Permanent magnet synchronous motor full electrical parameter online identification method and device based on composite wave

Abstract

The application discloses a kind of permanent magnet synchronous motor full electrical parameter online identification method and device based on composite wave, the application is by analyzing the influence of voltage error caused by dead time on parameter identification and carries out corresponding dead zone compensation;And in d-axis current injection certain amplitude and frequency bias composite wave, make it satisfy parameter identification sustained excitation condition;By the four different input data saved when motor steady state constructs three groups of effective current slope, and adopts least square method with forgetting factor to identify resistance, direct-axis inductance, cross-axis inductance three groups of parameters by referring to d-axis voltage equation;Finally, the three groups of parameters identified are as known quantity and are updated in q-axis voltage equation in real time, and save two different input data to construct a group of effective current slope and adopt least square method with forgetting factor to identify flux linkage parameter.The application solves the underdetermined problem of motor full electrical parameter identification, and the identification accuracy is higher, and the parameter identification error is about 3%.

Description

Technical Field

[0001] This invention belongs to the field of online parameter identification of embedded permanent magnet synchronous motors, and particularly relates to a method and device for online identification of all electrical parameters of permanent magnet synchronous motors based on composite waves. Background Technology

[0002] When a motor operates over a wide speed range, it may experience overheating and high current due to varying operating conditions. In such cases, motor parameters will change to varying degrees depending on the operating conditions. For example, under heavy loads, the iron core may saturate as the current increases, leading to a gradual decrease in stator inductance. Prolonged high-speed operation will cause the motor to heat up, increasing stator resistance and reducing the flux linkage of the permanent magnets. If updated motor parameters are not obtained, this will further affect controller performance and may even lead to motor failure. Therefore, online parameter identification is essential for the stable and efficient operation of motor drive systems, and it is of great significance in many fields such as high-performance closed-loop control, online fault diagnosis, and stator / rotor condition monitoring.

[0003] Existing online parameter identification methods often only identify certain specific electrical parameters of the motor, and methods capable of identifying all electrical parameters of the motor are rare. For example, the publicly available "Full electrical parameter identification method for permanent magnet synchronous linear motor (PMLSM) based on triangular wave injection and considering current differential term" (corresponding document: Full electrical parameter) This paper proposes an identification method for PMLSM based on triangular wave injection and considering current differential terms. While attempting to inject a bias triangular wave into the d-axis current, combined with least squares with a forgetting factor (FFRLS) and double-sampled data to construct an identification equation, achieving online identification of the three parameters of PMLSM—resistance, equivalent inductance (Ls), and flux linkage—and avoiding accuracy loss due to neglecting differential terms in steady state by introducing a current differential term, this method has significant limitations: First, it targets salient-pole PMLSMs, where the direct-axis and quadrature-axis inductances are approximately equal (Ld≈Lq=Ls), requiring only three parameters to be identified. However, the embedded permanent magnet synchronous motor (IPMSM) targeted in this invention has a salient-pole structure, where Ld≠Lq, requiring additional quadrature-axis inductance (Lq) identification, increasing the number of parameters to four. The three-parameter identification framework of this method cannot cover the four-parameter identification requirements, easily leading to underrank problems. Second, this method does not consider nonlinear factors during motor operation, especially in IPMSMs. The inverter on which it relies has a high switching frequency, and the voltage error caused by the accumulation of dead time is more significant. Such error will directly interfere with the accuracy of the input of the identification equation, thereby reducing the accuracy of parameter identification. However, this method does not propose any compensation scheme for dead time and cannot be adapted to the actual operation scenario of IPMSM.

[0004] In addition to the limitations of the linear motor identification methods mentioned above, existing online parameter identification methods for rotating motors also generally suffer from common problems: most methods do not consider nonlinear factors such as dead time during the identification process, leading to large errors in the identification results; and even if some methods focus on the current differential term, they often fail to meet the identification condition of "non-zero current differential term" while ensuring stable motor operation due to the simple design of the excitation signal (such as using only a triangular wave, which has weak noise immunity and is difficult to balance dynamic response and steady-state compatibility). Furthermore, there is no absolute steady state in the actual operation of the motor, and coupled with hardware circuit limitations, the sampling circuit has background noise and errors during operation. If these problems are not addressed through reasonable design, the accuracy of parameter identification will still be affected.

[0005] Therefore, further research is needed on an online identification method for all electrical parameters of salient-pole permanent magnet synchronous motors (IPMSMs). This method should be based on compensation for motor nonlinear factors (such as dead time), optimization of excitation signals to balance dynamic response and noise immunity, and full-rank identification of four parameters. This approach can better reflect the actual operating state of IPMSMs and is of great significance for solving problems such as the inability of existing methods to adapt to the four-parameter identification of salient-pole motors and the insufficient accuracy caused by ignoring nonlinear errors. This will improve the accuracy of online parameter identification of motors and the stability of the control system. Summary of the Invention

[0006] The purpose of this invention is to provide a method and apparatus for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves, to solve the aforementioned problems. This invention considers the nonlinear factors of the motor and closely matches the actual operating conditions of the motor. On the one hand, it achieves the identification of all four electrical parameters of the embedded permanent magnet synchronous motor; on the other hand, it improves the accuracy of the identification system model, with the identification result error being approximately 3%.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] Firstly, this aspect provides a method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves, the method comprising:

[0009] The nonlinear factors of the motor are obtained under the field-oriented control framework of the embedded permanent magnet synchronous motor, and the voltage error is compensated by the dead zone compensation algorithm.

[0010] When the motor reaches a stable operating state after initial acceleration, a bias composite wave with a set frequency and amplitude is injected into the d-axis current to meet the continuous excitation conditions for online motor identification.

[0011] After the enable signal identified by the given parameters is obtained, sample the dq-axis current, voltage and electric angular velocity data at four adjacent time points.

[0012] By sampling four sets of dq-axis current, voltage and electric angular velocity data, a voltage equation set with effective current slope d-axis is formed. The least squares method with forgetting factor is used to transform the resistance and inductance identification system model, thereby identifying three sets of electrical parameters of embedded permanent magnet synchronous motor resistance, direct-axis inductance and quadrature-axis inductance in real time.

[0013] The three sets of electrical parameters identified in real time are used as known quantities. Combined with any two adjacent sets of dq-axis current, voltage and electric angular velocity data, a set of q-axis voltage equations with effective slope is constructed. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model, and the last electrical parameter of the rotor flux linkage of the embedded permanent magnet synchronous motor is identified in real time.

[0014] In one implementation, the dead-zone compensation algorithm includes:

[0015] The angle between the actual current and the d-axis current is obtained by calculating the arctangent between the d-axis current and the q-axis current, then added to the motor electrical angle and subtracted. Obtain the electrical angle of phase A current;

[0016] Based on the mutual spacing of the three-phase currents A, B, and C The relationship between electrical angles is used to obtain the electrical angles of the three-phase currents;

[0017] The three-phase voltage is compensated in segments based on the magnitude of the electrical angle of the three-phase current, i.e., trapezoidal compensation voltage.

[0018] After the trapezoidal compensation voltage is converted to a two-phase stationary voltage coordinate system and subjected to coordinate transformation, it is applied to the output of the current loop PI to compensate for the voltage drop caused by the dead time of each switching cycle of the switching transistor, thereby achieving voltage replenishment.

[0019] In one embodiment, the bias composite wave with set frequency and amplitude is composed of a triangular wave with a constant local slope and a sine wave with a time-varying global slope. This composite waveform combines the fast dynamic response of the triangular wave with the excellent noise immunity of the sine wave, thereby achieving superior recognition performance.

[0020] In one embodiment, the amplitude of the composite wave is 5 to 10% of the rated current amplitude, the bias is -3 to -2A, and the frequency is 1 / 100 of the switching frequency.

[0021] In one implementation, the method considers the influence of the current differential term and uses the slope of adjacent current data points as the magnitude of the current differential term to form the condition for full-rank identification.

[0022] In one embodiment, the voltage equations with an effective current slope d-axis are:

[0023]

[0024] In the formula, , , They are respectively , , The d-axis voltage data at time 1; , , , They are respectively , , , The d-axis current data at any given time; , , They are respectively , , q-axis current data at time t; , , They are respectively , , Electric angular velocity data at any given time; For adjacent sampling periods; To identify the resistance of the embedded permanent magnet synchronous motor, To identify the embedded permanent magnet synchronous motor direct-axis inductance, The quadrature axis inductance of the embedded permanent magnet synchronous motor is identified.

[0025] The three sets of electrical parameters identified for the embedded permanent magnet synchronous motor—resistance, direct-axis inductance, and quadrature-axis inductance—are expressed as follows:

[0026]

[0027] in, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time-time parameter identification; , , They are respectively , , The d-axis voltage data at time 1; , , , They are respectively , , , The d-axis current data at any given time; , , They are respectively , , q-axis current data at time t; , , They are respectively , , Electric angular velocity data at any given time; For adjacent sampling periods; To identify the resistance of the embedded permanent magnet synchronous motor, To identify the embedded permanent magnet synchronous motor direct-axis inductance, The quadrature axis inductance of the embedded permanent magnet synchronous motor is identified.

[0028] In one embodiment, the q-axis voltage equations with effective slope are:

[0029]

[0030] In the formula, for The d-axis current data at any given time; , They are respectively , q-axis current data at time t; for Electric angular velocity data at any given time; for q-axis voltage data at time t; For adjacent sampling periods; , , This indicates the magnitude of the resistance and inductance identified in real time through the resistance and inductance identification model;

[0031] The identified rotor flux linkage of the embedded permanent magnet synchronous motor is represented as follows:

[0032]

[0033] In the formula, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time-time parameter identification; for The d-axis current data at any given time; , They are respectively , q-axis current data at time t; for Electric angular velocity data at any given time; for q-axis voltage data at time t; For adjacent sampling periods; , , This indicates the magnitude of the resistance and inductance identified in real time through the resistance and inductance identification model.

[0034] Secondly, the present invention provides an online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves, the device comprising:

[0035] The compensation module is used to determine the nonlinear factors of the motor under the field-oriented control framework of the embedded permanent magnet synchronous motor, and to compensate for voltage errors through a dead-zone compensation algorithm.

[0036] The composite wave injection module is used to inject a bias composite wave with a set frequency and amplitude into the d-axis current when the motor reaches a stable operating state after initial acceleration, so as to meet the continuous excitation conditions for online motor identification.

[0037] The acquisition module is used to sample four sets of dq-axis current, voltage, and electric angular velocity data at four adjacent time points after an enable signal identified by given parameters is received.

[0038] The online resistance and inductance identification module is used to combine four sets of sampled dq-axis current, voltage and electric angular velocity data into a voltage equation set with an effective current slope d-axis, and to transform the resistance and inductance identification system model using the least squares method with a forgetting factor, thereby identifying the three sets of electrical parameters of the embedded permanent magnet synchronous motor resistance, direct-axis inductance and quadrature-axis inductance in real time.

[0039] The flux linkage online identification module is used to take the three sets of electrical parameters identified in real time as known quantities, and combine them with any two adjacent sets of dq axis current, voltage and electric angular velocity data to form a set of q axis voltage equations with effective slopes. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model to identify the last electrical parameter of the permanent magnet flux linkage of the embedded permanent magnet synchronous motor in real time.

[0040] Thirdly, the present invention provides an embedded permanent magnet synchronous motor control system, including the above-mentioned online identification device for all electrical parameters of permanent magnet synchronous motor based on composite waves.

[0041] Fourthly, the present invention provides a storage medium storing computer-readable instructions, including a computer program / instructions, which are executed by one or more processors to perform the steps of the above-described method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves.

[0042] Beneficial effects:

[0043] This invention proposes a method for embedded permanent magnet synchronous motors. By considering the influence of the steady-state current differential term and the dead time due to motor nonlinearity, it solves the underrank problem in identifying all electrical parameters of the motor, enabling the identification of four electrical parameters. Simultaneously, the identification accuracy is high, with a maximum identification error of approximately 3% for each parameter. This invention addresses the challenges of difficult and inaccurate identification methods for all electrical parameters of motors, providing real-time and accurate identification. This is of significant importance for improving system efficiency and energy saving, enhancing control performance and dynamic response, and realizing motor condition monitoring and fault early warning. Attached Figure Description

[0044] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0045] Figure 1 This is a flowchart of an online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves, provided in an embodiment of the present invention.

[0046] Figure 2 This is a composite waveform diagram of d-axis current injection provided in one embodiment of the present invention;

[0047] Figure 3 This is an embodiment of the present invention showing the online identification results using the method of the present invention and the corresponding error magnitude;

[0048] Figure 4 This is an embodiment of the present invention showing the online identification results before dead zone compensation and the corresponding error magnitude;

[0049] Figure 5 The waveforms of electromagnetic torque and motor speed during online parameter identification are shown in one embodiment of the present invention.

[0050] Figure 6 This is a switching timing diagram of a single-phase bridge arm of the inverter considering dead time in one embodiment of the present invention;

[0051] Figure 7 This is a structural block diagram of an online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves, provided in an embodiment of the present invention.

[0052] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0053] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] To enhance understanding of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0055] like Figure 1 As shown, this embodiment provides an online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves. The method includes the following steps:

[0056] S100: The nonlinear factors of the motor are obtained under the field orientation control framework of the embedded permanent magnet synchronous motor, and the voltage error is compensated by the dead zone compensation algorithm.

[0057] Within the framework of field-oriented control of an embedded permanent magnet synchronous motor, the nonlinear factors of the motor are studied to compensate for the adverse effects of voltage errors caused by dead time on the accuracy of identification results.

[0058] Furthermore, the dead zone compensation algorithm specifically includes:

[0059] The angle between the actual current and the d-axis current is obtained by calculating the arctangent between the d-axis current and the q-axis current, then added to the motor electrical angle and subtracted. Obtain the electrical angle of phase A current;

[0060] Based on the mutual spacing of the three-phase currents A, B, and C The relationship between electrical angles is used to obtain the electrical angles of the three-phase currents;

[0061] The three-phase voltage is compensated in segments according to the magnitude of the electrical angle, i.e., trapezoidal compensation voltage;

[0062] Finally, a coordinate transformation is performed to convert the compensation voltage to a two-phase stationary voltage coordinate system, namely... and It acts on the PI output of the current loop to compensate for the voltage drop caused by the dead time of each switching cycle of the switching transistor, thereby compensating for the impact of voltage error caused by the dead time on the accuracy of the identification results.

[0063] S200: When the motor reaches a stable operating state after initial acceleration, a bias composite wave with a set frequency and amplitude is injected into the d-axis current to meet the continuous excitation conditions for online motor identification.

[0064] Furthermore, the bias composite wave with set frequency and amplitude is composed of a triangular wave with a constant local slope and a sine wave with a time-varying global slope. This composite waveform combines the fast dynamic response of the triangular wave with the excellent noise immunity of the sine wave, thereby achieving superior recognition performance.

[0065] In the preferred embodiment, the amplitude of the injected composite wave is 5 to 10% of the rated current amplitude, the bias of the injected composite wave is -3 to -2A, and the frequency of the composite wave is 1 / 100 of the switching frequency.

[0066] When performing full electrical parameter identification using a composite waveform with the aforementioned characteristics, a good continuous excitation effect can be obtained, the parameter identification results are relatively accurate, and the identification error is around 3%.

[0067] In this embodiment of the application, the method further considers the influence of the current differential term and uses the slope of adjacent current data points as the magnitude of the current differential term to form the condition for full-rank identification.

[0068] It is generally believed that the differential term of the current is zero when the motor is running in steady state. However, an absolutely steady state is impossible in actual operation, and there is background noise and error during sampling, so the differential term of the current is not zero. Therefore, by injecting a composite waveform into the d-axis current, the difference between adjacent currents divided by the sampling time is used to represent the magnitude of the differential term of the current. The slope of the injected composite wave includes not only the two fixed slopes of the triangular wave, but also the slope that varies in size within the period of the sine wave, making the current slope more diverse and thus increasing the effective slope signal-to-noise ratio of the current. At the same time, the slopes of two adjacent sampling points within one period of the composite waveform are symmetrical, which can minimize d-axis current fluctuations while meeting the continuous excitation conditions for online parameter identification.

[0069] Figure 2 The waveform of the injected composite wave is shown in the diagram. The main characteristics of the injected composite wave are the composite wave period. Composite amplitude Composite wave bias The magnitude of the current differential term is obtained by calculating the slope of the current at two adjacent sampling points in the composite waveform.

[0070] S300: After the enable signal identified by the given parameters is obtained, sample the dq axis current, voltage and electric angular velocity data at four adjacent time points.

[0071] In a synchronously rotating coordinate system under magnetic field orientation control, the stator voltage equation can be expressed as:

[0072] (1)

[0073] in, and These are the d-axis component and the q-axis component of the stator voltage, respectively. and These are the d-axis component and the q-axis component of the stator current, respectively. It is the stator resistance. and These are the direct-axis inductance and quadrature-axis inductance of the motor. It is the rotor permanent magnet flux linkage.

[0074] Once the motor reaches a stable state and the motor enable signal is activated, four adjacent time points are sampled. The data for the dq-axis current, voltage, and electric angular velocity at time t are denoted as follows: , , , Furthermore, adjacent sampling periods are denoted as .

[0075] S400: By sampling four sets of dq-axis current, voltage and electric angular velocity data, a voltage equation set with an effective current slope d-axis is formed, and the least squares method with forgetting factor is used to transform the resistance and inductance identification system model, thereby identifying three sets of electrical parameters of embedded permanent magnet synchronous motor resistance, direct-axis inductance and quadrature-axis inductance in real time.

[0076] Furthermore, during the identification process, the d-axis voltage equations are used to perform online identification of the motor resistance and inductance. The voltage equation set with an effective current slope on the d-axis is as follows:

[0077] (2)

[0078] In the resistance and inductance identification system model:

[0079] (3)

[0080] in, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time parameter identification. , , They are respectively , , The d-axis voltage data at time 1; , , , They are respectively , , , The d-axis current data at any given time; , , They are respectively , , q-axis current data at time t; , , They are respectively , , Electric angular velocity data at any given time; For adjacent sampling periods; To identify the resistance of the embedded permanent magnet synchronous motor, To identify the embedded permanent magnet synchronous motor direct-axis inductance, The quadrature axis inductance of the embedded permanent magnet synchronous motor is identified.

[0081] Using the least squares method with a forgetting factor, the three electrical parameters of the embedded permanent magnet synchronous motor, namely resistance, can be obtained in real time according to the formula of the identification system model. Direct-axis inductor and quadrature axis inductance .

[0082] Because the d-axis current signal is continuously excited, the magnitude of the d-axis current varies at different times, but the motor torque and speed remain relatively stable, and the linear correlation between adjacent time points is low. Since it is necessary to identify the three parameters according to the d-axis voltage equation, and to replace the differential term of the current in the voltage equation by calculating the current slope at adjacent time points, four sets of data are needed to construct three sets of effective current slopes to achieve full-rank identification of the resistance, direct-axis inductance, and quadrature-axis inductance. Similarly, when identifying the rotor flux linkage according to the q-axis voltage equation, two sets of data are also needed to construct one set of effective current slopes to complete the full-rank identification of the rotor flux linkage.

[0083] S500: The three sets of electrical parameters identified in real time are used as known quantities. Combined with any two adjacent sets of dq-axis current, voltage and electric angular velocity data, a set of q-axis voltage equations with effective slope is formed. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model to identify the last electrical parameter of the rotor flux linkage of the embedded permanent magnet synchronous motor in real time.

[0084] Furthermore, during the identification process, the q-axis voltage equations are used to perform online identification of the rotor permanent magnet flux linkage. The q-axis voltage equation set with effective slope is as follows:

[0085] (4)

[0086] The identified rotor flux linkage of the embedded permanent magnet synchronous motor is represented as follows:

[0087] (5)

[0088] In the formula, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time-time parameter identification, where , and This indicates the magnitude of the resistance and inductance identified in real time through the resistance and inductance identification model.

[0089] This invention injects a bias composite wave of a certain frequency and amplitude when the motor reaches a set stable operating state. Simultaneously, it samples the motor's dq-axis current, voltage, and electrical angular velocity in real time at adjacent moments. After a given identification enable signal, the data weights are dynamically adjusted using a least squares method with a forgetting factor to identify the resistance and inductance. The real-time identified resistance and inductance parameters are then used as known quantities to complete the identification of the flux linkage, ultimately achieving the identification of all four electrical parameters of the embedded permanent magnet synchronous motor. This is achieved by removing the stationary two-phase coordinate system compensation voltage. and The results of full electrical parameter identification before and after nonlinear compensation are compared to illustrate the feasibility and effectiveness of the full electrical parameter identification scheme in composite wave injection for embedded permanent magnet synchronous motors combined with dead zone compensation.

[0090] In one specific embodiment, the embedded permanent magnet synchronous motor has 4 pole pairs and a direct-axis inductance of 9.7. The quadrature-axis inductance of the motor is 13.7. The stator resistance of the motor is 1.42 ohms. Permanent magnet flux linkage 0.185 The motor operates at a speed of 1000 rpm and has a torque of 6. DC bus voltage 311V, switching frequency 10kHz, dead time 2 seconds. The injected composite wave has a DC bias of -2A, an amplitude of 1.5A, and a frequency of 100Hz.

[0091] Figure 3This presents the identification results of all electrical parameters of the motor after dead-zone nonlinear compensation according to the present invention, along with the corresponding error magnitudes. Considering that the motor undergoes nonlinear compensation, the four motor electrical parameters identified through bias composite wave injection have high accuracy. Among them, the identification error of flux linkage is 0.4%, the identification error of resistance is 2.45%, the identification error of direct-axis inductance is 3.29%, and the identification error of quadrature-axis inductance is 2.08%.

[0092] Figure 4 The images show the identification results of all electrical parameters of the motor before dead-zone nonlinear compensation in this invention, along with the corresponding error magnitudes. Without dead-zone compensation, the identification errors of the four electrical parameters of the embedded permanent magnet synchronous motor are relatively large. Specifically, the identification error for flux linkage is 7%, resistance is 12.32%, direct-axis inductance is 14.5%, and quadrature-axis inductance is 8.16%.

[0093] Figure 5 The waveforms of electromagnetic torque and motor speed during the online parameter identification process of this invention are shown. During the online identification process, the speed runs smoothly without speed fluctuations; the torque output has no obvious fluctuations, ensuring high-quality torque output and high-precision speed operation of the motor.

[0094] Figure 6 This invention considers the switching timing diagram of a single-phase bridge arm of the inverter when dead time is considered. To avoid simultaneous conduction of the upper and lower switching transistors of the voltage source inverter, a certain dead time needs to be inserted before the upper and lower transistors operate. Due to the existence of dead time, there is a certain difference between the actual voltage value and the ideal voltage value. This results in a certain deviation in the input voltage of the identification matrix, which in turn affects the accuracy of the parameter identification results. Indicates the on / off state of the upper bridge arm switch transistor. This indicates the on / off state of the lower bridge arm switch transistor, where 1 represents the on state and 0 represents the off state. The desired conduction time for the upper bridge arm switch management within one switching cycle. The dead time of the upper arm switch within one switching cycle. This represents the actual conduction time of the upper arm switch transistor within one switching cycle.

[0095] In one embodiment, an online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves is proposed, referring to... Figure 7 As shown (for the sake of system signal flow continuity and simplicity, the acquisition module is not included in...), Figure 7 (as shown in the image), the device includes:

[0096] The compensation module is used to determine the nonlinear factors of the motor under the field-oriented control framework of the embedded permanent magnet synchronous motor, and to compensate for voltage errors through a dead-zone compensation algorithm.

[0097] The composite wave injection module is used to inject a bias composite wave with a set frequency and amplitude into the d-axis current when the motor reaches a stable operating state after initial acceleration, so as to meet the continuous excitation conditions for online motor identification.

[0098] The acquisition module is used to sample four sets of dq-axis current, voltage, and electric angular velocity data at four adjacent time points after an enable signal identified by given parameters is received.

[0099] The online resistance and inductance identification module is used to combine four sets of sampled dq-axis current, voltage and electric angular velocity data into a voltage equation set with an effective current slope d-axis, and to transform the resistance and inductance identification system model using the least squares method with a forgetting factor, thereby identifying the three sets of electrical parameters of the embedded permanent magnet synchronous motor resistance, direct-axis inductance and quadrature-axis inductance in real time.

[0100] The flux linkage online identification module is used to take the three sets of electrical parameters identified in real time as known quantities, and combine them with any two adjacent sets of dq axis current, voltage and electric angular velocity data to form a set of q axis voltage equations with effective slopes. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model to identify the last electrical parameter of the permanent magnet flux linkage of the embedded permanent magnet synchronous motor in real time.

[0101] Specifically, a two-level three-phase full-bridge voltage source inverter outputs three-phase AC power to the motor, and the motor to be identified is an embedded permanent magnet synchronous motor. Dual closed-loop field-oriented control of the motor is achieved through speed PI controllers and current PI controllers. Since nonlinear factors are unavoidable in motor control, nonlinearity compensation is required before parameter identification to achieve high-precision results. Three-phase trapezoidal compensation voltages are output based on the electrical angles of the three-phase currents and the corresponding dead times, and then compensated to a two-phase stationary voltage coordinate system through coordinate transformation. Before parameter identification, a composite wave is injected into the d-axis motor. This composite wave consists of a triangular wave with a local constant slope and a sine wave with a global time-varying slope, ensuring that the motor meets the continuous excitation conditions during parameter identification. The identification model constructs full-rank identification conditions for the embedded permanent magnet synchronous motor by sampling the dq-axis current, voltage, and electrical angular velocity data at adjacent moments during steady-state motor operation, completing the full-rank identification of all electrical parameters. First, referring to the motor's d-axis voltage equation, a recursive least squares method with a forgetting factor is used to complete the identification of the motor resistance. Direct-axis inductor and quadrature axis inductance The identification of three electrical parameters is then performed. These three parameters, identified in real-time, are treated as known quantities. Following the motor's q-axis voltage equation, a recursive least squares method with a forgetting factor is used to complete the rotor flux linkage calculation. Parameter identification. Finally, the accuracy of all electrical parameters identification before and after dead-zone compensation was compared, verifying the effectiveness of the online identification method for all electrical parameters of the embedded permanent magnet synchronous motor based on composite wave injection and considering the current differential term.

[0102] It should be noted that the online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves provided in the above embodiments is only illustrated by the division of the above functional modules when executing the online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves provided in the above embodiments and the online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves belong to the same concept. The implementation process is detailed in the online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves, and will not be repeated here.

[0103] The functional modules in this embodiment of the invention can be integrated into one processing module, or each unit can exist as a separate physical entity, or two or more units can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module.

[0104] In one embodiment, an embedded permanent magnet synchronous motor control system is proposed, continuing with reference to... Figure 7 As shown, the system includes the aforementioned online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves.

[0105] Based on the description of the device, please refer to the description of the same or similar parts above, which will not be repeated here.

[0106] In one embodiment, a storage medium storing computer-readable instructions is proposed, including a computer program / instructions that are executed by one or more processors to perform the following steps: deriving the nonlinear factors of the motor within an embedded permanent magnet synchronous motor field-oriented control framework, and compensating for voltage errors using a dead-zone compensation algorithm; when the motor reaches a stable operating state after initial acceleration, injecting a bias composite wave of a set frequency and amplitude into the d-axis current to meet the continuous excitation conditions for online motor identification; after receiving an enable signal for parameter identification, sampling four sets of dq-axis current, voltage, and electrical angular velocity data at four adjacent time points; and using the sampled four sets of... The dq-axis current, voltage, and electric angular velocity data are combined to form a voltage equation system with an effective current slope on the d-axis. The least squares method with a forgetting factor is used to transform the resistance and inductance identification system model, thereby identifying three sets of electrical parameters of the embedded permanent magnet synchronous motor: resistance, direct-axis inductance, and quadrature-axis inductance. The three sets of electrical parameters identified in real time are used as known quantities and combined with any two adjacent sets of dq-axis current, voltage, and electric angular velocity data to form a voltage equation system with an effective slope on the q-axis. The least squares method with a forgetting factor is then used to transform the flux linkage identification system model, thereby identifying the last electrical parameter of the rotor flux linkage of the embedded permanent magnet synchronous motor in real time.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0108] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves, characterized in that, The method includes: The nonlinear factors of the motor are obtained under the field-oriented control framework of the embedded permanent magnet synchronous motor, and the voltage error is compensated by the dead zone compensation algorithm. When the motor reaches a stable operating state after initial acceleration, a bias composite wave with a set frequency and amplitude is injected into the d-axis current to meet the continuous excitation conditions for online motor identification. After the enable signal identified by the given parameters is obtained, sample the dq-axis current, voltage and electric angular velocity data at four adjacent time points. By sampling four sets of dq-axis current, voltage, and electric angular velocity data, a set of voltage equations with an effective current slope d-axis is formed. Then, a least squares method with a forgetting factor is used to transform the resistance and inductance identification system model, thereby enabling real-time identification of the resistance of the embedded permanent magnet synchronous motor. Direct-axis inductor quadrature axis inductance Three sets of electrical parameters; The three sets of electrical parameters identified in real time are used as known quantities. Combined with any two adjacent sets of dq-axis current, voltage and electric angular velocity data, a set of q-axis voltage equations with effective slope is constructed. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model, and the last electrical parameter of the rotor flux linkage of the embedded permanent magnet synchronous motor is identified in real time.

2. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 1, characterized in that: The dead zone compensation algorithm includes: The angle between the actual current and the d-axis current is obtained by calculating the arctangent between the d-axis current and the q-axis current, then added to the motor electrical angle and subtracted. Obtain the electrical angle of phase A current; Based on the mutual spacing of the three-phase currents A, B, and C The relationship between electrical angles is used to obtain the electrical angles of the three-phase currents; The three-phase voltage is compensated in segments based on the magnitude of the electrical angle of the three-phase current, i.e., trapezoidal compensation voltage. After the trapezoidal compensation voltage is converted to a two-phase stationary voltage coordinate system and subjected to coordinate transformation, it is applied to the output of the current loop PI to compensate for the voltage drop caused by the dead time of each switching cycle of the switching transistor, thereby achieving voltage replenishment.

3. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 1, characterized in that: The bias composite wave with set frequency and amplitude consists of a triangular wave with a constant local slope and a sine wave with a time-varying global slope. The slopes of two adjacent sampling points within one cycle of the composite waveform are symmetrical.

4. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 3, characterized in that: The amplitude of the composite wave is 5 to 10% of the rated current amplitude, the bias is -3 to -2A, and the frequency is 1 / 100 of the switching frequency.

5. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 4, characterized in that: The method considers the influence of the current differential term and uses the slope of adjacent current data points as the magnitude of the current differential term, thus forming the condition for full-rank parameter identification.

6. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 1, characterized in that: The voltage equations with an effective current slope d-axis are as follows: ， In the formula, , , They are respectively , , The d-axis voltage data at time 1; , , , They are respectively , , , The d-axis current data at any given time; , , They are respectively , , q-axis current data at time t; , , They are respectively , , Electric angular velocity data at any given time; For adjacent sampling periods; To identify the resistance of the embedded permanent magnet synchronous motor, To identify the embedded permanent magnet synchronous motor direct-axis inductance, The quadrature axis inductance of the embedded permanent magnet synchronous motor was identified; Identified embedded permanent magnet synchronous motor resistor Direct-axis inductor quadrature axis inductance The three sets of electrical parameters are expressed as follows: ， in, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time parameter identification. , , They are respectively , , The d-axis voltage data at time 1; , , , They are respectively , , , The d-axis current data at any given time; , , They are respectively , , q-axis current data at time t; , , They are respectively , , Electric angular velocity data at any given time; For adjacent sampling periods; To identify the resistance of the embedded permanent magnet synchronous motor, To identify the embedded permanent magnet synchronous motor direct-axis inductance, The quadrature axis inductance of the embedded permanent magnet synchronous motor is identified.

7. The method for online identification of all electrical parameters of a permanent magnet synchronous motor based on composite waves according to claim 1, characterized in that: The set of q-axis voltage equations with effective slope is as follows: ， In the formula, for The d-axis current data at any given time; , They are respectively , q-axis current data at time t; for Electric angular velocity data at any given time; for q-axis voltage data at time t; For adjacent sampling periods; , , This indicates the magnitude of the resistance and inductance identified in real time through the resistance and inductance identification model; The identified rotor flux linkage of the embedded permanent magnet synchronous motor is represented as follows: ， In the formula, express The motor parameters identified in real time express The voltage vector output at any given time. express The coefficient matrix for time-time parameter identification; for The d-axis current data at any given time; , They are respectively , q-axis current data at time t; for Electric angular velocity data at any given time; for q-axis voltage data at time t; For adjacent sampling periods; , , This indicates the magnitude of the resistance and inductance identified in real time through the resistance and inductance identification model.

8. An online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves, characterized in that: The device includes: The compensation module is used to determine the nonlinear factors of the motor under the field-oriented control framework of the embedded permanent magnet synchronous motor, and to compensate for voltage errors through a dead-zone compensation algorithm. The composite wave injection module is used to inject a bias composite wave with a set frequency and amplitude into the d-axis current when the motor reaches a stable operating state after initial acceleration, so as to meet the continuous excitation conditions for online motor identification. The acquisition module is used to sample four sets of dq-axis current, voltage, and electric angular velocity data at four adjacent time points after an enable signal identified by given parameters is received. The online resistance and inductance identification module is used to combine four sets of sampled dq-axis current, voltage and electric angular velocity data into a voltage equation set with an effective current slope d-axis, and to transform the resistance and inductance identification system model using the least squares method with a forgetting factor, thereby identifying the three sets of electrical parameters of the embedded permanent magnet synchronous motor resistance, direct-axis inductance and quadrature-axis inductance in real time. The flux linkage online identification module is used to take the three sets of electrical parameters identified in real time as known quantities, and combine them with any two adjacent sets of dq axis current, voltage and electric angular velocity data to form a set of q axis voltage equations with effective slopes. Then, the least squares method with forgetting factor is used to transform the flux linkage identification system model to identify the last electrical parameter of the permanent magnet flux linkage of the embedded permanent magnet synchronous motor in real time.

9. An embedded permanent magnet synchronous motor control system, characterized in that, The device includes the online identification device for all electrical parameters of a permanent magnet synchronous motor based on composite waves as described in claim 8.

10. A storage medium storing computer-readable instructions, comprising a computer program / instructions, characterized in that: The computer program / instructions are executed by one or more processors using the steps of the online identification method for all electrical parameters of a permanent magnet synchronous motor based on composite waves as described in any one of claims 1 to 7.

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