Induction motor parameter identification method and device based on steady-state T-type equivalent circuit

Abstract

The application is suitable for the technical field of induction motor parameter identification, and provides an induction motor parameter identification method and device based on a steady-state T-type equivalent circuit, which comprises the following steps: obtaining data parameters of a motor system, establishing a steady-state T-type equivalent circuit, obtaining theoretical impedance of the motor system, and the data parameters comprising at least some of the following parameters: voltage value, current value, stator resistance, rotor resistance, excitation inductance, stator leakage inductance, rotor leakage inductance, angular frequency of input phase voltage, rotating speed and slip rate; performing fast Fourier transform on the voltage value, the current value and the rotating speed to obtain first processing information; calculating the measured impedance of the motor system based on the first processing information; and obtaining motor identification parameters based on the theoretical impedance, the measured impedance and a quantum particle swarm algorithm. Compared with the existing method, the parameter identification method in the application has a faster convergence process, is not easy to fall into a local optimal point, can realize more accurate parameter estimation accuracy, and can realize online real-time identification of motor parameters.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of induction motor parameter identification, and particularly relates to an induction motor parameter identification method and device based on a steady-state T-type equivalent circuit. BACKGROUND

[0002] Motor parameter identification is divided into two categories in a broad sense. One is offline identification of parameters. Most of this type of identification method is based on a steady-state T-type equivalent circuit of a motor. Some specific excitation signals are injected into the motor, and various parameters of the motor are identified through some signal processing or optimization algorithm. The other is online identification technology of motor parameters. Currently, the main methods include recursive least squares algorithm, model reference adaptive algorithm, extended Kalman filter algorithm, artificial neural network algorithm, etc.

[0003] However, the online identification technology of motor parameters currently has a slow optimization speed and is prone to falling into a local optimal solution. SUMMARY

[0004] The embodiments of the application provide an induction motor parameter identification method and device based on a steady-state T-type equivalent circuit to solve the problem of slow optimization speed and easy falling into a local optimal solution.

[0005] The application is implemented by the following technical solutions:

[0006] In a first aspect, the embodiments of the application provide an induction motor parameter identification method based on a steady-state T-type equivalent circuit, including:

[0007] Obtaining data parameters of a motor system, establishing a steady-state T-type equivalent circuit, obtaining a theoretical impedance of the motor system, and the data parameters including at least some of a voltage value, a current value, a stator resistance, a rotor resistance, an excitation inductance, a stator leakage inductance, a rotor leakage inductance, an angular frequency of an input phase voltage, a rotating speed, and a slip rate.

[0008] Performing fast Fourier transform on the voltage value, the current value, and the rotating speed to obtain first processing information.

[0009] Calculating a measured impedance of the motor system based on the first processing information.

[0010] Obtaining motor identification parameters based on the theoretical impedance, the measured impedance, and a quantum particle swarm algorithm.

[0011] In combination with the first aspect, in some possible implementation manners, the theoretical impedance is:

[0012]

[0013] wherein R s is the stator resistance, R r is the rotor resistance, Lm Lm is the magnetizing inductance ls Lls is the stator leakage inductance lr Llr is the rotor leakage inductance, s is the slip, and ω is the angular frequency of the input phase voltage of the motor. The theoretical impedance of the A phase, the B phase, and the C phase is Z.

[0014] With reference to the first aspect, in some possible implementation manners, the measured impedance includes: an A-phase measured impedance Z real1 , a B-phase measured impedance Z real2 , and a C-phase measured impedance Z real3 . Wherein, u f1 is the amplitude of the voltage at the fundamental frequency point f1 of the A phase, i f1 is the amplitude of the current at the fundamental frequency point f1 of the A phase, θ1 is the phase of the voltage at the fundamental frequency point f1 of the A phase, θ2 is the phase of the current at the fundamental frequency point f1 of the A phase, the fundamental frequency point f1 of the A phase is the frequency point with the maximum amplitude in all frequency points collected by the A phase, the B-phase measured impedance Z real2 , and the C-phase measured impedance Z real3 are calculated in the same way as the A-phase measured impedance Z real1 .

[0015] With reference to the first aspect, in some possible implementation manners, the motor identification parameter is obtained based on the theoretical impedance, the measured impedance, and a quantum particle swarm algorithm, and specifically includes: establishing a fitness evaluation function based on the theoretical impedance, the first processing information, and the measured impedance. A particle swarm is randomly generated, and the particle swarm is initialized, iteratively calculated, and optimized based on the fitness evaluation function to obtain the motor identification parameter.

[0016] With reference to the first aspect, in some possible implementation manners, the fitness evaluation function is:

[0017]

[0018] Wherein,

[0019]

[0020]

[0021]

[0022]

[0023] R s is the stator resistance, R r is the rotor resistance, L m is the magnetizing inductance ls is the stator leakage inductance lrL is the leakage inductance of the rotor, s is the slip, ω is the angular frequency of the input phase voltage of the motor, and R is the resistance real2 Z is the measured impedance of the B phase real2 X is the real part real2 Z is the measured impedance of the B phase real2 R is the imaginary part real3 Z is the measured impedance of the C phase real3 X is the real part real3 Z is the measured impedance of the B phase real3 R is the imaginary part.

[0024] In some possible implementations, the particle swarm is initialized based on the fitness evaluation function, including: mapping the range of all particle positions in the particle swarm to an initial value space. The global extreme value gbest of the particle swarm is calculated based on the preset particle initial individual extreme value pbest. The attractor P, the average best position mbest, and the intermediate variable k are calculated based on the preset particle initial individual extreme value pbest and the global extreme value gbest of the particle swarm. The attractor P, the average best position mbest, and the intermediate variable k are converted into zero matrices.

[0025] In some possible implementations, the particle swarm is iteratively calculated and optimized based on the fitness evaluation function to obtain the motor identification parameter, and the method specifically includes: updating the average best position and the attractor to obtain an updated average best position and an updated average best position. The updated particle position is calculated based on the updated average best position and the updated average best position. If the specified number of iterations is not reached, the initial individual extreme value, the global extreme value, the attractor, the average best position, and the particle position information are updated, and the above steps are performed again. If the specified number of iterations is reached, the optimal particle position is calculated based on the updated particle position and the fitness evaluation function, and the motor identification parameter is obtained based on the optimal particle position.

[0026] The formula of the updated average best position is: L is the number of particles in the particle swarm.

[0027] The formula of the updated attractor is: P = r.pbest + (1-r)gbest, r ∈ [0, 1];

[0028] The updated particle position is x(t+1) = x(t) + a · (mbest(t) - x(t)) + c · (P - x(t)), where a and c are learning factors.

[0029]

[0030] wherein, t is the iteration number, x(t+1) is the updated population particle position, x(t) and mbest(t) are the particle position and the average best position obtained by the last iteration calculation, Fitness(xi-1 ) is the fitness value of the global optimal position of the last generation particle swarm, Fitness(x i ) is the fitness value of the i-th particle of the current generation.

[0031] In a second aspect, an induction motor parameter identification device based on a steady-state T-type equivalent circuit is provided, comprising:

[0032] An acquisition module is configured to acquire data parameters of a motor system, establish a steady-state T-type equivalent circuit, and obtain a theoretical impedance of the motor system. The data parameters include at least some of a voltage value, a current value, a stator resistance, a rotor resistance, an excitation inductance, a stator leakage inductance, a rotor leakage inductance, an angular frequency of an input phase voltage, a rotating speed, and a slip rate. A processing module is configured to perform a fast Fourier transform on the voltage value, the current value, and the rotating speed to obtain first processing information. A calculation module is configured to calculate a measured impedance of the motor system based on the first processing information. An identification module is configured to obtain motor identification parameters based on the theoretical impedance, the measured impedance, and a quantum particle swarm algorithm.

[0033] In a third aspect, a terminal device is provided, comprising a processor and a memory. The memory is configured to store a computer program. The processor is configured to execute the computer program to implement the induction motor parameter identification method based on the steady-state T-type equivalent circuit according to any one of the first aspect.

[0034] In a fourth aspect, a computer readable storage medium is provided. The computer readable storage medium stores a computer program. The computer program is configured to be executed by a processor to implement the induction motor parameter identification method based on the steady-state T-type equivalent circuit according to any one of the first aspect.

[0035] In a fifth aspect, a computer program product is provided. When the computer program product is executed on a terminal device, the terminal device is caused to execute the induction motor parameter identification method based on the steady-state T-type equivalent circuit according to any one of the first aspect.

[0036] It can be understood that the beneficial effects of the second aspect to the fifth aspect described above can be referred to the related description of the first aspect, and will not be repeated here.

[0037] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0038] This application establishes a steady-state T-type equivalent circuit to obtain the theoretical impedance, and then uses Fast Fourier Transform to obtain the measured impedance. Substituting the theoretical and measured impedances into a quantum particle swarm optimization (QPSO) algorithm, the motor identification parameters are calculated using the QPSO algorithm. The introduction of the QPSO algorithm improves the algorithm's global search capability and optimization accuracy, resulting in faster convergence and reducing the likelihood of getting trapped in local optima. Therefore, it solves the problems of slow optimization speed and susceptibility to local optima in current online motor parameter identification technologies.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art 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.

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of the induction motor parameter identification method based on a steady-state T-type equivalent circuit provided in an embodiment of this application.

[0042] Figure 2 This is a schematic flowchart of an embodiment of the induction motor parameter identification method based on a steady-state T-type equivalent circuit provided in this application;

[0043] Figure 3 This is a schematic diagram of the steady-state T-type equivalent circuit of an induction motor provided in an embodiment of this application;

[0044] Figure 4 This is a block diagram of the control strategy and parameter identification strategy of a motor system provided in an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the structure of an induction motor parameter identification device based on a steady-state T-type equivalent circuit provided in an embodiment of this application;

[0046] Figure 6 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0047] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0048] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0049] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0050] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0051] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0053] For example, embodiments of this application can be applied to, for example... Figure 1In the exemplary scenario shown, the induction motor data parameter acquisition device 10 acquires induction motor data parameters and sends them to the induction motor parameter identification device 20 based on the steady-state T-type equivalent circuit. The induction motor parameter identification device 20 based on the steady-state T-type equivalent circuit completes parameter identification and obtains the induction motor identification parameters.

[0054] The following combination Figure 1 The method for identifying parameters of an induction motor based on a steady-state T-type equivalent circuit, as described in this application, is explained in detail.

[0055] Figure 2 This is a schematic flowchart of an embodiment of the induction motor parameter identification method based on a steady-state T-type equivalent circuit provided in this application, with reference to... Figure 2 The detailed description of the induction motor parameter identification method based on the steady-state T-type equivalent circuit is as follows:

[0056] Step 101: Obtain the data parameters of the motor system, establish a steady-state T-type equivalent circuit, and obtain the theoretical impedance of the motor system.

[0057] For example, the data parameters include at least some of the following: voltage value, current value, stator resistance, rotor resistance, magnetizing inductance, stator leakage inductance, rotor leakage inductance, angular frequency of input phase voltage, speed, and slip.

[0058] For example, such as Figure 3 As shown, the theoretical impedance can be calculated using Kirchhoff's laws. The theoretical impedance is:

[0059]

[0060] Among them, R s For stator resistance, R r For rotor resistance, L m For magnetizing inductor, L ls For stator leakage inductance, L lr ω is the rotor leakage inductance, s is the slip, ω is the angular frequency of the motor input phase voltage, and the theoretical impedances of phases A, B, and C are all Z.

[0061] Step 102: Perform a fast Fourier transform on the voltage, current and rotation speed to obtain the first processing information.

[0062] Specifically, the sampling frequency Fs of the Fast Fourier Transform is 128Hz, and the number of points N is 256. Taking phase A of the motor as an example, the motor voltage, current, and speed information are synchronously sampled and saved every 0.0078125s. After collecting 256 points, the first data group Array1[u1; i1; ω] is formed. r1 Where u represents voltage sampling data, i represents current sampling data, and ωr This represents the rotational speed sampling data.

[0063] Calculations are performed on the voltage and current data to obtain the amplitude and phase data at each frequency point: The frequency point with the largest amplitude is the fundamental frequency point f1 in the first data set collected. The amplitude and phase information of the fundamental voltage and current at this frequency point are extracted to obtain:

[0064] Specifically, the actual slip of this motor under operating conditions can be determined by the fundamental frequency f1 and the speed information ω. r1 First, the sampled rotational speed information is processed by mean filtering: Using the fundamental frequency f1 as the synchronization frequency, the actual slip of the motor in this state is obtained according to the slip calculation formula:

[0065] For example, the corresponding data for phase B and phase C can also be obtained using the fast Fourier transform described above.

[0066] Step 103: Calculate the measured impedance of the motor system based on the first processing information.

[0067] For example, the measured impedance includes: the measured impedance Z of phase A. real1 Measured impedance Z of phase B real2 The measured impedance Z of phase C real3 , in, u f1 Let i be the amplitude of the voltage at the fundamental frequency point f1 of phase A. f1 Let f1 be the amplitude of the current at the fundamental frequency point f1 of phase A, θ1 be the phase of the voltage at the fundamental frequency point f1 of phase A, θ2 be the phase of the current at the fundamental frequency point f1 of phase A, and f1 be the frequency point with the largest amplitude among all frequency points collected for phase A. The measured impedance Z of phase B is given. real2 The measured impedance Z of phase C real3 The calculation method and the measured impedance Z of phase A are compared. real1 The calculation method is the same.

[0068] Step 104: Based on theoretical impedance, measured impedance and quantum particle swarm optimization algorithm, obtain motor identification parameters.

[0069] For example, motor identification parameters are obtained based on theoretical impedance, measured impedance, and quantum particle swarm optimization (QPSO). Specifically, this includes: establishing a fitness evaluation function based on theoretical impedance, first-processed information, and measured impedance; randomly generating a particle swarm; and performing initialization, iterative calculation, and optimization operations on the particle swarm based on the fitness evaluation function to obtain the motor identification parameters.

[0070] Specifically, the generated random particle swarm is [R s R r L ls L m ], dimension d = 4, size L = 25, maximum number of iterations T = 500, particle position range is [x min x max ], x min and x max The value is taken from the offline identification parameters of the induction motor and is ±50% of the corresponding value after corresponding conversion.

[0071] For example, the fitness evaluation function is:

[0072]

[0073] in,

[0074]

[0075]

[0076]

[0077]

[0078] R s For stator resistance, R r For rotor resistance, L m For magnetizing inductor, L ls For stator leakage inductance, L lr R is the rotor leakage inductance, s is the slip, ω is the angular frequency of the motor input phase voltage, and R is the rotor leakage inductance. real2 The measured impedance Z of phase B real2 Real part, X real2 The measured impedance Z of phase B real2 Imaginary part, R real3 The measured impedance Z of phase C real3 Real part, X real3 The measured impedance Z of phase B real3 The empty part.

[0079] For example, initializing the particle swarm based on the fitness evaluation function includes: mapping the range of all particle positions in the swarm to an initial value space; calculating the global extremum gbest of the particle swarm based on a preset initial individual particle extremum pbest; calculating the attractor P, the average best position mbest, and the intermediate variable k based on the preset initial individual particle extremum pbest and the global extremum gbest of the particle swarm; and transforming the attractor P, the average best position mbest, and the intermediate variable k into a zero matrix.

[0080] Exemplarily, iterative calculations and optimization operations are performed on the particle swarm based on the fitness evaluation function to obtain the motor identification parameters, which specifically include: updating the average best position and the attractor to obtain the updated average best position and the updated average best position. Based on the updated average best position and the updated average best position, the updated particle positions are calculated. If the specified number of iterations is not reached, update the initial individual extreme value, global extreme value, attractor, average best position, and the position information of each particle, and repeat the above steps. If the specified number of iterations is reached, then based on the updated positions of the particles and the fitness evaluation function, the optimal particle positions are calculated, and based on the optimal particle positions, the motor identification parameters are obtained.

[0081] The formula for the updated average best position is: L is the number of particles in the particle swarm.

[0082] The formula for the updated attractor is: P = r·pbest+(1 - r)gbest, r ∈ [0, 1];

[0083] The updated particle position is.

[0084]

[0085] Where t is the number of iterations, x(t + 1) is the updated position of the population particles, x(t) and mbest(t) are the particle position and the average optimal position obtained from the previous iteration calculation respectively, Fitness(x i-1 ) is the fitness value of the global optimal position of the previous generation particle swarm, and Fitness(x i ) is the fitness value of the i-th particle in the current generation.

[0086] Specifically, substitute the updated particle positions into the fitness evaluation function Fitness to calculate the current fitness value Fitness(x i ) of each particle, and at the same time update the individual extreme value pbest and the global optimal value gbest. The update rules are: if Fitness(x i ) < Fitness(pbest), then the position of pbest is updated to x i , otherwise pbest remains unchanged; if the minimum value min(Fitness(x i )) in the fitness values calculated this time < Fitness(gbest), then the position of gbest is updated to x i , otherwise, the position of gbest remains unchanged.

[0087] Specifically, when the specified number of iterations is reached, the output optimal example position is the final motor parameter, which is the result.

[0088] Specifically, such as Figure 4 As shown, based on the control strategy and parameter identification strategy of the motor system shown in the figure, the voltage value, current value and speed used in step 102 of this application can be obtained.

[0089] Specifically, to verify the difference between this method and the traditional variable-weight particle swarm optimization algorithm, in a specific embodiment, the identification parameter errors obtained by the two methods were compared. The comparison process is as follows:

[0090] The motor parameters used in the experiment were: number of pole pairs np = 2, power = 2.2 kW, and stator resistance R = 2.2 kW. s 6.4Ω, stator leakage inductance L ls 0.028H, rotor leakage inductance L lr =0.028H, mutual inductance L m The rotor resistance is 0.336H. r It is 4.8Ω.

[0091] First, start the motor and run it under normal operating conditions. Using this method, obtain a set of calculated real-time port impedance information of the motor's steady-state T-type equivalent circuit.

[0092] Then, using this information, two different algorithms were used to perform online identification of motor parameters, with each algorithm being tested 10 times.

[0093] First, let's compare the algorithm performance:

[0094] Traditional variable-weight particle swarm optimization (PSO) only finds the global optimum in 40 out of 100 parameter identifications, achieving the minimum fitness function and meeting the accuracy requirements. Furthermore, the average number of iterations required to reach the specified accuracy exceeds 300, indicating a slow optimization speed. In contrast, the algorithm used in this application finds the global optimum in all 100 parameter identifications, with the maximum number of iterations required not exceeding 100. Specific experimental data are shown in Table 1.

[0095] Table 1

[0096]

[0097]

[0098] Therefore, the quantum particle swarm optimization algorithm of this application has significant advantages in terms of stability and optimization convergence speed, and can identify various parameters of the motor online better and faster.

[0099] Then, the accuracy of the motor parameter identification results was compared. As shown in Table 2, the quantum particle swarm algorithm of this application has smaller identification error and higher identification accuracy.

[0100] Table 2: Comparison of parameter identification accuracy between the two algorithms

[0101]

[0102] Specifically, the above-mentioned induction motor parameter identification method based on steady-state T-type equivalent circuit can complete the online identification of induction motor parameters, that is, the motor identification parameters at any time can be obtained. In addition, the method improves the contraction and expansion factor in the quantum particle swarm algorithm during the calculation process, which improves the global search capability and optimization accuracy of the method, making the convergence process faster and less likely to get trapped in local optima.

[0103] The above-mentioned induction motor parameter identification method based on steady-state T-type equivalent circuit establishes a steady-state T-type equivalent circuit to obtain the theoretical impedance. The measured impedance is obtained based on fast Fourier transform. The theoretical and measured impedances are then substituted into the quantum particle swarm optimization algorithm. By improving the selection of the contraction expansion factor beta in the quantum particle swarm optimization algorithm, the global search capability and optimization accuracy of the algorithm are improved, making the convergence process faster and less prone to getting trapped in local optima. Finally, the motor identification parameters are obtained.

[0104] For example, based on the above-mentioned induction motor parameter identification method based on steady-state T-type equivalent circuit, the staff can make real-time adjustments to the motor system according to the actual situation, so as to realize real-time tracking of all parameters of the induction motor. That is, the corresponding operation of the motor system can be performed in a timely manner according to the induction motor identification parameters calculated by this method.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] Corresponding to the induction motor parameter identification method based on steady-state T-type equivalent circuit described in the above embodiments, Figure 5 The diagram shows a structural block diagram of an induction motor parameter identification device based on a steady-state T-type equivalent circuit provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0107] See Figure 5 The induction motor parameter identification device based on steady-state T-type equivalent circuit in this application embodiment may include an acquisition module 301, a processing module 302, a calculation module 303, and an identification module 304.

[0108] Optionally, module 301 is used to acquire data parameters of the motor system, establish a steady-state T-type equivalent circuit, and obtain the theoretical impedance of the motor system.

[0109] For example, the data parameters include at least some of the following: voltage value, current value, stator resistance, rotor resistance, magnetizing inductance, stator leakage inductance, rotor leakage inductance, angular frequency of input phase voltage, speed, and slip.

[0110] For example, the theoretical impedance is:

[0111]

[0112] Among them, R s For stator resistance, R r For rotor resistance, L m For magnetizing inductor, L ls For stator leakage inductance, L lr ω is the rotor leakage inductance, s is the slip, ω is the angular frequency of the motor input phase voltage, and the theoretical impedances of phases A, B, and C are all Z.

[0113] Optionally, the processing module 302 is used to perform a fast Fourier transform on the voltage value, current value, and rotation speed to obtain the first processing information.

[0114] Optionally, the calculation module 303 is used to calculate the measured impedance of the motor system based on the first processed information.

[0115] For example, the measured impedance includes: the measured impedance Z of phase A. real1 Measured impedance Z of phase B real2 The measured impedance Z of phase C real3 , in, u f1 Let i be the amplitude of the voltage at the fundamental frequency point f1 of phase A. f1 Let f1 be the amplitude of the current at the fundamental frequency point f1 of phase A, θ1 be the phase of the voltage at the fundamental frequency point f1 of phase A, θ2 be the phase of the current at the fundamental frequency point f1 of phase A, and f1 be the frequency point with the largest amplitude among all frequency points collected for phase A. The measured impedance Z of phase B is given. real2 The measured impedance Z of phase C real3 The calculation method and the measured impedance Z of phase A are compared. real1 The calculation method is the same.

[0116] Optionally, the identification module 304 is used to obtain motor identification parameters based on theoretical impedance, measured impedance and quantum particle swarm optimization algorithm.

[0117] For example, the identification module 304 is also used to: establish a fitness evaluation function based on theoretical impedance, first processed information, and measured impedance; randomly generate a particle swarm; and perform initialization, iterative calculation, and optimization operations on the particle swarm based on the fitness evaluation function to obtain motor identification parameters.

[0118] For example, the fitness evaluation function is:

[0119]

[0120] in,

[0121]

[0122]

[0123]

[0124]

[0125] R s For stator resistance, R r For rotor resistance, L m For magnetizing inductor, L ls For stator leakage inductance, L lr R is the rotor leakage inductance, s is the slip, ω is the angular frequency of the motor input phase voltage, and R is the rotor leakage inductance. real2 The measured impedance Z of phase B real2 Real part, X real2 The measured impedance Z of phase B real2 Imaginary part, R real3 The measured impedance Z of phase C real3 Real part, X real3 The measured impedance Z of phase B real3 The empty part.

[0126] For example, the identification module 304 is further configured to: update the average best position and attractor to obtain an updated average best position and an updated average best position. Based on the updated average best position and the updated average best position, calculate the updated particle position. If the specified number of iterations has not been reached, update the initial individual extreme value, global extreme value, attractor, average best position, and particle position information, and repeat the above steps. If the specified number of iterations has been reached, calculate the optimal particle position based on the updated particle position and fitness evaluation function, and obtain the motor identification parameters based on the optimal particle position.

[0127] For example, the updated formula for the average best position is: L is the number of particles in the particle swarm.

[0128] The updated attractor formula is: P = r·pbest + (1-r)gbest, r∈[0,1];

[0129] The updated particle positions are as follows.

[0130]

[0131] in, t represents the iteration number, x(t+1) is the updated population particle position, x(t) and mbest(t) are the particle position and average optimal position calculated in the previous iteration, respectively, and Fitness(x) is the position of the particle in the population. i-1 Fitness(x) represents the fitness value of the global optimal position of the previous generation particle swarm. i ) represents the fitness value of the i-th particle in the current era.

[0132] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0134] This application also provides a terminal device, see [link to relevant documentation] Figure 6 The terminal device 500 may include at least one processor 510 and a memory 520, the memory 520 being used to store a computer program 521. The processor 510 is used to call and run the computer program 521 stored in the memory 520 to implement the steps in any of the above method embodiments, for example... Figure 2 Steps 101 to 104 in the illustrated embodiment. Alternatively, when the processor 510 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 5 The functions of modules 301 to 304 are shown.

[0135] For example, computer program 521 may be divided into one or more modules / units, one or more of which are stored in memory 520 and executed by processor 510 to complete this application. The one or more modules / units may be a series of computer program segments capable of performing specific functions, which describe the execution process of the computer program in terminal device 500.

[0136] Those skilled in the art will understand that Figure 6 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0137] The processor 510 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0138] The memory 520 can be an internal storage unit of the terminal device or an external storage device, such as a plug-in hard drive, a smart media card (SMC), a secure digital card (SD), or a flash card. The memory 520 is used to store the computer program and other programs and data required by the terminal device. The memory 520 can also be used to temporarily store data that has been output or will be output.

[0139] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0140] The induction motor parameter identification method based on steady-state T-type equivalent circuit provided in this application embodiment can be applied to terminal devices such as computers, wearable devices, vehicle devices, tablet computers, laptop computers, netbooks, and mobile phones. This application embodiment does not impose any restrictions on the specific type of terminal device.

[0141] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various embodiments of the induction motor parameter identification method based on a steady-state T-type equivalent circuit described above.

[0142] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps in the various embodiments of the induction motor parameter identification method based on a steady-state T-type equivalent circuit.

[0143] 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 medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0144] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or 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 interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0147] 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.

[0148] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying parameters of an induction motor based on a steady-state T-type equivalent circuit, characterized in that, include: Obtain the data parameters of the motor system, establish a steady-state T-type equivalent circuit, and obtain the theoretical impedance of the motor system. The data parameters include at least some of the following: voltage value, current value, stator resistance, rotor resistance, magnetizing inductance, stator leakage inductance, rotor leakage inductance, angular frequency of input phase voltage, speed, and slip. The voltage value, the current value, and the rotational speed are subjected to a fast Fourier transform to obtain the first processed information; The measured impedance of the motor system is calculated based on the first processed information; Based on the theoretical impedance, the measured impedance, and the quantum particle swarm optimization algorithm, the motor identification parameters are obtained; The theoretical impedance is: ,in, R s For stator resistance, R r For rotor resistance, L m For excitation inductor, L ls For stator leakage inductance, L lr For rotor leakage inductance, s For slippage, ω Let be the angular frequency of the input phase voltage of the motor, and let the theoretical impedances of phases A, B, and C be . Z ; The measured impedance includes: the measured impedance of phase A. Measured impedance of phase B Measured impedance of phase C , ,in, , u f1 The fundamental frequency point of phase A f The voltage amplitude at point 1, i f1 The fundamental frequency point of phase A f The amplitude of the current at point 1, θ 1 represents the fundamental frequency of phase A. f The phase of the voltage at point 1, θ 2 is the fundamental frequency point of phase A. f The phase of the current at point 1, the fundamental frequency of phase A. f 1 represents the frequency with the largest amplitude among all frequency points collected in phase A, and the measured impedance of phase B. Measured impedance of phase C Calculation method and measured impedance of phase A The calculation methods are the same; The motor identification parameters obtained based on the theoretical impedance, the measured impedance, and the quantum particle swarm optimization algorithm specifically include: Based on the theoretical impedance, the first processed information, and the measured impedance, a fitness evaluation function is established. A particle swarm is randomly generated, and the particle swarm is initialized, iteratively calculated, and optimized based on the fitness evaluation function to obtain the motor identification parameters. The fitness evaluation function is: in, R s For stator resistance, R r For rotor resistance, L m For excitation inductor, L ls For stator leakage inductance, L lr For rotor leakage inductance, s For slippage, ω The angular frequency of the input phase voltage of the motor. Measured impedance of phase B Real part, Measured impedance of phase B virtual part, Measured impedance of phase C Real part, Measured impedance of phase B The empty part.

2. The induction motor parameter identification method based on a steady-state T-type equivalent circuit as described in claim 1, characterized in that, The initialization of the particle swarm based on the fitness evaluation function includes: Map the range of all particle positions in the particle swarm to the initial value space; Based on the preset initial individual extreme values ​​of particles pbest Calculate the global extremum of the particle swarm. gbest ; Based on the preset initial individual extreme value of the particle pbest and the global extremum of the particle swarm gbest The attractor was calculated. P Average best position mbest and intermediate variables k ; The attractor P The average best position mbest and the intermediate variables k Transform it into a zero matrix.

3. The induction motor parameter identification method based on a steady-state T-type equivalent circuit as described in claim 2, characterized in that, The step of iteratively calculating and optimizing the particle swarm based on the fitness evaluation function to obtain the motor identification parameters specifically includes: The average best position and the attractor are updated to obtain the updated average best position and the updated average best position; The updated particle positions are calculated based on the updated average best position and the updated average best position. If the specified number of iterations is not reached, update the initial individual extreme value, global extreme value, attractor, average best position and position information of each particle, and repeat the above steps. If the specified number of iterations is reached, the optimal particle position is calculated based on the updated particle position and the fitness evaluation function, and the motor identification parameters are obtained based on the optimal particle position. The formula for the updated average best position is: , L This represents the number of particles in the particle swarm. The formula for the updated attractor is: , ; The updated particle positions are: in, , , , t For the number of iterations, This refers to the updated population particle positions. and These are the particle position and the average optimal position calculated in the previous iteration, respectively. The fitness value represents the global optimal position of the previous generation particle swarm. For the contemporary first i The fitness value of each particle.

4. A method and apparatus for identifying parameters of an induction motor based on a steady-state T-type equivalent circuit, characterized in that, include: The acquisition module is used to acquire data parameters of the motor system, establish a steady-state T-type equivalent circuit, and obtain the theoretical impedance of the motor system. The data parameters include at least some of the following: voltage value, current value, stator resistance, rotor resistance, magnetizing inductance, stator leakage inductance, rotor leakage inductance, angular frequency of input phase voltage, speed, and slip. The processing module is used to perform a fast Fourier transform on the voltage value, the current value, and the rotational speed to obtain first processing information; The calculation module is used to calculate the measured impedance of the motor system based on the first processed information; The identification module is used to obtain motor identification parameters based on the theoretical impedance, the measured impedance, and the quantum particle swarm optimization algorithm. The theoretical impedance is: ,in, R s For stator resistance, R r For rotor resistance, L m For excitation inductor, L ls For stator leakage inductance, L lr For rotor leakage inductance, s For slippage, ω Let be the angular frequency of the input phase voltage of the motor, and let the theoretical impedances of phases A, B, and C be . Z ; The measured impedance includes: the measured impedance of phase A. Measured impedance of phase B Measured impedance of phase C , ,in, , u f1 The fundamental frequency point of phase A f The voltage amplitude at point 1, i f1 The fundamental frequency point of phase A f The amplitude of the current at point 1, θ 1 represents the fundamental frequency of phase A. f The phase of the voltage at point 1, θ 2 is the fundamental frequency point of phase A. f The phase of the current at point 1, the fundamental frequency of phase A. f 1 represents the frequency with the largest amplitude among all frequency points collected in phase A, and the measured impedance of phase B. Measured impedance of phase C Calculation method and measured impedance of phase A The calculation methods are the same; The identification module is also used for: Based on the theoretical impedance, the first processed information, and the measured impedance, a fitness evaluation function is established. A particle swarm is randomly generated, and the particle swarm is initialized, iteratively calculated, and optimized based on the fitness evaluation function to obtain the motor identification parameters. The fitness evaluation function is: in, R s For stator resistance, R r For rotor resistance, L m For excitation inductor, L ls For stator leakage inductance, L lr For rotor leakage inductance, s For slippage, ω The angular frequency of the input phase voltage of the motor. Measured impedance of phase B Real part, Measured impedance of phase B virtual part, Measured impedance of phase C Real part, Measured impedance of phase B The empty part.

5. A terminal device, characterized in that, include: A processor and a memory, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory to perform the induction motor parameter identification method based on a steady-state T-type equivalent circuit as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the induction motor parameter identification method based on a steady-state T-type equivalent circuit as described in any one of claims 1 to 3.

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