Multi-parameter collaborative stabilization design method for converters in multi-node DC power supply and distribution systems

Abstract

The embodiment of the present invention discloses a multi-parameter collaborative stabilization design method for converters of a multi-node DC power supply and distribution system, which relates to the technical field of DC power supply and distribution. In response to the broadband stable operation requirements of multi-node complex DC power supply and distribution systems, a universal multi-parameter collaborative stabilization design method is designed, which can consider the superposition effect of parameters on global stability and support the collaborative optimization of converter multi-parameters, thereby providing safety guarantees for the stable operation of multi-node complex DC power supply and distribution systems. The specific approach is a multi-node multi-port converter parameter collaborative adjustment method based on cross-point sensitivity, proposes a scalar analysis framework based on cross-point sensitivity in the Nyquist diagram, and combines nonlinear programming technology to construct a multi-parameter collaborative optimization model, breaking through the bottleneck of traditional methods in versatility and efficiency, and providing a universal solution for the stability design of multi-node complex DC power supply and distribution systems.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current (DC) power supply and distribution, and in particular to a multi-parameter collaborative stabilization design method for a converter of a multi-node DC power supply and distribution system. Background Art

[0002] Multi-node complex DC power supply and distribution systems are widely used in microgrids and renewable energy consumption due to their flexible power dispatch capabilities and high reliability requirements. In multi-node complex DC systems, the electrical characteristics and control strategies of each node subsystem potentially interact with each other. This, combined with the complexity of external environmental interference, can easily lead to resonant interactions or dynamic oscillations in multiple frequency ranges, ultimately resulting in degraded power quality, diminished operational efficiency, and even the risk of global loss of control.

[0003] Impedance / admittance analysis, with its advantage in describing port characteristics, eliminates the need for detailed system internal parameters and can assess stability using measured admittance data at busbar nodes. This makes it more practical in complex, multi-node DC power supply and distribution systems. Existing impedance analysis methods, using node impedance / admittance matrices or even back-ratio matrices, shift the sensitivity description to eigenvalue trajectories, constructing participation functions or factors to reveal which port is primarily responsible for potential instability.

[0004] However, these metrics only correlate with external port characteristics and fail to directly quantify the relationship between system parameters (such as PI regulator gain and virtual impedance) and global stability. This results in a long-term reliance on trial and error for parameter tuning. Alternatively, component sensitivity at specific frequencies, based on the chain rule, can be used to identify key parameters influencing system stability, thereby guiding the design of stable 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 scenarios where a single parameter is adjusted at a time. There is a lack of solutions to the superposition effect of sensitivity in multi-parameter adjustment, which ultimately leads to insufficient collaborative optimization capabilities of multi-port converter parameters, 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 multi-parameter collaborative stabilization design method for converters in a multi-node DC power supply and distribution system. The method can achieve multi-parameter collaborative stabilization design by quantifying the impact of parameter changes on stability, thereby improving the system's broadband stability and ensuring the safe and stable operation of the multi-node complex DC power supply and distribution system.

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

[0008] A multi-parameter collaborative stabilization design method for a converter of a multi-node DC power supply and distribution system, comprising:

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

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

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

[0012] S4. Determine the corresponding relationship between parameter setting values and relative setting values according to the sensitivity matrix, and then establish a nonlinear optimization model;

[0013] S5. Utilize the output results of the nonlinear optimization model. The output results include current parameter settings, which can be used to update Y(s) and perform verification. In practical applications, the determined parameter settings can stabilize the operating state of a multi-node complex DC power supply and distribution system.

[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 of the passive network part net (s), and the matrix Y containing the port admittance of each active port converter subsystem s (s); according to Y s (s) and Y net (s) constructs Y(s), and then performs 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 admittance of each active port converter subsystem, , where Y s1 (s) represents the output 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;

[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) denotes the eigenvalue trajectories corresponding to the 1st to nth active port converter subsystem ports, Y(jω)=Y(s)| s=jω Among them, there are elements at all element positions of Y(s), and during the parameter tuning process, the element form of Y(s) does not affect the execution of the entire process, so the specific matrix form of Y(s) is not described in detail.

[0017] Specifically, S2 includes: calculating the intersection points R of all eigenvalue trajectories and the negative real axis c， When any eigenvalue trajectory has an intersection with the negative real axis, it is determined that the DC power supply and distribution system does not meet the global stability requirement. Otherwise, time domain simulation verification is performed directly. ,ω r represents the phase crossing frequency of the eigenvalue trajectory and the negative real axis, R(ω) and X(ω) represent the eigenvalue trajectory λ k The real and imaginary parts of (jω); obtain the eigenvalue trajectory λ k (jω), ,Ψ k (jω) represents the kth row of Ψ(jω), Φ k (jω) represents the k-th column of Φ(jω).

[0018] Specifically, S3 includes: S31, calculating the k-th eigenvalue trajectory λ k (jω) contains the intersection point R ck,j (j=1, 2, …,g) for the parameter α in each port converter i Sensitivity S of (i=1, 2,…, h) ck,j (α i ), where g represents the number of intersections, and h represents the total number of parameters in the port converter; S32, repeat the above process until the intersection sensitivities of all eigenvalue trajectories are obtained, and the sensitivity matrix S is generated. ck , and then normalize S ck Convert to relative sensitivity matrix .

[0019] Among them, S31 includes: sensitivity ,in, , and Respectively represent Ψ k The u-th element of (jω) and Φ k The vth element of (jω), ω r Indicates the frequency corresponding to the intersection point, Y uv Represents the element in the uth row and vth column of Y(jω).

[0020] In S32, Sck Convert to relative sensitivity matrix ,include: , where α1~α h represents the h port converter parameters, represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,g , 0) for parameter α h Cross-point sensitivity, relative sensitivity matrix The matrix representation of ,in: , represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,g , 0) for parameter α h The relative cross-point sensitivity.

[0021] Specifically, S4 includes: establishing parameter setting value ∆α for the relative sensitivity matrix i Relative setting value ∆E i The conversion relationship of ; takes the overall minimum of the relative setting values of the parameters as the optimization goal, and constructs a nonlinear optimization model.

[0022] The conversion relationship is ; The nonlinear optimization model includes: , F is the optimization objective function; W i is α i The weight coefficient, W i The value of α i The setting priority is negatively correlated, α i The lower the setting priority, the higher the relative parameter setting value ∆E i The smaller the l i and b i is the relative setting value ∆E i The lower and upper bounds of the range of variation allowed during the stabilization design process, represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,j , 0) for parameter α i The relative cross-point sensitivity, Indicates the tuning margin during parameter tuning.

[0023] The multi-parameter collaborative stabilization design method for converters of a multi-node DC power supply and distribution system provided by an embodiment of the present invention establishes a port admittance model of each port converter subsystem and integrates it with a passive network to further construct the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system. 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. The eigenvalue trajectory λ around the origin is selected. k (jω) performs parameter stabilization design and calculates the eigenvalue trajectory λ k The intersection of (jω) and the negative real axis is 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. The relative adjustment amount of the parameter is iteratively solved based on the nonlinear optimization model, 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 time domain simulation verification is implemented after the stability conditions are met. This method proposes a scalar evaluation index based on the Nyquist plot intersection sensitivity. By quantifying the impact of parameter changes on stability, multi-parameter collaborative stabilization design is realized, the system broadband stability is improved, and thus the safe and stable operation of multi-node complex DC power supply and distribution systems is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

[0026] Figure 2This is a flowchart of a multi-parameter collaborative stabilization design method for a multi-node complex DC power supply and distribution system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[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 embodiments. The embodiments of the present invention will be described in detail below, with examples of the embodiments illustrated in the accompanying drawings. Throughout, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and intended only to explain the present invention and are not to be construed as limiting the present invention. Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" as used in the description of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when an element is referred to as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or intervening elements may be present. Furthermore, "connected" or "coupled" as used herein may include wireless connections or couplings. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which this invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and unless defined as such, will not be interpreted in an idealized or overly formal sense.

[0028] The general design purpose of this embodiment scheme is: to meet the broadband stable operation requirements of multi-node complex DC power supply and distribution systems, a universal multi-parameter collaborative stabilization design method is designed, which can consider the superposition effect of parameters on global stability and support the collaborative optimization of multiple parameters of port converters, thereby providing safety guarantees for the stable operation of multi-node complex DC power supply and distribution systems. The specific approach of this scheme is a multi-node multi-port converter parameter collaborative adjustment method based on cross-point sensitivity, and a scalar analysis framework based on cross-point sensitivity in the Nyquist diagram is proposed. A multi-parameter collaborative optimization model is constructed in combination with nonlinear programming technology, breaking through the bottleneck of traditional methods in universality and efficiency, and providing a universal solution for the stability design of multi-node complex DC power supply and distribution systems.

[0029] The general design idea of this embodiment is to establish the port admittance model of each port converter subsystem and integrate it with the passive network to further form the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system. 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. The eigenvalue trajectory λ around the origin is selected. k (jω) performs parameter stabilization design and calculates the eigenvalue trajectory λ k The intersection of (jω) and the negative real axis is 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. The relative adjustment amount of the parameter is iteratively solved based on the nonlinear optimization model, 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 features 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 multi-node complex DC power supply and distribution systems. Figure 2 As shown, the method includes the following steps:

[0032] Step 1: Establish the port admittance model of each port converter subsystem and integrate it with the passive network to form the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system. Then 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 (jω) Perform parameter stabilization design;

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

[0035] Step 4: Using the relative sensitivity matrix , establish the conversion relationship between the parameter setting value and its relative setting value, and construct a nonlinear optimization model based on the optimization goal of minimizing the overall relative setting value of the parameter 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 results to update the admittance matrix Y(s), and then conduct a new round of stability analysis and stabilization design;

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

[0038] Preferably, the step 1 comprises the following steps:

[0039] Step 1.1: Divide the multi-node complex DC power supply and distribution system into active port converter subsystems and passive networks based on the connection points between the port converters and the passive networks;

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

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

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

[0043] Preferably, the division diagram of the active port converter subsystem and the passive network in step 1.1 is as follows: Figure 1 As shown, Figure 1 Medium: Y s1 (s), Y s2 (s),…,Y sn (s) is the port admittance of the corresponding active port converter subsystem.

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

[0045] Preferably, the admittance matrix Y(s) of the entire system in step 1.3 is equal to the port admittance matrix Y of the active port converter subsystem. s (s) and the passive network admittance matrix Y net The relationship between (s) is: 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 eigenvalue trajectories of the admittance matrix Y(jω).

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

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

[0049] Step 2.2: When any eigenvalue trajectory has an intersection with the negative real axis, the system does not meet the global stability requirement. Otherwise, perform time domain simulation verification directly.

[0050] Step 2.3: Select the eigenvalue locus λ that contains the intersection point on the negative real axis k (jω) is used for parameter stabilization design.

[0051] Preferably, the eigenvalue trajectory λ in step 2.1 k (jω) intersects the negative real axis at point R c The calculation expression is: , where: r is the phase crossing frequency of the eigenvalue trajectory and the negative real axis, R(ω) and X(ω) represent the eigenvalue trajectory λ k The real and imaginary parts of (jω).

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

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

[0054] Step 3.1: According to the kth eigenvalue trajectory expression λ k (jω), calculate λ k (jω) contains the intersection point R ck,j (j=1, 2, …, g) for the parameter α in each port converter i Sensitivity S of (i=1, 2,…, h) ck,j (α i );

[0055] Step 3.2: Arrange all cross-point sensitivities into a sensitivity matrix S ck ;

[0056] Step 3.3: Combine the normalization principle and transform the sensitivity matrix S ck Convert to relative sensitivity matrix .

[0057] Preferably, in step 3.1, S ck,j (α i ) is: , Where: ,and and They are Ψ k The u-th element of (jω) and Φ k The vth element of (jω).

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

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

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

[0061] Step 4.1: Based on the relative sensitivity matrix , establish parameter setting values Its relative setting value Conversion relationship;

[0062] Step 4.2: Construct a nonlinear optimization model based on the optimization goal of minimizing the overall parameters relative to the set values.

[0063] Preferably, the parameter setting value in step 4.1 is Relative set value The conversion relationship is: .

[0064] Preferably, the nonlinear optimization model in step 4.2 is: , where: F is the optimization objective function; W i is α i The weight coefficient of α is larger, i The lower the setting priority, the higher the relative parameter setting value allowed. The smaller; l i and b i Is the relative setting value The lower and upper bounds of the range of variation allowed during the stability design process.

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

[0066] Step 5.1: Based on the iterative solution of the nonlinear optimization model, the relative setting values of each parameter are obtained;

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

[0068] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to this. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-parameter collaborative stabilization design method for converters in a multi-node DC power supply and distribution system, characterized in that: include: S1. Establish the admittance matrix Y(s) of the multi-node complex DC power supply and distribution system and obtain all eigenvalue trajectories of Y(s); S2. Determine the eigenvalue trajectory λ containing the intersection point on the negative real axis k (jω); S3. Calculate λ k The intersection of (jω) and the negative real axis is the sensitivity of the parameters in the port converter, and the sensitivity matrix is generated; S4. Determine the corresponding relationship between parameter setting values and relative setting values according to the sensitivity matrix, and then establish a nonlinear optimization model; S4 includes: establishing parameter setting values ∆α for the relative sensitivity matrix i Relative setting value ∆E i The conversion relationship of ; taking the overall minimum of the relative setting value of the parameters as the optimization goal, a nonlinear optimization model is constructed; The conversion relationship is ; The nonlinear optimization model includes: , F is the optimization objective function; W i is α i The weight coefficient, W i The value of α i The setting priority is negatively correlated, α i The lower the setting priority, the higher the relative parameter setting value allowed. The smaller the l i and b i Is the relative setting value The lower and upper bounds of the range of variation allowed during the stabilization design process, represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,j , 0) for parameter α i The relative cross-point sensitivity, Indicates the tuning margin during parameter tuning; S5. Utilize the output result of the nonlinear optimization model, wherein the output result includes the current parameter setting value.

2. The method according to claim 1, characterized in that S1 includes: The multi-node complex DC power supply and distribution system is divided into the active port converter subsystem part and the passive network part; Establish the admittance matrix Y of the passive network part net (s), and the admittance matrix Y of the active port converter subsystem port s (s); According to Y s (s) and Y net (s) constructs Y(s), and then performs 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, characterized in that 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) represents the eigenvalue loci corresponding to the 1st to nth active port converter subsystem ports, respectively. Y(jω) is another form of expression of Y(s), Y(jω)=Y(s)| s=jω .

4. The method according to claim 3, wherein S2 include: Calculate the intersection points R of all eigenvalue trajectories with the negative real axis c， in, ,ω r represents the phase crossing frequency of the eigenvalue trajectory and the negative real axis, R(ω) and X(ω) represent the eigenvalue trajectory λ k the real and imaginary parts of (jω); Get the eigenvalue trajectory λ k (jω), ,Ψ k (jω) represents the kth row of Ψ(jω), Φ k (jω) represents the k-th column of Φ(jω).

5. The method according to claim 4, characterized in that S3 include: S31, calculate the kth eigenvalue trajectory λ k (jω) contains the intersection point R ck,j (j=1, 2, …, g) for the parameter α in each port converter i Sensitivity S of (i=1, 2,…, h) ck,j (α i ), where g represents the number of cross points and h represents the total number of parameters in the port converter; S32, repeat the above process until the intersection sensitivity of all eigenvalue trajectories is obtained and the sensitivity matrix S is generated. ck , and then normalize S ck Convert to relative sensitivity matrix .

6. The method according to claim 5, characterized in that S31 includes: Sensitivity ,in, , and Respectively represent Ψ k The u-th element of (jω) and Φ k The vth element of (jω), ω r Indicates the phase crossover frequency corresponding to the crossover point, Y uv Represents the element in the uth row and vth column of Y(jω).

7. The method according to claim 6, characterized in that In S32, S ck Convert to relative sensitivity matrix ,include , where α1~α h represents the h port converter parameters, represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,g , 0) for parameter α h Cross-point sensitivity, relative sensitivity matrix The matrix representation of ,in: , represents the kth eigenvalue trajectory λ k (jω) the intersection point (R c,g , 0) for parameter α h The relative crossover sensitivity.

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