A method for optimizing MTPA control of a synchronous reluctance motor by using an online extremum seeking method

Abstract

The application discloses a synchronous reluctance motor MTPA control method based on an optimized online extremum search method and relates to the field of motor control. The method comprises the following steps: applying the extremum search method to the maximum torque / current ratio (MTPA) of a synchronous reluctance motor (SynRM); under a steady-state condition, constructing a lookup table by using an optimized current vector angle obtained by online extremum search; under a subsequent similar working condition, directly calling data in the lookup table to avoid a repeated search process and improve control efficiency; when the motor operation does not reach a steady state, performing interpolation calculation by using the existing data in the lookup table to estimate the MTPA current vector angle under the current working condition, thereby extending the MTPA control strategy to a non-steady-state condition. The method aims at the challenges in the traditional MTPA control strategy due to the nonlinearity of parameters and the dependence on a steady-state condition. By combining the online extremum search method and the real-time lookup table construction, the method improves the dynamic response speed, extends the application to a non-steady-state condition, and significantly improves the efficiency and performance of the SynRM.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor control, in particular to a synchronous reluctance motor MTPA control method based on optimized online extremum search method. BACKGROUND

[0002] In engineering practice, in order to realize high-precision MTPA control, the common method is to calibrate the motor used, and measure the torque output under different dq-axis current distribution. However, the traditional calibration method has many problems and limitations, including time-consuming, cumbersome and insufficient parameter universality. In order to obtain accurate and detailed parameters, it is necessary to investigate the output of the motor under different bus voltages and in the full speed range, and in addition, the influence of motor temperature on motor performance and measurement accuracy needs to be considered, which requires a lot of repetitive work, time-consuming and cumbersome. The introduction of online search algorithm can dynamically adjust and optimize the motor control parameters according to the current working state and environmental conditions of the motor, thereby improving the accuracy and stability of motor control and better meeting the control requirements under different working conditions. It is of great significance to introduce online search algorithm into motor control system, which can further improve the performance and efficiency of motor control. SUMMARY

[0003] The purpose of the present application is to provide a synchronous reluctance motor MTPA control method based on optimized online extremum search method, which can make the system run efficiently under complex working conditions, save cost and improve overall operation efficiency.

[0004] To achieve the above purpose, the present application provides the following scheme:

[0005] A synchronous reluctance motor MTPA control method based on optimized online extremum search method, characterized in that it comprises:

[0006] The online extremum search method is used to realize MTPA control of the synchronous reluctance motor, and a steady state detection (SSD) module is established: whether the drive is in transient or steady state mode is determined according to the change rate of torque and speed; an enable signal is generated by the SSD module to enable or disable the extremum search method.

[0007] In the steady state process: the search step of the online extremum search method is optimized by the simplified gradient descent method, that is, a larger step size is used when deviating from the MTPA working point far away to improve the search speed, and a smaller search step size is used when approaching the MTPA working point until the current vector angle converges, and the stable state convergent current angle is recorded in the online search result lookup table.

[0008] In the transition stage: make full use of the optimization value obtained in the past search stage to reduce unnecessary calculation and adjustment of system operating point, improve the response speed and stability of the system, and interpolate the existing search MTPA data near the current value to give the estimated reference current vector angle for the data not in the storage area.

[0009] Step one: determine whether the drive is in transient or steady state mode according to the change rate of torque and speed to establish a steady state detection (SSD) module.

[0010] Step two: use the simplified gradient descent method to optimize the search step of the online extreme value search method.

[0011] Step three: build an online search result lookup table to record the converged current angle in the steady state.

[0012] Step four: add a pre-judgment module, if the optimization value corresponding to the working condition has been searched and stored, it can be directly called to skip this search stage and avoid repeated search stages.

[0013] Step five: when the motor is not in the steady state in the intermediate transition stage, interpolate the existing search MTPA data near the current value to give the estimated reference current vector angle for the data not in the storage area.

[0014] Optionally, a steady state detection (SSD) module is established to determine whether the system is in a steady state.

[0015] Optionally, the search step of the online extreme value search method is optimized by using the simplified gradient descent method.

[0016] Optionally, an online search result lookup table is constructed to record the converged current angle in the steady state. According to the specific embodiments provided by the present application, the following technical effects are disclosed.

[0017] Optionally, a pre-judgment module is added, if the optimization value corresponding to the working condition has been searched and stored, it can be directly called to skip this search stage and avoid repeated search stages.

[0018] The application discloses a synchronous reluctance motor MTPA control method of an optimized online extremum search method. The method comprises the following steps: determining whether the motor is in a steady state or a quasi-steady state mode according to the variation of torque and rotating speed through a steady state detection (SSD) module. The module determines the activation or deactivation of the MTPA online search module, and ensures that the search process is only carried out when the motor is in a steady state; in view of the problem that the traditional online extremum search method has high steady state precision but slow search speed, a variable search step is adopted to optimize the search speed. By timely adjusting the search step, a larger step can be adopted to quickly approach when the MTPA working point deviates greatly, and a smaller step can be adopted to accurately search when the MTPA working point is close, so that oscillation is avoided; the high-precision MTPA control result obtained by the online extremum search method in the steady state is used to establish a searching value lookup table in real time, so that the repeated search stage is omitted, and the control strategy is extended to a non-steady state condition. In this way, when the motor encounters the same working condition in the subsequent operation process, the value in the lookup table can be directly called, so that the repeated search process is avoided, and the control efficiency is improved; when the motor is not in a steady state, the existing optimized value is used to approach the real MTPA reference value in the dynamic process through an interpolation algorithm, so that the deviation between the working point and the actual MTPA working point after reaching the steady state condition is reduced. The optimized online extremum search method makes the MTPA control of the synchronous reluctance motor more accurate, faster in response, and capable of adapting to a wider range of operating conditions. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0020] Figure 1 The figure is a schematic diagram of the MTPA control process of the synchronous reluctance motor of the optimized online extremum search method of the present application.

[0021] Figure 2 The figure is a block diagram of the extremum search method and a steady state discrimination block diagram.

[0022] Among them, (a) is a steady state discrimination module flow chart; (b) is a block diagram of the extremum search method;

[0023] Figure 3 The figure is a schematic diagram of the simplified gradient descent method under the constant torque load of the motor in the embodiment and a search result storage module flow chart.

[0024] Among them, (a) is a schematic diagram of the simplified gradient descent method under the constant torque load; (b) is a search result storage module flow chart.

[0025] Figure 4 Flow chart of interpolation module of synchronous reluctance motor control system of the application.

[0026] Figure 5 Flow chart of pre-judgment module of synchronous reluctance motor control system of the application.

[0027] Figure 6 Test of writing data module of online establishment of lookup table of the application.

[0028] Wherein (a) is the search process current simulation waveform; (b) is the search process current vector angle simulation waveform; (c) is the search value convergence judgment logic value; (d) is the storage area data;

[0029] Figure 7 Test of reading data and interpolation output module of online establishment of lookup table of the application.

[0030] Wherein (a) is the motor torque simulation waveform; (b) is the stator current simulation waveform; (c) is the motor speed simulation waveform; (d) is the current vector angle output value; DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the application will be apparently and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0032] The application aims to provide a synchronous reluctance motor MTPA control method of an optimized online extreme value search method, which can significantly reduce the stator current of the motor, improve energy efficiency, and has good dynamic performance and steady-state accuracy.

[0033] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the application will be further described in detail below with reference to the drawings and specific embodiments.

[0034] As shown in Figure 1 The application provides a synchronous reluctance motor MTPA high-performance control method, which comprises the following steps:

[0035] Step 100: establishing the MTPA control of the extreme value search method and establishing a steady-state check module (SSD); specifically comprising:

[0036] According to the change rate of torque and speed, it is determined whether the driving is in a transient or steady-state mode, and according to the determined motor operating state condition, the SSD module generates an enable signal, denoted as e(k); according to the value of e(k), the state of the motor is divided.

[0037] Step 200: Optimize the step selection in the MTPA search process using gradient descent method when the motor is in steady state, build an online search result lookup table and record the converged current vector angle. Specifically includes:

[0038] When using the traditional extremum search method, the search is usually performed with a given step size, and the search speed is inherently inferior. The execution of MTPA online search depends on the determination and maintenance of steady state conditions, so it is necessary to improve the utilization efficiency of the steady state stage as much as possible. The present application optimizes the search step size using gradient descent, thereby accelerating the convergence speed of the online maximum torque / maximum power tracking (MTPA) search result. And record the converged current vector angle to the online search result lookup table, so as to directly call in the subsequent similar working conditions, save the process of repeated search, improve the steady state performance.

[0039] Step 300: In the intermediate transition stage when the motor is not in steady state, the data not in the storage area is calculated by interpolation to give the estimated reference current vector angle. Specifically includes:

[0040] In the intermediate transition stage of the motor in non-steady state, due to the lack of previously stored optimization values, direct application of the reference value of the previous stage may not achieve accurate MTPA control, affecting the performance of the motor. In order to solve this problem, interpolation calculation method can be used to estimate the reference current vector angle of the current stage by using the existing MTPA data near the current value in the storage area. This method can provide an estimated value closer to the real MTPA working point, which helps to reduce the search period required after steady state, improve the dynamic response of the motor, and promote the motor to return to steady state faster, thereby optimizing the MTPA control effect.

[0041] Step 400: Before the motor executes the MTPA search stage, add a pre-judgment module to realize real-time judgment of the working condition of the current motor, match the data corresponding to similar working conditions in the established online MTPA lookup table, and if the optimization value of the corresponding working condition has been searched and stored, it can be directly called, specifically includes:

[0042] This module acquires the key parameters of the motor such as stator current and speed in real time, and sets the steady state condition threshold value such as the change rate of current and speed. The change rate of these parameters is calculated and compared with the threshold value to determine whether the motor is in steady state. If the motor is in steady state, the MTPA search algorithm is started to find the optimal current vector angle; if it is not in steady state, the search is not started to avoid misjudgment. The system will continuously monitor and update the steady state judgment result in real time, and record the optimal parameters under steady state in the lookup table, so as to directly apply in the subsequent similar working conditions, improve the control efficiency.

[0043] On the basis of the above technical solution, the following examples are provided.

[0044] In the following control link examples, the motor parameters are shown in Table 1:

[0045] Table 1

[0046]

[0047]

[0048] First, the synchronous reluctance motor operating conditions are determined:

[0049] As shown in (a) in the middle: Figure 2 The steady state detection (SSD) module is the key module of the MTPA control. Its purpose is to determine whether the drive is in transient or steady state mode according to the rate of change of torque and speed. Torque is generally not easy to measure online, and there are also accuracy problems in estimation, so current is used as a substitute. According to the determined motor operating state conditions, the SSD module generates an enable signal, denoted as e(k), which determines the activation or deactivation of the MTPA module. The MTPA module is responsible for searching the current vector angle of the MTPA control, and needs to ensure that the module should be activated or deactivated according to the transient or steady state of the motor. The following is the discrimination method of the SSD module:

[0050] (1) Transient process (e(k) = 0): If the rate of change of torque (approximated by current) or the rate of change of torque and speed exceeds its respective threshold, the motor is considered to be in a transient process. In this case, the SSD module sets e(k) to 0, which will disable the MTPA online search module.

[0051] (2) Steady state operation (e(k) = 1): If the rate of change of torque and speed is lower than its respective threshold, it indicates that the torque and speed are not changing rapidly, and the motor is considered to be in a steady state. In this case, the SSD module sets e(k) to 1, which activates the extremum search method module and enables MTPA tracking.

[0052] (3) Quasi-steady state operation (e(k) = 2): If the rate of change of torque is within its threshold range, but the rate of change of speed exceeds its threshold, it indicates that it is in a condition of constant torque but changing speed (quasi-steady state), and the SSD module sets e(k) to 2. Similar to steady state operation, this also activates the online search module and enables MTPA search. According to the above steady state discrimination principle, when the module output value e(k) is non-zero, the MTPA online search program can be started.

[0053] Second step: the gradient descent method is used to optimize the step selection in the MTPA search process when the motor is in steady state, and the online search result lookup table is constructed and the convergent current vector angle is recorded.

[0054] As Figure 3 (a) shows: with the real MTPA working point as the demarcation line, when the motor working point is located on the left side of the demarcation line, the gradient of the stator current variation is less than zero; when the working point is located on the right side of the demarcation line, the gradient of the stator current variation is greater than zero; when the motor works near the MTPA working point, the slope of the curve becomes more and more flat, and when the MTPA working point is reached, the gradient of the stator current variation is greater than zero.

[0055] The idea of the gradient descent method is to search along the gradient direction of the objective function. Based on the positive and negative of the real-time estimated gradient and the size of the absolute value, the position of the motor working point on the constant torque curve is determined, and then the search step to be used at this time is determined, that is, a larger step is used when deviating far from the MTPA working point to improve the search speed, and a smaller search step is used when approaching the MTPA working point to avoid oscillation and obtain good accuracy, until the motor working point shifts to the vicinity of the MTPA working point and the search value converges. According to the principle of the search algorithm described above, the function of the current on the current vector angle cannot be obtained under a certain working condition (torque), so the function expression cannot be used for numerical calculation. The process of online search is to change the current vector angle by a small step, and due to the limitation of the dynamic response time of the motor, it is not necessary to calculate the gradient at high speed, so the gradient of the current on the current vector angle can be obtained by approximating and simplifying the calculation according to the difference of the current before and after the change of the current vector angle. In the above analysis, the calculation process of the simplified stator current descent gradient is simple, which can be used for the selection of search step, and this simplification method also does not contain motor parameters, and is easy to implement.

[0056] According to the size of the gradient, the relative gradient size is multiplied by the minimum search step to assign the corresponding search step to different working conditions.

[0057] In order to better monitor the running state of the motor and judge whether the extremum search method converges, the torque of the motor is generally not easy to measure. It is necessary to monitor the measurable parameters such as current and speed in real time during the operation of the motor. Referring to the aforementioned steady state condition discrimination module, but setting a more stringent criterion to prevent storing data with errors. According to this, the convergence of the search process data can be effectively monitored, and if the search result converges, it means that the motor has realized the MTPA control under this working condition, and at this time the optimization value corresponding to this working condition can be recorded in the memory in real time.

[0058] Thirdly, when the motor is in the middle transition stage, the reference current vector angle is estimated by interpolating the MTPA data in the current interval.

[0059] As shown in Figure 4 For convenience, the current is divided into intervals of 1A for example, and the interpolation process starts from the reference value in the interval near the current value. The two closest reference values are selected for linear interpolation. Considering that the MTPA current vector angle of different motors is generally close when not saturated, if there is no reference value in the light saturation interval, the theoretical value is used for interpolation. In the deep saturation interval, the inductance saturation of different motors may not be the same, so the maximum reference value obtained by optimization is directly used, which is assumed to have reached the deep saturation region.

[0060] When the motor is in the middle transition stage, parameter optimization cannot be performed, and the storage area does not have previously stored optimization values. If the reference value of the previous stage is still used, it may not achieve the most accurate MTPA control, and the potential of the output torque cannot be fully utilized, which is not conducive to the motor reaching the steady state condition. The establishment of the steady state condition is crucial for MTPA control based on extreme value search. Therefore, in the middle transition stage, the reference current vector angle is estimated by interpolating the MTPA data in the current interval. This method is more accurate than the traditional method of directly using the reference value of the previous search stage, which helps to reduce the interpolation between the current vector angle and the true MTPA control vector angle after entering the steady state, and helps to reduce the search period after entering the steady state. In addition, it can also improve the dynamic response characteristics of the motor in the transition stage, which is also beneficial to the motor entering the steady state faster.

[0061] Fourthly, before the motor performs MTPA search stage, a pre-judgment module is added to realize real-time judgment of the current motor working condition, and the data corresponding to similar working conditions in the established online MTPA lookup table is matched. If the optimization value of the corresponding working condition has been searched and stored, it can be directly called.

[0062] As shown in Figure 5To ensure that the recorded values are correctly optimized, some speed is sacrificed to ensure accuracy. For example, the same optimal current angle output over multiple search periods can be considered a sign of convergence. Once the convergence condition is met, the optimal current angle corresponding to the current value can be stored for subsequent table lookup operations. In practical applications, variables can be indexed and modified by their serial numbers to read and modify data. Such a design can effectively manage and utilize recorded data, improving system real-time performance and stability.

[0063] The module needs to collect key operating parameters of the motor in real time, such as stator current, speed, etc. Set the specific conditions for judging the steady state, which usually includes the threshold of current rate of change, speed rate of change, etc. If the rate of change of these parameters is lower than the set threshold, it can be considered that the motor is in a steady state. Change rate calculation: Calculate the rate of change of the motor stator current and speed. This can be achieved by comparing the current collected parameter value and the parameter value at the previous time point. Determine the steady state: Compare the calculated rate of change with the preset threshold. If the rate of change is lower than the threshold, it is determined that the motor is in a steady state; if the rate of change exceeds the threshold, it is considered that the motor is in a non-steady state. Control decision: According to the steady state judgment result, decide whether to start the MTPA search algorithm. If the motor is in a steady state, start the search algorithm to find the optimal current vector angle; if the motor is in a non-steady state, do not start the search algorithm to avoid misjudgment in dynamic changes. Real-time update: During the operation of the motor, the pre-judgment module will continuously monitor and evaluate the running state of the motor and update the steady state judgment result in real time. Data recording: When the motor reaches a steady state and the MTPA search algorithm finds the optimal current vector angle, the parameters (such as current vector angle) under the working condition are recorded in the lookup table for subsequent use. Lookup table application: In the subsequent operation of the motor, if similar working conditions are encountered, the corresponding MTPA parameters can be directly read from the lookup table without the need for further search, thereby improving control efficiency.

[0064] As Figure 6As shown: (a) is the simulated waveform of the current during the search process; (b) is the simulated waveform of the current vector angle during the search process; (c) is the logic value for convergence judgment of the search value; (d) is the data in the storage area. It should be noted that, in order to test the performance of the optimized MTPA control strategy based on the online lookup table, a torque mutation response test was performed. To preliminarily verify the proposed control, the lookup table segment interval was set to 1A for ease of analysis. The operating conditions are consistent with the typical operating conditions mentioned above. The given speed is accelerated from 0 r / min to 500 r / min in 1 second. The motor performs the search phase of the aforementioned online MTPA control method under two different load conditions of 2 N·m and 6 N·m. The simulation analysis shows that the data writing module can normally write the optimization parameters that have reached convergence and store the current value and the corresponding current vector angle in the storage area.

[0065] like Figure 7 As shown: (a) is the simulated waveform of motor torque; (b) is the simulated waveform of stator current; (c) is the simulated waveform of motor speed; (d) is the output value of current vector angle. It should be noted that after completing the online MTPA current vector angle search under two specific load torques, the load torques in the following stages become 2N·m and 4N·m respectively, and load mutation tests are performed to test the storage effect of the online lookup table and the performance of the reading and interpolation modules respectively. From the above simulation analysis: the stator current changes with the torque and quickly reaches a stable state. The motor speed waveform will change slightly after the load mutation; the output value of the current vector angle, when the current reaches the existing search value of 2N·m load torque, after the sampling delay and the current drop time, the current vector angle quickly reaches the previously optimized search stage and stabilizes. Under the 4N·m load torque condition without optimization, the interpolation module takes effect and quickly gives a reference current vector angle of 55°, which is very close to the result obtained by the aforementioned search program. Due to the implicit errors caused by the threshold setting of the search program and other factors, the interpolated output, once stable, closely approximates the true value and remains unchanged.

[0066] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0067] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for MTPA control of a synchronous reluctance motor using an optimized online extremum seeking method, characterized in that, Comprise: MTPA control of synchronous reluctance motor is realized by using online extremum search method, a steady state detection (SSD) module is established to determine whether the drive is in transient or steady state mode according to the change rate of torque and speed, and an enable signal is generated by the SSD module to enable or disable the extremum search method; In the steady state process: the search step of the online extremum search method is optimized by the simplified gradient descent method, that is, a larger step is used when deviating far from the MTPA operating point to improve the search speed, and a smaller search step is used when approaching the MTPA operating point until the current vector angle converges, and the converged current angle in the stable state is recorded in the online search result lookup table; In the transition stage: make full use of the optimization values obtained in the past search stage to reduce unnecessary calculation and adjustment of the system operating point, improve the response speed and stability of the system, and for data not in the storage area, interpolation calculation is performed by reading the existing searched MTPA data near the current value to give the estimated reference current vector angle; Step one: establish a steady state detection (SSD) module to determine whether the drive is in transient or steady state mode according to the change rate of torque and speed; Step two: optimize the search step of the online extremum search method by using the simplified gradient descent method; Step three: build an online search result lookup table to record the converged current angle in the stable state; Step four: add a pre-judgment module, if the optimization value corresponding to the working condition has been searched and stored, it can be directly called to skip this search stage and avoid repeated search stages; Step five: when the motor is not in the steady state, for data not in the storage area, interpolation calculation is performed by reading the existing searched MTPA data near the current value to give the estimated reference current vector angle.

2. The MTPA control method of a synchronous reluctance motor based on an optimized online extremum seeking method according to claim 1, characterized in that, A steady state detection (SSD) module is established to determine whether the drive is in transient or steady state mode according to the change rate of torque and speed, as follows: According to the determined motor operating state condition, the SSD module generates an enable signal, denoted as e(k), which determines the activation or deactivation of the extremum search method MTPA module. The extremum search method module is responsible for searching the MTPA control current vector angle, and needs to ensure that the module is activated or deactivated according to the transient or steady state of the motor.

3. The MTPA control method of a synchronous reluctance motor based on an optimized online extremum seeking method according to claim 1, characterized in that, The search step of the online extremum search method is optimized by using the simplified gradient descent method, as follows: Based on the positive and negative of the real-time estimated gradient and the size of the absolute value, the position of the motor operating point on the constant torque curve is determined, and then the search step is determined.

4. The MTPA control method of a synchronous reluctance motor based on an optimized online extremum seeking method according to claim 1, characterized in that, A pre-judgment module is added, if the optimization value corresponding to the working condition has been searched and stored, it can be directly called to skip this search stage and avoid repeated search stages.

5. The MTPA control method of a synchronous reluctance motor based on an optimized online extremum seeking method according to claim 1, characterized in that, When the motor is not in the steady state, for data not in the storage area, interpolation calculation is performed by reading the existing searched MTPA data near the current value to give the estimated reference current vector angle, which includes: The flow of interpolation starts from all existing MTPA optimized reference values in the interval near the current stage current value, and selects the closest two qualified reference values for linear interpolation.

6. A control system characterized by, The control method according to claims 1-5 is performed.

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