Six-winding high-voltage frequency conversion speed regulation control method and system

Abstract

The invention discloses a six-winding high-voltage variable-frequency speed regulation control method and system, and belongs to the technical field of high-voltage variable-frequency speed regulation, and the method comprises the steps: building an electric-thermal-magnetic coupled six-dimensional nonlinear system model through collecting the structural parameters, thermosensitive data and an initial state of a motor; establishing a frequency domain regulation and control matrix among the six windings in combination with the operation condition and the load type; predicting a future disturbance power trend by using a weighted short-time Fourier analysis method, dynamically correcting the reference current of each winding, and generating six groups of PWM control signals; when the performance of the winding is degraded, fault-tolerant topology reconstruction and driving task redistribution are executed; periodically reconstructing a control matrix through a thermoelectric feedback mechanism, and executing parameter adaptive optimization according to a current balance coefficient and hot redundancy distribution; according to the method, high-precision current coordination control, frequency domain energy decoupling and winding thermal load balancing can be realized, and the speed regulation response speed, the stability and the fault-tolerant capability of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage variable-frequency speed regulation, and in particular to a six-winding high-voltage variable-frequency speed regulation control method and system. Background Art

[0002] Currently, high-voltage, high-power motors are widely used in industrial applications such as mining, metallurgy, petrochemicals, and water conservancy, with variable-frequency speed regulation systems becoming a key control method. Traditional variable-frequency speed regulation systems often use a three-winding structure and have relatively mature control algorithms. However, they are prone to current imbalance, motor temperature rise, and severe harmonic interference under high power density, large load disturbances, or extreme operating conditions.

[0003] Six-winding structures are increasingly being adopted in new-generation high-voltage variable-frequency systems due to their increased redundancy and thermal balance. However, existing speed control methods for six-winding high-voltage motors typically treat them as two independently controlled three-phase systems, failing to fully account for the coupling effects between the windings. This leads to problems such as delayed speed response, low energy efficiency, and insufficient system robustness.

[0004] Therefore, there is an urgent need for a speed control method for a six-winding high-voltage frequency conversion system that can comprehensively consider the dynamic response characteristics and energy distribution mechanism of each winding, thereby realizing multi-winding coordinated control and improving the overall response speed, current balance and energy efficiency of the system. Summary of the Invention

[0005] The purpose of the present invention is to provide a six-winding high-voltage variable frequency speed regulation control method and system to solve the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a six-winding high-voltage variable frequency speed regulation control method, comprising:

[0007] S100, collecting the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructing a six-dimensional nonlinear system model including the electric-thermal-magnetic coupling relationship;

[0008] S200, based on the target load type and operating conditions, a multivariable dynamic prediction model is used to establish a frequency domain control matrix among the six windings;

[0009] S300, based on the temperature rise trend of each winding and the historical disturbance trajectory, a weighted short-time Fourier analysis method is used to obtain a future power regulation prediction value, and a correction factor is assigned to each winding;

[0010] S400, updating the reference current of each winding in real time according to the correction factor, obtaining six sets of PWM control signals, and performing synchronous modulation in combination with the harmonic suppression factor;

[0011] S500: When any winding is monitored for performance degradation, perform fault-tolerant topology reconstruction, reallocate winding drive tasks, and construct optimal compensation paths for the remaining windings;

[0012] S600, during the entire control cycle, continuously integrating the thermal input data and the load disturbance prediction parameters to periodically reconstruct the control matrix;

[0013] S700: Using the current balance coefficient and the heat energy redundancy distribution during the speed regulation process as evaluation indicators, perform adaptive parameter optimization feedback.

[0014] Preferably, the construction of the six-dimensional nonlinear system model of electric-thermal-magnetic coupling includes:

[0015] Obtain the three-dimensional temperature field distribution of each winding, and build a coupled inductor model including temperature-dependent parameters based on the magnetic flux mutual inductance matrix;

[0016] Based on the nonlinear characteristics of each winding resistance changing with temperature, a dynamic electric loss expression is constructed, and a thermal resistance and capacitance network model of the heat conduction path is introduced to establish a multi-point heat transfer structure;

[0017] The current, flux and temperature of the six windings are taken as state variables, and a time-varying nonlinear state space function group is jointly constructed to form an electric-thermal-magnetic multi-field coupling simulation model.

[0018] Preferably, the establishing of the frequency domain control matrix includes:

[0019] The spectrum of the six winding current signals is extracted using weighted Fourier transform, a six-dimensional frequency response matrix is ​​constructed, and the coupling relationship between the main and auxiliary frequency bands between the windings is identified by calculating the frequency band overlap.

[0020] The frequency decoupling area is reconstructed into signal space by orthogonal spectrum projection method, the complementary frequency band features are extracted, and the frequency control function range of each winding group is defined;

[0021] The load type identification results, historical disturbance trajectories and thermal rise trend parameters are integrated to construct a dynamically updated frequency domain control matrix.

[0022] Preferably, obtaining the future power regulation prediction value includes:

[0023] Collect the historical disturbance current sequence of each winding and construct an energy weighting function with a thermal sensitivity factor to form a weight vector for frequency domain weighted analysis;

[0024] Based on the weighted short-time Fourier transform, the time-frequency analysis of the disturbance signal of each winding is performed to extract the evolution trajectory of the disturbance frequency and energy distribution over time;

[0025] The power density concentration trend is coupled with the winding thermal response model to calculate the power load prediction value in the next control cycle, which serves as the feedforward input of the current control strategy.

[0026] Preferably, updating the reference current of each winding in real time according to the correction factor includes:

[0027] The power correction factor corresponding to each winding is weightedly integrated with the target current trajectory in the basic frequency domain control matrix to form a preliminary reference current;

[0028] The dynamic temperature rise coefficient and disturbance feedback variables are introduced to construct an adaptive current regulation function under the current operating state and perform nonlinear adjustment on the preliminary reference current.

[0029] The disturbance trend in the next control cycle is estimated by using the differential evolution prediction algorithm, and the reference current sequence of each winding is corrected in real time.

[0030] Preferably, the redistribution of winding drive tasks includes:

[0031] According to the electromagnetic coupling strength and temperature rise rate of the faulty winding, a set of candidate windings with spatial compensation advantages and thermal margins is screened out from the remaining windings;

[0032] Construct a dynamic load compensation function, comprehensively consider the available bandwidth in the frequency domain, current redundancy, and system flux balance, and generate a drive task redistribution strategy table;

[0033] The control target of the alternative winding is dynamically injected into its PWM modulation module, and the frequency domain control matrix is ​​adjusted synchronously.

[0034] Preferably, the periodic reconstruction of the control matrix includes:

[0035] In each control cycle, the real-time temperature rise rate and current fluctuation data of each winding are collected to calculate the thermal interference factor and electrical interference intensity;

[0036] Combined with the load disturbance prediction value, a control factor matrix with thermal weight and frequency sensitivity coefficient is constructed, and each weight element in the original frequency domain control matrix is ​​adaptively modified;

[0037] The modified control matrix is ​​used to update each set of PWM modulation strategies.

[0038] Preferably, the current balance coefficient and the heat energy redundancy distribution during the speed regulation process are used as evaluation indicators, and the adaptive parameter optimization feedback is performed, including:

[0039] During the motor speed regulation process, the current values ​​and temperature rise information of the six windings are collected in real time to calculate the current balance coefficient and thermal energy redundancy distribution coefficient;

[0040] The current balance coefficient and the thermal energy redundancy distribution coefficient are compared with the corresponding preset thresholds respectively. If any indicator deviates from the normal operating range, the parameter adjustment mode is entered, including adjusting the PWM duty cycle, current reference value or frequency domain control matrix weight.

[0041] The present invention also provides a six-winding high-voltage variable-frequency speed regulation control system, comprising:

[0042] The state modeling module collects the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructs a six-dimensional nonlinear system model that includes the electrical-thermal-magnetic coupling relationship;

[0043] The load identification and frequency domain control building module uses a multivariable dynamic prediction model to establish a frequency domain control matrix among the six windings based on the target load type and operating conditions;

[0044] The disturbance sensing and power prediction module uses weighted short-time Fourier analysis to obtain future power regulation prediction values ​​based on the temperature rise trend of each winding and the historical disturbance trajectory, and assigns correction factors to each winding;

[0045] The current control and PWM modulation module updates the reference current of each winding in real time according to the correction factor, obtains six sets of PWM control signals, and synchronizes the modulation in combination with the harmonic suppression factor;

[0046] The fault-tolerant scheduling and topology reconstruction module monitors the performance degradation of any winding and performs fault-tolerant topology reconstruction, reallocates the winding drive tasks, and constructs the optimal compensation path for the remaining windings.

[0047] The control matrix dynamic update module continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the control matrix;

[0048] The evaluation and parameter optimization feedback module is run, and the current balance coefficient and thermal energy redundancy distribution during the speed regulation process are used as evaluation indicators to perform adaptive parameter optimization feedback.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] 1. The present invention achieves accurate dynamic modeling of high-voltage six-winding motors by constructing a six-dimensional nonlinear system model that integrates the coupling relationship of electrical, thermal, and magnetic multi-physical fields. Combined with load type identification, disturbance trend prediction, and temperature rise analysis, a speed control mechanism with clear division of labor in the frequency domain and adaptive adjustment as working conditions change is established, effectively improving the system's control sensitivity, current distribution balance, and frequency response accuracy.

[0051] 2. During operation, the present invention integrates thermal information and disturbance prediction results, performs closed-loop optimization feedback control based on current balance and thermal redundancy, and actively reconstructs the drive topology when the winding performance degrades, achieving efficient fault-tolerant scheduling and dynamic power redistribution, so that the system has stronger thermal stability, fault-tolerant operation capability and long-term operation safety, which is significantly better than traditional three-phase or passive control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 This is a mind map of the method of the present invention.

[0054] Figure 2 This is a mind map of the system modules of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] Example 1, please refer to Figure 1 As shown, the six-winding high-voltage variable frequency speed regulation control method described in this embodiment includes:

[0057] S100, collecting the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructing a six-dimensional nonlinear system model including the electric-thermal-magnetic coupling relationship;

[0058] S200, based on the target load type and operating conditions, a multivariable dynamic prediction model is used to establish a frequency domain control matrix among the six windings;

[0059] S300, based on the temperature rise trend of each winding and the historical disturbance trajectory, a weighted short-time Fourier analysis method is used to obtain a future power regulation prediction value, and a correction factor is assigned to each winding;

[0060] S400, updating the reference current of each winding in real time according to the correction factor, obtaining six sets of PWM control signals, and performing synchronous modulation in combination with the harmonic suppression factor;

[0061] S500: When any winding is monitored for performance degradation, perform fault-tolerant topology reconstruction, reallocate winding drive tasks, and construct optimal compensation paths for the remaining windings;

[0062] S600, during the entire control cycle, continuously integrating the thermal input data and the load disturbance prediction parameters to periodically reconstruct the control matrix;

[0063] S700: Using the current balance coefficient and the heat energy redundancy distribution during the speed regulation process as evaluation indicators, perform adaptive parameter optimization feedback.

[0064] Among them, S100 specifically includes:

[0065] S101: Obtain the structural parameter information of the six-winding motor, including the spatial arrangement coordinates of the six sets of stator windings, the number of winding coils, the mutual spacing, the conductor resistance and inductance values, and the winding capacitance distribution coefficient; accurately collect the flux coupling matrix between the windings , that is, the cross-inductance relationship between any two windings due to spatial coupling, which is used to describe the nonlinear effect of magnetic flux interaction; record the arrangement of each set of windings in the stator core slot and the yoke structure.

[0066] S102: Collect initial thermal information, embed thermistor / thermocouple arrays in each of the six windings, and obtain the initial temperature field distribution on the surface and inside of each winding , and form a three-dimensional thermal field through interpolation; identify the thermal resistance and heat capacity parameters of the heat dissipation paths such as the motor housing, core, and bearings, and establish an equivalent thermal network structure; obtain the basic thermal boundary conditions of the motor environment, including: initial ambient temperature, convection coefficient, heat conduction boundary form, etc.

[0067] S103: Construct a six-dimensional coupled variable model and establish a set of state variables for six windings , represents the current of each winding;

[0068] With magnetic flux ψ, temperature distribution T, resistance R(T), and magnetic permeability μ(T) as function variables, a coupling function group is formed: ;in: : terminal voltage of the i-th winding; : the equivalent resistance of the ith winding as a function of temperature T (unit: Ω); i represents the instantaneous current of the ith winding (unit: A); ψi: the magnetic flux of the ith winding (unit: Wb, Weber); : The first-order derivative of magnetic flux with respect to time, representing the induced electromotive force component. : total flux linkage of the i-th winding; : the current of the jth winding; is the temperature-dependent cross-inductance term; Indicates winding copper loss; represents the heat dissipation through heat conduction and convection; Represents the equivalent thermal capacity of each winding.

[0069] S104: Form a six-dimensional nonlinear system model, coupling the above three types of variables (electrical, electromagnetic, and thermal) to form a state space model , defined as: ; Where U is the control input (such as PWM voltage sequence), and the model is a strongly nonlinear multivariable time-varying system;

[0070] The system model supports the subsequent use of predictive control, state estimation and fault-tolerant reconstruction algorithms, and has a thermoelectric mutual feedback mechanism and a magnetic coupling asymmetric response mechanism, which can significantly improve control accuracy.

[0071] Among them, S200, based on the target load type and operating conditions, uses a multivariable dynamic prediction model to establish a frequency domain control matrix between the six windings, specifically including:

[0072] First, the real-time current sampling module on the inverter side obtains the instantaneous current data of the six windings, and combines it with historical operating data for preliminary filtering and normalization.

[0073] Use pattern recognition algorithms (such as DTW dynamic time warping or KNN nearest neighbor classification) to match current load characteristics to typical operating conditions: constant torque type (such as compressors), fan type (such as HVAC systems), high variable torque type (such as lifting equipment), etc.

[0074] Key load indicators, such as speed change rate, impact frequency, load disturbance amplitude, etc., are extracted as input parameters of the prediction model.

[0075] Perform weighted fast Fourier transform (W-FFT) on the current signal of each winding, introduce the disturbance sensitivity coefficient as a weighting factor, and construct a six-dimensional current spectrum matrix F=[F1(f),F2(f),...,F6(f)];

[0076] Analyze the primary response band and secondary response coupling band of each winding in different frequency ranges, and calculate the matrix based on the coupling band overlap to quantify the frequency domain coordination degree between windings;

[0077] The orthogonal spectrum projection method is used to reconstruct the signal space, remove the common mode interference frequency band, extract the effective frequency decoupling component, and form the functional frequency domain interval of each winding.

[0078] The frequency band division result is used as the control basis to construct a 6×N frequency domain control matrix Mfreq, where each element mij represents the control weight of the i-th winding in the j-th frequency band;

[0079] This matrix integrates variable inputs such as load disturbance history, temperature rise prediction trend, and current balance factor, and is dynamically updated through a weighted time-frequency regression prediction model (such as LSTM or dynamic Bayesian network).

[0080] Finally, the frequency domain control matrix serves as the input template of the PWM control strategy, guiding the target current trajectory of each winding group in each frequency band and realizing frequency domain coordinated control among the six windings.

[0081] Among them, S300 uses the weighted short-time Fourier analysis method (W-STFT) to obtain the future power regulation prediction value based on the temperature rise trend of each winding and the historical disturbance trajectory, and assigns a correction factor to each winding. The specific technical process includes the following:

[0082] Dynamically sample the thermal data of the six windings and collect the temperature rise rate of each winding within a certain time window in the past;

[0083] The sliding average filter algorithm is used to process the temperature data and calculate the temperature rise response coefficient of each winding under unit power conditions. , reflecting the thermal sensitivity of the winding to the load; Indicates the power change value of the i-th winding within a certain period of time (unit: watt, W), Indicates the temperature change of the winding during this time period.

[0084] An empirically weighted temperature rise response model is established to predict the impact of future power regulation on the thermal state of the winding.

[0085] The weighted short-time Fourier transform (W-STFT) method is used to extract the trajectory of the disturbance frequency changing with time using the historical load disturbance current sequence.

[0086] Analyze short-term power fluctuations and disturbance frequency transfer patterns to obtain the frequency domain load pressure prediction value that may occur in the next regulation cycle (f,t);

[0087] The temperature rise trend parameter is embedded as a weighting factor in the spectrum energy density calculation to form a "thermal weighted frequency response curve" to realize the power trend prediction under thermal load drive.

[0088] Calculate the expected load distribution ratio of each winding in future power regulation based on the predicted frequency response value and the thermal load capacity of each winding , its expression can be approximately expressed as: ;in is the temperature rise sensitivity coefficient, ε is the thermal balance compensation coefficient; It is introduced into the PWM current control module as a power correction factor to perform weighted adjustment on the original frequency domain control matrix to achieve thermal-aware adaptive optimization of the winding current control weight.

[0089] S400: Update the reference current of each winding in real time based on the correction factor to obtain six sets of PWM control signals, and perform synchronous modulation in combination with the harmonic suppression factor, specifically including:

[0090] The power correction factor obtained in the previous step S300 is Applied to the initial frequency domain control matrix or standard reference current trajectory; update reference current The expression is: ;in is the target current value originally calculated; this update mechanism supports dynamic calculation within each PWM control cycle and has millisecond-level real-time performance.

[0091] Six groups of real-time updates Input to SVPWM (space vector pulse width modulation) module or sine PWM module;

[0092] The control module converts each set of three-phase current target values ​​into gate pulse signals PWMi(t) according to the three-phase bridge inverter structure, corresponding to six sets of inverter drive signals;

[0093] During the modulation process, physical boundaries such as inverter dead zone and switching frequency limit are considered to ensure that the output voltage waveform is consistent with the updated current trajectory.

[0094] In order to improve the harmonic coupling problem of the multi-winding system in the frequency crossover band, the harmonic suppression factor γi(f) is introduced, which indicates that filtering or limiting should be strengthened in a specific frequency range;

[0095] Applying γi(f) to the reference current spectrum or PWM modulation index can achieve automatic voltage pulse width limitation or compensation in a specific frequency range. Harmonic suppression methods can include: frequency domain window function adjustment (such as Hamming window); active filter modulation factor; and harmonic detection feedback to adjust the modulation depth.

[0096] All six PWM signals use a unified clock reference and master synchronous modulation cycle to ensure phase matching of multi-winding voltage outputs;

[0097] A disturbance detection mechanism is introduced. If the reference current of a winding deviates beyond the threshold, the correction factor is automatically recalculated and the PWM modulation output is re-iterated.

[0098] A dynamic self-regulating fully parallel PWM output system is implemented to enhance the stability and robustness of the system in high-frequency disturbances and load switching.

[0099] S500: When any winding is monitored for performance degradation, perform fault-tolerant topology reconstruction, redistribute winding drive tasks, and construct optimal compensation paths for the remaining windings, specifically including:

[0100] Real-time monitoring of key operating parameters of six windings, including temperature rise rate, current fluctuation rate, and frequency domain power response intensity attenuation ratio; identification of windings with degraded performance using multi-factor threshold discrimination or principal component analysis (PCA);

[0101] Performance degradation criteria may include: temperature rise exceeding a set threshold, abnormal current amplitude fluctuation, or high-frequency power reduction exceeding 30% of the historical average.

[0102] Determine the phase group to which the faulty winding belongs and identify its impact on the overall system flux balance and power output;

[0103] If the degraded winding can still maintain some functions, it enters the derated operation mode;

[0104] If there is a complete failure, the fault-tolerant reconstruction process is initiated to activate functional redundancy and replace tasks for the remaining windings.

[0105] The system reconstructs the electromagnetic coupling matrix Lij′ and the winding path allocation table based on the winding spatial arrangement and coupling degree;

[0106] Adjust the current control weight in the frequency domain control matrix Mfreq to redistribute the frequency band and power carried by the original degenerate winding to the remaining windings;

[0107] Priority is given to allocating to windings with relatively weak spatial coupling, low current thermal load, and large frequency domain spare capacity to build the optimal compensation path.

[0108] Inject the new reference current target into the PWM modulation module and update the drive logic of the corresponding winding;

[0109] The controller simultaneously enters a fault-tolerant state, increases the frequency of real-time spectrum monitoring, shortens the update cycle, and enhances the system's dynamic response;

[0110] If multiple windings are degraded, the system will enter the backup operation topology in sequence according to priority to maintain system output continuity.

[0111] S600: During the entire control cycle, thermal input data and load disturbance prediction parameters are continuously integrated to periodically reconstruct the control matrix, specifically including:

[0112] Collect real-time temperature data of six windings every preset control period (e.g. 20-50ms) ;

[0113] Calculate the temperature rise rate through the first-order differential filter , identify the current heat load dynamics;

[0114] At the same time, the short-term temperature anomaly factors of each winding, such as peak temperature difference, gradient imbalance, etc., are extracted and input into the control system as thermal feedback variables.

[0115] Based on the load fluctuation trend over the past period, future disturbance power is predicted using weighted short-time Fourier analysis or autoregressive (AR) models.

[0116] Prediction results with disturbance intensity distribution map (f, t) indicates the load pressure that each winding will face in different frequency bands;

[0117] Synchronously analyze time domain indicators such as current imbalance and harmonic aggregation risk to form a disturbance response priority matrix.

[0118] The control system will thermal feedback variable with disturbance predictor variables (f, t) are commonly input into the control matrix reconstruction module; each element in the original frequency domain control matrix Mfreq Will be corrected in real time to: in, and is the thermal disturbance weight factor, The corrected element.

[0119] The modified matrix Mfreq′ will be used to update the PWM modulation strategy and the winding target current trajectory to achieve closed-loop adaptive evolution of the control parameters.

[0120] The control system monitors the system output stability before and after matrix adjustment in real time, including current balance, temperature rise rate change trend, and total harmonic distortion (THD);

[0121] If the system fluctuation is found to exceed the stability limit, the reconstruction frequency will be automatically slowed down or some matrix channels will be frozen to ensure the overall operation safety.

[0122] S700: Using the current balance coefficient and thermal energy redundancy distribution during the speed regulation process as evaluation indicators, perform adaptive parameter optimization feedback, specifically including:

[0123] Establish a multi-dimensional operation evaluation index system: During the entire speed regulation operation cycle, the system operation status evaluation index is calculated in real time, mainly including: current balance coefficient : Indicates the degree of fluctuation among the six winding currents, defined as: Where, : Instantaneous current value of six windings (unit: A); Indicates the standard deviation of the six groups of currents, which is used to measure the fluctuation / imbalance between the currents; mean indicates the average of the absolute values ​​of the six groups of currents, reflecting the current overall current level; It represents the current balance coefficient, and its value range is usually between 0 and 1. The closer it is to 1, the more consistent the six groups of currents are and the more balanced the system current distribution is.

[0124] Heat energy redundancy distribution coefficient : It is used to reflect the proportional redundancy between the current temperature rise of each winding and the acceptable temperature rise. The calculation method is: ;in is the control weight of the current winding. Indicates the current real-time temperature of the i-th winding (unit: °C or K); The maximum safe operating temperature allowed for the i-th winding (unit: °C or K). The thermal energy redundancy distribution coefficient represents the weighted sum of the remaining capacities of the six winding thermal loads under the current operating conditions of the overall system. A higher value indicates a more balanced thermal load and greater remaining capacity.

[0125] Will and As the feedback evaluation input for system operation quality, it is introduced into the optimization control module. A dynamic feedback trigger mechanism is set up. When any indicator falls below the set threshold (such as current balance below 0.8 or thermal redundancy less than 20%), the control parameter recalculation process is triggered.

[0126] The optimization module uses gradient descent, dynamic weight adjustment or model-based approximation methods (such as improved LMS) to modify core parameters, including: PWM duty cycle adjustment coefficient, current target correction factor or frequency band allocation weight in the control matrix.

[0127] All parameter optimization results will be fed back to the main controller to update the speed regulation strategy for the next cycle;

[0128] The feedback frequency can be set to a fixed period or event trigger;

[0129] Long-term statistical analysis can be introduced to improve the global optimality of parameter evolution and prevent over-adjustment or high-frequency disturbance interference;

[0130] The whole system forms a closed-loop self-optimization system with the electric and thermal status as the evaluation target, the control variable as the adjustment object, and the control matrix as the carrier.

[0131] Example 2, please refer to Figure 2 As shown, the six-winding high-voltage variable frequency speed regulation control system described in this embodiment includes:

[0132] The state modeling module collects the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructs a six-dimensional nonlinear system model that includes the electrical-thermal-magnetic coupling relationship;

[0133] The load identification and frequency domain control building module uses a multivariable dynamic prediction model to establish a frequency domain control matrix among the six windings based on the target load type and operating conditions;

[0134] The disturbance sensing and power prediction module uses weighted short-time Fourier analysis to obtain future power regulation prediction values ​​based on the temperature rise trend of each winding and the historical disturbance trajectory, and assigns correction factors to each winding;

[0135] The current control and PWM modulation module updates the reference current of each winding in real time according to the correction factor, obtains six sets of PWM control signals, and synchronizes the modulation in combination with the harmonic suppression factor;

[0136] The fault-tolerant scheduling and topology reconstruction module monitors the performance degradation of any winding and performs fault-tolerant topology reconstruction, reallocates the winding drive tasks, and constructs the optimal compensation path for the remaining windings.

[0137] The control matrix dynamic update module continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the control matrix;

[0138] The evaluation and parameter optimization feedback module is run, and the current balance coefficient and thermal energy redundancy distribution during the speed regulation process are used as evaluation indicators to perform adaptive parameter optimization feedback.

[0139] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A six-winding high-voltage variable frequency speed control method, characterized by: include: S100, collecting the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructing a six-dimensional nonlinear system model including the electric-thermal-magnetic coupling relationship; S200, based on the target load type and operating conditions, a multivariable dynamic prediction model is used to establish a frequency domain control matrix among the six windings; S300, based on the temperature rise trend of each winding and the historical disturbance trajectory, a weighted short-time Fourier analysis method is used to obtain a future power regulation prediction value, and a correction factor is assigned to each winding; S400, updating the reference current of each winding in real time according to the correction factor, obtaining six sets of PWM control signals, and performing synchronous modulation in combination with the harmonic suppression factor; S500: When any winding is monitored for performance degradation, perform fault-tolerant topology reconstruction, reallocate winding drive tasks, and construct optimal compensation paths for the remaining windings; S600, during the entire control cycle, continuously integrating the thermal input data and the load disturbance prediction parameters to periodically reconstruct the control matrix; S700: Using the current balance coefficient and the heat energy redundancy distribution during the speed regulation process as evaluation indicators, perform adaptive parameter optimization feedback.

2. A six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The construction of the six-dimensional nonlinear system model of electric-thermal-magnetic coupling includes: Obtain the three-dimensional temperature field distribution of each winding, and build a coupled inductor model including temperature-dependent parameters based on the magnetic flux mutual inductance matrix; Based on the nonlinear characteristics of each winding resistance changing with temperature, a dynamic electric loss expression is constructed, and a thermal resistance and capacitance network model of the heat conduction path is introduced to establish a multi-point heat transfer structure; The current, flux and temperature of the six windings are taken as state variables, and a time-varying nonlinear state space function group is jointly constructed to form an electric-thermal-magnetic multi-field coupling simulation model.

3. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The establishing of the frequency domain control matrix includes: The spectrum of the six winding current signals is extracted using weighted Fourier transform, a six-dimensional frequency response matrix is ​​constructed, and the coupling relationship between the main and auxiliary frequency bands between the windings is identified by calculating the frequency band overlap. The frequency decoupling area is reconstructed into signal space by orthogonal spectrum projection method, the complementary frequency band features are extracted, and the frequency control function range of each winding group is defined; The load type identification results, historical disturbance trajectories and thermal rise trend parameters are integrated to construct a dynamically updated frequency domain control matrix.

4. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The obtaining of the future power regulation prediction value includes: Collect the historical disturbance current sequence of each winding and construct an energy weighting function with a thermal sensitivity factor to form a weight vector for frequency domain weighted analysis; Based on the weighted short-time Fourier transform, the time-frequency analysis of the disturbance signal of each winding is performed to extract the evolution trajectory of the disturbance frequency and energy distribution over time; The power density concentration trend is coupled with the winding thermal response model to calculate the power load prediction value in the next control cycle, which serves as the feedforward input of the current control strategy.

5. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: Real-time updating of each winding reference current based on the correction factor includes: The power correction factor corresponding to each winding is weightedly integrated with the target current trajectory in the basic frequency domain control matrix to form a preliminary reference current; The dynamic temperature rise coefficient and disturbance feedback variables are introduced to construct an adaptive current regulation function under the current operating state and perform nonlinear adjustment on the preliminary reference current. The disturbance trend in the next control cycle is estimated by using the differential evolution prediction algorithm, and the reference current sequence of each winding is corrected in real time.

6. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The task of redistributing winding drive includes: According to the electromagnetic coupling strength and temperature rise rate of the faulty winding, a set of candidate windings with spatial compensation advantages and thermal margins is screened out from the remaining windings; Construct a dynamic load compensation function, comprehensively consider the available bandwidth in the frequency domain, current redundancy, and system flux balance, and generate a drive task redistribution strategy table; The control target of the alternative winding is dynamically injected into its PWM modulation module, and the frequency domain control matrix is ​​adjusted synchronously.

7. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The periodic reconstruction of the control matrix includes: In each control cycle, the real-time temperature rise rate and current fluctuation data of each winding are collected to calculate the thermal interference factor and electrical interference intensity; Combined with the load disturbance prediction value, a control factor matrix with thermal weight and frequency sensitivity coefficient is constructed, and each weight element in the original frequency domain control matrix is ​​adaptively modified; The modified control matrix is ​​used to update each set of PWM modulation strategies.

8. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The current balance coefficient and thermal energy redundancy distribution during the speed regulation process are used as evaluation indicators to perform adaptive parameter optimization feedback, including: During the motor speed regulation process, the current values ​​and temperature rise information of the six windings are collected in real time to calculate the current balance coefficient and thermal energy redundancy distribution coefficient; The current balance coefficient and the thermal energy redundancy distribution coefficient are compared with the corresponding preset thresholds respectively. If any indicator deviates from the normal operating range, the parameter adjustment mode is entered, including adjusting the PWM duty cycle, current reference value or frequency domain control matrix weight.

9. A six-winding high-voltage variable-frequency speed regulation control system, used to implement a six-winding high-voltage variable-frequency speed regulation control method according to any one of claims 1 to 8, characterized in that: include: The state modeling module collects the structural parameters, thermal information and initial operating status of the six windings of the high-voltage motor, and constructs a six-dimensional nonlinear system model that includes the electrical-thermal-magnetic coupling relationship; The load identification and frequency domain control building module uses a multivariable dynamic prediction model to establish a frequency domain control matrix among the six windings based on the target load type and operating conditions; The disturbance sensing and power prediction module uses weighted short-time Fourier analysis to obtain future power regulation prediction values ​​based on the temperature rise trend of each winding and the historical disturbance trajectory, and assigns correction factors to each winding; The current control and PWM modulation module updates the reference current of each winding in real time according to the correction factor, obtains six sets of PWM control signals, and synchronizes the modulation in combination with the harmonic suppression factor; The fault-tolerant scheduling and topology reconstruction module monitors the performance degradation of any winding and performs fault-tolerant topology reconstruction, reallocates the winding drive tasks, and constructs the optimal compensation path for the remaining windings. The control matrix dynamic update module continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the control matrix; The evaluation and parameter optimization feedback module is run, and the current balance coefficient and thermal energy redundancy distribution during the speed regulation process are used as evaluation indicators to perform adaptive parameter optimization feedback.

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