Permanent magnet state monitoring method based on digital twinning

Abstract

The invention discloses a permanent magnet state monitoring method based on digital twinning, and the method specifically comprises the steps: building a digital twinning model of a permanent magnet synchronous motor according to the nameplate parameters of the permanent magnet synchronous motor; further, optimizing control parameters and mechanical parameters of the permanent magnet synchronous motor by using a particle swarm optimization method to obtain an optimized digital twinborn model; thirdly, collecting the three-phase current of the actual permanent magnet synchronous motor driving system, comparing the three-phase current with the three-phase current output by the digital twin model, and verifying the precision of the model; on the basis, stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux linkage of the permanent magnet synchronous motor are regarded as to-be-optimized parameters, and accurate monitoring of the running state of the permanent magnet is achieved in combination with the constructed digital twinborn model. The method is easy to implement, small in calculation burden, free of adverse effects of motor parameter changes, high in monitoring precision and suitable for different types of permanent magnet synchronous motor driving systems, and the problems in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of reliability assessment of AC motor drive systems, and in particular relates to a permanent magnet state monitoring method based on digital twins. Background Art

[0002] Under the guidance of the goals of "carbon peak" and "carbon neutrality", the permanent magnet traction drive system that conforms to the concept of green and low-carbon development has become the key development direction of the urban rail transit system. As the core power source of urban rail trains, the reliable operation of the permanent magnet traction drive system is an effective guarantee for the green development of urban rail transit.

[0003] In the permanent magnet synchronous motor drive system, the permanent magnet flux is the key parameter to achieve torque performance maintenance. However, due to the continuous operation of high-power conditions, the confined space of underground tunnels, and the fully enclosed motor structure, coupled with the long-term effects of non-stable conditions such as frequent switching of traction / braking modes and variable loads, the permanent magnet temperature rises significantly, making the permanent magnet synchronous motor drive system face severe challenges of demagnetization failure. The permanent magnet demagnetization failure leads to a double vicious cycle in the permanent magnet synchronous motor drive system, accelerating the rise of the motor operating temperature and the demagnetization process of the permanent magnet, and even causing motor damage and train power loss, which seriously threatens the safe and reliable operation of urban rail trains.

[0004] Among the many permanent magnet state monitoring methods, the permanent magnet state monitoring method based on the motor model is widely popular due to its simple principle and easy implementation. However, this method depends on the motor parameters, resulting in low robustness. When the motor parameters change, the monitoring accuracy of this method decreases significantly. In addition, this method is not adaptable to complex working conditions. When the operating conditions change, the monitoring performance of this method will also be significantly affected. Summary of the invention

[0005] In view of the shortcomings of the existing permanent magnet state monitoring method based on motor model, the present invention provides a permanent magnet state monitoring method based on digital twin.

[0006] A permanent magnet state monitoring method based on digital twin of the present invention comprises the following steps:

[0007] Step 1: Establish a digital twin model of permanent magnet synchronous motor.

[0008] According to the nameplate parameters of the permanent magnet synchronous motor, the digital twin model of the permanent magnet synchronous motor is established using Matlab / Simulink, and the parameters are optimized using particle swarm optimization (PSO). The specific steps are as follows:

[0009] S1.1: Initialization of particle velocity and position: Determine the dimension of the particle according to the number of parameters to be optimized (such as control parameters and mechanical parameters of the permanent magnet synchronous motor drive system); further, randomly initialize the initial position and initial velocity of each particle in the solution space.

[0010] S1.2: Solution of fitness function.

[0011] The fitness function is constructed as:

[0012]

[0013] Where: i sam 、i sbm 、i scm is the measured three-phase current, i sa 、i sb 、i sc is the three-phase current of the twin model, and N is the number of data points.

[0014] The three-phase current of the actual permanent magnet synchronous motor is collected, and the fitness function of each particle is solved according to formula (1).

[0015] S1.3: Determination of the group optimal solution and the global optimal solution: According to the fitness function value of each particle, the group optimal solution and the global optimal solution of this iteration are recorded, that is, the particle position that minimizes the fitness value function.

[0016] S1.4: Update of particle speed and position: After determining the group optimal solution and the global optimal solution, the particle speed and position are updated according to the particle's initial speed, its knowledge of the optimal solution, and the knowledge of other particles on the optimal solution; in each iteration, the speed and position update formula of each particle is:

[0017]

[0018] Where: k is the number of iterations, v k and v k-1 is the speed of the kth and k-1th iterations; x k and x k-1 is the position of the kth and k-1th iterations; x pbest,k-1 represents the best position of the particle before the k-1th iteration; x gbest,k-1 represents the best position of the global particle before the k-1th iteration; c1 is the individual learning factor, the larger its value, the more the particle tends to choose the individual's best position; c2 is the social learning factor, the larger its value, the greater the possibility that the particle chooses the global optimal position; ω pis the inertia weight, the larger its value, the more inclined the particle is to maintain its original speed and direction and explore the position that has not appeared; r1 and r2 are random numbers on [0,1].

[0019] S1.5: Iteration termination: Repeat the above process until the maximum number of iterations is reached, then terminate the iteration and output the optimized digital twin model.

[0020] S1.6: Verification of twin model accuracy: After obtaining the optimized digital twin model, considering that the three-phase current can characterize the characteristics of the permanent magnet synchronous motor and is easy to measure, the three-phase current of the actual permanent magnet synchronous motor and the three-phase current output by the digital twin model are collected to verify the model accuracy.

[0021] Step 2: Specific implementation of permanent magnet state monitoring.

[0022] S2.1: Initialization of particle speed and position: The stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux of the permanent magnet synchronous motor are regarded as parameters to be optimized, and the dimension of the particle is determined; further, the initial position and initial speed of each particle are randomly initialized.

[0023] S2.2: Fitness function calculation: The fitness function is calculated based on the three-phase current provided by the digital twin model and the measured three-phase current.

[0024] S2.3: Iterative optimization of particle speed and position: Based on the calculated fitness function value, the particle swarm optimization algorithm is used to iteratively optimize the speed and position of each particle.

[0025] S2.4: Permanent magnet flux identification: After obtaining the optimized digital twin model, the identified permanent magnet flux is extracted to achieve effective monitoring of the permanent magnet status.

[0026] The beneficial technical effects of the present invention compared with the prior art are:

[0027] 1. The present invention only requires nameplate parameters and three-phase current information, and adopts particle swarm optimization algorithm to construct a digital twin model suitable for permanent magnet state monitoring, which is easy to implement and has a small computational burden.

[0028] 2. The digital twin model constructed by the present invention is not affected by changes in working conditions and motor parameters and has high robustness.

[0029] 3. The digital twin model constructed by the present invention can simultaneously identify the stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux, significantly improving the identification accuracy and effectively solving the problem of lack of rank in the state monitoring method based on the motor model.

[0030] Fourth, the permanent magnet state monitoring method studied in the present invention has good adaptability to different types of permanent magnet synchronous motors, and the state monitoring accuracy is high. In addition, the method has good versatility and can also be transplanted to other AC motor state monitoring methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the permanent magnet state monitoring method of the present invention.

[0032] Figure 2 It is a schematic diagram of the digital twin model of the present invention.

[0033] Figure 3 It is a parameter optimization method based on particle swarm optimization of the present invention.

[0034] Figure 4 This is a test result diagram of the digital twin model verification of the present invention.

[0035] Figure 5 It is the test performance of the permanent magnet state monitoring method of the present invention under steady state conditions.

[0036] Figure 6 It is the test performance of the permanent magnet state monitoring method of the present invention under the change of speed instruction.

[0037] Figure 7 It is the test performance of the permanent magnet state monitoring method of the present invention under load changes.

[0038] Figure 8 The present invention is a method for monitoring the state of a permanent magnet and is used to test the performance of a built-in permanent magnet synchronous motor.

[0039] Fig. 9 It is a comparison result of the permanent magnet state monitoring method of the present invention and the recursive least squares method test performance. DETAILED DESCRIPTION

[0040] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0041] The process of the permanent magnet state monitoring method based on digital twin of the present invention is as follows: Figure 1As shown in the figure, according to the nameplate parameters of the permanent magnet synchronous motor, a simulation model of the permanent magnet synchronous motor is built using Matlab / Simulink, and it is used as a digital twin model; further, the particle swarm optimization method is used to optimize the control parameters and mechanical parameters of the permanent magnet synchronous motor to obtain an optimized digital twin model; then, the three-phase current of the actual permanent magnet synchronous motor drive system is collected and compared with the three-phase current output by the digital twin model to verify the model accuracy; on this basis, the stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux of the permanent magnet synchronous motor are regarded as parameters to be optimized, and combined with the constructed digital twin model, accurate monitoring of the operating status of the permanent magnet is achieved.

[0042] The permanent magnet state monitoring method based on digital twin of the present invention uses the digital twin method to complete the accurate monitoring of the permanent magnet operation state under different working conditions, and specifically includes the following steps:

[0043] Step 1: Establish a digital twin model of permanent magnet synchronous motor.

[0044] According to the nameplate parameters of the permanent magnet synchronous motor, a digital twin model of the permanent magnet synchronous motor is established using Matlab / Simulink. Figure 2 However, since the control parameters and mechanical parameters of the permanent magnet synchronous motor drive system are unknown, particle swarm optimization (PSO) is used to optimize the parameters. The optimization method is as follows: Figure 3 As shown, the specific steps are as follows:

[0045] S1.1: Initialization of particle velocity and position: Determine the dimension of the particle according to the number of parameters to be optimized (such as control parameters and mechanical parameters of the permanent magnet synchronous motor drive system); further, randomly initialize the initial position and initial velocity of each particle in the solution space.

[0046] S1.2: Solution of fitness function.

[0047] The fitness function is constructed as:

[0048]

[0049] Where: i sam 、i sbm 、i scm is the measured three-phase current, i sa 、i sb 、i sc is the three-phase current of the twin model, and N is the number of data points.

[0050] The three-phase current of the actual permanent magnet synchronous motor is collected, and the fitness function of each particle is solved according to formula (1).

[0051] S1.3: Determination of the group optimal solution and the global optimal solution: According to the fitness function value of each particle, the group optimal solution and the global optimal solution of this iteration are recorded, that is, the particle position that minimizes the fitness value function.

[0052] S1.4: Update of particle speed and position: After determining the group optimal solution and the global optimal solution, the particle speed and position are updated according to the particle's initial speed, its knowledge of the optimal solution, and the knowledge of other particles on the optimal solution; in each iteration, the speed and position update formula of each particle is:

[0053]

[0054] Where: k is the number of iterations, v k and v k-1 is the speed of the kth and k-1th iterations; x k and x k-1 is the position of the kth and k-1th iterations; x pbest,k-1 represents the best position of the particle before the k-1th iteration; x gbest,k-1 represents the best position of the global particle before the k-1th iteration; c1 is the individual learning factor, the larger its value, the more the particle tends to choose the individual's best position; c2 is the social learning factor, the larger its value, the greater the possibility that the particle chooses the global optimal position; ω p is the inertia weight, the larger its value, the more inclined the particle is to maintain its original speed and direction and explore the position that has not appeared; r1 and r2 are random numbers on [0,1].

[0055] S1.5: Iteration termination: Repeat the above process until the maximum number of iterations is reached, then terminate the iteration and output the optimized digital twin model.

[0056] S1.6: Verification of twin model accuracy: After obtaining the optimized digital twin model, considering that the three-phase current can characterize the characteristics of the permanent magnet synchronous motor and is easy to measure, the three-phase current of the actual permanent magnet synchronous motor and the three-phase current output by the digital twin model are collected to verify the model accuracy.

[0057] Step 2: Specific implementation of permanent magnet state monitoring.

[0058] S2.1: Initialization of particle speed and position: The stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux of the permanent magnet synchronous motor are regarded as parameters to be optimized, and the dimension of the particle is determined; further, the initial position and initial speed of each particle are randomly initialized.

[0059] S2.2: Fitness function calculation: The fitness function is calculated based on the three-phase current provided by the digital twin model and the measured three-phase current.

[0060] S2.3: Iterative optimization of particle speed and position: Based on the calculated fitness function value, the particle swarm optimization algorithm is used to iteratively optimize the speed and position of each particle.

[0061] S2.4: Permanent magnet flux identification: After obtaining the optimized digital twin model, the identified permanent magnet flux is extracted to achieve effective monitoring of the permanent magnet status.

[0062] like Figure 4 As shown in Figure 2, the accuracy of the established digital twin model is verified by using the measured three-phase stator current. Figure 4 The results show that the three-phase current output by the digital twin can effectively track the measured stator current, and the error is limited to a small range. This means that the established digital twin model has high accuracy and can be used for permanent magnet state monitoring.

[0063] Figure 5 is the performance of the permanent magnet state monitoring method proposed under steady-state conditions. Figure 5 As shown in (a), the speed command and load are 500r / min and 10N·m respectively. From the test results, it can be seen that the proposed method can effectively monitor the actual operating state of the permanent magnet, and the monitoring error is controlled within a small range. Figure 5 As shown in (b), the speed command and load are 800 r / min and 12 N·m respectively. From the test results, it can be seen that the permanent magnet flux estimated by the proposed method can effectively track the actual permanent magnet flux, and the estimation error is within a reasonable range.

[0064] Figure 6 The performance of the proposed method for monitoring the state of permanent magnets under the condition of speed command changes. In this test, the speed command changes from 500r / min to 1000r / min, and the load is set to 10N·m. According to the test results, the proposed method can provide good state monitoring performance with small monitoring error.

[0065] Figure 7 The performance of the proposed method for monitoring the condition of permanent magnets under load variation conditions is shown in Figure 2. In this test, the speed command is set to 1000r / min and the load increases from 5N·m to 10N·m. According to the test results, the proposed method can provide good condition monitoring performance with a small monitoring error.

[0066] Furthermore, the permanent magnet state monitoring method studied was extended to the built-in permanent magnet synchronous motor drive system, and the test results are as follows: Figure 8As shown. In this test, the speed command and load are set to 1000r / min and 10N·m respectively. According to the test results, the studied method can provide good condition monitoring performance with small monitoring error. This means that the studied permanent magnet condition monitoring method can be applied to different types of permanent magnet synchronous motor drive systems.

[0067] Finally, the studied permanent magnet state monitoring method is compared with the permanent magnet state monitoring method based on recursive least squares. The test results are as follows: Fig. 9 As shown. Fig. 9 As shown in the figure, compared with the permanent magnet condition monitoring method based on recursive least squares, the studied method can provide better condition monitoring performance and smaller monitoring error.

[0068] The permanent magnet state monitoring method provided by the present invention is based on the nameplate parameters of the permanent magnet synchronous motor and combined with the particle swarm optimization algorithm to construct a digital twin model for permanent magnet state monitoring; on this basis, the stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux of the permanent magnet synchronous motor are regarded as parameters to be optimized, and the optimized digital twin model is used to effectively identify the permanent magnet parameters, thereby realizing effective monitoring of the permanent magnet operating state.

[0069] The present invention can realize accurate monitoring of the operating state of permanent magnets under different working conditions. More importantly, compared with the existing permanent magnet state monitoring method based on the motor model, the studied method only requires nameplate parameters and three-phase current information, is easy to implement, and has a small calculation burden; and the studied method is not affected by changes in working conditions and motor parameters, and has high monitoring accuracy, overcoming the problem that the existing permanent magnet state monitoring method based on the motor model has a decreased monitoring performance due to changes in motor parameters and working conditions; in addition, the studied method is applicable to a variety of operating conditions and different types of permanent magnet synchronous motors, and has high applicability.

[0070] The method of the present invention is easy to implement, has a small calculation burden, is not adversely affected by changes in motor parameters, has high monitoring accuracy, and is applicable to different types of permanent magnet synchronous motor drive systems, thereby solving the technical problems of existing permanent magnet state monitoring methods such as low robustness and poor adaptability.

Claims

1. A permanent magnet state monitoring method based on digital twin, characterized in that: The following steps are involved: Step 1: Establish a digital twin model of permanent magnet synchronous motor; According to the nameplate parameters of the permanent magnet synchronous motor, the digital twin model of the permanent magnet synchronous motor is established using Matlab / Simulink, and the parameters are optimized using particle swarm optimization. The specific steps are as follows: S1.1: Initialization of particle velocity and position: Determine the dimension of the particle according to the number of parameters to be optimized; further, randomly initialize the initial position and initial velocity of each particle in the solution space; S1.2: Solution of fitness function; The fitness function is constructed as: Where: i sam 、i sbm 、i scm is the measured three-phase current, i sa 、i sb 、i sc is the three-phase current of the twin model, N is the number of data points; The three-phase current of the actual permanent magnet synchronous motor is collected, and the fitness function of each particle is solved according to formula (1); S1.3: Determination of the group optimal solution and the global optimal solution: According to the fitness function value of each particle, record the group optimal solution and the global optimal solution of this iteration, that is, the particle position that minimizes the fitness value function; S1.4: Update of particle speed and position: After determining the group optimal solution and the global optimal solution, the particle speed and position are updated according to the particle's initial speed, its knowledge of the optimal solution, and the knowledge of other particles on the optimal solution; in each iteration, the speed and position update formula of each particle is: Where: k is the number of iterations, v k and v k-1 is the speed of the kth and k-1th iterations; x k and x k-1 is the position of the kth and k-1th iterations; x pbest,k-1 represents the best position of the particle before the k-1th iteration; x gbest,k-1 represents the best position of the global particle before the k-1th iteration; c1 is the individual learning factor, the larger its value, the more the particle tends to choose the individual's best position; c2 is the social learning factor, the larger its value, the greater the possibility that the particle chooses the global optimal position; ω p is the inertia weight, the larger its value, the more likely the particle is to maintain its original speed and direction and explore positions that have not appeared; r1 and r2 are random numbers on [0,1]; S1.5: Iteration termination: Repeat the above process until the maximum number of iterations is reached, then terminate the iteration and output the optimized digital twin model; S1.6: Verification of twin model accuracy: After obtaining the optimized digital twin model, considering that the three-phase current can characterize the characteristics of the permanent magnet synchronous motor and is easy to measure, the three-phase current of the actual permanent magnet synchronous motor and the three-phase current output by the digital twin model are collected to verify the model accuracy; Step 2: Specific implementation of permanent magnet state monitoring; S2.1: Initialization of particle speed and position: The stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux of the permanent magnet synchronous motor are regarded as parameters to be optimized, and the dimension of the particle is determined; further, the initial position and initial speed of each particle are randomly initialized; S2.2: Calculation of fitness function: Calculate the fitness function based on the three-phase current provided by the digital twin model and the measured three-phase current; S2.3: Iterative optimization of particle speed and position: According to the calculated fitness function value, the particle swarm optimization algorithm is used to iteratively optimize the speed and position of each particle; S2.4: Permanent magnet flux identification: After obtaining the optimized digital twin model, the identified permanent magnet flux is extracted to achieve effective monitoring of the permanent magnet status.

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