Method, device, electrical equipment and medium for setting parameters of quasi-resonant controller

Abstract

The embodiment of the present invention discloses a method, device, electrical equipment and medium for adjusting the parameters of a quasi-resonant controller. The adjustment method includes: obtaining the load parameter information of the load output by injecting harmonic current into the load, and controlling the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function for each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; obtaining the current quasi-resonant controller parameters, and calculating the corresponding current fitness function for each particle in the particle group corresponding to the current quasi-resonant controller parameters; adjusting the particle group corresponding to the particle group according to the adjustment fitness function or the current fitness function, and outputting the target standard resonant controller parameters. The technical solution of the embodiment of the present invention is to realize the online adjustment of the quasi-resonant controller parameters and improve the robustness of the quasi-resonant controller application.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of power grid harmonic current compensation, and in particular to a method, device, electrical equipment and medium for setting parameters of a quasi-resonant controller. Background Art

[0002] Quasi-proportional resonant control technology is widely used in active harmonic current compensation devices because it can achieve excellent command tracking and anti-interference performance for the frequency components corresponding to each resonant frequency point. With the growing demand for compensation of harmonic currents in the power grid, the importance of multi-frequency harmonic control is increasing.

[0003] When a quasi-proportional resonant controller is used to control the output current of a converter at multiple resonant frequency points, if the power system order is high, it will be difficult to directly adjust the parameters corresponding to each resonant frequency point through theoretical calculation. Summary of the invention

[0004] The embodiments of the present invention provide a method, an apparatus, an electrical device and a medium for adjusting the parameters of a quasi-resonant controller, so as to realize online adjustment of the parameters of the quasi-resonant controller and improve the robustness of the application of the quasi-resonant controller.

[0005] In a first aspect, an embodiment of the present invention provides a method for adjusting parameters of a quasi-resonant controller, the method comprising:

[0006] By injecting harmonic current into the load, load parameter information of the load output is obtained, and according to the load parameter information, the inverter circuit and the load model output are controlled to adjust the quasi-resonant controller parameters, so as to calculate the corresponding adjustment fitness function through each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters;

[0007] Acquire current quasi-resonant controller parameters, and calculate the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters;

[0008] According to the adjustment fitness function or the current fitness function, the particle group corresponding to it is adjusted, and the target standard resonant controller parameters are output.

[0009] In a second aspect, an embodiment of the present invention further provides a device for adjusting parameters of a quasi-resonant controller, the device comprising:

[0010] A load parameter information adjustment module, used for obtaining load parameter information of the load output by injecting harmonic current into the load, and controlling the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function through each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters;

[0011] A controller parameter calculation module, used to obtain current quasi-resonant controller parameters, and calculate the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters;

[0012] The controller parameter output module is used to adjust the particle group corresponding to it according to the adjustment fitness function or the current fitness function, and output the target standard resonant controller parameters.

[0013] In a third aspect, an embodiment of the present invention further provides an electrical device, the electrical device comprising:

[0014] one or more processors;

[0015] A storage device for storing a plurality of programs,

[0016] When at least one of the multiple programs is executed by the one or more processors, the one or more processors implement the method for adjusting the parameters of the quasi-resonant controller provided in the embodiment of the first aspect of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for adjusting the parameters of the quasi-resonant controller provided in the embodiment of the first aspect of the present invention.

[0018] The technical solution of the embodiment of the present invention obtains the load parameter information of the load output by injecting harmonic current into the load, and controls the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function for each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; obtain the current quasi-resonant controller parameters, and calculate the corresponding current fitness function for each particle in the particle group corresponding to the current quasi-resonant controller parameters; adjust the particle group corresponding to it according to the adjustment fitness function or the current fitness function, and output the target standard resonant controller parameters. The problem that the quasi-resonant controller parameters cannot be adjusted online and the parameters corresponding to each resonant frequency point are difficult to be adjusted directly through theoretical calculation in the prior art is solved, so as to realize the online adjustment of the quasi-resonant controller parameters and improve the robustness of the quasi-resonant controller application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of a method for adjusting parameters of a quasi-resonant controller provided in Embodiment 1 of the present invention;

[0020] Figure 2 is a flow chart of a method for adjusting parameters of a quasi-resonant controller provided in Embodiment 2 of the present invention;

[0021] Figure 3 It is a flow chart of tuning the particle swarm algorithm with social division of labor provided by an embodiment of the present invention;

[0022] Figure 4 It is a flow chart of re-optimizing the parameters of the quasi-resonance controller using the particle swarm algorithm provided by an embodiment of the present invention;

[0023] Figure 5 This is a flow chart of the initial optimization of the parameters of the quasi-resonance controller using the particle swarm algorithm provided by the embodiment of the present invention;

[0024] Figure 6 It is a circuit diagram of a single-phase converter using a particle swarm algorithm to adjust a quasi-resonant controller provided by an embodiment of the present invention;

[0025] Figure 7 It is a structural diagram of a device for adjusting parameters of a quasi-resonant controller provided in Embodiment 3 of the present invention;

[0026] Figure 8 It is a schematic diagram of the hardware structure of an electrical device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, rather than to limit the present invention.

[0028] It should also be noted that, for ease of description, only the part relevant to the present invention but not all content is shown in the accompanying drawings. It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processing or methods depicted as flow charts. Although the flow chart describes each operation (or step) as sequential processing, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the processing can be terminated, but it can also have additional steps not included in the accompanying drawings. The processing can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a method for adjusting the parameters of a quasi-resonant controller provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of online adjustment of the parameters of a multi-frequency quasi-resonant controller. The method can be performed by a quasi-resonant controller parameter adjustment device, which can be implemented in the form of software and / or hardware. Specifically, it includes the following steps:

[0031] S110, obtaining load parameter information of the load output by injecting harmonic current into the load, and controlling the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the adjustment fitness function corresponding to each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters.

[0032] The load is injected with a small signal by a quasi-proportional resonant controller in the quasi-proportional resonant controller electrical system, thereby outputting load parameter information. The load is selected and set according to the specific structure of the electrical system, and this embodiment does not impose any limitation on this.

[0033] Specifically, the harmonic current is injected into the load through the converter as the main circuit. The main circuit may include a quasi-resonant controller. The quasi-resonant controller may be a multi-frequency quasi-resonant controller or a quasi-proportional resonant controller. This embodiment does not impose any limitation on this.

[0034] The converter can be a single-phase converter that uses a particle swarm algorithm to tune a quasi-resonant controller. As a classic algorithm for single-objective optimization problems, the particle swarm algorithm can quickly converge to a relatively excellent solution in a multi-dimensional solution space by simulating the exploration logic of a group. It is often used in offline optimization of parameters for complex problems. In this embodiment, the particle swarm algorithm is used to optimize the parameters of the quasi-resonant controller to achieve online tuning of the parameters of the quasi-resonant controller.

[0035] The inverter circuit and the load model are established according to the load parameters. The inverter circuit and the load model perform optimized parameter combination output according to the load parameter information output by the load to achieve adjustment and update of the inverter circuit and the load model itself.

[0036] After each adjustment and update of the inverter circuit and the load model itself, the optimization result of each particle in the particle swarm corresponding to the adjustment of the quasi-resonant controller parameters is evaluated, that is, the adjustment fitness function corresponding to each particle in the particle swarm corresponding to the adjustment of the quasi-resonant controller parameters is obtained, thereby providing guidance for the update of the particle swarm.

[0037] It can be understood that the optimal parameter combination under the current inverter circuit and load model is achieved by minimizing the adjustment fitness function, wherein each particle in the particle group corresponding to the adjustment of the quasi-resonant controller parameters is a combination of the adjustment of the quasi-resonant controller parameters, which is expressed in vector form. The adjustment of the quasi-resonant controller parameters may include all proportional coefficients and resonance coefficients involved in the quasi-resonant controller.

[0038] The calculation method of adjusting the fitness function is

[0039]

[0040] Where Tsim is the simulation time; n is the highest harmonic number; i ref with I ref_i are the instantaneous value of the harmonic current reference value output at a certain time and the i-th harmonic amplitude; i out is the instantaneous value of the harmonic current.

[0041] S120, obtaining current quasi-resonant controller parameters, and calculating a current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters.

[0042] The current quasi-resonant controller parameters are a parameter group in the current electrical system fed back by the main circuit, that is, actual application results in the current electrical system.

[0043] Further, calculating the fitness function corresponding to each particle in the particle group corresponding to the current quasi-resonant controller parameter group includes: calculating the fitness function of each particle in the particle group corresponding to the current quasi-resonant controller parameter group by formula (1);

[0044]

[0045] Where Tsim is the simulation time; n is the highest harmonic number; i ref with I ref_i are the instantaneous value of the harmonic current reference value output at a certain time and the i-th harmonic amplitude; i out is the instantaneous value of the harmonic current.

[0046] Specifically, after each update of the particle swarm, the current quasi-resonant controller parameters are obtained, and the current quasi-resonant controller parameters are the results of optimizing the quasi-resonant controller parameters. Then, the optimization results are evaluated for each particle in the particle swarm corresponding to the current quasi-resonant controller parameters, that is, the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters is obtained, thereby providing guidance for the update of the particle swarm.

[0047] It can be understood that the optimal parameter combination of the current electrical system is achieved by minimizing the current fitness function, wherein each particle in the particle group corresponding to the current quasi-resonant controller parameter is a combination of the current quasi-resonant controller parameters, which is expressed in vector form. The current quasi-resonant controller parameters may include all proportional coefficients and resonance coefficients involved in the quasi-resonant controller.

[0048] S130, adjusting the particle swarm corresponding to the adjusted fitness function or the current fitness function, and outputting target standard resonant controller parameters.

[0049] Specifically, if it is detected that the difference between the target standard resonant controller parameter and the current quasi-resonant controller parameter is greater than the preset parameter threshold, the inverter circuit and the load model are adjusted according to the load parameter information, and after the inverter circuit and the load model are adjusted, the particle swarm corresponding thereto is adjusted according to the adjustment fitness function, and the target standard resonant controller parameter is output. If the target standard resonant controller parameter is obtained once by implementing steps S110 to S130, the particle swarm corresponding thereto is adjusted according to the current fitness function, and the new target standard resonant controller parameter is output again.

[0050] It can be understood that whenever external conditions change, that is, the inverter circuit and load model are adjusted or new standard resonant controller parameters are generated, the corresponding particle group is adjusted until the system stabilizes again, and then it is allowed to move according to the original update logic.

[0051] In this embodiment, the particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted, including: determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function; and adjusting the particle swarm corresponding to the adjusted fitness function or the current fitness function according to the optimal position of the particle swarm.

[0052] Furthermore, the particle group corresponding to the adjustment of the quasi-resonant controller parameters includes α-type particles, β-type particles and γ-type particles; accordingly, the particle group corresponding to the adjusted fitness function or the current fitness function is adjusted according to the optimal position of the particle group, including: controlling the α-type particles to be adjusted at a first update speed, controlling the β-type particles to be adjusted at a second update speed according to the optimal position, and controlling the γ-type particles to be adjusted at a directional update speed according to the optimal position.

[0053] Based on the above embodiment, after determining the optimal position of the corresponding particle group according to the adjusted fitness function or the current fitness function, it also includes: recording the optimal position of the particle group and recording the optimal position of the β-type particles.

[0054] It should also be noted that, considering that the particle swarm algorithm is updated randomly in the initial period of time, when the system is started, it is first explored offline to find the optimal solution. Specifically, the initial quasi-resonant controller parameters are obtained, and the initial fitness function of each particle in the particle swarm corresponding to the initial quasi-resonant controller parameters is calculated; it is determined whether the difference between the initial fitness function and the theoretical fitness function is greater than the fitness function threshold, and if so, the load parameter information is updated.

[0055] The technical solution of the embodiment of the present invention obtains the load parameter information of the load output by injecting harmonic current into the load, and controls the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function for each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; obtain the current quasi-resonant controller parameters, and calculate the corresponding current fitness function for each particle in the particle group corresponding to the current quasi-resonant controller parameters; adjust the particle group corresponding to it according to the adjustment fitness function or the current fitness function, and output the target standard resonant controller parameters. The problem that the quasi-resonant controller parameters cannot be adjusted online and the parameters corresponding to each resonant frequency point are difficult to be adjusted directly through theoretical calculation in the prior art is solved, so as to realize the online adjustment of the quasi-resonant controller parameters and improve the robustness of the quasi-resonant controller application.

[0056] Embodiment 2

[0057] Figure 2 This is a flow chart of a method for adjusting parameters of a quasi-resonant controller provided in Embodiment 2 of the present invention. This embodiment is optimized based on the above embodiment.

[0058] Accordingly, the method of this embodiment specifically includes:

[0059] S210, obtaining load parameter information of the load output by injecting harmonic current into the load, and controlling the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the adjustment fitness function corresponding to each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters.

[0060] S220, obtaining current quasi-resonant controller parameters, and calculating a current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters.

[0061] S230: Determine the optimal position of the corresponding particle swarm according to the adjusted fitness function or the current fitness function.

[0062] Starting from the prototype of the particle swarm algorithm, considering the social division of labor when an animal group hunts, the social division of labor of different types of particles is introduced on the basis of the traditional particle swarm algorithm and applied to this embodiment. The particle swarm corresponding to the adjustment of the quasi-resonant controller parameters includes α-type particles, β-type particles and γ-type particles, and the particle swarm corresponding to the current quasi-resonant controller parameters includes α-type particles, β-type particles and γ-type particles.

[0063] As particles at the bottom of society, α-type particles conduct random global exploration in the solution space, do not make any optimization actions, and are not affected by any existing excellent solutions, in order to ensure the randomness of the exploration.

[0064] β-type particles are similar to particles in traditional particle swarm optimization, and their number is equivalent to that of α-type particles. β-type particles are responsible for nearby optimization. They are affected by the historical optimal solutions of the group and themselves, and efficiently explore near the optimal solution of the group or their own subjective optimal solution. Their update method is:

[0065]

[0066] Where x(k) is the parameter set vector of any β-type particle at the kth step; x self_best (k), x swarm_best (k) are the historical optimal positions of the particle and the entire particle swarm up to the kth step; ω is the inertia parameter, which is generally 1; c1 and c2 are the steps contributed by the optimal solutions of the individual and the society in the optimization process; r1 and r2 are random numbers between 0 and 1 to ensure the randomness of the exploration.

[0067] The γ-type particle is the only hunter in the particle swarm and is actually used for parameter tuning. The purpose of setting this particle is to ensure that the actual parameters will not change suddenly during the optimization process. When updating, it always moves straight to the historical optimal solution of the swarm.

[0068] Furthermore, this embodiment records the optimal position of the particle group, that is, the optimal positions of the α-type particles, the β-type particles, and the γ-type particles, and records the optimal position of the individual β-type particles, so as to determine the optimal position of the particle group.

[0069] S240: Adjust the particle swarm corresponding to the adjusted fitness function or the current fitness function according to the optimal position of the particle swarm.

[0070] Based on the above embodiment, the tuning flow chart of the particle swarm algorithm with social division of labor is shown in Figure 3 First, the fitness function is calculated according to step S210 and step S220, and then the optimal position of the particle swarm is obtained through step S230, and the optimal position of the individual β-particle is recorded, the α-particle is controlled to be adjusted at a first update speed, the β-particle is controlled to be adjusted at a second update speed according to the optimal position, and the γ-particle is controlled to be adjusted at a directional update speed according to the optimal position, and finally the particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted.

[0071] Among them, the first update speed and the second update speed are random update speeds. This embodiment does not impose any restrictions on specific speed values. The specific speed values ​​are randomly generated according to the actual needs of the power system. The directional update speed is set according to the role of γ-type particles in the particle swarm.

[0072] S250, output standard resonant controller parameters.

[0073] Further, the flow chart of particle swarm algorithm to optimize the parameters of quasi-resonance controller can be found in Figure 4 Whenever the external conditions change, that is, the actual fitness function corresponding to the current quasi-resonant controller parameters differs greatly from the theoretical simulation, the load parameters in the inverter circuit and the load model are updated by detecting the load, the current particle group is unfrozen, and the optimization is started based on the original quasi-resonant controller parameters. If the application of the old γ-type particles causes the system to oscillate after the conditions suddenly change, then first, based on experience, the resonance coefficients in the γ-type particles are halved in each update step until the system stabilizes again, and then it is allowed to move according to the original update logic.

[0074] In addition, it should be noted that considering that the particle swarm algorithm has a strong randomness in the initial update, when the system is started, it first conducts offline exploration to find the optimal solution. Figure 5 The provided flow chart of the particle swarm algorithm for the initial optimization of the quasi-resonant controller parameters is to obtain the initial quasi-resonant controller parameters, that is, randomly initialize the particle swarm, and calculate the initial fitness function of each particle in the particle swarm corresponding to the initial quasi-resonant controller parameters; the particle swarm is updated, and after the position of the γ-type particles does not change for a period of time, the position of the particle swarm is temporarily frozen, and the main circuit starts to operate with power; it is determined whether the difference between the initial fitness function and the theoretical fitness function is greater than the fitness function threshold, if so, the load parameter information is updated, if not, the difference between the initial fitness function and the theoretical fitness function is calculated again, that is, at this time, the system enters the particle swarm algorithm quasi-resonant controller parameter optimization process again.

[0075] For example, Figure 6 is a circuit diagram of a single-phase converter using a particle swarm algorithm to adjust a quasi-resonant controller according to an embodiment of the present invention, see Figure 6The control structure of the main circuit 10 includes a quasi-proportional resonant controller, and the quasi-resonant controller parameters obtained by optimization are actually applied. The quasi-resonant controller parameters are the optimization results achieved by the particle swarm algorithm 20. When the actual result of applying the quasi-resonant controller parameters to the main circuit 10 is significantly different from the simulation result, the main circuit 10 adjusts the inverter circuit and the load model by detecting the change of the load parameters. Furthermore, after each update of the inverter circuit and the load model by itself or the particle swarm algorithm 20, the optimization result corresponding to each particle in the particle swarm algorithm 20 is evaluated, and the fitness function is calculated to provide guidance for the particle swarm update of the particle swarm algorithm 20.

[0076] The technical solution of the embodiment of the present invention adds social division of labor on the basis of the traditional particle swarm algorithm, improves the exploration characteristics of the particle swarm algorithm when applied to the online optimization problem of the parameters of the quasi-resonant controller, and the proposed particle swarm algorithm can be used for online optimization of the parameters involved in the quasi-proportional resonant controller, thereby improving the robustness of the multi-frequency quasi-resonant controller when applied to scenarios with load changes.

[0077] Embodiment 3

[0078] Figure 7 This is a structural diagram of a quasi-resonant controller parameter tuning device provided in Embodiment 3 of the present invention. This embodiment is applicable to the case of online tuning of multi-frequency quasi-resonant controller parameters.

[0079] like Figure 7 As shown, the device includes: a load parameter information adjustment module 710, a controller parameter calculation module 720 and a controller parameter output module 730, wherein:

[0080] A load parameter information adjustment module 710 is used to obtain load parameter information of the load output by injecting harmonic current into the load, and control the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function through each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters;

[0081] A controller parameter calculation module 720 is used to obtain current quasi-resonant controller parameters and calculate the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters;

[0082] The controller parameter output module 730 is used to adjust the particle group corresponding to it according to the adjustment fitness function or the current fitness function, and output the target standard resonant controller parameters.

[0083] The quasi-resonant controller parameter setting device of this embodiment obtains the load parameter information of the load output by injecting harmonic current into the load, and controls the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function for each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; obtain the current quasi-resonant controller parameters, and calculate the corresponding current fitness function for each particle in the particle group corresponding to the current quasi-resonant controller parameters; and set the particle group corresponding to the particle group according to the adjustment fitness function or the current fitness function, and output the target standard resonant controller parameters. The problem that the quasi-resonant controller parameters cannot be set online and the parameters corresponding to each resonant frequency point are difficult to be set directly through theoretical calculation in the prior art is solved, so as to realize the online setting of the quasi-resonant controller parameters and improve the robustness of the quasi-resonant controller application.

[0084] On the basis of the above embodiments, calculating the fitness function corresponding to each particle in the particle group corresponding to the current quasi-resonant controller parameter group includes:

[0085] The fitness function of each particle in the particle swarm corresponding to the current quasi-resonant controller parameter group is calculated by formula (1);

[0086]

[0087] Where Tsim is the simulation time; n is the highest harmonic number; i ref with I ref_i are the instantaneous value of the harmonic current reference value output at a certain time and the i-th harmonic amplitude; i out is the instantaneous value of the harmonic current.

[0088] Based on the above embodiments, before obtaining the load parameter information of the load output by injecting harmonic current into the load, the method further includes:

[0089] When it is detected that the difference between the target standard resonant controller parameter and the current quasi-resonant controller parameter is greater than a preset parameter threshold, the inverter circuit and the load model are adjusted according to the load parameter information.

[0090] On the basis of the above embodiments, the particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted, including:

[0091] Determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function;

[0092] The particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted according to the optimal position of the particle swarm.

[0093] On the basis of the above embodiments, the particle group corresponding to the adjustment of the quasi-resonant controller parameters includes α-type particles, β-type particles and γ-type particles;

[0094] Accordingly, adjusting the particle swarm corresponding to the adjusted fitness function or the current fitness function according to the optimal position of the particle swarm includes:

[0095] The α-type particles are controlled to be adjusted at a first update speed, the β-type particles are controlled to be adjusted at a second update speed according to the optimal position, and the γ-type particles are controlled to be adjusted at a directional update speed according to the optimal position.

[0096] On the basis of the above embodiments, after determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function, the method further includes:

[0097] The optimal position of the particle group is recorded, and the optimal position of the β-type particles is recorded.

[0098] Based on the above embodiments, the following further aspects are included:

[0099] Acquire initial quasi-resonant controller parameters, and calculate the initial fitness function of each particle in the particle swarm corresponding to the initial quasi-resonant controller parameters;

[0100] It is determined whether the difference between the initial fitness function and the theoretical fitness function is greater than a fitness function threshold; if so, the load parameter information is updated.

[0101] The quasi-resonant controller parameter tuning device provided in the above-mentioned embodiments can execute the quasi-resonant controller parameter tuning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the quasi-resonant controller parameter tuning method.

[0102] Embodiment 4

[0103] Figure 8 A schematic diagram of the structure of an electrical device provided in Embodiment 4 of the present invention is shown in FIG. Figure 8 As shown, the electrical device includes a processor 810, a memory 820, an input device 830, and an output device 840; the number of processors 810 in the electrical device can be one or more. Figure 8 A processor 810 is taken as an example; the processor 810, the memory 820, the input device 830 and the output device 840 in the electrical device can be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0104] The memory 820, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the method for setting the parameters of the quasi-resonant controller in the embodiment of the present invention (for example, the load parameter information adjustment module 710, the controller parameter calculation module 720 and the controller parameter output module 730 in the device for setting the parameters of the quasi-resonant controller). The processor 810 executes various functional applications and data processing of the electrical equipment by running the software programs, instructions and modules stored in the memory 820, that is, implements the above-mentioned method for setting the parameters of the quasi-resonant controller.

[0105] The memory 820 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 820 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 820 may further include a memory remotely arranged relative to the processor 810, and these remote memories may be connected to the electrical device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0106] The input device 830 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the electrical device. The output device 840 may include a display device such as a display screen.

[0107] Embodiment 5

[0108] Embodiment 5 of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions are used to execute a method for adjusting parameters of a quasi-resonant controller when executed by a computer processor, the method comprising:

[0109] Generate a seat power management interface after receiving a target seat power control signal including seat power control information and seat information sent by a seat power module;

[0110] Determining a target seat power corresponding to the seat information based on the seat power management interface;

[0111] The working state of the target seat power supply is controlled according to the seat power control information, and the target seat power supply state information is sent to the seat power supply module based on the working state to control the target seat power supply to perform corresponding operations according to the target seat power supply state information.

[0112] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the quasi-resonant controller parameter adjustment method provided in any embodiment of the present invention.

[0113] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for an electrical device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0114] It is worth noting that in the embodiment of the above-mentioned quasi-resonant controller parameter adjustment device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0115] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for adjusting parameters of a quasi-resonant controller, characterized in that: include: By injecting harmonic current into the load, load parameter information of the load output is obtained, and according to the load parameter information, the inverter circuit and the load model output are controlled to adjust the quasi-resonant controller parameters, so as to calculate the corresponding adjustment fitness function through each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; Acquire current quasi-resonant controller parameters, and calculate the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters; According to the adjustment fitness function or the current fitness function, the particle group corresponding to the adjustment fitness function is adjusted, and the target standard resonant controller parameters are output; The adjusted fitness function and the current fitness function are both obtained through formula (1): （1） in, T sim is the simulation time; n is the highest harmonic number; i ref and I ref_i are the instantaneous value of the harmonic current reference value output at a certain time and the i-th harmonic amplitude; i out is the instantaneous value of the harmonic current; The particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted, including: Determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function; The particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted according to the optimal position of the particle swarm.

2. The method according to claim 1, characterized in that Before obtaining the load parameter information output by the load by injecting harmonic current into the load, the method further includes: When it is detected that the difference between the target standard resonant controller parameter and the current quasi-resonant controller parameter is greater than a preset parameter threshold, the inverter circuit and the load model are adjusted according to the load parameter information.

3. The method according to claim 1, characterized in that The particle group corresponding to the adjustment of the quasi-resonant controller parameters includes α-type particles, β-type particles and γ-type particles; Accordingly, adjusting the particle swarm corresponding to the adjusted fitness function or the current fitness function according to the optimal position of the particle swarm includes: The α-type particles are controlled to be adjusted at a first update speed, the β-type particles are controlled to be adjusted at a second update speed according to the optimal position, and the γ-type particles are controlled to be adjusted at a directional update speed according to the optimal position.

4. The method according to claim 3, characterized in that After determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function, the method further includes: The optimal position of the particle group is recorded, and the optimal position of the β-type particles is recorded.

5. The method according to claim 1, characterized in that Also includes: Acquire initial quasi-resonant controller parameters, and calculate the initial fitness function of each particle in the particle swarm corresponding to the initial quasi-resonant controller parameters; It is determined whether the difference between the initial fitness function and the theoretical fitness function is greater than a fitness function threshold; if so, the load parameter information is updated.

6. A device for adjusting parameters of a quasi-resonant controller, characterized in that: include: A load parameter information adjustment module, used for obtaining load parameter information of the load output by injecting harmonic current into the load, and controlling the inverter circuit and the load model output to adjust the quasi-resonant controller parameters according to the load parameter information, so as to calculate the corresponding adjustment fitness function through each particle in the particle group corresponding to the adjusted quasi-resonant controller parameters; A controller parameter calculation module, used to obtain current quasi-resonant controller parameters, and calculate the current fitness function corresponding to each particle in the particle swarm corresponding to the current quasi-resonant controller parameters; A controller parameter output module, used to adjust the particle swarm corresponding to it according to the adjusted fitness function or the current fitness function, and output the target standard resonant controller parameters; The adjusted fitness function and the current fitness function are both obtained through formula (1): （1） in, T sim is the simulation time; n is the highest harmonic number; i ref and I ref_i are the instantaneous value of the harmonic current reference value output at a certain time and the i-th harmonic amplitude; i out is the instantaneous value of the harmonic current; The particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted, including: Determining the optimal position of the particle swarm corresponding to the adjusted fitness function or the current fitness function; The particle swarm corresponding to the adjusted fitness function or the current fitness function is adjusted according to the optimal position of the particle swarm.

7. An electrical device, characterized in that: The electrical equipment comprises: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for tuning the parameters of the quasi-resonant controller according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for adjusting parameters of a quasi-resonant controller as described in any one of claims 1 to 5 is implemented.

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