Permanent magnet synchronous motor control method and system for reducing heating loss

Abstract

The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a permanent magnet synchronous motor control method and system for reducing heating loss, and the method comprises the steps: collecting the current data of each phase of a stator in a permanent magnet synchronous motor and the vibration data of a bearing pedestal in real time; obtaining a change coefficient, a three-phase unbalance degree and a current change characteristic value of each phase in each time period; according to the fluctuation degree and the pulse characteristic of the vibration data in each time period, obtaining an abnormal vibration characteristic value in each time period; synthesizing the current change characteristic value and the abnormal vibration characteristic value to obtain a comprehensive change value of the permanent magnet synchronous motor in each time period; obtaining a difference coefficient in each time period; the rotating speed of the permanent magnet synchronous motor is regulated and controlled, the heating loss of the permanent magnet synchronous motor is reduced, and the prediction step length parameter of model prediction control is adjusted through the difference coefficient. The invention aims to reduce the heating loss of the permanent magnet synchronous motor in time.

Description

Technical Field

[0001] This application relates to the field of permanent magnet synchronous motor control technology, specifically to a permanent magnet synchronous motor control method and system for reducing heat loss. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in AC drive applications due to their advantages such as simple structure, low energy consumption, and rapid dynamic response. However, various losses generated during the operation of PMSMs are converted into heat energy, leading to increased motor temperature. Specifically, when the stator winding current in a PMSM is large and contains numerous harmonics, the main magnetic field waveform is distorted, generating a large number of harmonics. These harmonics induce a large number of eddy currents in the rotor permanent magnets, resulting in eddy current losses and a significant increase in rotor permanent magnet temperature. Furthermore, mechanical losses such as bearing friction and rotor-air friction further exacerbate motor heating.

[0003] Existing technologies generally use model predictive control to regulate the speed of permanent magnet synchronous motors (PMSMs) in order to reduce heat loss. The prediction step size parameter in model predictive control is determined based on the heat loss of the PMSM. However, under complex operating conditions such as variable speed and variable load, it is difficult to accurately assess the heat loss of the PMSM, which makes it impossible to effectively adjust the prediction step size parameter in model predictive control, thus making it difficult to reduce the heat loss of the motor in a timely manner. Summary of the Invention

[0004] In view of the above, it is necessary to provide a control method and system for permanent magnet synchronous motors that reduces heat loss. Compared with traditional control methods for permanent magnet synchronous motors that reduce heat loss, this method can effectively reduce the heat loss of the permanent magnet synchronous motor. In a first aspect, embodiments of this application provide a control method for a permanent magnet synchronous motor that reduces heat loss, the method comprising the following steps: Real-time acquisition of stator current data for each phase in a permanent magnet synchronous motor, as well as vibration data of the bearing housing; By analyzing the harmonic distortion and changes of the current data of each phase in each time period, the change coefficient of each phase in each time period is obtained. Combined with the analysis of the symmetrical components of the three-phase current data in each time period, the three-phase unbalance in each time period is obtained, and the characteristic value of the current change in each time period is obtained. By analyzing the fluctuations and pulse characteristics of vibration data within each time period, abnormal vibration characteristic values ​​for each time period can be obtained. By combining the current change characteristic value and the abnormal vibration characteristic value, the comprehensive change value of the permanent magnet synchronous motor in each time period is obtained; The predicted value of the comprehensive change value of multiple neighboring time periods for each time period is obtained. By comparing the comprehensive change value of multiple neighboring time periods for each time period with the predicted value, the difference coefficient within each time period is obtained. Model predictive control is used to regulate the speed of a permanent magnet synchronous motor, thereby reducing the heat loss of the permanent magnet synchronous motor. The prediction step size parameter of the model predictive control is adjusted by the difference coefficient.

[0005] In one embodiment, the process of the change coefficient is as follows: Obtain the spectrum of current data for each phase in each time period; record the ratio of the amplitude of each odd harmonic frequency to the amplitude of the fundamental frequency in the spectrum as the amplitude ratio; calculate the sum of all amplitude ratios in the spectrum. Obtain the fitting curve of the current data of each phase in each time period, and obtain the first derivative of the fitting curve at each time in each time period; the dispersion of all the first derivatives of the fitting curve in each time period is denoted as the variation dispersion. The variation coefficients are positively correlated with the sum and the variation dispersion, respectively.

[0006] In one embodiment, the process of obtaining the three-phase imbalance is as follows: Obtain the positive sequence component, negative sequence component, and zero sequence component of the three-phase current data for each time period; The ratio of the magnitude of the negative-order component to the magnitude of the positive-order component is denoted as the negative-order ratio. The ratio of the magnitude of the zero-sequence component to the magnitude of the positive-sequence component is denoted as the zero-sequence ratio. The three-phase imbalance is positively correlated with the negative sequence ratio and the zero sequence ratio, respectively.

[0007] In one embodiment, the three-phase imbalance is the average of the negative sequence ratio and the zero sequence ratio.

[0008] In one embodiment, the characteristic value of the current change is the product of the mean of the change coefficients of all phases in each time period and the three-phase unbalance.

[0009] In one embodiment, the process of obtaining the abnormal vibration characteristic value is as follows: Obtain the peak values ​​of vibration data in time series for each time period, and denote the dispersion of all peak values ​​in vibration data for each time period as peak dispersion. Obtain margin indices for vibration data in different time periods; The abnormal vibration characteristic values ​​are positively correlated with the peak dispersion and the margin index, respectively.

[0010] In one embodiment, the overall change value is a weighted sum of the normalized value of the current change characteristic value and the normalized value of the abnormal vibration characteristic value.

[0011] In one embodiment, the process of obtaining the difference coefficient is as follows: Calculate the difference between the predicted value and the comprehensive change value for each time period; The difference coefficient is the average of the differences between each time period and its nearest neighboring time periods.

[0012] In one embodiment, the method for adjusting the prediction step size parameter is as follows: The adjustment range of the prediction step size parameter for the preset model prediction control is defined, and the adjustment range includes multiple time periods. The product of the normalized value of the difference coefficient in the first time period within each adjustment interval and the preset value is used as the prediction step size parameter for the model prediction control in the next adjustment interval of each adjustment interval.

[0013] Secondly, embodiments of this application also provide a permanent magnet synchronous motor control system for reducing heat loss, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the permanent magnet synchronous motor control method for reducing heat loss described above.

[0014] This application has at least the following beneficial effects: This application calculates the variation coefficient, which integrates the harmonic distortion and variation of the current, and can accurately reflect the harmonic problems and waveform abnormalities in each phase current. By calculating the three-phase unbalance, the unbalance characteristics between the three-phase currents are considered. Then, by combining the variation coefficient and the three-phase unbalance, the characteristic value of current variation is obtained, which can accurately assess the intensity of eddy current loss caused by current quality problems in each time period. Furthermore, by analyzing the fluctuation degree and pulse characteristics of vibration data, abnormal vibration characteristic values ​​can be obtained, which can accurately reflect the loss caused by mechanical reasons in each time period. Furthermore, by combining the current change characteristic value with the abnormal vibration characteristic value to obtain the comprehensive change value, the heat loss of the synchronous permanent magnet motor under complex operating conditions can be evaluated more comprehensively and accurately, providing a more comprehensive reference for subsequent optimization control. Furthermore, considering the delayed effect of heat transfer, by obtaining the predicted value of the comprehensive change value and comparing it with the actual value, the delayed effect of heat transfer can be considered in advance. This allows for precise control of the speed of the permanent magnet synchronous motor based on its real-time heating status, thereby reducing the heat loss of the permanent magnet synchronous motor in a timely manner. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a permanent magnet synchronous motor control method for reducing heat loss, provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of obtaining the comprehensive change value; Figure 3 This is a schematic diagram illustrating the process of obtaining the difference coefficient. Detailed Implementation

[0017] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0019] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a permanent magnet synchronous motor control method and system for reducing heat loss provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a method for controlling a permanent magnet synchronous motor to reduce heat loss according to an embodiment of this application. The method includes the following steps: Step 1: Real-time acquisition of stator current data for each phase in the permanent magnet synchronous motor, as well as vibration data of the bearing housing.

[0022] The heat loss generated during the operation of a permanent magnet synchronous motor mainly originates from winding losses, eddy current losses, and mechanical losses. Winding losses are primarily determined by relevant parameters of the motor equipment, such as the cross-sectional area of ​​the conductors or the size of the slot space, and are difficult to reduce through control and adjustment. Eddy current losses are caused by the induction of a large number of eddy currents in the permanent magnet by harmonic components of current and voltage. Mechanical losses are mainly caused by friction generated during bearing rotation or additional friction due to rotor imbalance. To monitor the eddy current and mechanical loss status of the permanent magnet synchronous motor in real time, this application uses a high-precision current sensor to collect the phase current data of the stator in the permanent magnet synchronous motor in real time, and uses a vibration sensor to collect the vibration data of the bearing housing in real time.

[0023] In this embodiment, the acquisition frequency of both current data and vibration data is set to 2kHz. The acquisition frequency of both current data and vibration data is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0024] Step 2: Analyze the current and vibration data for each time period to obtain the change coefficients of each phase and the current change characteristic values ​​for each time period, and then obtain the comprehensive change value of the permanent magnet synchronous motor for each time period.

[0025] When a permanent magnet synchronous motor (PMSM) operates at high frequency or high speed, the rate of change of the magnetic field accelerates, leading to an increase in induced eddy currents in the core and permanent magnets. Furthermore, during sudden load changes or dynamic adjustments, bearing friction or rotor eccentricity can significantly increase mechanical losses. These factors combined result in significant abnormal heating of the PMSM under complex operating conditions. Therefore, a thorough analysis of the operating status of the PMSM is necessary.

[0026] Step 2.1: By analyzing the harmonic distortion and changes of the current data of each phase in each time period, obtain the change coefficient of each phase in each time period. Combined with the analysis of the symmetrical components of the three-phase current data in each time period, obtain the three-phase unbalance in each time period and obtain the current change characteristic value in each time period.

[0027] When the eddy current loss of a permanent magnet synchronous motor (PMSM) increases, it indicates the presence of a large number of harmonic magnetic fields inside the motor. These fields are generated by non-sinusoidal current excitation, while an ideal current is a standard sine wave. Non-sinusoidal current excitation manifests as the current containing significant harmonic components; that is, in addition to the fundamental frequency, there are abundant harmonic components of various orders. Furthermore, the more severe the distortion of the current waveform, the more obvious the local abnormal jitter characteristics in the waveform, and the more pronounced the imbalance characteristics between the currents of different phases. Therefore, by analyzing the abnormal waveform characteristics of the stator current, the state of eddy current loss in the PMSM can be reflected.

[0028] Based on the above analysis, a fixed-length time period is set to conduct a detailed analysis of the permanent magnet synchronous motor's state over a short period. By analyzing the harmonic distortion and changes in the current data of each phase within each time period, the variation coefficients of each phase within that time period are obtained. The specific process is as follows: Obtain the spectrum of current data for each phase in each time period; record the ratio of the amplitude of each odd harmonic frequency to the amplitude of the fundamental frequency in each spectrum as the amplitude ratio; calculate the sum of all amplitude ratios in the spectrum. Obtain the fitting curve of the current data of each phase in each time period, and obtain the first derivative of the fitting curve at each time in each time period; the dispersion of all the first derivatives of the fitting curve in each time period is denoted as the variation dispersion. The variation coefficients of each phase within each time period are positively correlated with the sum and the variation dispersion, respectively. The calculation of the derivative is a well-known technique and will not be elaborated upon in this application. It should be added that when calculating the amplitude ratio, if the amplitude of the fundamental frequency is 0, to avoid the denominator being 0 and thus unable to be calculated, the sum of the amplitude of the fundamental frequency and 0.01A is first calculated, and then the ratio of the amplitude of the odd harmonic frequency to the sum is used as the amplitude ratio. Here, 0.01A is merely one embodiment of this application; implementers can set it according to actual conditions, and this application does not impose any special limitations.

[0029] It should be noted that dispersion refers to the degree of unevenness in the distribution of data, which can be achieved by calculating the standard deviation, variance, coefficient of variation, etc. This application does not impose any special restrictions.

[0030] It should be noted that positive correlation means that the variables have the same direction of change; when one variable increases, the other variable also increases, and when one variable decreases, the other variable also decreases.

[0031] In this embodiment, the length of the time period is 1 second. The length of the time period is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0032] In this embodiment, the Fast Discrete Fourier Transform (FFT) technique is used to obtain the spectrum of current data. The FFT technique is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to obtain the spectrum of current data, implementers may adopt other existing feasible techniques, and this application does not impose any special restrictions.

[0033] In this embodiment, the fundamental frequency is 50 Hz.

[0034] In this embodiment, the odd harmonic frequencies involved in the calculation of the variation coefficients are N times the fundamental frequency. To balance the computational complexity and accuracy, N is an odd number in the interval [3,11]. The value of N is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0035] In this embodiment, the least squares method is used to obtain the fitting curve of the current data. The least squares method is a well-known technique and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the fitting curve of the current data, the implementer may use other existing techniques, such as local weighted regression, K-nearest neighbor regression, etc. This application does not impose any special restrictions.

[0036] In this embodiment, the dispersion of the first derivative is the coefficient of variation.

[0037] In this embodiment, the expression for the change coefficient of each phase within each time period is as follows: In the formula, This represents the change coefficient of the i-th phase within the t-th time period; The positive number represents the result of mapping the variation discreteness of the i-th phase within the t-th time period; This represents the sum of the i-th phase within the t-th time period. The purpose of mapping the variation dispersion to a positive number is to avoid the situation where the calculated result of the variation coefficient is forced to be 0 when the variation dispersion is 0. There are many methods to map data to positive numbers, such as calculating the sum of the data with a preset value greater than 0, or using the data as the exponent of an exponential function with the natural constant as the base. In this application, unless otherwise specified, the purpose of mapping data to positive numbers is achieved by calculating the sum of the data with a preset value greater than 0. The value of the preset value greater than 0 is preset by the user, and the implementer can set its specific value according to the actual situation. In this embodiment, the specific value of the preset value greater than 0 is 0.01.

[0038] In another embodiment, the expression for the change coefficient of each phase within each time period is: In the formula, This represents the change coefficient of the i-th phase within the t-th time period; This represents the dispersion of the change in the i-th phase within the t-th time period; Let represent the sum value of the i-th phase within the t-th time period.

[0039] It should be noted that: the more obvious the local abnormal jitter characteristics in the current waveform, the greater the change amplitude of the obtained first derivative, and the greater the calculated variation dispersion; the odd harmonic frequency energy is extracted from the spectrum of the current data, and combined with the change amplitude of the first derivative of the waveform at each time moment, the variation coefficient is calculated to reflect the harmonic distortion and waveform abnormal characteristics of each phase current in each time period; the larger the calculated variation coefficient, the greater the proportion of harmonic component energy contained in each phase current in each time period, and the more obvious the local abnormal jitter characteristics in the waveform.

[0040] Furthermore, the more pronounced the imbalance characteristics between the phase currents, the more severe the eddy current losses in the permanent magnet synchronous motor. Based on the above analysis, the three-phase imbalance degree in each time period is obtained by analyzing the symmetrical components of the three-phase current data in each time period. The specific process is as follows: The positive-sequence, negative-sequence, and zero-sequence components of the three-phase current data are obtained for each time period. Under the balanced state of the three-phase circuit, both the negative-sequence and zero-sequence components are relatively small. The higher the magnitude of the obtained negative-sequence or zero-sequence components, the more significant the imbalance characteristics exist in each time period. The ratio of the magnitude of the negative-sequence component to the magnitude of the positive-sequence component in each time period is recorded as the negative-sequence ratio; the ratio of the magnitude of the zero-sequence component to the magnitude of the positive-sequence component in each time period is recorded as the zero-sequence ratio; the average of the negative-sequence ratio and the zero-sequence ratio in each time period is taken as the three-phase imbalance in each time period. It should be noted that when calculating the negative-sequence ratio and the zero-sequence ratio, if the magnitude of the positive-sequence component is 0, to avoid the situation where the denominator is 0 and calculation is impossible, the cumulative sum of the magnitude of the positive-sequence component and 0.01A is first calculated, and then the ratios of the magnitude of the negative-sequence component and the magnitude of the zero-sequence component to the cumulative sum are taken as the negative-sequence ratio and the zero-sequence ratio, respectively. Here, 0.01A is merely one embodiment of this application; implementers can set it according to actual conditions, and this application does not impose any special limitations.

[0041] In this embodiment, the Fortescue transform is used to obtain the positive sequence component, negative sequence component and zero sequence component of the three-phase current data in each time period. The Fortescue transform is a well-known technology and will not be described in detail in this application.

[0042] It should be noted that the three-phase unbalance is used to reflect the unbalance characteristics of the three-phase current in the stator of the permanent magnet synchronous motor in each time period; the larger the calculated three-phase unbalance, the more significant the three-phase unbalance characteristics.

[0043] Because abnormal current response during load changes or dynamic adjustments can easily cause eddy current losses, the variation coefficients of each phase in each time period, as well as the three-phase imbalance in each time period, are comprehensively analyzed to obtain the characteristic values ​​of current variation in each time period, specifically: Calculate the average value of the change coefficients of all phases in each time period, and multiply the average value in each time period by the three-phase unbalance as the characteristic value of the current change in each time period.

[0044] It should be noted that the characteristic value of current change is used to reflect the intensity of eddy current loss caused by the deterioration of current quality in each time period; the larger the calculated characteristic value of current change, the greater the intensity of eddy current loss caused by the deterioration of current quality in each time period.

[0045] Step 2.2: Obtain the abnormal vibration characteristic values ​​for each time period by analyzing the fluctuation degree and pulse characteristics of the vibration data within each time period.

[0046] Furthermore, rotor eccentricity in permanent magnet synchronous motors is one of the key reasons for abnormal heating under complex operating conditions. When rotor eccentricity occurs, the air gap becomes uneven, causing periodic fluctuations in magnetic circuit saturation at the stator slot openings. This not only increases eddy current losses to some extent but also induces severe mechanical vibration and noise, further exacerbating local temperature rise. The more pronounced the abnormal vibration characteristics, the more severe the bearing friction and the greater the mechanical losses. The abnormal vibration characteristics of the bearing housing manifest as irregular, severe fluctuations and pulses. Based on this, the abnormal vibration characteristic values ​​for each time period are obtained by analyzing the fluctuation degree and pulse characteristics of vibration data within each time period. The specific process is as follows: Obtain the peak values ​​of vibration data in time series for each time period; under the influence of irregular and violent fluctuations, the more random the fluctuation amplitude of the peak values, the more the dispersion of all peak values ​​in the vibration data in each time period is recorded as the peak dispersion; the peak dispersion is used to reflect the degree of violent fluctuation of vibration data in each time period. Obtain the margin index of vibration data in each time period; the margin index is used to reflect the intensity of the pulse component in the vibration data of the bearing housing in the permanent magnet synchronous motor in each time period. The abnormal vibration characteristic values ​​in each time period are positively correlated with the peak dispersion and the margin index, respectively. The calculation of the margin index is a well-known technique and will not be elaborated upon in this application.

[0047] In this embodiment, the Automatic Multiscale-based Peak Detection (AMPD) algorithm is used to obtain the peak value of vibration data in time series. The AMPD algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the peak value of vibration data in time series, implementers may use other existing technologies, such as peak and trough detection algorithms, extreme point detection algorithms, etc. This application does not impose any special restrictions.

[0048] In this embodiment, the dispersion of the peak value is the standard deviation.

[0049] In this embodiment, the peak dispersion is mapped to a positive number, and the product of the mapped positive number and the margin index is used as the abnormal vibration characteristic value for each time period. The purpose of mapping the peak dispersion to a positive number is to avoid the situation where the calculated result of the abnormal vibration characteristic value is forced to be 0 when the peak dispersion is 0.

[0050] In another embodiment, the sum of the peak dispersion and the margin index is used as the abnormal vibration characteristic value for each time period.

[0051] It should be noted that the abnormal vibration characteristic value is used to reflect the abnormal vibration characteristics of the bearing housing in the permanent magnet synchronous motor in each time period. The larger the calculated abnormal vibration characteristic value, the more obvious the abnormal vibration characteristics are, and the greater the mechanical loss intensity of the permanent magnet synchronous motor.

[0052] Step 2.3: Combine the current change characteristic value and the abnormal vibration characteristic value to obtain the comprehensive change value of the permanent magnet synchronous motor in each time period.

[0053] Under complex operating conditions such as variable speed or variable load, permanent magnet synchronous motors (PMSMs) are more prone to eddy current losses and mechanical losses, both of which lead to significant heat generation. Since the characteristic value of current variation reflects the intensity of eddy current losses in a PMSM, while the characteristic value of abnormal vibration reflects the intensity of mechanical losses, a comprehensive change value for the PMSM is obtained by combining the characteristic values ​​of current variation and abnormal vibration in each time period. This comprehensive change value reflects the degree of influence of complex operating conditions on the heat generation losses of the PMSM in each time period, expressed as: In the formula, This represents the overall change value of the permanent magnet synchronous motor during the t-th time period; , All are preset constants greater than 0; This represents the normalized value of the current change characteristic value within the t-th time period; This represents the normalized value of the abnormal vibration characteristic value within the t-th time period.

[0054] In this embodiment, , The values ​​are 0.55 and 0.45 respectively. , The values ​​are all derived from experimental data.

[0055] In this embodiment, the Min-Max normalization method is used to obtain the normalized values ​​of the current change characteristic value and the abnormal vibration characteristic value, respectively. The Min-Max normalization method is a well-known technology and will not be described in detail in this application.

[0056] It should be noted that: by comprehensively considering the intensity of eddy current losses and the degree of mechanical losses existing in the permanent magnet synchronous motor under complex operating conditions, the heat generation characteristics of the permanent magnet synchronous motor are analyzed to obtain a comprehensive change value; the larger the calculated comprehensive change value, the more significant the influence of complex operating conditions on the heat generation loss of the permanent magnet synchronous motor in each time period. A schematic diagram of the process for obtaining the comprehensive change value is shown below. Figure 2 As shown.

[0057] Step 3: Obtain the predicted value of the comprehensive change value of multiple neighboring time periods for each time period. By comparing the comprehensive change value of multiple neighboring time periods for each time period with the predicted value, obtain the difference coefficient within each time period.

[0058] Furthermore, whether it's eddy current loss or mechanical loss, the heat generated inside the permanent magnet synchronous motor is transferred to the housing with a certain delay, resulting in a lag effect in the temperature data collected through the housing. Therefore, by predicting and analyzing the impact characteristics of heat loss, compared to directly relying on the permanent magnet synchronous motor housing temperature data, the overheating risk of the permanent magnet synchronous motor can be identified earlier and more proactively.

[0059] Based on the above analysis, predicted values ​​of the comprehensive change values ​​of each time period and several neighboring time periods are obtained. By comparing the comprehensive change values ​​of each time period and several neighboring time periods with the predicted values, the difference coefficient within each time period is obtained, which is used to reflect the dynamic changes in heat loss of the permanent magnet synchronous motor during operation. The specific process is as follows: Calculate the difference between the predicted value and the comprehensive change value for each time period; take the average of the differences for each time period and its nearest neighboring time periods as the difference coefficient for each time period. A schematic diagram of the process for obtaining the difference coefficient is shown below. Figure 3 As shown.

[0060] In this embodiment, to avoid having too many neighboring time periods and increasing computational complexity, and to avoid having too few neighboring time periods and being unable to capture enough historical information, the number of neighboring time periods for each time period is 20. The number of neighboring time periods is preset by the user. When the number of neighboring time periods is an integer within the range [20, 25], the implementer can set the number of neighboring time periods himself. This application does not impose any special restrictions.

[0061] In this embodiment, a first-order exponential smoothing algorithm is used to obtain the predicted value of the comprehensive change value. The first-order exponential smoothing algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the predicted value of the comprehensive change value, the implementer may use other existing technologies, such as Kalman filters, etc. This application does not impose any special restrictions.

[0062] Step 4: The speed of the permanent magnet synchronous motor is regulated by model predictive control to reduce the heat loss of the permanent magnet synchronous motor. The prediction step size parameter of the model predictive control is adjusted by the difference coefficient.

[0063] This application analyzes the abnormal changes in stator current and bearing housing vibration during the operation of a permanent magnet synchronous motor (PMSM), extracts the eddy current loss intensity and mechanical loss degree of the PMSM under complex operating conditions, and considers the delayed characteristics of heat transfer to analyze the heating characteristics of the PMSM. Based on this, model predictive control is used to adjust the speed of the PMSM to reduce heat loss. Specifically, the prediction step size parameter of the model predictive control is adjusted by the difference coefficient within each time period. When the difference coefficient is larger, the temperature rise characteristics are more obvious. In this case, a larger prediction step size parameter should be set to better suppress load disturbances and reduce the heat loss of the permanent magnet synchronous motor. Conversely, when the difference coefficient is smaller, the heat loss is less affected. In this case, a smaller prediction step size parameter should be set to improve the dynamic response of the control system. Based on the above analysis, firstly, to reduce computational delay interference, a preset adjustment range for the prediction step size parameter of the model predictive control is established, where each adjustment range contains multiple time periods. The product of the normalized value of the difference coefficient in the first time period within each adjustment range and the preset value is used as the prediction step size parameter for the next adjustment range in each adjustment range. Simultaneously, to avoid the problem of weak disturbance rejection capability due to excessively small prediction step size parameters, a lower limit of 10 is set for the prediction step size parameter.

[0064] In this embodiment, the length of the adjustment interval is 1 minute. The length of the adjustment interval is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0065] In this embodiment, the process of obtaining the normalized value of the difference coefficient is as follows: the difference coefficient is mapped to the [-3,3] interval using the Min-Max normalization method to ensure that the input value of the sigmoid function falls within its linear sensitive region and to prevent the output of the sigmoid function from always being 1 or 0. The sigmoid function is a well-known technology and will not be described in detail in this application.

[0066] In this embodiment, the preset value is 30. The preset value is obtained from historical experiments. The implementer can set it according to the actual situation. This application does not impose any special restrictions.

[0067] Furthermore, in the model predictive control, the actual speed data of the first time period within each adjustment range is input, and the inverter duty cycle signal is output to regulate the speed of the next adjustment range of each adjustment range, thereby reducing the heat loss of the permanent magnet synchronous motor in a timely manner.

[0068] Based on the same inventive concept as the above method, this application embodiment also provides a permanent magnet synchronous motor control system for reducing heat loss, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for controlling a permanent magnet synchronous motor to reduce heat loss.

[0069] In summary, this application, by calculating the variation coefficient, integrates the harmonic distortion and variation of the current, and can accurately reflect the harmonic problems and the degree of waveform abnormality in each phase current; by calculating the three-phase unbalance, it considers the unbalance characteristics between the three-phase currents; and by combining the variation coefficient and the three-phase unbalance, it obtains the current variation characteristic value, which can accurately assess the intensity of eddy current loss caused by current quality problems in each time period. Furthermore, by analyzing the fluctuation degree and pulse characteristics of vibration data, abnormal vibration characteristic values ​​can be obtained, which can accurately reflect the loss caused by mechanical reasons in each time period. Furthermore, by combining the current change characteristic value with the abnormal vibration characteristic value to obtain the comprehensive change value, the heat loss of the synchronous permanent magnet motor under complex operating conditions can be evaluated more comprehensively and accurately, providing a more comprehensive reference for subsequent optimization control. Furthermore, considering the delayed effect of heat transfer, by obtaining the predicted value of the comprehensive change value and comparing it with the actual value, the delayed effect of heat transfer can be considered in advance. This allows for precise control of the speed of the permanent magnet synchronous motor based on its real-time heating status, thereby reducing the heat loss of the permanent magnet synchronous motor in a timely manner.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0071] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A control method for a permanent magnet synchronous motor that reduces heat loss, characterized in that, The method includes the following steps: Real-time acquisition of stator current data for each phase in a permanent magnet synchronous motor, as well as vibration data of the bearing housing; By analyzing the harmonic distortion and changes of the current data of each phase in each time period, the change coefficient of each phase in each time period is obtained. Combined with the analysis of the symmetrical components of the three-phase current data in each time period, the three-phase unbalance in each time period is obtained, and the characteristic value of the current change in each time period is obtained. By analyzing the fluctuations and pulse characteristics of vibration data within each time period, abnormal vibration characteristic values ​​for each time period can be obtained. By combining the current change characteristic value and the abnormal vibration characteristic value, the comprehensive change value of the permanent magnet synchronous motor in each time period is obtained; The predicted value of the comprehensive change value of multiple neighboring time periods for each time period is obtained. By comparing the comprehensive change value of multiple neighboring time periods for each time period with the predicted value, the difference coefficient within each time period is obtained. Model predictive control is used to regulate the speed of a permanent magnet synchronous motor, thereby reducing the heat loss of the permanent magnet synchronous motor. The prediction step size parameter of the model predictive control is adjusted by the difference coefficient.

2. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 1, characterized in that, The process for the change coefficient is as follows: Obtain the spectrum of current data for each phase in each time period; record the ratio of the amplitude of each odd harmonic frequency to the amplitude of the fundamental frequency in the spectrum as the amplitude ratio. Calculate the sum of all amplitude ratios in the spectrum; Obtain the fitting curve of the current data of each phase in each time period, and obtain the first derivative of the fitting curve at each time in each time period; the dispersion of all the first derivatives of the fitting curve in each time period is denoted as the variation dispersion. The variation coefficients are positively correlated with the sum and the variation dispersion, respectively.

3. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 1, characterized in that, The process for obtaining the three-phase imbalance is as follows: Obtain the positive sequence component, negative sequence component, and zero sequence component of the three-phase current data for each time period; The ratio of the magnitude of the negative-order component to the magnitude of the positive-order component is denoted as the negative-order ratio. The ratio of the magnitude of the zero-sequence component to the magnitude of the positive-sequence component is denoted as the zero-sequence ratio. The three-phase imbalance is positively correlated with the negative sequence ratio and the zero sequence ratio, respectively.

4. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 3, characterized in that, The three-phase imbalance is the average of the negative sequence ratio and the zero sequence ratio.

5. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 1, characterized in that, The characteristic value of the current change is the product of the mean of the change coefficients of all phases in each time period and the three-phase unbalance.

6. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 1, characterized in that, The process for obtaining the abnormal vibration characteristic values ​​is as follows: Obtain the peak values ​​of vibration data in time series for each time period, and denote the dispersion of all peak values ​​in vibration data for each time period as peak dispersion. Obtain margin indices for vibration data in different time periods; The abnormal vibration characteristic values ​​are positively correlated with the peak dispersion and the margin index, respectively.

7. The method for controlling a permanent magnet synchronous motor to reduce heat loss as described in claim 1, characterized in that, The comprehensive change value is a weighted sum of the normalized value of the current change characteristic value and the normalized value of the abnormal vibration characteristic value.

8. The permanent magnet synchronous motor control method for reducing heat loss as described in claim 1, characterized in that, The process of obtaining the difference coefficient is as follows: Calculate the difference between the predicted value and the comprehensive change value for each time period; The difference coefficient is the average of the differences between each time period and its nearest neighboring time periods.

9. A method for controlling a permanent magnet synchronous motor to reduce heat loss as described in claim 1, characterized in that, The method for adjusting the prediction step size parameter is as follows: The adjustment range of the prediction step size parameter for the preset model prediction control is defined, and the adjustment range includes multiple time periods. The product of the normalized value of the difference coefficient in the first time period within each adjustment interval and the preset value is used as the prediction step size parameter for the model prediction control in the next adjustment interval of each adjustment interval.

10. A permanent magnet synchronous motor control system for reducing heat loss, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the permanent magnet synchronous motor control method for reducing heat loss as described in any one of claims 1-9.

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