A Harmonic Suppression Method for Permanent Magnet Synchronous Motor Based on Improved Particle Swarm Optimization Algorithm

Abstract

The present invention discloses a harmonic suppression method for a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm, which relates to the technical field of PMSM harmonic suppression, and includes: S1, obtaining harmonic current components by using the electrical angle and three-phase current of the permanent magnet synchronous motor; S2, constructing an algorithm value function according to the harmonic current components; S3, obtaining the harmonic suppression result of the permanent magnet synchronous motor based on the improved particle swarm optimization algorithm according to the harmonic current components and the algorithm value function. The present invention uses multiple synchronous rotating coordinate system transformations to obtain current components in the rotating reference frame, and then selects a corresponding LPF for filtering to extract current harmonics; the present invention decouples and compensates current harmonics by establishing a shifted multi-reference frame, and the algorithm is simpler compared with other methods; the present invention improves the amplitude and phase of dead zone compensation through the improved particle swarm optimization algorithm, which is not affected by motor parameters, has a fast iteration speed, and is not easily trapped in a local optimal solution.

Description

Technical Field

[0001] The present invention relates to the technical field of PMSM harmonic suppression, and particularly relates to a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) have the characteristics of simple structure, high power density, strong overload capacity, and easy maintenance, and are widely used in industrial robots, electric vehicles, aerospace and other fields. The magnitude of the current harmonics of PMSMs depends on various factors, such as the inverter feeding system, magnetic circuit saturation of the rectifier load system, manufacturing errors, dead time, noise, etc. These harmonic components will generate additional losses and torque ripples in the stator windings and iron cores of the motor, and easily cause the motor to overheat. This phenomenon will reduce the stability and reliability of the motor operation, and is not conducive to the application of PMSMs in high-precision and high-power occasions. At present, most of the existing methods for removing harmonics require peripheral circuits, high-precision current polarity detection, or motor parameters, which have complex calculations and cannot completely solve the harmonic problem. Summary of the Invention

[0003] The purpose of the present invention is to propose a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm to solve the harmonic problems caused by factors such as inverter nonlinearity and dead time in permanent magnet synchronous motors and improve the harmonic suppression effect.

[0004] To achieve the above purpose, the present invention provides a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm, including the following steps:

[0005] S1. Obtain harmonic current components by using the electrical angle and three-phase current of the permanent magnet synchronous motor;

[0006] S2. Construct an algorithm value function according to the harmonic current components;

[0007] S3. Obtain the harmonic suppression result of the permanent magnet synchronous motor based on the improved particle swarm optimization algorithm according to the harmonic current components and the algorithm value function.

[0008] Optionally, obtaining harmonic current components by using the electrical angle and three-phase current of the permanent magnet synchronous motor includes:

[0009] Obtain the electrical angle and three-phase current of the permanent magnet synchronous motor;

[0010] Perform a multiple synchronous coordinate system transformation according to the electrical angle and three-phase current of the permanent magnet synchronous motor to obtain current components in the rotating reference frame;

[0011] Filter the current components in the rotating reference frame based on a low-pass filter to obtain harmonic current components;

[0012] Among them, the current components in the rotating reference frame are the current components in the fifth rotating reference frame or the current components in the seventh rotating reference frame.

[0013] Optionally, the calculation formula of the algorithm value function is as follows:

[0014]

[0015] Among them, is the value of the algorithm value function, i d5th and i q5th respectively represent the 5th harmonic current values of the d-axis and q-axis during the operation of the PMSM, N sum represents the number of sampling points, i is the initial measurement value.

[0016] Optionally, obtaining the harmonic suppression result of the permanent magnet synchronous motor by using the improved particle swarm optimization algorithm according to the harmonic current components and the algorithm value function includes:

[0017] S3-1. Collect the steady-state operation parameters of the permanent magnet synchronous motor to obtain the fitness function of the initial state and initialize the target parameters;

[0018] S3-2. Set the range of compensation parameters and the number of algorithm iterations, and initialize the position and velocity of the particles;

[0019] S3-3. Use the harmonic current components and the algorithm value function to obtain the corresponding algorithm value function value as the particle fitness based on the improved particle swarm optimization algorithm;

[0020] S3-4. Update and generate the next generation of particles according to the particle fitness by using the update formula of the gray wolf algorithm and the surrounding update strategy of the improved particle swarm optimization algorithm to obtain the position and velocity of the new particles;

[0021] S3-5. Determine whether the algorithm iteration times are reached. If so, stop the iteration, and obtain the phase and amplitude of the dead zone compensation time corresponding to the target historical position of the swarm as the harmonic suppression result of the permanent magnet synchronous motor according to the position and velocity of the new particles. Otherwise, return to S3-3;

[0022] Among them, the target parameters include the inertia factor, the adjustment coefficient and the random matrix, the compensation parameters are the phase and amplitude of the dead zone compensation time, and the position of the particle corresponds to the combination of the phase and amplitude of different dead zone compensation times.

[0023] Optionally, updating and generating the next generation of particles according to the update formula of the grey wolf algorithm and the surrounding update strategy of the improved particle swarm algorithm using the particle fitness to obtain the positions and velocities of the new particles includes:

[0024] S3-4-1. Obtaining the corresponding historical particle fitness using the particle fitness;

[0025] S3-4-2. Obtaining the particle fitness as the fitness of the initial particle;

[0026] S3-4-3. Judging whether the fitness of the initial particle is less than the historical particle fitness. If so, updating the positions and velocities of the initial particle as the positions and velocities of the new particle, and directly executing S3-5. Otherwise, evolving the initial particle using the particle evolution strategy to obtain the evolved particle, and executing S3-4-4;

[0027] S3-4-4. Judging whether the fitness of the evolved particle is less than the fitness of the initial particle. If so, updating the positions and velocities of the initial particle using the evolved particle as the positions and velocities of the new particle, and directly executing S3-5. Otherwise, retaining the initial particle to obtain the fitness of the initial particle, and executing S3-4-5;

[0028] S3-4-5. Generating the next generation of particles according to the update formula of the grey wolf algorithm and the surrounding update strategy of the improved particle swarm algorithm using the fitness of the initial particle;

[0029] S3-4-6. Obtaining the fitness of the next generation of particles as the fitness of the initial particle according to the next generation of particles, and updating the target parameter, then returning to S3-4.

[0030] Optionally, the calculation formula of the particle evolution strategy is as follows:

[0031]

[0032] Wherein, x_worst ( t ) represents the particle with the worst fitness among the t rd iteration, N th particle, N represents that the number of example groups is N , Eworst ( t ) represents the position of the evolved particle, i , j , k are three different particles randomly selected from the particle swarm, argmax (·) is the maximum value obtaining function, fit (·) is a function, X 1( t ), X 2 ( t ),…, X N ( t ) are the positions of the 1st, 2nd, …, N th particles.

[0033] Optionally, the update formula of the Grey Wolf Algorithm is:

[0034]

[0035] Wherein, X 1 ( t ) is the position of the 1st particle, X 2 ( t ) is the position of the 2nd particle, X 3 ( t ) is the position of the 3rd particle, X α ( t ), X β ( t ), X δ ( t ) are the positions of the optimal solution, the sub-optimal solution, and the third-optimal solution respectively, A 1 , A 2 , A 3 are different adjustment coefficients respectively, , , are the distances between the individual wolves and the elite individual respectively, X ( t+1 ) is the updated particle position.

[0036] Optionally, the surrounding update strategy of the improved Particle Swarm Optimization Algorithm is:

[0037]

[0038] Wherein, is the moving distance of the updated particle, w is the inertia factor, is the Particle Swarm position, is the Particle Swarm position before update, c 1 , c 2 are random numbers between [0, 1], r1 , r 2 are the directions of the particle with the optimal solution and the sub - optimal solution respectively, , are the optimal solution and the sub - optimal solution respectively, is the position of the particle after being updated by the improved particle swarm algorithm.

[0039] Compared with the closest prior art, the beneficial effects of the present invention are:

[0040] The present invention uses multiple synchronous rotating coordinate system transformations to obtain the current components in the rotating reference frame, and then selects the corresponding LPF for filtering to extract current harmonics; the present invention decouples and compensates current harmonics by establishing a shifted multi - reference frame, and the algorithm is simpler compared with other methods; the present invention adjusts the amplitude and phase of dead - zone compensation through an improved particle swarm algorithm, which is not affected by motor parameters, has a fast iteration speed, and is not easily trapped in local optimal solutions. The present invention does not require an external circuit and a current polarity judgment algorithm, has simple calculations, an easy - to - understand algorithm, and can effectively remove the 5th and 7th harmonics; by simultaneously optimizing the amplitude and phase of compensation through an improved particle swarm algorithm, the harmonic suppression effect is improved, the characteristics of the grey wolf algorithm are integrated, the global search ability is enhanced, and being trapped in local optimal solutions is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 is the flowchart of a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to an embodiment of the present invention;

[0043] Figure 2 is the overall block diagram of the harmonic suppression strategy of a permanent magnet synchronous motor proposed in an embodiment of the present invention;

[0044] Figure 3 is the current flow diagram of a half - bridge driver under different switching states proposed in an embodiment of the present invention;

[0045] Figure 4 is the diagram of switch delay and voltage error caused by the non - linearity of the inverter proposed in an embodiment of the present invention;

[0046] Figure 5 is the schematic diagram of harmonic extraction principle proposed in an embodiment of the present invention;

[0047] Figure 6Flowchart of the improved particle swarm optimization algorithm proposed in the embodiments of the present invention. Detailed implementation manners

[0048] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] The terms used in the embodiments of the present invention are only for explaining the specific embodiments of the present invention, rather than aiming to limit the present invention.

[0050] Embodiment 1: As Figure 1 shown, the present invention provides a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm, including the following steps:

[0051] S1. Obtain harmonic current components by using the electrical angle and three-phase current of the permanent magnet synchronous motor;

[0052] S2. Construct an algorithm value function according to the harmonic current components;

[0053] S3. Obtain the harmonic suppression result of the permanent magnet synchronous motor based on the improved particle swarm optimization algorithm according to the harmonic current components and the algorithm value function.

[0054] S1 specifically includes:

[0055] Obtain the electrical angle and three-phase current of the permanent magnet synchronous motor;

[0056] Perform a multiple synchronous coordinate system transformation according to the electrical angle and three-phase current of the permanent magnet synchronous motor to obtain the current components in the rotating reference frame;

[0057] Perform filtering processing on the current components in the rotating reference frame based on a low-pass filter to obtain harmonic current components;

[0058] Among them, the current components in the rotating reference frame are the current components in the fifth rotating reference frame or the current components in the seventh rotating reference frame.

[0059] The calculation formula of the algorithm value function is as follows:

[0060]

[0061] Among them, is the value of the algorithm value function, i d5th and iq5th respectively represent the 5th harmonic current values of the d-axis and q-axis during the operation of the PMSM, N sum represents the number of sampling points, i is the initial measurement value.

[0062] S3 specifically includes:

[0063] S3-1. Collect the steady-state operation parameters of the permanent magnet synchronous motor to obtain the fitness function of the initial state and initialize the target parameters;

[0064] S3-2. Set the range of compensation parameters and the number of algorithm iterations, and initialize the position and velocity of the particles;

[0065] S3-3. Use the harmonic current component and the algorithm value function to obtain the corresponding algorithm value function value as the particle fitness based on the improved particle swarm algorithm;

[0066] S3-4. Update and generate the next generation of particles according to the update formula of the gray wolf algorithm and the enclosing update strategy of the improved particle swarm algorithm using the particle fitness to obtain the position and velocity of the new particles;

[0067] S3-5. Determine whether the algorithm iteration times are reached. If so, stop the iteration, and obtain the phase and amplitude of the dead zone compensation time corresponding to the target historical position of the swarm according to the position and velocity of the new particles as the harmonic suppression result of the permanent magnet synchronous motor. Otherwise, return to S3-3;

[0068] Among them, the target parameters include the inertia factor, adjustment coefficient and random matrix, the compensation parameters are the phase and amplitude of the dead zone compensation time, and the position of the particle corresponds to the combination of the phase and amplitude of different dead zone compensation times.

[0069] Specifically, after each iteration ends, determine whether the current iteration times have reached the predefined termination condition. If the termination condition is reached, stop the iteration process, and output the phase and amplitude of the dead zone compensation time corresponding to the best historical position of the swarm according to the position and velocity of the new particles obtained by the improved particle swarm algorithm. This is the optimal value obtained through the entire calculation process. This optimal value can minimize the previously defined value function, thereby achieving the best suppression effect on the 5th harmonic current, effectively compensating for the influence of the dead zone time on the motor operation, and improving the operation performance and stability of the permanent magnet synchronous motor.

[0070] S3-4 specifically includes:

[0071] S3-4-1. Use the particle fitness to obtain the corresponding historical particle fitness;

[0072] S3-4-2. Obtain the fitness of the particle as the fitness of the initial particle;

[0073] S3-4-3. Determine whether the fitness of the initial particle is less than the historical particle fitness. If so, update the position and velocity of the initial particle as the position and velocity of the new particle, and directly execute S3-5. Otherwise, use the particle evolution strategy to evolve the initial particle to obtain an evolved particle, and execute S3-4-4;

[0074] S3-4-4. Determine whether the fitness of the evolved particle is less than the fitness of the initial particle. If so, use the evolved particle to update the position and velocity of the initial particle as the position and velocity of the new particle, and directly execute S3-5. Otherwise, retain the initial particle to obtain the fitness of the initial particle, and execute S3-4-5;

[0075] S3-4-5. Generate the next generation of particles according to the fitness of the initial particle using the update formula of the gray wolf algorithm and the surrounding update strategy of the improved particle swarm algorithm;

[0076] S3-4-6. Obtain the fitness of the next generation of particles as the fitness of the initial particle, and update the target parameter, then return to S3-4.

[0077] Specifically, run the IPSO algorithm to start the iteration process. In each iteration, for each particle (representing a combination of phase and amplitude of the dead zone compensation time), substitute it into the motor control model, and combine the previously obtained harmonic current component data and the algorithm value function calculation formula to calculate the corresponding algorithm value function value. This algorithm value function value is used as the fitness evaluation index of the particle, that is, the fitness of the current particle; compare the fitness of the current particle (algorithm value function value) with the corresponding historical particle fitness, that is, the combination of phase and amplitude of the dead zone compensation time corresponding to the smallest algorithm value function value found in the history of the current particle itself. If the fitness of the current particle is better (that is, the current algorithm value function value is smaller), then update the best position and velocity of the individual to obtain the position and velocity of the new particle, and update the best historical position of the bee colony (that is, the combination of phase and amplitude of the dead zone compensation time corresponding to the smallest algorithm value function value found in the history of the entire particle swarm).

[0078] In each iteration, the particle with the worst fitness is evolved. The particle with the worst fitness in the particle swarm (i.e., the particle with the largest corresponding algorithm value function value) is selected, and its position is updated according to a specific evolution strategy. After the update, the fitness of the evolved particle (calculating the new algorithm value function value) is compared with the fitness before evolution. If the fitness of the evolved particle is better than that before evolution, the evolved particle replaces the non-evolved particle; if the fitness of the evolved particle is worse than that before evolution, the non-evolved particle is retained. In this way, the diversity of the particle swarm is increased, and the ability of the algorithm to search for the optimal phase and amplitude combination of the dead zone compensation time is improved.

[0079] The calculation formula of the particle evolution strategy is as follows:

[0080]

[0081] Where, x_worst ( t ) represents the particle with the worst fitness among the t th iteration, N th particle, N represents the number of particle swarms as N , Eworst ( t ) represents the position of the evolved particle, i , j , k are three different particles randomly selected from the particle swarm, argmax (·) is the maximum value obtaining function, fit (·) is the function, X 1 ( t ), X 2 ( t ), …, X N ( t ) are the positions of the 1st, 2nd, …, N th particles.

[0082] The update formula of the grey wolf algorithm is:

[0083]

[0084] Where, X 1 ( t ) is the position of the 1st particle, X 2 ( t ) is the position of the 2nd particle, X 3 ( t) is the position of the 3rd particle, X α ( t )、 X β ( t )、 X δ ( t ) are the positions of the optimal solution, sub-optimal solution, and third-optimal solution respectively, A 1 、 A 2 、 A 3 are different adjustment coefficients respectively, 、 、 are the distances of the wolf pack individuals from the elite individuals respectively, X ( t+1 ) is the updated particle position.

[0085] The surrounding update strategy of the improved particle swarm optimization algorithm is as follows:

[0086]

[0087] Among them, is the moving distance of the updated particle, w is the inertia factor, is the particle swarm position, is the particle swarm position before update, c 1 、 c 2 are random numbers between [0, 1], r 1 、 r 2 are the directions of the particle with respect to the optimal solution and sub-optimal solution respectively, 、 are the optimal solution and sub-optimal solution respectively, is the particle position after update using the improved particle swarm optimization algorithm.

[0088] Embodiment 2: The embodiment of the present application proposes a method for suppressing harmonics of a permanent magnet synchronous motor based on an improved particle swarm optimization algorithm, which does not require a peripheral circuit and a current polarity judgment algorithm, has simple calculation and an easy-to-understand algorithm, and can remove the 5th and 7th harmonics. Specifically, it includes: measuring the electrical angle of the PMSM and the three-phase current of the PMSM, and obtaining two current components and in two 5th-order rotating reference frames after multiple synchronous rotating coordinate system transformations; filtering the two current components in the two 5th-order rotating reference frames using a low-pass filter (LPF) to obtain the 5th-order harmonic current components id5 and i q5 , i.e., the 5th harmonic current values of the d-axis and q-axis during the operation of the PMSM i d5th and i q5th ; The improved particle swarm optimization algorithm is used to calculate the voltage compensation amplitude and phase.

[0089] As Figure 2 shown, the specific process steps of a permanent magnet synchronous motor harmonic suppression method based on the improved particle swarm optimization algorithm (hereinafter referred to as "harmonic suppression strategy") described in the embodiments of the present invention are described. Among them, the solid block diagram part is the general PMSM vector control structure, and the dashed block diagram part is the specific harmonic suppression strategy of this embodiment, that is, the Park transformation and LPF filtering are used to extract the harmonic amplitude of a specific frequency, the algorithm value function is constructed, and the improved particle swarm optimization algorithm is used to calculate the compensation harmonic amplitude and phase.

[0090] The PMSM is usually driven by a three-phase bipolar circuit including insulated gate bipolar transistors (IGBTs). When the current flows from the inverter to the load, it is defined as positive. As Figure 3 shown, it shows the current flow direction in the a-phase single-phase half-bridge under different switching states. When the a-phase current direction is positive, Q 1 conducts Q 2 turns off, Q 1 turns off Q 2 turns off, Q 1 conducts Q 2 when conducting, the current flow directions are shown as ①, ②, and ③ respectively; when the a-phase current direction is negative: Q 1 conducts Q 2 turns off, Q 1 turns off Q 2 turns off, Q 1 conducts Q 2 when conducting, the current flow directions are shown as ④, ⑤, and ⑥ respectively. As Figure 4 shown, considering the conduction voltage drop of the IGBT and the diode voltage drop v t , v d and the dead-time delay T d , the voltage of the a-phase upper-bridge IGBT Van , which explains the reasons for the voltage errors generated under the non-linear conditions of the inverter in different switching states.

[0091] Taking phase A as an example, when the pulse width modulation (PWM) switch is activated, the output voltage error of phase A during the switching can be expressed as follows:

[0092] (1)

[0093] Where, v t , v d represent the conduction voltage drop of the IGBT and the diode voltage drop respectively, R on is the IGBT resistance, u dc is the DC bus voltage, i a is the phase A current.

[0094] During ( t 1 - t 2 ) and ( t 3 - t 4 ) period, the dead-time delay T d is expressed as:

[0095] (2)

[0096] Where, t d is the dead-time caused by the PWM generator, t on , t off are the turn-on and turn-off delay times of the IGBT respectively. Considering the T pwm average error voltage during Δu :

[0097] (3)

[0098] In the formula, T pwm is the pulse width modulation period, T s1 represents the time of Q 1 turn-on Q 2 turn-off in the ideal state, Ts2 Represents the ideal state Q 1 Turn off Q 2 Turn-on time.

[0099] Average dead-time voltage Can be expressed as:

[0100] (4)

[0101] In the formula, sign (·) is a sign function. To compensate for the dead-time effect, the anti-dead time Is inserted into the duration( t 2 – t 3 ), and according to the second Kirchhoff's law, we get:

[0102] (5)

[0103] Combining (4) and (5), the following formula can be derived:

[0104] (6)

[0105] Wherein, Is the dead-time delay considering the conduction voltage drop of the inverter, i d Is the inverter current;

[0106] In most cases: u dc Is much greater than v t And v d ; v t And v Are numerically very close; v t Is proportional to the current. Therefore, the expression of formula (7)b can be rewritten as:

[0107] (7)

[0108] In the formula, a Is the fixed dead-time delay, b Is the current delay coefficient, , .

[0109] The phase delay is described as θ d , Considering the sampling error and formula (6), the actual three-phase dead-time compensation time can be described as:

[0110] (8)

[0111] Among them, 、 、 are the dead-time delay times of the three phases A, B, and C respectively, θ e is the initial phase, and are the estimators of θ d and respectively. According to Equation (8), directly compensating for the dead-time compensation by sampling the current polarity will lead to incorrect compensation and even affect the stability of motor control. In summary, in the control of PMSM, compensation based only on amplitude or only on phase is not sufficient to completely suppress the harmonic current caused by the inverter dead-time and sampling error. Therefore, it is necessary to use the IPSO algorithm to compensate for both amplitude and phase simultaneously.

[0112] Since the main harmonic components of PMSM are mainly the 5th, 7th, 11th, and 13th harmonics, and the amplitudes of these harmonics decrease with the increase of the order, therefore, the focus is mainly on the 5th and 7th harmonics to be studied.

[0113] In a stationary three-phase coordinate system, the 5th and 7th harmonic voltages in the three phases of a PMSM can be expressed as:

[0114] (9)

[0115] In the formula, u a 、 u b 、 u c are the three-phase voltages of A, B, and C respectively, ω is the motor speed, t is the running time, u 1 、 u 5 、 u 7 are the amplitudes of the fundamental voltage, the 5th and 7th harmonic voltages respectively, θ 1 、 θ 5 、 θ 7 are the initial phase angles of the fundamental voltage, the 5th and 7th harmonic voltages respectively.

[0116] Such as Figure 5As shown, the three-phase current is transformed into the rotating coordinate system through Clark transformation and Park transformation, and this process allows the extraction of the magnitudes of the 5th and 7th harmonic currents. Due to the periodic fluctuation of the motor, in order to improve the accuracy of the results, the average value of multiple points of the harmonic current is used as the final value function. Since the 5th and 7th harmonics are jointly caused by the non-linear characteristics of the drive circuit and the influence of dead time, when compensating the control circuit, the 5th and 7th harmonics will increase or decrease simultaneously. Therefore, in order to reduce the computational complexity and consider the actual physical meaning, the algorithm value function is defined as follows:

[0117] (10)

[0118] In the formula, i is the initial measurement value, N sum represents the number of sampling points, is the value of the algorithm value function, i d5th and i q5th respectively represent the 5th harmonic current values of the d-axis and q-axis during the operation of the PMSM. The obtained optimal values are the phase and amplitude of the dead-time compensation.

[0119] As Figure 6 shown, this is the flow chart of the improved particle swarm algorithm in this embodiment, that is, the flow chart of compensating the dead-time phase and amplitude using the improved algorithm strategy, and the specific flow steps of obtaining the harmonic amplitude using the hybrid grey wolf algorithm strategy described in this embodiment are described.

[0120] The process of using IPSO for PMSM harmonic elimination is as follows:

[0121] (1)Initial parameter acquisition: Before the algorithm takes effect, collect the steady-state operation parameters of the PMSM, including speed and the three-phase current of the PMSM, and calculate the fitness function of this initial state;

[0122] (2)Parameter initialization: Initialize the parameters, w is the inertia factor, A is the adjustment coefficient A 1 、 A 2 、 A 3 matrices, C is a random number c 1 、 c 2 matrices, set the range of compensation parameters, and initialize the N positions and velocities of the particles (representing potential solutions), and at the same time define the termination condition of the algorithm, that is, the number of algorithm iterations;

[0123] (3) IPSO algorithm execution: run the improved particle swarm algorithm to evaluate the fitness of each particle;

[0124] (4) Particle generation and control input: Generate the next generation of particles based on the update of the IPSO algorithm and perform boundary checks, calculate the optimal solution, suboptimal solution and third optimal solution for the fitness of the new generation of particles, and update the parameter inertia factor w , adjustment coefficient matrix A , random number matrix C , and input it into the control process of PMSM;

[0125] (5) Iteration and convergence check: Compare the fitness of the current particle with its best solution in history. If the current fitness is better, update the best position of the individual and the best historical position of the swarm at the same time to determine whether the current number of iterations has reached a predefined number.

[0126] In order to increase the diversity of the particle swarm and enhance the search ability of the algorithm, the particle with the worst fitness is evolved in each iteration. If the fitness of the evolved particle is better than that of the unevolved particle, the evolved particle will replace the unevolved particle. If the fitness of the evolved particle is worse than that of the unevolved particle, the unevolved particle will be retained. The evolution strategy for the particle with the worst fitness is as follows:

[0127]

[0128] in, x_worst ( t ) indicates the t At the iteration, N The particle with the worst fitness among the particles, N The number of example groups is N , Eworst ( t ) represents the position of the particle after evolution, i , j , k are three different particles randomly selected from the particle swarm, argmax (·) is the maximum value finding function, fit (·) is a function, X 1 ( t ), X 2 ( t ),…, X N ( t ) is the 1st, 2nd, ..., N Particle positions.

[0129] To avoid falling into local optimal solutions, the particle swarm optimization (PSO) update formula is improved by combining it with the grey wolf algorithm. The main reason is that during iterative updates, the grey wolf algorithm adopts an elite group guiding strategy, that is, it selects the three best elite individuals in the group for guiding updates, rather than just the best individual in the particle swarm. Moreover, the encircling guiding strategy of the grey wolf algorithm means that during the search process, other particles will approach the elite group in an encircling manner. Therefore, combining the particle swarm algorithm with the wolf pack algorithm can further improve the search ability of the algorithm. The PSO update formula is as follows:

[0130] (14)

[0131] Where, X ( t+1 ) is the updated particle position, t is the number of iterations, X p ( t ) is the position vector of the prey, X ( t ) is the position vector of the grey wolf, A and C are coefficient vectors, D is the particle movement distance.

[0132] The update formula of the grey wolf algorithm is:

[0133] (15)

[0134] Where, X α ( t )、 X β ( t )、 X δ ( t ) are the positions of the optimal solution, the sub-optimal solution, and the third optimal solution respectively, A 1 、 A 2 、 A 3 are different adjustment coefficients respectively, 、 、 are the distances of the wolf pack individuals from the elite individuals respectively.

[0135] Thus, the IPSO encircling update strategy is:

[0136]

[0137] Where, is the updated particle movement distance,w is the inertia factor, is the particle swarm position, is the particle swarm position before update, c 1 , c 2 is a random number between [0, 1], r 1 , r 2 are the directions of the particle to the optimal solution and the sub-optimal solution respectively, , are the optimal solution and the sub-optimal solution respectively, is the particle position after update using the improved particle swarm algorithm.

[0138] It can be seen from Equation (13) that the original individual experience of the particle swarm algorithm and the guidance of the optimal particle are retained, and the update strategy of the grey wolf algorithm is introduced. It not only includes the guidance strategy of the elite group of the grey wolf algorithm but also includes the encircling search strategy of the grey wolf algorithm. By introducing the grey wolf algorithm, the deficiencies existing in the iterative update formula of the particle swarm algorithm can be made up for.

[0139] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0141] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in the processFigure 1 one process or multiple processes and / or boxes Figure 1 the functions specified in one box or multiple boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or boxes Figure 1 one box or multiple boxes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A permanent magnet synchronous motor harmonic suppression method based on improved particle swarm algorithm, characterized in that: The specific steps include: S1. Obtain harmonic current components using the electrical angle and three-phase current of the permanent magnet synchronous motor; S2. constructing an algorithm value function according to the harmonic current component; S3, obtaining a harmonic suppression result of the permanent magnet synchronous motor based on an improved particle swarm algorithm according to the harmonic current component and the algorithm value function; Acquiring the harmonic suppression result of the permanent magnet synchronous motor by using the improved particle swarm algorithm according to the harmonic current component and the algorithm value function includes: S3-1, collecting the steady-state operating parameters of the permanent magnet synchronous motor to obtain the fitness function of the initial state and initialize the target parameters; S3-2, set the range of compensation parameters and the number of algorithm iterations and initialize the position and velocity of the particles; S3-3, using the harmonic current component and the algorithm value function to obtain a corresponding algorithm value function value as particle fitness based on an improved particle swarm algorithm; S3-4, according to the fitness of the particles, using the update formula of the grey wolf algorithm and the surrounding update strategy of the improved particle swarm algorithm to update and generate the next generation of particles to obtain the position and speed of the new particles; According to the particle fitness, the update formula of the gray wolf algorithm and the improved particle swarm algorithm are used to update the next generation of particles to obtain the position and speed of the new particles, including: S3-4-1. Using the particle fitness, obtain the corresponding historical particle fitness; S3-4-2, obtaining the fitness of the particle as the fitness of the initial particle; S3-4-3, determine whether the fitness of the initial particle is less than the fitness of the historical particle. If so, update the position and speed of the initial particle as the position and speed of the new particle, and directly execute S3-5. Otherwise, use the particle evolution strategy to evolve the initial particle to obtain the evolved particle, and execute S3-4-4; S3-4-4, determine whether the fitness of the evolved particle is less than the fitness of the initial particle. If so, use the evolved particle to update the position and speed of the initial particle as the position and speed of the new particle, and directly execute S3-5. Otherwise, retain the initial particle to obtain the fitness of the initial particle, and execute S3-4-5; S3-4-5, generating the next generation of particles according to the fitness of the initial particles using the update formula of the grey wolf algorithm and the surrounding update strategy of the improved particle swarm algorithm; S3-4-6, obtaining the fitness of the next generation particle according to the next generation particle as the fitness of the initial particle, and updating the target parameter, and returning to S3-4; S3-5, judging whether the number of iterations of the algorithm has been reached, if so, stopping the iteration, and obtaining the phase and amplitude of the dead zone compensation time corresponding to the target historical position of the swarm according to the position and speed of the new particle as the harmonic suppression result of the permanent magnet synchronous motor, otherwise, returning to S3-3; The target parameters include an inertia factor, an adjustment coefficient and a random matrix, the compensation parameters are a phase and an amplitude of a dead zone compensation time, and the position of the particle corresponds to a combination of phases and amplitudes of different dead zone compensation times.

2. The method for harmonic suppression of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to claim 1, characterized in that: Using the electrical angle and three-phase current of the permanent magnet synchronous motor to obtain the harmonic current components includes: Obtain the electrical angle and three-phase current of the permanent magnet synchronous motor; Performing multiple synchronous coordinate transformations according to the electrical angle of the permanent magnet synchronous motor and the three-phase current to obtain current components in a rotating reference system; Using the current component in the rotating reference system to obtain the harmonic current component by filtering based on a low-pass filter; The current component in the rotating reference frame is the current component in the fifth rotating reference frame or the current component in the seventh rotating reference frame.

3. The method for harmonic suppression of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to claim 1, characterized in that: The calculation formula of the algorithm value function is as follows: in, λ is the value of the algorithm value function, i d5th and i q5th They represent the 5th harmonic current values ​​of the d-axis and q-axis during the operation of the PMSM. N sum Indicates the number of sampling points.

4. The method for harmonic suppression of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to claim 1, characterized in that: The calculation formula of the particle evolution strategy is as follows: in, x_worst ( t ) indicates the t At the iteration, N The particle with the worst fitness among the particles, N The number of example groups is N , Eworst ( t ) represents the position of the particle after evolution, i , j , k are three different particles randomly selected from the particle swarm, argmax (·) is the maximum value finding function, fit (·) is a function, X 1 ( t ), X 2 ( t ),…, X N ( t ) is the 1st, 2nd, ..., N Particle positions.

5. The method for harmonic suppression of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to claim 4 is characterized in that: The update formula of the gray wolf algorithm is: in, X 1 ( t ) is the position of the first particle, X 2 ( t ) is the position of the second particle, X 3 ( t ) is the position of the third particle, X α ( t ), X β ( t ), X δ ( t ) are the optimal solution, the second optimal solution, and the third optimal solution respectively. A 1 , A 2 , A 3 are different adjustment coefficients, are the distances between individual wolves and elite individuals, X ( t+1 ) is the updated particle position.

6. The method for harmonic suppression of a permanent magnet synchronous motor based on an improved particle swarm algorithm according to claim 5, characterized in that: The bracket update strategy of the improved particle swarm algorithm is: in, is the distance the particle moves after updating, w is the inertia factor, is the particle group position, is the particle swarm position before updating, c 1 , c 2 is a random number between [0, 1], r 1 , r 2 are the directions of the particle and the optimal solution and suboptimal solution respectively. are the optimal solution and the suboptimal solution respectively. The particle positions are updated using the improved particle swarm algorithm.

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