Converter multi-parameter collaborative stabilization design method of multi-node direct current power supply and distribution system

Abstract

The embodiment of the invention discloses a converter multi-parameter collaborative stabilization design method for a multi-node direct-current power supply and distribution system, and relates to the technical field of direct-current power supply and distribution. Aiming at the broadband stable operation requirement of a multi-node complex direct current power supply and distribution system, a multi-parameter collaborative stabilization design method with universality is designed, the superimposed effect of parameters on the global stability influence can be considered, collaborative optimization of multiple parameters of a converter is supported, and the stability of the converter is improved. The specific method is a multi-node multi-port converter parameter collaborative setting method based on the cross point sensitivity, and a scalar analysis framework based on the cross point sensitivity in a Nyquist diagram is provided. A multi-parameter collaborative optimization model is constructed in combination with a nonlinear programming technology, the bottleneck of a traditional method in universality and efficiency is broken through, and a universal solution is provided for stability design of a multi-node complex direct current power supply and distribution system.

Description

Technical Field

[0001] The present invention relates to the technical field of DC power supply and distribution, and particularly to a method for collaborative multi-parameter stability design of converters in a multi-node DC power supply and distribution system. Background Art

[0002] Due to its flexible power scheduling ability and high reliability requirements, multi-node complex DC power supply and distribution systems are widely used in fields such as microgrids and renewable energy consumption. In a multi-node complex DC system, there are potential interactions between the electrical characteristics and control strategies of each node subsystem. Coupled with the complexity of external environmental disturbances, it is easy for the system to resonate and interact or dynamically oscillate in multiple frequency ranges, ultimately resulting in deteriorated power quality, reduced operation efficiency, and even the risk of global out-of-control.

[0003] With the advantage of port characteristic description, the impedance / admittance analysis method does not rely on detailed internal system parameters and can evaluate stability through measured admittance data of bus nodes. Therefore, it is more practical in multi-node complex DC power supply and distribution systems. Existing impedance analyses using node impedance / admittance matrices or even return ratio matrices transfer the object of sensitivity description to eigenvalue trajectories, constructing participation functions or participation factors to reveal which ports are mainly responsible for potential instability.

[0004] However, these indicators can only relate to the external characteristics of ports and cannot reflect the direct quantitative relationship between system parameters (such as PI regulator gain, virtual impedance value) and global stability, resulting in the long-term dependence on empirical trial and error in the parameter tuning process. There are also solutions that introduce component sensitivity based on the chain rule at a specific frequency to identify key parameters affecting system stability, thereby guiding the stability design of system parameters.

[0005] In some current studies, sensitivity and parameter tuning schemes have been proposed and applied, but most of these schemes are only applicable to the scenario of adjusting a single parameter at a time, lacking a solution for the superposition effect of sensitivity in multi-parameter regulation. Eventually, the collaborative optimization ability of multi-port converter parameters is insufficient, making it difficult to further improve the stability of multi-node complex DC power supply and distribution systems. Summary of the Invention

[0006] An embodiment of the present invention provides a method for collaborative multi-parameter stability design of converters in a multi-node DC power supply and distribution system, which can realize collaborative multi-parameter stability design, improve the broadband stability of the system, and thus ensure the safe and stable operation of the multi-node complex DC power supply and distribution system by quantifying the impact of parameter changes on stability.

[0007] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0008] A multi-parameter collaborative stability design method for converters in a multi-node DC power supply and distribution system, including:

[0009] S1. Establish the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system, and obtain all the eigenvalue trajectories of Y(s);

[0010] S2. Determine the eigenvalue trajectory λ k (jω) that contains the intersection point on the negative real axis;

[0011] S3. Calculate the sensitivity of the intersection point of λ k (jω) and the negative real axis to the parameters in the port converter, and generate a sensitivity matrix;

[0012] S4. According to the sensitivity matrix, determine the corresponding relationship between the parameter setting value and the relative setting value, and then establish a non-linear optimization model;

[0013] S5. Utilize the output result of the non-linear optimization model. The output result includes the current parameter setting value, and the Y(s) can be updated and verified by using the output result. In practical applications, the operating state of the multi-node complex DC power supply and distribution system can be stabilized according to the determined parameter setting value.

[0014] Specifically, S1 includes: dividing the multi-node complex DC power supply and distribution system into an active port converter subsystem part and a passive network part; establishing the admittance matrix Y net (s) of the passive network part, and the matrix Y s (s) containing the port admittances of each active port converter subsystem; constructing Y(s) according to Y s (s) and Y net (s), and then performing matrix operations on Y(s) to obtain its eigenvalue trajectory matrix Λ(jω), where s represents the Laplace operator, j represents the imaginary unit, and ω represents the angular frequency.

[0015] Y s (s) is used to represent the port admittances of each active port converter subsystem, , where Y s1 (s) represents the output admittance of the active port converter subsystem 1, s1~sn represent the admittance numbers of the active port converter subsystems 1~n, and n represents the number of active port converter subsystems;

[0016] Y(s)=Y s (s)+Y net (s), , where Ψ(jω) and Φ(jω) are the left and right eigenvector matrices of Y(jω) respectively, and λ1(jω), λ2(jω), …, λ n(jω) represent the eigenvalue trajectories corresponding to the ports of the 1st to the nth active port converter subsystems respectively, and Y(jω) = Y(s)| s=jω . Among them, there are elements at all positions of the elements of Y(s), and during the parameter tuning process, the form of the elements of Y(s) does not affect the execution of the entire process. Therefore, the specific matrix form of Y(s) will not be elaborated.

[0017] Specifically, S2 includes: calculating the intersection points R of all eigenvalue trajectories and the negative real axis c， When there are intersection points of any eigenvalue trajectory with the negative real axis, it is determined that the DC power supply and distribution system at this time does not meet global stability, otherwise, time-domain simulation verification is directly carried out; among them, , ω r represents the phase crossover frequency of the eigenvalue trajectory and the negative real axis, and R(ω) and X(ω) represent the real part and the imaginary part of the eigenvalue trajectory λ k (jω) respectively; obtaining the eigenvalue trajectory λ k (jω), , Ψ k (jω) represents the kth row of Ψ(jω), and Φ k (jω) represents the kth column of Φ(jω).

[0018] Specifically, S3 includes: S31, calculating the sensitivity S k of the intersection point R ck,j (j = 1, 2, …, g) contained in the kth eigenvalue trajectory λ i (jω) with respect to the parameters α ck,j (i = 1, 2, …, h) of each port converter, where g represents the number of intersection points and h represents the total number of parameters in the port converter; S32, repeating the above process until the intersection point sensitivities of all eigenvalue trajectories are obtained and a sensitivity matrix S i is generated, and then S ck is converted into a relative sensitivity matrix through normalization processing ck . .

[0019] Among them, S31 includes: sensitivity , where , and represent the u-th element of Ψ k (jω) and the v-th element of Φ k (jω) respectively, ω r represents the frequency corresponding to the intersection point, and Y uv represents the element in the u-th row and v-th column of Y(jω).

[0020] In S32, S ckConverted to relative sensitivity matrix , including: , where α1 to α h represent the parameters of h port converters, represents the k-th eigenvalue trajectory λ k (jω) at the intersection point (R c,g , 0) with respect to the parameter α h of the intersection point sensitivity, and the relative sensitivity matrix is represented in matrix form as , where: , represents the k-th eigenvalue trajectory λ k (jω) at the intersection point (R c,g , 0) with respect to the parameter α h of the relative intersection point sensitivity.

[0021] Specifically, S4 includes: establishing the conversion relationship between the parameter setting value ∆α i and the relative setting value ∆E i ; constructing a non-linear optimization model with the overall minimum of the relative parameter setting value as the optimization goal.

[0022] Among them, the conversion relationship is ; the non-linear optimization model includes: , F is the optimization objective function; W i is the weight coefficient of α i , and the value of W i is negatively correlated with the setting priority of α i . The lower the setting priority of α i , the smaller the allowed relative parameter setting value ∆E i . l i and b i are the lower and upper bounds of the allowable change range of the relative setting value ∆E i during the stability improvement design process, represents the k-th eigenvalue trajectory λ k (jω) at the intersection point (R c,j , 0) with respect to the parameter α i of the relative intersection point sensitivity, represents the setting margin during the parameter setting process.

[0023] The converter multi-parameter collaborative stability design method for the multi-node DC power supply and distribution system provided by the embodiments of the present invention constructs the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system by establishing the port admittance models of each port converter subsystem and integrating them with passive networks. For the admittance matrix Y(s), all its eigenvalue trajectories are solved, and the global stability of the system is analyzed according to the surrounding characteristics of each eigenvalue trajectory around the origin. Select the eigenvalue trajectory λ k (jω) for parameter stability design, calculate the intersection points of the eigenvalue trajectory λ k (jω) and the negative real axis, and the sensitivities of the parameters α i (i = 1, 2, …, h) in the port converter, and form the sensitivity matrix S ck . Using the normalization principle, the sensitivity matrix S ck is converted into the relative sensitivity matrix to ensure the comparability between different parameter sensitivities. Using the relative sensitivity matrix , the conversion relationship between the parameter setting value and the relative setting value is established. Based on the optimization goal of the overall minimum of the relative setting values of the parameters during the stability design process, a non-linear optimization model is constructed. The relative adjustment amount of the parameters is iteratively solved based on the non-linear optimization model, and the solution result is used to update the admittance matrix Y(s). A new round of stability analysis and stability design is carried out for the updated admittance matrix Y(s), and time-domain simulation verification is implemented after meeting the stability conditions. This method proposes a scalar evaluation index based on the cross-point sensitivity of the Nyquist diagram, realizes the multi-parameter collaborative stability design by quantifying the influence of parameter changes on stability, improves the broadband stability of the system, and thus ensures the safe and stable operation of the multi-node complex DC power supply and distribution system. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a schematic diagram of the small-signal Norton equivalent circuit of the multi-node complex DC power supply and distribution system provided by the embodiments of the present invention; where represents the port current of port converter n, represents the port voltage of port converter n, represents the equivalent controlled current source after Norton equivalent of port converter n.

[0026] Figure 2Flow chart of the multi-parameter collaborative stability design method applicable to multi-node complex DC power supply and distribution systems provided by the embodiments of the present invention. Detailed implementation manners

[0027] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be construed as a limitation of the present invention. Those skilled in the art of the present technology can understand that unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any unit and all combinations of one or more of the associated listed items. Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as herein.

[0028] General design purpose of the embodiment of the present solution: Aiming at the broadband stable operation requirements of multi-node complex DC power supply and distribution systems, a general multi-parameter collaborative stability design method is designed, which can consider the superposition effect of the influence of parameters on global stability, support the collaborative optimization of multi-parameters of port converters, and thus provide a safety guarantee for the stable operation of multi-node complex DC power supply and distribution systems. The specific approach of this solution is a parameter collaborative tuning method for multi-node multi-port converters based on cross-point sensitivity. A scalar analysis framework based on cross-point sensitivity in the Nyquist diagram is proposed, and a multi-parameter collaborative optimization model is constructed in combination with nonlinear programming technology, breaking through the bottleneck of traditional methods in terms of generality and efficiency, and providing a general solution for the stability design of multi-node complex DC power supply and distribution systems.

[0029] The general design idea of ​​this embodiment is: by establishing the port admittance model of each port converter subsystem and integrating it with the passive network, the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system is further constructed. For the admittance matrix Y(s), all its eigenvalue trajectories are solved, and the global stability of the system is analyzed based on the surrounding characteristics of each eigenvalue trajectory around the origin. Select the eigenvalue trajectory λ around the origin k (jω) performs parameter stabilization design and calculates the eigenvalue trajectory λ k The intersection of (jω) and the negative real axis is related to the parameter α in the port converter. i (i=1, 2,…, h) and form the sensitivity matrix S ck Using the normalization principle, the sensitivity matrix S ck Convert to relative sensitivity matrix , to ensure the comparability of the sensitivity of different parameters. , establish the conversion relationship between the parameter setting value and the relative setting value. Based on the optimization goal of minimizing the overall relative setting value of the parameter in the stabilization design process, a nonlinear optimization model is constructed. Based on the nonlinear optimization model, the relative adjustment amount of the parameter is iteratively solved, and the solution result is used to update the admittance matrix Y(s). A new round of stability analysis and stabilization design is carried out for the updated admittance matrix Y(s), and a time domain simulation verification is implemented after the stability conditions are met. The advantage of the scheme of this embodiment is that it makes full use of the scalar characteristics of the cross-point sensitivity and the function of indicating the direction of system stability enhancement when the parameters change. By describing the superposition effect of the influence of multiple parameter changes on stability, a parameter adjustment optimization model for stability is constructed, and a multi-parameter collaborative stabilization design of a multi-node complex DC power supply and distribution system is realized.

[0030] It should be noted that although the term "multi-node complex DC power supply and distribution system" is still a non-standard term, the characteristics it covers already exist and are applied in engineering practice. For example, the Zhangjiakou flexible substation and AC / DC distribution network demonstration project uses a multi-node complex DC power supply and distribution system.

[0031] The present invention discloses a multi-parameter collaborative stabilization design method suitable for a multi-node complex DC power supply and distribution system. Figure 2 As shown, the method comprises the following steps:

[0032] Step 1: Establish the port admittance model of each port converter subsystem, integrate it with the passive network, form the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system, and solve all eigenvalue trajectories of the admittance matrix Y(s);

[0033] Step 2: Analyze the global stability of the system based on the surrounding characteristics of each eigenvalue trajectory around the origin, and select the eigenvalue trajectory λ around the origink Design for parameter stabilization with (jω).

[0034] Step 3: Calculate the eigenvalue trajectory λ k The intersection points of (jω) and the negative real axis for the sensitivity of the parameters α in the port converter i (i = 1, 2, …, h), forming the sensitivity matrix S ck , and combine with the normalization principle to transform the sensitivity matrix S ck into the relative sensitivity matrix ;

[0035] Step 4: Use the relative sensitivity matrix , establish the conversion relationship between the parameter setting value and its relative setting value, and construct a nonlinear optimization model according to the optimization goal of the overall minimum of the relative setting value of the parameters in the stabilization design;

[0036] Step 5: Iteratively solve the relative adjustment amount of the parameters based on the nonlinear optimization model, and use the solution result to update the admittance matrix Y(s), and then perform a new round of stability analysis and stabilization design;

[0037] Step 6: Verification of the parameter design results.

[0038] Preferably, the said Step 1 includes the following steps:

[0039] Step 1.1: Based on the connection points of the port converter and the passive network, divide the multi-node complex DC power supply and distribution system into an active port converter subsystem and a passive network;

[0040] Step 1.2: Establish the passive network Y net (s), and the matrix Y s (s) containing the port admittances of each active port converter subsystem;

[0041] Step 1.3: Further construct the admittance matrix Y(s) of the entire system based on Y s (s) and Y net (s);

[0042] Step 1.4: For the admittance matrix Y(s), solve its eigenvalue trajectory matrix Λ(jω).

[0043] Preferably, the schematic diagram of the division of the active port converter subsystem and the passive network in the said Step 1.1 is as Figure 1 shown, Figure 1 where: Y s1 (s), Y s2 (s), …, Y sn (s) are the port admittances of the corresponding active port converter subsystems.

[0044] Preferably, the matrix Y in step 1.2 s (s) and the relationship between the port admittances of each active port converter subsystem is: .

[0045] Preferably, in step 1.3, the admittance matrix Y(s) of the entire system and the port admittance matrix Y s (s) and the admittance matrix Y of the passive network net (s) are related as: Y(s) = Y s (s) + Y net (s).

[0046] Preferably, the expression of the eigenvalue trajectory matrix Λ(jω) of the admittance matrix Y(s) in step 1.4 is: , where: Ψ(jω) and Φ(jω) are the left and right eigenvector matrices of Y(jω) respectively, and λ1(jω), λ2(jω), …, λ n (jω) are all the eigenvalue trajectories of the admittance matrix Y(jω).

[0047] Preferably, step 2 includes the following steps:

[0048] Step 2.1: Calculate the intersection point R of all eigenvalue trajectories with the negative real axis c ;

[0049] Step 2.2: When there is an intersection point of any eigenvalue trajectory with the negative real axis, the system does not satisfy global stability, otherwise directly perform time-domain simulation verification;

[0050] Step 2.3: Select the eigenvalue trajectory λ k (jω) containing the intersection point on the negative real axis for parameter stabilization design.

[0051] Preferably, the calculation expression of the intersection point R of the eigenvalue trajectory λ k (jω) with the negative real axis in step 2.1 is: c , where: ω is the phase cross frequency of the eigenvalue trajectory with the negative real axis, and R(ω) and X(ω) respectively represent the real part and imaginary part of the eigenvalue trajectory λ r (jω). k (jω).

[0052] Preferably, the calculation expression of the eigenvalue trajectory λ k (jω) in step 2.3 is: , where: Ψ k (jω) and Φ k (jω) are the k-th row of Ψ(jω) and the k-th column of Φ(jω) respectively.

[0053] Preferably, step 3 includes the following steps:

[0054] Step 3.1: According to the k-th eigenvalue trajectory expression λ k (jω), calculate the sensitivity S k (jω) of the intersection points R ck,j (j = 1, 2, …, g) to the parameters α i (i = 1, 2, …, h) of each port converter; ck,j (α i );

[0055] Step 3.2: Organize all the intersection point sensitivities into a sensitivity matrix S ck ;

[0056] Step 3.3: Combine the normalization principle to convert the sensitivity matrix S ck into a relative sensitivity matrix .

[0057] Preferably, the expression of S ck,j (α i ) in step 3.1 is: , where: , and and are the u-th element of Ψ k (jω) and the v-th element of Φ k (jω) respectively.

[0058] Preferably, the expression of S ck in step 3.2 is: .

[0059] Preferably, the calculation expression of in step 3.3 is: .

[0060] Preferably, step 4 includes the following steps:

[0061] Step 4.1: Based on the relative sensitivity matrix , establish the conversion relationship between the parameter setting value and its relative setting value ;

[0062] Step 4.2: Based on the optimization goal of the overall minimum of the relative parameter setting values, construct a non-linear optimization model.

[0063] Preferably, the conversion relationship between the parameter setting value and the relative setting value in step 4.1 is: .

[0064] Preferably, the non-linear optimization model in step 4.2 is as follows: , where: F is the optimization objective function; W i is the weight coefficient of α i , the larger its value, the lower the setting priority of α i , and the smaller the allowed relative parameter setting value ; l i and b i are the lower and upper bounds of the allowable variation range of the relative setting value during the stability enhancement design process.

[0065] Preferably, step 5 includes the following steps:

[0066] Step 5.1: Based on the iterative solution of the non-linear optimization model, obtain the relative setting values of each parameter;

[0067] Step 5.2: Update the admittance matrix Y(s) using the relative setting values, and perform a new round of stability analysis verification and stability enhancement design.

[0068] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the partial description of the method embodiments. The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for collaborative and stabilizing design of multiple parameters of a converter in a multi-node DC power supply and distribution system, characterized in that, Including: S1. Establish the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system, and obtain all the eigenvalue trajectories of Y(s); S2. Determine the eigenvalue locus λ k (jω) that contains the intersection point on the negative real axis; S3. Calculate λ k (jω)'s sensitivity to the parameters in the port converter at the intersection point with the negative real axis, and generate a sensitivity matrix; S4. Determine the correspondence between the parameter setting values and the relative setting values according to the sensitivity matrix, and then establish a nonlinear optimization model; S5. Utilize the output result of the nonlinear optimization model, and the output result includes the current parameter setting values.

2. The method according to claim 1, characterized in that S1 includes: Divide the multi-node complex DC power supply and distribution system into an active port converter subsystem part and a passive network part; Establish the admittance matrix Y of the passive network section net (s), and the admittance matrix Y s (s) of the ports of the active port converter subsystem; According to Y s (s) and Y net (s) to construct Y(s), and then perform matrix operations on Y(s) to obtain its eigenvalue trajectory matrix Λ(jω), where s represents the Laplace operator, j represents the imaginary unit, and ω represents the angular frequency.

3. The method according to claim 2, wherein Y s (s) is used to represent the port admittance of each active port converter subsystem, , where Y s1 (s) represents the port admittance of active port converter subsystem 1, s1 to sn represent the admittance numbers of active port converter subsystems 1 to n, and n represents the number of active port converter subsystems; Y(s)=Y s (s)+Y net (s), , where Ψ(jω) and Φ(jω) are the left and right eigenvector matrices of Y(jω) respectively, and λ1(jω), λ2(jω), …, λ n (jω) represent the eigenvalue trajectories corresponding to the ports of the first to the nth active-port converter subsystems respectively. Y(jω) is another form of Y(s), and Y(jω)=Y(s)| s=jω .

4. The method according to claim 1, wherein S2 Including: Calculate the intersection points R of all eigenvalue trajectories with the negative real axis c， where , ω r represents the phase crossover frequency of the eigenvalue trajectory with the negative real axis, and R(ω) and X(ω) respectively represent the real part and the imaginary part of the eigenvalue trajectory λ k (jω); Obtain the eigenvalue trajectory λ k (jω), , Ψ k (jω) represents the k-th row of Ψ(jω), Φ k (jω) represents the k-th column of Φ(jω).

5. The method according to claim 1, wherein S3 Including: S31. Calculate the k-th eigenvalue trajectory λ k The intersection point R contained in (jω) ck,j For each port converter, the sensitivity S of the parameter α i (i = 1, 2, …, h) ck,j (α i ), where g represents the number of intersection points, and h represents the total number of parameters in the port converter; S32. Repeat the above process until the cross-point sensitivities of all eigenvalue trajectories are obtained, and generate the sensitivity matrix S ck , and then convert S ck into a relative sensitivity matrix .

6. The method according to claim 5, wherein S31 includes: Sensitivity , where , and respectively represent the u-th element of Ψ k (jω) and the v-th element of Φ k (jω), ω r represents the phase crossover frequency corresponding to the crossover point, Y uv represents the element in the u-th row and v-th column of Y(jω).

7. The method according to claim 6, wherein In S32, convert S ck to a relative sensitivity matrix , including , where α1 to α h represent the parameters of h port converters, represents the k-th eigenvalue trajectory λ k (jω) at the intersection point (R c,g , 0) with respect to the parameter α h of the intersection point sensitivity, and the relative sensitivity matrix is represented in matrix form as , where: , represents the k-th eigenvalue trajectory λ k (jω) of the intersection point (R c,g , 0) with respect to the parameter α h of the relative intersection point sensitivity.

8. The method according to claim 6 or 7, characterized in that S4 Including: Establish the parameter setting value ∆α for the relative sensitivity matrix i and the conversion relationship with the relative setting value ∆E i ; Construct a nonlinear optimization model with the overall minimum of the relative parameter setting values as the optimization objective.

9. The method according to claim 8, wherein The conversion relationship is ; The nonlinear optimization model includes: ， F is the optimization objective function; W i is the weight coefficient of α i , and the value of W i is negatively correlated with the tuning priority of α i . The lower the tuning priority of α i , the smaller the allowed relative parameter setting value . l i and b i are the lower and upper bounds of the allowable variation range of the relative setting value during the stabilizer design process, denotes the relative cross-point sensitivity of the cross-point (R k (jω)) in the k-th eigenvalue trajectory λ c,j , 0) to the parameter α i , and represents the tuning margin during the parameter tuning process.

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