Motor rotor resistance identification method, device and equipment and medium
By constructing Lyapunov functions and adaptive laws, and based on motor current and voltage models, online identification of the rotor resistance of asynchronous motors is achieved using inverter software. This solves the problems of space occupation and detection delay caused by hardware dependence, and improves detection accuracy and system stability.
Patent Information
- Application Number
- CN202511800823.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-02
- Publication Date
- 2026-02-27
AI Technical Summary
Conventional asynchronous motor rotor resistance detection relies on additional hardware circuits and measuring instruments, which occupy a large space and involve a complex detection process, resulting in reduced detection accuracy and delays.
By constructing Lyapunov functions and adaptive laws, and based on motor current and voltage models, the rotor resistance can be identified online using inverter software, avoiding the use of hardware circuits and physical models.
This system enables online real-time detection of rotor resistance, improving detection accuracy and simplifying the detection process, while ensuring the stability of the asynchronous motor closed-loop vector rotor resistance adaptive system.
Smart Images
Figure CN121585049A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of frequency converter control technology, and in particular to a method, device, equipment, and medium for identifying the rotor resistance of a motor. Background Technology
[0002] Conventional detection of the closed-loop vector rotor resistance of an asynchronous motor relies on adding hardware circuits and measuring instruments beyond the frequency converter, and then establishing a complex physical model to obtain the resistance when the frequency converter is offline. Adding these additional hardware circuits and measuring instruments occupies significant physical space, resulting in a large footprint. Furthermore, establishing physical models for these components is complex and computationally intensive. Offline analysis for detecting the frequency converter's offline state suffers from a delay in detecting the actual rotor resistance due to the real-time operation of the frequency converter, leading to reduced accuracy.
[0003] Therefore, how to reduce the delay in rotor resistance detection, simplify the detection steps, and improve accuracy is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0004] The purpose of this application is to provide a method, device, equipment, and medium for identifying the rotor resistance of an electric motor, in order to solve the problems of conventional detection methods that require additional components, resulting in excessive physical space usage, complex physical model building processes, and delays in offline processing.
[0005] To address the aforementioned technical problems, this application provides a method for identifying the rotor resistance of an electric motor, comprising:
[0006] Error state variables are determined based on the state equations of rotor flux under the motor current model and the corresponding state equations of the adjustable reference model.
[0007] Based on the error state variables, state convergence, and parameter convergence, a Lyapunov function is constructed, and a rotor resistance adaptive law is constructed based on the stable parameters in the Lyapunov function.
[0008] The state equation of rotor flux linkage under the motor voltage model is determined based on the state equation of rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model.
[0009] The estimated rotor flux is determined based on the state equation of the adjustable reference model, and the estimated rotor flux and the rotor flux under the voltage model are input to the rotor resistance adaptive law to obtain the rotor resistance value.
[0010] On the one hand, a Lyapunov function is constructed based on the error state variables, state convergence, and parameter convergence, including:
[0011] The state error value is obtained by performing convergence processing on the error state variable based on the state convergence.
[0012] The parameter error value is obtained by performing convergence processing on the estimated rotor resistance value of the adjustable reference model based on the parameter convergence.
[0013] The Lyapunov function is constructed by performing positive definite processing based on the state error value and the parameter error value.
[0014] On the other hand, an adaptive law for rotor resistance is constructed based on the stable parameters in the Lyapunov function, including:
[0015] The stable parameters are obtained by differentiating the Lyapunov function; wherein the stable parameters are non-positive parameters.
[0016] The state equation for the estimated rotor resistance value is obtained by transforming the stable parameters, and is used as the adaptive law for rotor resistance.
[0017] On the other hand, the state equation of the rotor flux linkage under the motor voltage model is determined based on the state equation of the rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model, including:
[0018] The first integral formula is obtained by integrating the state equation of rotor flux linkage under the motor voltage model from the initial time to the target time.
[0019] The second integral formula is obtained by performing integration by parts on the first integral formula.
[0020] A voltage correction term is introduced into the other integral formulas besides the second integral formula in the first integral formula to obtain the third integral formula;
[0021] The state equation of rotor flux linkage under the motor voltage model is obtained by performing component processing of the third integral formula and the second integral formula in the stationary coordinate system.
[0022] On the other hand, after obtaining the rotor resistance value, the following steps are also included:
[0023] Obtain the current components of the motor in the dq coordinate system;
[0024] The slip frequency value is determined based on the rotor resistance, mutual inductance, and current components.
[0025] The estimated electric angular velocity value is determined based on the slip frequency value and the rotational speed value;
[0026] The rotor position is obtained by integrating the estimated electrical angular velocity value to track the actual rotor position of the motor.
[0027] On the other hand, after obtaining the rotor resistance value, the following steps are also included:
[0028] If the rotor resistance is greater than or equal to the preset resistance, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
[0029] On the other hand, after obtaining the rotor resistance value, the following steps are also included:
[0030] Obtain the constraint relationship between the motor's rotor windings and the motor temperature;
[0031] Obtain the initial rotor resistance, initial temperature, and rotor resistance temperature coefficient for electrons;
[0032] The corresponding rotor temperature value is determined based on the constraint relationship, the initial rotor resistance value, the initial temperature value, and the rotor resistance temperature coefficient.
[0033] If the rotor temperature exceeds the preset temperature value, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
[0034] To address the aforementioned technical problems, this application also provides a rotor resistance identification device for an electric motor, comprising:
[0035] The first determining module is used to determine the error state variables based on the state equation of the rotor flux under the motor current model and the state equation of the corresponding adjustable reference model.
[0036] The module is used to construct a Lyapunov function based on the error state variables, state convergence, and parameter convergence, and to construct a rotor resistance adaptive law based on the stable parameters in the Lyapunov function.
[0037] The second determining module is used to determine the state equation of the rotor flux linkage under the motor voltage model based on the state equation of the rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model.
[0038] The input module is used to determine the estimated rotor flux linkage based on the state equation of the adjustable reference model, and input the estimated rotor flux linkage and the rotor flux linkage under the voltage model into the rotor resistance adaptive law to obtain the rotor resistance value.
[0039] To address the aforementioned technical problems, this application also provides an electronic device, comprising:
[0040] Memory, used to store computer programs;
[0041] A processor for executing the computer program to implement the steps of the rotor resistance identification method for the motor as described.
[0042] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the described method for identifying the rotor resistance of an electric motor.
[0043] This application provides a method for identifying the rotor resistance of a motor. First, error state variables are determined based on the state equations of the rotor flux linkage under the motor current model and the corresponding state equations of the adjustable reference model. This lays the foundation for the subsequent construction of the Lyapunov function, providing essential error state variables. Second, the Lyapunov function is constructed based on the error state variables, state convergence, and parameter convergence. Here, state convergence and parameter convergence are achieved by matching the state equations of the rotor flux linkage under the current model (state variable parameters of the reference model) with the state equations of the adjustable parameter model (state variable parameters of the adjustable model) obtained by seeking the rotor resistance adaptive law using the stability of the constructed Lyapunov function. This completes the construction of the Lyapunov function. Then, based on the stability parameters in this function, the function is stabilized by taking the derivative of the trace. The rotor resistance adaptive law is then constructed based on the Lyapunov stability theorem, that is, the rotor resistance adaptive law is constructed under the condition that the function is stable. Finally, based on the state equation of the rotor flux linkage under the motor voltage model, the rotor flux linkage in the stationary coordinate system is obtained. Considering the dead zone effect, it is converted into the state equation of the rotor flux linkage under the voltage model, yielding the final rotor flux linkage under the voltage model. Then, the estimated rotor flux linkage is determined based on the state equation of the adjustable reference model. The estimated rotor flux linkage and the rotor flux linkage under the voltage model are input into the rotor resistance adaptive law to identify the rotor resistance value online. The entire process requires no additional hardware circuitry or measuring instruments, and is obtained solely through the software of the frequency converter, saving physical space. Furthermore, it eliminates the need for complex physical model construction, is independent of the motor power in the physical model, and the algorithm is relatively simple to implement. By constructing the rotor resistance adaptive law, online rotor resistance identification is achieved, ensuring the stable convergence of the asynchronous motor closed-loop vector rotor resistance adaptive system. Compared to offline detection, which has a delay, this application achieves online real-time detection, improving accuracy.
[0044] In addition, this application also provides a rotor resistance identification device, apparatus and medium for an electric motor, which has the same beneficial effects as the rotor resistance identification method for an electric motor described above. Attached Figure Description
[0045] To more clearly illustrate 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.
[0046] Figure 1 A flowchart illustrating a method for identifying the rotor resistance of an electric motor, provided as an embodiment of this application;
[0047] Figure 2 A block diagram illustrating the principle of online rotor resistance identification based on model reference adaptation for closed-loop vector control of asynchronous motors, provided in this application embodiment;
[0048] Figure 3 A flowchart of an over-temperature fault detection and processing method provided in this application embodiment;
[0049] Figure 4 A structural diagram of an inverse Γ-type equivalent circuit for an asynchronous motor provided in an embodiment of this application;
[0050] Figure 5 A block diagram of a model reference adaptive online rotor resistance identification system provided in this application embodiment;
[0051] Figure 6 A structural diagram of a rotor resistance identification device for an electric motor provided in an embodiment of this application;
[0052] Figure 7 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.
[0054] The core of this application is to provide a method, device, equipment, and medium for identifying the rotor resistance of an electric motor, in order to solve the problems of conventional detection methods that require additional components, resulting in excessive physical space occupation, complex physical model building processes, and delays in offline processing.
[0055] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0056] Asynchronous motors are widely used in industry, agriculture, and national defense, accounting for over 60% of the nation's total industrial electricity consumption. In practical applications, before normal operation, asynchronous motors require offline parameter self-learning to ensure stable and reliable operation. Asynchronous motor closed-loop vector control employs indirect field-oriented control (IFOC), which can achieve complete decoupling when motor parameters are accurate. However, with prolonged operation and increased temperature, motor parameters change, leading to inaccurate field orientation. This causes coupling between the current excitation component and torque component, resulting in increased torque and speed fluctuations and a decline in the overall performance of the motor control system.
[0057] In particular, changes in rotor resistance directly lead to deviations in the orientation of the rotor magnetic field in the closed-loop vector. Changes in rotor resistance are more complex; besides the effect of temperature rise, any operation that alters the rotor-side current frequency will change the rotor-side skin effect, thus causing a change in rotor resistance. The rotor resistance identification method for motors provided in this application can solve the above-mentioned technical problems.
[0058] Figure 1 A flowchart illustrating a method for identifying the rotor resistance of an electric motor, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes:
[0059] S11: Determine the error state variables based on the state equation of the rotor flux under the motor current model and the state equation of the corresponding adjustable reference model;
[0060] S12: Construct the Lyapunov function based on the error state variables, state convergence, and parameter convergence, and construct the rotor resistance adaptive law based on the stable parameters in the Lyapunov function.
[0061] S13: Determine the state equation of rotor flux linkage under the motor voltage model based on the state equation of rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model.
[0062] S14: Determine the estimated rotor flux linkage based on the state equation of the adjustable reference model, and input the estimated rotor flux linkage and the rotor flux linkage under the voltage model into the rotor resistance adaptive law to obtain the rotor resistance value.
[0063] Specifically, the motor current model uses the stationary coordinate system of the asynchronous motor current model as a reference model, which is the state equation of the rotor flux linkage. The adjustable reference model is obtained by estimation based on the state equation of the rotor flux linkage under the motor current model.
[0064] The specific formula for the state equation of rotor flux linkage under the motor current model is as follows:
[0065] ; (1)
[0066] in, For rotor flux linkage, This is the resistance value of the rotor. Mutual inductance value, This is the rotational speed value. This is the rotor current value; .
[0067] The specific formulas for the state equations of the adjustable reference model are as follows:
[0068] ; (2)
[0069] in, To estimate the rotor flux linkage, To estimate the rotor resistance.
[0070] The error state variables are determined using the above formulas (1) and (2), as follows:
[0071] ; (3)
[0072] in, As the first variable, As the second variable, As the third variable, As the fourth variable, As the fifth variable, It is the sixth variable;
[0073] First variable: ;
[0074] Second variable: ;
[0075] Third variable: ;
[0076] Fourth variable: ;
[0077] Fifth variable: ;
[0078] Sixth variable: .
[0079] In step S12, a Lyapunov function is constructed based on the error state variables, state convergence, and parameter convergence. State convergence determines the stability of the system's dynamic response, while parameter convergence affects control accuracy. The Lyapunov function must satisfy positive definiteness, and quadratic functions are the most commonly used form in industrial control systems. Sub-functions can be designed separately for state convergence and parameter convergence, and then superimposed to form the overall candidate function, ensuring both positive definiteness and simultaneously constraining both types of convergence. Based on the stable parameters in the Lyapunov function, an adaptive rotor resistance law is constructed. The derivative of its Lyapunov function must satisfy negative definiteness or semi-negative definiteness to ensure gradual convergence of the state and parameters.
[0080] The core of constructing the rotor resistance adaptive law based on the Lyapunov function is to establish a mathematical model of the induction motor (the core motor type adapted by the frequency converter). First, a system error equation containing rotor resistance error is established. Then, a positive definite Lyapunov function is constructed. The adaptive law is derived by taking the derivative and constraining its negative definiteness, ensuring that the rotor resistance estimate asymptotically converges to the true value.
[0081] The Lyapunov function needs to be globally positive definite and take the value of 0 only when all errors are 0. Considering that the system difference includes rotor flux error and rotor resistance error, the asynchronous motor body feature matrix satisfies negative definiteness, and the matrix diagonal elements between the first and second variables in the above formula (3) satisfy non-positive, so as to achieve Lyapunov function stability.
[0082] After transforming the non-positive parameters, an adaptive law for rotor resistance is constructed. It should be noted that the rotor resistance can only be obtained after the rotor flux linkage in the stationary coordinate system and the estimated rotor flux linkage are determined in the constructed adaptive law for rotor resistance.
[0083] After obtaining the rotor flux linkage under the voltage model through the state equation of the rotor flux linkage under the motor voltage model in step S13, the estimated rotor flux linkage is determined through the state equation of the adjustable reference model in step S14. The estimated rotor flux linkage and the rotor flux linkage under the voltage model are then applied to the rotor resistance adaptive law to obtain the rotor resistance value.
[0084] This application provides a method for identifying the rotor resistance of a motor. First, error state variables are determined based on the state equation of the rotor flux linkage under the motor current model and the state equation of the corresponding adjustable reference model. This lays the foundation for the subsequent construction of the Lyapunov function, providing essential error state variables. Second, the Lyapunov function is constructed based on the error state variables, state convergence, and parameter convergence. Here, state convergence and parameter convergence are achieved by matching the state equation of the rotor flux linkage under the current model (state variable parameters of the reference model) with the state equation of the adjustable parameter model (state variable parameters of the adjustable model) obtained by seeking the rotor resistance adaptive law using the stability of the constructed Lyapunov function. This completes the construction of the Lyapunov function. Then, based on the stability parameters in the function, the function is stabilized by taking the derivative of the trace. The rotor resistance adaptive law is constructed based on the Lyapunov stability theorem, that is, the rotor resistance adaptive law is constructed under the condition that the function is stable. Finally, based on the state equation of the rotor flux linkage under the motor voltage model, the rotor flux linkage in the stationary coordinate system is obtained. Considering the dead zone effect, it is converted into the state equation of the rotor flux linkage under the voltage model, yielding the final rotor flux linkage under the voltage model. Then, the estimated rotor flux linkage is determined based on the state equation of the adjustable reference model. The estimated rotor flux linkage and the rotor flux linkage under the voltage model are input into the rotor resistance adaptive law to identify the rotor resistance value online. The entire process requires no additional hardware circuitry or measuring instruments, and is obtained solely through the software of the frequency converter, saving physical space. Furthermore, it eliminates the need for complex physical model construction, is independent of the motor power in the physical model, and the algorithm is relatively simple to implement. By constructing the rotor resistance adaptive law, online rotor resistance identification is achieved, ensuring the stable convergence of the asynchronous motor closed-loop vector rotor resistance adaptive system. Compared to offline detection, which has a delay, this application achieves online real-time detection, improving accuracy.
[0085] In some embodiments, the Lyapunov function is constructed based on the error state variable, state convergence, and parameter convergence, including:
[0086] The state error value is obtained by performing convergence processing on the error state variable based on the state convergence property.
[0087] The parameter error value is obtained by performing convergence processing on the estimated rotor resistance value of the adjustable reference model based on parameter convergence.
[0088] Positive definite processing is performed based on the state error value and parameter error value to construct the Lyapunov function.
[0089] Specifically, combining the above formula (3), we obtain the error state variable, whose state convergence is: ; parameter convergence is Therefore, based on state convergence, the error state variables are converged to obtain the state error value. The rotor resistance of the adjustable reference model is estimated based on parameter convergence. The parameter error value is obtained by performing convergence processing.
[0090] Positive definite processing is performed based on the state error value and parameter error value to realize the Lyapunov function. The construction, in which, All are undetermined positive definite matrices, take The identity matrix is the mathematical symbol for the trace, representing the sum of the diagonal elements of the matrix.
[0091] In constructing the Lyapunov function, the parameter error value can be determined by combining the first and second variables of the error state variable mentioned above. The larger the parameter error value, the slower the parameter convergence. Conversely, the smaller the value, the faster the convergence, but it may oscillate. Therefore, it is necessary to ensure that the state error term is positive definite and the parameter error term is non-definite.
[0092] The process of constructing the Lyapunov function provided in this embodiment needs to satisfy the positive definite property, and at the same time, it includes the state convergence and parameter convergence corresponding to the state error term and parameter error term, respectively, so as to achieve double convergence.
[0093] In some embodiments, an adaptive law for rotor resistance is constructed based on the stability parameters in the Lyapunov function, including:
[0094] The stable parameters are obtained by differentiating the Lyapunov function; the stable parameters are non-positive parameters.
[0095] The state equation for estimating the rotor resistance value is obtained by transforming the stable parameters, which serves as the adaptive law for rotor resistance.
[0096] Specifically, the stable parameters are obtained by differentiating the Lyapunov function, as shown in the following formula:
[0097] ; (4)
[0098] Wherein, the stability parameter is Specifically:
[0099] ;
[0100] ;
[0101] It can be observed that, This is the feature matrix of the asynchronous motor itself, therefore The system is negative definite. Therefore, as long as the stability parameter is non-positive, the system is stable according to the Lyapunov function.
[0102] In other words, when the stability parameter is 0, that is: After transforming the stable parameters, we obtain the state equation for estimating the rotor resistance, which serves as the adaptive law for rotor resistance. Its formula (5) is as follows:
[0103] .
[0104] The rotor resistance adaptive law construction process provided in this embodiment allows the motor control system to track the changes in rotor resistance in real time, so as to realize online identification of rotor resistance and ensure that the asynchronous motor closed-loop vector rotor resistance adaptive system is stable and convergent.
[0105] In some embodiments, the state equation of the rotor flux linkage under the motor voltage model is determined based on the state equation of the rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model, including:
[0106] The first integral formula is obtained by integrating the state equation of rotor flux linkage under the motor voltage model from the initial time to the target time.
[0107] The second integral formula is obtained by integrating by parts into the first integral formula.
[0108] The third integral formula is obtained by introducing a voltage correction term into the other integral formulas besides the second integral formula within the first integral formula.
[0109] The state equation of rotor flux linkage under the voltage model is obtained by processing the components of the third integral formula and the second integral formula in the stationary coordinate system.
[0110] Specifically, the state equation of the rotor flux linkage under the motor voltage model is as follows:
[0111] ; (6)
[0112] in, This is the resistance value of the stator resistor. This is the leakage inductance value. This is the voltage value. This represents the current value.
[0113] The first integral formula is obtained by integrating from the initial time t0 to the target time t1. The specific formula is as follows:
[0114] ; (7)
[0115] By performing distributed integration on the first integral formula, we obtain the second integral formula, that is, the integral formula in formula (7). After integration, we obtain the following formula:
[0116] ; (8)
[0117] Since the current value at the initial moment is 0, it simplifies to: .
[0118] By introducing a voltage correction term into the remaining integral formulas (excluding the second integral formula) within the first integral formula, we obtain the third integral formula, namely:
[0119] ; (9)
[0120] The second and third formulas, after component processing in the stationary coordinate system, yield the final state equation of the rotor flux linkage under the motor voltage model, as follows:
[0121] ; (10)
[0122] in, It is the resistance value of the stator resistor. It is the leakage inductance value. and It is the final rotor flux linkage in the stationary coordinate system. and It is the voltage in a stationary coordinate system. and It is the current in a stationary coordinate system. and It is the equivalent loss voltage value of the dead zone in the stationary coordinate system.
[0123] It should be noted that the rotor flux linkage under the voltage model is obtained through the above formula (10), and then the estimated rotor flux linkage is determined through the state equation of the adjustable reference model. Applying this to formula (5), we obtain the derivative of the corresponding rotor resistance, and then integrate it to obtain the resistance value of the rotor.
[0124] This embodiment provides a method to determine the rotor flux linkage under the motor voltage model, so that it can be input into the rotor resistance adaptive law to obtain the rotor resistance value, thereby realizing online identification of the rotor resistance value.
[0125] In some embodiments, after obtaining the resistance value of the rotor, the method further includes:
[0126] Obtain the current components of the motor in the dq coordinate system;
[0127] The slip frequency value is determined based on the rotor resistance, mutual inductance, and current components.
[0128] The estimated electric angular velocity value is determined based on the slip frequency value and the rotational speed value;
[0129] The rotor position is obtained by integrating the estimated electrical angular velocity value to track the actual rotor position of the motor.
[0130] Specifically, changes in rotor resistance directly cause deviations in the rotor magnetic field orientation; therefore, rotor flux linkage orientation correction is necessary. The formula for calculating the slip frequency is as follows:
[0131] ; (11)
[0132] in, This is the resistance value of the rotor. Mutual inductance value, , All are current components in the dq coordinate system of the motor.
[0133] Figure 2 A block diagram illustrating the principle of online rotor resistance identification based on model reference adaptation for closed-loop vector control of asynchronous motors, as provided in this application embodiment, is shown below. Figure 2 As shown, under Model Reference Adaptive Theory (MRMS), the output rotor resistance is obtained. After obtaining the slip frequency, the difference between the output resistance and the rotational speed is processed to obtain the estimated electrical angular velocity. Then, integration is performed to obtain the rotor position, which tracks the actual rotor position. Furthermore, to prevent the integral stage from introducing deviations, a high-pass filter is added, with its cutoff frequency set very low to eliminate the cumulative error of the integral stage due to DC deviation. The block diagram of the Indirect Field Oriented Control (IFOC) system for an asynchronous motor is a typical structure for vector control of asynchronous motors. Its core principle is to achieve decoupling control of torque and flux linkage through coordinate transformation and closed-loop regulation. Through nested regulation of the speed loop and current loop, and the decoupling effect of coordinate transformation, precise and efficient control of motor speed and torque is achieved.
[0134] In this embodiment, the rotational speed and position of the asynchronous motor's closed-loop vector rotor flux linkage are corrected in real time during the online rotor resistance identification process, ensuring steady-state and dynamic performance during operation. The online correction method and scheme ensure complete decoupling of the excitation current and torque current, improving the steady-state and dynamic performance of the entire motor control system.
[0135] In some embodiments, after obtaining the resistance value of the rotor, the method further includes:
[0136] If the rotor resistance is greater than or equal to the preset resistance, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
[0137] Specifically, considering the changes in rotor resistance mentioned above, the increased temperature alters the skin effect on the rotor side, thus changing the rotor resistance. During a motor over-temperature fault, the corresponding online rotor resistance must meet the following requirements: the rotor resistance value... The resistance value is greater than or equal to the preset resistance value. .
[0138] Figure 3 A flowchart of an over-temperature fault detection and processing method provided in this application embodiment is shown below. Figure 3 As shown, the steps are as follows:
[0139] S21: Get the current rotor resistance value;
[0140] S22: Determine whether the rotor resistance is greater than or equal to the preset resistance; if yes, proceed to step S23; if no, return to step S21.
[0141] S23: Motor over-temperature fault;
[0142] S24: The frequency converter has stopped running.
[0143] The over-temperature protection provided in this embodiment can map motor over-temperature faults by the relationship between the rotor resistance value and the preset resistance value, estimate the rotor temperature value in real time, monitor the rotor status, overcome rotor servo effect and temperature rise influence, ensure complete decoupling of excitation current and torque current, and improve the steady-state performance and dynamic performance of the entire motor control system.
[0144] In other embodiments, after obtaining the rotor resistance value, the method further includes:
[0145] Obtain the constraint relationship between the motor's rotor windings and the motor temperature;
[0146] Obtain the initial rotor resistance, initial temperature, and rotor resistance temperature coefficient for electrons;
[0147] The corresponding rotor temperature value is determined based on the constraint relationship, the initial rotor resistance value, the initial temperature value, and the rotor resistance temperature coefficient.
[0148] If the rotor temperature exceeds the preset temperature value, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
[0149] Specifically, this enables online identification of the closed-loop vector rotor resistance of the asynchronous motor. During normal operation, rotor temperature rise analysis is required to monitor rotor health. The rotor windings and temperature satisfy the following constraints:
[0150] ;
[0151] in, This is the initial rotor resistance value. This is the initial temperature value. This is the rotor resistance temperature coefficient. The value of the rotor resistance temperature coefficient varies depending on the material. Copper is generally taken as 235, and aluminum is generally taken as 225.
[0152] Based on the above constraints, the rotor temperature value can be calculated. If the rotor temperature value exceeds the preset temperature value, an over-temperature fault is determined, and the over-temperature protection is activated.
[0153] In addition, a dual judgment can be made in combination with the above embodiments. The over-temperature protection mechanism will only be activated if both the above embodiments determine that an over-temperature fault has occurred.
[0154] The over-temperature protection provided in this embodiment calculates the rotor temperature value based on the rotor resistance and other parameters. During normal operation, it monitors the rotor temperature rise in real time and tracks the rotor's health status. This improves the diversity and flexibility of the over-temperature protection triggering mechanism.
[0155] Figure 4 A structural diagram of an inverse Γ-type equivalent circuit for an asynchronous motor provided in this application embodiment is shown below. Figure 4 As shown, this diagram describes the electrical relationship between the voltage, current, and rotor flux linkage on the stator side of an asynchronous motor. This relationship is fundamental for analyzing the dynamic characteristics and control strategies (such as field-oriented control) of asynchronous motors. Simplifying the complex electromagnetic coupling relationship into a series-parallel connection of circuit elements facilitates the analysis of the dynamic characteristics of the motor's voltage, current, and flux linkage, making it a key tool for designing asynchronous motor control algorithms. The differential equations for the stator voltage and rotor flux linkage can be derived using this circuit. In field-oriented control, decoupling control of the rotor flux linkage and torque is achieved by controlling the excitation and torque components of the stator current.
[0156] Figure 5 A block diagram of a model reference adaptive online rotor resistance identification system provided in this application embodiment is shown below. Figure 5 As shown, the parameters output by the voltage reference model and the current adjustable model are input to the adaptive mechanism to obtain the rotor resistance value, which is then input into the current adjustable model. The adaptive mechanism is the technical solution for the rotor resistance identification method of the motor provided in this embodiment.
[0157] The foregoing has described in detail various embodiments of the method for identifying the rotor resistance of an electric motor. Based on this, this application also discloses a rotor resistance identification device for an electric motor corresponding to the above method. Figure 6 This is a structural diagram of a rotor resistance identification device for an electric motor provided in an embodiment of this application. Figure 6 As shown, the rotor resistance identification device for the motor includes:
[0158] The first determining module 11 is used to determine the error state variables based on the state equation of the rotor flux under the motor current model and the state equation of the corresponding adjustable reference model.
[0159] Module 12 is used to construct a Lyapunov function based on the error state variables, state convergence, and parameter convergence, and to construct an adaptive law for rotor resistance based on the stable parameters in the Lyapunov function.
[0160] The second determining module 13 is used to determine the state equation of the rotor flux linkage under the motor voltage model based on the state equation of the rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model.
[0161] Input module 14 is used to determine the estimated rotor flux linkage based on the state equation of the adjustable reference model, and input the estimated rotor flux linkage and the rotor flux linkage under the voltage model into the rotor resistance adaptive law to obtain the rotor resistance value.
[0162] Since the embodiments of the device part correspond to the embodiments described above, please refer to the embodiments described in the method part for the embodiments of the device part, and will not be repeated here.
[0163] For a description of the rotor resistance identification device for an electric motor provided in this application, please refer to the above method embodiments. This application will not repeat the description here, as it has the same beneficial effects as the above-described rotor resistance identification method for electric motors.
[0164] Figure 7 A structural diagram of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown, the device includes:
[0165] Memory 21 is used to store computer programs;
[0166] Processor 22 is used to implement the steps of the rotor resistance identification method of the motor when executing a computer program.
[0167] The processor 22 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 22 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 22 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 22 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 22 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.
[0168] The memory 21 may include one or more computer-readable storage media, which may be non-transitory. The memory 21 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 21 is used to store at least the following computer program 211, which, after being loaded and executed by the processor 22, is capable of implementing the relevant steps of the motor rotor resistance identification method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 21 may also include an operating system 212 and data 213, etc., and the storage method may be temporary storage or permanent storage. The operating system 212 may include Windows, Unix, Linux, etc. The data 213 may include, but is not limited to, the data involved in the motor rotor resistance identification method, etc.
[0169] In some embodiments, the electronic device may further include a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27.
[0170] Those skilled in the field can understand, Figure 7 The structures shown do not constitute a limitation on electronic devices and may include more or fewer components than those shown.
[0171] The processor 22 implements the rotor resistance identification method of the motor provided in any of the above embodiments by calling the instructions stored in the memory 21.
[0172] For an introduction to the electronic device provided in this application, please refer to the above method embodiments. This application will not repeat the details here, but it has the same beneficial effects as the rotor resistance identification method of the above motor.
[0173] Furthermore, this application also provides a computer-readable storage medium storing a computer program, which, when executed by processor 22, implements the steps of the rotor resistance identification method for the motor described above.
[0174] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. 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 storage medium and executes all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0175] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments. This application will not repeat the description here, but it has the same beneficial effects as the above-described rotor resistance identification method for motors.
[0176] The present application provides a detailed description of a method, apparatus, device, and medium for identifying the rotor resistance of an electric motor. The various embodiments are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.
[0177] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.
Claims
1. A method for identifying the rotor resistance of an electric motor, characterized in that, include: Error state variables are determined based on the state equations of rotor flux under the motor current model and the corresponding state equations of the adjustable reference model. Based on the error state variables, state convergence, and parameter convergence, a Lyapunov function is constructed, and a rotor resistance adaptive law is constructed based on the stable parameters in the Lyapunov function. The state equation of rotor flux linkage under the motor voltage model is determined based on the state equation of rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model. The estimated rotor flux is determined based on the state equation of the adjustable reference model, and the estimated rotor flux and the rotor flux under the voltage model are input to the rotor resistance adaptive law to obtain the rotor resistance value.
2. The method for identifying the rotor resistance of an electric motor according to claim 1, characterized in that, Based on the error state variables, state convergence, and parameter convergence, a Lyapunov function is constructed, including: The state error value is obtained by performing convergence processing on the error state variable based on the state convergence. The parameter error value is obtained by performing convergence processing on the estimated rotor resistance value of the adjustable reference model based on the parameter convergence. The Lyapunov function is constructed by performing positive definite processing based on the state error value and the parameter error value.
3. The method for identifying the rotor resistance of an electric motor according to claim 2, characterized in that, Based on the stability parameters in the Lyapunov function, an adaptive law for rotor resistance is constructed, including: The stable parameters are obtained by differentiating the Lyapunov function; wherein the stable parameters are non-positive parameters. The state equation for the estimated rotor resistance value is obtained by transforming the stable parameters, and is used as the adaptive law for rotor resistance.
4. The method for identifying the rotor resistance of an electric motor according to claim 3, characterized in that, The state equation of rotor flux linkage under the motor voltage model is determined based on the state equation of rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model, including: The first integral formula is obtained by integrating the state equation of rotor flux linkage under the motor voltage model from the initial time to the target time. The second integral formula is obtained by performing integration by parts on the first integral formula. A voltage correction term is introduced into the other integral formulas besides the second integral formula in the first integral formula to obtain the third integral formula; The state equation of rotor flux linkage under the motor voltage model is obtained by performing component processing of the third integral formula and the second integral formula in the stationary coordinate system.
5. The method for identifying the rotor resistance of an electric motor according to any one of claims 1 to 4, characterized in that, After obtaining the rotor resistance value, the following steps are also included: Obtain the current components of the motor in the dq coordinate system; The slip frequency value is determined based on the rotor resistance, mutual inductance, and current components. The estimated electric angular velocity value is determined based on the slip frequency value and the rotational speed value; The rotor position is obtained by integrating the estimated electrical angular velocity value to track the actual rotor position of the motor.
6. The method for identifying the rotor resistance of an electric motor according to any one of claims 1 to 4, characterized in that, After obtaining the rotor resistance value, the following steps are also included: If the rotor resistance is greater than or equal to the preset resistance, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
7. The method for identifying the rotor resistance of an electric motor according to any one of claims 1 to 4, characterized in that, After obtaining the rotor resistance value, the following steps are also included: Obtain the constraint relationship between the motor's rotor windings and the motor temperature; Obtain the initial rotor resistance, initial temperature, and rotor resistance temperature coefficient for electrons; The corresponding rotor temperature value is determined based on the constraint relationship, the initial rotor resistance value, the initial temperature value, and the rotor resistance temperature coefficient. If the rotor temperature exceeds the preset temperature value, an over-temperature fault is identified in the motor, and the over-temperature protection is activated.
8. A rotor resistance identification device for an electric motor, characterized in that, include: The first determining module is used to determine the error state variables based on the state equation of the rotor flux under the motor current model and the state equation of the corresponding adjustable reference model. The module is used to construct a Lyapunov function based on the error state variables, state convergence, and parameter convergence, and to construct a rotor resistance adaptive law based on the stable parameters in the Lyapunov function. The second determining module is used to determine the state equation of the rotor flux linkage under the motor voltage model based on the state equation of the rotor flux linkage under the motor voltage model, so as to obtain the rotor flux linkage under the voltage model. The input module is used to determine the estimated rotor flux linkage based on the state equation of the adjustable reference model, and input the estimated rotor flux linkage and the rotor flux linkage under the voltage model into the rotor resistance adaptive law to obtain the rotor resistance value.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the rotor resistance identification method for an electric motor as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the rotor resistance identification method for an electric motor as described in any one of claims 1 to 7.