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

Abstract

The application discloses a six-winding high-voltage variable frequency speed regulation control method and system, belongs to the technical field of high-voltage variable frequency speed regulation, and constructs a six-dimensional nonlinear system model of electric-thermal-magnetic coupling by collecting the structure parameters, thermal sensitive data and initial state of a motor; a frequency domain regulation and control matrix among six windings is established in combination with an operation condition and a load type; a future disturbance power trend is predicted by using a weighted short-time Fourier analysis method, reference currents of the windings are dynamically corrected, and six groups of PWM control signals are generated; when performance degradation occurs in the windings, fault-tolerant topology reconstruction and driving task re-distribution are executed; the control matrix is periodically reconstructed through a thermal-electric feedback mechanism, and parameter adaptive optimization is executed according to a current balance coefficient and thermal redundancy distribution; the method can realize high-precision current coordinated control, frequency energy decoupling and winding thermal load balance, and significantly improves the speed regulation response speed, stability and fault tolerance of the system.

Description

Technical Field

[0001] This invention relates to the field of high-voltage variable frequency speed control technology, specifically to a six-winding high-voltage variable frequency speed control method and system. Background Technology

[0002] Currently, high-voltage, high-power motors are widely used in industrial settings such as mining, metallurgy, petrochemicals, and water conservancy, making variable frequency speed control systems a crucial control method. Traditional variable frequency speed control systems often employ a three-winding structure, and while their control algorithms are relatively mature, they are prone to problems such as current imbalance, motor temperature rise, and severe harmonic interference under high power density, large load disturbances, or extreme operating conditions.

[0003] The six-winding structure is gradually being applied to new-generation high-voltage frequency conversion systems due to its higher redundancy and thermal equalization capabilities. However, most existing speed control methods for six-winding high-voltage motors treat them as two independent three-phase systems, failing to fully consider the coupling effects between the windings. This results in problems such as lag in speed control response, low energy utilization efficiency, and insufficient system robustness.

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

[0005] The purpose of this invention is to provide a six-winding high-voltage variable frequency speed control method and system to address the shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a six-winding high-voltage variable frequency speed control method, comprising:

[0007] S100: Collect the structural parameters of the six windings of the high-voltage motor, thermistor information and its initial operating state, and construct a six-dimensional nonlinear system model that includes the electric-thermal-magnetic coupling relationship;

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

[0009] S300: Based on the temperature rise trend of each winding and the historical disturbance trajectory, the weighted short-time Fourier analysis method is used to obtain the predicted value of future power regulation, and correction factors are assigned to each winding.

[0010] S400: The reference current of each winding is updated in real time according to the correction factor to obtain six sets of PWM control signals, and synchronously modulated in combination with the harmonic suppression factor.

[0011] When S500 detects performance degradation in any winding, it performs fault-tolerant topology reconstruction, reallocates winding drive tasks, and constructs the optimal compensation path for the remaining windings.

[0012] S600 continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the control matrix.

[0013] S700 uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

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

[0015] The three-dimensional spatial temperature field distribution of each winding is obtained, and a coupled inductor model containing temperature-dependent parameters is constructed by combining the magnetic flux mutual inductance matrix.

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

[0017] The current, flux linkage, and temperature of the six windings are used as state variables to jointly construct a time-varying nonlinear state-space function set, forming an electro-thermal-magnetic multi-field coupled simulation model.

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

[0019] Weighted Fourier transform was used to extract the spectrum of six sets of winding current signals, a six-dimensional frequency response matrix was constructed, and the coupling relationship between the main and auxiliary frequency bands between windings was identified by frequency band overlap calculation.

[0020] The frequency decoupling region is reconstructed in signal space using the orthogonal spectrum projection method, complementary frequency band features are extracted, and the frequency control function range of each winding group is defined.

[0021] By integrating load type identification results, historical disturbance trajectories, and thermal rise trend parameters, a dynamically updated frequency domain control matrix is ​​constructed.

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

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

[0024] Time-frequency analysis of the disturbance signals of each winding is performed based on weighted short-time Fourier transform 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 predicted power load value in the next control cycle, which serves as the feedforward input for the current control strategy.

[0026] Preferably, real-time updating of the reference current for each winding group based on the correction factor includes:

[0027] The power correction factor corresponding to each winding is weighted and fused with the target current trajectory in the basic frequency domain control matrix to form a preliminary reference current.

[0028] By introducing a dynamic temperature rise coefficient and a disturbance feedback variable, an adaptive current regulation function is constructed under the current operating state to nonlinearly adjust the initial reference current;

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

[0030] Preferably, the reallocation winding drive task includes:

[0031] Based on 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 selected from the remaining windings.

[0032] A dynamic load compensation function is constructed, taking into account frequency domain spare bandwidth, current redundancy capability and system magnetic flux balance, and a drive task redistribution strategy table is generated.

[0033] The control target of the replacement 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, real-time temperature rise rate and current fluctuation data of each winding are collected, and thermal disturbance factor and electrical disturbance intensity are calculated.

[0036] By combining the predicted load disturbance values, a control factor matrix with thermal weights and frequency sensitivity coefficients is constructed, and the weight elements in the original frequency domain control matrix are adaptively corrected.

[0037] The corrected control matrix is ​​used to update the PWM modulation strategy for each group.

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

[0039] During the motor speed regulation operation, the current values ​​and temperature rise information of six sets of windings are collected in real time, and the current balance coefficient and thermal redundancy distribution coefficient are calculated.

[0040] The current balance coefficient and the thermal redundancy distribution coefficient are compared with their respective preset thresholds. If either index deviates from the normal operating range, the parameter adjustment mode is entered, which includes 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 control system, comprising:

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

[0043] The load identification and frequency domain control module establishes a frequency domain control matrix among the six windings based on the target load type and operating conditions using a multivariate dynamic prediction model.

[0044] The disturbance sensing and power prediction module uses a weighted short-time Fourier analysis method to obtain the predicted value of future power regulation 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 to obtain six sets of PWM control signals, and combines them with harmonic suppression factors for synchronous modulation.

[0046] The fault-tolerant scheduling and topology reconfiguration module monitors performance degradation in any winding, performs fault-tolerant topology reconfiguration, reallocates 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 operation evaluation and parameter optimization feedback module uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

[0049] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0050] 1. This invention constructs a six-dimensional nonlinear system model that integrates the coupling relationships of multiple physical fields such as electricity, heat, and magnetism, thereby achieving accurate dynamic modeling of a high-voltage six-winding motor. By combining load type identification, disturbance trend prediction, and temperature rise analysis, a speed control mechanism with clear frequency domain division and adaptive adjustment according to operating conditions is established, which effectively improves the system's control sensitivity, current distribution balance, and frequency response accuracy.

[0051] 2. During operation, this invention integrates thermal information and disturbance prediction results, executes closed-loop optimized feedback control based on current balance and thermal redundancy, and actively reconstructs the drive topology when the winding performance degrades, thereby achieving efficient fault-tolerant scheduling and dynamic power redistribution. This makes the system more thermally stable, fault-tolerant, and safe for long-term operation, which is significantly better than traditional three-phase or passive control strategies. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can 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 Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0057] S100: Collect the structural parameters of the six windings of the high-voltage motor, thermistor information and its initial operating state, and construct a six-dimensional nonlinear system model that includes the electric-thermal-magnetic coupling relationship;

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

[0059] S300: Based on the temperature rise trend of each winding and the historical disturbance trajectory, the weighted short-time Fourier analysis method is used to obtain the predicted value of future power regulation, and correction factors are assigned to each winding.

[0060] S400: The reference current of each winding is updated in real time according to the correction factor to obtain six sets of PWM control signals, and synchronously modulated in combination with the harmonic suppression factor.

[0061] When S500 detects performance degradation in any winding, it performs fault-tolerant topology reconstruction, reallocates winding drive tasks, and constructs the optimal compensation path for the remaining windings.

[0062] S600 continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the control matrix.

[0063] S700 uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

[0064] Specifically, S100 includes:

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

[0066] S102: Acquire initial thermistor information, embed thermistor / thermocouple arrays into each of the six windings, and obtain the initial temperature field distribution on the surface and inside of each winding. The system generates a three-dimensional thermal field through interpolation; identifies the thermal resistance and thermal capacity parameters of the heat dissipation paths of the motor casing, core, bearings, etc., and establishes an equivalent thermal network structure; and obtains the basic thermal boundary conditions of the environment in which the motor is located, including: initial ambient temperature, convection coefficient, and heat conduction boundary form.

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

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

[0069] S104: Form a six-dimensional nonlinear system model by coupling the above three types of variables (electric, electromagnetic, and thermal) to form a state-space model. Defined as: Where U is the control input (such as a 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 reconfiguration algorithms, and has thermoelectric feedback mechanism and magnetic coupling asymmetric response mechanism, which can significantly improve control accuracy.

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

[0072] First, the instantaneous current data of the six windings is obtained through the real-time current sampling module on the inverter side, and then preliminary filtering and normalization are performed in combination with historical operating data.

[0073] Pattern recognition algorithms (such as DTW dynamic time warping or KNN nearest neighbor classification) are used to match the current load characteristics to typical operating condition categories: constant torque type (such as compressor), fan type (such as HVAC system), high variable torque type (such as lifting equipment), etc.

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

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

[0076] The main response band and secondary response coupling band of each winding in different frequency bands are analyzed, and the matrix is ​​calculated based on the overlap of the coupling bands to quantify the frequency domain coordination between windings.

[0077] The signal space is reconstructed by applying the orthogonal spectrum projection method, removing the common-mode interference band, extracting the effective frequency decoupling components, and forming the functional frequency domain range of each winding.

[0078] Using the frequency band division results as the basis for regulation, a 6×N frequency domain regulation matrix Mfreq is constructed, where each element mij represents the control weight of the i-th winding in the j-th frequency band.

[0079] This matrix integrates input variables such as historical load disturbance trajectory, 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] Ultimately, the frequency domain modulation matrix serves as the input template for the PWM control strategy, guiding the target current trajectory of each winding in each frequency band, thereby achieving frequency domain coordinated control among the six windings.

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

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

[0083] Temperature data were processed using a moving average filtering algorithm, and the temperature rise response coefficient of each winding under unit power conditions was calculated. This reflects the thermal sensitivity of the winding to the load. This represents the power change of the i-th winding over a certain time period (unit: watts, W). This indicates the temperature change of the winding during that time period.

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

[0085] Using historical load disturbance current sequences, the trajectory of disturbance frequency variation over time is extracted using weighted short-time Fourier analysis (W-STFT).

[0086] Analyze short-term power fluctuations and disturbance frequency shift patterns to obtain predicted frequency domain load pressure values ​​that may occur in the next control cycle. (f,t);

[0087] By embedding the temperature rise trend parameter as a weighting factor into the spectral energy density calculation, a "thermal weighted frequency response curve" is formed, enabling power trend prediction driven by thermal load.

[0088] Based on the predicted frequency response and the thermal carrying capacity of each winding, calculate the expected load distribution ratio of each winding in future power regulation. Its expression can be approximated as: ;in ε is the temperature rise sensitivity coefficient, and ε is the thermal balance compensation coefficient; Introduced as a power correction factor into the PWM current control module, the original frequency domain control matrix is ​​weighted and adjusted to achieve thermal sensing adaptive optimization of the winding current control weight.

[0089] S400 updates the reference current of each winding in real time according to the correction factor to obtain six sets of PWM control signals, and synchronously modulates them in conjunction with the harmonic suppression factor, specifically including:

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

[0091] Six groups updated in real time Input to the SVPWM (Space Vector Pulse Width Modulation) module or the sine PWM module;

[0092] The control module converts the target value of each three-phase current into a gate pulse signal PWMi(t) based on 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] To improve the harmonic coupling problem in the frequency crossover band of multi-winding systems, a harmonic suppression factor γi(f) is introduced, which indicates that filtering or limiting should be strengthened in a specific frequency range.

[0095] By applying γi(f) to the reference current spectrum or PWM modulation index, automatic voltage pulse width limiting or compensation can be achieved within 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 adjustment of modulation depth.

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

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

[0098] A fully parallel PWM output system with dynamic self-adjustment is realized, which enhances the stability and robustness of the system under high-frequency disturbances and load switching.

[0099] When S500 detects performance degradation in any winding, it performs fault-tolerant topology reconstruction, reallocates winding drive tasks, and constructs the optimal compensation path 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; using multi-factor threshold discrimination method or principal component analysis (PCA) method to identify windings with degraded performance;

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

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

[0103] If the degraded winding can still retain some function, it will enter derating operation mode;

[0104] If the circuit fails completely, a fault-tolerant reconfiguration 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 weights in the frequency domain modulation matrix Mfreq to redistribute the frequency band and power carried by the original degraded winding to the remaining windings;

[0107] The optimal compensation path is constructed by prioritizing the allocation to windings with relatively weak spatial coupling, low current heat load, and large frequency domain spare capacity.

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

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

[0110] If multiple windings degrade, they will be switched to the standby operating topology in order of priority to maintain the continuity of system output.

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

[0112] Real-time temperature data of six windings are collected every preset control cycle (e.g., 20-50ms). ;

[0113] The rate of temperature change was calculated using a first-order differential filter. Identify the current dynamic heat load;

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

[0115] Based on the load fluctuation trend over a period of time, weighted short-time Fourier analysis or autoregressive model (AR) is used to predict future disturbance power.

[0116] The prediction results are presented in the disturbance intensity distribution map. (f,t) indicates the load pressure that each winding will face in different frequency bands;

[0117] Simultaneous analysis of time-domain indicators such as current imbalance and harmonic accumulation risk forms a disturbance response priority matrix.

[0118] The control system will use thermal feedback variables With disturbance predictor variables (f,t) common input control matrix reconstruction module; each element in the original frequency domain modulation matrix Mfreq It will be corrected in real time to: in, and Thermal perturbation weighting factor, These are the corrected elements.

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

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

[0121] If system fluctuations exceed stability limits, the reconfiguration frequency will be automatically slowed down or some matrix channels will be frozen to ensure overall operational safety.

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

[0123] Establish a multi-dimensional operation evaluation index system: During the entire speed regulation operation cycle, calculate the system operation status evaluation index in real time, mainly including: current balance coefficient. : This represents the degree of fluctuation between the currents in the six windings, defined as: In the formula, Instantaneous current values ​​of the six windings (unit: A); The standard deviation of the six current sets is used to measure the degree of fluctuation / imbalance between currents; mean is the average of the absolute values ​​of the six current sets, reflecting the current overall current level. This represents the current balance coefficient, which typically ranges from 0 to 1. The closer it is to 1, the more consistent the six current groups are and the more balanced the current distribution in the system.

[0124] Thermal redundancy distribution coefficient This reflects the proportional redundancy between the current temperature rise and the acceptable temperature rise of each winding. The calculation method is as follows: ;in This represents the control weight of the current winding. This represents 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) is indicated; the thermal redundancy distribution coefficient represents the weighted sum of the remaining capacity of the six windings under the current operating conditions of the overall system. A higher value indicates a more balanced thermal load and more sufficient remaining capacity.

[0125] Will and As a feedback evaluation input for system operation quality, it is introduced into the optimization control module; a dynamic feedback triggering mechanism is set up so that when any indicator is lower than the set threshold (such as current balance lower than 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 correct 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-triggered.

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

[0130] The system as a whole forms a closed-loop self-optimizing system with electrothermal state as the evaluation target, control variables as the adjustment objects, and control matrix as the carrier.

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

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

[0133] The load identification and frequency domain control module establishes a frequency domain control matrix among the six windings based on the target load type and operating conditions using a multivariate dynamic prediction model.

[0134] The disturbance sensing and power prediction module uses a weighted short-time Fourier analysis method to obtain the predicted value of future power regulation 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 to obtain six sets of PWM control signals, and combines them with harmonic suppression factors for synchronous modulation.

[0136] The fault-tolerant scheduling and topology reconfiguration module monitors performance degradation in any winding, performs fault-tolerant topology reconfiguration, reallocates 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 operation evaluation and parameter optimization feedback module uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A six winding high voltage variable frequency speed control method, characterized in that: include: S100. Collect the structural parameters of the six windings of the high-voltage motor, thermistor information and its initial operating state, and construct a six-dimensional nonlinear system model containing electric-thermal-magnetic coupling relationship. The six-dimensional nonlinear system model has a thermoelectric mutual feedback mechanism and a magnetic coupling asymmetric response mechanism, which are used for subsequent predictive control, state estimation and fault-tolerant reconstruction. S200: Based on the target load type and operating conditions, a multivariate dynamic prediction model is used to establish the frequency domain control matrix between the six windings; S300: Based on the temperature rise trend of each winding and the historical disturbance trajectory, the weighted short-time Fourier analysis method is used to obtain the predicted value of future power regulation, and correction factors are assigned to each winding. S400: The reference current of each winding is updated in real time according to the correction factor to obtain six sets of PWM control signals, and synchronously modulated in combination with the harmonic suppression factor. When S500 detects performance degradation in any winding, it performs fault-tolerant topology reconstruction, reallocates winding drive tasks, and constructs the optimal compensation path for the remaining windings. S600 continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the frequency domain control matrix. S700 uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

2. The 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 with electro-thermal-magnetic coupling includes: The three-dimensional spatial temperature field distribution of each winding is obtained, and a coupled inductor model containing temperature-dependent parameters is constructed by combining the magnetic flux mutual inductance matrix. Based on the nonlinear characteristics of the resistance of each winding changing with temperature, a dynamic electrical loss expression is constructed, and a thermal resistance-thermal capacity network model of the heat conduction path is introduced to establish a multi-point heat transfer structure. The current, flux linkage, and temperature of the six windings are used as state variables to jointly construct a time-varying nonlinear state-space function set, forming a six-dimensional nonlinear system model that includes electro-thermal-magnetic coupling relationships.

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

4. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The process of obtaining future power regulation prediction values ​​includes: Collect the historical disturbance current sequence of each winding and construct an energy weighting function with a thermistor to form a weight vector for frequency domain weighted analysis; Time-frequency analysis of the disturbance signals of each winding is performed based on weighted short-time Fourier transform 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 predicted power load value in the next control cycle, which serves as the feedforward input for the current control strategy.

5. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: The reference current for each winding group is updated in real time based on the correction factor, including: The power correction factor corresponding to each winding is weighted and fused with the target current trajectory in the frequency domain control matrix to form a preliminary reference current. By introducing a dynamic temperature rise coefficient and a disturbance feedback variable, an adaptive current regulation function is constructed under the current operating state to perform nonlinear adjustment on the initial reference current; The perturbation trend in the next control cycle is estimated by 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 reassignment winding drive task includes: Based on 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 selected from the remaining windings. A dynamic load compensation function is constructed, taking into account frequency domain spare bandwidth, current redundancy capability and system magnetic flux balance, to generate a drive task redistribution strategy table. The control target of the replacement 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 frequency domain modulation matrix includes: In each control cycle, real-time temperature rise rate and current fluctuation data of each winding are collected, and thermal disturbance factor and electrical disturbance intensity are calculated. By combining the predicted load disturbance values, a control factor matrix with thermal weights and frequency sensitivity coefficients is constructed, and the weight elements in the original frequency domain control matrix are adaptively corrected. The corrected frequency domain modulation matrix is ​​used to update the PWM modulation strategy for each group.

8. The six-winding high-voltage variable frequency speed control method according to claim 1, characterized in that: Using the current balance coefficient and thermal redundancy distribution during speed regulation as evaluation indicators, adaptive parameter optimization feedback is performed, including: During the motor speed regulation operation, the current values ​​and temperature rise information of six sets of windings are collected in real time, and the current balance coefficient and thermal redundancy distribution coefficient are calculated. The current balance coefficient and the thermal redundancy distribution coefficient are compared with their respective preset thresholds. If either index deviates from the normal operating range, the parameter adjustment mode is entered, which includes adjusting the PWM duty cycle, current reference value, or frequency domain control matrix weight.

9. A six-winding high-voltage variable frequency speed control system, used to implement the six-winding high-voltage variable frequency speed control method according to any one of claims 1-8, characterized in that: include: The state modeling module collects the structural parameters of the six windings of the high-voltage motor, thermistor information and its initial operating state, and constructs a six-dimensional nonlinear system model containing electro-thermal-magnetic coupling relationships. The six-dimensional nonlinear system model has a thermo-electric mutual feedback mechanism and a magnetic coupling asymmetric response mechanism, which are used for subsequent predictive control, state estimation and fault-tolerant reconstruction. The load identification and frequency domain control module establishes a frequency domain control matrix among the six windings based on the target load type and operating conditions using a multivariate dynamic prediction model. The disturbance sensing and power prediction module uses a weighted short-time Fourier analysis method to obtain the predicted value of future power regulation 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 to obtain six sets of PWM control signals, and combines them with harmonic suppression factors for synchronous modulation. The fault-tolerant scheduling and topology reconfiguration module monitors performance degradation in any winding, performs fault-tolerant topology reconfiguration, reallocates winding drive tasks, and constructs the optimal compensation path for the remaining windings. The frequency domain control matrix dynamic update module continuously integrates thermal input data and load disturbance prediction parameters throughout the entire control cycle to periodically reconstruct the frequency domain control matrix. The operation evaluation and parameter optimization feedback module uses the current balance coefficient and thermal redundancy distribution during the speed regulation process as evaluation indicators to perform adaptive parameter optimization feedback.

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