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

Abstract

The present invention provides a speed loop PI parameter self-learning method, medium and inverter of an elevator frequency converter, comprising the following steps: S1, controlling the elevator to enter a holding brake; S2, switching the elevator speed control from a speed closed-loop control mode to a 0-speed control mode, until the motor traction wheel speed reaches a 0-speed steady state; S3, controlling the elevator speed control from a 0-speed control mode to a speed closed-loop control mode, sending a self-learning speed instruction to the elevator, and after one instruction is completed, controlling the speed control to switch from the speed closed-loop control mode to the 0-speed control mode, until the motor traction wheel speed reaches a 0-speed steady state, switching to the next instruction until all instructions are completed, and controlling the speed control to switch from the speed closed-loop control mode to the 0-speed control mode, until the motor traction wheel speed reaches a 0-speed steady state; S4, reaching the number of iterations, completing self-learning; if the number of iterations is not reached, repeating step S3; S5, finding the KP with the smallest simultaneous value n and KI n , and set it as the control parameter.

Description

Technical Field

[0001] The invention belongs to the technical field of elevator control, and in particular relates to a speed loop PI parameter self-learning method, a medium and a frequency converter of 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. To cope with different configurations, a corresponding number of PI control parameters need to be developed. All these parameters must be stored in the memory to form a control parameter library, which takes up a lot of storage resources and makes the chip expensive. During the elevator design phase, due to the complex configuration of elevator specifications, the combination of different loads and speeds generates a large number of parameters. 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, which consumes manpower and time resources, affects delivery time, and causes 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 speed loop PI parameter self-learning method, comprising the steps of:

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

[0005] S2, switch the elevator speed control from the speed closed-loop control mode to the zero-speed control mode until the motor traction sheave speed reaches a zero-speed steady state;

[0006] S3, the elevator speed control is switched from the 0-speed control mode to the speed closed-loop control mode, and several self-learning speed commands are issued to the elevator. After one command is completed, the speed control is switched from the speed closed-loop control mode to the 0-speed control mode until the motor traction wheel speed reaches the 0-speed steady state, and then switched to the next command until all commands are given, and the speed control is switched from the speed closed-loop control mode to the 0-speed control mode until the motor traction wheel speed reaches the 0-speed steady state;

[0007] S4, if the number of iterations is reached, the self-learning is completed; if the number of iterations is not reached, step S3 is repeated;

[0008] S5, at △Max0~△Max n ,△Min0~△Min n , S0~Sn, find the KP that is the smallest at the same time n and Ki n , KP n Set as KP control parameter, and set KI n Set as KI control parameter.

[0009] As a further improvement of the present invention, before step S1, the following steps are also included:

[0010] S6. Set the number of iterations;

[0011] S7, set the adjustment range of speed loop PI parameters;

[0012] S8, setting the initial value and adjustment step of the current loop PI parameter;

[0013] S9. Set the mentor curve.

[0014] As a further improvement of the present invention, in the speed closed-loop control mode:

[0015] Using KP min +(n-1)*KP step KI min +(n-1)*KI step As PI parameter to control the elevator speed;

[0016] Continuously calculate the deviation between the tutor curve and the speed feedback curve, and list the maximum value of the deviation △Max at the end of a set of speed instructions. n 、Minimum value △Min n , and calculate the variance S of the tutor curve and the speed feedback curve during the given process of this set of speed instructions n ; Where n is the current iteration number.

[0017] As a further improvement of the present invention, the self-learning speed instruction includes: a step instruction, a ramp instruction, and a parabola excitation instruction.

[0018] As a further improvement of the present invention, in the calculation formula of the speed command, the step amplitude K of the step command is 0.5;

[0019] In the calculation formula of the ramp command, the speed slope a in the slope command is greater than or equal to the larger of the rated acceleration and rated deceleration of the elevator;

[0020] In the parabola instruction calculation formula, the slope ρ of the acceleration in the parabola instruction is greater than or equal to the larger of the rated acceleration and the rated deceleration speed of the elevator.

[0021] As a further improvement of the present invention, during the self-learning process, the running distance of the elevator is shortened.

[0022] As a further improvement of the present invention, the specific steps of shortening the running distance are as follows:

[0023] The running directions of two adjacent sets of speed instructions are opposite.

[0024] As a further improvement of the present invention, the specific steps of shortening the running distance are as follows:

[0025] Put the elevator in 0 speed control mode and the speed reaches steady-state 0 speed.

[0026] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium 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 speed loop PI parameter self-learning method.

[0027] 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 speed loop PI parameter self-learning method.

[0028] 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

[0029] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0030] Figure 1 This is a flow chart of the elevator inverter speed loop PI parameter self-learning method described in Example 1;

[0031] Figure 2 The speed command given and the tutor curve diagram for one iteration in Example 1;

[0032] Figure 3 In order not to exceed the maximum limit distance in Example 1, the speed of each iteration is specified in a given direction opposite to the previous one;

[0033] Figure 4 In embodiment 1, in order not to exceed the maximum limit distance, the direction of each speed instruction is defined according to the relationship between the car and the starting position;

[0034] Figure 5 This is a schematic diagram of the structure of the frequency converter described in Example 3. DETAILED DESCRIPTION

[0035] 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.

[0036] Example 1

[0037] This embodiment discloses a method for self-learning the speed loop PI parameters of an elevator inverter. Figure 1 and Figure 2 As shown, the steps include:

[0038] S6. 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, although the self-learning time will be short and the efficiency will be high, 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.

[0039] S7, set the adjustment range of the speed loop PI parameters; in the control parameter library, find the maximum values ​​of the speed 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.

[0040] S8, 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 ;

[0041] The initial value of KI is KI min , the adjustment step is KI step =(KI max -KI min ) / D m ;

[0042] S9. Set the mentor curve: The mentor curve is based on the feedback signal curve of the standard speed loop PI parameters under specific instructions, providing a reference standard for self-learning of PI parameters.

[0043] S1, control the elevator to enter the brake.

[0044] S2: Switch the elevator speed control from the speed closed-loop control mode to the zero-speed control mode until the motor traction wheel speed reaches a zero-speed steady state.

[0045] S3, control the elevator speed control to switch from 0 speed control mode to speed closed-loop control mode, send several self-learning speed instructions to the elevator, after one instruction is completed, control the speed control to switch from speed closed-loop control mode to 0 speed control mode, until the motor traction wheel speed reaches 0 speed steady state, then switch to the next instruction until all instructions are given, control the speed control to switch from speed closed-loop control mode to 0 speed control mode, until the motor traction wheel speed reaches 0 speed steady state.

[0046] In speed closed-loop control mode:

[0047] Using KP min +(n-1)*KP step KI min +(n-1)*KI step As PI parameter to control the elevator speed;

[0048] Continuously calculate the deviation between the mentor curve and the speed feedback curve (speed feedback is collected by the rotary encoder), and list the maximum value of the deviation △Max at the end of a set of speed instructions. n 、Minimum value △Min n , and calculate the variance S of the tutor curve and the speed feedback curve during the given process of this set of speed instructions n ; Where n is the current iteration number.

[0049] S4. If the number of iterations is reached, self-learning is completed and the process goes to step S5. If the number of iterations is not reached, step S3 is repeated.

[0050] S5, at △Max0~△Max n ,△Min0~△Min n 、S0~S n Find the KP that is the smallest at the same time n and KI n , KP n Set as KP control parameter, KI n Set as KI control parameter.

[0051] Self-learning speed instructions include: step instructions, ramp instructions, and parabola excitation instructions.

[0052] In the calculation formula of speed command v=K, the step amplitude K of the step command should not be too high, otherwise it will increase the running distance of self-learning and cause danger. K is preferably set to 0.5;

[0053] In the calculation formula of the ramp command v=at, the speed slope a in the slope command is greater than or equal to the larger of the rated acceleration and rated deceleration of the elevator;

[0054] Parabola instruction calculation formula v = ρt2 / 2, the slope ρ of the acceleration in the parabola instruction is greater than or equal to the larger of the rated acceleration and rated deceleration speed of the elevator.

[0055] During the self-learning process, in order to ensure safety and prevent the elevator from hitting the top or bottom, the operating range of the elevator needs to be limited, and the maximum stroke in the up and down directions should be set respectively. The integral value of the speed command of the above self-learning should be less than this maximum stroke, and a sufficient margin should be left, such as 1 meter. During the self-learning process, the running distance of the elevator is reduced. When the elevator is detected to exceed this stroke during the self-learning process, the power supply of the brake and the motor is immediately cut off to stop the elevator.

[0056] There are two specific ways to shorten the running distance:

[0057] 1. If Figure 3 As shown, the running directions of the two adjacent sets of speed instructions are opposite.

[0058] 2. If Figure 4 As shown, the elevator is in 0 speed control mode and the speed reaches a steady-state 0 speed.

[0059] In summary, this embodiment has the following technical effects:

[0060] 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;

[0061] 2. Designers do not need to select control parameters according to elevator specifications, which avoids mistakes and improves production efficiency.

[0062] Example 2

[0063] The present embodiment provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored, 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 elevator inverter speed loop PI parameter self-learning method of Example 1.

[0064] 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).

[0065] Example 3

[0066] This embodiment provides a frequency converter, which is applied to an elevator. Figure 5 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 speed loop PI parameter self-learning method of embodiment 1.

[0067] 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.

[0068] 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 self-learning method for speed loop PI parameters of an elevator inverter, characterized in that: Includes steps: S1, control the elevator to enter the brake; S2, switch the elevator speed control from the speed closed-loop control mode to the zero-speed control mode until the motor traction sheave speed reaches a zero-speed steady state; S3, control the elevator speed control to switch from 0 speed control mode to speed closed-loop control mode, send a number of self-learning speed instructions to the elevator, after one instruction is completed, control the speed control to switch from speed closed-loop control mode to 0 speed control mode, until the motor traction wheel speed reaches 0 speed steady state, then switch to the next instruction until all instructions are given, control the speed control to switch from speed closed-loop control mode to 0 speed control mode, until the motor traction wheel speed reaches 0 speed steady state; wherein, in the speed closed-loop control mode: Using KP min +(n-1)*KP step KI min +(n-1)*KI step As a PI parameter to control the elevator speed, KP min is the minimum value of proportional gain, KP step is the step size of each adjustment of the proportional gain; Continuously calculate the deviation between the tutor curve and the speed feedback curve, and list the maximum value of the deviation △Max at the end of a set of speed instructions. n 、Minimum value △Min n , and calculate the variance S of the tutor curve and the speed feedback curve during the given process of this set of speed instructions n ; Where n is the current iteration number, KI min is the initial value of KI, KI step To adjust the step length; S4, if the number of iterations is reached, the self-learning is completed and the process goes to step S5; if the number of iterations is not reached, step S3 is repeated; S5, at △Max0~△Max n ,△Min0~△Min n 、S0~S n Find the KP that is the smallest at the same time n and KI n , KP n Set as KP control parameter, and set KI n Set as KI control parameter, where △Max n is the maximum deviation value between the tutor curve and the speed feedback curve, △Min n is the minimum deviation between the tutor curve and the speed feedback curve, S n is the variance of the tutor curve and the speed feedback curve.

2. The elevator inverter speed loop PI parameter self-learning method according to claim 1 is characterized in that: Before step S1, the method further includes the following steps: S6. Set the number of iterations; S7, set the adjustment range of speed loop PI parameters; S8, setting the initial value and adjustment step of the current loop PI parameter; S9. Set the mentor curve.

3. The elevator inverter speed loop PI parameter self-learning method according to claim 1, characterized in that: The self-learning speed instructions include: step instructions, ramp instructions, and parabola excitation instructions.

4. The elevator inverter speed loop PI parameter self-learning method according to claim 3 is characterized in that: In the calculation formula of the speed command, the step amplitude K of the step command is 0.5; In the calculation formula of the ramp command, the speed slope a in the slope command is greater than or equal to the larger of the rated acceleration and rated deceleration of the elevator; In the parabola instruction calculation formula, the slope ρ of the acceleration in the parabola instruction is greater than or equal to the larger of the rated acceleration and the rated deceleration speed of the elevator.

5. The elevator inverter speed loop PI parameter self-learning method according to claim 3, characterized in that: During the self-learning process, the elevator's running distance is shortened.

6. The elevator inverter speed loop PI parameter self-learning method according to claim 5, characterized in that: Specific steps to shorten the running distance: The running directions of two adjacent sets of speed instructions are opposite.

7. The elevator inverter speed loop PI parameter self-learning method according to claim 5, characterized in that: Specific steps to shorten the running distance: Put the elevator in 0 speed control mode and the speed reaches steady-state 0 speed.

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 speed 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 speed loop PI parameter self-learning method as described in any one of claims 1 to 7.

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