An efficiency optimization control method and related equipment for a permanent magnet synchronous motor

Abstract

This invention provides an efficiency optimization control method and related equipment for a permanent magnet synchronous motor (PMSM). The method involves acquiring the inverter's output current; constructing an inverter loss model based on the output current and inverter loss coefficient; constructing a total loss model based on the electromagnetic loss model, the inverter loss model, and an equivalent conversion method; constructing a current population by treating multiple d-axis current values ​​as individuals; and optimizing the solution based on the total loss model, the current population, a PID search algorithm, and an artemisinin optimization algorithm to obtain the globally optimal solution and control the PMSM. In this scheme, the total loss model incorporates driver losses, enabling efficiency optimization of the entire motor drive system. Addressing the issue that traditional methods are not applicable to IPMSM, the method combines a PID search algorithm and an artemisinin optimization algorithm, using a simulated incremental PID controller adjustment mechanism to perform parameter search for the optimal motor efficiency, obtaining the globally optimal solution, thereby achieving optimal control of motor efficiency.

Description

Technical Field

[0001] This invention relates to the field of optimization control technology, specifically to an efficiency optimization control method and related equipment for a permanent magnet synchronous motor. Background Technology

[0002] Permanent magnet synchronous motors, with their advantages of small size and high power density, have been widely and deeply applied in many industries. However, how to further explore their potential for improving operating efficiency has become a core issue that researchers are relentlessly pursuing.

[0003] Existing efficiency optimization control techniques based on loss models construct a function to maximize the motor's efficiency or minimize its losses, thereby deriving the excitation current and flux linkage values ​​corresponding to the optimal efficiency state, and implementing precise control accordingly to achieve the efficiency optimization goal. This method has significant limitations, often neglecting the potential interference of driver switching actions on the overall motor efficiency. Furthermore, for interior permanent magnet synchronous motors (IPMSMs), the significant differences between their direct and quadrature axis parameters greatly increase the difficulty of determining the optimal direct axis current value. In practical applications, due to the high implementation difficulty, the model is usually simplified. However, this inevitably leads to a deviation between the approximate optimal solution and the actual optimal value, ultimately achieving only suboptimal efficiency control, failing to reach the ideal optimal efficiency level.

[0004] In summary, existing efficiency optimization control techniques based on loss models are not applicable to IPMSMs and do not consider motor driver losses, making it impossible to achieve optimal efficiency control, which is a problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an efficiency optimization control method and related equipment for a permanent magnet synchronous motor, so as to achieve the goal of optimal control of motor efficiency.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of this invention discloses an efficiency optimization control method for a permanent magnet synchronous motor, the method comprising:

[0008] Obtain the output current of the inverter in the permanent magnet synchronous motor drive system;

[0009] An inverter loss model is constructed based on the inverter's output current and a preset inverter loss coefficient.

[0010] Based on the preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method, a total loss model is constructed.

[0011] The current population is constructed by using multiple randomly generated d-axis current values ​​as individuals.

[0012] The global optimal solution is obtained by optimizing the solution based on the total loss model, the current population, the preset PID search algorithm, and the preset artemisinin optimization algorithm.

[0013] The permanent magnet synchronous motor is controlled based on the global optimal solution.

[0014] Optionally, the construction of the total loss model based on the preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method includes:

[0015] Based on the preset electromagnetic loss model and the inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and the q-axis current is constructed.

[0016] Based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

[0017] Optionally, the optimization solution based on the total loss model, the current population, a preset PID search algorithm, and a preset artemisinin optimization algorithm to obtain the global optimal solution includes:

[0018] Based on the total loss model, the current fitness value of all individuals in the current population is calculated, and the difference between the minimum current fitness value and the minimum historical fitness value is calculated to obtain the current deviation;

[0019] Based on the current deviation, past deviation, and the preset incremental PID formula, calculate the update step size;

[0020] The zero output factor is calculated based on the preset zero output factor calculation formula and the current deviation.

[0021] Based on the zero output factor and the update step size, the current population is updated to obtain the first population;

[0022] Based on the total loss model, the first fitness value of all individuals in the first population is calculated, and the first fitness variance is calculated based on each first fitness value.

[0023] Determine whether the variance of the first fitness is less than the variance threshold;

[0024] If so, the individual with the smallest first fitness value is taken as the global optimal solution;

[0025] If not, optimize the first population using the preset artemisinin optimization algorithm to obtain the second population;

[0026] Based on the total loss model, the second fitness value of all individuals in the second population is calculated, and the second fitness variance is calculated based on each second fitness value;

[0027] Determine whether the variance of the second fitness is less than the variance threshold;

[0028] If so, the individual with the smallest second fitness value is taken as the global optimal solution;

[0029] If not, take the second population as the current population, return to the step of calculating the current fitness value of all individuals in the current population based on the total loss model, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation.

[0030] Optionally, the step of calculating the update step size based on the current deviation, past deviations, and a preset incremental PID formula includes:

[0031] Calculate the current deviation Deviation from the previous time point The sum of these values ​​yields the deviation from the previous iteration recorded at the current moment. ;

[0032] The update step size is calculated based on a preset incremental PID formula; the incremental PID formula is: ;

[0033] in, , The previous iteration deviation is recorded at the previous time step, and t is the iteration number. To update the step size, This is the proportional adjustment coefficient. This is the integral adjustment factor. The differential adjustment coefficient is... , and It is a vector of random numbers.

[0034] Optionally, the formula for calculating the zero output factor includes:

[0035] Where t is the number of iterations, Zero output factor For the current deviation, Let L be a random number vector, and L be the Levy flight function. T is the maximum number of iterations.

[0036] Optionally, updating the current population to obtain the first population based on the zero output factor and the update step size includes:

[0037] Based on the zero output factor and the update step size, the update amount is calculated; the update amount is... , Zero output factor To update the step size, , It is an n-row, 1-column matrix composed of random numbers, where n is the number of individuals in the current population and T is the maximum number of iterations.

[0038] A second aspect of this invention discloses an efficiency optimization control device for a permanent magnet synchronous motor, the device comprising:

[0039] The acquisition unit is used to acquire the output current of the inverter in the permanent magnet synchronous motor drive system;

[0040] The first construction unit is used to construct an inverter loss model based on the inverter's output current and a preset inverter loss coefficient.

[0041] The second construction unit is used to construct a total loss model based on a preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method.

[0042] The third building unit is used to construct the current population by taking multiple randomly generated d-axis current values ​​as individuals.

[0043] The optimization and solution unit is used to perform optimization and solution based on the total loss model, the current population, the preset PID search algorithm and the preset artemisinin optimization algorithm to obtain the global optimal solution;

[0044] A control unit is used to control the permanent magnet synchronous motor based on the global optimal solution.

[0045] Optionally, the second building unit is specifically used for:

[0046] Based on the preset electromagnetic loss model and the inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and the q-axis current is constructed.

[0047] Based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

[0048] The third aspect of the present invention discloses a storage medium for storing a computer program, which, when executed, is specifically used to implement any of the efficiency optimization control methods for permanent magnet synchronous motors disclosed in the first aspect of the present invention.

[0049] A fourth aspect of this invention discloses an electronic device, comprising: a memory and a processor;

[0050] The memory is used to store computer programs;

[0051] The processor is used to execute the computer program, specifically to implement any of the efficiency optimization control methods for permanent magnet synchronous motors disclosed in the first aspect of the present invention.

[0052] Based on the above embodiments of the present invention, an efficiency optimization control method and related equipment for a permanent magnet synchronous motor (PMSM) are provided. The method involves obtaining the output current of the inverter in the PMSM drive system; constructing an inverter loss model based on the inverter output current and a preset inverter loss coefficient; constructing a total loss model based on a preset electromagnetic loss model, the inverter loss model, and an equivalent conversion method; constructing a current population using multiple randomly generated d-axis current values ​​as individuals; optimizing the solution based on the total loss model, the current population, a preset PID search algorithm, and a preset artemisinin optimization algorithm to obtain a globally optimal solution; and controlling the PMSM based on the globally optimal solution. In this scheme, driver losses are incorporated into the total loss model, enabling efficiency optimization of the entire motor drive system. Addressing the issue that traditional methods are not applicable to IPMSM, the method combines a PID search algorithm and an artemisinin optimization algorithm, using a simulated incremental PID controller adjustment mechanism to perform parameter search for the optimal motor efficiency, obtaining a globally optimal solution, and controlling the PMSM based on this globally optimal solution, thereby achieving optimal motor efficiency control. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0054] Figure 1 This is an equivalent circuit diagram of IPMSM considering iron loss in a two-phase rotating coordinate system disclosed in an embodiment of the present invention;

[0055] Figure 2 This is a flowchart of an efficiency optimization control method for a permanent magnet synchronous motor disclosed in an embodiment of the present invention;

[0056] Figure 3 This is a structural block diagram of a permanent magnet synchronous motor drive system disclosed in an embodiment of the present invention;

[0057] Figure 4 This is a structural diagram of an efficiency optimization control device for a permanent magnet synchronous motor disclosed in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] As can be seen from the background technology, existing efficiency optimization control technology based on loss models is not applicable to IPMSM and does not consider motor drive losses, which makes it impossible to achieve optimal efficiency control. This is a problem that urgently needs to be solved.

[0061] The specific details of existing efficiency optimization control techniques based on loss models are as follows:

[0062] The losses of an integrated permanent magnet synchronous motor (IPMSM) include copper losses, iron losses, stray losses, and mechanical losses. Copper losses refer to the heat loss generated when current passes through the stator windings. Since the stator of the motor contains a large number of coils and the current passing through the coils during operation is relatively large, copper losses are one of the main losses of the motor. Iron losses refer to the hysteresis and eddy current losses generated by the alternating flow of magnetic flux in the motor core. Its magnitude depends on the core material itself and is related to the current frequency and motor speed. Mechanical losses and stray losses account for a small proportion of the total motor losses and are not easy to control and detect. Therefore, only copper losses and iron losses are considered.

[0063] like Figure 1 The diagram shown is an equivalent circuit diagram of IPMSM considering iron loss in a two-phase rotating coordinate system, as disclosed in an embodiment of the present invention.

[0064] exist Figure 1 middle, and These are the d-axis voltage and the q-axis voltage, respectively; and These are the d-axis current and the q-axis current, respectively. and These are the active components of the stator current along the d-axis and the active components of the stator current along the q-axis. and These are the d-axis iron loss current components and the q-axis iron loss current components, respectively. Stator resistance; and These are the armature inductance along the d-axis and the armature inductance along the q-axis, respectively. For the magnetic flux linkage of the rotor permanent magnet; It is the mechanical angular velocity; This represents the number of pole pairs of the motor. Electric angular velocity; It represents the iron loss resistance, which is a function of motor speed; the higher the speed, the greater the iron loss resistance.

[0065] Based on the IPMSM equivalent circuit considering iron loss resistance, the equivalent voltage equation for the IPMSM in steady state can be written as follows:

[0066] (1);

[0067] According to Kirchhoff's Voltage Law (KVL), the steady-state expressions for the d-axis and q-axis iron loss current components are as follows:

[0068] (2);

[0069] Therefore, combining equation (2), according to Kirchhoff's Current Law (KCL), the nodal current equations under steady-state conditions can be obtained as follows:

[0070] (3);

[0071] The electromagnetic torque equation is:

[0072] (4);

[0073] Depend on Figure 1 It can be seen that the copper loss and iron loss of IPMSM can be expressed as follows:

[0074] (5);

[0075] Therefore, the total loss of IPMSM can be expressed as the sum of copper loss and iron loss:

[0076] (6);

[0077] Because stray losses and mechanical losses are ignored, the motor input power can be expressed as the sum of output power and total losses. Therefore, the expression for motor efficiency is:

[0078] (7);

[0079] The efficiency optimization control method based on the loss model establishes the objective function according to the motor loss equation and obtains the optimal d-axis current using the univariate function extremum method. Based on the IPMSM equivalent circuit considering iron loss resistance, and combining equations (2) and (5), the motor copper loss and iron loss are obtained as follows:

[0080] (8);

[0081] Therefore, the electromagnetic loss of IPMSM can be expressed as the sum of copper loss and iron loss:

[0082] (9);

[0083] Traditional control methods based on loss models require neglecting the reluctance torque term in the electromagnetic torque equation. Therefore, the simplified electromagnetic torque equation can be obtained as follows:

[0084] (10);

[0085] Substituting the simplified electromagnetic torque equation into equation (9), we can obtain the IPMSM electromagnetic loss and The expression is:

[0086] (11);

[0087] Therefore, combining equations (3) and (11), the optimal d-axis current during steady-state operation of the motor can be obtained as follows:

[0088] (12);

[0089] Therefore, this invention discloses an efficiency optimization control method and related equipment for permanent magnet synchronous motors. In this solution, driver losses are included when establishing the total loss model, enabling efficiency optimization of the entire motor drive system. To address the issue that traditional methods are not applicable to IPMSMs, a PID search algorithm and an artemisinin optimization algorithm are combined. The optimal point of motor efficiency is searched by simulating an incremental PID controller adjustment mechanism to obtain a globally optimal solution. The permanent magnet synchronous motor is then controlled based on the globally optimal solution, thereby achieving optimal efficiency control.

[0090] like Figure 2 The diagram shows a flowchart of an efficiency optimization control method for a permanent magnet synchronous motor disclosed in an embodiment of the present invention, including the following steps:

[0091] Step S101: Obtain the output current of the inverter in the permanent magnet synchronous motor drive system.

[0092] In this embodiment, the permanent magnet synchronous motor includes, but is not limited to, IPMSM.

[0093] It should be noted that the inverter is a key component that converts direct current (DC) to alternating current (AC) to drive a motor; it is essentially the motor driver. Inverter losses are equivalent to driver losses. During motor driving, losses occur due to switching operations and current flow in the electronic components. Therefore, it is necessary to obtain the inverter's output current to calculate switching and conduction losses, thus determining the inverter's total losses.

[0094] Step S102: Construct an inverter loss model based on the inverter's output current and the preset inverter loss coefficient.

[0095] In step S102, the inverter loss model is expressed as:

[0096] (13);

[0097] in and It is the inverter loss factor that is preset based on the specific inverter design, topology, and operating conditions. It is the inverter output current.

[0098] (14);

[0099] Step S103: Construct a total loss model based on the preset electromagnetic loss model, inverter loss model, and equivalent conversion method.

[0100] In the specific implementation of step S103, based on the preset electromagnetic loss model and inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and q-axis current is constructed; based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

[0101] It should be noted that, firstly, based on the IPMSM voltage and current equations considering iron losses, the d-axis iron loss current component and the q-axis iron loss current component, i.e., equation (2), can be equivalently converted to:

[0102] (15);

[0103] in:

[0104] (16);

[0105] Therefore, by equivalent transformation of equation (9), we can obtain the IPMSM electromagnetic loss model:

[0106] (17);

[0107] According to equations (13), (14), and (17), the total loss equations of the permanent magnet synchronous motor drive system with respect to the d-axis current and q-axis current can be obtained:

[0108] (18);

[0109] because ,according to Figure 1 It can be seen that, Therefore, we assume:

[0110] (19);

[0111] Therefore, based on the equivalent transformation of equations (4) and (19), the electromagnetic torque equation can be obtained as follows:

[0112] (20);

[0113] Substituting the electromagnetic torque equation (20) into equation (18) and eliminating... The total losses of the IPMSM drive system can be obtained. The relationship (i.e., the total loss model) is as follows:

[0114] (twenty one);

[0115] As can be seen from equation (21), when the motor is running in steady state, the total loss of the IPMSM drive system is a function of... The function of is thus obtained, therefore the total loss is obtained with respect to . When the value of is minimized, the point of minimum motor loss, i.e., the point of maximum efficiency, can be obtained.

[0116] However, equation (21) is a very complex equation, and it is very difficult to solve it directly. Therefore, in order to obtain the optimal point of motor efficiency, this embodiment of the invention proposes a control strategy for improving the energy efficiency of permanent magnet synchronous motor based on the adaptive APID search algorithm, namely steps S104 to S105.

[0117] The APID search algorithm refers to the combination of the artemisinin optimization algorithm and the PID-Search Algorithm (PSA). The APID search algorithm achieves parameter search by simulating the adjustment mechanism of an incremental PID controller. To address the problem of the original algorithm easily getting trapped in local optima, it introduces the local cleanup phase strategy and the post-consolidation phase strategy from the artemisinin optimization algorithm to re-explore and consolidate the population in the later stages, effectively enabling the algorithm to operate at the global optimum.

[0118] Step S104: Construct the current population by taking the multiple randomly generated d-axis current values ​​as individuals, and optimize the solution based on the total loss model, the current population, the preset PID search algorithm and the preset artemisinin optimization algorithm to obtain the global optimal solution.

[0119] It should be noted that equation (21) is a very complex equation, and it is very difficult to solve it directly. Therefore, in order to obtain the optimal motor efficiency, this embodiment of the invention proposes a control strategy for improving the energy efficiency of permanent magnet synchronous motor based on the adaptive APID search algorithm, namely step S104.

[0120] The APID search algorithm refers to the combination of the artemisinin optimization algorithm and the PID-Search Algorithm (PSA). The APID search algorithm achieves parameter search by simulating the adjustment mechanism of an incremental PID controller. To address the problem of the original algorithm easily getting trapped in local optima, it introduces the local cleanup phase strategy and the post-consolidation phase strategy from the artemisinin optimization algorithm to re-explore and consolidate the population in the later stages, effectively enabling the algorithm to operate at the global optimum.

[0121] The PID search algorithm is a novel heuristic optimization algorithm based on incremental PID control. This algorithm mainly achieves optimization through two steps: population initialization and incremental PID control. In this algorithm, the historical best individual in the population (the individual with the smallest fitness value in the past iterations) is abstracted as the target value, and each individual is abstracted as the actual value. The PID adjustment process is simulated to correct the deviation of each individual from the best individual, determine the position of the best individual, and update it to bring the entire population to a better state.

[0122] The specific implementation process of step S104 includes the following steps:

[0123] Step S201: Construct the current population using multiple randomly generated d-axis current values ​​as individuals.

[0124] In one embodiment, to address the problem of excessively long algorithm convergence time, the second-best d-axis current value obtained from the traditional loss model method is used as the initial individual in the population.

[0125] Understandably, at the start of the algorithm, the parameters are initialized, including the population size i, the number of iterations t, and the PID control parameters.

[0126] Step S202: Based on the total loss model, calculate the current fitness value of all individuals in the current population, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation.

[0127] It should be noted that the optimization problem consists of a set of variables to be optimized (d-axis current values), constraints (upper and lower bounds of the variables), and an objective function (total loss model). We can assume that the number of variables to be optimized in this set is d, the population size is n, and the upper and lower bounds of the variables are u and l, respectively. Then the current population can be represented as:

[0128] (twenty two);

[0129] This represents the j-th dimension of the i-th individual; and These are the upper and lower bounds of the j-th variable (dimension), respectively; It is a random number between 0 and 1.

[0130] It is understood that in this embodiment of the invention, there is only one variable to be optimized, namely the d-axis current value.

[0131] The current fitness value of all individuals in the current population is the loss value calculated by inputting all individuals in the current population into the total loss model.

[0132] The minimum historical fitness value refers to the minimum fitness value that has occurred in all previous iterations.

[0133] Step S203: Calculate the update step size based on the current deviation, past deviations and the preset incremental PID formula.

[0134] The specific implementation process of step S203 is as follows:

[0135] Calculate the current deviation Deviation from the previous time point The sum of these values ​​yields the deviation from the previous iteration recorded at the current moment. ;

[0136] The update step size is calculated based on a preset incremental PID formula; the incremental PID formula is:

[0137] (twenty three);

[0138] in, , The previous iteration deviation is recorded at the previous time step, and t is the iteration number. To update the step size, This is the proportional adjustment coefficient. This is the integral adjustment factor. The differential adjustment coefficient is... , and Let be an n-row, 1-column vector of random numbers between 0 and 1. The proportional adjustment coefficient, integral adjustment coefficient, and derivative adjustment coefficient can be set to 1, 0.8, and 1.2, respectively.

[0139] It should be noted that equation (23) is an improvement on the traditional incremental PID formula. The traditional incremental PID formula constructs the control quantity by linearly combining the proportional P, integral I, and derivative D of the deviation, as follows:

[0140] (twenty four);

[0141] In equation (24), This represents the increment of the PID controller output value when the number of iterations is t. This represents the deviation in the current calculation. This is the deviation from the previous calculation. This represents the deviation from the calculation two months ago.

[0142] The formula for calculating the deviation is as follows:

[0143] (25);

[0144] in, The individual corresponding to the smallest historical fitness value in the overall iterative process (, This refers to the individual with the smallest current fitness value in this iteration.

[0145] Step S204: Calculate the zero output factor based on the preset zero output factor calculation formula and the current deviation.

[0146] The formula for calculating the zero output factor includes:

[0147] (26);

[0148] Where t is the number of iterations. Zero output factor For the current deviation, Let L be a random number vector, and L be the Levy flight function. T is the maximum number of iterations.

[0149] Levy flight functions are as follows:

[0150] (27);

[0151] Where u and v are matrices of n rows and d columns of random numbers that follow a standard normal distribution. u and v follow a normal distribution. , , and This represents the parameter required to achieve the target variance.

[0152] (28);

[0153] Where Γ is the gamma function and β is the shape parameter of the step size distribution. .

[0154] Step S205: Based on the zero output factor and the update step size, update the current population to obtain the first population.

[0155] In the specific implementation of step S205, the update amount is calculated based on the zero output factor and the update step size; the update amount is... , Zero output factor To update the step size, , It is an n x 1 matrix composed of random numbers, where n is the number of individuals in the current population and T is the maximum number of iterations.

[0156] It should be noted that all individual updates are related to and Therefore, the population renewal formula is as follows:

[0157] (29);

[0158] In equation (29), For the updated population, This refers to the population before the update.

[0159] Step S206: Based on the total loss model, calculate the first fitness value of all individuals in the first population, and calculate the first fitness variance based on each first fitness value.

[0160] In this embodiment of the invention, fitness variance is used as the criterion for determining whether the PID search algorithm has fallen into a local optimum. The formula for calculating fitness variance is as follows:

[0161] (30);

[0162] in, Let be the fitness value of the i-th individual. This represents the average fitness value of all individuals in the population.

[0163] The calculation of the first fitness value for an individual in the first population is the same as the calculation of the fitness value for an individual in the current population, and will not be repeated here.

[0164] Step S207: Determine whether the variance of the first fitness is less than the variance threshold; if yes, proceed to step S211; if no, proceed to step S208.

[0165] In step S207, the variance threshold is a very small threshold, such as 0.001.

[0166] It should be noted that, in order to address the problem that the original PID search algorithm is prone to getting trapped in local optima, this embodiment of the invention adds a judgment condition, namely, when the variance of the first fitness is not less than the variance threshold, the local elimination phase strategy and the post-consolidation phase strategy in the artemisinin optimization algorithm are adopted to explore and consolidate the population again in the later stage.

[0167] Step S208: The first population was optimized using a pre-defined artemisinin optimization algorithm to obtain the second population.

[0168] In step S208, the local purge phase strategy and the post-consolidation phase strategy from the artemisinin optimization algorithm are used to further explore and consolidate the later-stage population. The specific formula for the local purge phase strategy is as follows:

[0169] (31);

[0170] (32);

[0171] In equation (31), Let r7 be the i-th individual in the optimized population, and r7 be a random number. This represents the normalized fitness value, where 'a' is a possible value. Random values ​​between , , This indicates the location of a random population. Provides a reference location for search direction. Random perturbations are introduced to explore new regions. When the population fitness variance... At that time, local individuals are reset according to equation (31) to avoid error accumulation in the composite algorithm. This is a very small threshold, which can be set to 0.001.

[0172] The post-consolidation phase strategy retains a 5% random mutation probability to ensure the algorithm continuously approaches the global optimum. The specific formula is as follows:

[0173] (33);

[0174] in, This represents the sub-vector of the current best solution in the j-th dimension, which is the optimal solution of the algorithm, and rand is the probability of random mutation.

[0175] Step S209: Based on the total loss model, calculate the second fitness value of all individuals in the second population, and calculate the second fitness variance based on each second fitness value.

[0176] The calculation process for fitness value and fitness variance has been explained above and will not be repeated here.

[0177] Step S210: Determine whether the variance of the second fitness is less than the variance threshold; if yes, proceed to step S212; if no, proceed to step S202.

[0178] Understandably, when returning to the iteration, the second fitness value of all individuals in the second population can also be used as the current fitness value of all individuals in the current population to avoid redundant calculations and improve the algorithm speed.

[0179] Step S211: Select the individual with the smallest first fitness value as the global optimal solution.

[0180] Step S212: Select the individual with the smallest second fitness value as the global optimal solution.

[0181] It is understandable that the individuals corresponding to the smallest first fitness value and the smallest second fitness value are the best individuals in their respective populations, that is, the individuals with the smallest loss value.

[0182] Step S105: Control the permanent magnet synchronous motor based on the global optimal solution.

[0183] In step S105, the global optimal solution is the globally optimal d-axis current value. Controlling the d-axis current of the permanent magnet synchronous motor based on the globally optimal d-axis current value can ensure the lowest total loss of the drive system and achieve optimal control of motor efficiency.

[0184] like Figure 3 The diagram shown is a structural block diagram of a permanent magnet synchronous motor drive system disclosed in an embodiment of the present invention.

[0185] in, and These are the d-axis voltage and the q-axis voltage, respectively; and These are the d-axis current and the q-axis current, respectively. and These are the active components of the stator current along the d-axis and the active components of the stator current along the q-axis. and These are the d-axis iron loss current components and the q-axis iron loss current components, respectively. Stator resistance; and These are the armature inductance along the d-axis and the armature inductance along the q-axis, respectively. For the magnetic flux linkage of the rotor permanent magnet; It is the mechanical angular velocity; This represents the number of pole pairs of the motor. Electric angular velocity; It represents the iron loss resistance, which is a function of motor speed; the higher the speed, the greater the iron loss resistance.

[0186] It should be noted that the APID search algorithm and the drive system loss model (i.e., the total loss model) are the improvements in this application, while the rest is the basic framework of permanent magnet synchronous motor control.

[0187] Based on the above-disclosed embodiment of the present invention, an efficiency optimization control method for a permanent magnet synchronous motor is proposed. Compared with traditional methods, this method incorporates driver losses when establishing the total loss model, enabling it to optimize the overall efficiency of the motor drive system. Furthermore, addressing the issue that traditional methods are not applicable to IPMSM, an adaptive APID search algorithm is proposed. This method uses a simulated incremental PID controller adjustment mechanism to search for parameters that optimize motor efficiency. It uses the suboptimal d-axis current obtained by the traditional loss model-based control method as the initial search point. Simultaneously, it introduces the local clearing phase strategy and post-consolidation phase strategy from the artemisinin optimization algorithm to further explore and consolidate the later population, effectively enabling the algorithm to operate at the global optimum and reducing the search time. After obtaining the global optimum, the permanent magnet synchronous motor is controlled based on this global optimum, thereby achieving optimal efficiency control.

[0188] like Figure 4 The diagram shown is a structural diagram of an efficiency optimization control device for a permanent magnet synchronous motor disclosed in an embodiment of the present invention, comprising:

[0189] Acquisition unit 401 is used to acquire the output current of the inverter in the permanent magnet synchronous motor drive system;

[0190] The first building unit 402 is used to build an inverter loss model based on the inverter's output current and a preset inverter loss coefficient.

[0191] The second building unit 403 is used to build a total loss model based on a preset electromagnetic loss model, inverter loss model and equivalent conversion method;

[0192] The third building unit 404 is used to construct the current population by using multiple randomly generated d-axis current values ​​as individuals.

[0193] The optimization and solution unit 405 is used to perform optimization and solution based on the total loss model, the current population, the preset PID search algorithm and the preset artemisinin optimization algorithm to obtain the global optimal solution;

[0194] Control unit 406 is used to control the permanent magnet synchronous motor based on the global optimal solution.

[0195] Optionally, the second building unit 403 is specifically used for:

[0196] Based on the preset electromagnetic loss model and inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and q-axis current is constructed.

[0197] Based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

[0198] Optionally, the optimization solution element 405 is used specifically for:

[0199] Based on the total loss model, the current fitness value of all individuals in the current population is calculated, and the difference between the minimum current fitness value and the minimum historical fitness value is calculated to obtain the current deviation.

[0200] The update step size is calculated based on the current deviation, past deviation, and the preset incremental PID formula.

[0201] The zero output factor is calculated based on the preset zero output factor calculation formula and the current deviation.

[0202] Based on the zero output factor and the update step size, update the current population to obtain the first population;

[0203] Based on the total loss model, the first fitness value of all individuals in the first population is calculated, and the first fitness variance is calculated based on each first fitness value.

[0204] Determine if the variance of the first fitness is less than the variance threshold;

[0205] If so, the individual with the smallest first fitness value is taken as the global optimal solution;

[0206] If not, optimize the first population using the preset artemisinin optimization algorithm to obtain the second population;

[0207] Based on the total loss model, the second fitness value of all individuals in the second population is calculated, and the second fitness variance is calculated based on each second fitness value.

[0208] Determine whether the variance of the second fitness is less than the variance threshold;

[0209] If so, the individual with the smallest second fitness value is taken as the global optimal solution;

[0210] If not, take the second population as the current population, return to the step of calculating the current fitness value of all individuals in the current population based on the total loss model, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation.

[0211] Optionally, based on the current deviation, past deviations, and a preset incremental PID formula, the update step size is calculated, including:

[0212] Calculate the current deviation Deviation from the previous time point The sum of these values ​​yields the deviation from the previous iteration recorded at the current moment. ;

[0213] The update step size is calculated based on a preset incremental PID formula; the incremental PID formula is: ;

[0214] in, , The previous iteration deviation is recorded at the previous time step, and t is the iteration number. To update the step size, This is the proportional adjustment coefficient. This is the integral adjustment factor. The differential adjustment coefficient is... , and It is a vector of random numbers.

[0215] Optional formulas for calculating the zero output factor include:

[0216] Where t is the number of iterations, Zero output factor For the current deviation, Let L be a random number vector, and L be the Levy flight function. T is the maximum number of iterations.

[0217] Optionally, based on the zero output factor and the update step size, the current population is updated to obtain the first population, including:

[0218] The update amount is calculated based on the zero output factor and the update step size; the update amount is... , Zero output factor To update the step size, , It is an n x 1 matrix composed of random numbers, where n is the number of individuals in the current population and T is the maximum number of iterations.

[0219] Based on the efficiency optimization control device for a permanent magnet synchronous motor disclosed in the above embodiments of the present invention, compared with traditional methods, this method incorporates driver losses when establishing the total loss model, enabling it to optimize the overall efficiency of the motor drive system. Furthermore, addressing the issue that traditional methods are not applicable to IPMSM, an adaptive APID search algorithm is proposed. This method uses a simulated incremental PID controller adjustment mechanism to search for parameters to achieve the optimal motor efficiency, using the suboptimal d-axis current obtained by the traditional loss model-based control method as the initial search point. Simultaneously, it introduces the local clearing phase strategy and post-consolidation phase strategy from the artemisinin optimization algorithm to further explore and consolidate the later population, effectively enabling the algorithm to operate at the global optimum and reducing the search time. After obtaining the global optimum, the permanent magnet synchronous motor is controlled based on the global optimum, thereby achieving optimal efficiency control.

[0220] This invention also provides an electronic device, which includes a memory and a processor.

[0221] The memory is used to store computer programs;

[0222] The processor is used to execute computer programs, specifically to implement the efficiency optimization control method for permanent magnet synchronous motors provided in any embodiment of this application.

[0223] This application also provides a computer storage medium for storing a computer program, which, when executed, is specifically used to implement the efficiency optimization control method for a permanent magnet synchronous motor provided in any embodiment of this application.

[0224] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0225] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0226] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An efficiency optimization control method for a permanent magnet synchronous motor, characterized in that, The method includes: Obtain the output current of the inverter in the permanent magnet synchronous motor drive system; An inverter loss model is constructed based on the inverter's output current and a preset inverter loss coefficient. Based on the preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method, a total loss model is constructed. The current population is constructed by using multiple randomly generated d-axis current values ​​as individuals. The global optimal solution is obtained by optimizing the solution based on the total loss model, the current population, the preset PID search algorithm, and the preset artemisinin optimization algorithm. Control the permanent magnet synchronous motor based on the global optimal solution; The optimization solution is obtained by optimizing the total loss model, the current population, the preset PID search algorithm and the preset artemisinin optimization algorithm to obtain the global optimal solution, including: calculating the current fitness value of all individuals in the current population based on the total loss model, and calculating the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation. Based on the current deviation, past deviation, and the preset incremental PID formula, calculate the update step size; The zero output factor is calculated based on the preset zero output factor calculation formula and the current deviation. Based on the zero output factor and the update step size, the current population is updated to obtain the first population; Based on the total loss model, the first fitness value of all individuals in the first population is calculated, and the first fitness variance is calculated based on each first fitness value. Determine whether the variance of the first fitness is less than the variance threshold; If so, the individual with the smallest first fitness value is taken as the global optimal solution; If not, optimize the first population using the preset artemisinin optimization algorithm to obtain the second population; Based on the total loss model, the second fitness value of all individuals in the second population is calculated, and the second fitness variance is calculated based on each second fitness value; Determine whether the variance of the second fitness is less than the variance threshold; If so, the individual with the smallest second fitness value is taken as the global optimal solution; If not, take the second population as the current population, return to the step of calculating the current fitness value of all individuals in the current population based on the total loss model, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation.

2. The method according to claim 1, characterized in that, The total loss model is constructed based on the preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method, including: Based on the preset electromagnetic loss model and the inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and the q-axis current is constructed. Based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

3. The method according to claim 1, characterized in that, The step of calculating the update step size based on the current deviation, past deviations, and a preset incremental PID formula includes: Calculate the current deviation Deviation from the previous time point The sum of these values ​​yields the deviation from the previous iteration recorded at the current moment. ; The update step size is calculated based on a preset incremental PID formula; the incremental PID formula is: ; in, , The previous iteration deviation is recorded at the previous time step, and t is the iteration number. To update the step size, This is the proportional adjustment coefficient. This is the integral adjustment factor. The differential adjustment coefficient is... , and It is a vector of random numbers.

4. The method according to claim 1, characterized in that, The formula for calculating the zero output factor includes: Where t is the number of iterations, Zero output factor For the current deviation, Let L be a random number vector, and L be the Levy flight function. T is the maximum number of iterations.

5. The method according to claim 1, characterized in that, The step of updating the current population based on the zero output factor and the update step size to obtain the first population includes: Based on the zero output factor and the update step size, the update amount is calculated; the update amount is... , Zero output factor To update the step size, , It is an n x 1 matrix composed of random numbers, where n is the number of individuals in the current population, T is the maximum number of iterations, and t is the number of iterations.

6. An efficiency optimization control device for a permanent magnet synchronous motor, characterized in that, The device includes: The acquisition unit is used to acquire the output current of the inverter in the permanent magnet synchronous motor drive system; The first construction unit is used to construct an inverter loss model based on the inverter's output current and a preset inverter loss coefficient. The second construction unit is used to construct a total loss model based on a preset electromagnetic loss model, the inverter loss model, and the equivalent conversion method. The third building unit is used to construct the current population by taking multiple randomly generated d-axis current values ​​as individuals. The optimization and solution unit is used to perform optimization and solution based on the total loss model, the current population, the preset PID search algorithm and the preset artemisinin optimization algorithm to obtain the global optimal solution; Control unit, used to control permanent magnet synchronous motor based on the global optimal solution; Specifically, the optimization solution unit is used to: calculate the current fitness value of all individuals in the current population based on the total loss model, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation; Based on the current deviation, past deviation, and the preset incremental PID formula, calculate the update step size; The zero output factor is calculated based on the preset zero output factor calculation formula and the current deviation. Based on the zero output factor and the update step size, the current population is updated to obtain the first population; Based on the total loss model, the first fitness value of all individuals in the first population is calculated, and the first fitness variance is calculated based on each first fitness value. Determine whether the variance of the first fitness is less than the variance threshold; If so, the individual with the smallest first fitness value is taken as the global optimal solution; If not, optimize the first population using the preset artemisinin optimization algorithm to obtain the second population; Based on the total loss model, the second fitness value of all individuals in the second population is calculated, and the second fitness variance is calculated based on each second fitness value; Determine whether the variance of the second fitness is less than the variance threshold; If so, the individual with the smallest second fitness value is taken as the global optimal solution; If not, take the second population as the current population, return to the step of calculating the current fitness value of all individuals in the current population based on the total loss model, and calculate the difference between the minimum current fitness value and the minimum historical fitness value to obtain the current deviation.

7. The apparatus according to claim 6, characterized in that, The second building unit is specifically used for: Based on the preset electromagnetic loss model and the inverter loss model, the total loss equation of the permanent magnet synchronous motor drive system with respect to the d-axis current and the q-axis current is constructed. Based on the electromagnetic torque equation obtained by pre-analyzing the iron loss equivalent circuit of the permanent magnet synchronous motor drive system, the total loss equation is equivalently transformed to obtain the total loss model.

8. A storage medium, characterized in that, Used to store computer programs, which, when executed, are specifically used to implement the efficiency optimization control method for permanent magnet synchronous motors as described in any one of claims 1 to 5.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program, specifically to implement the efficiency optimization control method for the permanent magnet synchronous motor as described in any one of claims 1 to 5.

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