Method for identifying multi-process parameters of induction motor

Abstract

The invention discloses a multi-process parameter identification method for an induction motor. According to the method, different parameter identification strategies are provided for different common working conditions in combination with an induction motor transient mathematical model. For motor starting and steady-state operation conditions, current deviation and impedance deviation are respectively adopted as objective functions, and motor parameters are identified by using an improved particle swarm algorithm. On the basis, accurate parameters such as stator and rotor resistance, stator and rotor leakage inductance and the like are provided for a control strategy during long-term operation of the motor by introducing a parameter change coefficient and considering nonlinear characteristics such as skin effect, magnetic circuit saturation, temperature and the like. According to the method, historical operation data of the motor are fully utilized, invasive experiments such as locked-rotor and signal injection do not need to be additionally carried out, and the method has the advantages of being good in engineering practicability and accurate in identification result.

Description

Technical Field

[0001] The invention belongs to the technical field of online motor parameters, and in particular relates to a multi-pass parameter identification method taking induction into account. Background Art

[0002] With the development of power electronics technology, digital signal processing technology, etc., the control performance of induction motor drive systems has been continuously improved. The vector control and sensorless control schemes widely used in industrial production are highly dependent on the accuracy of motor parameters. Excessive identification errors in motor parameters will lead to a decrease in the dynamic response performance of the drive system or even a loss of stability. Therefore, induction motor parameter identification is of great significance to improving motor control performance.

[0003] Common induction motor parameter identification methods can be divided into two categories: offline identification and online identification. The traditional offline identification method is to obtain the stator and rotor resistance and inductance parameters of the induction motor based on the induction motor equivalent circuit through no-load and stalled experiments. In addition, there are also offline identification methods that obtain mutual inductance, leakage inductance, and resistance parameters through single-phase AC experiments and DC attenuation experiments, that is, injecting specific AC or DC voltage to achieve motor parameters. Because the motor is in a static or abnormal operating condition, the motor parameter identification results obtained by this type of offline identification method often deviate greatly from the motor parameters under actual working conditions. The online identification method mainly uses the least squares method, Kalman filtering method, model reference adaptation, genetic algorithm, etc., combined with the measurement results of voltage, current, speed, etc. to perform online real-time identification of parameters such as stator and rotor resistance and inductance. The main problems with existing induction motor parameter identification methods include:

[0004] (1) Traditional parameter identification methods often require experimental testing of the motor under specific working conditions. Factors such as measurement errors, inaccurate model assumptions, and differences between experimental and actual working conditions lead to limited accuracy of parameter identification, and it is difficult to identify all the main electrical and mechanical parameters of the induction motor at one time.

[0005] (2) The motor is affected by nonlinear factors such as magnetic circuit saturation, skin effect, and temperature in different operating states (such as motor starting, steady-state operation, etc.). Currently, most parameter identification methods fail to accurately take into account the impact of the above factors on the motor parameters. Summary of the invention

[0006] In order to solve the problem that the traditional motor parameter identification method has low accuracy and cannot take into account the influence of various nonlinear factors such as magnetic circuit saturation, skin effect, temperature and so on on the parameters during operation, the present invention provides a multi-process parameter identification method for an induction motor. Based on the mathematical model of the induction motor in different working conditions of motor starting and steady-state operation, different methods are used for parameter identification. Among them, the current deviation is used as the objective function when the motor starts, and the impedance deviation is used as the objective function when the motor is running steadily, and the improved particle swarm algorithm is used to identify the motor parameters. On this basis, the impedance variation coefficient is introduced to take into account the influence of nonlinear factors, which has the advantages of accurate identification results, good engineering practicality and convenient application. To achieve the above purpose, the present invention adopts the following technical scheme: the multi-process parameter identification method for an induction motor includes: a power supply, an induction motor, a voltage sensor, a current sensor, a signal processing module, a motor working condition judgment module, a parameter identification module, and a motor historical operation database.

[0007] The power supply is connected to the induction motor and provides the required voltage for the induction motor; the voltage sensor is connected in parallel with the induction motor terminal and measures the motor terminal voltage; the current sensor is connected in series with the motor terminal and measures the stator current of the motor. The signal processing module is connected to the voltage sensor and the current sensor, and the measured voltage and current signals are passed through a low-pass filter to filter out the noise caused by the sensor, environment and other factors; the motor working condition judgment module is connected to the signal processing module, and judges whether the motor is in the starting, steady-state operation and other working conditions through state quantities such as voltage and current; the parameter identification module is connected to the motor working condition judgment module, and selects a suitable parameter identification method according to the current motor operating condition, and transmits the current parameter identification result to the motor historical operation database, which is convenient for the operation and maintenance personnel to confirm and view.

[0008] Furthermore, when the induction motor is started, motor parameter identification is performed according to the following steps.

[0009] Step 1: Establish a transient mathematical model of the motor when starting. The induction motor model with stator current and rotor flux as state variables is shown in equations (1) to (4), and the initial values ​​of the state variables are all 0.

[0010]

[0011] In the formula, x is the state variable; A is the system matrix; B is the input matrix; u is the input phasor; i sα 、i sβ , rα , rβ are the stator current and rotor flux in the αβ stationary coordinate system; u sα 、u sβ is the stator voltage in the αβ stationary coordinate system; ω r is the electrical angular velocity; R s , Rr is the stator and rotor resistance; L ss , L rr and L m They are the total stator inductance, the total rotor inductance and the excitation inductance respectively; n p is the number of motor pole pairs; J is the moment of inertia; σ is the leakage inductance coefficient, T r is the rotor time constant, T r =L rr / R r ; γ is an intermediate variable, T L is the load torque.

[0012] Step 2: Measure the stator ABC three-phase currents and obtain the αβ-axis stator currents through coordinate transformation, as shown in equation (5).

[0013]

[0014] In the formula, k represents the kth data point; is the three-phase current of the electronic device measured in the kth step; is the stator αβ axis current after the kth step of transformation.

[0015] Step 3: Determine the objective function F of the improved particle swarm algorithm, taking the deviation between the stator current simulation value and the measured value as the objective function, as shown in formula (6).

[0016]

[0017] In the formula, the superscript "st" is the parameter when the motor starts; K is the total number of motor starting data; The αβ axis stator current is calculated based on the motor model for parameter identification; It is the measured αβ axis stator current.

[0018] Step 4: Use particle swarm with shrinkage factor to identify the parameters of induction motor, as shown in equations (7) and (8).

[0019]

[0020] In the formula, v id (u), x id (u) is the speed and position of the i-th particle in the d-dimensional search space at the u-th iteration; r1 and r2 are two independent random numbers between 0 and 1; c1 and c2 are acceleration factors; p id (u), p gd (u) is the position of the individual and global optimal particles in the u-th iteration in the d-dimensional search space; χ is the contraction factor; κ is the balance parameter that controls the exploration and search capabilities, κ∈[0,1];

[0021] The individual optimal position iteration formula of the i-th particle is shown in formula (9).

[0022]

[0023] In the formula, p i (u) = [p i1 (u), p i2 (u),…,p id (u)], is the individual optimal position of the i-th particle after the u-th iteration; x i (u+1)=[x i1 (u+1), x i2 (u+1),…,x id (u+1)], which is the position of the i-th particle after the u+1-th iteration.

[0024] The global optimal solution is obtained through equation (10).

[0025] [p g1 ,p g2 ,…,p gd ]=min{p1,p2,…,p N} (10)In the formula, N is the total number of particles.

[0026] Furthermore, when the induction motor is running in a steady state, motor parameter identification is performed according to the following steps.

[0027] Step 1: Establish the steady-state mathematical model of the motor. When the motor is in steady-state operation, the mathematical model is expressed by phasors, as shown in equations (11) and (12).

[0028]

[0029] In the formula, are the phasors of stator voltage and stator current, respectively, Z is the total impedance of the motor; L sl , L rl is the stator and rotor leakage inductance; ω B is the synchronous electrical angular velocity; s is the slip rate; j is the imaginary unit.

[0030] Step 2: Calculate the total motor impedance by measuring the ABC three-phase voltage and current, as shown in formula (13).

[0031]

[0032] In the formula, is the total motor impedance calculated using the measured voltage and current.

[0033] Step 3: Introduce parameter variation coefficient to take into account the influence of nonlinear factors such as main magnetic circuit saturation and leakage magnetic circuit saturation on motor parameters. The motor parameter identification values ​​during steady-state operation are obtained through equations (14) to (18).

[0034]

[0035] In the formula, the superscript "pre" indicates the last identification result of the motor parameters. It should be pointed out that according to the different nonlinear effects on the motor, steady-state operation is divided into two cases: 1) the motor has just entered the steady state; 2) the steady-state operation has been running for a long time. When the motor has just entered the steady state, the identification result of the start-up is used; when the motor has been running in the steady state for a long time, the identification result of the last steady-state operation is used.

[0036] The meaning and value range of the introduced parameter variation coefficient are shown in Table 1.

[0037] Table 1 Meaning and value range of correction coefficient

[0038]

[0039] Step 4: Estimate the motor steady-state slip s using the rotor slot harmonics of the stator current, as shown in equation (19).

[0040]

[0041] In the formula, f sh is the rotor slot harmonic frequency; f B is the fundamental frequency; Z2 is the number of rotor slots.

[0042] Step 5: Calculate the identification value of the total motor impedance according to the identification parameters of steps 3 and 4, as shown in formula (12).

[0043] Step 6: Determine the objective function F of the improved particle swarm algorithm. During steady-state operation, the deviation between the measured value and the identified value of the total motor impedance is used as the objective function, as shown in formula (20).

[0044]

[0045] Wherein, the superscript "ss" indicates the steady-state parameter, Re indicates the real part of impedance, and Im indicates the imaginary part of impedance.

[0046] Step 7: Use the improved particle swarm algorithm with shrinkage factor to identify the steady-state parameters of the motor, as shown in formula (7).

[0047] Furthermore, the current operating state of the motor is determined based on the measured current and estimated speed of the motor, and then the corresponding parameter identification method is selected. The practical criteria for the motor operating conditions are shown in Table 2. Where I is the effective value of the stator current; I N is the effective value of the rated current.

[0048] Table 2 Motor operating condition criteria

[0049]

[0050] Furthermore, after the motor starting or steady-state parameters are identified, the identification results are stored in the motor historical operation database, which is convenient for operation and maintenance personnel to view in real time or for the next motor parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a connection schematic diagram of the present invention;

[0052] Figure 2 It is a parameter identification flow chart of the present invention;

[0053] Figure 3 Measure the A-phase current of the induction motor;

[0054] Figure 4 The fitness iteration curve of the particle swarm algorithm when the present invention is applied to the motor start-up;

[0055] Figure 5 It is the fitness iteration curve of the particle swarm algorithm when the motor just enters the steady state. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The present embodiment provides an induction motor multi-process parameter identification method, including: a power supply, an induction motor, a voltage sensor, a current sensor, a signal processing module, a motor operating condition judgment module, a parameter identification module, and a motor historical operation database.

[0058] Specifically, the power supply is connected to the induction motor and provides the required voltage for the induction motor; the voltage sensor is connected in parallel with the induction motor terminal and measures the motor terminal voltage; the current sensor is connected in series with the motor terminal and measures the stator current of the motor. The signal processing module is connected to the voltage sensor and the current sensor, and the measured voltage and current signals are passed through a low-pass filter to filter out noise caused by factors such as sensors and the environment; the motor operating condition judgment module is connected to the signal processing module, and determines whether the motor is in the starting, steady-state operation and other operating conditions through state quantities such as voltage and current; the parameter identification module is connected to the motor operating condition judgment module, and selects a suitable parameter identification method according to the current motor operating condition, and transmits the current parameter identification result to the motor historical operation database for easy confirmation and viewing by the operation and maintenance personnel.

[0059] Specifically, when the motor is started, the motor parameters are identified according to the following steps.

[0060] Step 1: Establish a transient mathematical model of the motor when starting. The induction motor model with stator current and rotor flux as state variables is shown in equations (1) to (4), and the initial values ​​of the state variables are all 0.

[0061]

[0062] In the formula, x is the state variable; A is the system matrix; B is the input matrix; u is the input phasor; i sα 、i sβ , rα , rβ are the stator current and rotor flux in the αβ stationary coordinate system; u sα 、u sβ is the stator voltage in the αβ stationary coordinate system; ω r is the electrical angular velocity; R s , R r is the stator and rotor resistance; L ss , L rr and L m They are the total stator inductance, the total rotor inductance and the excitation inductance respectively; n p is the number of motor pole pairs; J is the moment of inertia; σ is the leakage inductance coefficient, T r is the rotor time constant, T r =L rr / R r ; γ is an intermediate variable, T L is the load torque.

[0063] Step 2: Measure the stator ABC three-phase currents and obtain the αβ-axis stator currents through coordinate transformation, as shown in equation (5).

[0064]

[0065] In the formula, k represents the kth data point; is the three-phase current of the electronic device measured in the kth step; is the stator αβ axis current after the kth step of transformation.

[0066] Step 3: Determine the objective function F of the improved particle swarm algorithm, taking the deviation between the stator current simulation value and the measured value as the objective function, as shown in formula (6).

[0067]

[0068] In the formula, the superscript "st" is the parameter when the motor starts; K is the total number of motor starting data; The αβ axis stator current is calculated based on the motor model for parameter identification; It is the measured αβ axis stator current.

[0069] Step 4: Use particle swarm with shrinkage factor to identify the parameters of induction motor, as shown in formula (7).

[0070]

[0071] In the formula, v id (u), x id (u) is the speed and position of the i-th particle in the d-dimensional search space at the u-th iteration; r1 and r2 are two independent random numbers between 0 and 1; c1 and c2 are acceleration factors; p id (u), p gd (u) is the position of the individual and global optimal particles in the u-th iteration in the d-dimensional search space; χ is the contraction factor; κ is the balance parameter that controls the exploration and search capabilities, κ∈[0,1];

[0072] The individual optimal position iteration formula of the i-th particle is shown in formula (9).

[0073]

[0074] In the formula, p i (u) = [p i1 (u), p i2 (u),…,p id (u)], is the individual optimal position of the i-th particle after the u-th iteration; x i (u+1)=[x i1 (u+1), x i2 (u+1),…,x id (u+1)], which is the position of the i-th particle after the u+1-th iteration.

[0075] The global optimal solution is obtained through equation (10).

[0076] [p g1 ,p g2 ,…,p gd ]=min{p1,p2,…,p N} (10)

[0077] In the formula, N is the total number of particles.

[0078] Specifically, during steady-state operation, motor parameter identification is performed according to the following steps.

[0079] Step 1: Establish the steady-state mathematical model of the motor. When the motor is in steady-state operation, the mathematical model is expressed by phasors, as shown in equations (11) and (12).

[0080]

[0081] In the formula, are the phasors of stator voltage and stator current, respectively, Z is the total impedance of the motor; L sl , L rl is the stator and rotor leakage inductance; ω B is the synchronous electrical angular velocity; s is the slip rate; j is the imaginary unit.

[0082] Step 2: Calculate the total motor impedance by measuring the ABC three-phase voltage and current, as shown in formula (13).

[0083]

[0084] In the formula, is the total motor impedance calculated using the measured voltage and current.

[0085] Step 3: Introduce parameter variation coefficient to take into account the influence of nonlinear factors such as main magnetic circuit saturation and leakage magnetic circuit saturation on motor parameters. The motor parameter identification values ​​during steady-state operation are obtained through equations (14) to (18).

[0086]

[0087] In the formula, the superscript "pre" indicates the last identification result of the motor parameters. It should be pointed out that according to the different nonlinear effects on the motor, steady-state operation is divided into two cases: 1) the motor has just entered the steady state; 2) the steady-state operation has been running for a long time. When the motor has just entered the steady state, the identification result of the start-up is used; when the motor has been running in the steady state for a long time, the identification result of the last steady-state operation is used.

[0088] The meaning and value range of the introduced parameter variation coefficient are shown in Table 1.

[0089] Table 1 Meaning and value range of correction coefficient

[0090]

[0091] Step 4: Estimate the motor steady-state slip using the rotor slot harmonics of the stator current, as shown in equation (19).

[0092]

[0093] In the formula, f sh is the rotor slot harmonic frequency; f B is the fundamental frequency; Z2 is the number of rotor slots.

[0094] Step 5: Calculate the identification value of the total motor impedance according to the identification parameters of steps 3 and 4, as shown in formula (12).

[0095] Step 6: Determine the objective function F of the improved particle swarm algorithm. During steady-state operation, the deviation between the measured value and the identified value of the total motor impedance is used as the objective function, as shown in formula (20).

[0096]

[0097] Wherein, the superscript "ss" indicates the steady-state parameter, Re indicates the real part of impedance, and Im indicates the imaginary part of impedance.

[0098] Step 7: Use the improved particle swarm algorithm with shrinkage factor to identify the steady-state parameters of the motor, as shown in formula (7).

[0099] Specifically, the current operating state of the motor is determined based on the measured current and estimated speed of the motor, and then the corresponding parameter identification method is selected. The practical criteria for the motor operating conditions are shown in Table 2. Where I is the effective value of the stator current; I N is the effective value of the rated current.

[0100] Table 2 Motor operating condition criteria

[0101]

[0102] Specifically, after identifying the motor starting or steady-state parameters, the identification results are stored in the motor historical operation database to facilitate real-time viewing by operation and maintenance personnel or for the next motor parameter identification.

[0103] Application Examples

[0104] In this application example, Figure 1 In the induction motor parameter identification system shown in the figure, the power supply supplies power to the induction motor whose parameters are to be identified, and uses a voltage transformer and a current transformer to measure the terminal voltage and the stator current. Figure 2The parameter identification is performed according to the flowchart shown in the figure. The results are shown in Table 3. The motor terminal voltage is 380V rated voltage. When the motor starts, the A phase current is Figure 3 The fitness iteration curve of the improved particle swarm algorithm is shown in Figure 4 shown.

[0105] Table 3 Motor starting parameter identification results

[0106]

[0107] Secondly, according to the parameter identification results in Table 3, the motor parameters when it just enters the steady state are identified, and the influence of nonlinear factors such as magnetic circuit saturation and skin effect is taken into account through the parameter change coefficient. The coefficients obtained by the improved particle swarm algorithm are shown in Table 4. The corresponding motor parameters are shown in Table 5. The fitness iteration curve of the improved particle swarm algorithm is shown in Figure 5 shown.

[0108] Table 4 Parameter variation coefficient identification results

[0109]

[0110] Table 5 Motor steady-state parameter identification results

[0111]

[0112] From the identification results, it can be seen that the parameter identification scheme proposed by the present invention can accurately identify the motor parameters of different working conditions such as motor starting and steady-state operation. By taking into account the nonlinear factors such as skin effect and magnetic circuit saturation through the parameter variation coefficient, reliable parameter support is provided for applications such as motor online control and motor operation energy efficiency analysis.

[0113] As described above, the present invention has been described in detail. Obviously, as long as the modifications do not substantially deviate from the inventive concept and effect of the present invention, the modifications that are obvious to those skilled in the art are also included in the protection scope of the present invention.

Claims

1. A method for multi-process parameter identification of an induction motor, characterized in that: include: Power supply, induction motor, voltage sensor, current sensor, signal processing module, motor operating condition judgment module, parameter identification module, motor historical operation database. The power supply is connected to the induction motor and provides the required voltage for the induction motor; the voltage sensor is connected in parallel to the induction motor terminal and measures the motor terminal voltage; the current sensor is connected in series to the motor terminal and measures the stator current of the motor. The signal processing module is connected to the voltage sensor and the current sensor, and the measured voltage and current signals are passed through a low-pass filter to filter out noise caused by factors such as the sensor and the environment; the motor operating condition judgment module is connected to the signal processing module, and judges whether the motor is in the starting, steady-state operation and other operating conditions through state quantities such as voltage and current; The parameter identification module is connected to the motor operating condition judgment module, selects the appropriate parameter identification method according to the current motor operating condition, and transmits the current parameter identification result to the motor historical operation database for easy confirmation and viewing by the operation and maintenance personnel.

2. The induction motor multi-process parameter identification method according to claim 1, characterized in that: When the motor starts, follow the steps below to identify the motor parameters. Step 1: Establish a transient mathematical model of the motor when starting. The induction motor model with stator current and rotor flux as state variables is shown in equations (1) to (4), and the initial values ​​of the state variables are all 0. In the formula, x is the state variable; A is the system matrix; B is the input matrix; u is the input phasor; i sα 、i sβ , rα , rβ are the stator current and rotor flux in the αβ stationary coordinate system; u sα 、u sβ is the stator voltage in the αβ stationary coordinate system; ω r is the electrical angular velocity; R s , R r is the stator and rotor resistance; L ss , L rr and L m They are the total stator inductance, the total rotor inductance and the excitation inductance respectively; n p is the number of motor pole pairs; J is the moment of inertia; σ is the leakage inductance coefficient, T r is the rotor time constant, T r =L rr / R r ; γ is an intermediate variable, T L is the load torque. Step 2: Measure the stator ABC three-phase currents and obtain the αβ-axis stator currents through coordinate transformation, as shown in equation (5). In the formula, k represents the kth data point; is the three-phase current of the electronic device measured in the kth step; is the stator αβ axis current after the kth step of transformation. Step 3: Determine the objective function F of the improved particle swarm algorithm, taking the deviation between the stator current simulation value and the measured value as the objective function, as shown in formula (6). In the formula, the superscript "st" is the parameter when the motor starts; K is the total number of motor starting data; The αβ axis stator current is calculated based on the motor model for parameter identification; It is the measured αβ axis stator current. Step 4: Use particle swarm with shrinkage factor to identify the parameters of induction motor, as shown in equations (7) and (8). In the formula, v id (u), x id (u) is the speed and position of the i-th particle in the d-dimensional search space at the u-th iteration; r1 and r2 are two independent random numbers between 0 and 1; c1 and c2 are acceleration factors; p id (u), p gd (u) is the position of the individual and global optimal particles in the u-th iteration in the d-dimensional search space; χ is the contraction factor; κ is the balance parameter that controls the exploration and search capabilities, κ∈[0,1]; The individual optimal position iteration formula of the i-th particle is shown in formula (9). In the formula, p i (u) = [p i1 (u), p i2 (u),…,p id (u)], is the individual optimal position of the i-th particle after the u-th iteration; x i (u+1)=[x i1 (u+1), x i2 (u+1),…,x id (u+1)], which is the position of the i-th particle after the u+1-th iteration. The global optimal solution is obtained through equation (10). [p g1 ,p g2 ,…,p gd ]=min{p1,p2,…,p N } (10) In the formula, N is the total number of particles.

3. The method for multi-process parameter identification of an induction motor according to claim 2, characterized in that: During steady-state operation, follow the steps below to identify the motor parameters. Step 1: Establish the steady-state mathematical model of the motor. When the motor is in steady-state operation, the mathematical model is expressed by phasors, as shown in equations (11) and (12). In the formula, are the phasors of stator voltage and stator current, respectively, Z is the total impedance of the motor; L sl , L rl is the stator and rotor leakage inductance; ω B is the synchronous electrical angular velocity; s is the slip rate; j is the imaginary unit. Step 2: Calculate the total motor impedance by measuring the ABC three-phase voltage and current, as shown in formula (13). In the formula, is the total motor impedance calculated using the measured voltage and current. Step 3: Introduce parameter variation coefficient to take into account the influence of nonlinear factors such as main magnetic circuit saturation and leakage magnetic circuit saturation on motor parameters. The motor parameter identification values ​​during steady-state operation are obtained through equations (14) to (18). In the formula, the superscript "pre" indicates the last identification result of the motor parameters. It should be pointed out that according to the different nonlinear effects on the motor, steady-state operation is divided into two cases: 1) the motor has just entered the steady state; 2) the steady-state operation has been running for a long time. When the motor has just entered the steady state, the identification result of the start-up is used; when the motor has been running in the steady state for a long time, the identification result of the last steady-state operation is used. The meaning and value range of the introduced parameter variation coefficient are shown in Table 1. Table 1 Meaning and value range of correction coefficient Step 4: Estimate the motor steady-state slip s using the rotor slot harmonics of the stator current, as shown in equation (19). In the formula, f sh is the rotor slot harmonic frequency; f B is the fundamental frequency; Z2 is the number of rotor slots. Step 5: Calculate the identification value of the total motor impedance according to the identification parameters of steps 3 and 4, as shown in formula (12). Step 6: Determine the objective function F of the improved particle swarm algorithm. During steady-state operation, the deviation between the measured value and the identified value of the total motor impedance is used as the objective function, as shown in formula (20). Wherein, the superscript "ss" indicates the steady-state parameter, Re indicates the real part of impedance, and Im indicates the imaginary part of impedance. Step 7: Use the improved particle swarm algorithm with shrinkage factor to identify the steady-state parameters of the motor, as shown in formula (7).

4. The method for multi-process parameter identification of an induction motor according to claim 3, characterized in that: The current operating state of the motor is determined based on the measured current and estimated speed of the motor, and then the corresponding parameter identification method is selected. The practical criteria for the motor operating conditions are shown in Table 2. Where I is the effective value of the stator current; I N is the effective value of the rated current. Table 2 Motor operating condition criteria 5. The method for multi-process parameter identification of an induction motor according to claim 4, characterized in that: After identifying the motor starting or steady-state parameters, the identification results are stored in the motor historical operation database for real-time viewing by operation and maintenance personnel or for the next motor parameter identification.

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