Elevator inverter current loop PI parameter self-learning method, medium and inverter

Abstract

The present invention provides an elevator inverter current loop PI parameter self-learning method, medium and inverter, comprising the steps of: S1, controlling the elevator brake to enter a closed state; S2, giving a rotating q-axis current command Iq*, so that the rotation frequency of the current command Iq* changes from a first frequency to a second frequency, and then changes from the second frequency to the first frequency; S3, in a cycle, counting the variance S of the current feedback Iqfb and the current command Iq* in each frequency segment k ; S4, at the end of a cycle, the difference S0~S in the cycle is obtained N The maximum value S nmax , minimum value S nmin and the mean value S nave ; S5, if the number of loops reaches the number of iterations, jump to step S6; if the number of iterations is not reached, jump to step S2 to enter the next loop; S6, find the maximum value S in multiple loops nmax , minimum value S nmin , average value S nave At the same time, the smallest cycle k, KP k The invention can improve production efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of elevator control, and in particular relates to a current loop PI parameter self-learning method, a medium and a frequency converter for an elevator frequency converter. Background Art

[0002] During the elevator development phase, developers need to design appropriate control parameters for each elevator specification. Elevator specifications are complex, and the combination of different loads and speeds, different motor configurations, and different inverter specifications generate a large number of parameters. Control parameters generally include motor parameters (such as number of poles, back electromotive force, winding resistance and inductance, etc.) and PI control parameters. All these parameters must be stored in the memory to form a control parameter library, which occupies a large amount of storage resources and makes the chip expensive. During the elevator design phase, due to the complex configuration of elevator specifications and the combination of different loads and speeds, a large number of parameters are generated. Designers need to select the corresponding control parameters from the existing control parameter library according to the elevator specifications, which often leads to misconfiguration or even errors, resulting in defects. Poor on-site feedback and redesign consume manpower and time resources, affect delivery time, and cause economic losses. Summary of the invention

[0003] In order to overcome the above technical defects, the first aspect of the present invention provides an elevator inverter current loop PI parameter self-learning method, comprising the steps of:

[0004] S1, control the elevator brake to enter the closed state;

[0005] S2, giving a rotating q-axis current command Iq*, so that the rotation frequency of the current command Iq* changes from the first frequency to the second frequency, and then changes from the second frequency to the first frequency. The change from the first frequency to the second frequency or from the second frequency to the first frequency is one cycle;

[0006] S3. In one cycle, the variance S of the current feedback Iqfb and the current command Iq* in each frequency band is counted. k , k∈[0,N];

[0007] S4. At the end of a cycle, the difference S0~S in the cycle is obtained. N The maximum value S nmax , minimum value S nmin and the mean value S nave ;

[0008] S5. If the number of cycles reaches the number of iterations, jump to step S6; if the number of iterations is not reached, jump to step S2 to enter the next cycle;

[0009] S6. Find the maximum value S in multiple cycles nmax , minimum value S nmin, average value S nave At the same time, the smallest cycle l, KP l Set as KP control parameter.

[0010] As a further improvement of the present invention, before step S1, the following steps are also included:

[0011] S7, setting the number of iterations;

[0012] S8, setting the adjustment range of the current loop PI parameters;

[0013] S9. Set the initial value and adjustment step of the current loop PI parameters.

[0014] As a further improvement of the present invention, in step S2:

[0015] Using KP min +(n-1)*KP step KI min +(n-1)*KI step As a PI parameter to control the elevator speed; where n is the current iteration number, KI min is the initial value of KI, KI step To adjust the step length.

[0016] As a further improvement of the present invention, the amplitude of the current command Iq* is equal to the q-axis current corresponding to the rated torque of the motor.

[0017] As a further improvement of the present invention, the second frequency is greater than or equal to the maximum operating angular frequency covered by the system.

[0018] As a further improvement of the present invention, the second frequency is 1.5 times the maximum operating angular frequency covered by the system.

[0019] As a further improvement of the present invention, the variance S k It is calculated using the following formula:

[0020]

[0021] Where M is the number of samples in each frequency segment.

[0022] As a further improvement of the present invention, the sampling number is greater than or equal to ten times of the second frequency.

[0023] A second aspect of the present invention provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the above-mentioned elevator inverter current loop PI parameter self-learning method.

[0024] According to a third aspect of the present invention, a frequency converter is provided for use in an elevator, wherein the frequency converter comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the above-mentioned elevator frequency converter current loop PI parameter self-learning method.

[0025] Compared with the prior art, the present invention has the following beneficial effects: it adopts a self-learning method to retain only the adjustment range of the control parameters, and the control parameter library does not need to be written into the controller, which can greatly save the storage space occupied by the control parameter library and save chip costs; designers do not need to select control parameters according to elevator specifications, which avoids errors and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0027] Figure 1 This is a flow chart of the elevator inverter current loop PI parameter self-learning method described in Example 1;

[0028] Figure 2 This is a self-learning timing diagram with waiting time between cycles in Example 1;

[0029] Figure 3 This is a timing diagram of self-learning without waiting time between cycles in Example 1;

[0030] Figure 4 This is a schematic diagram of the structure of the frequency converter described in Example 3. DETAILED DESCRIPTION

[0031] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0032] It should be noted that S1 and S2 mentioned in the text are merely the numbers of each step, and the order of implementation of each step has nothing to do with the size of the numbers of S1 and S2.

[0033] Example 1

[0034] This embodiment discloses a method for self-learning the current loop PI parameters of an elevator inverter. Figure 1 to Figure 3 As shown, the steps include:

[0035] S7. Set the number of iterations. The number of iterations needs to take into account both efficiency and accuracy. If the number of iterations is too large, the self-learning time will be long and the efficiency will be low, but the learning accuracy will be high. If the number of iterations is too small, the self-learning time will be short and the efficiency will be high, but the learning accuracy will be low. It is recommended to set a fixed number of iterations D m , when the number of iterations reaches D m When the learning is forced to end.

[0036] S8. Set the adjustment range of the current loop PI parameters; in the control parameter library, find the maximum values ​​of the current loop proportional gain P and integral gain I in all specifications (KP max KI max ) and minimum value (KP min KI min ) and set them as the upper and lower limits of KP and KI.

[0037] S9, set the initial value and adjustment step of the current loop PI parameter; the initial value of KP is KP min , the adjustment step is KP step =(KP max -KP min ) / D m ; The initial value of KI is KI min , the adjustment step is KI step =(KI max -KI min ) / D m ;

[0038] S1, control the elevator brake to enter the closed state. During the entire self-learning process, the elevator brake is always in the closed state.

[0039] S2. Given a rotating q-axis current command Iq*, the rotation frequency of the current command Iq* gradually increases from 0HZ to NHZ, and then gradually decreases from NHZ to 0HZ. The change from 0HZ to NHZ or the change from NHZ to 0HZ is a cycle; N ≥ the maximum operating angular frequency covered by the system. It is recommended to set N to 1.5 times the maximum operating angular frequency covered by the system. The time of one cycle can be defined according to the efficiency requirements. It is generally recommended to be 1 second. In addition, the amplitude of Iq* should not be too small. It is recommended to be the q-axis current corresponding to the rated torque of the motor. The amplitude of the current command Iq* can also be set to be variable, but it has little effect on the test results.

[0040] like Figure 2 and Figure 3As shown in the figure, considering the improvement of efficiency, the loops are seamlessly connected, but due to the different PI parameters between the loops, the control effect of the first frequency segment of the next loop will be affected by the previous loop, which will affect the evaluation results of the PI parameters to a certain extent. If you want to improve the learning accuracy of the PI parameters, you can cut off the current after a loop ends, wait for a waiting time, and then give the current command of the next loop.

[0041] S3. In one cycle, the variance S of the current feedback Iqfb and the current command Iq* in each frequency band is counted. k ,k∈[0,N], specifically.

[0042]

[0043] M is the number of samples in each frequency band. To ensure accuracy, M ≥ 10N is recommended. A frequency band is generally defined as a range of frequency changes of 1 Hz, such as 0 Hz to 1 Hz, 1 Hz to 2 Hz. The size of the frequency band can be customized. The narrower the frequency band bandwidth, the higher the accuracy.

[0044] S4. At the end of a cycle, the difference S0~S in the cycle is obtained. N The maximum value S nmax , minimum value S nmin and the mean value S nave .

[0045] S5. If the number of loops reaches the number of iterations D m , jump to step S6; if the number of iterations D is not reached m , then jump to step S2 to enter the next cycle.

[0046] S6. Find the maximum value S in multiple cycles nmax , minimum value S nmin , average value S nave At the same time, the smallest cycle l, KP l Set as KP control parameter.

[0047] In summary, this embodiment has the following technical effects:

[0048] 1. Using the self-learning method, the control parameter library does not need to be written into the controller, and only the adjustment range of the control parameters is retained, which can greatly save the storage space occupied by the control parameter library and save chip costs;

[0049] 2. Designers do not need to select control parameters according to elevator specifications, which avoids mistakes and improves production efficiency.

[0050] Example 2

[0051] This embodiment provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the elevator inverter current loop PI parameter self-learning method of Example 1.

[0052] Optionally, the computer readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid state drive (SSD), or an optical disk, etc. Among them, the random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0053] Example 3

[0054] This embodiment provides a frequency converter, which is applied to an elevator. Figure 4 As shown, it includes a processor and a storage, wherein program codes are stored in the storage, and the processor executes the program codes to execute the elevator inverter current loop PI parameter self-learning method of embodiment 1.

[0055] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented with hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

[0056] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for self-learning the current loop PI parameters of an elevator inverter, characterized in that: Includes steps: S1, control the elevator brake to enter the closed state; S2, given the rotating q-axis current command Iq*, so that the rotation frequency of the current command Iq* changes from the first frequency to the second frequency, and then changes from the second frequency to the first frequency. The change from the first frequency to the second frequency or from the second frequency to the first frequency is one cycle; use KP min +(n-1)*KP step KI min +(n-1)*KI step As a PI parameter to control the elevator speed; where n is the current cycle number, KI min is the initial value of KI, KI step is the adjustment step size of the KI parameter in each cycle; S3. In one cycle, the variance S of the current feedback Iqfb and the current command Iq* in each frequency band is counted. k , k∈[0,N], N is the number of frequency bands; S4. At the end of a cycle, the difference S0~S in the cycle is obtained. N The maximum value S nmax , minimum value S nmin and the mean value S nave ; S5. If the number of cycles reaches the number of iterations, jump to step S6; if the number of iterations is not reached, jump to step S2 to enter the next cycle; S6. Find the maximum value S in multiple cycles nmax , minimum value S nmin , average value S nave At the same time, for the smallest cycle l, KPl is set as the KP control parameter, and KPl is the KP in the lth cycle.

2. The elevator inverter current loop PI parameter self-learning method according to claim 1 is characterized in that: Before step S1, the method further includes the following steps: S7, setting the number of iterations; S8, setting the adjustment range of the current loop PI parameters; S9. Set the initial value and adjustment step of the current loop PI parameters.

3. The elevator inverter current loop PI parameter self-learning method according to claim 1, characterized in that: The amplitude of the current command Iq* is equal to the q-axis current corresponding to the rated torque of the motor.

4. The elevator inverter current loop PI parameter self-learning method according to claim 1, characterized in that: The second frequency is greater than or equal to the maximum operating angular frequency covered by the system.

5. The elevator inverter current loop PI parameter self-learning method according to claim 1, characterized in that: The second frequency is 1.5 times the maximum operating angular frequency covered by the system.

6. The elevator inverter current loop PI parameter self-learning method according to claim 1, characterized in that: VarianceS k It is calculated using the following formula: Where M is the number of samples in each frequency segment.

7. The elevator inverter current loop PI parameter self-learning method according to claim 6, characterized in that: The sampling number is greater than or equal to ten times the second frequency.

8. A computer-readable storage medium, characterized in that: The readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by a processor to implement the elevator inverter current loop PI parameter self-learning method as described in any one of claims 1 to 7.

9. A frequency converter, characterized in that: Applied in an elevator, the frequency converter includes a processor and a memory, the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the elevator frequency converter current loop PI parameter self-learning method as described in any one of claims 1 to 7.

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