Converter control method, device, medium and program product

Abstract

The invention discloses a converter control method and device, a medium and a program product. The method comprises the following steps: setting a value of a control parameter of a virtual oscillator as an initial control parameter value; wherein the number of the control parameters is multiple; optimizing the control parameters of the plurality of virtual oscillators through a target optimization algorithm to obtain a plurality of target control parameters, so that target function values of the virtual oscillators corresponding to the plurality of target control parameters meet preset optimization requirements; wherein the target function value is used for indicating the stability of a power system where a target converter corresponding to the virtual oscillator is located; and controlling a target converter corresponding to the virtual oscillator according to the target control parameter. According to the scheme, manual parameter setting is not needed, the problems that manual setting is low in efficiency and depends on the experience of workers, and the control parameters need to be manually adjusted are solved, the implementation difficulty of power system control is lowered, and the stability of the power system is improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a converter control method, device, medium and program product. Background Technology

[0002] New power systems often improve the efficiency of renewable energy utilization by increasing its proportion. However, this increased proportion can lead to lower stability in these systems. Current solutions primarily address system stability issues caused by renewable energy through grid construction technology, with grid-forming converters (GFMs) being crucial equipment for ensuring stable grid operation.

[0003] Currently, Virtual Oscillator Control (VOC) is one of the key control methods for grid-connected converters. It can simulate the dynamic characteristics of synchronous generators, providing voltage support and frequency stability for high-proportion renewable energy power systems. The VOC method primarily relies on manual experience to set multiple control parameters to achieve control of the grid-connected converter.

[0004] However, setting multiple control parameters for the VOC method based on manual experience is inefficient, relies on manual verification, and is difficult to implement. Furthermore, the multiple control parameters of the VOC method need to be adjusted according to certain operating environments; in this case, manual adjustment is inefficient and the accuracy of parameter settings is poor, potentially leading to new types of power system instability. Summary of the Invention

[0005] This application provides a converter control method, device, medium, and program product to address the shortcomings of existing technologies where multiple control parameters of the VOC method are set manually based on experience, resulting in low efficiency, reliance on manual verification, and high implementation difficulty. Furthermore, the multiple control parameters of the VOC method require adjustment based on certain operating environments, which leads to low efficiency and poor accuracy of parameter settings, potentially causing instability issues in new power systems.

[0006] In a first aspect, this application provides a converter control method, the method comprising:

[0007] The values ​​of the control parameters of the virtual oscillator are set to the initial control parameter values; wherein, there are multiple control parameters;

[0008] The control parameters of multiple virtual oscillators are optimized using a target optimization algorithm to obtain multiple target control parameters, so that the objective function values ​​of the virtual oscillators corresponding to the multiple target control parameters meet the preset optimization requirements; wherein, the objective function values ​​are used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located;

[0009] The target converter corresponding to the virtual oscillator is controlled according to the target control parameters.

[0010] Secondly, this application provides a converter control device, the device comprising:

[0011] The setting module is used to set the values ​​of the control parameters of the virtual oscillator to the initial control parameter values; wherein, there are multiple control parameters;

[0012] An optimization module is used to optimize the control parameters of multiple virtual oscillators using a target optimization algorithm to obtain multiple target control parameters, so that the objective function values ​​of the virtual oscillators corresponding to the multiple target control parameters reach preset optimization requirements; wherein, the objective function values ​​are used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located;

[0013] The control module is used to control the target converter corresponding to the virtual oscillator according to the target control parameters.

[0014] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the converter control method as described in any embodiment of this application.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the converter control method as described in any embodiment of this application.

[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the converter control method as described in any embodiment of this application.

[0017] The proposed solution sets the control parameters of the virtual oscillator as initial control parameter values. Multiple control parameters are used. A target optimization algorithm is employed to optimize the control parameters of the multiple virtual oscillators, resulting in multiple target control parameters. These target control parameters ensure that the objective function values ​​of the virtual oscillators corresponding to these parameters meet preset optimization requirements. The objective function values ​​indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located. The target converter corresponding to the virtual oscillator is then controlled according to these target control parameters. In other words, the proposed solution optimizes the control parameters of the virtual oscillator using a target optimization algorithm to obtain multiple target control parameters. The target converter corresponding to the virtual oscillator is then controlled according to these multiple target control parameters. This eliminates the need for manual parameter setting, avoiding the inefficiency and reliance on operator experience, as well as the need for manual adjustment of control parameters. This reduces the difficulty of power system control and improves the stability of the power system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the converter control method provided in this application;

[0020] Figure 2 This is another schematic diagram of the converter control method provided in this application;

[0021] Figure 3 This is a schematic diagram of the converter control device provided in this application;

[0022] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] Figure 1This is a flowchart illustrating a converter control method provided in this application. This method can be executed by a converter control device, which can be implemented using software and / or hardware. In a specific embodiment, the device can be applied in an electronic device, such as a computer. The following embodiments will illustrate this using the application of the device in an electronic device as an example. Figure 1 The method may specifically include the following steps:

[0025] Step 101: Set the control parameters of the virtual oscillator to the initial control parameter values.

[0026] There are multiple control parameters.

[0027] Specifically, the control parameters of the target virtual oscillator are the set of parameters required for the virtual oscillator to operate under ideal conditions, including bias voltage, inductance, capacitance, resistance, dead-time function slope, etc. The initial control parameter values ​​are the starting values ​​set for each control parameter before the optimization algorithm begins. Setting the bias voltage parameter to the target virtual oscillator's control parameter value provides a reasonable starting point during the optimization process. This ensures that the optimization algorithm starts from an initial point close to the target value, thus converging to the optimal solution more quickly. The initial value of each control parameter is set to the target value. This ensures that the optimization algorithm starts from an initial point close to the target value.

[0028] Optionally, the control parameters include bias voltage parameters, current gain, inductance, capacitance, the slope of the dead-time function, and resistance.

[0029] Specifically, control parameters include bias voltage parameters, current gain, inductance, capacitance, the slope of the dead-time function, and resistance. These parameters collectively determine the dynamic response, steady-state accuracy, and frequency stability of the virtual oscillator, reflecting its stability. The bias voltage parameter adjusts the initial operating point of the virtual oscillator, ensuring stable operation within a specified voltage range under different load conditions. Optimization goals can include reducing the steady-state voltage error and improving dynamic response speed by optimizing the bias voltage. The current gain adjusts the current feedback characteristics of the system, ensuring it operates at the lowest possible voltage during rated operation, thereby improving system efficiency. Optimization goals can include reducing current error and improving steady-state accuracy by optimizing the current gain. The inductance parameter, along with the capacitance, determines the oscillator's natural frequency, affecting the system's dynamic response. Optimization goals can include ensuring the oscillator's frequency matches the system's angular frequency, achieving synchronous control. The slope of the dead-time function adjusts the system's nonlinear characteristics, preventing over-response under small signals and reducing harmonic distortion. Optimizing the dead-time function ensures appropriate response characteristics under small signals, avoiding unnecessary oscillations. Resistance parameters are used to adjust the damping characteristics of a system and prevent system oscillations. By optimizing the resistance value, it is ensured that the system's damping characteristics are appropriate, avoiding excessive oscillations or slow response.

[0030] Step 102: Optimize the control parameters of multiple virtual oscillators using a target optimization algorithm to obtain multiple target control parameters, so that the objective function values ​​of the virtual oscillators corresponding to the multiple target control parameters meet the preset optimization requirements.

[0031] The objective function value is used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located.

[0032] Specifically, in virtual oscillator control, multiple control parameters (such as bias voltage, inductance, capacitance, resistance, dead-zone function slope, etc.) jointly determine the performance of the virtual oscillator. To achieve optimal performance under different operating conditions, these parameters need to be optimized. The optimization goal is to make the objective function values ​​of the virtual oscillator corresponding to its multiple target control parameters meet preset optimization requirements. Objective optimization algorithms are systematic search methods used to find optimal solutions in complex parameter spaces. Commonly used optimization algorithms include genetic algorithms and particle swarm optimization algorithms. These algorithms dynamically adjust parameters by simulating natural phenomena or physical processes to find the global optimum. The objective function is a pre-defined function used to evaluate the performance of the virtual oscillator under given parameters, indicating the stability of the power system where the target converter is located. Stability can be reflected in steady-state voltage error, dynamic response time, and frequency stability. Steady-state voltage error refers to the deviation between the system's steady-state voltage and the target voltage. Dynamic response time refers to the time required for the system to transition from one state to another. Frequency stability refers to the frequency fluctuations of the system when the load changes. The objective function can also include other performance indicators such as harmonic distortion and system efficiency. The preset optimization requirement is that the objective function meets constraints, such as defining the range of parameter values ​​and other constraints, to ensure the feasibility and physical meaning of the optimization process. The optimization process involves selecting an optimization algorithm to dynamically adjust the parameters. Through multiple iterations, the parameter set is gradually adjusted to minimize the objective function. Each iteration includes calculating the fitness value; that is, for each parameter set, its objective function value is calculated, the parameter set is updated according to the rules of the optimization algorithm, and the parameter set with the smallest fitness value (i.e., the objective function value) is selected as the current optimal solution. The optimization process stops when a termination condition is met. The termination condition can be reaching the maximum number of iterations or the change in fitness value being less than a certain threshold. After the optimization process is complete, the optimal parameter set is output, which makes the objective function value of the virtual oscillator meet the preset optimization requirements. By optimizing multiple control parameters of the virtual oscillator using an objective optimization algorithm, a set of optimal parameters can be found, making the objective function value of the virtual oscillator meet the preset optimization requirements. This method not only improves the system performance but also enhances its stability and robustness under different operating conditions.

[0033] Optionally, the preset optimization requirements include the following: the voltage range corresponding to the bias voltage parameter of the virtual oscillator is the target voltage range, and / or the voltage corresponding to the current gain of the virtual oscillator reaches the minimum value, and / or the frequency of the virtual oscillator is consistent with the angular frequency of the power system corresponding to the target converter, the slope of the dead zone function of the virtual oscillator and the resistance meet the first preset requirements, and the error value of the virtual oscillator reaches the minimum value.

[0034] Specifically, the optimized bias voltage parameters should ensure that the output voltage of the virtual oscillator remains stable within the target voltage range, which is a pre-set voltage range during system design, such as 220V to 240V. Adjusting the bias voltage parameters ensures stable operation of the virtual oscillator's output voltage within the target voltage range, reducing steady-state voltage error. The optimized current gain should minimize the output voltage of the virtual oscillator during rated operation. Adjusting the current gain reduces system current error, improves steady-state accuracy, and ensures the system operates at the lowest possible voltage during rated operation, thereby improving system efficiency. The optimized frequency of the virtual oscillator should match the angular frequency of the power system corresponding to the target converter. The angular frequency of the power system is the reference frequency. Adjusting the inductor and capacitor ensures that the natural frequency of the virtual oscillator matches the angular frequency of the power system, achieving synchronous control. The slope of the optimized dead-time function should meet a first preset requirement, which is a condition pre-set during system design, such as the slope of the dead-time function being greater than the reciprocal of the resistance. By adjusting the slope and resistance of the dead-zone function, the damping characteristics of the system are ensured to be appropriate, avoiding excessive oscillation or slow response. The error value of the optimized virtual oscillator should be minimized. The error value is an indicator of system performance, such as steady-state voltage error, dynamic response time, and frequency stability. By adjusting all control parameters, the system error value is minimized, thereby improving the overall system performance.

[0035] For example, setting the voltage gain To generate the required output voltage, i.e. ,in This is the rated voltage. Adjust the bias voltage parameters. This ensures the system can operate within a specified voltage range under different load conditions. Adjust the current gain k. i This ensures that the system operates at the lowest possible voltage during rated operation. The inductance L and capacitance C parameters of the oscillator are determined according to Formula 1.

[0036] Formula 1

[0037] in, ω is the angular frequency of the power system.

[0038] Next, the oscillator resistance R and the slope of the dead-time function are optimized according to Formula 2. To satisfy Formula 2.

[0039] Formula 2

[0040] Alternatively, other parameters of VOC can be selected to meet synchronization standards, such as those in Formula 3.

[0041] Formula 3

[0042] in, The impedance of the filter in the power system, This represents the impedance of the oscillator in the power system.

[0043] Finally, all parameters of VOC should be optimized according to Equation 4 to minimize the error value, thereby obtaining a pure sinusoidal modulated signal.

[0044] Formula 4

[0045] in, This is the error value.

[0046] Step 103: Control the target converter corresponding to the virtual oscillator according to the target control parameters.

[0047] Specifically, after completing the optimization process, the optimization algorithm outputs a set of optimal control parameters. These parameters are obtained by minimizing the objective function. The optimal control parameters output by the optimization algorithm are applied to the control system of the virtual oscillator, thereby controlling the target converter. The virtual oscillator control system adjusts its control logic according to these optimal parameters to achieve efficient and precise control of the target converter. The control system dynamically adjusts the control parameters by monitoring the converter's output in real time, ensuring stable operation of the converter under different operating conditions. The target converter adjusts its output voltage, current, and frequency according to the control signal from the virtual oscillator. Controlling the target converter corresponding to the virtual oscillator according to the target control parameters can be achieved using a proportional-integral-derivative (PID) control algorithm. With the optimized control parameters, the target converter can maintain good dynamic response and stability in weak power grids, large disturbances, and multi-machine parallel systems. The target converter adjusts its output voltage, current, and frequency according to the control signal from the virtual oscillator, thereby achieving efficient and precise control.

[0048] The proposed solution sets the control parameters of the virtual oscillator as initial control parameter values. Multiple control parameters are used. A target optimization algorithm is employed to optimize the control parameters of the multiple virtual oscillators, resulting in multiple target control parameters. These target control parameters ensure that the objective function values ​​of the virtual oscillators corresponding to these parameters meet preset optimization requirements. The objective function values ​​indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located. The target converter corresponding to the virtual oscillator is then controlled according to these target control parameters. In other words, the proposed solution optimizes the control parameters of the virtual oscillator using a target optimization algorithm to obtain multiple target control parameters. The target converter corresponding to the virtual oscillator is then controlled according to these multiple target control parameters. This eliminates the need for manual parameter setting, avoiding the inefficiency and reliance on operator experience, as well as the need for manual adjustment of control parameters. This reduces the difficulty of power system control and improves the stability of the power system.

[0049] Figure 2 This is another schematic flowchart of the converter control method provided in this application. In this embodiment... Figure 1 Based on the illustrated embodiments and various optional implementation schemes, the optimization process of control parameters is described in detail when the target optimization algorithm is the Whale Migration Algorithm (WMA). For example... Figure 2 As shown, the method may include the following steps:

[0050] Step 201: Set the control parameters of the virtual oscillator to the initial control parameter values.

[0051] Step 202: Randomly generate the initial whale population location between the lower and upper bounds of the preset search space.

[0052] Each initial population position of whales represents a candidate solution for the control parameters.

[0053] Specifically, initializing the whale population requires pre-defining the lower and upper bounds of the search space. Each control parameter has a preset value range, which defines the lower (L) and upper (U) bounds of the search space. Initial whale positions are randomly generated between the lower and upper bounds of the search space. Each whale position represents a candidate solution for a control parameter. At the beginning of the algorithm, an initial population, or a group of migrating whales, is formed. ,in This represents the population size (number of individuals). Each vector... , (and () represents a candidate solution, used in the whale migration algorithm to solve for a dimension of (). Given a problem, migrating whale pods are randomly generated between the lower and upper bounds of the search space for the given problem, as shown in Equation 5.

[0054] Formula 5

[0055] in, It is the lower bound vector of the search space. It is the upper bound vector of the search space. It generates a dimension of The range of values ​​is within random vectors, This represents the Hadamard product (element-by-element product).

[0056] Step 203: Calculate the fitness value of each whale's initial population location, and sort the multiple whale initial population locations according to the fitness values ​​to obtain the sorting results.

[0057] Specifically, within each migratory whale pod, more experienced individuals, with superior location information and higher objective function values, guide and direct the group, leading other members towards their destination. The WMA algorithm introduces parameters... It represents the number of more experienced whales (i.e., leaders) who have a superior position and objective function value.

[0058] Suppose that at any given moment, the actual location of the entire migrating whale pod is described by a single point in the ocean. Therefore, in this paper, we define... For the present The average of the number of leadership positions, i.e.:

[0059] Formula 6

[0060] Formula 6 can improve the convergence speed of WMA and enhance the algorithm's ability to escape local optima.

[0061] Suppose that all whales in the population are sorted in descending order according to their fitness value or their position, based on Formula 7.

[0062] Formula 7

[0063] in, The individual that performs best will be denoted as [insert name hereafter]. ,and This represents the worst-performing individual. The best-performing individuals are the leaders (whales) listed below.

[0064] All calves typically have a natural tendency to imitate. Therefore, in the WMA model, each less experienced individual (the calves)... Its movement is influenced by the previous neighboring individual. The strong influence, because It is superior in terms of objective function value. .

[0065] This kind of The effect can be expressed in the model as Equation 8.

[0066] Formula 8

[0067] in, Indicates the interval A randomly generated random vector of dimension D. This indicates element-wise multiplication. This vector is added to the motion update formula.

[0068] Optionally, the fitness value for each whale's initial population position can be calculated via step 2031.

[0069] Step 2031: Calculate the fitness value corresponding to each whale position based on the performance index of the virtual oscillator.

[0070] Specifically, in each generation, the fitness value of each whale at its new location is calculated. If the fitness value of the new location is better than the fitness value of the current location, then update the whale's location:

[0071] Formula 9

[0072] At the end of each WMA iteration, the migratory whale pod is sorted from best to worst fitness, and the top... The optimal individual is selected as the leader. The mathematical model of the above formula reflects the cooperative migration behavior of whale pods in WMA, achieving a crucial balance between global and local search efficiency.

[0073] Step 204: Determine the positions of multiple leader whales based on the sorting results, and update the initial population position of whales based on the positions of multiple leader whales to obtain the updated whale positions.

[0074] Specifically, after determining the positions of several top-performing individuals as leader whales based on the ranking results, the current position of the entire migratory whale pod in the ocean is assumed to be the average of the current positions of all experienced whales. .if and The distance between the (optimal individuals) gradually decreases, indicating that the entire whale pod is gradually approaching. In this situation, the inexperienced calves must also begin moving in the same direction, which is determined by vector formula 10.

[0075] Formula 10

[0076] in, Indicates the interval The dimensions generated internally are random vectors, This represents element-wise multiplication. This vector is added to the motion update formula. Ultimately, it is used to represent the... The motion equation for the new position of an inexperienced individual is Equation 11, thereby enabling the updated whale position to be obtained according to Equation 11.

[0077] Formula 11

[0078] Among them, the new location Only when It will only replace the current position when .

[0079] Optionally, determining the positions of multiple leader whales based on the sorting results can be achieved through step 2041.

[0080] Step 2041: Based on the sorting results, specific locations in the preset Earth's gravitational and magnetic fields, and the main migration path direction of whales, determine the locations of multiple leader whales.

[0081] Specifically, within migrating whale pods, more experienced individuals (leaders) are responsible for identifying and selecting the optimal route to their destination. Whale migration is influenced by two key factors. The first is a pre-defined location within the Earth's gravitational and magnetic fields, represented in the model as vectors. This indicates that, secondly, whales migrate almost in a straight line from the starting point. Move to the finish line This is a fact, therefore vectors are used in the model. express.

[0082] In implementation, considering the random movements of whales around the main migration route (e.g., foraging or escaping predators), two random parameter vectors were introduced. and Its goal is to ensure The influence of vectors in the equations of motion is greater than Vectors, thus making the pod as a whole more likely to move toward its destination.

[0083] Finally, according to the following equations of motion, the first... The search path of a leader whale can be represented by Formula 12.

[0084] Formula 12

[0085] in, and For interval A random vector within, with dimension , Represents a position vector. It is a relative direction vector.

[0086] Optionally, updating the initial whale population position based on the positions of multiple leader whales can be achieved through steps 2042 to 2043.

[0087] Step 2042: Calculate the movement direction and stride length of the less experienced whale based on the position of the previous neighboring individual and the objective function value.

[0088] Specifically, in the sorted whale population, each whale has a previous neighbor. The position of the previous neighbor is usually better than the current whale's fitness value, and therefore can be used as a reference. The objective function value is used to evaluate the performance of each whale position. The smaller the fitness value, the better the performance of that position. The average position of the leader is the top N with the smallest fitness value. L The average position of each whale is taken as the average position. This average position improves the algorithm's convergence speed and enhances its ability to escape local optima. The optimal individual's position is the position of the whale with the smallest fitness value. This position is the best solution in the current population and can serve as a reference point for the global search. The movement direction and step size of less experienced whales represent the direction and step size of a single movement towards the optimal solution.

[0089] Step 2043: Update the initial population position of whales based on the positions, movement directions and strides of multiple leader whales to obtain the updated whale positions.

[0090] Specifically, based on the positions, directions of movement, and step lengths of multiple leader whales, the whale's position is moved by the corresponding step length towards the multiple leader whales in each update, updating the initial population position of the whales and obtaining the updated whale position.

[0091] Step 205: Use the updated whale position as the new initial whale population position, return to step 203, and continue until the iteration termination condition is met. Determine the control parameters of the virtual oscillator corresponding to the updated whale position as the target control parameters so that the objective function values ​​of the virtual oscillators corresponding to multiple target control parameters reach the preset optimization requirements.

[0092] Specifically, the algorithm terminates when it reaches the maximum number of iterations or meets other stopping conditions (such as the change in the objective function value being less than a certain threshold). Finally, the position of the optimal whale is returned as the optimal solution.

[0093] Step 206: Control the target converter corresponding to the virtual oscillator according to the target control parameters.

[0094] The proposed scheme uses the updated whale locations as the new initial population locations and repeatedly performs fitness value calculation and sorting steps until the iteration termination condition is met. This allows the whale migration algorithm to progressively optimize the control parameters of the virtual oscillator. Ultimately, the control parameters of the virtual oscillator corresponding to the updated whale locations are determined as the target control parameters, which enable the virtual oscillator to achieve the preset optimization requirements. This process achieves a balance between global and local search, improving the efficiency and robustness of the optimization algorithm.

[0095] Figure 3 This is a schematic diagram of a converter control device provided in this application, which is suitable for executing the converter control method provided in this application. Figure 3 As shown, the device may specifically include:

[0096] Setting module 301 is used to set the values ​​of the control parameters of the virtual oscillator to initial control parameter values; wherein, the number of control parameters is multiple.

[0097] The optimization module 302 is used to optimize the control parameters of multiple virtual oscillators through a target optimization algorithm to obtain multiple target control parameters, so that the target function values ​​of the virtual oscillators corresponding to the multiple target control parameters reach the preset optimization requirements; wherein, the target function values ​​are used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located.

[0098] The control module 303 is used to control the target converter corresponding to the virtual oscillator according to the target control parameters.

[0099] In one embodiment, the control parameters of the setting module 301 include bias voltage parameters, current gain, inductance, capacitance, slope of the dead-time function, and resistance.

[0100] In one embodiment, the preset optimization requirements of the optimization module 302 include the voltage range corresponding to the bias voltage parameter of the virtual oscillator being the target voltage range, and / or the voltage corresponding to the current gain of the virtual oscillator reaching the minimum value, and / or the frequency of the virtual oscillator being consistent with the angular frequency of the power system corresponding to the target converter, the slope of the dead zone function of the virtual oscillator and the resistance satisfying the first preset requirement, and the error value of the virtual oscillator reaching the minimum value.

[0101] In one embodiment, the target optimization algorithm of the optimization module 302 is a whale migration algorithm. The optimization module 302 is specifically used to: randomly generate initial whale population positions between the lower and upper bounds of a preset search space; wherein each initial whale population position represents a candidate solution of the control parameters; calculate the fitness value of each initial whale population position, and sort multiple initial whale population positions according to the fitness value to obtain a sorting result; determine the positions of multiple leader whales according to the sorting result, and update the initial whale population positions according to the positions of the multiple leader whales to obtain updated whale positions; use the updated whale positions as new initial whale population positions, and return to execute the steps of "calculating the fitness value of each initial whale population position and sorting multiple initial whale population positions according to the fitness value to obtain a sorting result" until the iteration termination condition is met, and determine the control parameters of the virtual oscillator corresponding to the updated whale positions as the target control parameters.

[0102] In one embodiment, the optimization module 302 is specifically used to calculate the fitness value of each initial population position of the whale based on the performance index of the virtual oscillator.

[0103] In one embodiment, the optimization module 302, in updating the initial population position of whales based on the positions of the plurality of leader whales to obtain the updated whale position, is specifically configured to: calculate the movement direction and step size of less experienced whales based on the position of the previous neighboring individual and the objective function value; update the initial population position of whales based on the positions of the plurality of leader whales and the movement direction and step size to obtain the updated whale position.

[0104] In one embodiment, the optimization module 302, in determining the positions of the multiple leader whales based on the sorting results, is specifically used to: determine the positions of the multiple leader whales based on the sorting results, a specific position in a preset Earth's gravitational and magnetic fields, and the main migration path direction of the whales.

[0105] The apparatus of this application sets the control parameters of a virtual oscillator to initial control parameter values; wherein there are multiple control parameters; the control parameters of the multiple virtual oscillators are optimized using a target optimization algorithm to obtain multiple target control parameters, so that the objective function value of the virtual oscillator corresponding to the multiple target control parameters reaches a preset optimization requirement; wherein the objective function value is used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located; and the target converter corresponding to the virtual oscillator is controlled according to the target control parameters. In other words, the scheme of this application optimizes the control parameters of the virtual oscillator using a target optimization algorithm to obtain multiple target control parameters, and controls the target converter corresponding to the virtual oscillator according to the multiple target control parameters, thereby eliminating the need for manual parameter setting. This avoids the problems of low efficiency and reliance on operator experience in manual parameter setting, as well as the need for manual adjustment of control parameters, reducing the difficulty of power system control and improving the stability of the power system.

[0106] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the converter control method provided in any of the above embodiments.

[0107] This application also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the converter control method provided in any of the above embodiments.

[0108] The following is for reference. Figure 4 It shows a schematic diagram of the structure of an electronic device 400 suitable for implementing the present application. Figure 4 The electronic device shown is merely an example and should not impose any limitations on the functionality and scope of this application.

[0109] like Figure 4 As shown, the electronic device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0110] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0111] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined above in the system of this application.

[0112] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The modules and / or units described in this application can be implemented in software or hardware. The described modules and / or units can also be located in a processor; for example, a processor can be described as including a setup module, an optimization module, and a control module. The names of these modules do not necessarily limit the module itself.

[0115] In another aspect, this application also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to perform the following operations:

[0116] The control parameters of the virtual oscillator are set to initial control parameter values; there are multiple control parameters; the control parameters of the multiple virtual oscillators are optimized using a target optimization algorithm to obtain multiple target control parameters, so that the objective function values ​​of the virtual oscillators corresponding to the multiple target control parameters reach the preset optimization requirements; the objective function values ​​are used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located; the target converter corresponding to the virtual oscillator is controlled according to the target control parameters.

[0117] According to the technical solution of this application, the control parameters of the virtual oscillator are set as initial control parameter values; wherein, there are multiple control parameters; the control parameters of the multiple virtual oscillators are optimized through a target optimization algorithm to obtain multiple target control parameters, so that the objective function value of the virtual oscillator corresponding to the multiple target control parameters reaches the preset optimization requirements; wherein, the objective function value is used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located; the target converter corresponding to the virtual oscillator is controlled according to the target control parameters. That is, the solution of this application optimizes the control parameters of the virtual oscillator through a target optimization algorithm to obtain multiple target control parameters, and controls the target converter corresponding to the virtual oscillator according to the multiple target control parameters, thereby eliminating the need for manual parameter setting, thus avoiding the problems of low efficiency and reliance on staff experience in manual setting, and the need for manual adjustment of control parameters, reducing the difficulty of power system control and improving the stability of the power system.

[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the converter control method provided in any embodiment of this application.

[0119] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A converter control method, characterized in that, The method includes: The values ​​of the control parameters of the virtual oscillator are set to the initial control parameter values; wherein, there are multiple control parameters; The control parameters of multiple virtual oscillators are optimized using a target optimization algorithm to obtain multiple target control parameters, so that the objective function values ​​of the virtual oscillators corresponding to the multiple target control parameters meet the preset optimization requirements; wherein, the objective function values ​​are used to indicate the stability of the power system where the target converter corresponding to the virtual oscillator is located; The target converter corresponding to the virtual oscillator is controlled according to the target control parameters.

2. The method according to claim 1, characterized in that, The control parameters include bias voltage parameters, current gain, inductance, capacitance, the slope of the dead-time function, and resistance.

3. The method according to claim 2, characterized in that, The preset optimization requirements include that the voltage range corresponding to the bias voltage parameters of the virtual oscillator is the target voltage range, and / or, The voltage corresponding to the current gain of the virtual oscillator reaches its minimum value, and / or, The frequency of the virtual oscillator is consistent with the angular frequency of the power system corresponding to the target converter, the slope of the dead zone function of the virtual oscillator and the resistance satisfy the first preset requirement, and the error value of the virtual oscillator reaches the minimum value.

4. The method according to any one of claims 1 to 3, characterized in that, The target optimization algorithm is the whale migration algorithm. The optimization of the control parameters of multiple virtual oscillators using this algorithm yields multiple target control parameters, including: Initial whale population positions are randomly generated between the lower and upper bounds of a preset search space; wherein each initial whale population position represents a candidate solution for the control parameters. Calculate the fitness value of each initial whale population location, and sort the multiple initial whale population locations according to the fitness value to obtain the sorting result; The positions of multiple leader whales are determined based on the sorting results, and the initial population position of the whales is updated based on the positions of the multiple leader whales to obtain the updated whale positions. The updated whale position is used as the new initial whale population position. The process of "calculating the fitness value of each initial whale population position and sorting the multiple initial whale population positions according to the fitness value to obtain the sorting result" is repeated until the iteration termination condition is met. The control parameters of the virtual oscillator corresponding to the updated whale position are then determined as the target control parameters.

5. The method according to claim 4, characterized in that, The calculation of the fitness value for each initial population location of the whale includes: The fitness value corresponding to each whale position is calculated based on the performance index of the virtual oscillator.

6. The method according to claim 5, characterized in that, The step of updating the initial population position of whales based on the positions of the multiple leader whales to obtain the updated whale positions includes: Based on the position of the previous neighboring individual and the objective function value, calculate the movement direction and stride length of the less experienced whale; Based on the positions of the multiple leader whales and their movement direction and step length, the initial population position of the whales is updated to obtain the updated whale position.

7. The method according to claim 6, characterized in that, Determining the positions of multiple leader whales based on the sorting results includes: Based on the sorting results, specific locations within the preset Earth's gravitational and magnetic fields, and the main migration path of the whales, the positions of the multiple leader whales are determined.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the converter control method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the converter control method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the converter control method as described in any one of claims 1 to 7.

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