A netting controller parameter optimization method and device

Abstract

The application discloses a kind of follow net controller parameter optimization method and device, by constructing the dynamic model of follow net controller in the power system to be analyzed, the dynamic analysis is carried out to the dynamic model of follow net controller, state vector equation is obtained, using the extended inner domain method based on Lyapunov second method, combined with the solution of the attraction domain of the power system to be analyzed based on sum of squares theory, obtain the attraction domain estimation result in the power system to be analyzed;System fault state time domain simulation is carried out to the power system to be analyzed, obtain real critical cut-off time and the numerical value of state variable at each time, combined with Lyapunov function to obtain the error index of attraction domain estimation result;If error index is greater than preset index, based on state vector equation, optimal parameter search is carried out using axial search, and parameter optimal result is obtained, the method is by optimizing the parameter of follow net controller in power grid system, improve the attraction domain estimation effect of follow net controller acting on power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system automation, in particular to a method and device for optimizing parameters of a grid-following controller. BACKGROUND

[0002] Transient stability is one of the important problems to be solved in power systems. Analyzing the stability of a power system helps to ensure that the power system operates safely and efficiently, and if the stability is not analyzed, the impact of a fault on the stability cannot be estimated, and the system may become unstable in unexpected situations. In addition, if the stability of the system is ensured without analyzing the stability of the system, the operation of the system must be made conservative, thereby reducing efficiency. With the rapid development of social economy and technology, the scale of the power system is increasing, the proportion of renewable energy is gradually increasing, and the number of power electronic devices in the power grid is also increasing, making the stability problem of the power system more complex.

[0003] Existing stability evaluation methods can be divided into methods not based on Lyapunov functions and methods based on Lyapunov functions. The method based on Lyapunov function is committed to finding a stable region of the equilibrium point after a fault, and if the initial state of the system after the fault is located within the stable region, it can be asserted that the system will eventually converge to the equilibrium point. The method based on the sum of squares is a new algorithm tool proposed in recent decades, which can systematically construct a Lyapunov function and determine a large enough approximate attractor domain in the model of the power system considering the transfer conductance. However, the result of the attractor domain estimation is greatly affected by the condition of the system vector field, and the equilibrium surface or unstable equilibrium point in the vector field close to the equilibrium point may hinder the expansion of the attractor domain to be estimated, resulting in an overly conservative attractor domain estimation result. SUMMARY

[0004] To solve the above technical problems, the present application provides a method and device for optimizing parameters of a grid-following controller, which optimizes the parameters of the grid-following controller in the power grid system and improves the attractor domain estimation effect of the grid-following controller acting on the power system.

[0005] The first aspect of the present application provides a method for optimizing parameters of a grid-following controller, the method comprising:

[0006] constructing a dynamic model of the grid-following controller in the power system to be analyzed, performing dynamic analysis on the dynamic model of the grid-following controller, and obtaining a state vector equation;

[0007] using an extended inner domain method based on Lyapunov's second method, combining the sum of squares theory to solve the attractor domain of the power system to be analyzed, and obtaining the attractor domain estimation result in the power system to be analyzed;

[0008] A time-domain simulation of the fault state of the power system to be analyzed is performed to obtain the true critical removal time and the state variable values ​​at each moment. The state variable values ​​at each moment are substituted into the Lyapunov function to obtain multiple Lyapunov function values. The moment corresponding to the Lyapunov function value that meets the preset conditions is selected as the estimated critical removal time. The true critical removal time and the estimated critical removal time are used to obtain the error index of the attraction domain estimation result.

[0009] If the error index is greater than the preset index, the optimal parameter search is performed based on the state vector equation using axial search. When the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed, and the updated attraction domain estimation result is obtained. Based on the updated attraction domain estimation result, the pattern search and axial search are used to continue searching for the optimal solution of the parameters of the grid controller until the step size is less than the second preset value, and the optimal parameter result is obtained.

[0010] In a possible implementation of the first aspect, a dynamic analysis is performed on a dynamic model of the network controller to obtain a state vector equation, including:

[0011] The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are:

[0012]

[0013] Among them, Δδ c is the controller’s equivalent relative phase angle, Δδ c =θ pll -θ ref , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, is the q-axis voltage at the common coupling point where the grid-following control power supply is connected to the grid, f n is the system rated frequency, is the rated speed of the system, K p and K i It is an adjustable parameter of the network controller.

[0014] The state vector equation of the reference synchronous generator is:

[0015]

[0016] in, is the relative speed of the reference synchronous generator, H ref is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, to refer to the mechanical power input of the synchronous generator, to refer to the electrical power output of the synchronous generator.

[0017] The state vector equation set of the rest of the synchronous generators in the system is:

[0018]

[0019] wherein, δ i is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, H i is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the mechanical power input of the synchronous generator, is the electrical power output of the synchronous generator.

[0020] In a possible implementation manner of the first aspect, an extended interior point method based on Lyapunov's second method is adopted, and a quadratic sum theory is used to solve the attraction domain of the power system to be analyzed, so as to obtain the attraction domain estimation result in the power system to be analyzed, including:

[0021] setting a criterion for ending the loop γ 0 : = 0, i: = 1, j: = 1, selecting an initial Lyapunov function, and solving by using an attraction domain estimation problem model, wherein the attraction domain estimation problem model is:

[0022] find (V 0 , λ1, λ3, s2)

[0023] s.t.

[0024]

[0025] wherein, is a set of quadratic sum polynomials, is a set of n-1 dimensional polynomial vectors, G is an equality constraint generated in a process of trigonometric function elimination of a system model, β is a positive number, r(z) is a positive definite polynomial, λ1 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in a positive point theorem, and s2 is a quadratic sum polynomial variable added to the inequality constraint β-r(z) in the positive point theorem; and are quadratic sum polynomials,

[0026] setting setting V (i) (z): = V (i-1) (z), γ: = γ(i-1) , transform the attraction domain estimation problem into the first square sum planning problem, obtain the first square sum optimization problem model and solve it to obtain the first solution result, where the first square sum optimization problem is:

[0027] (SOSP1)max c

[0028] st

[0029]

[0030] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ2 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, s1, s2, s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem, is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0031] Using the first solution result as a variable, the first square sum planning problem is transformed into a second square sum planning problem, and the second square sum optimization problem model is obtained and solved to obtain the second solution result. The second square sum optimization problem model is:

[0032] (SOSP2)maxβ

[0033] st

[0034]

[0035] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ1, λ2, and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s1, s2, and s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem. and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0036] The second solution results β and V(z) are stored as β (i) and V (i) (z), if β (i) -β (i-1) <∈β , then if j>1 and and If the maximum absolute value of the coefficient is less than the preset criterion, the estimation result of the attraction domain and the current Lyapunov function are obtained. If not, V is set. (0) (z):=V (i) (z), P 0 (z):=V (i )(z),β (0) :=c (i ), i: = 1, j = j + 1, re-solving the first square sum optimization problem model and the second square sum optimization problem model until the attraction domain estimation result and the Lyapunov function are obtained;

[0037] If β (i) -β (i-1) >∈ β Otherwise, set i=i+1 and re-solve the first and second square sum optimization problem models until the attraction domain estimation results and Lyapunov function are obtained.

[0038] In a possible implementation of the first aspect, the calculation formula of the error index is:

[0039]

[0040] Among them, t ref represents the true critical resection time, t cal Represents the estimated critical resection time.

[0041] In a possible implementation of the first aspect, performing optimal parameter search using axial search includes:

[0042] Along K p Direction search, if Then order like Then order Otherwise,

[0043] Along K i Direction search. Then order like Then order Otherwise, Among them, K p and K i is an adjustable parameter of the network controller, and d is the step size of the axial search.

[0044] In a possible implementation of the first aspect, searching for an optimal solution for parameters of a network controller using a pattern search includes:

[0045] set up Along y k =x k -x k-1 Direction search, if Δ(x k -αy k )<Δ(y k ), let x k+1 =x k -αy k Otherwise, let x k+1 =x k .

[0046] A second aspect of an embodiment of the present invention provides a network controller parameter optimization device, the device comprising:

[0047] A construction module is used to construct a dynamic model of a grid controller in the power system to be analyzed, perform dynamic analysis on the dynamic model of the grid controller, and obtain a state vector equation;

[0048] An estimation module is used to solve the attraction domain of the power system to be analyzed by using an extended inner domain method based on Lyapunov's second method and combining it with the sum of squares theory to obtain an estimation result of the attraction domain in the power system to be analyzed;

[0049] An error determination module is used to perform a time-domain simulation of the system fault state of the power system to be analyzed, obtain the true critical removal time and the state variable values ​​at each moment, substitute the state variable values ​​at each moment into the Lyapunov function to obtain multiple Lyapunov function values, select the moment corresponding to the Lyapunov function value that meets the preset conditions as the estimated critical removal time, and use the true critical removal time and the estimated critical removal time to obtain the error index of the attraction domain estimation result;

[0050] The parameter calculation module is used to search for the optimal parameters using axial search based on the state vector equation if the error index is greater than the preset index. When the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed to obtain an updated attraction domain estimation result. Based on the updated attraction domain estimation result, the pattern search and axial search are used to continue searching for the optimal solution of the parameters of the grid controller until the step size is less than the second preset value to obtain the optimal parameter result.

[0051] In a possible implementation of the second aspect, a dynamic analysis is performed on a dynamic model of the network controller to obtain a state vector equation, including:

[0052] The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are:

[0053]

[0054] Among them, Δδ c is the controller’s equivalent relative phase angle, Δδ c =θ pll -θ ref , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, is the q-axis voltage at the common coupling point where the grid-following control power supply is connected to the grid, f n is the system rated frequency, is the rated speed of the system, K p and K i It is an adjustable parameter of the network controller.

[0055] The state vector equation of the reference synchronous generator is:

[0056]

[0057] in, is the relative speed of the reference synchronous generator, H ref is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electrical power output.

[0058] The state vector equations of the remaining synchronous generators in the system are:

[0059]

[0060] Among them, δ i is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, H i is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the synchronous generator mechanical power input, The synchronous generator power output.

[0061] A third aspect of an embodiment of the present invention provides a computer device, including:

[0062] memory for storing computer programs;

[0063] The processor is configured to implement the network controller parameter optimization method of the first aspect when executing the computer program.

[0064] A fourth aspect of an embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the network controller parameter optimization method according to the first aspect are implemented.

[0065] The embodiment of the present invention provides a method of constructing a dynamic model of a grid controller in the power system to be analyzed, dynamically analyzing the dynamic model of the grid controller, obtaining a state vector equation, adopting an extended inner domain method based on Lyapunov's second method, combining with the sum of squares theory to solve the attraction domain of the power system to be analyzed, and obtaining an estimation result of the attraction domain in the power system to be analyzed; performing a time domain simulation of the system fault state of the power system to be analyzed, obtaining the true critical resection time and the state variable values ​​at each moment, substituting the state variable values ​​at each moment into the Lyapunov function, obtaining multiple Lyapunov function values, selecting the moment corresponding to the Lyapunov function value that meets the preset conditions as the estimated critical resection time, and using the true critical resection time to estimate the critical resection time. The error index of the attraction domain estimation result is obtained by dividing the time and estimating the critical resection time; if the error index is greater than the preset index, the optimal solution of the parameters of the grid controller is searched by axial search based on the state vector equation. When the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed to obtain an updated attraction domain estimation result. Based on the updated attraction domain estimation result, the optimal solution of the parameters of the grid controller is continued to be searched by pattern search and axial search until the step size is less than the second preset value to obtain the optimal parameter result. This method improves the attraction domain estimation effect of the grid controller on the power system by optimizing the parameters of the grid controller in the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 : A flow chart of a parameter optimization method according to an embodiment of the network controller parameter optimization method provided by the present invention;

[0067] Figure 2 : A structural block diagram of a network controller according to an embodiment of the network controller parameter optimization method provided by the present invention;

[0068] Figure 3 : A flow chart of an algorithm for estimating the power system attraction domain of a grid controller according to one embodiment of the grid controller parameter optimization method provided by the present invention;

[0069] Figure 4 : A flow chart of a network controller parameter optimization algorithm for a network controller according to one embodiment of a network controller parameter optimization method provided by the present invention;

[0070] Figure 5 : A calculation example data diagram of an embodiment of the network controller parameter optimization method provided by the present invention;

[0071] Figure 6 : A diagram of the domain of attraction of an example of an embodiment of the network controller parameter optimization method provided by the present invention;

[0072] Figure 7 : A device block diagram of an embodiment of the network controller parameter optimization method provided by the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0074] Currently, although sum-of-squares-based methods can systematically construct Lyapunov functions and determine a sufficiently large approximate attraction domain in power system models that consider transfer conductance, the estimated attraction domain is significantly affected by the state of the system's vector field. Equilibrium surfaces or unstable equilibrium points close to equilibrium points in the vector field may hinder the expansion of the estimated attraction domain, resulting in overly conservative results.

[0075] Based on this, an embodiment of the present application provides a method and device for optimizing the parameters of a network controller, which aims to find suitable network controller parameters when running the algorithm for estimating the attraction domain so that the dynamic characteristics of the entire system are more uniform, so as to obtain the maximized attraction domain estimation results and improve computational efficiency.

[0076] Please refer to Figure 1 , which is a flow chart of an embodiment of a method for optimizing network controller parameters provided by an embodiment of the present invention, including steps S101 to S104, each of which is specifically as follows:

[0077] S101. Construct a dynamic model of a grid controller in the power system to be analyzed, perform dynamic analysis on the dynamic model of the grid controller, and obtain a state vector equation.

[0078] In this embodiment, a dynamic model of the network controller in the system to be analyzed is designed, and based on the dynamic model, a transfer function and a state vector equation group of the network controller are derived.

[0079] In some embodiments, step S101 of "dynamically analyzing the dynamic model of the network controller to obtain a state vector equation" includes but is not limited to the following steps:

[0080] The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are:

[0081]

[0082] Among them, Δδ c is the controller’s equivalent relative phase angle, Δδ c =θ pll -θ ref , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, is the q-axis voltage at the common coupling point where the grid-following control power supply is connected to the grid, f n is the system rated frequency, is the rated speed of the system, K p and K i It is an adjustable parameter of the network controller.

[0083] The state vector equation of the reference synchronous generator is:

[0084]

[0085] in, is the relative speed of the reference synchronous generator, H ref is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electrical power output.

[0086] The state vector equations of the remaining synchronous generators in the system are:

[0087]

[0088] Among them, δ i is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, H i is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the synchronous generator mechanical power input, The synchronous generator power output.

[0089] Specifically, the model structure of the network controller is as follows: Figure 2 As shown, in which Figure 2 middle, is the phase a component of the common connection point voltage, is the b-phase component of the common connection point voltage, is the c-phase component of the common connection point voltage, is the d-axis component of the common connection point voltage, is the q-axis component of the common connection point voltage, K p is the proportional adjustment coefficient of the grid controller, K i is the integral adjustment coefficient of the grid controller, ω n is the rated speed, f n is the rated frequency, θ pll is the phase difference of the phase-locked loop.

[0090] The dynamic characteristics of the system include the speed and phase difference between the synchronous generator and the grid-controlled power supply. The state vector equation of the power supply connected to the power system using the grid-controlled power supply in the dynamic analysis is:

[0091]

[0092] Among them, Δδ c is the controller’s equivalent relative phase angle, Δδ c =θ pll -θ ref , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, is the q-axis voltage at the common coupling point where the grid-following control power supply is connected to the grid, f n is the system rated frequency, is the rated speed of the system, K p and K i It is an adjustable parameter of the network controller.

[0093] At the same time, the models of the system reference synchronous generator and the remaining synchronous generators are constructed. The state vector equation of the system reference synchronous generator is:

[0094]

[0095] in, is the relative speed of the reference synchronous generator, H ref is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electrical power output.

[0096] The state vector equations of the remaining synchronous generators in the system are:

[0097]

[0098] Among them, δ i is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, H i is the moment of inertia of the synchronous generator, is a damping coefficient of the synchronous generator, is a mechanical power input of the synchronous generator, is an electrical power output of the synchronous generator.

[0099] S102, an extended interior domain method based on the second Lyapunov method is adopted, and an attraction domain of the power system to be analyzed is solved by combining a square sum theory to obtain an attraction domain estimation result in the power system to be analyzed.

[0100] In the embodiment, a solution algorithm of the controller parameter optimization index is designed, the system attraction domain estimation algorithm adopts the extended interior domain method based on the second Lyapunov method, and the attraction domain of the power system to be analyzed is solved by combining the square sum theory to obtain the attraction domain estimation result in the power system to be analyzed.

[0101] In some embodiments, the step S102 of “adopting the extended interior domain method based on the second Lyapunov method, combining the square sum theory to solve the attraction domain of the power system to be analyzed, and obtaining the attraction domain estimation result in the power system to be analyzed” includes but is not limited to the following steps:

[0102] Setting a criterion for ending the loop γ 0 : = 0, i : = 1, j : = 1, selecting an initial Lyapunov function, and solving by using an attraction domain estimation problem model, wherein the attraction domain estimation problem model is:

[0103] find (V 0 , λ1, λ3, s2)

[0104] s.t.

[0105]

[0106] wherein, is a set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is an equality constraint generated in a system model de-trigonometric function process, β is a positive number, r(z) is a positive definite polynomial, λ1 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in a positive point theorem, and s2 is a square sum polynomial variable added to the inequality constraint β-r(z) in the positive point theorem; and is a square sum polynomial,

[0107] Setting Setting V (i) (z) : = V (i-1) (z), γ : = γ (i-1), transforming the attraction domain estimation problem into a first square sum planning problem, obtaining a first square sum optimization problem model and solving it to obtain a first solution result, wherein the first square sum optimization problem is:

[0108] (SOSP1)max c

[0109] st

[0110]

[0111] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ2 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, s1, s2, s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem, is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0112] Using the first solution result as a variable, the first square sum planning problem is transformed into a second square sum planning problem, a second square sum optimization problem model is obtained and solved, and a second solution result is obtained, wherein the second square sum optimization problem model is:

[0113] (SOSP2)maxβ

[0114] st

[0115]

[0116] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ1, λ2, and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s1, s2, and s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem. and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0117] The second solution results β and V(z) are stored as β (i) and V (i) (z), if β (i) -β (i-1) <∈β , then if j>1 and and If the maximum absolute value of the coefficient is less than the preset criterion, the estimation result of the attraction domain and the current Lyapunov function are obtained. If not, V is set. (0) (z):=V (i) (z), P 0 (z):=V (i) (z), β (0) :=c (i) , i:=1, j=j+1, resolving the first sum-of-squares optimization problem model and the second sum-of-squares optimization problem model until the attraction domain estimation result and the Lyapunov function are obtained;

[0118] If β (i) -β (i-1) >∈ β Otherwise, set i=i+1 and re-solve the first sum-of-squares optimization problem model and the second sum-of-squares optimization problem model until the attraction domain estimation result and the Lyapunov function are obtained.

[0119] Specifically, the process of the power system attraction region estimation algorithm is as follows: Figure 3 As shown in Figure 2, Feasratio is the quality indicator for solving the SOSP2 optimization problem. The algorithm uses an extended inner domain method based on Lyapunov's second method, combined with the sum of squares theory for optimization. The basic iterative process of the algorithm is as follows:

[0120] Step 1: Set the criteria for loop end Assume γ 0 :=0,i:=1,j:=1. The initial Lyapunov function V is selected by solving the following square sum optimization problem 0 .

[0121] find(V 0 ,λ1,λ3,s2)

[0122] st

[0123]

[0124] in, is the set of square sum polynomials, It is a set of n-1 dimensional polynomial vectors. The algorithm converts the original multiple equality constraints and inequality constraints into polynomial form through the P-satz theorem. G is the equality constraint generated during the process of de-trigonometric function of the system model. β is a positive number, r(z) is a positive definite polynomial, λ1 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s2 is the sum of squares polynomial variable added to the inequality constraint β-r(z) in the positive point theorem; and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0125] Step 2: Setup

[0126] Step 3: Set V (i) (z):=V (i-1) (z),γ:=γ (i-1) , and solve the following sum-of-squares optimization problem

[0127] (SOSP1)max c

[0128] st

[0129]

[0130] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ2 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, s1, s2, s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem, is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0131] Store the solution results c, s2(z), and s3(z) as c (i) 、

[0132] Step 4: Set c:=c (i) , And solve the following sum-of-squares optimization problem:

[0133] (SOSP2)maxβ

[0134] st

[0135]

[0136] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ1, λ2, and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s1, s2, and s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem. and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0137] in is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated in the process of detrigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ1, λ2, and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s1, s2, and s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem. and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0138] Store the solution results β and V(z) as β (i) and V (i) (z).

[0139] Step 5: If β (i) -β (i-1) <∈ β , then go to step 6, otherwise set i=i+1 and go to step 3.

[0140] Step 6: If j>1 and and The maximum absolute value of the coefficient is less than ∈ l , go to step 7. Otherwise, set V (0) (z):=V (i) (z), P 0 (z):=V (i) (z), β (0) :=c (i) , i:=1, j=j+1, and go to step 2.

[0141] Step 7: Output V (i) (z) and c (i) .

[0142] S103. Perform a time domain simulation of the fault state of the power system to be analyzed to obtain the true critical resection time and the state variable values ​​at each moment, substitute the state variable values ​​at each moment into the Lyapunov function to obtain multiple Lyapunov function values, select the moment corresponding to the Lyapunov function value that meets the preset conditions as the estimated critical resection time, and use the true critical resection time and the estimated critical resection time to obtain the error index of the attraction domain estimation result.

[0143] In this embodiment, the parameter optimization index of the grid controller is the error of the critical clearing time (CCT) of a specific fault in the power system.

[0144] After obtaining the attraction domain estimation results using the aforementioned power system attraction domain estimation algorithm, a specific fault point and fault type are selected within the power system, and a time-domain simulation of the power system's post-fault state is performed to determine its accurate critical reversal time. Simultaneously, the state variable values ​​at each moment in the time-domain simulation are substituted into the Lyapunov function obtained from the attraction domain estimation. The moment when the Lyapunov function value first exceeds 1 is selected as the estimated critical reversal time. The error index of the attraction domain estimation result is derived using the true critical reversal time and the estimated critical reversal time.

[0145] In some embodiments, the "error indicator" in step S103 further includes but is not limited to the following steps:

[0146] The calculation formula of the error index is:

[0147]

[0148] Among them, t ref represents the true critical resection time, t cal Represents the estimated critical resection time.

[0149] Specifically, the calculation formula of the error index is:

[0150]

[0151] Among them, t ref represents the true critical resection time, t cal Represents the estimated critical resection time.

[0152] S104. If the error index is greater than the preset index, the optimal solution of the parameters of the grid controller is searched using the axial search based on the state vector equation. When the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed, and an updated attraction domain estimation result is obtained. Based on the updated attraction domain estimation result, the optimal solution of the parameters of the grid controller is continued to be searched using the pattern search and the axial search until the step size is less than the second preset value, and the optimal parameter result is obtained.

[0153] In this embodiment, when the error of the estimation result of the attraction domain is large, the pattern search method is used to search for the optimal solution of the two parameters of the network controller. Specifically, the algorithm flow of the network controller parameter optimization is as follows: Figure 4 As shown in the figure, the pattern search method is divided into two parts, axial search and pattern search, which run alternately. Each cycle includes one axial search and one pattern search, so as to approach the optimal value faster. At the same time, the internal iteration process of the attraction domain estimation algorithm is modified during the axial search iteration. When the search step size is reduced to a certain extent, the initial Lyapunov function obtained in the previous iteration is used as the initial Lyapunov function of the current iteration, thereby reducing the calculation time. That is, when the step size d of the axial search is reduced to a certain extent, the iterative process of the attraction domain estimation algorithm skips steps 1 and 2 and uses the initial Lyapunov function V calculated in the previous step of the axial search instead. 0 and l1 and l2 are input into step 3.

[0154] By selecting appropriate parameter optimization metrics, the aforementioned network-tracking controller parameter optimization algorithm automates both the system's domain of attraction solution and accuracy assessment, enabling automated optimization, reducing the risk of human error and improving algorithm efficiency. Furthermore, modifications to the domain of attraction estimation algorithm effectively alleviate the algorithm's computational burden for higher-order systems, enabling the system to quickly obtain optimal controller parameters.

[0155] In some embodiments, the step S104 of "using axial search to search for optimal parameters" further includes but is not limited to the following steps:

[0156] Along K p Direction search, if Then order like Then order Otherwise,

[0157] Along K i Direction search. Then order like Then order Otherwise, Among them, K p and K i is an adjustable parameter of the network controller, and d is the step size of the axial search.

[0158] Specifically, when the algorithm is running, the network controller parameter K is first set p and K i The initial value of , the axial search strategy is:

[0159] Step 11: Along the K p Direction search, if Then order like Then order Otherwise,

[0160] Step 12: Along the K i Direction search. Then order like Then order Otherwise, Among them, K p and K i is an adjustable parameter of the network controller, and d is the step size of the axial search.

[0161] In some embodiments, in step S104, "searching for an optimal solution for the parameters of the network controller using a pattern search" further includes but is not limited to the following steps:

[0162] set up Along y k =x k -x k-1 Direction search, if Δ(x k -αy k )<Δ(y k ), let x k+1 =x k -αy k Otherwise, let x k+1 =x k .

[0163] Specifically, the pattern search strategy is:

[0164] set up Along y k =x k -x k-1 Direction search, if Δ(x k -αy k )<Δ(y k ), let x k+1 =x k -αy k Otherwise, let x k+1 =x k .

[0165] The parameters of the grid-following controller include integral coefficient and proportional coefficient. The parameterization index is selected as the error of the critical clearing time of a specified fault at a specified node in the grid estimated by the attraction domain.

[0166] This method designs a dynamic model of the grid controller and derives the controller transfer function and state vector equations. An algorithm is then developed to solve the controller parameter optimization metrics. Using the error in the estimated attraction domain of the system as a metric, representative nodes (or multiple nodes) and fault types are selected from the system. The critical cut-off time (CCT) is calculated using both time-domain simulation and the estimated attraction domain using the algorithm. The error between the two is used as a metric for the error in the estimation. Finally, a strategy for optimizing the grid controller parameters is designed. Initial values ​​for the controller parameters are set, the attraction domain estimation algorithm is run, and the accuracy of the resulting attraction domain is evaluated. A pattern search method is used to search for the optimal solution for the controller parameters. Once the adjustment range of the pattern search method is reduced to the set accuracy, the internal iteration process of the attraction domain estimation algorithm is modified to reduce computational time. Furthermore, when running the attraction domain estimation algorithm, appropriate grid controller parameters are found to achieve a more uniform dynamic characteristic across the entire system, thereby maximizing the attraction domain estimation result and improving computational efficiency. This enhances the effectiveness of the grid controller in estimating the attraction domain of the power system.

[0167] This application also implements the technical solution provided in the embodiment of this application, selects a 3-machine 9-node system model, which has two synchronous generators and a motor controlled by the grid, and sets the initial parameters to Step size d=6,ε1=1,ε2=1. Figure 5 is the parameter K in the iterative process of the optimization algorithm p ,K i The values ​​selected are: parameter groups 2, 3, 4 and 6, 7, 8, 9, 10, and 11 are the results of the axial search, and parameter group 5 is the result of the axial search. During the iteration process, the optimization index Δ gradually decreases, and the step size also gradually decreases as the search fails, eventually obtaining the optimal result.

[0168] Select the parameter groups 1, 2, 4, 5, 10, and 11 with the reduced optimization index Δ to draw the attraction domain image as shown in the figure. Figure 6 As shown in the figure, the horizontal and vertical coordinates are selected respectively. As the optimization index Δ decreases, the range of the attraction domain gradually expands. Only when the adjustment range from data group 10 to data group 11 is extremely small, there is a non-positive correlation, which is enough to prove the rationality of the selection of the optimization index Δ.

[0169] This method maximizes the performance of the doubly-fed wind turbine converter. During the initial stages of a power system transient oscillation, the switching controller allows the four output variables of the doubly-fed wind turbine to converge to a certain neighborhood near the equilibrium point at the fastest possible speed. A conventional vector controller is then used, leveraging its optimality near the equilibrium point and allowing the system to asymptotically stabilize to the original equilibrium point. Furthermore, the switching controller proposed in this method only includes logical operations, resulting in a smaller phase lag between its output and input than a conventional vector controller. This allows the switching controller to respond more quickly to oscillations in the doubly-fed wind turbine's output variables. Furthermore, the switching controller's control signal has only two values, facilitating control signal transmission.

[0170] The application of the above method in the coordinated control of doubly fed wind turbines can greatly improve the transient stability of the power system operation containing large-scale wind power.

[0171] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0172] In some embodiments, as Figure 7 As shown, it shows a block diagram of a network controller parameter optimization device 700 provided in an embodiment of the present application, including: a construction module 701, an estimation module 702, an error determination module 703 and a parameter calculation module 704, wherein:

[0173] A construction module 701 is used to construct a dynamic model of a grid controller in the power system to be analyzed, perform dynamic analysis on the dynamic model of the grid controller, and obtain a state vector equation;

[0174] An estimation module 702 is configured to employ an extended inner domain method based on Lyapunov's second method in combination with the sum-of-squares theory to solve the attraction domain of the power system to be analyzed, thereby obtaining an estimation result of the attraction domain in the power system to be analyzed;

[0175] Error determination module 703 is configured to perform time-domain simulation of the power system under analysis and the fault state to obtain the true critical removal time and the state variable values ​​at each time instant, substitute the state variable values ​​at each time instant into the Lyapunov function to obtain multiple Lyapunov function values, select the time instant corresponding to the Lyapunov function value that meets preset conditions as the estimated critical removal time, and use the true critical removal time and the estimated critical removal time to obtain an error indicator for the attraction domain estimation result;

[0176] The parameter calculation module 704 is used to search for the optimal solution of the parameters of the grid controller based on the state vector equation using axial search if the error index is greater than the preset index. When the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed to obtain an updated attraction domain estimation result. Based on the updated attraction domain estimation result, the pattern search and axial search are used to continue searching for the optimal solution of the parameters of the grid controller until the step size is less than the second preset value to obtain the optimal parameter result.

[0177] In one embodiment, a dynamic analysis is performed on the dynamic model of the network controller to obtain a state vector equation, including:

[0178] The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are:

[0179]

[0180] Among them, Δδ c is the controller’s equivalent relative phase angle, Δδ c =θ pll -θ ref , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, is the q-axis voltage at the common coupling point where the grid-following control power supply is connected to the grid, f n is the system rated frequency, is the rated speed of the system, K p and K i It is an adjustable parameter of the network controller.

[0181] The state vector equation of the reference synchronous generator is:

[0182]

[0183] in, is the relative speed of the reference synchronous generator, H ref is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electrical power output.

[0184] The state vector equations of the remaining synchronous generators in the system are:

[0185]

[0186] Among them, δ i is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, H i is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the synchronous generator mechanical power input, The synchronous generator power output.

[0187] In one embodiment, an extended inner domain method based on Lyapunov's second method is used in combination with the sum-of-squares theory to solve the attraction domain of the power system to be analyzed, and an estimation result of the attraction domain in the power system to be analyzed is obtained, including:

[0188] Set the criterion for loop end γ 0 :=0,i:=1,j:=1, select the initial Lyapunov function and use the attraction domain estimation problem model to solve it, where the attraction domain estimation problem model is:

[0189] find(V 0 ,λ1,λ3,s2)

[0190] st

[0191]

[0192] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated in the process of detrigonometric function of the system model, β is a positive number, r(z) is a positive definite polynomial, λ1 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s2 is the sum of squares polynomial variable added to the inequality constraint β-r(z) in the positive point theorem; and is a square sum polynomial,

[0193] set up Setting V (i) (z):=V (i-1)(z), γ=γ (i-1) , transform the attraction domain estimation problem into the first square sum planning problem, obtain the first square sum optimization problem model and solve it to obtain the first solution result, where the first square sum optimization problem is:

[0194] (SOSP1)max c

[0195] st

[0196]

[0197] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ2 and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, s1, s2, s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem, is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0198] Using the first solution result as a variable, the first square sum planning problem is transformed into a second square sum planning problem, and the second square sum optimization problem model is obtained and solved to obtain the second solution result. The second square sum optimization problem model is:

[0199] (SOSP2)maxβ

[0200] st

[0201]

[0202] in, is the set of square sum polynomials, is a set of n-1 dimensional polynomial vectors, G is the equality constraint generated during the process of de-trigonometric function of the system model, β and c are positive numbers, p is a positive definite polynomial, λ1, λ2, and λ3 are n-1 dimensional polynomial variables added to the equality constraint G in the positive point theorem, and s1, s2, and s3 are square sum polynomial variables added to the inequality constraint in the positive point theorem. and is a square sum polynomial, used to ensure that each constraint is semi-positive definite,

[0203] The second solution results β and V(z) are stored as β (i) and V (i) (z), if β (i) -β(i-1) <∈ β , then if j>1 and and If the maximum absolute value of the coefficient is less than the preset criterion, the estimation result of the attraction domain and the current Lyapunov function are obtained. If not, V is set. (0) (z):=V (i) (z), P 0 (z):=V (i) (z), β (0) :=c (i) , i: = 1, j = j + 1, re-solve the first square sum optimization problem model and the second square sum optimization problem model until the attraction domain estimation result and Lyapunov function are obtained;

[0204] If β (i) -β (i-1) >∈ β Otherwise, set i=i+1 and re-solve the first and second square sum optimization problem models until the attraction domain estimation results and Lyapunov function are obtained.

[0205] In one embodiment, the error index is calculated as follows:

[0206]

[0207] Among them, t ref represents the true critical resection time, t cal Represents the estimated critical resection time.

[0208] In one embodiment, the axial search is used to search for the optimal solution of the parameters of the network controller, including:

[0209] Along K p Direction search, if Then order like Then order Otherwise,

[0210] Along K i Direction search. Then order like Then order Otherwise, Among them, K p and K i is an adjustable parameter of the network controller, and d is the step size of the axial search.

[0211] In one embodiment, searching for an optimal solution for network controller parameters using a pattern search includes:

[0212] set up Along y k =x k -x k-1 Direction search, if Δ(x k -αy k )<Δ(y k ), let x k+1 =x k -αy k Otherwise, let x k+1 =x k .

[0213] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0214] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the above steps are implemented when the computer program is executed by a processor; the computer-readable storage medium provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0215] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0216] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0217] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for optimizing network controller parameters, characterized in that: include: Constructing a dynamic model of a grid controller in the power system to be analyzed, and performing dynamic analysis on the dynamic model of the grid controller to obtain a state vector equation; An extended inner domain method based on Lyapunov's second method is used in combination with the sum-of-squares theory to solve the attraction domain of the power system to be analyzed, thereby obtaining an estimation result of the attraction domain in the power system to be analyzed; Performing a time-domain simulation of a system fault state on the power system to be analyzed to obtain a true critical removal time and state variable values ​​at each moment, substituting the state variable values ​​at each moment into a Lyapunov function to obtain multiple Lyapunov function values, selecting the moment corresponding to the Lyapunov function value that meets preset conditions as the estimated critical removal time, and using the true critical removal time and the estimated critical removal time to obtain an error index of the attraction domain estimation result; If the error index is greater than the preset index, the optimal parameter search is performed based on the state vector equation using axial search. When the step size is less than the first preset value, the attraction domain of the power system to be analyzed is solved using the initial Lyapunov function obtained last time to obtain an updated attraction domain estimation result. Based on the updated attraction domain estimation result, the optimal solution of the parameters of the grid controller is continued to be searched using pattern search and axial search until the step size is less than the second preset value and the optimal parameter result is obtained. The calculation formula of the error index is: in, represents the real critical resection time, represents the estimated critical resection time; The method of searching for optimal parameters by using axial search includes: along Direction search, if , then let ;like , then let Otherwise, ; along Direction search, , then let ;like , then let Otherwise, ,in, and To be the adjustable parameters of the network controller, is the step size of the axial search.

2. The method for optimizing network controller parameters according to claim 1, wherein: The dynamic analysis of the dynamic model of the network controller is performed to obtain a state vector equation, including: The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are: in, is the controller equivalent relative phase angle, , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, The common coupling point for the grid control power supply to be connected to the grid Shaft voltage, is the system rated frequency, is the rated speed of the system, and It is an adjustable parameter for the network controller; The state vector equation of the reference synchronous generator is: in, is the relative speed of the reference synchronous generator, is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electric power output; The state vector equations of the remaining synchronous generators in the system are: in, is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the synchronous generator mechanical power input, The synchronous generator power output.

3. The method for optimizing network controller parameters according to claim 1, wherein: The extended inner domain method based on Lyapunov's second method is used in combination with the sum-of-squares theory to solve the attraction domain of the power system to be analyzed, and the estimation result of the attraction domain in the power system to be analyzed is obtained, including: Set the criterion for loop end , , , select the initial Lyapunov function and solve it using the attraction domain estimation problem model, where the attraction domain estimation problem model is: in, is the set of square sum polynomials, yes A set of n-dimensional polynomial vectors, The equality constraints generated during the detrigonometric function process of the system model, ; is a positive number, is a positive definite polynomial, 、 is the equality constraint in the punctuality theorem Additional -dimensional polynomial variables, For the inequality constraints in the punctual theorem Additional sum of squares polynomial variables; and is a square sum polynomial, ; set up , ,set up , , transforming the attraction domain estimation problem into a first square sum planning problem, obtaining a first square sum optimization problem model and solving it to obtain a first solution result, wherein the first square sum optimization problem is: in, is the set of square sum polynomials, A set of n-dimensional polynomial vectors, The equality constraints generated during the detrigonometric function process of the system model, ; 、 is a positive number, is a positive definite polynomial, 、 is the equality constraint in the punctuality theorem Additional -dimensional polynomial variables, 、 、 is the sum of squares polynomial variable added to the inequality constraint in the punctual theorem, is a square sum polynomial, used to ensure that each constraint is semi-positive definite, ; Using the first solution result as a variable, the first square sum planning problem is transformed into a second square sum planning problem, a second square sum optimization problem model is obtained and solved, and a second solution result is obtained, wherein the second square sum optimization problem model is: in, is the set of square sum polynomials, A set of n-dimensional polynomial vectors, The equality constraints generated during the detrigonometric function process of the system model, 、 is a positive number, is a positive definite polynomial, 、 、 is the equality constraint in the punctuality theorem Additional -dimensional polynomial variables, 、 、 is the sum of squares polynomial variable added to the inequality constraint in the punctual theorem, and is a square sum polynomial, used to ensure that each constraint is semi-positive definite, ; The second solution result Stored as and ,like , then if and and If the maximum absolute value of the coefficient is less than the preset criterion, the estimation result of the attraction domain and the current Lyapunov function are obtained. If not, set , , , , , resolving the first sum-of-squares optimization problem model and the second sum-of-squares optimization problem model until an attraction domain estimation result and a Lyapunov function are obtained; like , otherwise , re-solving the first sum-of-squares optimization problem model and the second sum-of-squares optimization problem model until the attraction domain estimation result and the Lyapunov function are obtained.

4. The method for optimizing network controller parameters according to claim 1, wherein: The method of searching for an optimal solution for the parameters of the network controller using a pattern search includes: set up ,along Direction search, if ,make Otherwise, let .

5. A network controller parameter optimization device, characterized in that: The method for optimizing network controller parameters according to any one of claims 1 to 4 comprises: A construction module is used to construct a dynamic model of a grid controller in the power system to be analyzed, and to perform dynamic analysis on the dynamic model of the grid controller to obtain a state vector equation; an estimation module, configured to solve the attraction domain of the power system to be analyzed by using an extended inner domain method based on Lyapunov's second method in combination with the sum-of-squares theory, and obtain an estimation result of the attraction domain in the power system to be analyzed; an error determination module, configured to perform a time-domain simulation of a system fault state of the power system to be analyzed, obtain a true critical removal time and a state variable value at each moment, substitute the state variable value at each moment into a Lyapunov function to obtain a plurality of Lyapunov function values, select a moment corresponding to a Lyapunov function value that satisfies a preset condition as an estimated critical removal time, and use the true critical removal time and the estimated critical removal time to obtain an error indicator of the attraction domain estimation result; A parameter calculation module is used to search for the optimal parameters using axial search based on the state vector equation if the error index is greater than the preset index; when the step size is less than the first preset value, the initial Lyapunov function obtained last time is used to solve the attraction domain of the power system to be analyzed to obtain an updated attraction domain estimation result; based on the updated attraction domain estimation result, the pattern search and axial search are used to continue searching for the optimal solution of the parameters of the grid controller until the step size is less than the second preset value to obtain the optimal parameter result.

6. The network controller parameter optimization device according to claim 5, characterized in that: The dynamic analysis of the dynamic model of the network controller is performed to obtain a state vector equation, including: The dynamic model of the grid controller is dynamically analyzed, and the state vector equations of the grid control power supply are obtained in the dynamic analysis. The state vector equations are: in, is the controller equivalent relative phase angle, , is the equivalent speed deviation of the controller, is the speed deviation of the system reference synchronous generator, The common coupling point for the grid control power supply to be connected to the grid Shaft voltage, is the system rated frequency, is the rated speed of the system, and It is an adjustable parameter for the network controller; The state vector equation of the reference synchronous generator is: in, is the relative speed of the reference synchronous generator, is the reference moment of inertia of the synchronous generator, is the reference synchronous generator damping coefficient, is the reference synchronous generator mechanical power input, is the reference synchronous generator electric power output; The state vector equations of the remaining synchronous generators in the system are: in, is the phase angle of the synchronous generator, is the relative speed of the synchronous generator, is the moment of inertia of the synchronous generator, is the damping coefficient of the synchronous generator, is the synchronous generator mechanical power input, The synchronous generator power output.

7. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the network controller parameter optimization method according to any one of claims 1 to 4 when executing the computer program.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the network controller parameter optimization method according to any one of claims 1 to 4.

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