Wind power network construction type VSG control adaptive parameter optimization method

Abstract

The invention provides a wind power networking type VSG control adaptive parameter optimization method, and the method comprises the steps: firstly, determining a constraint relation between a virtual inertia and a damping coefficient based on the system performance of a wind power networking type VSG; based on the constraint relation between the virtual inertia and the damping coefficient, an adaptive parameter optimization module is established, and based on the adaptive parameter optimization module, a particle swarm optimization algorithm of fuzzy logic is introduced, and values of the virtual inertia and the damping coefficient are iteratively optimized by dynamically adjusting an inertia weight and a learning factor in the particle swarm optimization algorithm. And finally outputting the optimal combination of the virtual inertia and the damping coefficient. The virtual inertia and the damping coefficient can be flexibly adjusted according to the dynamic change of the operation condition of the power grid, and the stability and the electric energy quality of the power grid are improved; meanwhile, adjustment of the virtual inertia and the damping coefficient considers the constraint relation between the virtual inertia and the damping, the virtual inertia and the damping coefficient are effective at the same time, and the condition that the parameter optimization effect is limited is avoided.

Description

Technical Field

[0001] The present application relates to the field of power system control technology, and in particular to a method for optimizing adaptive parameters of wind power grid-type VSG control. Background Art

[0002] With the large-scale integration of renewable energy into the grid, power system stability faces new challenges. Traditional power systems rely on the inertia of synchronous generators to maintain frequency stability. However, renewable energy sources such as wind power are connected to the grid through power electronic converters, which lack inherent inertia and damping properties. To address this, virtual synchronous generator technology has been proposed. By simulating the operating characteristics of synchronous generators, it provides virtual inertia and damping support for the grid. However, in traditional grid-based virtual synchronous control, the virtual inertia and damping coefficients are usually set at fixed values ​​and cannot be flexibly adjusted according to the dynamic changes in the grid operating conditions, thus affecting the stability and power quality; and the traditional parameter optimization method does not take into account the coupling relationship between virtual inertia and damping, and optimizes the virtual inertia and damping separately, resulting in limited parameter optimization effect. Summary of the Invention

[0003] The purpose of this application is to provide a method for optimizing adaptive parameters of wind power grid-type VSG control in order to solve the above problems.

[0004] The present application provides a method for optimizing adaptive parameters of wind power grid-type VSG control, comprising the following steps: S1: Based on the system performance of the wind power grid VSG, determine the constraint relationship between the virtual inertia and the damping coefficient; S2: establishing an adaptive parameter optimization module based on the constraint relationship between the virtual inertia and the damping coefficient; S3: Based on the adaptive parameter optimization module, a fuzzy logic particle swarm optimization algorithm is introduced. By dynamically adjusting the inertia weight and learning factor in the particle swarm algorithm, the values ​​of the virtual inertia and damping coefficient are iteratively optimized, and finally the optimal combination of virtual inertia and damping coefficient is output.

[0005] According to the technical solution provided by this application, step S3 includes the following steps: S31: Initialize the particle swarm and randomly generate the current position of the particle With current speed ; S32: Substitute the current position of each particle into the adaptive parameter optimization module to calculate the current fitness value; S33: Based on the current fitness value and the historical optimal fitness value of each particle, update the individual optimal position and the global optimal position; S34: According to the current fitness value of the particle and the average fitness value of the population Dynamically adjust the inertia weight ; S35: Dynamically adjust the learning factor according to the distance between the particle and the individual optimal position and the global optimal position; the learning factor includes the cognitive learning factor and social learning factors ; S36: Update the particle's velocity based on the adjusted inertia weight and learning factor using formula 1 , update the particle position using formula 2; Formula 1; Formula 2; in: For particles In the The first iteration The speed of dimension, is the inertia weight, is the cognitive learning factor, is the social learning factor, and is a random number between 0 and 1, For particles The individual optimal position of dimensional coordinates, The first position of the global optimal dimensional coordinates; k is the number of iterations; S37: According to the updated particle position , determine the current virtual inertia and current damping coefficient; S38: Determine whether the stopping condition is met. If so, output the current virtual inertia and the current damping coefficient as the globally optimal virtual inertia and the damping coefficient. Otherwise, return to step S32 to continue iterating. The stopping condition includes: reaching a preset maximum number of iterations or the change in the global optimal fitness value in multiple consecutive iterations is less than a first threshold.

[0006] According to the technical solution provided by this application, step S34 includes the following steps: S341: If ,but , to enhance global search capabilities; S342: If ,but , to balance global and local search capabilities; S343: If ,but , to enhance local search capabilities; in, is the inertia weight, is the maximum value of the inertia weight, is the median value of the inertia weight, is the minimum value of the inertia weight.

[0007] According to the technical solution provided by this application, step S35 includes the following steps: S351: Calculate the distance between the particle and the individual optimal position , and the distance between the particle and the global optimal position ; S352: Based on the distance between all particles in the population and the individual's optimal position , calculate the average distance between the particles in the population and the individual optimal position ; S353: Based on the distance between all particles in the population and the global optimal position , calculate the average distance between the particles in the population and the global optimal position ; S354: When When , the cognitive learning factor is updated to , the social learning factor is updated to Otherwise, cognitive learning factor and social learning factors Keep the current value; in: is the adjusted cognitive learning factor, is the adjusted social learning factor, and α and β are the preset thresholds.

[0008] According to the technical solution provided by this application, step S33 includes the following steps: S331: Determine whether the current fitness value of the particle is greater than its historical optimal fitness value; S332: If the current fitness value is greater than the historical optimal fitness value, the current position of the particle is updated to the individual optimal position; S333: Traverse the individual optimal positions of all particles, screen out the position with the best fitness value, and determine it as the global optimal position.

[0009] According to the technical solution provided by this application, the parameter value range corresponding to the position of the particle is: the virtual inertia satisfies , the damping coefficient satisfies ; in: is the minimum permissible value of virtual inertia, is the maximum allowable value of virtual inertia, is the minimum allowable value of the damping coefficient, is the maximum allowable value of the self-simulated coefficient.

[0010] According to the technical solution provided in this application, step S2 includes the following steps: S21: Based on the system performance of wind power grid VSG, the fitness function is defined as: ; in: is the weight coefficient; is the frequency deviation, is the frequency change rate, is the power deviation; S22: Introducing a constraint violation function to characterize the constraint relationship between the virtual inertia and the damping parameter, wherein the constraint violation function is: ; in: is the optimal damping ratio, is the filter inductor on the VSG grid side, is the phase voltage amplitude at the inverter output side, is the phase voltage amplitude at the grid connection point; S23: The penalty function method is used to combine the constraint violation function with the fitness function to construct the total fitness function: ; in, is the penalty coefficient.

[0011] According to the technical solution provided by this application, the minimum allowable value of virtual inertia is calculated using Formula 3 and Formula 4 respectively. and the maximum allowable value of virtual inertia ; Formula 3; Formula 4; in: is the lower limit of the system inertia time constant, is the upper limit of the system inertia time constant, is the system rated apparent power, is the synchronous speed.

[0012] According to the technical solution provided by this application, the minimum allowable value of the damping coefficient is calculated using Formula 5 and Formula 6 respectively: and the maximum allowable value of the damping coefficient ; Formula 5; Formula 6; in: is the minimum damping ratio, is the minimum undamped natural oscillation angular frequency, is the maximum undamped natural oscillation angular frequency, is the maximum allowable value of virtual inertia.

[0013] Compared with the prior art, the present invention has the following advantages: The present application provides a method for adaptive parameter optimization of wind power grid-type VSG control. First, based on the system performance of the wind power grid-type VSG, the constraint relationship between the virtual inertia and the damping coefficient is determined; then, based on the constraint relationship between the virtual inertia and the damping coefficient, an adaptive parameter optimization module is established, and based on the adaptive parameter optimization module, a fuzzy logic particle swarm optimization algorithm is introduced. By dynamically adjusting the inertia weight and learning factor in the particle swarm algorithm, the values ​​of the virtual inertia and the damping coefficient are iteratively optimized, and finally the optimal combination of virtual inertia and damping coefficient is output; it can be seen that the present application designs an adaptive parameter optimization module based on the constraint relationship between the virtual inertia and the damping coefficient, so as to flexibly adjust according to the dynamic changes of the power grid operating conditions, thereby improving the stability and power quality of the power grid; at the same time, the fuzzy logic particle swarm optimization algorithm is introduced to seek the optimal virtual inertia and damping parameters, thereby improving the effect of adaptive optimization of model parameters and avoiding limitation of parameter optimization effect.

[0014] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution in this embodiment, the following is a brief introduction to the drawings required for the description of the embodiment. 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 any creative work.

[0016] Figure 1 A flowchart of a method for optimizing adaptive parameters of a wind power grid-type VSG control provided in an embodiment of the present application; Figure 2Functional block diagram of the wind power grid-type VSG model provided in the embodiment of the present application; Figure 3 This is a control principle diagram of the grid-side converter provided in an embodiment of the present application.

[0017] In the figure: 1. Permanent magnet synchronous generator; 2. AC-DC converter; 3. DC-AC converter; 4. abc-dq coordinate transformation module; 5. SVPWM generation module; 6. SPWM generation module; 7. PI control module; 8. dq-abc coordinate transformation module, 9. VSG control module. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. The description in this section is only exemplary and explanatory and should not have any limiting effect on the scope of protection of the present application. Specifically, the embodiments described 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 making creative work should fall within the scope of protection of the present application.

[0019] It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0020] In order to make the technical solution of the present application clearer and easier to understand, a method for optimizing adaptive parameters of wind power grid-type VSG control provided in an embodiment of the present application is introduced below.

[0021] It should be noted that this method can be executed by a controller. For ease of understanding, the method is introduced below from the perspective of the controller.

[0022] like Figure 1 As shown, this embodiment provides a method for optimizing adaptive parameters of wind power grid-type VSG control, which includes the following steps: S1: Based on the system performance of the wind power grid VSG, determine the constraint relationship between the virtual inertia and the damping coefficient; Specifically, refer to Figure 2 and Figure 3As shown, the wind power grid-type VSG (Virtual Synchronous Generator) consists of a main circuit and a control circuit, wherein the main circuit includes a permanent magnet synchronous generator 1, an AC-DC converter 2 (AC-DC converter), a DC-AC converter 3 (DC-AC converter), a filter composed of inductors and capacitors, and a grid-side voltage source; the control circuit is divided into machine-side control and grid-side control, the machine-side control includes an abc-dq coordinate transformation module 4 (coordinate transformation from a three-phase stationary coordinate system to a two-phase rotating coordinate system), a PI control module 7, a speed control module, and an SVPWM generation module 5 for generating SVPWM waves (space vector pulse width modulation waves); the grid-side control includes a dq-abc coordinate transformation module (coordinate transformation from a two-phase rotating coordinate system to a three-phase stationary coordinate system), a power calculation module, a VSG control module 9 with adaptive parameter adjustment, and an SPWM generation module 6 for generating SPWM waves (sinusoidal pulse width modulation waves); The equation of motion for the VSG system is as follows:

[0023] in, is the moment of inertia, is the damping coefficient, is the electromagnetic torque, is the mechanical torque, is the angular velocity, is the rated angular velocity; Active power regulation satisfies the formula ,in: is the reference value of active power, is the initial active power, is the active power droop coefficient; Reactive power regulation satisfies the formula: ,in: is the virtual internal potential amplitude, is the initial virtual internal potential amplitude, is the reactive power regulation coefficient, is the reactive power reference value, is the actual reactive power; In the voltage and current double closed loop, the current loop generally uses a PI controller. The output of the PI controller for the current loop is for: ;in is the proportional coefficient of the current loop PI controller, is the integral coefficient of the current loop PI controller, is the current reference value, is the actual current, is the Laplace operator; The voltage loop also uses a PI controller, and the voltage loop PI controller output for: ;in is the proportional coefficient of the voltage loop PI controller, is the integral coefficient of the voltage loop PI controller, is the voltage reference value, is the actual voltage; Specifically, the values ​​of virtual inertia and damping parameters directly affect the stability and dynamic response of the system. By determining their association with system performance indicators, it is possible to ensure that the optimized parameter combination can provide good frequency support and damping effects under different operating conditions, avoiding system oscillation or instability. At the same time, system performance usually involves multiple objectives (such as minimizing overshoot and shortening adjustment time), and the impact of virtual inertia and damping parameters on these objectives may conflict (for example, increasing the damping parameter can reduce overshoot, but may increase adjustment time). By determining their association with system performance indicators, a balance between multiple objectives can be achieved during the optimization process. Therefore, in this application, it is also necessary to determine the relationship between the virtual inertia and damping parameters and the system performance indicators, as follows: The second-order system transfer function of VSG can be expressed as: ;in, is the undamped natural frequency, is the damping ratio, is a coefficient related to the power-frequency droop characteristic; is a complex frequency domain variable; For a second-order system, the overshoot The calculation formula is: ; From this formula we can see that the damping ratio It has a direct impact on the overshoot. When it increases, the overshoot decreases; It can be seen that increasing the damping coefficient , will make Increase, thereby reducing overshoot, the system can recover to a stable state faster after being disturbed, and reduce oscillation.

[0024] For a second-order system, the adjustment time The approximate calculation formula (within a 2% or 5% error band) is: When the error band is 2%, ; When the error band is 5%, ; Depend on and It can be seen that: Increase virtual inertia , will make Reduce the adjustment time Increase; increase the damping coefficient , will make Increase, so that the adjustment time reduce; In summary, virtual inertia and damping coefficient The effects on overshoot and adjustment time are as follows: Increasing the damping coefficient It will reduce the overshoot and the adjustment time (in In the case of ), increase the virtual inertia This will increase the adjustment time.

[0025] Specifically, in this application, the constraint relationship between the virtual inertia and the damping parameters is established through the power balance equation and small signal linearization analysis, as follows: The frequency dynamic equation of the virtual synchronous generator (VSG) is: ; In steady state, , , in a three-phase system, the electromagnetic torque is ,in p is the polar logarithm, and is the component of the stator flux on the shaft, and is the component of the stator current on the shaft; Assume that the output voltage amplitude of the virtual synchronous machine is , the grid voltage amplitude is , considering the filter inductance When the voltage drop is ; According to the power balance relationship (in: and are the inverter output current and the grid current, is the power factor angle), we get (in and are the inverter output power and the grid power respectively); Combined with the above equations, by performing small signal analysis on the power set up , , , ,in is the steady-state value, is a small signal disturbance; Will Expand to get:

[0026] Linearize the dynamic equation around the steady-state operating point, ignoring the second-order after , Similarly, we get ; The power difference equation is: ; Subsequently, decompose the variables of the electromagnetic torque equation; Let , , , , where is the steady-state value, is the small-signal perturbation; Ignoring the second-order small quantities and , the linearization gives:

[0027] Using (when b << a), for Approximately make

[0028] Substitute into the expression and ignore the second-order small quantities, we get:

[0029] Substitute the linearized power equation and electromagnetic torque equation into the frequency dynamic equation, and finally obtain the constraint relationship between the virtual inertia and damping parameters as: (where is the optimal damping ratio, is the filter inductor on the grid side of the virtual synchronous machine, is the amplitude of the phase voltage at the inverter output side, is the amplitude of the phase voltage at the grid connection point).

[0030] S2: Based on the constraint relationship between the virtual inertia and damping coefficient, establish an adaptive parameter optimization module; In this embodiment, optionally, step S2 includes the following steps: S21: Based on the system performance of the grid-forming VSG, define the fitness function as: ; Where: is the weight coefficient; is the frequency deviation, is the frequency change rate, is the power deviation; S22: Introducing a constraint violation function to characterize the constraint relationship between the virtual inertia and the damping parameter, wherein the constraint violation function is: ; in: is the optimal damping ratio, is the filter inductor on the VSG grid side, is the phase voltage amplitude at the inverter output side, is the phase voltage amplitude at the grid connection point; S23: The penalty function method is used to combine the constraint violation function with the fitness function to construct the total fitness function: ; in, is the penalty coefficient.

[0031] Specifically, the total fitness function is the adaptive parameter optimization module; Specifically, when When the deviation occurs (i.e. the relationship between virtual inertia and damping is not satisfied), the penalty term It will increase the value of the total fitness function and encourage particles to move in the direction that satisfies the constraints during the search process; In this embodiment, the total fitness function is used to retain the optimization objectives of the system performance indicators (frequency deviation, frequency change rate, power deviation), while also forcing the constraint relationship between the virtual inertia and the damping coefficient. This ensures that the parameter optimization results meet both the system stability requirements and the inherent constraints of the virtual inertia and the damping coefficient, thereby improving the reliability and effectiveness of the adaptive parameter optimization module.

[0032] S3: Based on the adaptive parameter optimization module, a fuzzy logic particle swarm optimization algorithm is introduced. By dynamically adjusting the inertia weight and learning factor in the particle swarm algorithm, the values ​​of the virtual inertia and damping coefficient are iteratively optimized, and finally the optimal combination of virtual inertia and damping coefficient is output.

[0033] In this embodiment, optionally, step S3 includes the following steps: S31: Initialize the particle swarm and randomly generate the current position of the particle With current speed ; Specifically, the position of each particle represents a set of candidate solutions for fictitious inertia and damping coefficients, is the value of virtual inertia, To determine the value of the damping parameter, the initial current position is randomly generated within a preset range to ensure that the search space covers all possible parameter combinations and avoid the optimization process falling into a local optimum; The speed of each particle Indicates the adjustment direction and amplitude of virtual inertia and damping parameters; is the adjustment speed of the virtual inertia, The adjustment speed of the damping parameters; the initial current speed is randomly generated within a reasonable range to ensure that the particles have sufficient exploration ability in the search space, which serves as the basis for subsequent iterative optimization through dynamic adjustment and improves the convergence speed of the algorithm; at the same time, this application ensures that the initial generated particle position meets the physical constraints of the system by setting a reasonable range for virtual inertia and damping parameters; Specifically, in this embodiment, the parameter value range corresponding to the position of the particle is: the virtual inertia satisfies , the damping coefficient satisfies , so that the particles in the particle swarm are generated within a reasonable range; in: is the minimum permissible value of virtual inertia, is the maximum allowable value of virtual inertia, is the minimum allowable value of the damping coefficient, is the maximum allowable value of the damping coefficient; Specifically, in the power system, the virtual inertia is related to the inertia time constant H of the system, and the relationship is: (in is the virtual inertia, is the inertia time constant, is the system rated apparent power, is the synchronous speed); Lower limit of the system inertia time constant The maximum frequency change rate allowed by the system can be and the system's rated power change rate To determine; the specific formula is: ; Therefore, the minimum allowable value of virtual inertia is ; Upper limit of the system inertia time constant It is usually determined by the inertia limit of the actual rotating equipment (such as generator) in the system, or by the maximum inertia reserve requirement of the system design; if the inertia time constant corresponding to the largest inertia device in the system is ,but ;The maximum allowable value of the corresponding virtual inertia is: ; use and Conclusion , when the virtual inertia is known, the minimum allowable value of the damping coefficient is ; Maximum allowable value of the damping coefficient The parameters of the damping device are usually determined by the maximum value that the system can withstand, or by the requirements of the damping state that the system will not appear; For the critical damping case , assuming the maximum allowable value of the virtual inertia and the maximum undamped natural oscillation angular frequency , then the maximum allowable value of the damping coefficient for (in: is the undamped natural frequency, is the damping ratio, is the minimum damping ratio, is the minimum undamped natural oscillation angular frequency, is the maximum undamped natural oscillation angular frequency, is the maximum allowable value of virtual inertia).

[0034] S32: Substitute the current position of each particle into the adaptive parameter optimization module to calculate the current fitness value; (i.e., substitute the current position of each particle into the total fitness function); S33: Based on the current fitness value and the historical optimal fitness value of each particle, update the individual optimal position and the global optimal position; Specifically, in this embodiment, preferably, step S33 includes the following steps: S331: Determine whether the current fitness value of the particle is greater than its historical optimal fitness value; S332: If the current fitness value is greater than the historical optimal fitness value, the current position of the particle is updated to the individual optimal position; S333: Traverse the individual optimal positions of all particles, screen out the position with the best fitness value, and determine it as the global optimal position.

[0035] Specifically, if the current fitness value of a particle is greater than its historical optimal fitness value, the current position of the particle is updated to the individual optimal position. If the current fitness value is less than or equal to the historical optimal fitness value, the historical position is maintained as the individual optimal position. ; Then, traverse the individual optimal positions of all particles , filter out the position with the best fitness value and determine it as the global optimal position ; Specifically, the present application updates the individual optimal position so that each particle can find a better solution in the local range; and by updating the global optimal position, the entire algorithm can find the optimal solution in the entire search space; it can be seen that the present application continuously updates the individual optimal position and the global optimal position, and the algorithm can adapt to complex and changeable optimization problems and improve optimization efficiency and accuracy.

[0036] S34: According to the current fitness value of the particle and the average fitness value of the population Dynamically adjust the inertia weight ; Specifically, in the iterative optimization process of the particle swarm algorithm, the inertia weight is an important parameter that controls the search behavior of particles. By dynamically adjusting the inertia weight according to the relationship between the current fitness value of the particle and the average fitness value of the population, the global search ability and local search ability of the algorithm can be effectively balanced, thereby improving the optimization efficiency and accuracy.

[0037] In this embodiment, optionally, step S34 includes the following steps: S341: If ,but , to enhance global search capabilities; S342: If ,but , to balance global and local search capabilities; S343: If ,but , to enhance local search capabilities; in, is the inertia weight, is the maximum value of the inertia weight, is the median value of the inertia weight, is the minimum value of the inertia weight.

[0038] S35: Dynamically adjust the learning factor according to the distance between the particle and the individual optimal position and the global optimal position; the learning factor includes the cognitive learning factor and social learning factors ; Specifically, in the iterative optimization process of the particle swarm algorithm, the learning factor is an important parameter that controls the movement of particles to the individual optimal position and the global optimal position. By dynamically adjusting the learning factor according to the distance between the particles and the individual optimal position and the global optimal position, the local search and global search capabilities of the particles can be effectively balanced, thereby improving the optimization efficiency and accuracy.

[0039] In this embodiment, optionally, step S35 includes the following steps: S351: Calculate the distance between the particle and the individual optimal position , and the distance between the particle and the global optimal position ; S352: Based on the distance between all particles in the population and the optimal position of the individual , calculate the average distance between the particles in the population and the individual optimal position ; S353: Based on the distance between all particles in the population and the global optimal position , calculate the average distance between the particles in the population and the global optimal position ; S354: When When , the cognitive learning factor is updated to , the social learning factor is updated to Otherwise, cognitive learning factor and social learning factors Keep the current value; in: is the adjusted cognitive learning factor, is the adjusted social learning factor, and α and β are the preset thresholds.

[0040] Specifically, in this embodiment, the distance between the particle and the individual optimal position is , and the distance between the particle and the global optimal position (in is the current position of the particle, is the optimal position of an individual, is the global optimal position), if Very small and If it is very large, it means that the particle is close to the individual optimum but far from the global optimum. In this case, the social learning factor can be appropriately increased. , reducing cognitive learning factors , to guide particles toward the global optimum.

[0041] S36: Update the particle's velocity based on the adjusted inertia weight and learning factor using formula 1 , update the particle position using formula 2; Formula 1; Formula 2; in: For particles In the The first iteration The speed of dimension, is the inertia weight, is the cognitive learning factor, is the social learning factor, and is a random number between 0 and 1, For particles The individual optimal position of dimensional coordinates, The first position of the global optimal dimensional coordinates; kis the number of iterations; S37: According to the updated particle position , determine the current virtual inertia and current damping coefficient; Specifically, the adjustment of virtual inertia is achieved by updating the position of the particle; assuming that the particle The position at the iteration is expressed as ,and A certain dimension of corresponds to the value of virtual inertia, and the update formula of virtual inertia can be expressed as: , Among them are particles In the The corresponding virtual inertia value at the iteration; Similar to the virtual inertia, the damping coefficient update formula can be expressed as: ,in It is a particle In the The damping value corresponding to the iteration; In each iteration, the particle velocity update formula is ; The position update formulas are: .

[0042] S38: Determine whether the stopping condition is met. If so, output the current virtual inertia and the current damping coefficient as the globally optimal virtual inertia and the damping coefficient. Otherwise, return to step S32 to continue iterating. The stopping condition includes: reaching a preset maximum number of iterations or the change in the global optimal fitness value in multiple consecutive iterations is less than a first threshold.

[0043] Specifically, in this embodiment, the value range of the first threshold is 0.1%~1%.

[0044] Working Principle: This application designs an adaptive parameter optimization module based on the constraint relationship between virtual inertia and damping coefficient, so as to make flexible adjustments according to the dynamic changes of grid operating conditions, thereby improving grid stability and power quality. At the same time, a fuzzy logic particle swarm optimization algorithm is introduced to seek the optimal virtual inertia and damping parameters, thereby improving the effect of model parameter adaptive optimization and avoiding limitations on parameter optimization effects.

[0045] 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. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of this application, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for optimizing adaptive parameters of wind power grid-type VSG control, characterized in that: The steps include: S1: Based on the system performance of the wind power grid VSG, determine the constraint relationship between the virtual inertia and the damping coefficient; S2: establishing an adaptive parameter optimization module based on the constraint relationship between the virtual inertia and the damping coefficient; S3: Based on the adaptive parameter optimization module, a fuzzy logic particle swarm optimization algorithm is introduced. By dynamically adjusting the inertia weight and learning factor in the particle swarm algorithm, the values ​​of the virtual inertia and damping coefficient are iteratively optimized, and finally the optimal combination of virtual inertia and damping coefficient is output.

2. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 1, characterized in that: Step S3 includes the following steps: S31: Initialize the particle swarm and randomly generate the current position of the particle With current speed ; S32: Substitute the current position of each particle into the adaptive parameter optimization module to calculate the current fitness value; S33: Based on the current fitness value and the historical optimal fitness value of each particle, update the individual optimal position and the global optimal position; S34: According to the current fitness value of the particle and the average fitness value of the population Dynamically adjust the inertia weight ; S35: Dynamically adjust the learning factor based on the distance between the particle and the individual optimal position and the global optimal position; The learning factors include cognitive learning factors and social learning factors ; S36: Update the particle's velocity based on the adjusted inertia weight and learning factor using formula 1 , update the particle position using formula 2; Formula 1; Formula 2; in: For particles In the The first iteration The speed of dimension, is the inertia weight, is the cognitive learning factor, is the social learning factor, and is a random number between 0 and 1, For particles The individual optimal position of dimensional coordinates, The first position of the global optimal dimensional coordinates; k is the number of iterations; S37: According to the updated particle position , determine the current virtual inertia and current damping coefficient; S38: Determine whether the stopping condition is met. If so, output the current virtual inertia and the current damping coefficient as the globally optimal virtual inertia and the damping coefficient. Otherwise, return to step S32 to continue iterating. The stopping condition includes: reaching a preset maximum number of iterations or the change in the global optimal fitness value in multiple consecutive iterations is less than a first threshold.

3. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 2, characterized in that: Step S34 includes the following steps: S341: If ,but , to enhance global search capabilities; S342: If ,but , to balance global and local search capabilities; S343: If ,but , to enhance local search capabilities; in, is the inertia weight, is the maximum value of the inertia weight, is the median value of the inertia weight, is the minimum value of the inertia weight.

4. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 2, characterized in that: Step S35 includes the following steps: S351: Calculate the distance between the particle and the individual optimal position , and the distance between the particle and the global optimal position ; S352: Based on the distance between all particles in the population and the individual's optimal position , calculate the average distance between the particles in the population and the individual optimal position ; S353: Based on the distance between all particles in the population and the global optimal position , calculate the average distance between the particles in the population and the global optimal position ; S354: When When , the cognitive learning factor is updated to , the social learning factor is updated to ; Otherwise, the cognitive learning factor and social learning factors Keep the current value; in: is the adjusted cognitive learning factor, is the adjusted social learning factor, and α and β are the preset thresholds.

5. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 2, characterized in that: Step S33 includes the following steps: S331: Determine whether the current fitness value of the particle is greater than its historical optimal fitness value; S332: If the current fitness value is greater than the historical optimal fitness value, the current position of the particle is updated to the individual optimal position; S333: Traverse the individual optimal positions of all particles, screen out the position with the best fitness value, and determine it as the global optimal position.

6. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 2, characterized in that: The parameter value range corresponding to the position of the particle: the virtual inertia satisfies , the damping coefficient satisfies ; in: is the minimum permissible value of virtual inertia, is the maximum allowable value of virtual inertia, is the minimum allowable value of the damping coefficient, is the maximum allowable value of the damping coefficient.

7. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 1, characterized in that: Step S2 includes the following steps: S21: Based on the system performance of wind power grid VSG, the fitness function is defined as: ； in: is the weight coefficient; is the frequency deviation, is the frequency change rate, is the power deviation; S22: Introducing a constraint violation function to characterize the constraint relationship between the virtual inertia and the damping parameter, wherein the constraint violation function is: ; in: is the optimal damping ratio, is the filter inductor on the VSG grid side, is the phase voltage amplitude at the inverter output side, is the phase voltage amplitude at the grid connection point; S23: The penalty function method is used to combine the constraint violation function with the fitness function to construct the total fitness function: ; in, is the penalty coefficient.

8. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 6, characterized in that: Use formula 3 and formula 4 to calculate the minimum allowable value of virtual inertia and the maximum allowable value of virtual inertia ; Formula 3; Formula 4; in: is the lower limit of the system inertia time constant, is the upper limit of the system inertia time constant, is the system rated apparent power, is the synchronous speed.

9. A method for optimizing adaptive parameters of wind power grid-type VSG control according to claim 6, characterized in that: Use Formula 5 and Formula 6 to calculate the minimum allowable value of the damping coefficient respectively and the maximum allowable value of the damping coefficient ; Formula 5; Formula 6; in: is the minimum damping ratio, is the minimum undamped natural oscillation angular frequency, is the maximum undamped natural oscillation angular frequency, is the maximum allowable value of virtual inertia.

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