A method, apparatus, medium, processor, and power system for current sharing control in a power system.

Abstract

This invention discloses a current sharing control method, device, medium, processor, and power system for a power supply system. The power system includes at least two power modules with their outputs connected in parallel. The power system controls the output current sharing of each power module through a PI controller and a current sharing loop. The current sharing control method employs an ant colony optimization algorithm to find the optimal parameter combination Kp and Ki for adjusting the parameters of the PI controller. Kp and Ki are the X-axis and Y-axis values ​​when an ant is at a certain position, respectively. The current sharing control method includes: an initialization setting step; an objective function establishment and calculation step; an ant position update step; a superior individual selection step; a genetic mutation judgment step; an iteration step; and an optimal parameter combination determination step. This invention incorporates genetic mutation into the classic ant colony optimization algorithm, which can effectively avoid the algorithm getting trapped in local optima and improve the stability of the power supply system.

Description

Technical Field

[0001] This invention relates to the field of power supply technology, and in particular to a power supply system current sharing control method, device, medium, processor, and power supply system. Background Technology

[0002] As the modular power supply market matures, low-voltage input high-power modular power supplies are gaining popularity among customers. However, in some low-voltage, high-power applications, a single modular power supply cannot meet the load power requirements, necessitating the consideration of parallel output connection. Parallel connection of multiple medium / low-power power supplies not only meets the load power requirements and reduces stress, but also allows for the application of redundancy technology, improving the reliability of the power supply system.

[0003] The primary issue to address when paralleling modular power supply outputs is current sharing. This ensures a uniform distribution of current and thermal stress among the modules, preventing one or more modules from operating at their current limits and reducing the reliability of the power system. Implementing current sharing in industrial power supplies presents several challenges. Analog current sharing schemes increase cost, space requirements, and adjustment complexity, but offer better dynamic current sharing performance. Digital current sharing schemes can save on current sharing circuitry and reduce adjustment complexity, but their dynamic current sharing performance is affected by loop parameters, resulting in poorer control.

[0004] Traditional digital current sharing PI controllers not only cannot adaptively change parameter values ​​during the control process, but also require a large number of experiments to obtain suitable parameters, which increases a lot of work. On this basis, ant colony optimization can be added to find the optimal parameter combination of the PI controller by taking advantage of its superior search capability and adaptability.

[0005] However, the classic ant colony optimization algorithm has problems such as slow convergence speed and easy getting trapped in local optima, which leads to a certain probability of inaccurate optimization coefficients. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to provide a power system current sharing control method, device, medium, processor and power system, which, based on an improved ant colony algorithm, can adaptively adapt the parameters of the digital current sharing PI controller and effectively solve the problem of uneven current distribution after dynamic load in parallel output of industrial power supplies.

[0007] As a first aspect of the present invention, the technical solution of the power system current sharing control method embodiment is as follows:

[0008] A current sharing control method for a power system, wherein the power system includes at least two power modules, the outputs of which are connected in parallel, and the power system controls the output current sharing of each power module through a PI controller and a current sharing loop. The current sharing control method employs an ant colony algorithm to find the optimal parameter combination Kp and Ki for parameter adjustment of the PI controller, where Kp and Ki are the X-axis and Y-axis values ​​respectively when an ant is located at a certain position. The current sharing control method includes the following steps:

[0009] S100. Initialization setup step: Initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, randomly generate the number of ants and place them in different spatial nodes.

[0010] S200, Objective function establishment and calculation steps: Establish the objective function, the value of which is used to measure the difference between the actual measured output value and the expected output value of the power system, and calculate the objective function value of each ant based on the parameter combination of the location of each ant;

[0011] S300, Ant position update step: Based on the pheromone on the path between each spatial node and the objective function value of each ant, calculate the probability of each ant moving to the next position, select the target spatial node for each ant to move to, and update the position of each ant.

[0012] S400, the selection of superior individuals: based on the ant fitness assessment mechanism, select a portion of the ants with the best fitness as superior individuals. These superior individuals will be used for genetic variation.

[0013] S500, Genetic mutation judgment step: Determine whether to perform genetic mutation based on preset conditions. If the mutation conditions are met, perform genetic mutation on the excellent individuals and update the parameter combination of the location of each mutated ant.

[0014] S600, Iteration step: Repeat steps S200 to S500 until the number of iterations set in the initialization is reached or the stopping criterion is met;

[0015] S700, the optimal parameter combination determination step: find the optimal value among all objective function values, and determine the parameter combination corresponding to the current position of the ant as the optimal parameter combination.

[0016] Preferably, the objective function described in step S200 is:

[0017]

[0018] In the formula: T ref T represents the desired output value of the power supply system.git t represents the actual measured output value of the power supply system; t represents time.

[0019] Preferably, in step S300, the probability of each ant moving to the next position is calculated using a roulette wheel method.

[0020] Preferably, the probability calculation formula in step S300 is:

[0021]

[0022] In the formula: Let be the probability that ant m moves from spatial node i to spatial node j at time t; i and j represent two different spatial nodes. β is the pheromone factor; β is the heuristic function factor; τ ij (t) represents the pheromone intensity of an ant moving from node i to node j at time t; η ij (t) represents the objective function described in step S200; T ac This represents a spatial node that an ant can move to.

[0023] Preferably, the pheromone update rule is as follows:

[0024]

[0025] In the formula: This represents the pheromone level of ant m moving from spatial node i to spatial node j in the Nth iteration; i and j represent two different spatial nodes; ρ is the pheromone evaporation coefficient, L is the pheromone constant, and E... m Let M be the cumulative error of ant m over time t, M be the total number of ants, and N be the number of iterations.

[0026] Preferably, the pheromone evaporation rule is as follows:

[0027]

[0028] In the formula: ρ max ω is the maximum volatility coefficient, and ω is the weighting coefficient. The maximum volatility coefficient and the weighting coefficient can be adjusted according to different product characteristics.

[0029] Preferably, the genetic mutation rule of step S500 is as follows: the mutation probability is set by the mutation operator. If the random number is less than the mutation probability, the ant’s current spatial node is randomly selected and exchanged with a random spatial node in all spatial nodes. This exchange is repeated 5 times to complete the mutation; otherwise, it is skipped.

[0030] As a second aspect of the present invention, the technical solution of the power system current sharing control device embodiment is as follows:

[0031] A current sharing control device for a power system, wherein the power system includes at least two power modules, the outputs of which are connected in parallel, and the power system controls the output current sharing of each power module through a PI controller and a current sharing loop. The current sharing control device employs an ant colony algorithm to find the optimal parameter combination Kp and Ki for parameter adjustment of the PI controller, where Kp and Ki are the X-axis and Y-axis values ​​respectively when an ant is located at a certain position. The current sharing control device includes the following units:

[0032] An initialization setting unit is used to initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, the number of ants is randomly generated and placed in different spatial nodes.

[0033] The objective function establishment and calculation unit is used to establish an objective function. The value of the objective function is used to measure the difference between the actual measured output value and the expected output value of the power system, and to calculate the objective function value of each ant based on the parameter combination of the location of each ant.

[0034] The ant position update unit is used to calculate the probability of each ant moving to the next position based on the pheromone on the path between each spatial node and the objective function value of each ant, select the target spatial node for each ant to move to, and update the position of each ant.

[0035] The superior individual selection unit is used to select a subset of ants with the best fitness as superior individuals based on the ant fitness assessment mechanism. These superior individuals will be used for genetic variation.

[0036] The genetic mutation judgment unit is used to determine whether to perform genetic mutation based on preset conditions. If the mutation conditions are met, genetic mutation is performed on the excellent individuals, and the parameter combination of the location of each mutated ant is updated.

[0037] The iteration unit is used to control the objective function establishment and calculation unit, the ant position update unit, the excellent individual selection unit and the genetic mutation judgment unit to execute their respective tasks in sequence and loop until the number of iterations set in the initial setting is reached or the stopping criterion is met.

[0038] The optimal parameter combination determination unit is used to find the optimal value among all objective function values ​​and determine the parameter combination corresponding to the current position of the ant at that optimal value as the optimal parameter combination.

[0039] As a third aspect of the present invention, the embodiment of the computer-readable storage medium is as follows:

[0040] A computer-readable storage medium comprising a stored program that executes the control method described in any of the first aspects above.

[0041] As a fourth aspect of the present invention, the provided embodiment of the processor is as follows:

[0042] A processor, wherein the processor is configured to run a program, which, when running, performs the control method described in any of the first aspects above.

[0043] As a fifth aspect of the present invention, an embodiment of the power supply system is provided as follows:

[0044] A power supply system comprising at least two power modules, the outputs of which are connected in parallel, wherein the power supply system controls the output current sharing of each power module through a PI controller and a current sharing loop, wherein the power supply system includes the power supply system current sharing control device described in any of the second aspects above.

[0045] Traditional current-sharing PI controllers are difficult to adjust. Adjusting the PI controller parameters requires trial and error and optimization based on the specific characteristics and requirements of the power system, resulting in low efficiency and an inability to adapt to complex power systems and changing operating conditions. This invention incorporates an ant colony algorithm into the PI controller, offering the following advantages and benefits compared to existing technologies:

[0046] (1) In this embodiment of the invention, the ant colony algorithm is applied to the current sharing PI controller. By utilizing the excellent search capability and adaptability of the ant colony algorithm, the parameters of the current sharing PI controller are quickly and adaptively adjusted, which enables the PI controller to have good adaptability and adapt to the optimization requirements under different working conditions, load changes and other conditions, thereby improving the stability and robustness of the power supply system.

[0047] (2) In this embodiment of the invention, genetic mutation is added to the classic ant colony optimization algorithm, which can effectively avoid the algorithm from getting trapped in local optima;

[0048] (3) Ant colony algorithm has a relatively simple principle and is easy to implement. It does not require too much mathematical calculation. At the same time, the parameter tuning of ant colony algorithm is relatively easy. This makes the embodiments of the present invention have good feasibility and debuggability in practical applications. Attached Figure Description

[0049] Figure 1 This is a hardware schematic diagram of the current sharing control of the power supply system of the present invention;

[0050] Figure 2 This is a flowchart of the power system current sharing control method according to the first embodiment of the present invention;

[0051] Figure 3 This is a specific flowchart of the power system current sharing control method according to the first embodiment of the present invention;

[0052] Figure 4 For power supply system Figure 3 Current ripple diagram of the output of the current sharing control method;

[0053] Figure 5 The current ripple diagram for a power supply system using a traditional PI controller output;

[0054] Figure 6 This is a comparison diagram of the algorithm iteration of the power system current sharing control method according to the first embodiment of the present invention;

[0055] Figure 7 This is a schematic block diagram of the power system current sharing control device according to the second embodiment of the present invention. Detailed Implementation

[0056] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0058] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the purposes of describing embodiments of this application herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] It should be understood that in the specification, claims, and drawings, when a step is described as continuing into another step, the step may directly continue into that other step or be continued into that other step through a third step; when an element / unit is described as "continuing" into another element / unit, the element / unit may be "directly connected" to that other element / unit or "connected" to that other element / unit through a third element / unit.

[0060] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0061] The power supply system involved in this embodiment of the invention includes at least two power supply modules, with the outputs of each power supply module connected in parallel. The power supply system controls the output current sharing of each power supply module through a PI controller and a current sharing loop. Figure 1 This is a hardware schematic diagram of the current sharing control of the power supply system according to the present invention. The embodiment of the present invention is based on a traditional PI controller. The current sharing loop calculates the average output current value of N power supply modules (i.e., Figure 1 The given current ref(t) in the circuit, and the local output current value (i.e. Figure 1 The actual current feed(t) is subtracted to obtain the error value (i.e., ... Figure 1 The error value is used as the input variable, and the output value is calculated by the PI controller. This output value is then accumulated at the input of the current sharing loop to achieve current sharing. However, the parameters of a traditional PI controller are fixed values ​​during the control process. Therefore, this can affect the stability of the controller when facing dynamic loads. Figure 1 An improved ant colony algorithm from an embodiment of the present invention has been incorporated, which continuously updates the parameter combination K using the ant colony algorithm. p and K i During the update process, genetic mutation is incorporated to prevent the algorithm from getting trapped in local optima until the iteration is complete and the optimal parameter combination K is found. p and K i Adjusting the parameters of the PI control can achieve a current sharing effect, ultimately resulting in a faster response speed and a more stable current sharing error range.

[0062] First Embodiment

[0063] This embodiment provides a current sharing control method for a power supply system. The power supply system includes at least two power modules, with their outputs connected in parallel. The power supply system controls the output current sharing of each power module through a PI controller and a current sharing loop. The current sharing control method uses an ant colony algorithm to find the optimal parameter combination Kp and Ki for adjusting the parameters of the PI controller. Kp and Ki are the X-axis and Y-axis values, respectively, when the ant is at a certain position. Figure 2 For a flowchart of the unit system current sharing control method of the first embodiment of the present invention, please refer to [link / reference]. Figure 2The current sharing control method for power systems includes the following steps:

[0064] S100 Initialization setup step: Initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, randomly generate a number of ants and place them in different spatial nodes.

[0065] As an example, the parameter settings can be: the number of ants M is 50; the number of iterations is 100; in the two-dimensional space, the spatial range of Kp is divided into (300-800), the spatial range of Ki is divided into (20-200), and several spatial nodes representing the positions of the ants are set within this spatial range. It should be noted that these parameter settings are just examples, and those skilled in the art can choose them as needed in specific implementations.

[0066] S200, Objective function establishment and calculation steps: Establish the objective function, the value of which is used to measure the difference between the actual measured output value and the expected output value of the power system, and calculate the objective function value of each ant based on the parameter combination of the location of each ant;

[0067] The purpose of setting the objective function is to minimize the error of the power supply system based on the performance indicators of the PI controller. In practice, the objective function can be set as the accumulated square error over time t.

[0068] The specific expression for the objective function is as follows:

[0069]

[0070] In the formula: is the desired output value of the power supply system; T git t represents the actual measured output value of the power supply system; t represents time.

[0071] S300, Ant position update step: Based on the pheromone on the path between each spatial node and the objective function value of each ant, calculate the probability of each ant moving to the next position, select the target spatial node for each ant to move to, and update the position of each ant.

[0072] The probability of an ant moving to the next position can be calculated using a roulette wheel method.

[0073] The probability calculation formula is as follows:

[0074]

[0075] In the formula: Let be the probability that ant m moves from spatial node i to spatial node j at time t; i and j represent two different spatial nodes. β is the pheromone factor; β is the heuristic function factor; τij (t) represents the pheromone intensity of an ant moving from node i to node j at time t; η ij (t) represents the objective function described in step S200; Tac represents the spatial node that the ant can move.

[0076] The pheromone update rules are as follows:

[0077]

[0078] In the formula: This represents the pheromone level of ant m moving from node i to node j in the Nth iteration; ρ is the pheromone evaporation coefficient, L is the pheromone constant, and E... m Let M be the cumulative error of ant m over time t, M be the total number of ants, and N be the number of iterations.

[0079] Furthermore, this embodiment can adopt an adaptive adjustment method for pheromone evaporation rules to improve the global search capability in the early stage and the iteration speed in the later stage of the ant colony algorithm. The specific pheromone evaporation rules are as follows:

[0080]

[0081] In the formula: ρ max ω is the maximum volatility coefficient, and ω is the weighting coefficient. The maximum volatility coefficient and the weighting coefficient can be adjusted according to different product characteristics.

[0082] By adopting an adaptive pheromone evaporation coefficient, the search range and convergence speed of the algorithm can be improved. Specifically, in the early stage of ant search, the pheromone evaporation coefficient is increased to enhance the random search ability of the ants and find potential optimal solutions. In the later stage of ant search, the pheromone evaporation coefficient is gradually reduced to narrow the search range and quickly converge to the optimal parameter combination.

[0083] S400, the selection of superior individuals: based on the ant fitness assessment mechanism, select a portion of the ants with the best fitness as superior individuals. These superior individuals will be used for genetic variation.

[0084] The fitness is determined by the objective function, and the optimal population can be set to, for example, one-fifth of the total number of ants.

[0085] S500, Genetic mutation judgment step: Determine whether to perform genetic mutation based on preset conditions. If the mutation conditions are met, perform genetic mutation on the superior individuals and update the parameter combination of the location of each mutated ant.

[0086] Furthermore, the genetic mutation rule is as follows: the mutation probability is set by the mutation operator. If the random number is less than the mutation probability, the ant's current spatial node is randomly selected and exchanged with a random spatial node among all spatial nodes. This exchange is repeated 5 times to complete the mutation; otherwise, it is skipped.

[0087] S600, Iteration step: Repeat steps S200 to S500 until the number of iterations set in the initialization is reached or the stopping criterion is met;

[0088] S700, the optimal parameter combination determination step: find the optimal value among all objective function values, and determine the parameter combination corresponding to the current position of the ant at that optimal value as the optimal parameter combination.

[0089] Figure 3 This is a specific flowchart of the power system current sharing control method according to the first embodiment of the present invention. After optimizing the PI controller parameters using this flowchart, the output current ripple captured by an oscilloscope under dynamic load is only 11.92A (see...). Figure 4 The traditional PI controller has an output current ripple exceeding 185.09A (see...). Figure 5 Furthermore, the comparison charts were obtained through algorithm iteration (see...). Figure 6 As can be clearly seen, the improved ant colony algorithm in the power system current sharing control method of this embodiment has a faster iteration speed, which is significantly better than the traditional ant colony algorithm.

[0090] In summary, the power system current sharing control method of this embodiment is feasible for solving the problem of uneven output current when digital power modules are connected in parallel under dynamic conditions.

[0091] Second Embodiment

[0092] The second embodiment of the present invention provides a current sharing control device for a power system. The power system includes at least two power modules, with the outputs of each power module connected in parallel. The power system controls the output current sharing of each power module through a PI controller and a current sharing loop. The current sharing control device uses an ant colony algorithm to find the optimal parameter combination Kp and Ki for adjusting the parameters of the PI controller. Kp and Ki are the X-axis and Y-axis values, respectively, when the ant is located at a certain position. Figure 7 This is a schematic block diagram of a power system current sharing control device according to a second embodiment of the present invention. The power system current sharing control device includes the following units:

[0093] 100. Initialization setting unit, used to initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, a number of ants are randomly generated and placed in different spatial nodes.

[0094] 200. Objective function establishment and calculation unit, used to establish the objective function. The value of the objective function is used to measure the difference between the actual measured output value and the expected output value of the power supply system, and to calculate the objective function value of each ant based on the parameter combination of the location of each ant.

[0095] 300. Ant position update unit, used to calculate the probability of each ant moving to the next position based on the pheromone on the path between each spatial node and the objective function value of each ant, select the target spatial node for each ant to move to, and update the position of each ant.

[0096] 400. The Excellent Individual Selection Unit is used to select a subset of ants with the best fitness as excellent individuals based on the ant fitness assessment mechanism. These excellent individuals will be used for genetic variation.

[0097] 500. Genetic variation judgment unit, used to determine whether to perform genetic variation based on preset conditions. If the variation conditions are met, genetic variation is performed on the superior individuals, and the parameter combination of the location of each mutated ant is updated.

[0098] 600. Iteration unit, used to control the objective function establishment and calculation unit 200, ant position update unit 300, excellent individual selection unit 400 and genetic mutation judgment unit 500 to execute their respective tasks in sequence and loop until the number of iterations set in the initialization is reached or the stopping criterion is met;

[0099] 700. Optimal parameter combination determination unit, used to find the optimal value among all objective function values, and determine the parameter combination corresponding to the current position of the ant at that optimal value as the optimal parameter combination.

[0100] The technical means adopted by the control device in this embodiment are consistent with the control method in the first embodiment, and the beneficial effects are the same, so they will not be described in detail. In addition, the preferred technical means or further improved means of each step in the control method of the first embodiment can be extended to the corresponding unit in this embodiment, and will not be described in detail in this embodiment.

[0101] Third Embodiment

[0102] If the unit integrated into the control device in the second embodiment described above is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0103] Therefore, the third embodiment of the present invention provides a computer-readable storage medium, including a stored program that performs the method of any specific implementation of the first embodiment.

[0104] Fourth embodiment

[0105] The fourth embodiment of the present invention provides a processor for running a program, wherein the program executes the method of any specific implementation method in the first embodiment.

[0106] Fifth embodiment

[0107] The fifth embodiment of the present invention provides a power supply system, which includes at least two power modules, the outputs of each power module are connected in parallel, and the power supply system controls the output current sharing of each power module through a PI controller and a current sharing loop, wherein: the power supply system includes any of the current sharing control devices in the second embodiment.

[0108] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention. These modifications and improvements will not affect the effectiveness and practicality of the present invention. Equivalent substitutions or changes made to the technical solution and inventive concept of the present invention are all within the scope of protection of the present invention.

Claims

1. A current sharing control method for a power supply system, wherein the power supply system includes at least two power modules, the outputs of each power module are connected in parallel, and the power supply system controls the output current sharing of each power module through a PI controller and a current sharing loop, characterized in that, The power system current sharing control method uses an ant colony algorithm to find the optimal parameter combination Kp and Ki for parameter adjustment of the PI controller. Kp and Ki are the X-axis and Y-axis values ​​when the ant is located at a certain position, respectively. The power system current sharing control method includes the following steps: S100. Initialization setup step: Initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, randomly generate the number of ants and place them in different spatial nodes. S200, Objective function establishment and calculation steps: Establish the objective function, the value of which is used to measure the difference between the actual measured output value and the expected output value of the power system, and calculate the objective function value of each ant based on the parameter combination of the location of each ant; S300, Ant position update step: Based on the pheromone on the path between each spatial node and the objective function value of each ant, calculate the probability of each ant moving to the next position, select the target spatial node for each ant to move to, and update the position of each ant. S400, the selection of superior individuals: based on the ant fitness assessment mechanism, select a portion of the ants with the best fitness as superior individuals. These superior individuals will be used for genetic variation. S500, Genetic mutation judgment step: Determine whether to perform genetic mutation based on preset conditions. If the mutation conditions are met, perform genetic mutation on the excellent individuals and update the parameter combination of the location of each mutated ant. S600, Iteration step: Repeat steps S200 to S500 until the number of iterations set in the initialization is reached or the stopping criterion is met; S700, the optimal parameter combination determination step: find the optimal value among all objective function values, and determine the parameter combination corresponding to the current position of the ant as the optimal parameter combination.

2. The power system current sharing control method according to claim 1, characterized in that, The objective function mentioned in step S200 is: In the formula: T ref T represents the desired output value of the power supply system. git t represents the actual measured output value of the power supply system; t represents time.

3. The power system current sharing control method according to claim 1, characterized in that: In step S300, a roulette wheel method is used to calculate the probability of each ant moving to the next position.

4. The power system current sharing control method according to claim 3, characterized in that, The probability calculation formula mentioned in step S300 is: In the formula: Let be the probability that ant m moves from spatial node i to spatial node j at time t; i and j represent two different spatial nodes. β is the pheromone factor; β is the heuristic function factor; τ ij (t) represents the pheromone intensity of an ant moving from node i to node j at time t; η ij (t) represents the objective function described in step S200; T ac This represents a spatial node that an ant can move to.

5. The power system current sharing control method according to claim 1, characterized in that, The pheromone update rule is as follows: In the formula: This represents the pheromone of ant m moving from spatial node i to spatial node j in the Nth iteration; i and j represent two different spatial nodes. ρ is the pheromone evaporation coefficient, L is the pheromone constant, and E m Let M be the cumulative error of ant m over time t, M be the total number of ants, and N be the number of iterations.

6. The power system current sharing control method according to claim 5, characterized in that, The pheromone volatilization rule is as follows: In the formula: ρ max ω is the maximum volatility coefficient, and ω is the weighting coefficient. The maximum volatility coefficient and the weighting coefficient can be adjusted according to different product characteristics.

7. The power system current sharing control method according to claim 1, characterized in that, The genetic mutation rule for step S500 is as follows: the mutation probability is set by the mutation operator. If the random number is less than the mutation probability, the ant's current spatial node is randomly selected and exchanged with a random spatial node among all spatial nodes. This exchange is repeated 5 times to complete the mutation; otherwise, it is skipped.

8. A current sharing control device for a power system, wherein the power system includes at least two power modules, the outputs of each power module are connected in parallel, and the power system controls the output current sharing of each power module through a PI controller and a current sharing loop, characterized in that, The power system current sharing control device uses an ant colony algorithm to find the optimal parameter combination Kp and Ki for parameter adjustment of the PI controller. Kp and Ki are the X-axis and Y-axis values ​​when the ant is located at a certain position, respectively. The power system current sharing control device includes the following units: An initialization setting unit is used to initialize the parameters of the PI controller, including the number of ants, the number of iterations, and several spatial nodes representing the positions of the ants. Then, the number of ants is randomly generated and placed in different spatial nodes. The objective function establishment and calculation unit is used to establish an objective function. The value of the objective function is used to measure the difference between the actual measured output value and the expected output value of the power system, and to calculate the objective function value of each ant based on the parameter combination of the location of each ant. The ant position update unit is used to calculate the probability of each ant moving to the next position based on the pheromone on the path between each spatial node and the objective function value of each ant, select the target spatial node for each ant to move to, and update the position of each ant. The superior individual selection unit is used to select a subset of ants with the best fitness as superior individuals based on the ant fitness assessment mechanism. These superior individuals will be used for genetic variation. The genetic mutation judgment unit is used to determine whether to perform genetic mutation based on preset conditions. If the mutation conditions are met, genetic mutation is performed on the excellent individuals, and the parameter combination of the location of each mutated ant is updated. The iteration unit is used to control the objective function establishment and calculation unit, the ant position update unit, the excellent individual selection unit and the genetic mutation judgment unit to execute their respective tasks in sequence and loop until the number of iterations set in the initial setting is reached or the stopping criterion is met. The optimal parameter combination determination unit is used to find the optimal value among all objective function values ​​and determine the parameter combination corresponding to the current position of the ant at that optimal value as the optimal parameter combination.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method according to any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 7 when it runs.

11. A power supply system comprising at least two power modules, the outputs of which are connected in parallel, wherein the power supply system controls the current sharing of the output current of each power module through a PI controller and a current sharing loop, characterized in that: The power supply system includes the power supply system current sharing control device as described in claim 9.

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