Permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization

Abstract

The invention relates to the technical field of permanent magnet synchronous motor control, and discloses a permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization, comprising the following steps: S1, collecting three-phase current, rotor position electrical angle and rotating speed information of a three-phase permanent magnet synchronous motor, and converting the current into a dq coordinate system through coordinate transformation; s2, predicting the dq-axis current of the next control period according to the dq-axis voltage vector of the previous control period; s3, based on the reference rotating speed of the motor, a q-axis current reference value is generated through a rotating speed controller, and a d-axis current reference value is set to be zero. Through voltage vector preselection and cost function optimization, the number of candidate voltage vectors is reduced from 37 to 4, the calculation complexity is significantly reduced, and the control real-time performance is improved. Meanwhile, the steady-state performance is improved, current ripples and torque ripples are reduced, the requirements for rapid dynamic response and low computing power are met, and an efficient solution is provided for high-precision control of the permanent magnet synchronous motor.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor control, and specifically to a speed regulation method for permanent magnet motors based on current prediction and discrete space vector optimization. Background Art

[0002] Due to its high power density and high efficiency, permanent magnet synchronous motors are widely used in the field of precision control. However, the rapid and accurate control of its speed, torque, and current has always been a technical difficulty. Although the traditional field-oriented control can achieve good static performance through coordinate decoupling, due to its dependence on PI controllers, its dynamic response speed is slow, and a large amount of time is required to debug parameters to adapt to complex environments. The direct torque control method achieves fast response through a simple switching table, but in high-precision application scenarios, torque ripple and uncontrollable switching frequency significantly limit its performance.

[0003] In recent years, model predictive current control, as an emerging control strategy, has received attention due to its fast response speed and simple structure. However, since only one voltage vector is applied within the control period, this method results in large current ripple and poor steady-state performance. Especially under high-precision control requirements, traditional model predictive control is difficult to meet the requirements. To address this issue, researchers have proposed virtual voltage vector synthesis technology, which expands the voltage vector set through discrete space vector modulation, thereby effectively reducing current ripple and torque ripple.

[0004] Although the discrete space vector modulation method significantly improves the steady-state performance, the cost is a substantial increase in computational complexity. Since the number of virtual voltage vectors is much larger than that of real voltage vectors, the current prediction values of candidate voltages need to be calculated one by one within each control period, and the optimal voltage vector is selected through a cost function. This sharp increase in computational amount leads to an enhanced dependence of the controller on hardware computing power, and the real-time performance and economy are significantly reduced. Especially on low-computing-power devices, the engineering application of this method becomes difficult, becoming a bottleneck for the further development of model predictive control.

[0005] Aiming at the above problems, there is an urgent need for a motor control method that can not only maintain the superiority of steady-state performance but also significantly reduce the computational burden. Based on this, the present invention proposes a speed regulation method for permanent magnet motors based on current prediction and discrete space vector optimization. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a speed regulation method for permanent magnet motors based on current prediction and discrete space vector optimization, which solves the problems of poor steady-state performance and high computational complexity in traditional model predictive current control.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions: A permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization, comprising the following steps:

[0008] S1. Collect the three-phase current, rotor position electrical angle, and speed information of a three-phase permanent magnet synchronous motor, and convert the current to the dq coordinate system through coordinate transformation;

[0009] S2. Predict the dq-axis current of the next control period according to the dq-axis voltage vector of the previous control period;

[0010] S3. Based on the reference speed of the motor, use a speed controller to generate a q-axis current reference value, and set the d-axis current reference value to zero;

[0011] S4. Calculate the dq-axis reference voltage according to the reference current, and convert the reference voltage to the reference voltage in the stationary coordinate system;

[0012] S5. Select the sector and quadrant where the candidate voltage vector is located based on the reference voltage;

[0013] S6. Calculate the current prediction value under the action of the candidate voltage vector, and select the optimal voltage vector through a cost function;

[0014] S7. Output the inverter switching signal corresponding to the optimal voltage vector and apply it to the inverter.

[0015] Preferably, the S1 step specifically includes the following steps:

[0016] S1.1. Collect the three-phase current signals of a three-phase permanent magnet synchronous motor;

[0017] S1.2. Obtain the rotor position electrical angle and speed signals through a motor sensor;

[0018] S1.3. Use coordinate transformation to convert the collected three-phase current from the three-phase stationary coordinate system to the dq-axis coordinate system, and output the d-axis current and q-axis current.

[0019] Preferably, the S2 step uses a one-beat delay compensation method to predict the dq-axis current, and the one-beat delay compensation method includes the following steps:

[0020] S2.1. Predict the dq-axis current of the next control period based on the dq-axis voltage vector of the previous control period and the motor parameters of the current period;

[0021] S2.2. Compensate the d-axis and q-axis current values of the current period using the stator resistance, inductance, electrical angular velocity, and permanent magnet flux of the motor;

[0022] S2.3. The corrected d-axis and q-axis currents are further predicted through the inductor, voltage vector, and motor magnetic flux models to obtain the d-axis current and q-axis current in the next control period.

[0023] Preferably, the S3 step specifically includes the following steps:

[0024] S3.1. Collect the deviation between the actual current speed of the motor and the reference speed.

[0025] S3.2. Calculate the integral value and proportional value of the speed error through the speed controller.

[0026] S3.3. Generate the q-axis current reference value according to the calculation result of the speed error.

[0027] S3.4. Set the d-axis current reference value to zero to achieve decoupled control of the motor torque and magnetic flux.

[0028] Preferably, in S4, the dq-axis reference voltages are calculated in the following manner:

[0029] S4.1. Calculate the dq-axis reference voltages based on the stator resistance, d-axis inductance, and q-axis inductance of the motor, combined with the dq-axis reference currents and electrical angular velocity.

[0030] S4.2. Use the inverse coordinate transformation method to convert the dq-axis reference voltages into the α-axis reference voltage and β-axis reference voltage in the stationary coordinate system.

[0031] Preferably, the selection of the candidate voltage vector in the S5 step includes the following steps:

[0032] S5.1. Determine the sector where the candidate voltage vector is located according to the magnitude and direction of the α-axis and β-axis reference voltages in the stationary coordinate system.

[0033] S5.2. Determine the candidate quadrant based on the combination of the virtual voltage vector and the actual voltage vector in the sector.

[0034] S5.3. The candidate voltage vectors include the zero voltage vector, active voltage vector, and virtual voltage vector synthesized by two or more real voltage vectors.

[0035] Preferably, in the S6 step, when calculating the current prediction value under the action of the candidate voltage vector, the following formula is used:

[0036] d-axis current prediction value:

[0037]

[0038] q-axis current prediction value:

[0039]

[0040] Among them, and are the dq-axis components of the candidate voltage vector, T s is the sampling period, R is the internal resistance of the motor stator, L d , L q is the inductance, ω e is the electrical angular velocity, ψ f is the permanent magnet flux linkage, i d (k + 1) and i q (k + 1) are the d-axis and q-axis current values in the (k + 1)-th control period respectively.

[0041] Preferably, the cost function in step S6 is as follows:

[0042]

[0043] Among them, and are the dq-axis current reference values, and are the predicted dq-axis current values under the action of the candidate voltage vector.

[0044] The present invention provides a speed regulation method for a permanent magnet motor based on current prediction and discrete space vector optimization.

[0045] It has the following beneficial effects:

[0046] 1. The present invention adopts a model predictive control technical solution based on discrete space vector modulation. By preselecting discrete voltage vectors and screening the optimal voltage vector, the technical effect of significantly improving the steady-state performance is achieved. Compared with the technical solution of directly calculating a large number of virtual voltage vectors in the prior art, the discrete voltage vector preselection method proposed by the present invention reduces the candidate voltage vectors from 37 to 4, effectively reducing the computational complexity and solving the problem of excessive computational burden of traditional model predictive control.

[0047] 2. The present invention significantly optimizes the current control performance through the deadbeat prediction method combined with the partition screening of voltage vectors, achieving both steady-state and dynamic performance. Compared with the technical solution in the prior art that uses a single voltage vector action resulting in large steady-state current ripple, the present invention ensures smooth output current and small torque ripple through the cost function selection of candidate voltage vectors, overcomes the deficiency of poor steady-state performance of traditional methods, and retains the advantage of fast dynamic response. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic diagram of the drive topology structure of the permanent magnet synchronous motor of the present invention;

[0049] Figure 2Schematic diagram of the actual voltage vector space corresponding to the two-level inverter of the present invention;

[0050] Figure 3 Schematic diagram of the control block diagram of the low-computation model predictive current control method for a permanent magnet synchronous motor based on discrete space vector modulation provided by the present invention;

[0051] Figure 4 Schematic diagram of the control flow of the low-computation model predictive current control method for a permanent magnet synchronous motor based on discrete space vector modulation provided by the present invention;

[0052] Figure 5 Schematic diagram of the virtual voltage space of the present invention;

[0053] Figure 6 Quadrant distribution of sector I of the present invention;

[0054] Figure 7 Schematic diagram of the simulation test results under steady state of the present invention;

[0055] Figure 8 Simulation test result diagram under dynamic conditions of the present invention. Detailed implementation manners

[0056] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] Please refer to the attached Figure 1 - attached Figure 8 , the embodiments of the present invention provide a permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization, including the following steps:

[0058] S1. Collect the three-phase current, rotor position electrical angle and speed information of the three-phase permanent magnet synchronous motor, and convert the current to the dq coordinate system through coordinate transformation;

[0059] In the method of the present invention, the acquisition of the operating parameters of the three-phase permanent magnet synchronous motor and the current coordinate transformation are the basis and key steps of the entire control process. By collecting the three-phase current, rotor position electrical angle and speed information in real time and converting the current from the three-phase stationary coordinate system to the dq coordinate system, the necessary initial data can be provided for subsequent current prediction and voltage vector selection.

[0060] In general, operating parameters such as three-phase current, electrical angle of rotor position, and rotational speed are the basic input data for the control system. By collecting these parameters in real time and converting them to the rotating coordinate system dq, decoupled control of the current can be achieved, providing support for subsequent optimization.

[0061] In this embodiment, the specific implementation method is as follows:

[0062] First, collect the three-phase current of the three-phase permanent magnet synchronous motor, denoted as i a 、i b and i c . These current components are the instantaneous currents in the stator windings of the motor and are obtained in real time through current sensors (such as Hall sensors or current transformers).

[0063] At the same time, use a position sensor (such as a resolver or an encoder) to obtain the electrical angle of the rotor position, denoted as θ e . This electrical angle is a key input quantity for subsequent coordinate transformation and is used to achieve current conversion from the three-phase stationary coordinate system to the rotating coordinate system.

[0064] In addition, the rotational speed n of the motor is obtained by directly measuring through a speed sensor or by differentiating the electrical angle signal of the rotor position. In the case of no speed sensor, a method based on back electromotive force estimation can also be used to calculate the rotational speed n.

[0065] Specifically, the collected three-phase currents i a 、i b 、i c are the current components in the stationary coordinate system. To achieve subsequent decoupled control, it is necessary to convert the current components in the three-phase stationary coordinate system to the rotating coordinate system dq. This step requires introducing the electrical angle θ e of the rotor position, and the coordinate transformation is completed through the following formula:

[0066] The converted d-axis current:

[0067]

[0068] The converted q-axis current:

[0069]

[0070] where: i a 、i b 、i c are the three-phase current components; θ e is the electrical angle of the rotor position; i d and i q are the d-axis current and q-axis current in the rotating coordinate system respectively.

[0071] It should be noted that in the rotating coordinate system, the d-axis current i d is usually related to the magnetic flux control of the motor, while the q-axis current i q is related to the torque control. The transformation completed by the above formula can achieve complete decoupling of the current, providing accurate basic data for subsequent current prediction.

[0072] Generally, the collected current, position, and speed signals are affected by noise and interference. In some embodiments, these signals can be filtered. For example, a low-pass filter can be used for the collected signals of θ e and n to reduce the influence of mechanical vibration and electromagnetic interference on the measurement accuracy.

[0073] In addition, in an actual control system, if there is a delay in the data collected by the sensor, the influence of the delay on the collected data can also be reduced by the method of interpolation prediction, thereby further improving the control accuracy.

[0074] As an option, in the case of no speed sensor, the speed n can be estimated by the back electromotive force of the motor. This estimation method derives the back electromotive force from the voltage and current relationship of the motor stator winding, and then calculates the electrical angular velocity ω e and the mechanical speed n.

[0075] To ensure the clarity of the formula, the parameters in the formula are defined as follows:

[0076] i a 、i b 、i c : Current components in the three-phase stationary coordinate system;

[0077] i d 、i q : Current components in the rotating coordinate system dq;

[0078] θ e : Electrical angular position of the rotor, describing the angular position of the rotor magnetic pole relative to the stator reference coordinate system;

[0079] n: Mechanical speed, with the unit of rpm;

[0080] ω e : Electrical angular velocity, with the unit of radian / second, which can be calculated by the formula ω e = 2πn / 60.

[0081] In some embodiments, dynamic error correction can also be added during the current acquisition and coordinate transformation. For example, according to the known motor parameters (such as inductance and resistance) and system states (such as motor speed and current), the collected signals can be corrected online to compensate for the errors of the sensor. This method can further improve id and i q computation accuracy.

[0082] As another option, a distributed acquisition method can also be adopted, where the three-phase current and position signals are respectively sent to multiple parallel processing modules to improve data processing efficiency, which is particularly suitable for motor control scenarios with high dynamic response.

[0083] Through the above steps, the present invention accurately transforms the current components in the three-phase stationary coordinate system to the rotating coordinate system dq, realizing the decoupling and preliminary processing of the current components, laying a foundation for subsequent current prediction, cost function calculation, and voltage vector selection. The entire implementation process closely combines real-time acquisition and mathematical transformation to ensure control accuracy and reliability.

[0084] S2. Predict the dq-axis current in the next control period according to the dq-axis voltage vector in the previous control period;

[0085] In the implementation process of the present invention, current prediction is one of the core steps of motor control. By using a mathematical model based on the dq-axis voltage vector and motor parameters in the previous control period, the dq-axis current in the next control period is predicted, providing accurate reference data for subsequent current control and voltage vector selection.

[0086] Generally, the dq-axis current of a permanent magnet synchronous motor is affected by factors such as the voltage vector in the previous period, motor parameters (such as inductance, resistance), and rotor electrical angular velocity. By adopting a one-beat delay compensation method, accurate prediction of the current in the next control period can be completed within the current period.

[0087] In this embodiment, the process of predicting the dq-axis current includes the following:

[0088] First, according to the d-axis voltage u d (k - 1) and q-axis voltage u q (k - 1) in the previous control period, combined with the dq-axis current i d (k) and i q (k) in the current period, use the mathematical model of the permanent magnet synchronous motor to calculate the predicted values i d (k + 1) and i q (k + 1) of the dq-axis current in the next control period.

[0089] The prediction formulas are as follows:

[0090] d-axis current prediction formula:

[0091]

[0092] q-axis current prediction formula:

[0093]

[0094] Among them: T s is the sampling period, representing the time interval of each cycle of the control system; L d and L q are the d-axis and q-axis inductances respectively, reflecting the inductance characteristics of the motor in different axial directions; R is the internal resistance of the motor stator, used to describe the resistance characteristics of the motor winding; ω e is the electrical angular velocity of the rotor, and its relationship with the rotational speed n and the number of pole pairs P is ψ f is the permanent magnet flux linkage, representing the magnetic flux generated by the rotor permanent magnet; u d (k - 1) and u q (k - 1) are the d-axis and q-axis voltage vector components of the previous control cycle.

[0095] In the above formula, the prediction of the d-axis current i d (k + 1) is affected by the following factors:

[0096] The d-axis voltage u d (k - 1): Provides the main driving force directly affecting the change of the d-axis current.

[0097] The coupling effect of the q-axis current and the electrical angular velocity of the rotor ω e L q i q (k): Reflects the indirect influence of the q-axis inductance, current, and electrical angular velocity on the d-axis current.

[0098] The stator resistance - Ri d (k): Reflects the consumption of current by the motor stator winding resistance.

[0099] For the q-axis current i q (k + 1), its change is affected by the following factors:

[0100] The q-axis voltage u q (k - 1): Is the main driving force of the q-axis current.

[0101] The coupling effect of the permanent magnet flux linkage and the d-axis current -ω e (L d i d (k) + ψ f ): Reflects the influence of the rotor permanent magnet flux linkage and its coupling with the d-axis current on the q-axis current.

[0102] The stator resistance - Ri q (k): Reflects the energy loss of the resistance on the q-axis current.

[0103] Through the above formula, the predicted values of the dq-axis currents i for the next control cycle can be calculated in real timed (k + 1) and i q (k + 1). These predicted values provide a basis for subsequent cost function calculation and optimal voltage vector selection.

[0104] In some embodiments, online correction can be performed on the changes of parameters in the prediction formula. For example, for the temperature drift of the stator resistance R, the operating temperature of the motor can be detected in real time and the value of R can be dynamically adjusted to improve the prediction accuracy.

[0105] In addition, at higher speeds, the change of the electrical angular velocity ω e has a greater impact on current prediction. For this reason, the reliability of the prediction results can be further improved by adding a high-precision speed estimation module.

[0106] Through the above steps, the present invention realizes the dq-axis current prediction based on the voltage vector of the previous control cycle. By introducing motor parameters and control models, this method effectively improves the real-time performance and accuracy of current prediction, providing accurate inputs for subsequent control links.

[0107] S3. Based on the reference speed of the motor, use a speed controller to generate a q-axis current reference value, and set the d-axis current reference value to zero;

[0108] In the present invention, to achieve precise speed control of the motor, it is necessary to compare the reference speed with the actual speed and generate a corresponding q-axis current reference value through a speed controller At the same time, the d-axis current reference value is set to zero to achieve decoupled control of the motor flux linkage and torque.

[0109] Generally, the speed control of the motor is achieved through closed-loop feedback. The core of the speed controller is to calculate the speed error and generate a suitable current reference value according to the error, so as to adjust the motor torque to reach the desired speed.

[0110] In this embodiment, the specific implementation manner of step S3 includes the following contents:

[0111] First, collect the current actual speed n and compare it with the set reference speed n ref to calculate the speed error Δn:

[0112] Δn = n ref - n

[0113] where n ref is the target speed and n is the actual speed.

[0114] Then, input the speed error Δn into a proportional-integral (PI) controller, and the PI controller generates a q-axis current reference value according to the speed error The calculation formula of the PI controller is as follows:

[0115]

[0116] Among them: K p is the proportionality coefficient of the PI controller, which is used to adjust the response speed of the system; K i is the integral coefficient of the PI controller, which is used to eliminate the steady-state error; ∫Δndt is the integral value of the speed error.

[0117] Specifically, the function of the PI controller is:

[0118] When the speed error is large, the proportional part K p ·Δn provides a fast response ability;

[0119] When the speed error persists, the integral part K i ·∫Δndt provides a correction ability to ensure that the speed error tends to zero.

[0120] The generated q-axis current reference value is directly used for subsequent current control and optimization calculations, and reflects the torque component required by the motor.

[0121] In this embodiment, to achieve decoupled control of the current, the d-axis current reference value is set to zero. This is because in a permanent magnet synchronous motor, the d-axis current is mainly related to the magnetic flux, and the permanent magnet already provides sufficient magnetic flux, so there is no need to supplement it additionally through the d-axis current. Therefore, setting can simplify the control and concentrate all control precision on the q-axis current.

[0122] Collection and feedback of speed:

[0123] The speed n is usually collected by a speed sensor, or obtained by differential calculation of the angle signal of a position sensor (such as a resolver or an encoder).

[0124] The accuracy of speed feedback directly affects the calculation of the q-axis current reference value. For this reason, the collected signal can be filtered to eliminate noise interference.

[0125] Setting of PI controller parameters:

[0126] K p and K i The selection of needs to be determined according to the dynamic response requirements of the motor. Generally, when the proportionality coefficient K p is larger, the system response speed is accelerated, but oscillations may be introduced; when the integral coefficient K i is larger, the steady-state error is reduced, but the system response becomes slower.

[0127] In some embodiments, K can be dynamically adjusted through an adaptive adjustment algorithm p and K i to balance rapidity and stability.

[0128] Setting of d-axis current:

[0129] The magnetic flux of the permanent magnet synchronous motor is provided by the rotor permanent magnet. Therefore, the d-axis current can be set to zero. This setting method simplifies the control model, eliminates the coupling effect between the d-axis and the q-axis at the same time, and avoids complex decoupling operations.

[0130] Steady state and dynamic response:

[0131] During steady-state operation, the speed error Δn is small, and the output of the PI controller is mainly determined by the integral part to maintain stable operation.

[0132] During dynamic operation (such as speed change or load mutation), the PI controller can respond quickly, and a large is output through the proportional part to meet the dynamic performance requirements.

[0133] In some embodiments, a feedforward control link can be added on the basis of the PI controller. By introducing a feedforward term based on the motor mathematical model, the dynamic response ability can be further improved. For example, the required q-axis current can be directly estimated according to the load change and superimposed on the output of the PI controller, so as to speed up the response speed.

[0134] In addition, in the scenario without a speed sensor, the speed n can be calculated through an estimation method based on the back electromotive force. This method uses the voltage and current relationship of the motor stator winding and combines the known motor parameters to indirectly deduce the speed.

[0135] Through the above steps, the present invention generates the q-axis current reference value based on the reference speed and the speed controller, and sets the d-axis current reference value to zero, completing the preliminary setting of the current. This process realizes the decoupling control of the current and lays a foundation for subsequent current prediction and optimization calculation.

[0136] S4. Calculate the dq-axis reference voltage according to the reference current, and convert the reference voltage into the reference voltage in the stationary coordinate system;

[0137] In the present invention, the dq-axis reference voltage and is calculated through a method based on deadbeat model prediction. Subsequently, the calculated dq-axis reference voltage is converted into the reference voltage in the stationary plane coordinate system (α-β) through the inverse Park transformation and Meanwhile, in combination with the rules of discrete space vector modulation, preselect the candidate voltage vectors, including determining the candidate sectors and candidate quadrants.

[0138] Generally, the discrete space vector modulation method can effectively improve the accuracy and efficiency of motor control. By calculating the reference voltage and preselecting the space vector, determine the candidate voltage vectors, optimize the control performance, and provide accurate input data for the subsequent cost function calculation.

[0139] In this embodiment, the specific implementation method is as follows:

[0140] In a possible implementation manner, according to the dq-axis reference current and combined with the predicted values i d (k + 1), i q (k + 1) of the dq-axis current in the current period, use the following formula to calculate the dq-axis reference voltage:

[0141] d-axis reference voltage:

[0142]

[0143] q-axis reference voltage:

[0144]

[0145] Where: L d , L q are the d-axis and q-axis inductances respectively; T s is the sampling period; R is the stator resistance of the motor; ω e is the electrical angular velocity of the rotor; ψ f is the magnetic flux of the permanent magnet.

[0146] The core of the formula is based on the deadbeat control method, and the voltage vector is calculated by compensating the error between the predicted value and the reference value of the dq-axis current.

[0147] Next, through the inverse Park coordinate transformation, convert the calculated dq-axis reference voltage into the stationary plane reference voltage and The conversion formula is as follows:

[0148] Stationary plane reference voltage:

[0149]

[0150] Where: θ e is the electrical angular position of the rotor, which is the key input quantity for the inverse Park transformation.

[0151] Through the inverse Park transformation, the reference voltage in the rotating coordinate system is mapped to the stationary coordinate system, providing input for subsequent preselection of voltage vectors.

[0152] Obtained through calculation and Determine the sector where the candidate voltage vector is located. The discrete voltage vectors are defined by the following formula:

[0153]

[0154] where: V x , V y , V z are the real voltage vectors of the two-level inverter represented in Figure 2 ; x, y, z are adjacent voltage vectors, satisfying x, y, z ∈ {0, 1,..., 7}.

[0155] The discrete voltage vectors can be divided into three categories:

[0156] When V x , V y , V z contain two zero voltage vectors, the synthesized virtual vector is a small virtual vector;

[0157] When V x , V y , V z contain one zero voltage vector, the synthesized virtual vector is a medium virtual vector;

[0158] When V x , V y , V z do not contain zero voltage vectors, the synthesized virtual vector is a large virtual vector.

[0159] According to and the magnitudes of, the candidate sectors of the reference voltage vector can be determined by the following table:

[0160]

[0161] For example, when and , the candidate sector is Figure 5 sector I in, i.e., SI, and its schematic diagram is as shown in Figure 6 .

[0162] S5. Select the sector and quadrant where the candidate voltage vector is located based on the reference voltage;

[0163] After determining the candidate sector, by comparing And the middle virtual vectors in the candidate sectors that are not in the same direction as the real vector, select the quadrant where the candidate voltage is located in the candidate sector, that is, the candidate quadrant. The voltage vectors in the candidate quadrant are the candidate voltage vectors. The method for selecting the candidate quadrant is shown in the following table:

[0164]

[0165]

[0166] For example, when the candidate sector is Sector I, when and the candidate quadrant is Figure 5 quadrant ① in, that is the candidate voltage is V 110 、V 120 、V 122 、V 222 .

[0167] Through this step, the dq-axis reference voltage is calculated according to the deadbeat model prediction method, and combined with the inverse Park transformation and the discrete space vector modulation rule, the conversion of the reference voltage and the preselection of the candidate voltage vector are completed, providing a basis for subsequent optimal control. The whole method ensures the control accuracy and improves the operation efficiency at the same time.

[0168] S6. Calculate the predicted current value under the action of the candidate voltage vector, and select the optimal voltage vector through the cost function;

[0169] In the present invention, by predicting the current for the action of each candidate voltage vector V n and combining the deviation between the reference current value and the predicted value, the cost function is calculated, so as to select the optimal voltage vector V opt . This step makes full use of the mathematical model of the permanent magnet synchronous motor and the distribution characteristics of the candidate voltage vector, not only ensuring the dynamic response performance of the motor, but also improving the steady-state performance and significantly reducing the calculation complexity.

[0170] In a possible implementation manner, according to the candidate voltage vector V n obtained in step 5, predict the next moment values of the dq-axis currents and The prediction formulas are as follows:

[0171] d-axis current prediction formula:

[0172]

[0173] q-axis current prediction formula:

[0174]

[0175] Explanation of formula parameters:

[0176] i d (k + 1), i q (k + 1) is the dq-axis current of the current prediction period; is the candidate voltage vector V n The next-period dq-axis current under the action; is the candidate voltage vector V n The components in the dq coordinate system; L d , L q is the d-axis and q-axis inductance; ω e is the rotor electrical angular velocity; ψ f is the permanent magnet flux linkage; R is the stator internal resistance; T s is the sampling period.

[0177] In the implementation process of the present invention, by substituting the dq components n of the candidate voltage vector V into the above formula, predictive calculations are carried out for each candidate voltage vector one by one to obtain the dq-axis current values at the next moment under its action

[0178] After obtaining the predicted current values under the action of each candidate voltage vector, the effect of each candidate vector is evaluated through the cost function g, and finally the voltage vector V opt .

[0179] Cost function formula:

[0180]

[0181] Explanation of formula parameters:

[0182] is the reference current value of the dq-axis; is the candidate voltage vector V n The predicted dq-axis current value under the action; g is the cost function value, used to evaluate the control effect of the candidate voltage vector.

[0183] Selection method:

[0184] Traverse all candidate voltage vectors V n , and calculate their corresponding cost function values one by one;

[0185] Compare the cost function values g of all candidate voltage vectors, and select the voltage vector V with the smallest cost function value opt .

[0186] Assume the reference current value is The predicted current value of the current period is i d (k + 1) = 0.4, iq (k + 1)=0.7. Given that the candidate voltage vectors are V 110 、V 120 、V 122 、V 222 , and their components in the dq coordinate system are

[0187] According to the formula, for each candidate voltage vector V n calculate

[0188] Use the cost function formula to calculate the cost function value g of each candidate voltage vector;

[0189] Compare the g values of all candidate voltage vectors. For example:

[0190] g V110 =0.02;

[0191] g V120 =0.015;

[0192] g V122 =0.03;

[0193] g V222 =0.025;

[0194] Select g V120 =0.015 as the minimum value, and the corresponding optimal voltage vector V opt =V 120 .

[0195] Through simulation tests on the MATLAB / Simulink platform, verify the effectiveness of the method of the present invention. The test scenarios include two working conditions: steady-state operation and dynamic speed change:

[0196] Steady-state operation:

[0197] Set the speed to 500 r / min. The results show that the three-phase current waveform of the method proposed by the present invention is smoother, and the total harmonic distortion rate (THD) of the phase current is 9.2%, which is much lower than 26.2% of the traditional model predictive control method;

[0198] The ripple of the dq-axis current is significantly reduced, and the torque fluctuation of the motor is effectively suppressed.

[0199] Dynamic change:

[0200] When the speed suddenly changes from 500 r / min to 1000 r / min, the method of the present invention completes the speed regulation within 50 ms;

[0201] The simulation results show that the method of the present invention not only inherits the excellent dynamic performance of the traditional method, but also improves the steady-state operation performance.

[0202] S7. Output the inverter switching signals corresponding to the optimal voltage vector and apply them to the inverter.

[0203] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A speed regulation method for a permanent magnet motor based on current prediction and discrete space vector optimization, characterized in that It includes the following steps: S1. Collect the three-phase current, rotor position electrical angle, and speed information of the three-phase permanent magnet synchronous motor, and convert the current to the dq coordinate system through coordinate transformation; S2. Predict the dq-axis current of the next control period based on the dq-axis voltage vector of the previous control period; S3. Based on the reference speed of the motor, use the speed controller to generate the q-axis current reference value, and set the d-axis current reference value to zero; S4. Calculate the dq-axis reference voltage according to the reference current, and convert the reference voltage to the reference voltage in the stationary coordinate system; S5. Select the sector and quadrant where the candidate voltage vector is located based on the reference voltage; S6. Calculate the predicted current value under the action of the candidate voltage vector, and select the optimal voltage vector through the cost function; S7. Output the inverter switching signal corresponding to the optimal voltage vector and apply it to the inverter.

2. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, wherein The specific steps of step S1 include the following steps: S1.

1. Collect the three-phase current signals of the three-phase permanent magnet synchronous motor; S1.

2. Obtain the rotor position electrical angle and speed signals through the motor sensor; S1.

3. Use coordinate transformation to convert the collected three-phase current from the three-phase stationary coordinate system to the dq-axis coordinate system, and output the d-axis current and q-axis current.

3. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, wherein Step S2 uses a one-beat delay compensation method to predict the dq-axis current, and the one-beat delay compensation method includes the following steps: S2.

1. Predict the dq-axis current of the next control period based on the dq-axis voltage vector of the previous control period and the motor parameters of the current period; S2.

2. Compensate the d-axis and q-axis current values of the current period using the stator resistance, inductance, electrical angular velocity, and permanent magnet flux of the motor; S2.

3. The corrected dq-axis current is further predicted through the inductance, voltage vector, and motor flux model to obtain the d-axis current and q-axis current of the next control period.

4. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, wherein The specific steps of step S3 include the following steps: S3.

1. Collect the deviation between the actual speed of the current motor and the reference speed; S3.

2. Calculate the integral value and proportional value of the speed error through the speed controller; S3.

3. Generate the q-axis current reference value according to the calculation result of the speed error; S3.

4. Set the d-axis current reference value to zero to achieve decoupling control of the motor torque and flux linkage.

5. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, characterized in that In step S4, the dq-axis reference voltage is calculated in the following way: S4.

1. Calculate the dq-axis reference voltage according to the stator resistance, d-axis inductance, and q-axis inductance of the motor, combined with the dq-axis reference current and electrical angular velocity; S4.

2. Use the inverse coordinate transformation method to convert the dq-axis reference voltage to the α-axis reference voltage and β-axis reference voltage in the stationary coordinate system.

6. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, characterized in that The selection of the candidate voltage vector in step S5 includes the following steps: S5.

1. Determine the sector where the candidate voltage vector is located according to the magnitude and direction of the α-axis and β-axis reference voltages in the stationary coordinate system; S5.

2. Determine the candidate quadrant based on the combination of the virtual voltage vector and the actual voltage vector in the sector; S5.

3. The candidate voltage vector includes a zero voltage vector, an active voltage vector, and a virtual voltage vector synthesized by two or more real voltage vectors.

7. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, characterized in that In the step S6, when calculating the predicted current value under the action of the candidate voltage vector, the following formula is used: Predicted d-axis current value: Predicted q-axis current value: wherein, and are the dq-axis components of the candidate voltage vector, T s is the sampling period, R is the internal resistance of the motor stator, L d , L q are the inductances, ω e is the electrical angular velocity, ψ f is the permanent magnet flux linkage, i d (k + 1) and i q (k + 1) are the d- and q-axis current values of the (k + 1)-th control period, respectively.

8. The permanent magnet motor speed regulation method based on current prediction and discrete space vector optimization according to claim 1, wherein The cost function in the step S6 is as follows: Among them, and are the dq-axis current reference values, and are the predicted dq-axis current values under the action of the candidate voltage vectors.

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