Induction motor parameter on-line identification method and device based on parameter directional solidification

Abstract

The invention discloses an induction motor parameter on-line identification method and device based on parameter directional solidification. The method comprises the following steps: step S01, acquiring all parameters required to be identified by an induction motor model; step S02, classifying parameters required to be identified into non-key parameters and non-key parameters; step S03, monitoring the operation state of the power grid and the load characteristic parameters of the induction motor in real time, and performing online identification on each parameter in the induction motor model according to the parameters monitored in real time; in the process of carrying out online identification on each parameter in the induction motor model, firstly, non-key parameters in the parameters needing to be identified are assigned, parameter optimization is carried out to identify each key parameter, the identified key parameters are input into the induction motor model, and then each non-key parameter is identified. And repeatedly executing until an identification result meeting a preset requirement is obtained. The method has the advantages of being simple in implementation method, high in identification efficiency and precision, high in real-time performance and robustness and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of load modeling of a distribution network, and in particular to an online identification method and device for induction motor parameters based on parameter-oriented solidification method (POSM). Background Art

[0002] Due to the access of distributed power sources such as new energy and electric vehicles, the current distribution network is facing more and more complex load characteristics and higher and higher requirements for operation and management. Therefore, distribution network load modeling has become one of the key technologies to ensure the safe and stable operation of the power grid and improve energy efficiency. An accurate load model can help better predict and adjust the operation status of the power grid to adapt to the changing power demand and supply. As one of the key load types in the distribution network, the current and power changes of the induction motor during its startup and operation will have a significant impact on the stability and power quality of the power grid. Therefore, it is of great significance to accurately identify the parameters of the induction motor and then establish an accurate mathematical model to achieve efficient management and optimal control of the distribution network.

[0003] Traditional induction motor parameter identification methods usually establish a simplified mathematical model of the induction motor and use simulation software and offline testing to identify and verify unknown parameters. However, this type of method cannot reflect the changes in motor parameters in real time, nor can it effectively deal with the uncertainty and volatility in power grid operation. Therefore, it is not suitable for rapidly changing power grid environments and complex load conditions, thereby limiting the flexibility and accuracy of distribution network dispatching and control. Moreover, since the induction motor model involves multiple parameters, the traditional identification method is to directly identify all parameters as a whole, which makes identification difficult and it is difficult to achieve fast, accurate and real-time online identification. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in response to the technical problems existing in the prior art, the present invention provides an online identification method and device for induction motor parameters based on parameter directional solidification, which has a simple implementation method, high identification efficiency and accuracy, and strong real-time and robustness. The method and device can adapt to changes in the power grid environment and adjust the identification strategy in real time to ensure the accuracy and reliability of online identification.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is: An online identification method for induction motor parameters based on parameter directional solidification, comprising the following steps: Step S01. Obtain all parameters required for identification of the induction motor model; Step S02. Classify the parameters to be identified into non-key parameters and non-key parameters; Step S03. Real-time monitoring of the grid operation state and the load characteristic parameters of the induction motor, and online identification of various parameters in the induction motor model according to the real-time monitored grid operation state and the load characteristic parameters of the induction motor; In the process of online identification of various parameters in the induction motor model, non-key parameters in the parameters to be identified are first assigned values, parameter optimization is performed to identify various key parameters, the identified key parameters are input into the induction motor model and then the identification of various non-key parameters is performed, and the execution is repeated until an identification result that meets the preset requirements is obtained.

[0006] Furthermore, the induction motor model is used to describe the dynamic behavior of the motor under different working conditions and simulate the response of the motor under various load changes. The stator part of the induction motor model consists of a stator resistor and a stator reactance The rotor part consists of a rotor resistor. and a rotor reactance , which is used to simulate the resistance loss and leakage loss of the rotor winding.

[0007] Furthermore, the parameters required to be identified in the induction motor model include: stator resistance, stator reactance, rotor resistance, rotor reactance, excitation resistance, mechanical torque coefficient and load rate, and the mechanical torque coefficient is the relationship between the mechanical torque output by the motor under different operating conditions and the electrical parameters.

[0008] Furthermore, in step S02, the parameters to be identified are classified according to the degree of influence on the performance indicators of the induction motor, and the performance indicators include any one or more of efficiency, output power, and stability.

[0009] Further, step S03 includes: Step S301. Acquire the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time, wherein the grid operation state parameters include bus voltage and phase angle, and the load characteristic parameters of the induction motor include active power and reactive power data; Step S302: assigning values ​​to non-key parameters in the induction motor model; Step S303. Calculate the fitness value of the intelligent optimization algorithm when each key parameter takes different values ​​according to the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time; Step S304. Sort the fitness values ​​of the intelligent optimization algorithm obtained when each key parameter takes different values, and determine the iteration results of each key parameter; Step S305. Determine whether the current iteration result satisfies the objective function. If not, return to step S303. Otherwise, output all key parameters identified and proceed to step S306. Step S306: Substitute the identified key parameters into the induction motor model; Step S307. Calculate the fitness value of the intelligent optimization algorithm when each non-key parameter takes different values ​​according to the real-time measured grid operation state parameters and the load characteristic parameters of the induction motor; Step S308. Sort the fitness values ​​of the intelligent optimization algorithm when the non-key parameters take different values, and determine the iteration results of each non-key parameter; Step S309. Determine whether the preset termination criteria are currently met, if yes, proceed to step S310, otherwise return to step S307; Step S310: Determine whether the preset two-layer criterion is currently met. If the iteration is complete, output the global optimal solution; otherwise, return to step S303.

[0010] Furthermore, the fitness value of the intelligent optimization algorithm is calculated according to the following formula:

[0011] in, is the fitness value, , are respectively the active power and reactive power of the actual bus output, , is the active and reactive power output by the induction motor model, is the number of sample points.

[0012] Furthermore, the active power output by the induction motor model is calculated as follows:

[0013] The reactive power is calculated as follows:

[0014] in, is the equivalent stator resistance of the induction motor, is the equivalent rotor resistance of the induction motor, is the equivalent rotor reactance of the induction motor, is the equivalent resistance of the induction motor, is the equivalent impedance of the induction motor, is the slip of the induction motor.

[0015] Furthermore, in step S03, typical values ​​or standard values ​​are used to assign values ​​to non-key parameters.

[0016] An induction motor parameter online identification device based on parameter directional solidification comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the above method.

[0017] A computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed by a processor.

[0018] Compared with the prior art, the advantages of the present invention are: 1. The present invention monitors the operation status of the power grid and the load characteristics of the induction motor in real time, and adopts a double-layer iterative optimization method of first directing and solidifying a part of non-key parameters to identify key parameters, and then substituting key parameters to identify non-key parameters. This can realize rapid online identification and dynamic update of induction motor parameters, accurately reflect the load characteristics of the induction motor, greatly improve the accuracy and efficiency of distribution network load modeling, so as to better carry out load prediction and scheduling decisions, and at the same time can effectively reduce operating costs and energy consumption, and improve the stability of the power grid, power quality and energy utilization efficiency.

[0019] 2. The present invention is based on online real-time identification of induction motor parameters. Compared with the traditional offline identification method, it can also capture the dynamic changes of motor parameters in time, thereby ensuring the timeliness and accuracy of the model.

[0020] 3. The present invention can identify and stabilize the key and stable parts of the motor parameters through parameter directional solidification technology, which can effectively improve the robustness of the identification results and reduce the impact of factors such as changes in the power grid environment or measurement noise. It can also dynamically adjust the identification strategy and parameter model according to changes in the power grid operation status and load characteristics, so that the mathematical model of the induction motor can better adapt to the actual power grid environment and improve the flexibility and reliability of power grid dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the implementation flow of the method for online identification of induction motor parameters based on parameter directional solidification in this embodiment.

[0022] Figure 2 It is a schematic diagram of the structural principle of the induction motor model used in this embodiment.

[0023] Figure 3 It is a detailed implementation flow diagram of online identification of induction motor parameters in a specific application embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0025] The present invention monitors the operation status of the power grid and the load characteristics of the induction motor in real time, and then realizes the parameter identification of the induction motor model online. In the identification process, the key parameters and non-key parameters are divided, and the non-key parameters are assigned values ​​to identify the key parameters first, that is, the key parameters are identified in advance based on a fixed part of the non-key parameters of the induction motor, the stable part of the motor parameters is first identified and solidified, and then the key parameters are identified, and then the non-key parameters are identified in turn by using the identified key parameters. Iterative optimization can be performed according to the results of each identification, and the identification strategy with the minimum deviation from the actual output is obtained, so as to improve the accuracy and robustness of parameter identification, and the online identification and dynamic update of the induction motor parameters can be realized, the identification strategy can be adjusted in real time to adapt to the changes in the power grid environment, the changes in the motor parameters can be reflected in real time, and the uncertainty and volatility in the operation of the power grid can be effectively dealt with. Not only can the accuracy and efficiency of the distribution network load modeling be greatly improved, but also the operation cost can be effectively reduced, the stability and power quality of the power grid can be improved, and the flexibility, accuracy and reliability of the distribution network dispatching and control can be improved.

[0026] like Figure 1 As shown, the steps of the induction motor parameter online identification method based on parameter directional solidification in this embodiment include: Step S01: Obtain all parameters required for identification of the induction motor model.

[0027] The induction motor model can describe the dynamic behavior of the motor under different operating conditions, such as starting, acceleration, deceleration and shutdown, and simulate the response of the motor under various load changes, such as changes in current, voltage and slip. The slip is the ratio of the difference between the rotor speed and the synchronous speed to the synchronous speed. It is a key parameter in the analysis of induction motors and affects the torque and power of the motor, as shown in Equation (1).

[0028] (1) in, is the slip, usually expressed as a dimensionless decimal or percentage, is the synchronous speed, that is, the theoretical maximum speed when there is no slip. is the actual speed of the rotor.

[0029] The rotor speed is the actual rotation speed of the rotor, which can be measured in revolutions per minute (RPM) or angular velocity (in radians per second, rad / s). The synchronous speed is the theoretical maximum speed determined by the power frequency and the number of poles of the motor. For a given power frequency and number of poles, the synchronous speed is fixed, as shown in equation (2).

[0030] (2) in, is the power frequency, is the number of poles of the motor.

[0031] In this embodiment, the induction motor model uses an equivalent circuit model of an induction motor, such as Figure 2 As shown in the figure, the stator part of the induction motor model consists of a stator resistor and a stator reactance The rotor part consists of a rotor resistor. and a rotor reactance , which is used to simulate the resistance loss and leakage loss of the rotor winding. The equivalent circuit model can simulate the mechanical characteristics of the motor through the electrical parameters, such as the torque-speed curve, to predict the behavior of the motor at different load rates and speeds.

[0032] In this embodiment, the parameters that need to be identified for the induction motor model specifically include stator resistance, stator reactance, rotor resistance, rotor reactance, excitation resistance, mechanical torque coefficient, and load rate, among which stator resistance ( ) represents the heat loss caused by the current passing through the winding during the operation of the motor, and the stator reactance ( ) reflects the impedance of the stator winding to the change of magnetic flux. In an induction motor, the reactance is mainly caused by hysteresis and eddy current effects. Hysteresis is a nonlinear characteristic of the magnetic material in the induction motor under the action of an alternating magnetic field. The eddy current effect refers to the closed current generated in the conductive material of the motor (such as the core of the stator and rotor) due to the action of the alternating magnetic field. Rotor resistance ( ) is closely related to the dynamic response characteristics of the motor, such as the start-up, acceleration and deceleration process. Its size will affect the rate of change of the rotor current, and thus affect the dynamic performance of the motor. The dynamic response characteristics of the motor are the ability of the motor to change its electrical and mechanical characteristics when facing external input changes (such as load changes, power supply voltage fluctuations, etc.). Rotor reactance ( ) determines the impedance of the rotor current, which in turn affects the rotor copper loss. The load factor is the ratio of the motor's actual output power to its rated power, which can measure the motor's efficiency and operating conditions.

[0033] Copper loss can be calculated using the following formula (3): (3) in, is the rotor copper loss, is the effective value of the rotor phase current, is the total copper resistance of the rotor winding.

[0034] The excitation resistance is the resistance used to simulate the magnetic field generated in the stator winding. This resistance is not a physical component that actually exists in the motor, but is used in the equivalent circuit model to represent the energy loss required to magnetize the motor.

[0035] The mechanical torque coefficient refers to the relationship between the mechanical torque output by the motor under different operating conditions and the electrical parameters, which can be calculated using the following formula (4): (4) in, is the mechanical torque, is the initial torque, , , is the torque coefficient, is the slip of the motor.

[0036] Step S02: Classify the parameters to be identified into non-important parameters and non-important parameters.

[0037] In this embodiment, the parameters to be identified can be classified according to the degree of influence on the performance indicators of the induction motor, and the performance indicators include efficiency, output power, stability, etc. For example, considering that the parameters that have a significant influence on the performance indicators of the induction motor (such as efficiency, output power, stability, etc.) are difficult to measure directly, or the measurement cost is high, and there are interactions between the parameters, and these interactions will affect the overall behavior of the motor, such parameters can be classified as key parameters, and the remaining parameters that have a small influence on the performance indicators of the induction motor are classified as non-key parameters.

[0038] Step S03. After assigning values ​​to the non-key parameters in the parameters to be identified, perform parameter optimization to identify the key parameters, input the identified key parameters into the induction motor model and then identify the non-key parameters, and repeat the process until an identification result that meets the preset requirements is obtained.

[0039] Since the induction motor model has many identification parameters and direct identification is difficult, this embodiment first directs and solidifies a part of non-key parameters, and after identifying the key parameters, substitutes the identified key parameters into the original model, and further identifies the non-key parameters, and iterates in this way until all parameters converge. This method can significantly reduce the amount of calculation and processing time. At the same time, it can ensure that each parameter is considered and optimized through the iterative method, so as to gradually improve the parameters, which is helpful to establish induction motor models in different environments to meet the requirements of various simulation experiments, so that a more accurate and reliable induction motor model can be efficiently established.

[0040] In this embodiment, for non-important parameters, typical values ​​or standard values ​​may be first used for assignment as initial estimated values ​​in the parameter optimization and iteration process, and then corrected and refined through actual measurement data.

[0041] In this embodiment, an intelligent optimization algorithm can be used for parameter optimization. The intelligent optimization algorithm can perform a global search in the parameter space to find the optimal or approximately optimal parameter combination, thereby ensuring the global convergence and accuracy of the model. The intelligent optimization algorithm can specifically use genetic algorithms, particle swarm optimization, simulated annealing, ant colony algorithms, etc.

[0042] As an optional implementation, Figure 3 As shown, the specific steps of step S03 are as follows: Step S301. Acquire the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time, the grid operation state parameters include bus voltage and phase angle, etc., and the load characteristic parameters of the induction motor include active power and reactive power data, etc.; Step S302: assign values ​​to non-key parameters in the induction motor model; set the number of outer optimization iterations i=1; Step S303. Calculate the fitness value of the intelligent optimization algorithm when each key parameter takes different values ​​according to the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time; Step S304. Sort the fitness values ​​of the intelligent optimization algorithm obtained when each key parameter takes different values, and determine the iteration results of each key parameter; Step S305. Determine whether the current iteration result satisfies the objective function. If not, return to step S303. Otherwise, output all key parameters identified and proceed to step S306. Step S306: Substitute the identified key parameters into the induction motor model and set the number of outer optimization iterations j=1; Step S307. Calculate the fitness value of the intelligent optimization algorithm when each non-key parameter takes different values ​​according to the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time; Step S308. Sort the fitness values ​​of the intelligent optimization algorithm when the non-key parameters take different values, and determine the iteration results of each non-key parameter; Step S309. Determine whether the preset termination criteria are currently met, if yes, proceed to step S310, otherwise return to step S307; Step S310: Determine whether the preset two-layer criterion is currently met. If the iteration is complete, output the global optimal solution; otherwise, return to step S303.

[0043] In the above steps, the objective function can be set according to the actual iteration target. The preset termination criteria can be set according to the actual identification target, such as reaching a specified number of iterations or all parameters reaching a specified convergence target, etc. In this embodiment, by first assigning non-key parameters and identifying key parameters as the first layer, and substituting key parameters into non-key parameters as the second layer, the corresponding double-layer criteria are preset for the two-layer identification process, such as whether the number of iterations of the two layers reaches a specified value or whether the iteration results of the two layers meet the preset requirements, etc., which can be specifically configured according to actual needs.

[0044] In the above step S302, the fitness value of the intelligent optimization algorithm can be calculated according to the following formula: (5) in, is the fitness value, , are respectively the active power and reactive power of the actual bus output, , is the active and reactive power output by the induction motor model (i.e., the equivalent motor model), is the number of sample points.

[0045] The active power of the induction motor model can be calculated as follows: (6) Reactive power can be calculated as follows: (7) in, is the equivalent stator resistance of the induction motor, is the equivalent rotor resistance of the induction motor, is the equivalent rotor reactance of the induction motor, is the equivalent resistance of the induction motor, is the equivalent impedance of the induction motor, is the slip of the induction motor.

[0046] The non-important parameters in the induction motor model are first assigned values, that is, they are substituted into the above formulas (5) to (6) to calculate the active and reactive power output by the induction motor model. Then, the real-time active and reactive power output by the induction motor can be obtained according to the grid operation status parameters and the load characteristic parameters of the induction motor measured in real time. The fitness values ​​of the intelligent optimization algorithm at different sample points are calculated in real time according to formula (5). The optimal parameters can be identified by sorting the fitness values. Combined with the double-layer iterative process (first assigning values ​​to non-important parameters to identify important parameters, and then substituting important parameters to identify non-important parameters), the parameters in the induction motor model can be quickly and accurately iterated.

[0047] In a specific application embodiment, a set of initial parameters is randomly generated for the intelligent optimization algorithm, which can of course also be given based on experience or typical values ​​according to actual needs; then the search range and step size of the parameters are determined, and a suitable intelligent optimization algorithm, such as genetic algorithm, particle swarm optimization, simulated annealing, ant colony algorithm, etc., is selected according to the available computing resources, expected performance indicators, etc., and the algorithm is run to search for parameters, and the result of each iteration is evaluated according to the objective function. The parameters are continuously updated during the iteration process to gradually approach the optimal solution, and finally the results of the optimization algorithm are analyzed to evaluate whether the found parameters meet the predetermined goals. Further, the identified parameters can be verified using an independent test data set to ensure the generalization ability of the model. The above-mentioned determination of the parameter search range includes defining the upper and lower limits of the parameter values ​​to ensure that the search process does not exceed the physically possible or meaningful values. The step size determines the amplitude of the parameter update during the search process, that is, the "step" unit of the iterative search, which can be fixed or adaptively adjusted, for example, dynamically adjusted according to the progress of the search process. The initially defined parameters can also include basic behaviors and strategies during the search process, such as the direction, speed and range of the search, to further improve the performance and efficiency of the search.

[0048] The present invention monitors the operation status of the power grid and the load characteristics of the induction motor in real time, and adopts a double-layer iterative optimization method of first directionally solidifying a part of non-key parameters to identify key parameters, and then substituting the key parameters to identify non-key parameters. This can realize rapid online identification and dynamic update of the induction motor parameters, accurately reflect the load characteristics of the induction motor, greatly improve the accuracy and efficiency of distribution network load modeling, thereby better performing load prediction and scheduling decisions, and at the same time effectively reducing operating costs and energy consumption, and improving the stability of the power grid, power quality and energy utilization efficiency.

[0049] The present invention can realize online real-time identification of induction motor parameters. Compared with the traditional offline identification method, it can capture the dynamic changes of motor parameters in time and ensure the timeliness and accuracy of the model. And through the parameter directional solidification technology, it can identify and stabilize the key and stable parts of the motor parameters, thereby improving the robustness of the identification results, reducing the impact of factors such as changes in the power grid environment or measurement noise, and can also dynamically adjust the identification strategy and parameter model according to the changes in the power grid operation status and load characteristics, so that the mathematical model of the induction motor can better adapt to the actual power grid environment and improve the flexibility and reliability of power grid scheduling. By making full use of signal processing technology and data fusion algorithms, the present invention can also improve data processing capabilities, can process large quantities of data at the same time, extract useful information from them, and improve the accuracy and efficiency of parameter identification.

[0050] This embodiment further provides an induction motor parameter online identification device based on parameter directional solidification, comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the above method.

[0051] It is understandable that the above method of this embodiment can be executed by a single device, such as a computer or server, etc., and can also be applied to a distributed scenario and completed by multiple devices in cooperation with each other. In the case of a distributed scenario, one of the multiple devices can only execute one or more steps in the above method of this embodiment, and multiple devices interact to complete the above method. The processor can be implemented in the form of a general-purpose CPU, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, etc., for executing related programs to implement the above method of this embodiment. The memory can be implemented in the form of a read-only memory ROM, a random access memory RAM, a static storage device, and a dynamic storage device. The memory can store an operating system and other applications. When the above method of this embodiment is implemented by software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0052] This embodiment further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0053] Those skilled in the art should understand that the above-mentioned embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. An online identification method for induction motor parameters based on parameter directional solidification, characterized in that the steps include: Step S01. Obtain all parameters required for identification of the induction motor model; Step S02. Classify the parameters to be identified into non-key parameters and non-key parameters; Step S03. Real-time monitoring of the grid operation state and the load characteristic parameters of the induction motor, and online identification of various parameters in the induction motor model according to the real-time monitored grid operation state and the load characteristic parameters of the induction motor; In the process of online identification of various parameters in the induction motor model, non-key parameters in the parameters to be identified are first assigned values, parameter optimization is performed to identify various key parameters, the identified key parameters are input into the induction motor model and then the identification of various non-key parameters is performed, and the execution is repeated until an identification result that meets the preset requirements is obtained.

2. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 1, characterized in that: The induction motor model is used to describe the dynamic behavior of the motor under different working conditions and simulate the response of the motor under various load changes. In the induction motor model, the stator part consists of a stator resistor and a stator reactance The rotor part consists of a rotor resistor. and a rotor reactance , which is used to simulate the resistance loss and leakage loss of the rotor winding.

3. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 2, characterized in that: The parameters required to be identified in the induction motor model include: stator resistance, stator reactance, rotor resistance, rotor reactance, excitation resistance, mechanical torque coefficient and load rate. The mechanical torque coefficient is the relationship between the mechanical torque output by the motor under different operating conditions and the electrical parameters.

4. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 1, characterized in that: In step S02, the parameters to be identified are classified according to the degree of influence on the performance indicators of the induction motor, and the performance indicators include any one or more of efficiency, output power, and stability.

5. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 1, characterized in that: Step S03 includes: Step S301. Acquire the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time, wherein the grid operation state parameters include bus voltage and phase angle, and the load characteristic parameters of the induction motor include active power and reactive power data; Step S302: assigning values ​​to non-key parameters in the induction motor model; Step S303. Calculate the fitness value of the intelligent optimization algorithm when each key parameter takes different values ​​according to the grid operation state parameters and the load characteristic parameters of the induction motor measured in real time; Step S304. Sort the fitness values ​​of the intelligent optimization algorithm obtained when each key parameter takes different values, and determine the iteration results of each key parameter; Step S305. Determine whether the current iteration result satisfies the objective function. If not, return to step S303. Otherwise, output all key parameters identified and proceed to step S306. Step S306: Substitute the identified key parameters into the induction motor model; Step S307. Calculate the fitness value of the intelligent optimization algorithm when each non-key parameter takes different values ​​according to the real-time measured grid operation state parameters and the load characteristic parameters of the induction motor; Step S308. Sort the fitness values ​​of the intelligent optimization algorithm when the non-key parameters take different values, and determine the iteration results of each non-key parameter; Step S309. Determine whether the preset termination criteria are currently met, if yes, proceed to step S310, otherwise return to step S307; Step S310: Determine whether the preset two-layer criterion is currently met. If the iteration is complete, output the global optimal solution; otherwise, return to step S303.

6. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 5, characterized in that: In step S302, the fitness value of the intelligent optimization algorithm is calculated according to the following formula: in, is the fitness value, , are respectively the active power and reactive power of the actual bus output, , is the active and reactive power output by the induction motor model, is the number of sample points.

7. The method for online identification of induction motor parameters based on parameter directional solidification according to claim 6, characterized in that: The active power output by the induction motor model is calculated as follows: The reactive power is calculated as follows: in, is the equivalent stator resistance of the induction motor, is the equivalent rotor resistance of the induction motor, is the equivalent rotor reactance of the induction motor, is the equivalent resistance of the induction motor, is the equivalent impedance of the induction motor, is the slip of the induction motor.

8. The method for online identification of induction motor parameters based on parameter directional solidification according to any one of claims 1 to 7, characterized in that: In step S03, typical values ​​or standard values ​​are used to assign values ​​to non-important parameters.

9. An induction motor parameter online identification device based on parameter directional solidification, comprising a processor and a memory, wherein the memory is used to store a computer program, characterized in that: The processor is configured to execute the computer program to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images