Method, device and equipment for enhancing low-speed power generation stability of speed sensorless motor and storage medium

Abstract

The invention discloses a speed sensorless motor low-speed power generation stability enhancement method, which comprises the following steps: establishing an induction motor mathematical model, and deducing a traditional rotating speed adaptive law; based on the deduced traditional rotating speed self-adaptive law, introducing an observation rotor flux linkage and current error correction term, introducing a correction coefficient, and obtaining a new rotating speed self-adaptive law; and according to the characteristic value distribution of the expansion error coefficient matrix, obtaining a correction coefficient of the rotation speed adaptive law. The invention further discloses a device for enhancing the low-speed power generation stability of the speed sensorless motor, corresponding equipment and a storage medium. According to the speed sensorless motor low-speed power generation stability enhancement method provided by the embodiment of the invention, the stability and reliability of a motor driving system can be improved, and the performance of the whole motor driving system is improved.

Description

Technical Field

[0001] This application relates to the field of motor technology, and more specifically, to a method, apparatus, device, and storage medium for enhancing the stability of low-speed power generation in a sensorless motor. Background Technology

[0002] With the rapid development of industrial automation and electric vehicle technology, sensorless motor drive systems are increasingly favored due to their low cost and high reliability. Among numerous sensorless control methods, the technology based on full-order flux linkage observers is widely used due to its high accuracy and good dynamic performance. However, this system often encounters stability issues under low-speed power generation conditions, hindering its further promotion in precision speed regulation and high-efficiency applications. Under low-speed power generation conditions, the motor speed is low, the back electromotive force is small, and the observation error is easily amplified, leading to system instability.

[0003] Currently, common methods for improving the stability of sensorless motors at low speeds include feedback matrix design, speed adaptive law improvement, and signal injection. Among these, improvement methods based on speed adaptive laws, such as locally linearizing the rotor flux in the rotating coordinate system, introducing phase shift angles, or rotor flux linkage errors, can alleviate stability issues to some extent. However, the eigenvalue distribution of the system's extended error coefficient matrix still shows that some eigenvalues ​​are distributed in the right half of the characteristic plane, i.e., the unstable region. This indicates that traditional methods cannot completely eliminate unstable eigenvalues, leading to potential instability phenomena such as oscillations and speed fluctuations during low-speed power generation, thus limiting the development of sensorless motors in low-speed, high-precision applications. Summary of the Invention

[0004] In view of at least one defect or improvement need of the prior art, the present invention provides a method, apparatus, device and storage medium for enhancing the stability of low-speed power generation of a sensorless motor, which can solve at least one of the problems existing in the background art.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a method for enhancing the low-speed power generation stability of a sensorless motor is provided, the method comprising: A mathematical model of the induction motor is established, and the traditional speed adaptive law is derived. Based on the derived traditional speed adaptive law, an error correction term for the observed rotor flux linkage and current is introduced, and a correction coefficient is introduced to obtain a new speed adaptive law. The correction coefficients of the speed adaptive law are obtained based on the eigenvalue distribution of the extended error coefficient matrix.

[0006] Furthermore, the aforementioned method for enhancing the stability of low-speed power generation in a sensorless motor, specifically includes establishing a mathematical model of the induction motor and deriving the traditional speed adaptive law, which includes: At rest in two phases α - β A mathematical model of the induction motor is established in the coordinate system to obtain the expressions for stator current, rotor flux linkage, and stator voltage. Combined with the mathematical model of the full-order flux linkage observer, the error vector equation is obtained. Define the Lyapunov function, ignore the rotor flux error term, and derive the expression for the traditional speed adaptive law.

[0007] Furthermore, the aforementioned method for enhancing the stability of low-speed power generation in a sensorless motor, specifically includes the introduction of observation rotor flux linkage and current error correction terms, the introduction of correction coefficients, and the acquisition of a new speed adaptive law, which includes: Transform the traditional speed adaptive law into a two-phase... d - q Form in a rotated coordinate system; By using the rotor flux orientation vector control method, a speed error extended error vector is introduced to obtain a linearized extended error vector equation; By adding a dot product term of the observed rotor flux linkage and current error to the traditional speed adaptive law and introducing a correction coefficient, a new expression for the speed adaptive law is obtained.

[0008] Furthermore, in the aforementioned method for enhancing the stability of low-speed power generation in a sensorless motor, the step of obtaining the correction coefficients of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix specifically includes: The extended error coefficient matrix of the modified speed adaptive law is calculated; The extended error coefficient matrix is ​​used as a function of the correction coefficient and the rotor speed, and the surface variation of the function is plotted. Based on the motor's operating status and load conditions, determine the range of values ​​for the correction coefficients so that the determinant of the extended error coefficient matrix is ​​less than 0.

[0009] Furthermore, the above-mentioned method for enhancing the stability of low-speed power generation of a sensorless motor is characterized in that the new speed adaptive law is used in a sensorless induction motor drive system.

[0010] According to a second aspect of the present invention, a device for enhancing the stability of low-speed power generation of a sensorless motor is also provided, comprising: The first acquisition module is used to establish a mathematical model of the induction motor and derive the traditional speed adaptive law; The second acquisition module is used to acquire a new speed adaptive law by introducing an observation rotor flux linkage and current error correction term and a correction coefficient based on the derived traditional speed adaptive law. The third acquisition module is used to acquire the correction coefficients of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix.

[0011] According to a third aspect of the present invention, a sensorless motor low-speed power generation stability enhancement device is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the methods described above.

[0012] According to a fourth aspect of the invention, a storage medium is also provided, which stores a computer program executable by a sensorless motor low-speed power generation stability enhancement device, wherein when the computer program is run on the sensorless motor low-speed power generation stability enhancement device, the sensorless motor low-speed power generation stability enhancement device performs the steps of any of the methods described above.

[0013] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: This application provides a method for enhancing the stability of sensorless motors during low-speed power generation. By introducing observation of rotor flux and current error correction terms and correction coefficients into the traditional speed adaptive law, it effectively solves the stability problem of existing sensorless motor drive systems during low-speed power generation. Based on establishing a mathematical model of the induction motor and deriving the traditional speed adaptive law, a new speed adaptive law is designed to more accurately estimate and adjust the motor speed. The correction coefficients are obtained based on the eigenvalue distribution of the extended error coefficient matrix, ensuring that the eigenvalues ​​of the error coefficient matrix are stably distributed in the left half of the characteristic plane, enabling the sensorless motor to maintain stable operation under low-speed power generation conditions. In this way, this application not only improves the stability and reliability of the motor drive system but also expands the application range of sensorless motors in low-speed, high-precision application scenarios, reduces costs and maintenance workload, and improves the overall performance of the motor drive system. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for enhancing the stability of low-speed power generation in a sensorless motor, as provided in an embodiment of this application. Figure 2 The embodiments provided in this application are based on Follow and A schematic diagram of the stability analysis of the changes; Figure 3 This is a schematic diagram of the unstable region of low-speed power generation provided in an embodiment of this application; Figure 4 Matrix provided for embodiments of this application A schematic diagram of the real distribution of unstable eigenvalues; Figure 5 The extended error coefficient matrix provided for the embodiments of this application Determinant follows k and A schematic diagram of the changes; Figure 6 The corrected error coefficient matrix eigenvalue distribution provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0017] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0018] Figure 1 This is a flowchart illustrating a method for enhancing the stability of low-speed power generation in a sensorless motor, as provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for enhancing the stability of low-speed power generation of a sensorless motor includes the following steps: A mathematical model of the induction motor is established, and the traditional speed adaptive law is derived. Based on the derived traditional speed adaptive law, an error correction term for the observed rotor flux linkage and current is introduced, and a correction coefficient is introduced to obtain a new speed adaptive law. The correction coefficients of the speed adaptive law are obtained based on the eigenvalue distribution of the extended error coefficient matrix.

[0019] Specifically, in the two-phase static state α - β In a coordinate system, a mathematical model of the induction motor is constructed. This model reflects the electrical characteristics of the motor under different operating conditions, including the relationships between physical quantities such as stator current, rotor flux linkage, and stator voltage, as well as the influence of various motor parameters such as stator resistance, rotor resistance, stator inductance, and rotor inductance on motor operation. Based on the above mathematical model of the induction motor, a mathematical model of a full-order flux linkage observer is established to perform real-time observation and estimation of the motor's flux linkage, providing a foundation for the subsequent derivation of the speed adaptive law. Combining the mathematical models of the induction motor and the full-order flux linkage observer, an error vector equation is obtained, which reflects the deviation between the actual operating state and the observed state of the motor. Based on this, a Lyapunov function is defined, and using Lyapunov stability theory, a traditional speed adaptive law is derived. This speed adaptive law can automatically adjust the speed estimate according to the motor's operating state to achieve sensorless control of the motor speed.

[0020] The traditional speed adaptive law is transformed from a stationary coordinate system to a two-phase system. d - q The form in a rotating coordinate system is used to better integrate with the motor's vector control, achieving precise motor control. Under rotor flux-oriented vector control, speed error is introduced to construct an extended error vector, further considering the motor's dynamic characteristics under low-speed power generation conditions, allowing the error vector to more comprehensively reflect the motor's operating state. The eigenvalue distribution of the extended error coefficient matrix is ​​analyzed to determine the boundaries of the unstable region in low-speed power generation. To improve system stability, a correction term for observing rotor flux and current errors is added to the traditional speed adaptive law, and a correction coefficient is introduced, thus designing a new speed adaptive law. This ensures that the eigenvalues ​​of the error coefficient matrix are stably distributed in the left half of the characteristic plane, enhancing the stability of sensorless motor low-speed power generation.

[0021] Based on the new speed adaptive law, the extended error coefficient matrix is ​​reconstructed and compared with the corrected matrix to determine the stability of the corrected matrix under different operating conditions. By analyzing the relationship between the determinant of the extended error coefficient matrix and the changes in the correction coefficient and rotor speed, a function surface graph is plotted to determine the stable region of the sensorless induction motor drive system. Based on the operating characteristics of the motor under different load conditions, the range of values ​​for the correction coefficient is determined to ensure that the determinant of the extended error coefficient matrix is ​​less than 0 in the unstable region of low-speed power generation. This ensures that all eigenvalues ​​are distributed in the left half of the characteristic plane, effectively enhancing the stability of low-speed power generation of the sensorless motor.

[0022] This application provides a method for enhancing the stability of sensorless motors during low-speed power generation. By introducing observation of rotor flux and current error correction terms and correction coefficients into the traditional speed adaptive law, it effectively solves the stability problem of existing sensorless motor drive systems during low-speed power generation. Based on establishing a mathematical model of the induction motor and deriving the traditional speed adaptive law, a new speed adaptive law is designed to more accurately estimate and adjust the motor speed. The correction coefficients are obtained based on the eigenvalue distribution of the extended error coefficient matrix, ensuring that the eigenvalues ​​of the error coefficient matrix are stably distributed in the left half of the characteristic plane, enabling the sensorless motor to maintain stable operation under low-speed power generation conditions. In this way, this application not only improves the stability and reliability of the motor drive system but also expands the application range of sensorless motors in low-speed, high-precision application scenarios, reduces costs and maintenance workload, and improves the overall performance of the motor drive system.

[0023] Optionally, the method for enhancing the stability of low-speed power generation of a sensorless motor provided in this application embodiment, wherein establishing a mathematical model of the induction motor and deriving a traditional speed adaptive law specifically includes: At rest in two phases α - β A mathematical model of the induction motor is established in the coordinate system to obtain the expressions for stator current, rotor flux linkage, and stator voltage. Combined with the mathematical model of the full-order flux linkage observer, the error vector equation is obtained. Define the Lyapunov function, ignore the rotor flux error term, and derive the expression for the traditional speed adaptive law.

[0024] Specifically, in the two-phase static state α-β Establish a mathematical model of the induction motor in a coordinate system:

[0025] in, Stator current Rotor flux Stator voltage ; superscript s Represents two-phase static α-β Coordinate system; , , , , , , ; , , , , , , ; , , , These are stator resistance, rotor resistance, stator inductance, and rotor inductance, respectively.

[0026] Similar to the mathematical model of an induction motor, the mathematical model of a full-order flux linkage observer can be expressed as follows:

[0027] Where '^' represents the estimated value. To be A In Replace with The matrix after that.

[0028] The error vector equation is obtained as follows

[0029] in Stator resistance error Rotor flux error ; Speed ​​error .

[0030] Define the Lyapunov function as

[0031] in λ It is a positive constant. Because of the function... V It is positive definite; therefore, according to Lyapunov's second stability theorem, the necessary and sufficient condition for the system to be asymptotically stable at the origin is:

[0032] Among them, the subscript α , β Representing each physical quantity α , β Quantity.

[0033] Because the rotor flux linkage error is very small, it is usually ignored in practical applications. Therefore, the traditional speed adaptive law is:

[0034] in, Indicates proportional gain. This represents the integral gain.

[0035] Optionally, the method for enhancing the stability of low-speed power generation in a sensorless motor provided in this application embodiment, which involves introducing an observation rotor flux linkage and current error correction term, introducing a correction coefficient, and obtaining a new speed adaptive law, specifically includes: Transform the traditional speed adaptive law into a two-phase... d - q Form in a rotated coordinate system; By using the rotor flux orientation vector control method, a speed error extended error vector is introduced to obtain a linearized extended error vector equation; By adding a dot product term of the observed rotor flux linkage and current error to the traditional speed adaptive law and introducing a correction coefficient, a new expression for the speed adaptive law is obtained.

[0036] Specifically, the traditional speed adaptive law expression is transformed into a two-phase... d - q Adaptive law of rotational speed in rotating coordinate system

[0037] Among them, superscript r Representing two phases d - q Rotate coordinate system, subscript d , q Representing each physical quantity d , q Quantity.

[0038] Using rotor flux orientation vector control, the rotor flux is aligned with... d The axes coincide, that is, there is

[0039] Introducing speed error to expand the error vector

[0040] Therefore, the extended error vector equation can be linearized at the motor operating point as follows:

[0041] Among them, the extended error coefficient matrix

[0042] in, , , , , .

[0043] calculate Two boundary lines of the unstable region of low-speed power generation can be obtained as follows:

[0044] Analysis of the extended error coefficient matrix The determinant of the matrix is ​​plotted, and the matrix determinant is plotted as a function of slip speed and synchronous speed, as shown below. Figure 2As shown. By Figure 2 It is known that near the region where the synchronous speed is 0, there exists a region where the matrix determinant is greater than 0, meaning there are unstable eigenvalues. Therefore, using the traditional speed adaptive law will cause the sensorless induction motor drive system to have unstable eigenvalues ​​in the unstable region of low-speed power generation.

[0045] To improve the stability of sensorless induction motor drive systems, a dot product term for observing rotor flux linkage and current error is added to the traditional speed adaptive law, and a correction coefficient is introduced, namely...

[0046] in, k This is the correction coefficient for the speed adaptive law.

[0047] The above formula can be simplified to

[0048] Recalculate the expanded error coefficient matrix using the modified speed adaptive law, and name it as .matrix The first four lines and Same, fifth line

[0049] in, , , , , .

[0050] When the motor operates in the unstable region of low-speed power generation, the slip speed and rotor speed can be described as having a linear relationship. Therefore, based on the expressions for the two boundary lines of the unstable region of low-speed power generation, the slip speed can be assumed to be...

[0051] Therefore, the extended error coefficient matrix The determinant of can be represented as

[0052] in, , , .

[0053] Differentiating the above equation, we get

[0054] Set the slip speed of the motor under full load to Traditional low-speed power generation instability areas, such as Figure 3The shaded area is shown in the figure. When the motor is fully loaded and operating on an unstable boundary line, the motor speed is... .

[0055] The real distribution of the instability eigenvalues ​​in the unstable region of low-speed power generation using the extended error coefficient matrix of the traditional speed adaptive law is as follows: Figure 4 As shown. From Figure 4 As can be seen, in the unstable region of low-speed power generation, at most one eigenvalue is distributed in the right half of the characteristic plane. Therefore, as long as the determinant of the extended error coefficient matrix is ​​less than 0, it can be guaranteed that all eigenvalues ​​are distributed in the left half of the characteristic plane.

[0056] Optionally, the method for enhancing the stability of low-speed power generation in a sensorless motor provided in this application embodiment, wherein obtaining the correction coefficient of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix, specifically includes: The extended error coefficient matrix of the modified speed adaptive law is calculated; The extended error coefficient matrix is ​​used as a function of the correction coefficient and the rotor speed, and the surface variation of the function is plotted. Based on the motor's operating status and load conditions, determine the range of values ​​for the correction coefficients so that the determinant of the extended error coefficient matrix is ​​less than 0.

[0057] Specifically, the extended error coefficient matrix Considered as a correction factor k With rotor speed The function, plotting the surface changes of the function as follows Figure 5 As shown. From Figure 5 As can be seen from this, the stable region of the sensorless induction motor drive system, i.e. The area only appears in k When >0, in the expanded error coefficient matrix. In the determinant, Always less than 0. When k When greater than 0, and All are greater than 0. Therefore, when k When the sign is determined, This can be viewed as a cubic function of the rotor speed. The curve of this function is shown below. Figure 5 The light-colored curve in region 1 shows the derivative curve of the function. The derivative curve opens downwards, its axis of symmetry is located on the positive x-axis, and it intersects the y-axis on the negative y-axis.

[0058] The distribution of the derivative curve can be categorized into two cases: one where the maximum value is less than or equal to 0, and the other where the maximum value is greater than 0. For the first case, we have...

[0059] From the above formula, we can derive that...

[0060] However, when h As the expression approaches 1, the right-hand side of the above equation becomes infinitely large. Therefore, k It will also become infinitely large and uncertain. k The specific value to be taken.

[0061] In the second scenario, when the rotor speed is 0, When the rotor speed gradually increases from 0, It will first decrease, then increase, and finally decrease again. According to... Figure 3 As shown, when the load on the motor does not exceed the rated load, it is only necessary to ensure that the rotor speed is within the range specified in the diagram. and below It can be less than 0. That is...

[0062] The above formula can be transformed into

[0063] The right side of the above equation can be seen as h The function, defined as The function is monotonically increasing from 0 to positive infinity. h There is a discontinuity at point =1. Considering h The range of values ​​is ,therefore, k It can be set to

[0064] In summary, the novel adaptive speed law designed based on the eigenvalue distribution of the extended error coefficients is as follows: .

[0065] In one embodiment, the rotor speed is set to The slip speed varies with the rotor speed as set in this application. The error coefficient matrix after speed adaptive law correction is plotted. The variation of eigenvalue distribution with slip speed is as follows: Figure 6 As shown in the figure, the arrows indicate the change in eigenvalue distribution caused by the change in motor load from heavy to light. It can be seen from the figure that the eigenvalues ​​of the corrected error coefficient matrix are all distributed in the left half of the characteristic plane, verifying the effectiveness of the method proposed in this application.

[0066] Optionally, the present application provides a method for enhancing the stability of low-speed power generation of a sensorless motor, characterized in that the new speed adaptive law is used in a sensorless induction motor drive system.

[0067] Specifically, the new speed adaptive law for sensorless induction motor drive systems can be implemented in the following way: First, the novel speed adaptive law proposed in this application is integrated into the control algorithm of the sensorless induction motor drive system. During the system initialization phase, based on the motor's nameplate parameters and the actual operating environment, various parameters in the model are precisely set, such as stator resistance, rotor resistance, stator inductance, and rotor inductance, to ensure that the mathematical model can accurately reflect the actual operating characteristics of the motor.

[0068] Next, during motor operation, measurable electrical signals such as stator current and stator voltage are acquired in real time, and flux linkage is observed using a full-order flux linkage observer. The observed flux linkage values ​​and the acquired current signals are processed, and combined with the observed rotor flux linkage and current error correction term in the new speed adaptive law, the speed estimate is calculated and updated in real time. This correction term can effectively compensate for speed estimation errors caused by parameter changes, load fluctuations, and other factors, improving the accuracy of speed estimation.

[0069] Simultaneously, based on the correction coefficients determined by the eigenvalue distribution characteristics of the extended error coefficient matrix, the gain of the new speed adaptive law is dynamically adjusted to ensure that the eigenvalues ​​of the error coefficient matrix remain stably distributed in the left half of the characteristic plane throughout the entire operation. This gives the sensorless induction motor drive system stronger stability and anti-interference capabilities under low-speed power generation conditions. Even under complex conditions such as sudden load changes and parameter perturbations, the system can quickly and accurately adjust the speed estimate to maintain stable motor operation, effectively avoiding instability phenomena such as oscillations and speed fluctuations that may occur in traditional methods. This significantly improves the performance and reliability of the sensorless induction motor drive system in low-speed power generation scenarios.

[0070] This application embodiment also provides a sensorless motor low-speed power generation stability enhancement device, including: The first acquisition module is used to establish a mathematical model of the induction motor and derive the traditional speed adaptive law; The second acquisition module is used to acquire a new speed adaptive law by introducing an observation rotor flux linkage and current error correction term and a correction coefficient based on the derived traditional speed adaptive law. The third acquisition module is used to acquire the correction coefficients of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix.

[0071] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0072] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0073] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0078] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0079] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for enhancing the stability of low-speed power generation in a sensorless motor, characterized in that, Includes the following steps: A mathematical model of the induction motor is established, and the traditional speed adaptive law is derived. Based on the derived traditional speed adaptive law, an error correction term for the observed rotor flux linkage and current is introduced, and a correction coefficient is introduced to obtain a new speed adaptive law. The correction coefficients of the speed adaptive law are obtained based on the eigenvalue distribution of the extended error coefficient matrix.

2. The method for enhancing the stability of low-speed power generation of a sensorless motor as described in claim 1, characterized in that, The establishment of the mathematical model of the induction motor and the derivation of the traditional speed adaptive law specifically include: At rest in two phases α - β A mathematical model of the induction motor is established in the coordinate system to obtain the expressions for stator current, rotor flux linkage, and stator voltage. Combined with the mathematical model of the full-order flux linkage observer, the error vector equation is obtained. Define the Lyapunov function, ignore the rotor flux error term, and derive the expression for the traditional speed adaptive law.

3. The method for enhancing the stability of low-speed power generation of a sensorless motor as described in claim 1, characterized in that, The introduction of the rotor flux linkage and current error correction term, the introduction of correction coefficients, and the acquisition of a new speed adaptive law specifically include: Transform the traditional speed adaptive law into a two-phase... d - q Form in a rotated coordinate system; By using the rotor flux orientation vector control method, a speed error extended error vector is introduced to obtain a linearized extended error vector equation; By adding a dot product term of the observed rotor flux linkage and current error to the traditional speed adaptive law and introducing a correction coefficient, a new expression for the speed adaptive law is obtained.

4. The method for enhancing the stability of low-speed power generation of a sensorless motor as described in claim 1, characterized in that, The step of obtaining the correction coefficients of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix specifically includes: The extended error coefficient matrix of the modified speed adaptive law is calculated; The extended error coefficient matrix is ​​used as a function of the correction coefficient and the rotor speed, and the surface variation of the function is plotted. Based on the motor's operating status and load conditions, determine the range of values ​​for the correction coefficients so that the determinant of the extended error coefficient matrix is ​​less than 0.

5. A method for enhancing the stability of low-speed power generation in a sensorless motor as described in any one of claims 1 to 4, characterized in that, The new speed adaptive law is used in sensorless induction motor drive systems.

6. A device for enhancing the stability of low-speed power generation in a sensorless motor, characterized in that, include: The first acquisition module is used to establish a mathematical model of the induction motor and derive the traditional speed adaptive law; The second acquisition module is used to acquire a new speed adaptive law by introducing an observation rotor flux linkage and current error correction term and a correction coefficient based on the derived traditional speed adaptive law. The third acquisition module is used to acquire the correction coefficients of the speed adaptive law based on the eigenvalue distribution of the extended error coefficient matrix.

7. A device for enhancing the stability of low-speed power generation of a sensorless motor, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, It stores a computer program that can be executed by a sensorless motor low-speed power generation stability enhancement device. When the computer program is run on the sensorless motor low-speed power generation stability enhancement device, the sensorless motor low-speed power generation stability enhancement device performs the steps of the method described in any one of claims 1 to 5.

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