A method and system for multi-parameter collaborative control of a traction machine frequency converter

Abstract

The application discloses a kind of multi-parameter collaborative control method and system of traction machine frequency converter, is specifically related to elevator traction machine frequency converter control technical field, for solving the mechanical resonance problem caused by parameter discrete setting in prior art;It is by real-time acquisition speed, torque and current parameters, extract harmonic component and calculate energy distribution ratio to identify resonance state, and then modal decomposition and modal participation factor calculation are carried out to resonance band signal, according to determine key parameters and adjustment direction, finally realize the effective suppression of mechanical resonance by multi-parameter collaborative adjustment set value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of elevator hoisting machine frequency converter control, and more particularly, to a multi-parameter collaborative control method and system for a hoisting machine frequency converter. BACKGROUND

[0002] As the core driving component of the elevator system, the running performance of the elevator hoisting machine is directly related to the ride comfort, leveling accuracy and equipment reliability of the entire elevator. Modern elevators generally use the method of driving asynchronous hoisting machines with frequency converters, which realizes the control of the running state of the hoisting machine by respectively adjusting the speed, torque and current and other core parameters of the frequency converter. In the prior art, the control strategy for the above-mentioned parameters is mostly based on the setting and optimization of independent control loops, aiming to ensure the stability and response speed of a single control loop. However, this separate parameter setting method cannot fully consider the dynamic coupling and mutual influence between different control loops.

[0003] Due to the insufficient consideration of the coordination between multiple control parameters of the hoisting machine frequency converter in the existing control method, the system is prone to mechanical resonance at a specific frequency when dealing with complex operating conditions, and the elevator will vibrate and produce noise when running at a specific speed range. This not only significantly affects the ride comfort, but also poses a potential risk of fatigue damage to the hoisting system and mechanical structure. The existing technology cannot effectively avoid or suppress such resonance phenomenon caused by the interaction of multiple parameters while ensuring the dynamic response performance of the system. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a multi-parameter collaborative control method and system for a hoisting machine frequency converter to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A multi-parameter collaborative control method for a hoisting machine frequency converter, comprising the following steps:

[0007] S1, acquiring the speed parameter, torque parameter and current parameter of the hoisting machine frequency converter in real time;

[0008] S2, extracting the harmonic component based on the speed parameter, torque parameter and current parameter and calculating the harmonic energy distribution ratio, and determining that it falls into a predefined mechanical resonance frequency range when the harmonic energy distribution ratio exceeds a preset ratio threshold;

[0009] S3, when falling into the predefined mechanical resonance frequency range, performing modal decomposition on the signal components of the speed parameter, torque parameter and current parameter within the resonance frequency band and calculating the modal participation factor of each parameter;

[0010] S4. Based on the magnitude of the modal participation factor, identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions;

[0011] S5. Based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, coordinate the setting values ​​of speed parameters, torque parameters and current parameters in the inverter control circuit.

[0012] Furthermore, the speed, torque, and current parameters of the traction machine frequency converter are acquired in real time, including:

[0013] Speed, torque, and current parameters are collected in real time synchronously through the feedback channel of the inverter control circuit.

[0014] The collected speed, torque, and current parameters are timestamped and aligned.

[0015] Generate time-consistent datasets of speed, torque, and current parameters.

[0016] Furthermore, harmonic components are extracted based on speed, torque, and current parameters, and the harmonic energy distribution ratio is calculated. When the harmonic energy distribution ratio exceeds a preset threshold, it is determined to fall within a predefined mechanical resonance frequency range, including:

[0017] Frequency domain transformations were performed on the time-consistent speed, torque, and current parameters to obtain their corresponding spectral distributions; the fundamental component and each harmonic component were then separated from the spectral distributions.

[0018] The ratio of the energy value of each harmonic component to the total energy value is calculated as the harmonic energy distribution ratio;

[0019] The proportion of harmonic energy distribution of the main harmonic components is compared with a preset proportion threshold.

[0020] When the proportion of harmonic energy distribution of any major harmonic component exceeds a preset proportion threshold, it is determined to fall into the predefined mechanical resonance frequency range.

[0021] Furthermore, extracting harmonic components and calculating the harmonic energy distribution ratio also includes: normalizing the amplitude of each harmonic component in the spectral distribution and calculating the normalized harmonic energy distribution ratio.

[0022] Furthermore, when falling within a predefined mechanical resonant frequency range, modal decomposition is performed on the signal components of the speed, torque, and current parameters within the resonant frequency band, and the modal participation factors of each parameter are calculated, including:

[0023] Extract the resonant frequency band signal components within the predefined mechanical resonant frequency range from the speed parameters, torque parameters, and current parameters;

[0024] Multiple eigenmode functions are obtained by performing eigenmode decomposition on the resonant frequency band signal components.

[0025] The modal participation factors corresponding to the velocity, torque, and current parameters are calculated based on the energy distribution of each intrinsic mode function.

[0026] Furthermore, performing modal decomposition and calculating the modal participation factors of each parameter also includes: calculating the instantaneous frequency of each intrinsic modal function and selecting intrinsic modal functions within the mechanical resonance frequency range to participate in the modal participation factor calculation.

[0027] Furthermore, based on the magnitude of the modal participation factor, the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions are identified, including:

[0028] By comparing the modal participation factors of speed, torque, and current parameters, the parameter with the largest modal participation factor is identified as the key parameter.

[0029] The adjustment direction is determined based on the energy contribution characteristics of key parameters in mechanical resonance.

[0030] Furthermore, identifying key parameters and their adjustment directions also includes: establishing a trend analysis model for the modal participation factors and determining the adjustment direction based on real-time trends.

[0031] Furthermore, based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, the setpoints of speed, torque, and current parameters in the inverter control loop are adjusted in a coordinated manner, including:

[0032] The settings of key parameters are modified based on the direction adjustment;

[0033] Based on the coupling relationship between key parameters and other parameters, the set values ​​of the remaining parameters in speed, torque and current parameters are adjusted synchronously according to a preset ratio to complete the coordinated adjustment of multiple parameters.

[0034] On the other hand, the present invention provides a multi-parameter coordinated control system for a traction machine frequency converter, comprising the following modules:

[0035] The parameter acquisition module is used to acquire the speed, torque, and current parameters of the traction machine frequency converter in real time.

[0036] The interval judgment module is used to extract harmonic components and calculate the harmonic energy distribution ratio based on speed parameters, torque parameters and current parameters. When the harmonic energy distribution ratio exceeds the preset ratio threshold, it is judged to fall into the predefined mechanical resonance frequency interval.

[0037] The factor calculation module is used to perform modal decomposition of the signal components of speed parameters, torque parameters, and current parameters within the resonant frequency band and calculate the modal participation factor of each parameter when they fall within a predefined mechanical resonant frequency range.

[0038] The parameter identification module is used to identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions based on the magnitude of the modal participation factor.

[0039] The coordinated adjustment module is used to coordinately adjust the set values ​​of speed, torque and current parameters in the inverter control loop based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. By acquiring speed, torque, and current parameters in real time and performing harmonic energy analysis, early characteristics of mechanical resonance can be accurately captured, thereby enabling rapid identification and early warning of resonance states. Through modal decomposition and modal participation factor calculation, the contribution of each parameter to the resonance phenomenon can be precisely quantified, thereby identifying the key parameters with the greatest impact and their adjustment directions. This parameter identification mechanism based on energy distribution and modal analysis effectively avoids the resonance risk caused by neglecting parameter coupling in traditional discrete tuning, significantly improving the stability and comfort of elevator operation.

[0042] 2. By employing a multi-parameter collaborative adjustment strategy, the setpoints for speed, torque, and current are simultaneously optimized after identifying key parameters, ensuring dynamic matching and coordination between various control loops. This integrated control approach not only effectively suppresses mechanical vibration and noise at specific frequencies but also reduces fatigue damage to the traction system, extending equipment lifespan. Furthermore, it possesses excellent adaptive capabilities, enabling it to adapt to parameter changes under different operating conditions. Thus, while ensuring the system's dynamic response performance, it continuously maintains the elevator's leveling accuracy and operational reliability. Attached Figure Description

[0043] Figure 1 This is a flowchart of a multi-parameter coordinated control method for a traction machine frequency converter according to the present invention;

[0044] Figure 2 This is a schematic diagram of the structure of a multi-parameter coordinated control system for a traction machine frequency converter according to the present invention. Detailed Implementation

[0045] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1: Figure 1 This invention provides a multi-parameter coordinated control method for a traction machine frequency converter, which includes the following steps:

[0047] S1. Real-time acquisition of speed, torque, and current parameters of the traction machine frequency converter;

[0048] S2. Extract harmonic components based on speed parameters, torque parameters and current parameters and calculate the harmonic energy distribution ratio. When the harmonic energy distribution ratio exceeds the preset ratio threshold, it is judged to fall into the predefined mechanical resonance frequency range.

[0049] S3. When the frequency falls within the predefined mechanical resonance frequency range, perform mode decomposition on the signal components of the speed parameter, torque parameter, and current parameter within the resonance frequency band and calculate the mode participation factor of each parameter.

[0050] S4. Based on the magnitude of the modal participation factor, identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions;

[0051] S5. Based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, coordinate the setting values ​​of speed parameters, torque parameters and current parameters in the inverter control circuit.

[0052] S1. Real-time acquisition of speed, torque, and current parameters of the traction machine frequency converter, specifically implemented as follows:

[0053] In the process of acquiring the speed, torque, and current parameters of the traction machine inverter in real time, the speed, torque, and current parameters are first synchronously acquired in real time through the feedback channel of the inverter control loop. The feedback channel refers to the circuit path inside the inverter used to monitor and transmit operating status data. These paths are directly connected to the speed sensor, torque sensor, and current sensor to ensure the continuous flow of data from the sensor to the processing unit. Real-time synchronous acquisition is achieved by setting a unified sampling clock in the inverter control system. This sampling clock triggers all sensors to acquire data simultaneously at fixed time intervals. For example, the sampling clock can be generated based on the inverter's internal crystal oscillator to ensure that the speed, torque, and current parameters are captured at the same time, thereby avoiding data inconsistency caused by differences in acquisition time. The speed parameter is obtained by measuring the angular velocity or linear velocity of the traction machine rotor through an encoder or Hall effect sensor. The torque parameter is obtained by measuring the torque of the drive shaft through a strain gauge or magnetoelastic sensor. The current parameter is obtained by measuring the current value of the motor winding through a current transformer or Hall effect sensor. All parameters are transmitted to the central processing unit in the form of analog or digital signals through the feedback channel during the acquisition process.

[0054] The acquired speed, torque, and current parameters undergo timestamp alignment. Timestamp alignment involves attaching a precise timestamp to each acquired data point and then adjusting the data sequence based on these timestamps to align all parameter data points on the same time reference. The timestamps are generated by a high-precision timer inside the inverter, which records the acquisition time of each data point with microsecond-level resolution. Specific alignment methods include comparing the timestamps of the speed, torque, and current parameters to identify time deviations caused by transmission delays or sampling jitter. Then, interpolation algorithms are used to adjust the position of the data points. For example, for data points with small time deviations, linear interpolation is used to calculate the parameter values ​​at a unified time point; for data points with large time deviations, a sliding window averaging method is used to smooth the data sequence, ensuring that all parameters remain consistent in the time dimension. Timestamp alignment also involves verifying data validity, such as checking the continuity and consistency of the timestamps, eliminating abnormal data points caused by sensor malfunctions or signal interference, and using interpolation to supplement missing data to ensure the integrity of the data sequence.

[0055] Generate time-consistent datasets of speed, torque, and current parameters. A time-consistent dataset refers to a structured collection of speed, torque, and current parameters that have been timestamped and aligned, where each data point corresponds to the same sampling time. The dataset generation method includes arranging the aligned speed, torque, and current parameters in chronological order and storing them as an array or table. For example, a two-dimensional array can be used where rows represent time points and columns represent speed, torque, and current parameter values, respectively. To ensure dataset consistency, data normalization is also performed during the generation process, such as changing the units of the speed parameters. The units for torque parameters and current parameters are standardized to meters per second (m / s) and Newton-meters (Nm), respectively, to avoid affecting subsequent analysis due to differences in units. The dataset also includes metadata information, such as the acquisition time range, parameter source, and sampling interval. This information is used for data verification and traceability in subsequent steps. After generating a time-consistent dataset, the system temporarily stores the dataset in a buffer. The size of the buffer is dynamically adjusted according to the traction machine's operating cycle. For example, the buffer capacity is set to be able to store data for at least one complete operating cycle to ensure that data is not lost or overwritten during continuous operation, thereby providing complete and consistent input data for subsequent harmonic energy analysis.

[0056] S2. Harmonic components are extracted based on speed, torque, and current parameters, and the harmonic energy distribution ratio is calculated. When the harmonic energy distribution ratio exceeds a preset threshold, it is determined to fall within a predefined mechanical resonance frequency range. Specifically, the implementation is as follows:

[0057] Based on time-consistent speed, torque, and current parameter datasets, frequency domain transformations are first performed on the speed, torque, and current parameters to obtain their corresponding spectral distributions. Frequency domain transformation refers to the analysis method of converting time-domain signals into frequency-domain signals, specifically implemented using the Fast Fourier Transform (FFT) algorithm. This algorithm obtains the spectral distribution by decomposing the time-domain signal into sinusoidal components of different frequencies. During the FFT, the input time-consistent speed, torque, and current parameters need to be preprocessed, including adding a Hanning window function to the data sequence to reduce spectral leakage. The Hanning window function is expressed as the product of weighting coefficients and data points, where the weighting coefficients are calculated based on the position of the data points within the window. The spectral distribution contains frequency components and their corresponding amplitude information. The resolution of the frequency components is determined by the sampling frequency and the number of transformation points. For example, when the sampling frequency is 1000 Hz and the number of transformation points is 1024, the frequency resolution is approximately 0.98 Hz. The obtained spectral distribution is stored in the form of an amplitude-frequency relationship, where the amplitude represents the intensity of each frequency component in the original signal.

[0058] The fundamental frequency component and harmonic components are separated from the spectral distribution. The fundamental frequency component corresponds to the operating fundamental frequency of the traction machine, while the harmonic components are frequency components that are integer multiples of the fundamental frequency. The separation method involves identifying the amplitude peak value corresponding to the fundamental frequency in the spectral distribution, and then extracting the amplitude peak values ​​corresponding to the second harmonic, third harmonic, and so on up to the highest harmonic frequency from the spectral distribution, using the fundamental frequency as a reference. The fundamental frequency is identified by finding the low-frequency component with the largest amplitude in the spectral distribution; for example, the frequency corresponding to the maximum amplitude value in the range of 5 Hz to 50 Hz is taken as the fundamental frequency. The extraction range of each harmonic component is determined according to the mechanical characteristics of the traction machine; for example, up to the 20th harmonic component, i.e., the harmonic component with a frequency 20 times the fundamental frequency. The frequency values ​​and corresponding amplitudes of the separated fundamental and harmonic components are recorded respectively, forming a fundamental component dataset and a harmonic component dataset.

[0059] The ratio of the energy value of each harmonic component to the total energy value is used as the harmonic energy distribution ratio. The harmonic energy value is calculated by squared the amplitude of the corresponding harmonic component. The total energy value is the sum of the energy values ​​of the fundamental component and all harmonic components. The specific calculation process includes first calculating the energy value of each harmonic component, i.e., the square of its corresponding amplitude; then adding the energy values ​​of all harmonic components to obtain the total energy value; and finally dividing the energy value of each harmonic component by the total energy value to obtain the harmonic energy distribution ratio of that harmonic component. The calculation of the energy value of each harmonic component must maintain consistent dimensions; for example, when the amplitude unit is volts, the energy value unit is volt squared. The harmonic energy distribution ratio is expressed as a percentage, and the sum of the harmonic energy distribution ratios of all harmonic components is 100%.

[0060] The amplitude of each harmonic component in the spectral distribution is normalized, and the normalized harmonic energy distribution ratio is calculated. Amplitude normalization is the process of converting the amplitude of each harmonic component into a relative value. Specifically, the amplitude of each harmonic component is divided by the amplitude of the fundamental component to obtain the normalized amplitude. When calculating the harmonic energy distribution ratio based on the normalized amplitude, the square of the normalized amplitude is used as the normalized energy value, and then the normalized harmonic energy distribution ratio is calculated according to the ratio of the normalized energy value to the total normalized energy value. Normalization eliminates the influence of the absolute magnitude of the signal amplitude on the analysis results, making the harmonic energy distribution ratios under different operating conditions comparable.

[0061] The harmonic energy distribution ratio of the main harmonic components is compared with a preset ratio threshold. The main harmonic components refer to harmonic components with harmonic orders within a specific range, such as the 3rd to 10th harmonics. The preset ratio threshold is determined through experimental data analysis based on the mechanical resonance characteristics of the traction machine. Specifically, under known operating conditions where mechanical resonance occurs, the harmonic energy distribution ratio of each main harmonic component is statistically analyzed, and the upper quartile of the statistical distribution is taken as the preset ratio threshold. Different preset ratio thresholds can be set for each main harmonic component; for example, the preset ratio threshold for the 3rd harmonic can be set to 5%, and the preset ratio threshold for the 5th harmonic can be set to 3%. The comparison process includes checking whether the harmonic energy distribution ratio of each main harmonic component exceeds the corresponding preset ratio threshold.

[0062] When the harmonic energy distribution ratio of any major harmonic component exceeds a preset ratio threshold, it is determined that the system has fallen into a predefined mechanical resonance frequency range. The predefined mechanical resonance frequency range refers to a frequency range known to easily induce mechanical resonance, obtained through traction machine modal testing experiments, for example, the 15 Hz to 35 Hz frequency range. The determination process involves determining that the current operating state of the system falls into the mechanical resonance frequency range when the frequency of a major harmonic component is detected to be within the predefined mechanical resonance frequency range and the harmonic energy distribution ratio of that harmonic component exceeds the corresponding preset ratio threshold. The determination result is recorded as a flag signal to trigger subsequent modal decomposition processing. The entire harmonic energy analysis process is performed in real time, with the analysis cycle synchronized with the data acquisition cycle; for example, a complete harmonic energy distribution ratio calculation and determination is performed every 100 milliseconds.

[0063] S3. When the frequency falls within the predefined mechanical resonance frequency range, perform modal decomposition on the signal components of the speed, torque, and current parameters within the resonance frequency band and calculate the modal participation factor for each parameter. Specifically, the implementation is as follows:

[0064] When the system is determined to fall within a predefined mechanical resonance frequency range, mode decomposition processing is initiated on the signal components of the speed, torque, and current parameters within the resonance band. First, the resonance band signal components within the predefined mechanical resonance frequency range are extracted from the time-consistent dataset of speed, torque, and current parameters. The predefined mechanical resonance frequency range refers to a specific frequency range easily inducing mechanical resonance, determined through experimental testing, such as the 20 Hz to 40 Hz frequency range. The extraction process employs a digital bandpass filter, which allows frequency components within the predefined mechanical resonance frequency range to pass through while attenuating frequency components outside this range. The digital bandpass filter is designed based on a finite impulse response filter structure, and its transfer function is determined by the filter order and cutoff frequency. The lower cutoff frequency is set as the lower limit frequency of the predefined mechanical resonance frequency range, and the upper cutoff frequency is set as the upper limit frequency of the predefined mechanical resonance frequency range. During the filtering process, the speed, torque, and current parameters are filtered separately to obtain the corresponding resonance band signal components. These signal components retain the oscillating characteristics of the original parameters within the mechanical resonance frequency range.

[0065] Eigenmode decomposition (EMD) is performed on the resonant frequency band signal components to obtain multiple eigenmode functions. Eigenmode decomposition is an adaptive signal processing method that decomposes complex signals into a finite number of eigenmode functions. The decomposition process is implemented using an empirical mode decomposition algorithm, which extracts the eigenmode functions of the signal through multiple iterative screening processes. Specific steps include identifying all extreme points in the resonant frequency band signal components, connecting all extreme points using cubic spline interpolation to form upper and lower envelopes, calculating the mean of the upper and lower envelopes to obtain the mean envelope, subtracting the mean envelope from the original resonant frequency band signal components to obtain candidate eigenmode functions, and repeating the above process until the candidate eigenmode functions meet the eigenmode function criteria, ultimately obtaining a series of eigenmode functions. Each eigenmode function represents the vibration mode of the signal at different time scales and satisfies the following conditions: the number of extreme points and the number of zero-crossing points within the entire data segment do not differ by more than one, and the mean of the upper envelope defined by local maxima and the lower envelope defined by local minima is zero at any given time.

[0066] The instantaneous frequencies of each intrinsic mode function (EMF) are calculated, and EMFs falling within the mechanical resonance frequency range are selected for the modal participation factor calculation. The instantaneous frequency calculation employs the Hilbert transform method. An analytic signal is obtained by performing a Hilbert transform on each EMF, and then the rate of change of the phase angle of the analytic signal over time is calculated to obtain the instantaneous frequency. Specifically, the calculation process includes performing a Hilbert transform on each EMF to obtain its orthogonal components, constructing an analytic signal, calculating the instantaneous phase, and differentiating the instantaneous phase to obtain the instantaneous frequency. The selection process compares the average instantaneous frequency of each EMF with a predefined mechanical resonance frequency range, retaining only EMFs whose average instantaneous frequency falls within this range for subsequent calculations. The average instantaneous frequency is calculated by taking the arithmetic mean of the instantaneous frequencies of the EMFs over the entire time series.

[0067] The modal participation factors corresponding to the speed, torque, and current parameters are calculated based on the energy distribution of each intrinsic mode function (EMF). The energy distribution is obtained by calculating the energy value of each EMF, which is the integral of the square of the EMF amplitude. The modal participation factor characterizes the contribution of each parameter to mechanical resonance. The calculation method involves first calculating the energy value of each EMF, then summing the energy values ​​of all EMFs corresponding to the same parameter to obtain the total energy of that parameter, and finally dividing the total energy of each parameter by the sum of the total energies of all parameters to obtain the modal participation factor. Specifically, the calculation process includes calculating the energy value of each EMF after EMF decomposition of the resonant frequency band signal components for the speed, torque, and current parameters. The energy value is obtained by numerically integrating the square of the EMF amplitude in the time domain, with the integration interval being the entire data acquisition duration. The energy values ​​of all intrinsic mode functions are summed according to parameter categories to obtain the total energy of velocity parameters, torque parameters, and current parameters. Finally, the total energy of each parameter is divided by the sum of the total energies of the three parameters to obtain the corresponding modal participation factor. The sum of the modal participation factors of all parameters is 1.

[0068] S4. Based on the magnitude of the modal participation factor, identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions, specifically implemented as follows:

[0069] Based on the modal participation factors (MOFs) of velocity, torque, and current parameters obtained from modal decomposition, the identification and adjustment direction of key parameters are determined. First, the magnitudes of these three MOFs are compared, and the parameter with the largest MOF is identified as the key parameter. The comparison process uses a numerical sorting method, arranging the three MOFs from largest to smallest value, and selecting the parameter corresponding to the MOF with the largest value as the key parameter. When two or more MOFs have equal values, the key parameters are determined according to a preset priority order. This priority order is set based on the parameter's sensitivity to mechanical resonance; for example, velocity parameters take precedence over torque parameters, and torque parameters take precedence over current parameters. The key parameter identification results are recorded as parameter type identifiers for subsequent adjustment direction determination.

[0070] The adjustment direction is determined based on the energy contribution characteristics of key parameters during mechanical resonance. Energy contribution characteristics refer to the magnitude and variation of energy contributed by the key parameter during mechanical resonance. The determination of the adjustment direction is based on the principle of energy conservation. When the modal participation factor of a key parameter is large, it indicates that the parameter contributes more energy to mechanical resonance. In this case, the adjustment direction is to reduce the setpoint of the parameter to decrease its energy contribution. The specific determination method includes analyzing the energy distribution characteristics of the key parameter within the mechanical resonance frequency range. If the energy of the key parameter is concentrated near the resonance frequency, the adjustment direction is to reduce the gain or setpoint of the parameter; if the energy distribution of the key parameter is more dispersed, the adjustment direction is to adjust the dynamic response characteristics of the parameter. The adjustment direction is represented by an incremental sign; for example, a positive sign indicates increasing the parameter setpoint, and a negative sign indicates decreasing the parameter setpoint.

[0071] A trend analysis model for modal participation factors is established to determine the adjustment direction based on real-time trends. This trend analysis model is a mathematical model constructed through time series analysis of modal participation factors over multiple consecutive sampling periods. The model construction process includes collecting a certain number of recent historical data points for modal participation factors, such as data from the last 10 sampling periods. The least squares method is used to fit a trend line showing the modal participation factors changing over time, and the slope of the trend line is calculated as a trend indicator. The sign of the trend indicator indicates the direction of increase or decrease in the modal participation factor, and the absolute value represents the rate of change. When determining the adjustment direction based on real-time trends, if the trend indicator shows a continuous increase in the modal participation factor, the adjustment direction is to decrease the key parameter setting by a larger margin; if the trend indicator shows a continuous decrease in the modal participation factor, the adjustment direction is to adjust the key parameter setting by a smaller margin. The trend analysis model also includes a trend confirmation mechanism, requiring that the trend be consistent across multiple consecutive sampling periods before adopting the trend. For example, if the trend indicator shows the same sign for three consecutive sampling periods, the trend is confirmed as valid.

[0072] In determining the adjustment direction, the coupling effects between different parameters are also considered. When the key parameter is identified as the speed parameter, the adjustment direction must simultaneously consider the indirect effects on the torque and current parameters. In this case, the determination of the adjustment direction is based on the dynamic coupling relationship between the speed and torque parameters, as well as the energy transfer relationship between the speed and current parameters. For example, when the speed parameter is the key parameter and its modal participation factor shows an upward trend, the adjustment direction is determined to reduce the proportional gain coefficient of the speed loop, while appropriately increasing the integral time constant of the torque loop to balance the system response. The final determination of the adjustment direction needs to be verified through multi-parameter coordination. The verification method includes simulating the energy distribution changes of the system after adjustment to ensure that the modal participation factors of each parameter tend to be balanced after adjustment. The entire process of key parameter identification and adjustment direction determination runs in real time, updating the identification results and adjustment direction once per control cycle to ensure that the system can quickly respond to changes in the mechanical resonance state.

[0073] S5. Based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, coordinate the set values ​​of speed, torque, and current parameters in the inverter control loop. Specifically, this is implemented as follows:

[0074] Based on the key parameters and their adjustment directions obtained from the key parameter identification process, multi-parameter coordinated adjustment begins. First, the setpoints of the key parameters are modified according to the adjustment direction, which refers to the direction of parameter modification determined through prior analysis, such as increasing or decreasing the setpoint. The process of modifying the key parameter setpoints is implemented in the inverter control loop, and the specific method varies depending on the type of key parameter. When the key parameter is a speed parameter, the setpoint is modified by adjusting the setpoint of the speed loop, and the modification amount is determined jointly by the adjustment direction and the magnitude of the modal participation factor. For example, when the adjustment direction is to decrease the setpoint, the speed parameter setpoint decreases in increments of a certain step size, which is proportional to the magnitude of the modal participation factor. When the key parameter is a torque parameter, the setpoint is modified by adjusting the torque setpoint of the torque loop, and the modification amount is calculated based on the energy contribution of the torque parameter in mechanical resonance. When the key parameter is a current parameter, the setpoint is modified by adjusting the current setpoint of the current loop, and the modification amount considers the influence of the current parameter on system stability. The setting modification process also includes a modification range limitation mechanism to ensure that the modified setting value does not exceed the safe operating range of the frequency converter. For example, the speed parameter setting value modification range shall not exceed 10% of the rated speed, the torque parameter setting value modification range shall not exceed 15% of the rated torque, and the current parameter setting value modification range shall not exceed 20% of the rated current.

[0075] Based on the coupling relationship between key parameters and other parameters, the setpoints of the remaining parameters among speed, torque, and current parameters are adjusted synchronously according to preset ratios. Coupling relationships refer to the mutual influence and interaction between different control parameters, which are obtained through a combination of offline testing and online identification. Offline testing involves changing the setpoints of speed, torque, and current parameters under different operating conditions of the inverter, recording the response changes of other parameters, and thus establishing a coupling coefficient matrix between parameters. Online identification involves dynamically updating the coupling coefficients by monitoring the interactive effects during parameter changes in real time. The preset ratio is an adjustment weight determined based on the coupling coefficients. For example, when the key parameter is speed, the preset ratio for torque is set to 0.3 times the speed parameter modification amount, and the preset ratio for current is set to 0.2 times the speed parameter modification amount. The synchronous adjustment process is executed in order of the strength of the coupling relationship: first, the parameter with the strongest coupling relationship to the key parameter is adjusted; then, the parameter with the second strongest coupling relationship is adjusted; and finally, the parameter with the weakest coupling relationship is adjusted. The adjustment amount is calculated based on the modification amount of the key parameter setting value and the corresponding preset ratio. For example, when the speed parameter is a key parameter and its setting value is reduced by a certain amount, the torque parameter setting value is reduced accordingly by a preset ratio, and the current parameter setting value is reduced accordingly by a preset ratio.

[0076] After completing the multi-parameter coordinated adjustment, the adjustment effect is verified. Verification methods include monitoring changes in the mechanical vibration level and harmonic energy distribution ratio of the system after adjustment. The mechanical vibration level is collected in real time by vibration sensors installed on the traction machine, and the harmonic energy distribution ratio is calculated through real-time spectrum analysis. If the mechanical vibration level decreases and the harmonic energy distribution ratio decreases after adjustment, the coordinated adjustment is deemed effective; if the mechanical vibration level does not improve significantly or the harmonic energy distribution ratio does not decrease, the coupling relationship and preset ratio are reassessed, and a second round of coordinated adjustment is performed. The entire coordinated adjustment process operates in a closed-loop control mode, executing a complete adjustment and verification cycle in each control cycle to ensure the system can quickly suppress mechanical resonance and maintain stable operation. The adjustment process also includes a historical record function for parameter setpoints, used to analyze the effectiveness of the adjustment strategy and optimize subsequent adjustment parameters. The ultimate goal of multi-parameter coordinated adjustment is to achieve the optimal combination of speed, torque, and current parameter setpoints, thereby effectively suppressing mechanical resonance and improving system operational stability.

[0077] Example 2: Figure 2 A schematic diagram of a multi-parameter coordinated control system for a traction machine frequency converter is provided according to the present invention. The multi-parameter coordinated control system for a traction machine frequency converter includes the following modules:

[0078] The parameter acquisition module is used to acquire the speed, torque, and current parameters of the traction machine frequency converter in real time.

[0079] The interval judgment module is used to extract harmonic components and calculate the harmonic energy distribution ratio based on speed parameters, torque parameters and current parameters. When the harmonic energy distribution ratio exceeds the preset ratio threshold, it is judged to fall into the predefined mechanical resonance frequency interval.

[0080] The factor calculation module is used to perform modal decomposition of the signal components of speed parameters, torque parameters, and current parameters within the resonant frequency band and calculate the modal participation factor of each parameter when they fall within a predefined mechanical resonant frequency range.

[0081] The parameter identification module is used to identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions based on the magnitude of the modal participation factor.

[0082] The coordinated adjustment module is used to coordinately adjust the set values ​​of speed, torque and current parameters in the inverter control loop based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction.

[0083] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0084] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0085] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0089] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-parameter coordinated control method for a traction machine frequency converter, characterized in that, Includes the following steps: S1. Real-time acquisition of speed, torque, and current parameters of the traction machine frequency converter; S2. Extract harmonic components based on speed parameters, torque parameters and current parameters and calculate the harmonic energy distribution ratio. When the harmonic energy distribution ratio exceeds the preset ratio threshold, it is judged to fall into the predefined mechanical resonance frequency range. S3. When the frequency falls within a predefined mechanical resonance frequency range, perform modal decomposition on the signal components of the speed, torque, and current parameters within the resonance frequency band and calculate the modal participation factor for each parameter, including... Extract the resonant frequency band signal components within the predefined mechanical resonant frequency range from the speed parameters, torque parameters, and current parameters; Multiple eigenmode functions are obtained by performing eigenmode decomposition on the resonant frequency band signal components. The modal participation factors corresponding to the velocity, torque, and current parameters are calculated based on the energy distribution of each intrinsic mode function. S4. Based on the magnitude of the modal participation factor, identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions; S5. Based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, coordinate the setting values ​​of speed parameters, torque parameters and current parameters in the inverter control circuit.

2. The multi-parameter coordinated control method for a traction machine frequency converter according to claim 1, characterized in that, Real-time acquisition of speed, torque, and current parameters of the traction machine frequency converter, including: Speed, torque, and current parameters are collected in real time synchronously through the feedback channel of the inverter control circuit. The collected speed, torque, and current parameters are timestamped and aligned. Generate time-consistent datasets of speed, torque, and current parameters.

3. The multi-parameter coordinated control method for a traction machine frequency converter according to claim 1, characterized in that, Harmonic components are extracted based on speed, torque, and current parameters, and the proportion of harmonic energy distribution is calculated. When the proportion of harmonic energy distribution exceeds a preset threshold, it is determined to fall within a predefined mechanical resonance frequency range, including: Frequency domain transformation is performed on the time-consistent speed, torque, and current parameters to obtain their corresponding spectral distributions; the fundamental component and each harmonic component are then separated from the spectral distributions. The ratio of the energy value of each harmonic component to the total energy value is calculated as the harmonic energy distribution ratio; The proportion of harmonic energy distribution of the main harmonic components is compared with a preset proportion threshold. When the proportion of harmonic energy distribution of any major harmonic component exceeds a preset proportion threshold, it is determined to fall into the predefined mechanical resonance frequency range.

4. The multi-parameter coordinated control method for a traction machine frequency converter according to claim 3, characterized in that, Extracting harmonic components and calculating the proportion of harmonic energy distribution also includes: normalizing the amplitude of each harmonic component in the spectrum distribution and calculating the proportion of the normalized harmonic energy distribution.

5. The multi-parameter coordinated control method for a traction machine frequency converter according to claim 1, characterized in that, The modal decomposition and calculation of modal participation factors for each parameter also include: calculating the instantaneous frequency of each intrinsic modal function and selecting intrinsic modal functions within the mechanical resonance frequency range to participate in the modal participation factor calculation.

6. The multi-parameter coordinated control method for a traction machine frequency converter according to claim 1, characterized in that, Based on the magnitude of the modal participation factor, the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions are identified, including: By comparing the modal participation factors of speed, torque, and current parameters, the parameter with the largest modal participation factor is identified as the key parameter. The adjustment direction is determined based on the energy contribution characteristics of key parameters in mechanical resonance.

7. A multi-parameter coordinated control method for a traction machine frequency converter according to claim 6, characterized in that, Identifying key parameters and their adjustment directions also includes: establishing a trend analysis model for modal participation factors and determining the adjustment direction based on real-time trends.

8. A multi-parameter coordinated control method for a traction machine frequency converter according to claim 1, characterized in that, Based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction, the setpoints of speed, torque, and current parameters in the inverter control loop are adjusted in a coordinated manner, including: The settings of key parameters are modified based on the direction adjustment; Based on the coupling relationship between key parameters and other parameters, the set values ​​of the remaining parameters in speed, torque and current parameters are adjusted synchronously according to a preset ratio to complete the coordinated adjustment of multiple parameters.

9. A multi-parameter coordinated control system for a traction machine frequency converter, used to implement the multi-parameter coordinated control method for a traction machine frequency converter as described in any one of claims 1-8, characterized in that, Includes the following modules: The parameter acquisition module is used to acquire the speed, torque, and current parameters of the traction machine frequency converter in real time. The interval judgment module is used to extract harmonic components and calculate the harmonic energy distribution ratio based on speed parameters, torque parameters and current parameters. When the harmonic energy distribution ratio exceeds the preset ratio threshold, it is judged to fall into the predefined mechanical resonance frequency interval. The factor calculation module is used to perform modal decomposition of the signal components of speed parameters, torque parameters, and current parameters within the resonant frequency band and calculate the modal participation factor of each parameter when they fall within a predefined mechanical resonant frequency range. The parameter identification module is used to identify the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment directions based on the magnitude of the modal participation factor. The coordinated adjustment module is used to coordinately adjust the set values ​​of speed, torque and current parameters in the inverter control loop based on the key parameters that have the greatest impact on mechanical resonance suppression and their adjustment direction.

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