, characterized in that: It also includes a self-learning optimization module for system parameters, which is implemented as follows: S601, run the genetic algorithm optimization program once every 24 hours, and the optimization variables include the benchmark coefficients of α, β, γ, and η; S602, using elite retention strategy and adaptive mutation rate, mutation rate μ gen Dynamic adjustment based on system stability index S; S603: The optimization result is encrypted by a digital certificate and stored in a non-volatile memory, and becomes effective at the next power-on.

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