An optimal stator current angle correction method for salient-pole synchronous motors

Abstract

The present invention relates to a method for correcting the optimal stator current angle of a salient-pole synchronous motor. When the stator flux of the motor is changed, an improved hybrid model flux observer based on extended Kalman filtering is used to calculate the stator flux to achieve iterative correction of the optimal stator current angle calculation formula. When the rotor flux of the motor is changed, an FFRLS-based rotor flux online identification algorithm is used to calculate #imgabs0# to achieve the optimal stator current angle. The stator flux observer and the rotor excitation flux observer are used to estimate the stator flux and the rotor excitation flux, and the optimal stator current angle is brought in for iteration to achieve optimized maximum torque-to-current ratio control of the salient-pole synchronous motor under strong magnetic field saturation. The estimated flux is used to form an iterative optimization correction of the flux to achieve the purpose of correcting the optimal stator current angle, and then the optimized given current is calculated, which reduces the stator current and copper loss of the motor, alleviates the heat dissipation burden, and thus improves the cruising range of the electric aircraft.

Description

Technical Field

[0001] The invention belongs to the technical field of motor drive control and relates to an optimal stator current angle correction method for a salient-pole synchronous motor. Background Art

[0002] Compared with salient pole motors, salient pole motors are widely used in aircraft, automobile, shipbuilding and other industries due to their advantages such as high power density, high efficiency, high reliability and wide speed regulation range. Generally speaking, salient pole motors use the maximum torque per ampere (MTPA) control technology in order to use the minimum motor stator current value to achieve the given command torque. However, the MTPA control system based on the traditional motor mathematical model ignores the changes in motor parameters caused by magnetic field saturation, causing the optimal stator current angle of the motor to shift under the given command torque, thereby increasing the motor stator current value. Taking the salient pole synchronous motor for multi-electric aircraft as an example, Figure 1 As shown in the figure, during the takeoff phase (rolling and climbing phases), the rapid change in the motor's stator current causes the stator magnetic field to enter a saturated state. The nonlinearity of the magnetic circuit causes changes in the motor's orthogonal axis inductance parameters. Operating the motor at maximum stator current causes a rapid rise in motor temperature before the aircraft enters the cruise phase, resulting in changes in the rotor excitation flux. The saturation of the motor's stator-rotor magnetic field leads to changes in motor parameters, which in turn causes a shift in the optimal stator current angle in the MTPA control system. Therefore, a method for correcting the optimal stator current angle for a salient-pole synchronous motor under strong magnetic field saturation is designed to optimize the MTPA control strategy and reduce the effective value of the stator current while meeting the load torque requirement, thereby reducing copper loss.

[0003] MTPA control can be categorized into offline and online methods based on their calculation method. While offline methods are easy to implement, they lack robustness when searching for the MTPA operating point and struggle to track parameter variations and differences between motor parameters. With the increasing performance of microprocessors, online adaptive methods are gaining increasing attention to track the optimal operating point of MTPA control algorithms.

[0004] Han Zexiu et al. analyzed the stator current angle error and proposed an error compensation method, which improved the accuracy of the stator current reference angle. However, this method approximated the permanent magnet flux parameters, reducing the error compensation accuracy. (See the reference: "Improved Online Maximum-Torque-Per-Ampere Algorithm for Speed-Controlled Interior Permanent Magnet Synchronous Machine.") Separately, Atsushi Shinohara et al. proposed a maximum torque-to-current ratio control strategy based on direct torque control. This strategy calculates the given flux using the given torque and the MTPA control law. This strategy is applicable to permanent magnet synchronous motor drive systems where the given quantities are torque and stator flux. This method considers variations in dq-axis inductance, eliminating the need for table lookup and improving control accuracy solely through iteration. However, it does not account for variations in permanent magnet flux caused by wide temperature variations. (See the reference: "Direct Calculation Method of Reference Flux Linkage for Maximum Torque per Ampere Control in DTC-Based IPMSM Drives.")

[0005] Patent application number CN202110619796.9 discloses a sensorless parameter error compensation strategy for an internal permanent magnet synchronous motor. By obtaining the deviation coefficients μ and λ, the permanent magnet flux and quadrature-axis inductance parameters are corrected in real time online. These corrected parameters are used to calculate the stator flux setpoint for the motor's maximum torque-to-current ratio control, estimate the MT-axis current, and calculate the load angle. This method improves rotor position estimation accuracy and enhances system robustness, but the lack of online correction of the d-axis inductance reduces control accuracy and performance to a certain extent. Patent application number CN202011312450.6 discloses a permanent magnet synchronous motor control method using torque and stator flux estimators. To address the low observation accuracy of permanent magnet synchronous motors at low speeds, a stator flux observer based on a current model is used to achieve online identification of flux and speed. Although the high-frequency current signal injection method does not rely on motor parameters, it generates speed ripple, which in turn results in additional losses. Furthermore, the injection signal frequency is limited by the controller sampling frequency, which limits the speed regulation range. Patent application number CN201410121053.9 discloses a control method for a salient-pole synchronous motor. The control process is insensitive to parameters, there is no curve fitting, and the robustness is effectively improved compared to the MTPA method. However, the reluctance torque is not fully utilized, and the control performance needs to be further improved. Summary of the Invention

[0006] Technical problems to be solved

[0007] In order to avoid the shortcomings of the existing technology, the present invention proposes a method for correcting the optimal stator current angle of a salient-pole synchronous motor. The technical problem to be solved is: when a multi-electric / all-electric aircraft is taking off and is under strong magnetic field saturation, the motor parameters change, causing the optimal stator current angle of the motor to shift.

[0008] Technical Solution

[0009] A method for correcting the optimal stator current angle of a salient pole synchronous motor is characterized in that: when considering the stator-rotor magnetic field saturation, the optimal stator current angle α is calculated by the stator flux dq axis component ψ d , ψ q 、Rotor excitation flux ψ f Iterative calculation.

[0010] Optimized maximum torque current ratio (MTPA) control of salient pole synchronous motor based on the output current reference value of the speed loop proportional integral (PI) controller and the stator current angle α to calculate the dq axis component reference value of the stator current α represents the stator current vector The angle between the positive half axis and the q axis ranges from (-π / 2 to π / 2).

[0011] The specific steps are as follows:

[0012] Step 1. Get the initial value of the stator current angle α0: Before the stator-rotor magnetic field of the motor enters saturation, if the stator current i s Given, the optimal stator current vector angle is given by the nominal motor excitation flux ψ f , d-axis inductance L d0 and q-axis inductance L q0 The calculation result is as follows:

[0013]

[0014] Where, L d0 is the direct-axis inductance before entering magnetic field saturation; L q0 is the quadrature-axis inductance before magnetic field saturation; ψ f is the rotor excitation flux;

[0015] Step 2: When considering the change of the motor stator flux, the improved hybrid model flux observer based on the extended Kalman filter (EKF) is used to calculate the stator flux dq axis components. At the same time, when considering the change of the motor rotor flux, the rotor excitation flux is calculated using the rotor flux online identification algorithm based on FFRLS.

[0016] Step 3: Based on the initial value of the stator current angle α0 obtained in step 1, the two observers in step 2 simultaneously calculate the updated rotor excitation flux and the stator flux dq axis component Then calculate the stator according to the following iterative formula

[0017] Current angle α:

[0018]

[0019] Where, α k represents the optimal stator current angle at the current moment, α k-1 Indicates the optimal stator current angle at the previous moment; represents the estimated rotor excitation flux at the current moment, It represents the estimated rotor excitation flux at the previous moment; Indicates the given stator current at the last moment;

[0020]

[0021] They represent the estimated stator direct-axis flux and quadrature-axis flux at the previous moment respectively; Calculated using the saturated inductance model.

[0022] The dq axis current obtained according to the stator current vector angle is:

[0023]

[0024] Where, Indicates the direct axis given current at the current moment, Indicates the quadrature-axis given current at the current moment.

[0025] When the stator current of the motor changes rapidly and the stator magnetic field enters saturation, the stator flux observer is used to complete the online estimation of the stator flux: the stator flux dq axis component It is obtained by the improved hybrid model flux observer based on the extended Kalman filter EKF. The input signal of the observer is the rotor position θ obtained by the position sensor. e and reference command voltage

[0026] First, in the current model, the EKF algorithm is used to obtain the direct-axis and quadrature-axis inductances. The online identification process of the motor direct-axis inductance and quadrature-axis inductance parameters based on EKF is as follows:

[0027] In each running step, ignoring the change in motor speed, the electrical time constant of the motor is much smaller than the mechanical time constant of the motor, so the differential term of the dq-axis inductance in the system state variable is approximately zero. Then, with the dq-axis inductance as the parameter to be identified and the dq-axis current as the state variable, the time domain state equation of the IPMSM is:

[0028]

[0029] Where, ω r is the motor electrical angular velocity;

[0030] Selecting the AC and DC axis currents as the output of the identification system, the output equation of the IPMSM nonlinear mathematical model is:

[0031]

[0032] The first-order forward Euler method is used to discretize the state equation in the time domain, and the state equation of IPMSM in the discrete domain is obtained as follows:

[0033]

[0034] Where, T s is the sampling period or running step of the EKF algorithm;

[0035] The output equation is:

[0036]

[0037] Then, according to the input u(k-1) and the last state estimate To predict the state vector at time k, then:

[0038]

[0039] In the formula, “^” represents the estimated value of the state and “~” represents the predicted value of the state:

[0040]

[0041]

[0042] At this time, the output corresponding to the predicted state quantity is

[0043]

[0044] Where,

[0045] Depend on have to

[0046]

[0047] Where,

[0048] Therefore, the error covariance matrix is:

[0049]

[0050] Where, Q = E{VV T}, V represents the system noise, and E{} represents the digital expected value;

[0051] According to the above error covariance matrix The EKF algorithm gain matrix is ​​calculated as:

[0052]

[0053] Where, R=E{WW T}, W represents the measurement noise, and E{} represents the digital expected value;

[0054] The Kalman gain matrix K(k) and the predicted output vector and the actual output vector y(k), the predicted state vector Perform feedback correction to obtain the estimated state vector Right now:

[0055]

[0056] Finally, the next state estimation is performed and the error covariance matrix is ​​estimated in advance, that is:

[0057]

[0058] Complete the online identification of the motor's direct-axis inductance and quadrature-axis inductance based on the EKF. In this process, the system noise covariance Q and the measurement noise covariance matrix R take fixed values, and the initial value P0 of the error covariance matrix takes a diagonal matrix.

[0059] After obtaining the orthogonal-axis inductance through online identification, the current model can be calibrated. The calculation formula for the calibrated current model is as follows:

[0060]

[0061] In the formula, ^ represents the estimated value; k represents the kth moment;

[0062] Secondly, a nonlinear low-pass filter is used to replace the ideal integration link to complete the voltage model correction; the mathematical form of the nonlinear low-pass filter is as follows:

[0063]

[0064] Where, ωc is the cutoff frequency of the low-pass filter; ω r is the stator voltage / current angular frequency, which is equal to the motor electrical angular velocity when the motor is in steady-state operation; s is the Laplace operator; They are the α-β axis components of the back electromotive force respectively; are the α-β axis components of the stator flux respectively;

[0065] Finally, the corrected current model and improved voltage model are used as the input and feedback of the Gopinath observer, respectively. The mathematical form of the Gopinath observer is as follows:

[0066]

[0067] In the Gopinath observer, ξ represents the damping of the flux observation system, which ranges from 0 to 1; ω0 represents the bandwidth of the flux observation system, which ranges from 0 to 1000. The output of the Gopinath regulator is Δu αβ , and back EMF Add and integrate to get the actual output flux of the improved hybrid model stator flux observer based on EKF Then, the dq axis components of the stator flux are obtained through rotation transformation.

[0068] When the motor rotor excitation flux changes, a rotor excitation flux observer needs to be designed to complete the online estimation of the rotor excitation flux; It is estimated by the recursive least squares rotor flux online identification algorithm with forgetting factor. The input signal of this module is the dq axis current i obtained by current sensor acquisition and calculation. d ,i q and reference command voltage

[0069] The voltage equation of the salient polarity motor under steady-state operation can be written as follows:

[0070]

[0071] The backward difference method is used to discretize the above formula to obtain the discretized mathematical model:

[0072]

[0073] Where, subscript k or superscript k represents the kth moment;

[0074] make The stator resistance and rotor excitation flux are identified according to the following recursive iterative formula of FFRLS:

[0075]

[0076] Where λ represents the forgetting factor, which is between 0 and 1; the subscript k-1 or superscript k-1 represents the k-1th moment.

[0077] The stator flux observer and rotor excitation flux observer are used to estimate the stator flux and rotor excitation flux respectively, and the optimal stator current angle is brought into the iterative formula to calculate the corrected optimal stator current angle, thereby realizing the optimized maximum torque-to-current ratio control of the salient polarity synchronous motor under strong magnetic field saturation.

[0078] Beneficial effects

[0079] The present invention proposes an optimal stator current angle correction method for a salient-pole synchronous motor.

[0080] When considering the stator flux variation of the motor, an improved hybrid model flux observer based on the extended Kalman filter (EKF) is used to calculate the stator flux dq axis components. This is to achieve iterative correction of the optimal stator current angle calculation formula in step 1.

[0081] When considering the change of motor rotor flux, the rotor flux online identification algorithm based on FFRLS is used to calculate the rotor flux. This is to achieve further optimization and correction of the optimal stator current angle calculation formula in step 1.

[0082] When the stator current of the motor changes rapidly and causes the stator magnetic field to enter saturation, a stator flux observer needs to be designed to complete the online estimation of the stator flux.

[0083] When the motor rotor excitation flux changes, a rotor excitation flux observer needs to be designed to complete the online estimation of the rotor excitation flux.

[0084] The stator flux observer and rotor excitation flux observer are used to estimate the stator flux and rotor excitation flux respectively, and the optimal stator current angle iterative formula given in claim 1 is brought into play to calculate the corrected optimal stator current angle, thereby realizing the optimized maximum torque-to-current ratio control of the salient polarity synchronous motor under strong magnetic field saturation.

[0085] This paper proposes a method for correcting the optimal stator current angle for a salient-pole synchronous motor. This method addresses the problem of shifting the optimal stator current angle due to changes in motor parameters under strong magnetic field saturation. By separately estimating the stator flux and rotor excitation flux under strong magnetic field saturation, the estimated fluxes are used to form an iterative optimization correction to achieve the optimal stator current angle. This method then calculates the optimized given current, reducing the motor's stator current and copper loss, alleviating the heat dissipation burden, and ultimately increasing the range of electric aircraft.

[0086] Compared with the prior art, the method has the following advantages:

[0087] (1) There is no need to inject current / voltage signals, which avoids the problem of speed regulation range being limited due to the limitation of injection signal frequency. At the same time, it also avoids the problems of fluctuation and loss caused by the injection of current / voltage signals.

[0088] (2) By simultaneously estimating the stator-rotor flux linkage and adopting the flux linkage iterative optimization correction method to correct the optimal stator current angle, not only the convergence of the algorithm is guaranteed, but also the accuracy of the optimal stator current angle is improved, thereby reducing the effective value of the stator current of the motor under MTPA control. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 : Complete flight conditions of electric aircraft

[0090] Figure 2 :Block diagram of optimized vector control based on stator-rotor flux estimation

[0091] Figure 3 :An improved hybrid model flux observer based on EKF

[0092] Figure 4 :Nonlinear low-pass filter principle block diagram

[0093] Figure 5 :Stator flux estimation waveform based on improved hybrid model of EKF

[0094] Figure 6 : Stator resistance-permanent magnet flux estimation waveform based on FFRLS, where They are the stator resistance reference value, the rotor excitation flux reference value, R s , ψ f They are the estimated value of stator resistance and the estimated value of rotor excitation flux.

[0095] (a) Stator resistance and estimated error waveform (b) Permanent magnet flux and estimated error waveform

[0096] Figure 7 : Motor stator phase current simulation waveform,

[0097] (a) Before optimization (steady-state amplification) (b) After optimization (steady-state amplification)

[0098] Figure 8 : Experimental waveform of phase A stator current under low speed and light load

[0099] (a) Typical MTPA control (before optimization)

[0100] (b) Optimized vector control based on stator-rotor flux estimation (after optimization)

[0101] Figure 9:A-phase stator current experimental waveform under high speed and heavy load

[0102] (a) Typical MTPA control (before optimization)

[0103] (b) Optimized vector control based on stator-rotor flux estimation (after optimization) DETAILED DESCRIPTION

[0104] The present invention will now be further described with reference to the embodiments and accompanying drawings:

[0105] In the present invention, the optimized vector control block diagram based on stator-rotor flux estimation is as follows: Figure 2 The specific implementation is as follows:

[0106] (1) Mathematical model of salient-pole synchronous motor

[0107] When considering the magnetic field saturation effect, the motor stator voltage equation is:

[0108]

[0109] Where u d 、u q Respectively represent the stator voltage d-axis component and stator voltage q-axis component; R s is the stator phase resistance; i d 、i q Respectively represent the stator current d-axis component and the stator current q-axis component; ω r represents the rotor electrical angular velocity; ψ f is the rotor excitation flux; L d (i d ,i q ), L q (i d ,i q ) represent the direct-axis inductance and quadrature-axis inductance when the magnetic field is saturated.

[0110] In steady state, the voltage equation can be expressed as

[0111]

[0112] The torque equation can be obtained from the flux equation as follows:

[0113]

[0114] Where, ψ f i q It is called the excitation torque, (L d (i d ,i q )-L q (i d ,i q ))id i q It is called reluctance torque, p n Indicates the number of pole pairs of the motor.

[0115] (2) Stator flux estimation

[0116] During takeoff, the main motor's stator current rapidly changes, causing the stator magnetic field to saturate. This causes changes in the stator's dq-axis inductance, shifting the optimal stator current vector angle. To correct the optimal stator current angle and thereby reduce the effective value of the stator current, a stator flux observer is designed that accounts for stator magnetic field saturation. When considering magnetic field saturation, the mathematical model of a salient-pole motor is nonlinear, so the stator flux observer must be adaptable to state estimation for nonlinear systems. Because the Extended Kalman Filter (EKF) can be used for state estimation of nonlinear systems and considers the effects of both system and measurement noise on state estimation, the design of an improved hybrid model flux observer based on the EKF is used as an example.

[0117] The present invention designs an improved hybrid model stator flux observer based on EKF. Figure 3 First, in the current model, the EKF algorithm is used to identify the orthogonal axis inductance online and complete the current model correction. The calculation formula of the corrected current model is as follows:

[0118]

[0119] Where ^ represents the estimated value; k represents the kth moment.

[0120] Secondly, the traditional voltage model usually uses the estimated flux of the ideal integration link, which has the advantages of small dependence on motor parameters. However, in practice, due to the initial value of the integration and sampling errors, the ideal integration link is still prone to DC bias problems. Therefore, in order to avoid this problem from the source, the following is used: Figure 4 The nonlinear low-pass filter shown replaces the ideal integrator to correct the voltage model. This nonlinear low-pass filter not only solves the DC bias problem caused by the ideal integrator, but also adapts to the estimation of flux linkage over a wide speed range. Figure 4 In, ω c is the low-pass filter cutoff frequency, ω r is the stator voltage / current angular frequency.

[0121] Finally, the corrected current model and the improved voltage model are used as Gopinath observers ( Figure 3 ) input and feedback, through which the stator flux is adjusted, and then the stator flux is estimated online. The identification results are as follows Figure 5 shown.

[0122] In the Gopinath observer, ξ represents the damping of the flux observation system, which ranges from 0 to 1; ω0 represents the bandwidth of the flux observation system, which ranges from 0 to 1000. The output of the Gopinath regulator is Δu αβ , and back EMF Add and integrate to get the actual output flux of the improved hybrid model stator flux observer based on EKF Then, the dq axis components of the stator flux are obtained through rotation transformation.

[0123] (3) Rotor flux estimation

[0124] During takeoff, the speed of a multi-electric / all-electric aircraft changes rapidly, causing the rotor flux to enter a saturated state. The rotor excitation flux ψ f Changes cause the optimal stator current angle to change. In order to further correct the optimal stator current angle and thereby reduce the effective value of the stator current, it is necessary to design a rotor flux observer that takes into account the rotor magnetic field saturation effect. Since the main motor speed changes rapidly during takeoff, the rotor flux observer needs to adapt to the system state estimation with rapidly changing speeds. Since the Forgotten Factor Recursive Least Squares (FFRLS) algorithm with forgetting factor does not require the computer to store a large amount of data when online identifying motor parameters, it is easy to implement high-speed online parameter identification of the motor, and the algorithm is relatively simple. Therefore, here we only take the FFRLS algorithm online identification of rotor flux as an example to illustrate.

[0125] Using the steady-state voltage equation to identify the flux avoids the problem of phase shift. The voltage equation for the salient polarity motor in steady-state operation can be written as follows:

[0126]

[0127] The discretized mathematical model can be obtained by using the backward difference method to discretize the above formula.

[0128]

[0129] Wherein, subscript k or superscript k represents the kth moment.

[0130] make The stator resistance and rotor excitation flux are identified according to the following recursive iterative formula of FFRLS. The identification results are as follows: Figure 6 shown.

[0131]

[0132] Where λ represents the forgetting factor, which is between 0 and 1; the subscript k-1 or superscript k-1 represents the k-1th moment.

[0133] (4) Maximum torque current ratio control principle

[0134] Define the stator current vector angle α as the stator current vector The angle between it and the positive half axis of the q axis is in the range of -π / 2≤α≤π / 2.

[0135] In the dq synchronous rotating reference frame, when the stator current vector amplitude i s When is a constant, if the angle α between the stator current vector and the positive half axis of the q axis is known, the dq axis current calculation formula is:

[0136]

[0137] Substitute the torque equation considering the magnetic field saturation effect into it, then

[0138]

[0139] In the formula, if i s As a given value, the torque is a function of the angle α. At this time, the maximum torque current ratio control problem can be equivalent to finding an optimal stator current angle α to maximize the output torque when the output stator current amplitude remains unchanged, that is,

[0140]

[0141] Among them, i smax The maximum stator current allowed for main motor operation.

[0142] In order to solve this problem, let the derivative of the electromagnetic torque with respect to the stator current angle be equal to zero, that is,

[0143]

[0144] Among them, Δ s The expression is as follows.

[0145]

[0146] In order to observe the changing law of the optimal stator current angle, the above formula is transformed to obtain:

[0147]

[0148] Where, L Δ (i d ,i q )=L d (i d ,iq )-L q (i d ,i q ), the first part on the right side of the equation is the deviation of the optimal stator current angle caused by the change of the rotor excitation flux, and the second part is the deviation of the optimal stator current angle caused by the change of the direct-axis inductance and the quadrature-axis inductance.

[0149] Estimation of stator flux dq-axis components using an improved hybrid model based on EKF And the rotor excitation flux estimated online by FFRLS algorithm The direct-axis inductance and quadrature-axis inductance in the dq coordinate system are:

[0150]

[0151] In the formula, when the direct axis current i d =-i s When sinα≈0, take Among them L d0 is the direct-axis inductance value when the motor magnetic field is not saturated; when the quadrature-axis current i q =i s When cosα≈0, Among them L q0 is the quadrature-axis inductance when the motor magnetic field is not saturated.

[0152] Then, the optimal stator current vector angle under strong magnetic field saturation is obtained as

[0153]

[0154] If the stator current amplitude is known According to the optimal stator current angle, the dq axis current is obtained as follows:

[0155]

[0156] Where, Indicates the direct axis given current at the current moment, Indicates the quadrature-axis given current at the current moment.

[0157] The dq axis currents obtained by the above process are used as givens to implement optimized vector control based on stator-rotor flux estimation. Under low-speed and light-load conditions, the given speed command is 1000r / min, and the load torque is 2.4N·m; when it switches to high-speed and heavy-load conditions at 0.3s, the given speed command is 4000r / min, and the load torque is 35.6N·m. The rotor excitation flux is changed from 0.0336Wb to 0.0302Wb in 0.8s. Based on the MATLAB / Simulink platform, an optimized vector control strategy based on stator-rotor flux is established in the vector control simulation, and the optimal stator current angle of the typical MTPA control is corrected, and then the given dq axis current is calculated to obtain the optimized vector control based on stator-rotor flux estimation. The simulation waveforms of the three-phase current of the motor stator before and after optimization are as follows. Figure 7 As shown in the figure. In the low-speed, light-load steady-state phase, the effective value of the A-phase stator current before optimization was 8.205A, and after optimization, it was 8.169A. The phase current after compensation was 0.036A lower than the phase current before compensation, and the motor copper loss was reduced by 0.006W. In the high-speed, heavy-load steady-state phase, the effective value of the A-phase current before compensation was 107.6A, and after compensation, it was 107.1A. The phase current after compensation was 0.5A lower than the phase current before compensation, and the motor copper loss was reduced by 1.530W.

[0158] In addition, based on the construction of IPMSM drive control experimental test platform, the propeller load is simulated and the power side DC bus voltage U dc =270V, under low speed and light load conditions and high speed and heavy load conditions, when the speed and current enter the steady state, the stator current i of the motor phase A under the typical MTPA control and the optimized vector control based on stator-rotor flux estimation is observed using an oscilloscope. a like Figure 8 、 Figure 9 shown.

[0159] At low speed and light load, after the motor stator current enters the steady state, the typical MTPA controls the motor A phase stator current i a =11.85A, optimized vector control motor A phase stator current i based on stator-rotor flux estimation a =10.79A. Compared with the typical MTPA control, the optimized vector control based on stator-rotor flux estimation reduces the motor phase A stator current by 1.06A and the motor copper loss by 0.248W. Under high speed and heavy load, after the motor stator current enters the steady state, the typical MTPA control motor phase A stator current i a =123.11A, optimized vector control motor A phase stator current i based on stator-rotor flux estimation a=118.53 A. Compared with the typical MTPA control, the optimized vector control based on stator-rotor flux estimation reduces the stator current of motor phase A by 4.58 A and the motor copper loss by 13.28 W.

[0160] From the simulation and experimental results under low-speed and light-load and high-speed and heavy-load working conditions, it can be seen that the optimized vector control designed in the present invention reduces the effective value of the motor phase current, thereby reducing the copper loss of the motor and saving energy.

[0161] In addition, the present invention only takes the improved hybrid model stator flux observer based on EKF and the rotor flux observer based on FFRLS as examples. Other stator flux estimation and rotor flux estimation methods are also within the scope of protection of this patent.

Claims

1. A method for optimizing the stator current angle of a salient-pole synchronous motor, characterized by: When considering the stator-rotor magnetic field saturation, the optimal stator current angle α is determined by the stator flux dq axis component ψ d , ψ q 、Rotor excitation flux ψ f Iterative calculation; the specific steps are as follows: Step 1. Get the initial value of the stator current angle α0: Before the stator-rotor magnetic field of the motor enters saturation, if the stator current i s Given, the optimal stator current vector angle is given by the nominal motor excitation flux ψ f , d-axis inductance L d0 and q-axis inductance L q0 The calculation result is as follows: Where, L d0 is the direct-axis inductance before entering magnetic field saturation; L q0 is the quadrature-axis inductance before magnetic field saturation; ψ f is the rotor excitation flux; Step 2: When considering the change of the motor stator flux, the stator flux dq axis component is calculated using the flux observer based on the extended Kalman filter EKF At the same time, when considering the change of the motor rotor flux, the rotor excitation flux is calculated using the rotor flux online identification algorithm based on FFRLS. Step 3: Based on the initial value of the stator current angle α0 obtained in step 1, the two observers in step 2 simultaneously calculate the updated rotor excitation flux and the stator flux dq axis component The stator current angle α is then calculated according to the following iterative formula: Where, α k represents the optimal stator current angle at the current moment, α k-1 Indicates the optimal stator current angle at the previous moment; represents the estimated rotor excitation flux at the current moment, It represents the estimated rotor excitation flux at the previous moment; Indicates the given stator current at the last moment; They represent the estimated stator direct-axis flux and quadrature-axis flux at the previous moment respectively; Calculated using the saturated inductance model.

2. The optimal stator current angle correction method for a salient-pole synchronous motor according to claim 1, characterized in that: Optimized maximum torque current ratio (MTPA) control of salient pole synchronous motor based on the output current reference value of the speed loop proportional integral (PI) controller and the stator current angle α to calculate the dq axis component reference value of the stator current α represents the stator current vector The angle between the positive half axis and the q axis ranges from (-π / 2 to π / 2).

3. The method for optimizing the stator current angle of a salient-pole synchronous motor according to claim 2, wherein: According to the stator current vector angle, the dq axis current is obtained as: Where, Indicates the direct axis given current at the current moment, Indicates the quadrature-axis given current at the current moment.

4. The method for optimizing the stator current angle of a salient-pole synchronous motor according to claim 1, wherein: When the stator current of the motor changes rapidly and the stator magnetic field enters saturation, the stator flux observer is used to complete the online estimation of the stator flux: the stator flux dq axis component It is obtained by the flux observer based on the extended Kalman filter EKF. The input signal of the observer is the rotor position θ obtained by the position sensor. e and reference command voltage First, in the current model, the EKF algorithm is used to obtain the direct-axis and quadrature-axis inductances. The online identification process of the motor direct-axis inductance and quadrature-axis inductance parameters based on EKF is as follows: In each running step, ignoring the change in motor speed, the electrical time constant of the motor is much smaller than the mechanical time constant of the motor, so the differential term of the dq-axis inductance in the system state variable is approximately zero. Then, with the dq-axis inductance as the parameter to be identified and the dq-axis current as the state variable, the time domain state equation of the IPMSM is: Where, ω r is the motor electrical angular velocity; Selecting the AC and DC axis currents as the output of the identification system, the output equation of the IPMSM nonlinear mathematical model is: The first-order forward Euler method is used to discretize the state equation in the time domain, and the state equation of IPMSM in the discrete domain is obtained as follows: Where, T s is the sampling period or running step of the EKF algorithm; The output equation is: Then, according to the input u(k-1) and the last state estimate To predict the state vector at time k, then: In the formula, "^" represents the estimated value of the state, and "~" represents the predicted value of the state: At this time, the output corresponding to the predicted state quantity is Where, Depend on have to Where, Therefore, the error covariance matrix is: Where, Q = E{VV T }, V represents the system noise, and E{} represents the digital expected value; According to the above error covariance matrix The EKF algorithm gain matrix is ​​calculated as: Where, R=E{WW T }, W represents the measurement noise, and E{} represents the digital expected value; The Kalman gain matrix K(k) and the predicted output vector and the actual output vector y(k), the predicted state vector Perform feedback correction to obtain the estimated state vector Right now: Finally, the next state estimation is performed and the error covariance matrix is ​​estimated in advance, that is: Completed online identification of motor direct-axis inductance and quadrature-axis inductance based on EKF; In this process, the system noise covariance Q and the measurement noise covariance matrix R take fixed values, and the initial value of the error covariance matrix P0 takes a diagonal matrix; After obtaining the orthogonal-axis inductance through online identification, the current model can be calibrated. The calculation formula for the calibrated current model is as follows: In the formula, ^ represents the estimated value; k represents the kth moment; Secondly, a nonlinear low-pass filter is used to replace the ideal integration link to complete the correction of the voltage model; The mathematical form of a nonlinear low-pass filter is as follows: Where, ω c is the cutoff frequency of the low-pass filter; ω r is the stator voltage / current angular frequency, which is equal to the motor electrical angular velocity when the motor is in steady state operation; s is the Laplace operator; They are the α-β axis components of the back electromotive force respectively; are the α-β axis components of the stator flux respectively; Finally, the corrected current model and the corrected voltage model are used as the input and feedback of the Gopinath observer, respectively. The mathematical form of the Gopinath observer is as follows: In the Gopinath observer, ξ represents the damping of the flux observation system, which ranges from 0 to 1; ω0 represents the bandwidth of the flux observation system, which ranges from 0 to 1000. The output of the Gopinath regulator is Δu αβ , and back EMF Add and integrate to get the actual output flux of the flux observer based on EKF Then, the dq axis components of the stator flux are obtained through rotation transformation.

5. The method for optimizing the stator current angle of a salient-pole synchronous motor according to claim 1, wherein: When the motor rotor excitation flux changes, a rotor excitation flux observer needs to be designed to complete the online estimation of the rotor excitation flux; It is estimated by the recursive least squares rotor flux online identification algorithm with forgetting factor. The input signal of this module is the dq axis current i obtained by current sensor acquisition and calculation. d ,i q and reference command voltage The voltage equation of the salient polarity motor under steady-state operation can be written as follows: The backward difference method is used to discretize the above formula to obtain the discretized mathematical model: Where, subscript k or superscript k represents the kth moment; make The stator resistance and rotor excitation flux are identified according to the following recursive iterative formula of FFRLS: Where λ represents the forgetting factor, which is between 0 and 1; the subscript k-1 or superscript k-1 represents the k-1th moment.

6. The method for optimizing the stator current angle of a salient-pole synchronous motor according to claim 1, wherein: The stator flux observer and rotor excitation flux observer are used to estimate the stator flux and rotor excitation flux respectively, and the optimal stator current angle is brought into the iterative formula to calculate the corrected optimal stator current angle, thereby realizing the optimized maximum torque-to-current ratio control of the salient polarity synchronous motor under strong magnetic field saturation.

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