Parameter optimization method and device of multiple network-type converters, equipment and medium

Abstract

The application discloses a parameter optimization method, device and equipment of multiple grid-connected type converters and a medium, relates to the technical field of electrical engineering, and includes an additional damping controller in the input end of the control loop of the multiple grid-connected type converters. The method comprises the following steps: performing modal analysis on a state space model containing the grid-connected type converters and an alternating current power grid to obtain characteristic values of each oscillation mode, so as to obtain expected damping ratios under each operating condition; a target parameter optimization model is constructed with the maximum target expected damping ratio as an optimization target; the target expected damping ratio is the minimum expected damping ratio; based on the optimization target of the target parameter optimization model, an attractive force search mechanism, an adaptive step mechanism and a contrarian learning mechanism, a target parameter combination is searched in a space formed by parameter combinations of the multiple grid-connected type converters; and the target parameter combination is deployed to the multiple grid-connected type converters. The method overcomes the combination explosion problem of parameters of the multiple grid-connected type converters and improves the optimization efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical engineering, in particular to a parameter optimization method, device and equipment of multiple grid-forming converters and a medium. BACKGROUND

[0002] High penetration of renewable energy sources, such as wind farms and photovoltaic power stations, significantly affects the dynamics of the power system, including the reduction of system inertia and damping performance, resulting in the stability of the power system being affected. Grid-forming converters can provide inertia and damping support for the grid, so they play an important role in the field of new power systems and are widely used today. For the grid using grid-forming converters, due to the strong interaction between the grid-forming converters and the alternating current grid, the system stability may be deteriorated, and this interaction is affected by the parameters of the controller, so the controller parameters need to be optimized to improve the system stability.

[0003] However, the number of converters is large, and there is no clear relationship between the decision variables (i.e. the parameters of the grid-forming converters) and the objective function, which is a non-convex and non-linear optimization problem, and the controller parameter design has a combination explosion problem, and the search efficiency is low when the parameters are optimized by traditional methods, and it is easy to fall into local optimization, and the optimization accuracy is low.

[0004] In summary, how to overcome the combination explosion problem of the parameters of multiple grid-forming converters and improve the optimization efficiency and accuracy is a problem to be solved in the field. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a parameter optimization method, device, equipment and medium of multiple grid-forming converters, which overcomes the combination explosion problem of the parameters of multiple grid-forming converters and improves the optimization efficiency and accuracy. The specific scheme is as follows:

[0006] In a first aspect, the present application discloses a parameter optimization method of multiple grid-forming converters, which includes an additional damping controller in the control loop input end of the multiple grid-forming converters; the method comprises:

[0007] Performing modal analysis on a state space model containing the grid-forming converters and the alternating current grid to obtain characteristic values of each oscillation mode, and obtaining expected damping ratios under each operating condition based on the characteristic values;

[0008] Based on the expected damping ratios, a target parameter optimization model is constructed with the maximum target expected damping ratio as the optimization objective; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios;

[0009] Based on the optimization objective of the target parameter optimization model, the attraction search mechanism, the adaptive step mechanism and the opposite learning mechanism, a target parameter combination is searched in a space formed by parameter combinations of the plurality of network-forming converters;

[0010] The target parameter combination is deployed into the plurality of network-forming converters.

[0011] Optionally, the parameters in the parameter combination of each network-forming converter are respectively a virtual inertia time constant, a virtual damping coefficient and parameters of an additional damping controller, and the parameters of the additional damping controller include a first time constant, a second time constant and a gain.

[0012] Optionally, a modal analysis is performed on a state space model containing the network-forming converters and the alternating current power grid to obtain characteristic values of each oscillation mode, and an expected damping ratio under each operating condition is obtained based on the characteristic values, including:

[0013] A state space model containing the network-forming converters and the alternating current power grid is constructed; wherein the state space model includes a state matrix, a control input matrix and a control output matrix;

[0014] The state space model is decoupled through linear transformation to obtain a diagonal matrix; wherein the diagonal matrix contains characteristic values of each oscillation mode;

[0015] Characteristic values of each oscillation mode are used to obtain a damping ratio of each oscillation mode, and an expected damping ratio under each operating condition is obtained based on the damping ratio of each oscillation mode.

[0016] Optionally, the target parameter optimization model with the optimization objective of maximizing the target expected damping ratio is constructed based on the expected damping ratio, including:

[0017] The target parameter optimization model is constructed based on the expected damping ratio, including a target function with the optimization objective of maximizing the target expected damping ratio, parameter range constraint conditions of the plurality of network-forming converters, probability stability constraint conditions, expected damping ratio constraint conditions and the state space model;

[0018] The target parameter optimization model is:

[0019] ;

[0020] wherein, is an expected damping ratio, L is a decision variable set containing parameter combinations of each network-forming converter, is a weakly damped oscillation mode set, 、 is respectively a state vector and an output vector of a network-forming converter, is an input vector composed of active power and reactive power reference values, A is a state matrix, B is a control input matrix, C is a control output matrix, is a virtual inertia time constant, , is a minimum value, a maximum value of the virtual inertia time constant, respectively, is a virtual damping coefficient, , is a minimum value, a maximum value of the virtual damping coefficient, respectively, is a first time constant, , is a minimum value, a maximum value of the first time constant, respectively, is a second time constant, , is a minimum value, a maximum value of the second time constant, respectively, K is a gain of an additional damping controller, , is a minimum value, a maximum value of the gain of the additional damping controller, respectively, n is an nth oscillation mode, is a probability stability index.

[0021] Optionally, the optimization target based on the target parameter optimization model, the attractive force search mechanism, the adaptive step length mechanism and the opposite learning mechanism search for a target parameter combination in a space composed of parameter combinations of the plurality of network type converters, comprising:

[0022] initializing the parameter combination of the network type converter, and determining the parameter combination of the network type converter as an individual, determining the target expected damping ratio of each individual, and determining the target expected damping ratio of the individual as an individual attribute;

[0023] initializing an adaptive step length control coefficient and an attractive force coefficient, and setting a boundary value of the adaptive step length control coefficient, a boundary value of the attractive force coefficient, a maximum iteration number and a number of the individuals;

[0024] searching for a target individual with a maximum individual attribute in a space composed of all the individuals based on the attractive force search mechanism, the adaptive step length mechanism and the opposite learning mechanism, and determining the target individual as a target parameter combination.

[0025] Optionally, searching for a target individual with a maximum individual attribute in a space composed of all the individuals based on the attractive force search mechanism, the adaptive step length mechanism and the opposite learning mechanism, comprising:

[0026] randomly determining a current individual from all the individuals, and judging whether the current individual is an individual with a maximum individual attribute among all the individuals;

[0027] determining the current individual as a candidate individual if the current individual is an individual with the largest individual attribute among all the individuals;

[0028] if the current individual is not an individual with the largest individual attribute among all the individuals, determining a reference individual randomly from all the individuals, and judging whether a first individual attribute of the current individual is less than a second individual attribute of the reference individual;

[0029] if the first individual attribute is less than the second individual attribute, updating an adaptive step control coefficient according to an attractive force and an Euclidean distance between the current individual and the reference individual, and determining a new current individual based on the adaptive step control coefficient;

[0030] judging whether the current individual has traversed all the individuals, and if not, jumping back to the step of judging whether the current individual is an individual with the largest individual attribute among all the individuals, and if yes, determining the current individual as a candidate individual;

[0031] determining a target individual based on the candidate individual and an adversarial learning mechanism.

[0032] Optionally, the determining of the target individual based on the candidate individual and the adversarial learning mechanism comprises:

[0033] randomly moving at a position of the candidate individual to obtain a moved individual, and determining an individual at an opposite position of the moved individual;

[0034] if the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position and the current individual has not traversed all the individuals, determining the moved individual as a new current individual, and jumping back to the step of judging whether the current individual is an individual with the largest individual attribute among all the individuals;

[0035] if the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position and the current individual has traversed all the individuals, determining the moved individual as the target individual;

[0036] if the individual attribute of the moved individual is less than the individual attribute of the individual at the opposite position and the current individual has not traversed all the individuals, determining the individual at the opposite position as a new current individual, and jumping back to the step of judging whether the current individual is an individual with the largest individual attribute among all the individuals;

[0037] if the individual attribute of the moved individual is less than the individual attribute of the individual at the opposite position and the current individual has traversed all the individuals, determining the individual at the opposite position as the target individual.

[0038] In a second aspect, the application discloses a parameter optimization device for multiple grid-forming converters, an additional damping controller being included in an input end of a control loop of the multiple grid-forming converters; the device comprises:

[0039] a damping ratio obtaining module, configured to perform modal analysis on a state space model containing the grid-forming converters and an alternating current power grid to obtain eigenvalues of each oscillation mode, and obtain expected damping ratios under each operating condition based on the eigenvalues;

[0040] a model constructing module, configured to construct a target parameter optimization model with an optimization target of maximizing a target expected damping ratio based on the expected damping ratios; the target expected damping ratio is a minimum expected damping ratio in the expected damping ratios;

[0041] a parameter optimization module, configured to search for a target parameter combination in a space formed by parameter combinations of the multiple grid-forming converters based on the optimization target of the target parameter optimization model, an attraction search mechanism, an adaptive step mechanism and a counter-learning mechanism;

[0042] an optimal parameter deployment module, configured to deploy the target parameter combination to the multiple grid-forming converters.

[0043] In a third aspect, the application discloses an electronic device, comprising:

[0044] a memory, configured to save a computer program;

[0045] a processor, configured to execute the computer program to implement steps of the parameter optimization method for the multiple grid-forming converters disclosed in the foregoing.

[0046] In a fourth aspect, the application discloses a computer readable storage medium, configured to store a computer program; wherein the computer program is executed by a processor to implement steps of the parameter optimization method for the multiple grid-forming converters disclosed in the foregoing.

[0047] The application has the beneficial effects that: the application includes an additional damping controller in the control loop input end of multiple network-type converters; the method includes: performing modal analysis on a state space model containing the network-type converters and an alternating current power grid to obtain characteristic values of each oscillation mode, and obtaining expected damping ratios under each operating condition based on the characteristic values; constructing a target parameter optimization model with the maximum target expected damping ratio as the optimization target based on the expected damping ratios; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios; searching for a target parameter combination in a space formed by the parameter combinations of the multiple network-type converters based on the optimization target of the target parameter optimization model, an attractive force search mechanism, an adaptive step size mechanism, and a contrarian learning mechanism; and deploying the target parameter combination to the multiple network-type converters. As can be seen, the application adds an additional damping controller in the control loop input end of the network-type converter, which can suppress the oscillation of the converter and improve the stability of the system. The expected damping ratio of each oscillation mode is accurately quantified by modal analysis of the state space model, and then an optimization model with the maximum target expected damping ratio as the core is constructed. The target expected damping ratio is the minimum expected damping ratio among the expected damping ratios. The attractive force search mechanism and the adaptive step size mechanism are used to efficiently search for the optimal parameter combination in the high-dimensional space of the multiple converter parameter combinations, which can greatly improve the search efficiency. The contrarian learning mechanism can explore more diversified search space areas, avoiding local optimization. That is, the traditional method overcomes the combination explosion problem in high-dimensional nonlinear optimization. Through the balance between global search and local optimization, the optimal parameter combination is quickly converged. The optimal parameter combination enables the system to maintain superior damping characteristics under multiple operating conditions, effectively suppresses power oscillation, and ultimately improves the stability of the power system through the deployment of the target parameter combination. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings.

[0049] Figure 1 A parameter optimization method flow chart of multiple network-type converters disclosed by the application;

[0050] Figure 2 A specific network-type converter control chart containing an additional damping controller disclosed by the application;

[0051] Figure 3 A specific network-type converter control chart disclosed by the application;

[0052] Figure 4 A specific iterative optimization flowchart disclosed in the present application;

[0053] Figure 5 A specific individual position update schematic diagram disclosed in the present application;

[0054] Figure 6 A specific fitness convergence curve schematic diagram disclosed in the present application;

[0055] Figure 7 A specific impact of different controller parameter combinations of a network-forming type converter on rotor angle oscillation schematic diagram disclosed in the present application; wherein (a) is a rotor angle response curve diagram of No. 1 generator under different controller parameter combinations, (b) is a rotor angle response curve diagram of No. 5 generator under different controller parameter combinations, and (c) is a rotor angle response curve diagram of No. 10 generator under different controller parameter combinations;

[0056] Figure 8 A specific impact of different controller parameter combinations of a network-forming type converter on power oscillation on a transmission line schematic diagram disclosed in the present application; wherein (a) is an active power response curve diagram corresponding to line 01-39 under different controller parameter combinations, (b) is an active power response curve diagram corresponding to line 06-07 under different controller parameter combinations, and (c) is an active power response curve diagram corresponding to line 23-24 under different controller parameter combinations;

[0057] Figure 9 A specific parameter optimization device structure schematic diagram of a plurality of network-forming type converters disclosed in the present application;

[0058] Figure 10 A specific electronic device structure diagram disclosed in the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] High penetration of renewable energy sources, such as wind farms and photovoltaic power plants, significantly affects the dynamics of the power system, including the reduction of system inertia and damping performance, resulting in the stability of the power system being affected. Grid-forming converters can provide inertia and damping support for the grid, so they play an important role in the field of new power systems and are widely used today. For the grid using grid-forming converters, due to the strong interaction between the grid-forming converters and the alternating current grid, the system stability may be deteriorated, and this interaction is affected by the parameters of the controller, so the controller parameters need to be optimized to improve the system stability.

[0061] However, the number of converters is large, and there is no clear relationship between the decision variables (i.e., the parameters of the grid-forming converter) and the objective function, which is a non-convex nonlinear optimization problem, and there is a combination explosion problem in the design of the controller parameters, and the search efficiency is low when the parameters are optimized by the traditional method, and it is easy to fall into local optimization, and the optimization accuracy is low.

[0062] Therefore, the application correspondingly provides a parameter optimization scheme for multiple grid-forming converters, which overcomes the combination explosion problem of the parameters of the multiple grid-forming converters and improves the optimization efficiency and accuracy.

[0063] Referring to Figure 1 The embodiments of the application disclose a parameter optimization method for multiple grid-forming converters, and an additional damping controller is included in the input end of the control loop of the multiple grid-forming converters; the method comprises the following steps:

[0064] Step S11: Perform modal analysis on the state space model containing the grid-forming converter and the alternating current grid to obtain the characteristic values of each oscillation mode, and obtain the expected damping ratio under each operating condition based on the characteristic values.

[0065] When the grid-forming converter is used, the converter has similar characteristics to the synchronous generator, and the converter may participate in the oscillation, in order to suppress the oscillation and improve the stability, for example Figure 2 A specific grid-forming converter control diagram containing an additional damping controller is shown in the figure, and an additional damping controller is added to the input end of the grid-forming control loop.

[0066] In the embodiments, the parameters in the parameter combination of each grid-forming converter are respectively a virtual inertia time constant, a virtual damping coefficient, and a parameter of an additional damping controller, and the parameter of the additional damping controller includes a first time constant, a second time constant, and a gain.

[0067] As Figure 2 As shown in the figure, is the initial frequency, is the adjusted frequency, is the system output frequency, P refP is an active power reference value v is an actual value, is a phase difference, is a virtual inertia time constant, is a virtual damping coefficient, the additional damping controller introduces three parameters, which are a first time constant , a second time constant and a gain K of the additional damping controller, so in addition to the need to optimize and , the parameters (i.e. the first time constant, the second time constant and the gain) of the additional damping controller also need to be optimized to ensure that the system damping is improved and the system stability is improved, that is, , , , and K are a set of parameter combinations.

[0068] In the embodiment, the state space model comprising the grid-forming converter and the alternating current power grid is subjected to modal analysis to obtain characteristic values of each oscillation mode, and expected damping ratios under each operating condition are obtained based on the characteristic values, including: constructing a state space model comprising the grid-forming converter and the alternating current power grid; wherein the state space model comprises a state matrix, a control input matrix and a control output matrix; decoupling the state space model through linear transformation to obtain a diagonal matrix; wherein the diagonal matrix comprises characteristic values of each oscillation mode; obtaining damping ratios of each oscillation mode by using the characteristic values of each oscillation mode, and obtaining expected damping ratios under each operating condition based on the damping ratios of each oscillation mode.

[0069] For example Figure 3 , a specific grid-forming converter control diagram is shown, the grid-forming converter adjusts the active power it emits through the power angle signal of the alternating current end of the converter, so as to ensure that it can be synchronized with the alternating current power grid. According to the control loop of the grid-forming converter and the topology of the alternating current power grid, a state space model of the entire system including the grid-forming converter and the alternating current power grid is established, and the state space model can be expressed as:

[0070] ;

[0071] In the formula, A, B and C are respectively a state matrix, a control input matrix and an output matrix, ∆u is an input vector composed of active and reactive power reference values, and ∆x and ∆y are respectively a state vector and an output vector of the grid-forming converter.

[0072] Based on the above state space model, linear transformation is performed on the state space model , wherein is a right eigenvector a matrix composed of the elements of the state space model, eliminating the cross-coupling between the state variables, decoupling the state space model through a linear transformation, and finally transforming the original state space in the state space model into

[0073] ;

[0074] wherein z is a new state vector obtained through the linear transformation, Λ is a diagonal matrix whose elements are the eigenvalues of A, that is, the diagonal matrix contains the eigenvalues of each oscillation mode, B' is a controllability matrix, and C' is an observability matrix, that is, the state space model is decoupled through a linear transformation to obtain a transformed state space model containing a diagonal matrix, a controllability matrix, and an observability matrix. Through modal analysis, the eigenvalues of each oscillation mode can be obtained, denoted as , , wherein is the real part of the eigenvalue of each oscillation mode, is the imaginary part of the eigenvalue of each oscillation mode.

[0075] The damping ratio of each oscillation mode is obtained using the eigenvalues of each oscillation mode , and the formula is as follows:

[0076] ;

[0077] Further, the expected damping ratio under each operating condition is obtained based on the damping ratio of each oscillation mode , and the formula is as follows:

[0078] ;

[0079] wherein N sample represents the total number of random scenarios, that is, different operating conditions of the power system.

[0080] Step S12: Based on the expected damping ratio, a target parameter optimization model is constructed with the maximum target expected damping ratio as the optimization objective; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios.

[0081] In this embodiment, the target parameter optimization model with the maximum target expected damping ratio as the optimization objective is constructed based on the expected damping ratio, including: based on the expected damping ratio, a target parameter optimization model is constructed including a target function with the maximum target expected damping ratio as the optimization objective, parameter range constraint conditions of the plurality of network-forming type converters, probability stability constraint conditions, expected damping ratio constraint conditions, and the state space model;

[0082] The target parameter optimization model is:

[0083] ;

[0084] wherein, is a set of decision variables including parameter combinations of each grid-forming converter, is a set of weakly damped oscillation modes, , are a state vector and an output vector of the grid-forming converter respectively, is an input vector composed of active power and reactive power reference values, A is a state matrix, B is a control input matrix, C is a control output matrix, is a virtual inertia time constant, , are a minimum value and a maximum value of the virtual inertia time constant respectively, is a virtual damping coefficient, , are a minimum value and a maximum value of the virtual damping coefficient respectively, is a first time constant, , are a minimum value and a maximum value of the first time constant respectively, is a second time constant, , are a minimum value and a maximum value of the second time constant respectively, K is a gain of an additional damping controller, , are a minimum value and a maximum value of the gain of the additional damping controller respectively, n is an nth oscillation mode, is a probabilistic stability index.

[0085] The optimization design of the converter aims to improve the damping performance of the system and better and faster suppress oscillation, and therefore the system selects maximizing the minimum expected damping ratio as the objective function, that is, the expected damping ratio is used to construct the objective function including the optimization objective of maximizing the target expected damping ratio, and the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios, and the objective function is:

[0086] ;

[0087] wherein, is an expected damping ratio, L is a set of decision variables including parameter combinations of each grid-forming converter, that is, L = [H v1 ,D v1 ,T b1 ,T a1 ,K1,···,H vNgfm ,D vNgfm ,T bgfm ,T aNgfm ,K Ngfm ] T , Ngfm represents the number of converters, For a set of weakly damped oscillation modes (i.e. a set of COMs), a weakly damped oscillation mode refers to a vibration mode with an expected damping ratio less than 0.1 under random operating conditions, i.e. where S om is the set of all oscillation modes.

[0088] The constraint conditions are constructed so that the parameters of the grid-forming converter and the operating state of the system should be limited within a certain range, and more importantly, the probability stability of the system should be guaranteed to ensure the stability of the system under random operating conditions, wherein the constraint conditions include parameter range constraint conditions of multiple grid-forming converters, probability stability constraint conditions and expected damping ratio constraint conditions. The parameter range constraint conditions of multiple grid-forming converters are specifically:

[0089] ;

[0090] In the formula, is a virtual inertia time constant, , are the minimum value and the maximum value of the virtual inertia time constant, is a virtual damping coefficient, , are the minimum value and the maximum value of the virtual damping coefficient, is a first time constant, , are the minimum value and the maximum value of the first time constant, is a second time constant, , are the minimum value and the maximum value of the second time constant, and K is the gain of the additional damping controller, , are the minimum value and the maximum value of the gain of the additional damping controller, and n is the nth oscillation mode.

[0091] The probability stability P uns evaluates the stability of the system under random scenarios. According to the law of large numbers and the central limit theorem, the probability stability index P uns can be expressed as:

[0092] ;

[0093] where and are the stable state of the kth random scenario and the expected value of all N sample scenarios, and N sample represents the total number of random scenarios, i.e. different operating conditions of the power system. uns,k The calculation is as follows:

[0094] ;

[0095] wherein is the real part of the eigenvalue of each oscillation mode, and m represents an oscillation mode under a random scenario.

[0096] In order to eliminate weakly damped oscillation modes, the parameters of the GFM-ESs should ensure that all the desired damping ratios of the oscillation modes are greater than 0.05, that is, the desired damping ratio constraint condition is:

[0097] ;

[0098] wherein, is the desired damping ratio, is a set of weakly damped oscillation modes, and n is the nth oscillation mode.

[0099] The target parameter optimization model is constructed in combination with the objective function, the constraint condition and the state space model, and the target parameter optimization model is specifically:

[0100] ;

[0101] wherein, is the desired damping ratio, L is a set of decision variables including the parameter combination of the grid-forming converter, is a set of weakly damped oscillation modes, , are respectively a state vector and an output vector of the grid-forming converter, is an input vector composed of active power and reactive power reference values, A is a state matrix, B is a control input matrix, and C is a control output matrix, is a virtual inertia time constant, , are respectively a minimum value and a maximum value of the virtual inertia time constant, is a virtual damping coefficient, , are respectively a minimum value and a maximum value of the virtual damping coefficient, is a first time constant, , are respectively a minimum value and a maximum value of the first time constant, is a second time constant, , are respectively a minimum value and a maximum value of the second time constant, and K is a gain of the additional damping controller, , are respectively a minimum value and a maximum value of the gain of the additional damping controller, and n is the nth oscillation mode, is a probability stability index.

[0102] Step S13: searching for a target parameter combination in a space formed by a plurality of parameter combinations of the grid-connected converter based on the optimization objective of the target parameter optimization model, the attraction search mechanism, the adaptive step mechanism, and the opposite learning mechanism.

[0103] In this embodiment, the searching for a target parameter combination in a space formed by a plurality of parameter combinations of the grid-connected converter based on the optimization objective of the target parameter optimization model, the attraction search mechanism, the adaptive step mechanism, and the opposite learning mechanism comprises: initializing the parameter combination of the grid-connected converter, and determining the parameter combination of the grid-connected converter as an individual, determining the target expected damping ratio of each individual, and determining the target expected damping ratio of the individual as an individual attribute; initializing an adaptive step control coefficient and an attraction coefficient, and setting a boundary value of the adaptive step control coefficient, a boundary value of the attraction coefficient, a maximum iteration number, and a number of the individuals; searching for a target individual with a maximum individual attribute in a space formed by all the individuals based on the attraction search mechanism, the adaptive step mechanism, and the opposite learning mechanism, and determining the target individual as a target parameter combination.

[0104] In the parameter optimization design of the grid-connected converter, there is a combination explosion problem in the parameter design of the grid-connected converter, there is no clear relationship between the decision variable (the parameter of the converter) and the objective function, and it is a black box problem. In this embodiment, the maximum target expected damping ratio is taken as an objective function, the minimum expected damping ratio is optimized through an optimization algorithm, and the optimal parameter of the converter is found, so the minimum expected damping ratio is defined as an attribute of an individual (each individual represents a candidate parameter combination of the converter), that is, the parameter combination of the grid-connected converter is initialized, and the parameter combination of the grid-connected converter is determined as an individual L, the target expected damping ratio of each individual is determined, and the target expected damping ratio of the individual is determined as an individual attribute. The formula of the individual attribute I is:

[0105] ;

[0106] Wherein, I represents an attribute of the individual.

[0107] In order to improve the search efficiency, a and are adaptively scaled in each iteration. The parameter a is a random control parameter, which determines the random moving step length of the individual. A larger value should be set initially to encourage exploration, and then gradually reduced over time to focus on a certain space to find the optimal value. The parameter is an attraction coefficient, which controls the reduction rate of the attraction of the individual with the distance. It starts from a low value to promote exploration, and gradually increases over time to focus on local search, that is, the boundary value of the adaptive step control coefficient is set. The adaptive and a can be represented as:

[0108] ;

[0109] Where the subscript "0" represents the initial value; t represents the number of iterations in the program; L max L min The positional boundary, composed of the parameters of the grid-type converter, limits the number of individuals; d a The decreasing factor aims to reduce random motion as the number of iterations increases. for The maximum value of N; iter It is the maximum number of iterations, i.e., the initial adaptive step size control coefficient and attraction coefficient, and the boundary value of the attraction coefficient.

[0110] For example Figure 4 The flowchart illustrates a specific iterative optimization process. Based on an attraction search mechanism, an adaptive step size mechanism, and an adversarial learning mechanism, it searches for the target individual with the maximum individual attribute in a space composed of all individuals, and defines the target individual as the target parameter combination. During the search for the target individual with the maximum individual attribute in the space composed of all individuals, the individual positions need to be continuously updated, and the fitness of the individuals after the position update is compared to obtain the optimal fitness, i.e., the optimal solution. If the difference in precision between the fitness of the previous iteration and the previous iteration is within a preset precision threshold, or if the maximum number of iterations has been reached, the iterative optimization ends, and the optimal solution, i.e., the optimal parameters, is output. If the maximum number of iterations has not been reached and the difference in precision between the fitness of the previous iteration and the previous iteration is not within the preset precision threshold, further iterative optimization is required. In other words, besides setting a maximum number of iterations N... iter Furthermore, after each iteration, the damping ratio between the updated position and the previous position is compared. If the difference in damping ratio is less than 1e-3, the iteration is terminated. Therefore, the optimization may be completed before reaching the maximum number of iterations, which improves the optimization efficiency of the algorithm.

[0111] In the embodiment, the target individual with the maximum individual attribute is searched in the space formed by all the individuals based on the attraction search mechanism, the adaptive step length mechanism and the opposite learning mechanism, comprising: randomly determining a current individual from all the individuals, and judging whether the current individual is the individual with the maximum individual attribute among all the individuals; if the current individual is the individual with the maximum individual attribute among all the individuals, the current individual is determined as a candidate individual; if the current individual is not the individual with the maximum individual attribute among all the individuals, a reference individual is randomly determined from all the individuals, and whether the first individual attribute of the current individual is less than the second individual attribute of the reference individual is judged; if the first individual attribute is less than the second individual attribute, the adaptive step length control coefficient is updated according to the attraction and the Euclidean distance between the current individual and the reference individual, and a new current individual is determined based on the adaptive step length control coefficient; whether the current individual has been traversed through all the individuals is judged, if not, the step of judging whether the current individual is the individual with the maximum individual attribute among all the individuals is re-jumped to, if yes, the current individual is determined as the candidate individual; the target individual is determined based on the candidate individual and the opposite learning mechanism.

[0112] For example Figure 5 A specific individual position update diagram is shown, a current individual L i is randomly determined from all the individuals, and whether the current individual is the individual with the maximum individual attribute among all the individuals is judged; if the current individual is the individual with the maximum individual attribute among all the individuals, the current individual is determined as a candidate individual; if the current individual is not the individual with the maximum individual attribute among all the individuals, a reference individual L j is randomly determined from all the individuals, and whether the first individual attribute of the current individual is less than the second individual attribute of the reference individual is judged; if the first individual attribute is less than the second individual attribute, the adaptive step length control coefficient is updated according to the attraction and the Euclidean distance between the current individual and the reference individual, and a new current individual is determined based on the adaptive step length control coefficient, that is, when L j is attracted, the individual at the position L i moves to the individual at the position L j with a small random step length, and the attraction between the individuals at the positions L i and L j is represented as: i j i j

[0113] ;

[0114] wherein,​​​​ L i L j The attraction between individual (which is equivalent to the converter parameter combination i) and reference individual does not include the individual in the opposite position; b0 is the initial attraction; d ij L i L j The Euclidean (geometric) distance between positions L

[0115] The Euclidean distance formula is:

[0116] ;

[0117] N dim is the problem dimension, where N dim = the number of converters in the system Ngfm x the number of converter parameters to be optimized in the system network type.

[0118] According to the attraction β ij between individuals and the distance d ij , the optimization algorithm moves a certain step L i,step (i.e. adaptive step control coefficient) to update the position of the individual in each iteration. The formula of the adaptive step control coefficient is:

[0119] ;

[0120] Where rand is a uniformly distributed random number between 0 and 1.

[0121] According to the step L i,step , update the position L i :

[0122] ;

[0123] In this way, the individual is updated when the attribute of the first individual is less than the attribute of the second individual. Next, it is judged whether all individuals have been traversed, if not, it is returned to the step of judging whether the current individual is the individual with the largest attribute among all individuals, i.e. continue iteration optimization, if yes, the current individual is determined as the candidate individual.

[0124] If the current individual is the individual with the largest attribute among all individuals, or after the individual is updated, all individuals have been traversed, the candidate individual is obtained, and therefore the target individual is determined based on the candidate individual and the opposite learning mechanism.

[0125] In the embodiment, the determining the target individual based on the candidate individual and the opposite learning mechanism comprises: randomly moving on the position of the candidate individual to obtain a moved individual, and determining an individual on the opposite position of the moved individual; if the individual attribute of the moved individual is greater than the individual attribute of the individual on the opposite position and all the individuals have not been traversed at present, determining the moved individual as a new current individual, and jumping back to the step of judging whether the current individual is the individual with the greatest individual attribute among all the individuals; if the individual attribute of the moved individual is greater than the individual attribute of the individual on the opposite position and all the individuals have been traversed at present, determining the moved individual as the target individual; if the individual attribute of the moved individual is less than the individual attribute of the individual on the opposite position and all the individuals have not been traversed at present, determining the individual on the opposite position as the new current individual, and jumping back to the step of judging whether the current individual is the individual with the greatest individual attribute among all the individuals; if the individual attribute of the moved individual is less than the individual attribute of the individual on the opposite position and all the individuals have been traversed at present, determining the individual on the opposite position as the target individual.

[0126] Since the candidate individual is the individual with the best individual attribute, the candidate individual will not be attracted by other individuals, and therefore the target individual is determined based on the candidate individual by updating the individual through random movement, i.e., the individual update formula based on the candidate individual is:

[0127] ;

[0128] In order to explore more diversified search space regions and avoid local optimum, the opposite-based learning is introduced into the optimization algorithm, i.e., the opposite learning mechanism is introduced. In a specific iteration, the individual with poor attribute will not only be attracted by the individual with good attribute, but also move in the opposite direction. For a given position L i , the boundaries are L min and L max , and the opposite position L i,oppo is calculated as:

[0129] ;

[0130] wherein L min and L max are the individual position boundaries composed of the maximum value and the minimum value of the GFM-ESs parameters. The individual L i obtained through the individual update formula based on the candidate individual is a moved individual L i , and the individual attribute of the moved individual L i is compared with the individual attribute of the individual L i,oppocomparing the individual attribute of the moved individual with the individual attribute of the individual at the opposite position, determining the moved individual as the new current individual if the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position, determining the individual at the opposite position as the new current individual if the individual attribute of the moved individual is less than the individual attribute of the individual at the opposite position, and determining the new current individual as the target individual if all the individuals have been traversed, that is, determining the moved individual as the new current individual if the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position and all the individuals have not been traversed, and jumping back to the step of judging whether the current individual is the individual with the maximum individual attribute among all the individuals; determining the moved individual as the target individual if the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position and all the individuals have been traversed; determining the individual at the opposite position as the new current individual if the individual attribute of the moved individual is less than the individual attribute of the individual at the opposite position and all the individuals have not been traversed, and jumping back to the step of judging whether the current individual is the individual with the maximum individual attribute among all the individuals; and determining the individual at the opposite position as the target individual if the individual attribute of the moved individual is less than the individual attribute of the individual at the opposite position and all the individuals have been traversed.

[0131] Step S14: deploying the target parameter combination to the plurality of network-forming converters.

[0132] After obtaining the optimal parameter combination of the plurality of network-forming converters, the target parameter combination is deployed to the plurality of network-forming converters, and the system stability of the plurality of network-forming converters is better at this time.

[0133] The embodiment takes the parameters of the network-forming converter with the plurality of additional damping controllers as the object, constructs a target function taking the maximum value of the minimum expected damping ratio as the target function and a constraint condition, optimizes the target function through an optimization algorithm, and obtains the optimization result of the parameters (virtual inertia time constant H v , virtual damping coefficient D v , time constant T b , T a , and additional damping controller gain K) of the network-forming converter with the plurality of additional damping controllers. The adaptive parameter and the opposite learning mechanism are used to explore more diversified search space regions and avoid local optimization. The adaptive parameter greatly improves the efficiency of the algorithm by scaling a and in each iteration. The network-forming converter with the additional damping controller introduces time constants T b and T a, damping controller gain K. The minimum expected damping ratio is defined as an individual attribute, and adaptive parameters and a competitive learning mechanism are introduced into the optimization algorithm.

[0134] The application has the advantages that: the control loop input end of the plurality of networked converter includes an additional damping controller; the method comprises: performing modal analysis on a state space model containing the networked converter and an alternating current power grid to obtain characteristic values of each oscillation mode, and obtaining expected damping ratios under each operating condition based on the characteristic values; based on the expected damping ratios, a target parameter optimization model is constructed with the maximum target expected damping ratio as the optimization target; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios; based on the optimization target of the target parameter optimization model, the attractive search mechanism, the adaptive step size mechanism, and the competitive learning mechanism, a target parameter combination is searched in a space formed by the parameter combinations of the plurality of networked converters; and the target parameter combination is deployed to the plurality of networked converters. As can be seen, the application adds an additional damping controller to the control loop input end of the networked converter, which can suppress the oscillation of the converter and improve the stability of the system. The expected damping ratio of each oscillation mode is accurately quantified by modal analysis of the state space model, and then an optimization model with the maximum target expected damping ratio as the core is constructed, the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios, the attractive search mechanism and the adaptive step size mechanism are used to efficiently optimize in the high-dimensional space of the multi-converter parameter combination, which can greatly improve the optimization efficiency, and the competitive learning mechanism can explore more diversified search space areas to avoid local optimum. That is, the traditional method overcomes the combination explosion problem in high-dimensional nonlinear optimization, balances global search and local optimization, quickly converges to the optimal parameter combination, and the optimal parameter combination enables the system to maintain superior damping characteristics under multiple operating conditions, effectively suppresses power oscillation, and ultimately improves the stability of the power system through the deployment of the target parameter combination.

[0135] The networked converter is integrated into a 39-bus system, and the parameters (i.e., H v , D v , T b , T a , and K) of each networked converter are optimized to illustrate the application.

[0136] (1) Parameter settings:

[0137] Since there are five networked converters, the dimension of the optimization problem is N dim =25. To obtain the optimal parameters, 40 individuals are created to search the parameter space, the number of iterations N iter is set to 1000, and other parameters are set as follows:

[0138] Table 1 Parameter setting

[0139]

[0140] (2) Simulation analysis:

[0141] Through the code writing of python, the Python-PowerFactory automatic simulation framework is established to realize and verify the proposed algorithm. After 1000 iterations, all the optimal parameters of the grid-forming converter are successfully found, denoted as P opt , see Table 2. At the same time, in order to evaluate the effectiveness of the optimal parameters, two other groups of parameters are used for comparison, denoted as Pother1 and Pother2.

[0142] Table 2 Optimal parameters and comparison parameters of GFM-BESs

[0143]

[0144] From Table 2, P opt Compared with the other two groups of parameters, through the optimization algorithm, the minimum expected damping ratio is improved, the damping performance is effectively improved, and the stability is improved. At the same time, from the convergence graph of the minimum expected damping ratio, as shown in a specific fitness convergence curve diagram Figure 6 , when the optimal parameters are used, after 1000 iterations, the minimum expected damping ratio of the test system is improved from 6.4% to 9.1%.

[0145] As shown in a specific grid-forming converter different controller parameter combination influence on rotor angle oscillation diagram Figure 7 and a specific grid-forming converter different controller parameter combination influence on power oscillation on transmission line diagram Figure 8 , time domain simulation is carried out, and the comparison of system dynamics response with optimal parameters and other two parameter sets is compared, as shown in a specific grid-forming converter different controller parameter combination influence on rotor angle oscillation diagram Figure 8 , the grid-forming converter can quickly suppress the power oscillation caused by short-circuit fault within 10s, and the power oscillation on the transmission line is also well suppressed. In contrast, the grid-forming converter using other parameters (i.e. Pother1 and Pother2) cannot obtain satisfactory damping performance, and there is long-term oscillation of active power on the transmission line.

[0146] It can be seen that the embodiment solves the "black box" optimization problem, optimizes the parameters of multiple grid-forming converters, improves the damping performance of the system, and thus improves the stability of the system. The embodiment uses adaptive parameters and a mechanism based on opposition learning, expands the optimization range of the algorithm, and greatly improves the efficiency of the algorithm.

[0147] Referring to Figure 9 As shown in the accompanying drawings, the embodiments of the present application disclose a parameter optimization device of multiple grid-forming converters, an additional damping controller is included in the input end of the control loop of the multiple grid-forming converters; the device comprises:

[0148] a damping ratio obtaining module 11, configured to perform modal analysis on a state space model containing the grid-forming converters and an alternating current power grid to obtain characteristic values of each oscillation mode, and obtain expected damping ratios under each operating condition based on the characteristic values;

[0149] a model constructing module 12, configured to construct a target parameter optimization model with an optimization target of maximizing a target expected damping ratio based on the expected damping ratios; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios;

[0150] a parameter optimization module 13, configured to search for a target parameter combination in a space formed by parameter combinations of the multiple grid-forming converters based on the optimization target of the target parameter optimization model, an attraction search mechanism, an adaptive step mechanism and a counter-learning mechanism;

[0151] an optimal parameter deployment module 14, configured to deploy the target parameter combination to the multiple grid-forming converters.

[0152] The application has the beneficial effects that: the application includes an additional damping controller in the control loop input end of multiple network-type converters; the method includes: performing modal analysis on a state space model containing the network-type converters and an alternating current power grid to obtain characteristic values of each oscillation mode, and obtaining expected damping ratios under each operating condition based on the characteristic values; constructing a target parameter optimization model with the maximum target expected damping ratio as the optimization target based on the expected damping ratios; the target expected damping ratio is the minimum expected damping ratio among the expected damping ratios; searching for a target parameter combination in a space formed by parameter combinations of the multiple network-type converters based on the optimization target of the target parameter optimization model, an attractive force search mechanism, an adaptive step size mechanism, and a contrarian learning mechanism; and deploying the target parameter combination to the multiple network-type converters. As can be seen, the application adds an additional damping controller in the control loop input end of the network-type converter, which can suppress the oscillation of the converter and improve the system stability. The modal analysis of the state space model accurately quantifies the expected damping ratio of each oscillation mode, and then an optimization model with the maximum target expected damping ratio as the core is constructed. The target expected damping ratio is the minimum expected damping ratio among the expected damping ratios. The attractive force search mechanism and the adaptive step size mechanism are used to efficiently search for the optimal parameter combination in the high-dimensional space of multiple converter parameter combinations, which can greatly improve the search efficiency. The contrarian learning mechanism can explore more diversified search space areas and avoid local optimization. In other words, the traditional method overcomes the combination explosion problem in high-dimensional nonlinear optimization. Through the balance between global search and local optimization, the optimal parameter combination is quickly converged. The optimal parameter combination enables the system to maintain superior damping characteristics under multiple operating conditions, effectively suppresses power oscillation, and ultimately improves the stability of the power system through the deployment of the target parameter combination.

[0153] Further, the embodiment of the application further provides an electronic device. Figure 10 The electronic device 20 structure diagram shown in the figure cannot be considered as any limitation on the use range of the application.

[0154] Figure 10 The structure of the electronic device provided by the embodiment of the application is shown in the figure. Specifically, it can include at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the related steps in the parameter optimization method of the multiple network-type converters executed by the electronic device disclosed in any of the preceding embodiments.

[0155] In this embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device; the communication interface 24 is configured to create a data transmission channel between the electronic device and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which will not be specifically limited herein; the input and output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which will not be specifically limited herein.

[0156] The processor 21 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 21 can also include a main processor and a coprocessor. The main processor is a processor for processing data in a wake-up state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 can be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 can also include an AI (Artificial Intelligence) processor configured to process machine learning-related computing operations.

[0157] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222, and data 223, etc. The storage mode can be temporary storage or permanent storage.

[0158] The operating system 221 is used to manage and control each hardware device on the electronic device and the computer program 222, so as to realize the operation and processing of the processor 21 on the mass data 223 in the memory 22, and can be Windows, Unix, Linux, etc. In addition to the computer program capable of completing the parameter optimization method of the plurality of networked type current transformers disclosed by the electronic device, the computer program 222 can further include a computer program capable of completing other specific work. The data 223 can include the data transmitted by the external device received by the electronic device, and can also include the data collected by the self input and output interface 25, etc.

[0159] Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by the processor to realize the parameter optimization method of the plurality of networked type current transformers disclosed above. The specific steps of the method can refer to the corresponding contents disclosed in the foregoing embodiments, and will not be described here.

[0160] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0161] Those skilled in the art will further appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or any combination thereof. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein in terms of their functionality, which has been described generally and symbolically in flow charts. Having thus described the functionality of the examples in terms of a process, it is appreciated that this functionality can be implemented by one or more types of electrical circuits or computer software, which are collectively referred to herein as a "circuit" that can carry out a variety of operations described herein. The circuit can include a variety of different types of general purpose or special purpose circuits, or combinations thereof. In addition, it is further noted that the embodiments disclosed herein can be modified to comprise more or less steps or operations than those disclosed herein, and such modifications are contemplated and considered within the scope of embodiments of the present application. The steps or operations of the methods or algorithms described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in Random Access Memory (RAM), flash memory, Read-only memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, hard disk can be used as a non-transitory storage medium to store software modules.

[0162] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but rather are used to distinguish one element from another, and are more especially used for the purpose of distinction from other elements in the specification. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0163] The above describes in detail the parameter optimization method, device, equipment and medium of the plurality of networked type converters provided by the application. The principles and implementation manners of the application are described by using specific examples. The above description of the examples is only used to help understand the method of the application and the core idea thereof. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges will be changed according to the idea of the application. In conclusion, the content of the specification should not be understood as a limitation of the application.

Claims

1. A parameter optimization method for multiple grid-type converters, characterized in that, The method includes an additional damping controller in the input terminal of the control loop of multiple grid-type converters; the method includes: Modal analysis is performed on the state-space model containing the grid-type converter and the AC power grid to obtain the characteristic values ​​of each oscillation mode, and the expected damping ratio under each operating condition is obtained based on the characteristic values. Based on the desired damping ratio, an optimization model for target parameters is constructed with the goal of maximizing the target desired damping ratio; the target desired damping ratio is the minimum desired damping ratio among all the desired damping ratios. Based on the optimization objective, attraction search mechanism, adaptive step size mechanism, and opposition learning mechanism of the target parameter optimization model, the target parameter combination is searched in the space formed by the parameter combinations of multiple grid-type converters; the parameters in the parameter combination of each grid-type converter are the virtual inertia time constant, the virtual damping coefficient, and the parameters of the additional damping controller, and the parameters of the additional damping controller include the first time constant, the second time constant, and the gain. The target parameters are combined and deployed to multiple of the grid-type converters; The modal analysis of the state-space model including the grid-type converter and the AC power grid to obtain the characteristic values ​​of each oscillation mode, and the acquisition of the expected damping ratio under each operating condition based on the characteristic values, includes: A state-space model is constructed, comprising the grid-connected converter and the AC power grid. The state-space model includes a state matrix, a control input matrix, and a control output matrix. The state-space model is decoupled using a linear transformation to obtain a diagonal matrix. This diagonal matrix contains the eigenvalues ​​of each oscillation mode. The damping ratio of each oscillation mode is obtained using its eigenvalues, and the desired damping ratio under each operating condition is obtained based on the damping ratio of each oscillation mode.

2. The parameter optimization method for multiple grid-type converters according to claim 1, characterized in that, The objective parameter optimization model, constructed based on the desired damping ratio with the objective of maximizing the target desired damping ratio, includes: Based on the desired damping ratio, a target parameter optimization model is constructed, which includes an objective function with the goal of maximizing the target desired damping ratio, parameter range constraints of multiple grid-type converters, probabilistic stability constraints, desired damping ratio constraints, and the state-space model. The target parameter optimization model is as follows: ； in, Let L be the desired damping ratio, and L be the set of decision variables containing the parameter combinations of each grid-type converter. It is a set of weakly damped oscillation modes. , These are the state vector and output vector of the grid-type converter, respectively. Let A be the input vector composed of active power and reactive power reference values, B be the state matrix, and C be the control input matrix. The virtual inertial time constant. , These are the minimum and maximum values ​​of the virtual inertial time constant, respectively. This is the virtual damping coefficient. , These are the minimum and maximum values ​​of the virtual damping coefficient, respectively. The first time constant, , These are the minimum and maximum values ​​of the first time constant, respectively. The second time constant, , These represent the minimum and maximum values ​​of the second time constant, respectively, and K is the gain of the additional damping controller. , , , represent the minimum and maximum values ​​of the gain of the additional damping controller, respectively, where n is the nth oscillation mode. It is a probability stability index.

3. The parameter optimization method for multiple grid-type converters according to claim 1, characterized in that, The optimization objective, attraction search mechanism, adaptive step size mechanism, and opposition learning mechanism based on the target parameter optimization model search for the target parameter combination in the space formed by the parameter combinations of multiple grid-type converters, including: Initialize the parameter combination of the grid-type converter, and determine the parameter combination of the grid-type converter as an individual, determine the target expected damping ratio of each individual, and determine the target expected damping ratio of the individual as an individual attribute; Initialize the adaptive step size control coefficient and the attraction coefficient, and set the boundary values ​​of the adaptive step size control coefficient, the boundary value of the attraction coefficient, the maximum number of iterations, and the number of individuals; Based on the attraction search mechanism, the adaptive step size mechanism, and the opposition learning mechanism, the target individual with the maximum attribute of all individuals is found in the space composed of all individuals, and the target individual is determined as the target parameter combination.

4. The parameter optimization method for multiple grid-type converters according to claim 3, characterized in that, The method of finding the target individual with the maximum attribute in the space composed of all individuals based on the attraction search mechanism, adaptive step size mechanism, and opposition learning mechanism includes: Randomly select the current individual from all the individuals and determine whether the current individual has the largest individual attribute among all the individuals. If the current individual has the largest individual attribute among all the individuals, then the current individual is determined as a candidate individual; If the current individual is not the individual with the largest individual attribute among all the individuals, then a reference individual is randomly determined from all the individuals, and it is determined whether the first individual attribute of the current individual is less than the second individual attribute of the reference individual; If the first individual attribute is less than the second individual attribute, the adaptive step size control coefficient is updated based on the attractiveness and Euclidean distance between the current individual and the reference individual, and a new current individual is determined based on the adaptive step size control coefficient. Determine whether all individuals have been traversed. If not, jump back to the step of determining whether the current individual is the one with the largest individual attribute among all individuals. If so, determine the current individual as a candidate individual. The target individual is determined based on the candidate individuals and the opposition learning mechanism.

5. The parameter optimization method for multiple grid-type converters according to claim 4, characterized in that, The process of determining the target individual based on the candidate individuals and the opposition learning mechanism includes: The candidate individual is randomly moved to obtain the moved individual, and the individual at the opposite position of the moved individual is determined. If the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position and all individuals have not been traversed yet, then the moved individual is determined as the new current individual, and the process jumps back to the step of determining whether the current individual is the individual with the largest individual attribute among all individuals. If the individual attribute of the moved individual is greater than the individual attribute of the individual at the opposite position, and all the individuals have been traversed, then the moved individual is determined as the target individual. If the individual attribute of the moved individual is less than the individual attribute of the individual in the opposite position and all individuals have not been traversed yet, then the individual in the opposite position is determined as the new current individual, and the process jumps back to the step of determining whether the current individual is the individual with the largest individual attribute among all individuals. If the individual attribute of the moved individual is less than the individual attribute of the individual in the opposite position, and all individuals have been traversed, then the individual in the opposite position is determined as the target individual.

6. A parameter optimization apparatus for multiple grid-type converters, used to implement the parameter optimization method for multiple grid-type converters as described in any one of claims 1 to 5, characterized in that, The control loop input of multiple grid-connected converters includes an additional damping controller; the device includes: The damping ratio acquisition module is used to perform modal analysis on the state-space model containing the grid-type converter and the AC power grid to obtain the characteristic values ​​of each oscillation mode, and to obtain the expected damping ratio under each operating condition based on the characteristic values. The model building module is used to construct a target parameter optimization model based on the expected damping ratio, with the goal of maximizing the target expected damping ratio; the target expected damping ratio is the minimum expected damping ratio among all the expected damping ratios. The parameter optimization module is used to find the target parameter combination in the space formed by the parameter combinations of multiple grid-type converters based on the optimization objective, attraction search mechanism, adaptive step size mechanism and opposition learning mechanism of the target parameter optimization model. The optimal parameter deployment module is used to deploy the target parameter combination to multiple of the grid-type converters.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the parameter optimization method for a plurality of grid-type converters as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the parameter optimization method for a plurality of grid-type converters as described in any one of claims 1 to 5.

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