Generator harmonic global optimization method and related device

Abstract

The invention discloses a global optimization method of generator harmonic waves and a related device, and relates to the technical field of generator control, a global optimization model of the generator harmonic waves is established firstly, the optimization objective of the global optimization model is to maximize the efficiency of a generator system, and the generator system comprises a generator and a controller. The global optimization model is optimized and solved, optimal control parameters are obtained, the control parameters comprise a carrier period and dead zone time, the efficiency of a generator system composed of a generator and a controller can be optimized, equivalently, space harmonics and time harmonics are optimized at the same time, and global optimization of the harmonics of the generator is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of generator control, and in particular to a global optimization method for generator harmonics taking switching losses into consideration and a related device. Background Art

[0002] Generator control research is an important trend in current research. The harmonic problem of the generator has a serious impact on its heating and efficiency. Therefore, the suppression and optimization of harmonics in the generator control process has become an important part of generator control research. The harmonics generated by the generator are composed of spatial harmonics and temporal harmonics. Spatial harmonics are generated by the generator, and temporal harmonics are generated by the controller that controls the generator. Spatial harmonics are related to the generator speed, and temporal harmonics are mainly affected by control parameters such as dead time, carrier ratio, and modulation ratio. In order to analyze and optimize harmonics, numerical analysis and finite element analysis are often used to solve the harmonic loss model, and optimization is performed on this basis.

[0003] At present, the influence of control parameters such as dead time, carrier ratio and modulation ratio on harmonics has been widely discussed, and many studies have optimized the spatial harmonics generated by the generator itself based on the influence of control parameters. Different from the control of mechanical devices, generator control must not only consider the spatial harmonics of the generator itself, but also the influence of the time harmonics generated by the controller on the generator and the overall generator system. However, current studies have explained the influence of control parameters on the spatial harmonics generated by the generator itself and the optimization method, and only considered the efficiency of the generator, and basically showed a monotonic relationship of positive correlation or negative correlation, so there is no need for optimization. That is, the traditional method is to optimize the efficiency of the generator itself, which is equivalent to optimizing only the spatial harmonics, but in fact it will also affect the efficiency of the controller, and the time harmonics also need to be optimized. Summary of the invention

[0004] The purpose of this application is to provide a global optimization method and related devices for generator harmonics, which can optimize the efficiency of a generator system composed of a generator and a controller, which is equivalent to optimizing spatial harmonics and temporal harmonics at the same time, thereby achieving global optimization of generator harmonics.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a global optimization method for generator harmonics, the global optimization method for generator harmonics comprising:

[0007] Establishing a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller;

[0008] The global optimization model is optimized and solved to obtain optimal control parameters; the control parameters include carrier period and dead time.

[0009] Optionally, the global optimization model is:

[0010]

[0011] Among them, eff is the efficiency of the generator system; Pe1 is the useful power of the generator; Pe is the total power of the generator.

[0012] Optionally, optimizing and solving the global optimization model to obtain optimal control parameters specifically includes: optimizing and solving the global optimization model using a particle swarm algorithm to obtain optimal control parameters;

[0013] The particle swarm algorithm is used to optimize and solve the global optimization model to obtain the optimal control parameters, which specifically includes:

[0014] Generate an initial population; the initial population includes a plurality of particles, and the position of each particle represents a value of a control parameter;

[0015] Calculating the fitness function value of each particle in the initial population; the fitness function value is the difference between 1 and the efficiency of the generator system;

[0016] Determine the global optimal position of the current iteration based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration; in the first iteration, the fitness function value corresponding to the global optimal position of the previous iteration is a preset value;

[0017] Determine whether the iteration termination condition is met;

[0018] If so, the global optimal position of the current iteration is used as the optimal control parameter;

[0019] If not, the initial population is updated to obtain an updated population, and the updated population is used as the initial population for the next iteration, and the process returns to the step of "calculating the fitness function value of each particle in the initial population".

[0020] Optionally, calculating the fitness function value of each particle in the initial population specifically includes:

[0021] Establishing a joint simulation model of the generator system; the joint simulation model includes a simulation model of the generator in the generator system and a simulation model of the controller in the generator system;

[0022] For each particle in the initial population, taking the position of the particle as input, running the joint simulation model under a preset working condition to obtain the total power of the generator and the useful power of the generator;

[0023] Based on the total power of the generator and the useful power of the generator, the efficiency of the generator system is calculated;

[0024] Based on the efficiency of the generator system, the fitness function value of the particle is calculated.

[0025] Optionally, determining the global optimal position of the current iteration based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration specifically includes:

[0026] Based on the fitness function value of each particle in the initial population, determining the smallest particle with the smallest fitness function value in the initial population;

[0027] Determine whether the fitness function value of the minimum particle is less than the fitness function value corresponding to the global optimal position of the previous iteration;

[0028] If yes, the position of the smallest particle is taken as the global optimal position of the current iteration;

[0029] If not, the global optimal position of the previous iteration is used as the global optimal position of the current iteration. Optionally, the iteration termination condition is that the fitness function value corresponding to the global optimal position of the current iteration is less than or equal to the target fitness function value, or the current iteration number reaches the maximum iteration number.

[0030] In a second aspect, the present application provides a global optimization device for generator harmonics, the global optimization device for generator harmonics comprising:

[0031] A model building module is used to build a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller;

[0032] The optimization solution module is used to optimize and solve the global optimization model to obtain the optimal control parameters; the control parameters include carrier period and dead time.

[0033] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described global optimization methods for generator harmonics.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for global optimization of generator harmonics.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for global optimization of generator harmonics.

[0036] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0037] The present application provides a global optimization method and related devices for generator harmonics. A global optimization model for generator harmonics is first established. The optimization goal of the global optimization model is to maximize the efficiency of the generator system. The generator system includes a generator and a controller. The global optimization model is then optimized and solved to obtain the optimal control parameters. The control parameters include carrier period and dead time. The present application uses carrier period and dead time as optimization variables and maximizes the efficiency of the generator system as the optimization goal to establish a global optimization model for generator harmonics. Subsequently, the global optimization model for generator harmonics is optimized and solved to obtain the optimal control parameters. The efficiency of the generator system composed of a generator and a controller can be optimized, which is equivalent to optimizing spatial harmonics and time harmonics at the same time to achieve global optimization of generator harmonics. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is an application environment diagram of a global optimization method for generator harmonics provided in Example 1 of the present application.

[0040] Figure 2 A schematic flow chart of a global optimization method for generator harmonics provided in Example 1 of the present application.

[0041] Figure 3 A schematic diagram of the framework of a global optimization method for generator harmonics provided in Example 1 of the present application.

[0042] Figure 4 A schematic diagram of the optimization solution process of the particle swarm algorithm provided in Example 1 of the present application.

[0043] Figure 5A schematic diagram of the functional modules of a global optimization device for generator harmonics provided in Example 2 of the present application.

[0044] Figure 6 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] Example 1

[0047] The global optimization method of generator harmonics provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in the figure, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The global optimization method of generator harmonics can be implemented by the server or the terminal alone, or by the terminal and the server together.

[0048] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0049] like Figure 2 As shown, a global optimization method for generator harmonics is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0050] Step S1, establishing a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller.

[0051] Step S2, optimizing and solving the global optimization model to obtain optimal control parameters; the control parameters include carrier period and dead time.

[0052] By implementing the above steps S1 to S2, compared with the traditional method, if the influence of switching loss on the efficiency of the generator system is taken into account and the time harmonics are further optimized, the optimization space will be larger. Therefore, this embodiment adds switching loss to the optimization considerations and proposes a global optimization method for generator harmonics taking switching loss into consideration. The optimization space is larger and the efficiency of the generator system composed of a generator and a controller can be optimized, which is equivalent to optimizing the spatial harmonics and the time harmonics at the same time, realizing the global optimization of the generator harmonics, improving the efficiency of the generator system, and achieving the coordinated optimization of the efficiency of the generator and the efficiency of the controller.

[0053] In this embodiment, the generator may be a high-speed generator, which means a generator with a rotation speed exceeding 10000 r / min. At the same time, the generator in this embodiment may also be replaced by a motor, that is, the global optimization method provided in this embodiment may also be used to optimize a motor system composed of a motor and a controller.

[0054] The optimization of spatial harmonics is mainly based on the optimization of the generator itself, while the analysis of time harmonics does not require approximation through finite elements. Generally, mathematical modeling or simple numerical simulation can be performed directly. Therefore, the optimization of time harmonics is usually the optimization of related control parameters. Based on this, this embodiment proposes a global optimization method for generator harmonics considering switching losses, such as Figure 3 As shown, the specific process is as follows:

[0055] (1) Model establishment: A simulation model of the generator is established based on Ansys (i.e. Figure 3 The generator finite element model in the figure) is used to build a simulation model of the controller based on Matlab / Simulink (i.e. Figure 3 The Simulink control model in ), the simulation model of the generator and the simulation model of the controller constitute a joint simulation model of the generator system. Through the joint simulation of the joint simulation model, the carrier period Ts and the dead time deadtime are used as input to calculate the motor loss and the switch loss, and the efficiency eff of the generator system under the carrier period Ts and the dead time deadtime is determined. Eff is the efficiency under steady state and can be obtained by calculating the ratio of output power to input power over a period of time. The optimization parameters such as the carrier period Ts, the efficiency eff of the generator system and the dead time deadtime are provided for the next optimization algorithm.

[0056] (2) Particle swarm optimization: Considering that the particle swarm algorithm can meet the nonlinear and uncertainty requirements of the system, and the optimization is global and has a fast calculation speed, the particle swarm algorithm is used for optimization in this embodiment. Based on the joint simulation model, an optimization function of the efficiency eff of the generator system and the carrier period Ts and the dead time deadtime is constructed, eff = function (Ts, deadtime), and the efficiency of the generator system is selected as the global optimization model. The essence of optimization is to determine the independent variables, dependent variables and fitness function of the global optimization model. The independent variables are the carrier period Ts and the dead time deadtime, which are called optimization variables, and the dependent variable is the efficiency eff of the generator system, which is called the optimization target. In order to optimize the efficiency of the generator system, the fitness function is selected as fitness = 1-eff, and fitness is the fitness function value. After determining the fitness function, the particle swarm algorithm is used for optimization with the goal of minimizing the fitness function. It is judged whether the fitness function value is less than or equal to the target fitness function value or whether the number of iterations reaches the maximum number of iterations, and the final optimization result, that is, the optimal control parameters, is obtained.

[0057] In this embodiment, when determining the optimization variables: according to the harmonic characteristics of the generator, the speed of the generator and the carrier ratio, modulation ratio and dead time of the controller are all influencing factors of the harmonics of the generator. The rule is that the harmonics increase with the increase of speed, the decrease of carrier ratio, the decrease of modulation ratio and the increase of dead time. Among them, the modulation ratio is related to the bus voltage, the speed of the generator, the power of the generator and the parameters of the generator. After the generator is selected, the parameters of the generator, the rated speed of the generator and the rated power of the generator are determined. The bus voltage is set according to the needs of the project. Therefore, under the rated state, the speed of the generator and the modulation ratio of the controller remain unchanged. Although the dead time is related to the device parameters, it can be set manually to avoid the upper and lower bridge arms from being turned on at the same time. Therefore, the speed and modulation ratio are no longer used as optimization variables. The carrier ratio and dead time are determined as optimization variables. At the same time, considering the association between the carrier ratio and the carrier period, the carrier ratio is replaced by the carrier period, so the carrier period and dead time are finally selected as optimization variables.

[0058] This embodiment determines the optimization target: According to the harmonic analysis results, the optimization variables will affect the harmonics, but the harmonic components are complex. If the harmonics are used as the optimization target, spectrum analysis needs to be used, which is difficult to calculate and is not conducive to the optimization of the generator. According to the harmonic loss model, harmonics will affect the loss of the generator, so optimizing the loss of the generator is actually optimizing the harmonics. However, the traditional method only considers the loss of the generator itself, and basically presents a monotonic relationship of positive correlation or negative correlation, and there is no need for optimization. In fact, the carrier period and dead time will affect the switching loss of the controller, thereby indirectly affecting the loss of the entire generator system. At this time, the overall loss of the generator-controller no longer changes monotonically, and there is more room for optimization. Therefore, the generator loss and switching loss, that is, the total loss of the generator system, are selected as the optimization target. For a generator, the efficiency of the generator depends on the power of the generator and the loss of the generator. There is a constraint relationship between the efficiency and the total loss. Therefore, this embodiment finally selects the efficiency of the generator system as the optimization target. For a certain working condition, the total power Pe of the generator is determined, and the total power of the generator is equal to the sum of the useful power Pe1 of the generator and the loss power Pe2 of the generator, that is, Pe=Pe1+Pe2, and the efficiency of the generator system eff=Pe1 / Pe.

[0059] When determining the optimization method in this embodiment, the particle swarm algorithm is used for optimization, considering that the particle swarm algorithm can meet the nonlinear and uncertainty requirements of the system, and the optimization is global and has a fast calculation speed. Figure 4 As shown, first, initialize the position and speed of each particle, and the position of each particle corresponds to a set of carrier period Ts and dead time deadtime values. Then, perform joint simulation based on the joint simulation model, calculate the fitness function value of each particle, and a series of (Ts, deadtime, fitness) can be obtained through the joint simulation process of each particle. By comparing the fitness function values ​​of all particles, the global optimal position (which is the position of the particle with the smallest fitness function value) is obtained. Then determine whether the end condition is met. If the end condition is met, the optimization algorithm ends and the global optimal position is output; otherwise, update the speed and position of each particle, perform joint simulation based on the joint simulation model, calculate the fitness function value of each particle, and update the historical optimal position of each particle and the global optimal position of the group by comparing the fitness function values ​​of all particles. Repeat this process until the end condition is met and the optimization algorithm ends. Among them, the end condition is that the current number of iterations reaches the set maximum number of iterations, or the fitness function value corresponding to the global optimal position is less than or equal to the target fitness function value.

[0060] This embodiment is widely applicable to all generator systems composed of generators and controllers. For different generators and controllers, the specific generator simulation model and controller simulation model established are also different. This embodiment establishes a generator simulation model based on the structural parameters and working parameters of the generator, so that the generator simulation model has the same function as the real generator, and establishes a controller simulation model based on the structural parameters and working parameters of the controller, so that the controller simulation model has the same function as the real controller. The operation process of the joint simulation model is: taking the position of the particle (i.e., the carrier cycle and the dead time) as input, adjusting the working parameters of the controller simulation model, and then applying the preset working conditions, the preset working conditions include the load and the speed of the generator, so that the generator simulation model and the controller simulation model are simulated and run under the preset working conditions, and the useful power of the generator corresponding to the carrier cycle and the dead time under the preset working conditions and the total power of the generator are calculated, and then the ratio of the useful power of the generator to the total power of the generator is calculated to obtain the efficiency of the generator system, and then the difference between 1 and the efficiency of the generator system is calculated to obtain the fitness function value of the particle. It should be noted that since the control parameters of the controller are taken into account when determining the useful power and total power of the generator, the efficiency finally determined is the efficiency of the entire generator system.

[0061] Since the switching loss is mainly generated by the converter included in the controller, the simulation model of the controller may be a simulation model of the converter in the controller.

[0062] In S1, a global optimization model of generator harmonics is established. The optimization goal of the global optimization model is to maximize the efficiency of the generator system. The global optimization model is:

[0063]

[0064] Among them, eff is the efficiency of the generator system; Pe1 is the useful power of the generator; Pe is the total power of the generator.

[0065] In S2, the global optimization model is optimized and solved to obtain the optimal control parameters, which specifically includes: using a particle swarm algorithm to optimize and solve the global optimization model to obtain the optimal control parameters.

[0066] Among them, the particle swarm algorithm is used to optimize and solve the global optimization model to obtain the optimal control parameters, including:

[0067] (1) Generate an initial population, which includes multiple particles. The position of each particle represents a value of a control parameter.

[0068] (2) Calculate the fitness function value of each particle in the initial population. The fitness function value is the difference between 1 and the efficiency of the generator system.

[0069] Among them, calculating the fitness function value of each particle in the initial population specifically includes: establishing a joint simulation model of the generator system, the joint simulation model includes a simulation model of the generator in the generator system and a simulation model of the controller in the generator system; for each particle in the initial population, taking the position of the particle as input, running the joint simulation model under preset working conditions to obtain the total power of the generator and the useful power of the generator; based on the total power of the generator and the useful power of the generator, calculating the efficiency of the generator system; based on the efficiency of the generator system, calculating the fitness function value of the particle.

[0070] (3) Based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration, the global optimal position of the current iteration is determined. In the first iteration, the fitness function value corresponding to the global optimal position of the previous iteration is a preset value.

[0071] Among them, based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration, the global optimal position of the current iteration is determined, specifically including: based on the fitness function value of each particle in the initial population, determining the smallest particle with the smallest fitness function value in the initial population; judging whether the fitness function value of the smallest particle is less than the fitness function value corresponding to the global optimal position of the previous iteration; if so, taking the position of the smallest particle as the global optimal position of the current iteration; if not, taking the global optimal position of the previous iteration as the global optimal position of the current iteration.

[0072] Among them, the preset value can be 1.

[0073] (4) Determine whether the iteration termination condition is met.

[0074] The iteration termination condition is that the fitness function value corresponding to the global optimal position of the current iteration is less than or equal to the target fitness function value, or the current iteration number reaches the maximum iteration number.

[0075] (5) If yes, the global optimal position of the current iteration is taken as the optimal control parameter.

[0076] (6) If not, the initial population is updated to obtain an updated population, and the updated population is used as the initial population for the next iteration, and the process returns to the step of "calculating the fitness function value of each particle in the initial population".

[0077] This embodiment proposes a global optimization method for generator harmonics taking switching losses into consideration, using carrier period and dead time as optimization variables, generator losses and switching losses (i.e., total losses of the generator system) as optimization targets, and actually using the efficiency of the generator system as the optimization target to achieve global optimization of harmonics and efficiency of the generator system. Compared with the traditional method, in the selection of optimization targets, due to the complexity of harmonic components, harmonics that require spectrum analysis and are difficult to calculate are not selected, but generator losses affected by harmonics are selected, and considering that the optimization variables will also affect the efficiency of the controller, i.e., the switching losses, the overall loss of the generator-controller no longer changes monotonically, so the generator system loss is selected as the optimization target, and the optimization space is larger.

[0078] The embodiment of the present application also provides an application scenario, which applies the above-mentioned global optimization method of generator harmonics. Specifically, the global optimization method of generator harmonics provided in this embodiment can be applied in the generator harmonic optimization scenario, which includes an optimization link and an application link. The optimization link is used to globally optimize the generator harmonics to obtain the optimal control parameters, and the application link is used to apply the optimal control parameters to the actual generator. The global optimization method of generator harmonics provided in this embodiment belongs to the optimization link.

[0079] Example 2

[0080] Based on the same inventive concept, the embodiment of the present application also provides a global optimization device for generator harmonics for implementing the global optimization method for generator harmonics involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiment of the global optimization device for generator harmonics provided below can refer to the limitations of the global optimization method for generator harmonics above, and will not be repeated here.

[0081] like Figure 5 As shown, a global optimization device for generator harmonics is provided, and the global optimization device for generator harmonics includes:

[0082] The model building module M1 is used to build a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller.

[0083] The optimization solution module M2 is used to optimize and solve the global optimization model to obtain the optimal control parameters; the control parameters include carrier period and dead time.

[0084] Example 3

[0085] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a global optimization method for generator harmonics is implemented.

[0086] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0087] In an exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the global optimization method of generator harmonics described in Example 1.

[0088] Example 4

[0089] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the global optimization method for generator harmonics described in Embodiment 1 is implemented.

[0090] Example 5

[0091] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the global optimization method for generator harmonics described in Example 1.

[0092] Any reference to memory used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0093] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A global optimization method for generator harmonics, characterized in that: The global optimization method of generator harmonics includes: Establishing a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller; The global optimization model is optimized and solved to obtain optimal control parameters; the control parameters include carrier period and dead time.

2. The global optimization method of generator harmonics according to claim 1, characterized in that: The global optimization model is: Among them, eff is the efficiency of the generator system; Pe1 is the useful power of the generator; Pe is the total power of the generator.

3. The global optimization method of generator harmonics according to claim 1, characterized in that: Optimizing and solving the global optimization model to obtain optimal control parameters, specifically comprising: optimizing and solving the global optimization model using a particle swarm algorithm to obtain optimal control parameters; The particle swarm algorithm is used to optimize and solve the global optimization model to obtain the optimal control parameters, which specifically includes: Generate an initial population; the initial population includes a plurality of particles, and the position of each particle represents a value of a control parameter; Calculating the fitness function value of each particle in the initial population; the fitness function value is the difference between 1 and the efficiency of the generator system; Determine the global optimal position of the current iteration based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration; in the first iteration, the fitness function value corresponding to the global optimal position of the previous iteration is a preset value; Determine whether the iteration termination condition is met; If so, the global optimal position of the current iteration is used as the optimal control parameter; If not, the initial population is updated to obtain an updated population, and the updated population is used as the initial population for the next iteration, and the process returns to the step of "calculating the fitness function value of each particle in the initial population".

4. The global optimization method of generator harmonics according to claim 3, characterized in that: Calculating the fitness function value of each particle in the initial population, specifically including: Establishing a joint simulation model of the generator system; the joint simulation model includes a simulation model of the generator in the generator system and a simulation model of the controller in the generator system; For each particle in the initial population, taking the position of the particle as input, running the joint simulation model under a preset working condition to obtain the total power of the generator and the useful power of the generator; Based on the total power of the generator and the useful power of the generator, the efficiency of the generator system is calculated; Based on the efficiency of the generator system, the fitness function value of the particle is calculated.

5. The global optimization method of generator harmonics according to claim 3, characterized in that: Based on the fitness function value of each particle in the initial population and the fitness function value corresponding to the global optimal position of the previous iteration, the global optimal position of the current iteration is determined, specifically including: Based on the fitness function value of each particle in the initial population, determining the smallest particle with the smallest fitness function value in the initial population; Determine whether the fitness function value of the minimum particle is less than the fitness function value corresponding to the global optimal position of the previous iteration; If yes, the position of the smallest particle is taken as the global optimal position of the current iteration; If not, the global optimal position of the previous iteration is used as the global optimal position of the current iteration.

6. The global optimization method of generator harmonics according to claim 3, characterized in that: The iteration termination condition is that the fitness function value corresponding to the global optimal position of the current iteration is less than or equal to the target fitness function value, or the current iteration number reaches the maximum iteration number.

7. A global optimization device for generator harmonics, characterized in that: The global optimization device for generator harmonics comprises: A model building module is used to build a global optimization model of generator harmonics; the optimization goal of the global optimization model is to maximize the efficiency of the generator system; the generator system includes a generator and a controller; The optimization solution module is used to optimize and solve the global optimization model to obtain the optimal control parameters; the control parameters include carrier period and dead time.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the global optimization method for generator harmonics according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the global optimization method for generator harmonics according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the global optimization method for generator harmonics according to any one of claims 1 to 6 is implemented.

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