A Digital Twin Variable Frequency Speed Regulation Prediction Control Method and System

Abstract

The present invention discloses a digital twin variable frequency speed regulation predictive control method and system. By establishing an online cloud adjustable model, the operating conditions of the load that are ahead of the actual operating time are predicted, including the control proportional coefficient in the load at a certain future moment K p , integral coefficient K i and differential coefficient K d rationality, pre-eliminate possible future faults, give fault alarm prompts for the predicted faults, predict the change state of the load working conditions, and further improve the reliability and control performance of the variable frequency speed regulation system.

Description

Technical Field

[0001] The present invention relates to the field of variable frequency speed regulation control, and specifically relates to a digital twin variable frequency speed regulation predictive control method and system. Background Art

[0002] Digital twin technology is a technology that establishes a model in a virtual space-time system, uses information such as multi-dimensions, multi-space-time scales, multi-disciplines, multi-physical quantities, and multi-probabilities of physical entities for fusion, analysis, simulation, and mining, and completes the mapping of physical entities in the virtual space, thereby reflecting the whole life cycle process of the corresponding physical entity. It is an effective method to solve the problem of cyber-physical fusion and has been successfully applied in many industrial fields. However, the application of digital twin technology in the field of variable frequency speed regulation control is still in its infancy.

[0003] As an energy-saving and emission-reduction component, the frequency converter has many advantages such as variable frequency speed regulation, energy saving and emission reduction, high-voltage soft start, and improving system stability, and is widely used in various industries such as fans and pumps, machine tools, rail transit, and oil drilling. However, once any abnormal fault occurs in the frequency converter, it often requires professional debugging personnel to go to the site for fault diagnosis and debugging, which is time-consuming, laborious, and inefficient. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a digital twin variable frequency speed regulation predictive control method and system. The method can predict the operating conditions of the load ahead of the actual operating time by establishing an online cloud adjustable model, and makes three aspects of predictions, predicting the rationality of the control proportional coefficient , integral coefficient and differential coefficient in the load at a certain future moment, pre-eliminating possible future faults, giving fault alarm prompts for the predicted faults, and predicting the change state of the load working conditions.

[0005] The present invention provides a digital twin variable frequency speed regulation predictive control method, including the following steps:

[0006] Step S1, based on the actual mathematical model of the permanent magnet synchronous motor, discretize the motion equation and voltage equation of the permanent magnet synchronous motor within the sampling period T c .

[0007] Step S2, construct a speed dynamic equation and a current dynamic equation based on the discretized motor motion equation and voltage equation described in Step S1:

[0008] Speed dynamic equation: ,

[0009] Current dynamic equation: ,

[0010] Among them, ,

[0011] ,

[0012] ,

[0013] ,

[0014] Among them, n(t + 1) is the motor speed at time t + 1, n(t) is the motor speed at time t, I(t + 1) is the d-q axis current of the permanent magnet synchronous motor at time t + 1, and I(t) is the d-q axis current of the permanent magnet synchronous motor at time t. is the damping coefficient, T c is the sampling period, J is the moment of inertia, and π is the pi. and are the d-q axis inductances. is the stator resistance. is the electrical angular velocity. is the input electromagnetic torque at time t, and U(t) is the d-q axis voltage at time t.

[0015] Step S3: According to the speed dynamic equation and current dynamic equation described in Step S2, verify that the assumptions of the model-free adaptive control theory are satisfied. When , there must exist a pseudo-gradient , thus constructing a speed dynamic linear equation. When , there must exist a pseudo-Jacobian matrix , thus constructing a current dynamic linear equation:

[0016] Speed dynamic linear equation: ,

[0017] Current dynamic linear equation: ,

[0018] Among them,

[0019] ,

[0020] ,

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] Among them, is the difference in rotational speed between time t+1 and time t, is the difference in d-q axis current between time t+1 and time t, is the pseudo-gradient, which is and at time t to form a matrix, is the pseudo-Jacobian matrix, which is and at time t to form a matrix, and T represents the transpose of any matrix.

[0026] Step S4: Construct the model-free adaptive input torque control criterion function and the input voltage control criterion function respectively, so that the output rotational speed error and current error are close to zero, and establish the control algorithm;

[0027] Input torque control criterion function:

[0028] ,

[0029] Input voltage control criterion function:

[0030] ,

[0031] where, is the input torque control criterion function, is the input voltage control criterion function, is the input electromagnetic torque at time t, U(t) is the input d-q axis voltage at time t, is the reference rotational speed at time t+1, is the d-q axis reference current at time t+1, and are the weight coefficients, is the input electromagnetic torque at time t-1, U(t-1) is the input d-q axis voltage at time t-1, is the absolute value symbol, is the norm symbol.

[0032] Derive the above criterion function and set its derivative equal to zero. Considering the generality of the algorithm, introduce the step size factor, and the input torque control algorithm and the input voltage control algorithm can be obtained as follows: ,

[0033] ,

[0034] ,

[0035] where, , is the torque step size factor, , is the voltage step factor, is the difference in rotational speed between time t and time t-1, is the difference in d-q axis current between time t and time t-1.

[0036] Step S5, perform online estimation of the rotational speed pseudo-gradient and the current pseudo-Jacobian matrix, and construct the rotational speed pseudo-gradient estimation criterion function and the current pseudo-Jacobian matrix estimation criterion function respectively:

[0037] ,

[0038] ,

[0039] where:

[0040] ,

[0041] ,

[0042] where, is the rotational speed pseudo-Jacobian matrix estimation criterion function, is the current pseudo-Jacobian matrix estimation criterion function, is the reference rotational speed at time t, are the d-q axis reference currents at time t, n(t-1) is the rotational speed at time t-1, I(t-1) is the d-q axis current at time t-1, is the estimated value of the rotational speed pseudo-gradient at time t-1, is the estimated value of the current pseudo-Jacobian matrix at time t-1.

[0043] Find the extreme values of their respective criterion functions, and use the matrix inversion lemma to obtain the online estimated values of the pseudo-gradient and pseudo-Jacobian matrix with their respective introduced step factors:

[0044] ,

[0045] ,

[0046] where, is the estimated value of the rotational speed pseudo-gradient at time t, is the rotational speed pseudo-gradient step factor, is the current pseudo-Jacobian matrix step factor, is the estimated value of the current pseudo-Jacobian matrix at time t.

[0047] Step S6, integrating Steps S1 - S5, when the system is operating normally, enter S7; when or , enter S5 and reset the pseudo-gradient and pseudo-Jacobian matrix in Step S5 to their initial values.

[0048] Step S7: Based on the online cloud adjustable model established according to Steps S1 - S6, the cloud receives the actual operation data, and can predict the operation status of the load ahead of the actual operation time. Three aspects of predictions are made to adjust the actual model in advance.

[0049] When the load is operating at time k, the observed actual operating speed and actual current of the load are respectively and , and at the same time, the online cloud adjustable model based on the model - free adaptive control algorithm predicts the speed of the load and the current of the load after time k. t is the time after time k. Set the speed hysteresis width and the current hysteresis width . At time k, it satisfies:

[0050] ,

[0051] Then, the cloud Wifi function transmits the possible fault problems back to the local computer to issue a warning prompt, and at the same time, an alarm is issued through the alarm device set at the load end.

[0052] Step S8: After receiving the warning reminder, judge the accuracy of the proportional coefficient , integral coefficient and derivative coefficient given in the controller. If it is found that the settings of the above three coefficients are unreasonable, then calculate and adjust the proportional coefficient , integral coefficient and derivative coefficient in the speed loop and current loop to ensure the correct speed and current output at the load end. At the same time, monitor the operation of the load according to the predicted speed and current after time k, and adjust the load - carrying situation of the load in real - time.

[0053] The present invention further provides a digital - twin variable - frequency speed - regulation prediction control system for the above - mentioned digital - twin variable - frequency speed - regulation prediction control method, including a digital - twin online cloud system, a prediction module, an actual load end, and a controller; the digital - twin online cloud system includes an online cloud adjustable model and an online estimation of the pseudo - Jacobian matrix. The digital - twin online cloud system is connected to the local end of the digital - twin online cloud system and the actual load end through WIFI, obtains the speed and current data output from the load end and transmits them back to the local end of the digital - twin online cloud system. The local end of the digital - twin online cloud system passes the prediction data through the digital - twin online cloud system to the actual load end after passing through the online cloud adjustable model, realizing three - end interaction.

[0054] The technical effects of the present invention are as follows: pre-excluding possible future faults, giving fault alarm prompts for the predicted faults, predicting the change state of the load working conditions, adjusting the load control parameters in advance, and calibrating the actual operation model in the cloud. Description of the Drawings

[0055] Figure 1 It is a flowchart of the steps of a digital twin variable frequency speed regulation prediction control method provided by the present invention;

[0056] Figure 2 It is a principle flowchart of a digital twin variable frequency speed regulation prediction control system provided by the present invention. Specific Embodiments

[0057] The following are only the preferred embodiments of the present invention. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments, so as to facilitate those skilled in the art of the present technology to understand the present invention. It should be noted that for those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims without departing from the principle of the present invention, all inventions and creations using the concept of the present invention are within the scope of protection.

[0058] Figure 1 It is a flowchart of the steps of a digital twin variable frequency speed regulation prediction control method provided by the present invention, as Figure 1 shown, a digital twin variable frequency speed regulation prediction control method includes the following steps:

[0059] Step S1, based on the actual mathematical model of the permanent magnet synchronous motor, within the sampling period T c discretize the motion equation and voltage equation of the permanent magnet synchronous motor.

[0060] Step S2, construct a speed dynamic equation and a current dynamic equation based on the discretized motor motion equation and voltage equation described in step S1:

[0061] Speed dynamic equation: ,

[0062] Current dynamic equation: ,

[0063] [[ID= forty-two]]Wherein, ,

[0064] ,

[0065] ,

[0066] ,

[0067] Among them, n(t + 1) is the motor speed at time t + 1, n(t) is the motor speed at time t, I(t + 1) is the d-q axis current of the permanent magnet synchronous motor at time t + 1, and I(t) is the d-q axis current of the permanent magnet synchronous motor at time t. is the damping coefficient, T c is the sampling period, J is the moment of inertia, and π is the pi. and are the d-q axis inductances. is the stator resistance. is the electrical angular velocity. is the input electromagnetic torque at time t, and U(t) is the d-q axis voltage at time t.

[0068] Step S3: According to the speed dynamic equation and current dynamic equation described in Step S2, verify that the assumptions of the model-free adaptive control theory are satisfied. When holds, there must exist a pseudo-gradient , and thus a speed dynamic linear equation is constructed. When holds, there must exist a pseudo-Jacobian matrix , and thus a current dynamic linear equation is constructed:

[0069] Speed dynamic linear equation: ,

[0070] Current dynamic linear equation: ,

[0071] Among them,

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] ,

[0077] ,

[0078] Among them, is the difference in speed between time t + 1 and time t, is the difference in d-q axis current between time t + 1 and time t, is the pseudo-gradient, which is the and at time t in Step S2 to form a matrix. is the pseudo-Jacobian matrix, which is the and The matrix formed at time t, where T represents the transpose of any matrix.

[0079] Step S4: Construct the model-free adaptive input torque control criterion function and the input voltage control criterion function respectively, so that the output speed error and current error are close to zero, and establish a control algorithm.

[0080] Input torque control criterion function:

[0081] ,

[0082] Input voltage control criterion function:

[0083] ,

[0084] where, is the input torque control criterion function, is the input voltage control criterion function, is the input electromagnetic torque at time t, U(t) is the input d-q axis voltage at time t, is the reference speed at time t + 1, is the d-q axis reference current at time t + 1, and are weight coefficients, is the input electromagnetic torque at time t - 1, U(t - 1) is the input d-q axis voltage at time t - 1, is the absolute value symbol, is the norm symbol.

[0085] Derive the above criterion function and set its derivative equal to zero. Considering the generality of the algorithm and introducing a step size factor, the input torque control algorithm and the input voltage control algorithm can be obtained as follows: ,

[0086] ,

[0087] ,

[0088] where, , is the torque step size factor, , is the voltage step size factor, is the difference in speed between time t and time t - 1, is the difference in d-q axis current between time t and time t - 1.

[0089] Step S5: Perform online estimation of the speed pseudo-gradient and the current pseudo-Jacobian matrix, and construct the speed pseudo-gradient estimation criterion function and the current pseudo-Jacobian matrix estimation criterion function respectively:

[0090] ,

[0091] ,

[0092] Wherein:

[0093] ,

[0094] ,

[0095] Wherein, is the rotational speed pseudo-Jacobian matrix estimation criterion function, is the current pseudo-Jacobian matrix estimation criterion function, is the reference rotational speed at time t, is the d-q axis reference current at time t, n(t - 1) is the rotational speed at time t - 1, and I(t - 1) is the d-q axis current at time t - 1, is the estimated value of the rotational speed pseudo-gradient at time t - 1, is the estimated value of the current pseudo-Jacobian matrix at time t - 1.

[0096] Find the extrema of their respective criterion functions, and use the matrix inversion lemma to obtain the online estimated values of the pseudo-gradient and pseudo-Jacobian matrix introduced with their respective step factors:

[0097] ,

[0098] ,

[0099] Wherein, is the estimated value of the rotational speed pseudo-gradient at time t, is the rotational speed pseudo-gradient step factor, is the current pseudo-Jacobian matrix step factor, is the estimated value of the current pseudo-Jacobian matrix at time t.

[0100] Step S6, integrating steps S1 - S5, when the system is operating normally, enter S7; when or , enter S5, and reset the pseudo-gradient and pseudo-Jacobian matrix in step S5 to their initial values.

[0101] Step S7, based on the online cloud-tunable model established according to steps S1 - S6 using the model-free adaptive control algorithm, receive the actual operation data from the cloud, and then it is possible to predict the operation status of the load ahead of the actual operation time, and make three aspects of predictions to make adjustments to the actual model in advance.

[0102] When the load is operating at time k, the observed actual operating rotational speed and actual current of the load are respectively and Meanwhile, based on the model-free adaptive control algorithm, the online cloud adjustable model predicts the rotational speed of the load after the k-th moment and the current of the load , where t is the time after the k-th moment, and the rotational speed hysteresis width is set and the current hysteresis width , and at the k-th moment, it satisfies:

[0103] ,

[0104] Then, the cloud Wifi function transmits the possible fault problems back to the local computer, issues a warning prompt, and at the same time, an alarm is issued through the alarm device set at the load end.

[0105] Step S8, after receiving the warning reminder, judge the accuracy of the proportional coefficient , integral coefficient and differential coefficient given in the controller. If it is found that the settings of the above three coefficients are unreasonable, then adjust the proportional coefficient , integral coefficient and differential coefficient in the speed loop and current loop through calculation to ensure the correct rotational speed and current output at the load end. At the same time, monitor the operating conditions of the load based on the predicted rotational speed and current after the k-th moment, and make real-time adjustments to the load's load-bearing situation.

[0106] Figure 2 is the principle flow chart of a digital twin variable frequency speed regulation prediction control system provided by the present invention. As Figure 2 shown, a digital twin variable frequency speed regulation prediction control system for the above-mentioned digital twin variable frequency speed regulation prediction control method includes a digital twin online cloud system, a prediction module, an actual load end, and a controller; the digital twin online cloud system includes an online cloud adjustable model and an online estimation of the pseudo-Jacobian matrix. The digital twin online cloud system is connected to the local end of the digital twin online cloud system and the actual load end through WIFI, obtains the rotational speed and current data output by the load end, and transmits them back to the local end of the digital twin online cloud system. After passing through the online cloud adjustable model, the local end of the digital twin online cloud system transmits the prediction data to the actual load end through the digital twin online cloud system, realizing three-end interaction.

[0107] Although the specific implementation manners of the invention have been described in detail in conjunction with the accompanying drawings, it should not be construed as a limitation on the protection scope of this patent. Within the scope described in the claims, various modifications and deformations that can be made by those skilled in the art without creative efforts still fall within the protection scope of this patent.

Claims

1. A digital twin variable frequency speed regulation predictive control method, characterized in that, It includes the following steps: Step S1, based on the actual mathematical model of the permanent magnet synchronous motor, within the sampling period T c discretize the motion equation and voltage equation of the permanent magnet synchronous motor; Step S2, based on the discretized motor motion equation and voltage equation described in step S1, construct a rotational speed dynamic equation and a current dynamic equation: the rotational speed dynamic equation is used to describe the relationship between the motor rotational speed and time, and the current dynamic equation is used to describe the relationship between the d-q axis currents of the motor and time; Step S3: According to the rotational speed dynamic equation and current dynamic equation described in Step S2, verify that the assumptions of the model-free adaptive control theory are satisfied. Under the premise conditions of and , respectively construct the rotational speed dynamic linearization equation and current dynamic linearization equation; Rotational speed dynamic linear equation: , Current dynamic linear equation: , wherein, is the difference in rotational speed between the (t + 1)-th moment and the t-th moment, is the difference in d-q axis currents between the (t + 1)-th moment and the t-th moment, is the pseudo-gradient, is the pseudo-Jacobian matrix, and T represents the transpose of an arbitrary matrix, is the difference in the rotational speed and torque vectors between the t-th moment and the (t - 1)-th moment, is the difference in the current and voltage vectors between the t-th moment and the (t - 1)-th moment; Step S4, construct an input torque control criterion function and an input voltage control criterion function, and generate an adaptive input torque control algorithm and an input voltage control algorithm by minimizing the rotational speed error and the current error and introducing a step factor; ， ， ， Among them, , is the torque step factor, , is the voltage step factor, is the input electromagnetic torque at time t, is the input electromagnetic torque at time t - 1, U(t) is the d-q axis voltage at time t, and U(t - 1) is the d-q axis voltage at time t - 1, 、 are the elements of the pseudo-gradient described in step S3, 、 are the elements of the pseudo-Jacobian matrix described in step S3, is the reference speed at time t + 1, are the d-q axis reference currents at time t + 1, is the difference in speed between time t and time t - 1, is the difference in d-q axis currents between time t and time t - 1, is the symbol for taking the norm, and are the weight coefficients; Step S5, in combination with the matrix inversion lemma, estimate the rotational speed pseudo-gradient and the current pseudo-Jacobian matrix in real time online, and optimize the estimation accuracy by dynamically adjusting the step factor; the online estimated values of the rotational speed pseudo-gradient and the current pseudo-Jacobian matrix after optimizing the estimation accuracy are as follows: ， ， Among them, is the estimated value of the rotational speed pseudo-gradient at time t, is the estimated value of the rotational speed pseudo-gradient at time t-1, is the estimated value of the current pseudo-Jacobian matrix at time t, is the estimated value of the current pseudo-Jacobian matrix at time t-1, is the difference between the rotational speed and torque vectors at times t-1 and t-2, is the difference between the current and voltage vectors at times t-1 and t-2, n(t) is the rotational speed at time t, n(t-1) is the rotational speed at time t-1, I(t) is the d-q axis current at time t, and I(t-1) is the d-q axis current at time t-1, is the rotational speed pseudo-gradient step factor, is the current pseudo-Jacobian matrix step factor; Step S6, according to steps S1 - S5, determine whether it is necessary to reset the initial values of the pseudo-gradient and the pseudo-Jacobian matrix for the system operating state, and the judgment conditions are as follows: when the system is operating normally, enter S7; when the preconditions described in S3 are not met, enter S5 and reset the initial values of the rotational speed pseudo-gradient and the current pseudo-Jacobian matrix in step S5; Step S7, establish an online cloud-tunable model based on the model-free adaptive control algorithm according to steps S1 - S6, receive the actual operation data through the cloud-tunable model, predict the future load conditions, and trigger a fault warning when the predicted rotational speed or current exceeds the preset hysteresis range; Step S8: Dynamically adjust the proportional coefficient of the controller according to the warning result , integral coefficient and differential coefficient , and optimize the output performance of the load end in real time.

2. A digital twin variable frequency speed regulation prediction control system for implementing the digital twin variable frequency speed regulation prediction control method according to claim 1, characterized in that, The digital twin variable frequency speed regulation prediction control system includes a digital twin online cloud system, a prediction module, an actual load end, a controller, and a local end of the digital twin online cloud system; the digital twin online cloud system includes an online cloud-tunable model, an online estimation of the pseudo-gradient and the pseudo-Jacobian matrix, the local end of the digital twin online cloud system is connected to the digital twin online cloud system and the actual load end through WIFI, the local end of the digital twin online cloud system obtains the rotational speed and current data output by the load end and transmits them back to the digital twin online cloud system, and after passing through the online cloud-tunable model, transmits the prediction data to the actual load end through the digital twin online cloud system to achieve three-end interaction.

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