Converter transformer behavior model order reduction method and device, equipment and storage medium

Abstract

The invention discloses a converter transformer behavior model order reduction method and device, equipment and a storage medium. The method comprises the following steps: establishing a simulation physical model of a converter transformer; obtaining a state parameter time sequence of the converter transformer based on the simulation physical model, and judging whether the state parameter time sequence has a chaotic characteristic or not; and when the state parameter time sequence has the chaotic characteristic, performing order reduction processing on the simulation physical model based on a Lyapunov method to obtain a simplified simulation physical model after order reduction. Whether a state parameter time sequence of the converter transformer has chaotic characteristics or not is judged, whether key states and variables which greatly influence dynamic behaviors of the model exist in a simulation physical model or not is determined, order reduction is performed on the simulation physical model based on a Lyapunov method, the key states and variables which greatly influence the dynamic behaviors of the model are reserved, and the dynamic behavior of the converter transformer is simulated. And obtaining a simplified simulation physical model so as to ensure that the simplified model can still accurately describe the main dynamic characteristics of the converter transformer system while simplifying the model.

Description

Technical Field

[0001] The present invention relates to the technical field of model order reduction, and in particular to a method and device, equipment and storage medium for reducing the order of a converter transformer behavior model. Background Art

[0002] As a key equipment in the power system, the construction of a digital twin model of the converter transformer can optimize the performance and energy utilization of the converter transformer by simulating different operation strategies, and can monitor the operation status of the converter transformer in real time, find problems in time and deal with them, which helps to reduce power outage time and losses. It is conducive to fully mastering the design and manufacturing technology of the converter transformer, promoting the localization of the converter transformer, and filling the domestic gap. At the same time, this can also promote the further improvement and development of the design and manufacturing level of domestic AC and DC transmission equipment, and lay the foundation for the development of UHV AC and DC transmission and transformation equipment.

[0003] At present, due to the large number of external components and complex structure of the converter transformer, the simulation calculation efficiency of the digital twin model of the converter transformer is low, and it often takes dozens of hours of calculation, which greatly affects the synchronization of the visualization of multi-physics field simulation results. Summary of the invention

[0004] Based on this, it is necessary to propose a method and device, equipment and storage medium for reducing the order of the converter transformer behavior model to address the above problems, so as to effectively simplify the model.

[0005] To achieve the above-mentioned purpose, the present application provides, in a first aspect, a method for reducing the order of a converter transformer behavior model, the method comprising:

[0006] Establish a simulation physical model of the converter transformer;

[0007] Obtaining a state parameter time series of the converter transformer based on the simulation physical model, and determining whether the state parameter time series has chaotic characteristics;

[0008] When the state parameter time series has chaotic characteristics, the simulation physical model is reduced in order based on the Lyapunov method to obtain a simplified simulation physical model after the reduction in order.

[0009] Furthermore, the Lyapunov method is used to reduce the order of the simulation physical model to obtain a simplified simulation physical model after the reduction, which specifically includes:

[0010] Constructing a full-order system equation of the converter transformer;

[0011] Substituting the state parameters of the converter transformer into the full-order system equation to obtain the system matrix of the converter transformer;

[0012] Constructing a Lyapunov equation according to the system matrix, and reducing the order of the full-order system equation according to the solution of the Lyapunov equation to obtain a reduced-order system equation;

[0013] A simplified simulation physical model is constructed based on the reduced-order system equations.

[0014] Furthermore, constructing a Lyapunov equation according to the system matrix, and reducing the order of the full-order system equation according to the solution of the Lyapunov equation to obtain the reduced-order system equation specifically includes:

[0015] Based on the system matrix and the preset control matrix and observation matrix, the Lyapunov equation is constructed for the i-th time, and the initial value of i is 1;

[0016] Solving the Lyapunov equation to obtain eigenvalues ​​of the control matrix and the observation matrix, and eigenvectors corresponding to the eigenvalues ​​in the system matrix;

[0017] Based on the balanced truncation method, the system matrix is ​​reduced in order by using the eigenvalue and the eigenvector corresponding to the eigenvalue in the system matrix to obtain a reduced-order system matrix;

[0018] Substituting the reduced-order system matrix into the full-order system equation to obtain the reduced-order system equation;

[0019] Calculating a model error between the reduced-order system equation and the full-order system equation;

[0020] When the model error is not less than a preset error threshold, the reduced-order system matrix is ​​used as the system matrix, and i=i+1, and the step of constructing the Lyapunov equation for the i-th time according to the system matrix and the preset control matrix and observation matrix is ​​continued until the model error is less than the error threshold.

[0021] Furthermore, the Lyapunov equation is constructed as follows:

[0022]

[0023] Wherein, A, B, C are system matrices, P is the control matrix, and Q is the observation matrix.

[0024] Furthermore, the balanced truncation method is based on which the eigenvalue and the eigenvector corresponding to the eigenvalue in the system matrix are used to reduce the order of the system matrix to obtain the reduced-order system matrix, specifically including:

[0025] Compare the eigenvalue with a preset eigenvalue threshold to obtain a first eigenvalue that is less than the eigenvalue threshold;

[0026] Constructing an equation according to the control matrix and the observation matrix and solving it to obtain a non-singular transformation matrix, wherein the non-singular transformation matrix is ​​used to balance the control matrix and the observation matrix, so that the balanced control matrix is ​​the same as the observation matrix and is a diagonal matrix;

[0027] Using the non-singular transformation matrix to perform equivalent transformation on the system matrix to obtain a balanced system matrix;

[0028] The eigenvector corresponding to the first eigenvalue in the balanced system matrix is ​​truncated and removed to obtain a reduced-order system matrix.

[0029] Furthermore, the non-singular transformation matrix is ​​obtained by solving the following equation:

[0030]

[0031] In the formula, is the balanced control matrix, T is the non-singular transformation matrix, is the balanced observation matrix, σ1,σ2,…,σ n is the eigenvector corresponding to the matrix, and n is the matrix order.

[0032] Furthermore, the balanced system matrix is:

[0033]

[0034] In the formula, A, B, C, D are system matrices, T is a non-singular transformation matrix, is the balanced system matrix.

[0035] To achieve the above-mentioned purpose, the second aspect of the present application provides a converter transformer behavior model reduction device, the device comprising a simulation unit, a parameter acquisition unit and a model reduction unit;

[0036] The simulation unit is used to establish a simulation physical model of the converter transformer;

[0037] The parameter acquisition unit is used to obtain the state parameter time series of the converter transformer based on the simulation physical model, and determine whether the state parameter time series has chaotic characteristics;

[0038] The model order reduction unit is used to reduce the order of the simulation physical model based on the Lyapunov method when the state parameter time series has chaotic characteristics, so as to obtain a simplified simulation physical model after the order reduction.

[0039] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0040] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer device, including a memory and a processor, characterized in that the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in the first aspect.

[0041] The embodiments of the present invention have the following beneficial effects:

[0042] The embodiment of the present invention proposes a method for reducing the order of a converter transformer behavior model, which includes: establishing a simulation physical model of a converter transformer; obtaining a state parameter time series of the converter transformer based on the simulation physical model, and judging whether the state parameter time series has chaotic characteristics; when the state parameter time series has chaotic characteristics, reducing the order of the simulation physical model based on the Lyapunov method to obtain a simplified simulation physical model after the reduction. By judging whether the state parameter time series of the converter transformer has chaotic characteristics, determining whether there are key states and variables that have a greater impact on the dynamic behavior of the model in the simulation physical model, reducing the order of the simulation physical model based on the Lyapunov method, retaining the key states and variables with a greater impact, and removing the key states and variables with a smaller impact, to obtain a simplified simulation physical model, so as to simplify the model while ensuring that the simplified model can still accurately describe the main dynamic characteristics of the converter transformer system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0044] in:

[0045] Figure 1 Schematic diagram of the flow of a converter transformer behavior model reduction method according to an embodiment of the present invention;

[0046] Figure 2 1 is a structural block diagram of a converter transformer behavior model reduction device according to an embodiment of the present invention;

[0047] Figure 3 1 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Due to the complex internal structure of the converter transformer, its simulation physical model is also extremely complex, resulting in a long simulation time during the operation of the simulation physical model, which in turn causes the behavioral state parameters between the converter transformer simulation physical model and the actual converter transformer on site to be out of sync.

[0050] Based on this, an embodiment of the present invention proposes a method for reducing the order of the behavioral model of a converter transformer. By reducing the order of the model by only retaining the key behavioral state parameters of the converter transformer, the reduced simulation physical model can realize fast simulation calculation of multiple physical fields, reduce simulation time, and improve the synchronization efficiency of the state parameters between the digital twin model of the converter transformer and the real transformer on site.

[0051] See also Figure 1 , Figure 1 1 is a flow chart of a method for reducing the order of a converter transformer behavior model in an embodiment of the present invention, the method comprising:

[0052] Step 110: Establish a simulation physical model of the converter transformer.

[0053] In the embodiment of the present invention, a simulation physical model of the converter transformer is established based on the detailed behavior of the converter transformer, and subsequent physical field simulations are all calculated based on the simulation physical model.

[0054] Specifically, to establish a detailed behavioral model of the converter transformer, finite element simulation software can be used, and linear or nonlinear transient or steady-state solvers can be used to obtain the distribution of multiple physical fields inside the converter transformer as state parameters. The state parameters include dynamic characteristics in multiple aspects such as electrical, thermal, and mechanical. According to the actual physical size and state parameters of the converter transformer, a 1:1 simulation physical model is constructed. Since the internal structure of the actual converter transformer is complex, the simulation physical model can also be simplified as necessary.

[0055] Step 120: Obtain a state parameter time series of the converter transformer based on the simulation physical model, and determine whether the state parameter time series has chaotic characteristics.

[0056] The behavior state parameters of the converter transformer are physical information extracted from the online monitoring information, test information, diagnostic information and actual operation information of the converter transformer, which can reflect the trend of the change of the operation state of the converter transformer. The behavior state parameters are variables that describe the dynamic behavior of the converter transformer at a specific moment, including parameters such as voltage, current, temperature, and mechanical stress.

[0057] In the embodiment of the present invention, the dynamic behavior variables of the converter transformer at different times are obtained based on the simulation physical model, and then the behavior state parameter time series is obtained. The Lyapunov exponent of the behavior state parameter of the converter transformer is calculated, and the Lyapunov exponent of the behavior state parameter is used as a quantitative indicator to judge whether the behavior state parameter time series has chaotic characteristics, and then whether the behavior state parameter time series of the converter transformer has chaotic characteristics is judged.

[0058] The Lyapunov index is a simple and easy-to-implement quantitative indicator for judging whether a time series has chaotic characteristics. The Lyapunov index quantitatively describes the degree to which two initially close chaotic attractor orbits separate exponentially after evolving over time from the perspective of initial value sensitivity and randomness of the chaotic system. When the Lyapunov index is greater than zero, the chaotic attractor orbits separate quickly (or diverge), that is, they are extremely sensitive to initial values, the system has initial value sensitivity and randomness, and exhibits chaotic characteristics; when the Lyapunov index is less than zero, the chaotic attractor orbits move closer to each other (or converge), and the operation is stable, that is, they are insensitive to initial values, and the system does not have chaotic characteristics; when the Lyapunov index is equal to zero, the distance between the chaotic attractor orbits remains unchanged, and the system exhibits periodic characteristics.

[0059] In an embodiment of the present invention, when the time series of the behavior state parameters of the converter transformer has chaotic characteristics, it means that there are key states and variables in the behavior state parameters of the converter transformer that have a great influence on the dynamic behavior of the simulated physical model. Based on this, the key states and variables can be retained in the process of order reduction to ensure that the main dynamic characteristics of the converter transformer system can be accurately described while simplifying the model. It can be understood that when the Lyapunov exponent is greater than zero, it is considered that the time series of the state parameters of the converter transformer has chaotic characteristics.

[0060] In one embodiment of the present invention, the Lyapunov exponent of the behavior state parameter of the converter transformer can be calculated in the following way:

[0061] A. For the converter transformer state parameter time series {x1, x2…, x t}, if the embedding dimension of the time series is m and the time delay is τ, then the point in the phase space at the t-(m-1)τth moment is:

[0062] X t-(m-1)τ = (x t-(m-1)τ , x t-(m-1)τ+1 , …, x t ) (1)

[0064] With X t-(m-1)τ as the center point, assume X k = (x k , x k + τ, …, x k+(m-1)τ ). Let k < t - (m - 1)τ be the point in the existing phase space closest to X t-(m-1)τ . Define the Euclidean distance between X t-(m-1)τ and X k as:

[0065] d = ∥∥X t-(m-1)τ - X k ∥∥ (2)

[0067] At the next moment, X t-(m-1)τ and X k evolve into X t-(m-1)τ+1 and X k+1 respectively. Assume the maximum Lyapunov exponent of the simulation physical model is λ. The Lyapunov exponent can be calculated based on the behavioral state parameters at different moments:

[0068]

[0069] When the Lyapunov exponent is greater than zero, the simulation physical model has extremely high sensitivity to the initial conditions and exhibits chaotic characteristics; when the Lyapunov exponent is less than zero, the simulation physical model tends to be stable; when the Lyapunov exponent is equal to zero, the simulation physical model exhibits periodic behavior.

[0070] Step 130: When the time series of the state parameters has chaotic characteristics, perform order reduction processing on the simulation physical model based on the Lyapunov method to obtain a reduced simplified simulation physical model.

[0071] In the embodiments of the present invention, only when the Lyapunov exponent is greater than zero will the model order reduction operation be performed. Based on the Lyapunov method, the key states and variables that have a greater impact on the dynamic behavior of the simulation physical model are identified, and these key states and variables are retained during the order reduction process, while removing the key states and variables that have a smaller impact on the dynamic behavior of the simulation physical model, so as to ensure that the reduced model can still accurately describe the main dynamic characteristics of the system.

[0072] The method for reducing the order of the behavior model of the converter transformer in the embodiment of the present invention determines whether the state parameter time series of the converter transformer has chaotic characteristics, determines whether there are key states and variables in the simulation physical model that have a greater impact on the dynamic behavior of the model, reduces the order of the simulation physical model based on the Lyapunov method, retains the key states and variables with greater impact, removes the key states and variables with less impact, and obtains a simplified simulation physical model, so as to simplify the model while ensuring that the simplified model can still accurately describe the main dynamic characteristics of the converter transformer system.

[0073] In one embodiment of the present invention, step 130, performing order reduction processing on the simulation physical model based on the Lyapunov method to obtain a simplified simulation physical model after order reduction, specifically includes:

[0074] A. Construct the full-order system equation of the converter transformer.

[0075] In the embodiment of the present invention, the full-order system equation of the converter transformer may be:

[0076]

[0077] Where x is the behavior state parameter, u is the input vector, and y is the output vector. A, B, C, and D are the structure of the converter transformer and the system matrix defined by the simulation model.

[0078] B. Substitute the state parameters of the converter transformer into the full-order system equation to obtain the system matrix of the converter transformer.

[0079] C. Construct the Lyapunov equation according to the system matrix, and reduce the full-order system equation according to the solution of the Lyapunov equation to obtain the reduced-order system equation.

[0080] D. Construct a simplified simulation physical model based on the reduced-order system equations.

[0081] Specifically, there are multiple eigenvalues ​​in the solution of the Lyapunov equation. The larger the eigenvalue, the more significant the impact on the dynamic behavior of the system. Therefore, the size of the solution of the Lyapunov equation can be used to reduce the order of the full-order system equation, and only the eigenvalues ​​that affect the dynamic behavior of the system are retained to ensure the accuracy of the simulation physical model and the simulation efficiency after the reduction.

[0082] In one embodiment of the present invention, C. constructing a Lyapunov equation according to the system matrix, and reducing the order of the full-order system equation according to the solution of the Lyapunov equation to obtain the reduced-order system equation, specifically includes:

[0083] C1. Construct the Lyapunov equation for the i-th time based on the system matrix and the preset control matrix and observation matrix, where the initial value of i is 1.

[0084] Specifically, the following Lyapunov equation is constructed using system matrices A, B, and C:

[0085]

[0086] In the formula, P is the control matrix and Q is the observation matrix, which reflect the controllability and observability of the simulation physical model. A, B, and C are system matrices.

[0087] C2. Solve the Lyapunov equation to obtain the eigenvalues ​​of the control matrix and the observation matrix, as well as the eigenvectors corresponding to the eigenvalues ​​in the system matrix.

[0088] In the embodiment of the present invention, the small eigenvalues ​​of the control matrix P and the observation matrix Q correspond to the eigenvectors in the system matrices A, B, and C that are difficult to control and difficult to observe. Therefore, the small eigenvalues ​​of the control matrix and the observation matrix can be solved by Lyapunov equations, and the difficult-to-control and difficult-to-observe system states can be found based on the small eigenvalues, so as to remove the difficult-to-control and difficult-to-observe eigenvectors from the system matrix.

[0089] C3. Based on the balanced truncation method, the system matrix is ​​reduced in order using the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues ​​in the system matrix to obtain the reduced-order system matrix.

[0090] To simplify the process of system order reduction, in one embodiment of the present invention, a non-singular transformation matrix is ​​constructed so that the control matrix P = observation matrix Q, and it is a diagonal matrix. Since the eigenvalues ​​are the same, the state that is difficult to observe is also difficult to control. Therefore, to facilitate subsequent analysis, the non-singular transformation matrix can be calculated, and the system matrix can be balanced based on the non-singular transformation matrix, and then the balanced system matrix can be reduced in order using the balanced stage method.

[0091] In one embodiment of the present invention, C3, based on the balanced truncation method, uses the eigenvalue and the eigenvector corresponding to the eigenvalue in the system matrix to reduce the order of the system matrix to obtain the reduced-order system matrix, specifically including:

[0092] C31. Compare the eigenvalue with a preset eigenvalue threshold to obtain a first eigenvalue that is smaller than the eigenvalue threshold.

[0093] In the embodiment of the present invention, the small eigenvalue of the control matrix and the observation matrix is ​​the first eigenvalue. The eigenvalue threshold ε is preset, and when there is an eigenvalue whose absolute value is less than the eigenvalue threshold ε in the eigenvalues ​​of the control matrix and the observation matrix, the eigenvalue whose absolute value is less than the eigenvalue threshold ε is taken as the first eigenvalue. By obtaining the small eigenvalue of the control matrix and the observation matrix, the eigenvalue with less influence on the dynamic behavior of the simulated physical model can be determined, so that the model can be reduced based on the small eigenvalue.

[0094] C32. Construct equations based on the control matrix and the observation matrix and solve them to obtain a non-singular transformation matrix. The non-singular transformation matrix is ​​used to balance the control matrix and the observation matrix so that the balanced control matrix is ​​the same as the observation matrix and is a diagonal matrix.

[0095] In the embodiment of the present invention, in order to realize the model reduction construction formula (6), the non-singular transformation matrix is ​​obtained by solving formula (6).

[0096]

[0097] In the formula, is the balanced control matrix, T is the non-singular transformation matrix, is the balanced observation matrix, σ1,σ2,…,σ n is the eigenvector corresponding to the matrix, and n is the matrix order.

[0098] C33. Use the non-singular transformation matrix to perform equivalent transformation on the system matrix to obtain the balanced system matrix.

[0099] Since the non-singular transformation matrix T is non-singular, the matrix eigenvalues ​​before and after balancing are the same. Apply the non-singular transformation matrix T to the system S to obtain the balanced system matrix:

[0100]

[0101] In the formula, A, B, C, D are system matrices, T is a non-singular transformation matrix, is the balanced system matrix.

[0102] C34. Truncate and remove the eigenvector corresponding to the first eigenvalue in the balanced system matrix to obtain a reduced-order system matrix.

[0103] After the system matrix is ​​balanced, the first eigenvalue is used to remove the corresponding eigenvector in the balanced system matrix to cut off the state that is difficult to control and observe, and the balanced system The order of is reduced from n to r.

[0104]

[0105] It is the approximate system matrix after order reduction by the balanced truncation method.

[0106] C4. Substitute the reduced-order system matrix into the full-order system equation to obtain the reduced-order system equation.

[0107] C5. Calculate the model error between the reduced-order system equation and the full-order system equation.

[0108] Specifically, the reduced-order system equations are transformed into state-space equations, the reduced-order system matrices are input into the state-space equation module in the finite element simulation platform, the operating data of the reduced-order simulation physical model is recorded and compared with the operating data of the full-order simulation physical model before reduction, and the model error between the two is calculated.

[0109] C6. When the model error is not less than the preset error threshold, the reduced-order system matrix is ​​used as the system matrix, and i=i+1, and the steps of constructing the Lyapunov equation for the i-th time according to the system matrix and the preset control matrix and observation matrix are continued until the model error is less than the error threshold.

[0110] In the embodiment of the present invention, if the model error is less than the preset error threshold, for example, the model error is less than 1%, it means that the accuracy of the reduced-order simulation physical model meets the requirements, and the reduced-order simulation physical model can be directly put into operation. If the model error is not less than the preset error threshold, it means that the error between the two is large, and the reduction operation needs to be performed again, and steps C1-C6 are repeated until the model error between the two is less than the error threshold.

[0111] In one embodiment of the present invention, the model error can be calculated by the following formula:

[0112]

[0113] In the embodiment of the present invention, the norm of the system matrix before and after order reduction is calculated to determine the model error, and then determine whether to continue iteration based on the model error.

[0114] The embodiment of the present invention introduces the order reduction method based on Lyapunov exponents and equations into the multi-physics field calculation process of the converter transformer, which can effectively identify the key states and variables in the system and avoid the loss of accuracy that may be caused by blind order reduction. Combined with the dynamic characteristics of the system, it can more accurately describe the dynamic behavior of the system; it is suitable for different types of converter transformers and different operating scenarios, and has strong versatility and flexibility. By reducing the computational complexity of the converter transformer, the simulation speed and accuracy of the digital twin of the converter transformer can be improved, providing strong support for the stable operation and fault analysis of the power system.

[0115] An embodiment of the present invention provides a device for reducing the order of a converter transformer behavior model. Figure 2 , Figure 2 2 is a structural block diagram of a converter transformer behavior model reduction device in an embodiment of the present invention. The device includes a simulation unit 201, a parameter acquisition unit 202 and a model reduction unit 203.

[0116] The simulation unit 201 is used to establish a simulation physical model of the converter transformer.

[0117] The parameter acquisition unit 202 is used to obtain the state parameter time series of the converter transformer based on the simulation physical model, and determine whether the state parameter time series has chaotic characteristics.

[0118] The model order reduction unit 203 is used to reduce the order of the simulation physical model based on the Lyapunov method when the state parameter time series has chaotic characteristics, so as to obtain a simplified simulation physical model after the order reduction.

[0119] The converter transformer behavior model reduction device proposed in the embodiment of the present invention determines whether the state parameter time series of the converter transformer has chaotic characteristics, determines whether there are key states and variables in the simulation physical model that have a greater impact on the dynamic behavior of the model, reduces the order of the simulation physical model based on the Lyapunov method, retains the key states and variables with greater impact, removes the key states and variables with less impact, and obtains a simplified simulation physical model, so as to simplify the model while ensuring that the simplified model can still accurately describe the main dynamic characteristics of the converter transformer system.

[0120] Figure 3 FIG. 1 shows an internal structure diagram of a computer device in one embodiment of the present invention. The computer device may be a terminal or a system. Figure 3 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0122] In one embodiment, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, the processor executes each step in the above method embodiment. A person of ordinary skill in the art can understand that all or part of the processes in the above embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium, and the program can include the processes of the embodiments of the above methods when executed. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many 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), etc.

[0123] The technical features of the above embodiments may 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.

[0124] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for reducing the order of a converter transformer behavior model, characterized in that: The method comprises: Establish a simulation physical model of the converter transformer; Obtaining a state parameter time series of the converter transformer based on the simulation physical model, and determining whether the state parameter time series has chaotic characteristics; When the state parameter time series has chaotic characteristics, the simulation physical model is reduced in order based on the Lyapunov method to obtain a simplified simulation physical model after the reduction in order.

2. The method according to claim 1, characterized in that The reducing the order of the simulation physical model based on the Lyapunov method to obtain the reduced-order simplified simulation physical model specifically includes: Constructing a full-order system equation of the converter transformer; Substituting the state parameters of the converter transformer into the full-order system equation to obtain the system matrix of the converter transformer; Constructing a Lyapunov equation according to the system matrix, and reducing the order of the full-order system equation according to the solution of the Lyapunov equation to obtain a reduced-order system equation; A simplified simulation physical model is constructed based on the reduced-order system equations.

3. The method according to claim 2, characterized in that The step of constructing a Lyapunov equation according to the system matrix and reducing the order of the full-order system equation according to the solution of the Lyapunov equation to obtain the reduced-order system equation specifically includes: Based on the system matrix and the preset control matrix and observation matrix, the Lyapunov equation is constructed for the i-th time, and the initial value of i is 1; Solving the Lyapunov equation to obtain eigenvalues ​​of the control matrix and the observation matrix, and eigenvectors corresponding to the eigenvalues ​​in the system matrix; Based on the balanced truncation method, the system matrix is ​​reduced in order by using the eigenvalue and the eigenvector corresponding to the eigenvalue in the system matrix to obtain a reduced-order system matrix; Substituting the reduced-order system matrix into the full-order system equation to obtain the reduced-order system equation; Calculating a model error between the reduced-order system equation and the full-order system equation; When the model error is not less than a preset error threshold, the reduced-order system matrix is ​​used as the system matrix, and i=i+1, and the step of constructing the Lyapunov equation for the i-th time according to the system matrix and the preset control matrix and observation matrix is ​​continued until the model error is less than the error threshold.

4. The method according to claim 3, characterized in that The Lyapunov equation construction method is as follows: Wherein, A, B, C are system matrices, P is the control matrix, and Q is the observation matrix.

5. The method according to claim 3, characterized in that The method of reducing the order of the system matrix by using the eigenvalue and the eigenvector corresponding to the eigenvalue in the system matrix based on the balanced truncation method to obtain the reduced-order system matrix specifically includes: Compare the eigenvalue with a preset eigenvalue threshold to obtain a first eigenvalue that is less than the eigenvalue threshold; Constructing an equation according to the control matrix and the observation matrix and solving it to obtain a non-singular transformation matrix, wherein the non-singular transformation matrix is ​​used to balance the control matrix and the observation matrix, so that the balanced control matrix is ​​the same as the observation matrix and is a diagonal matrix; Using the non-singular transformation matrix to perform equivalent transformation on the system matrix to obtain a balanced system matrix; The eigenvector corresponding to the first eigenvalue in the balanced system matrix is ​​truncated and removed to obtain a reduced-order system matrix.

6. The method according to claim 5, characterized in that The non-singular transformation matrix is ​​obtained by solving the following equation: In the formula, is the balanced control matrix, T is the non-singular transformation matrix, is the balanced observation matrix, σ1,σ2,…,σ n is the eigenvector corresponding to the matrix, and n is the matrix order.

7. The method according to claim 5, characterized in that The balanced system matrix is: In the formula, A, B, C, D are system matrices, T is a non-singular transformation matrix, is the balanced system matrix.

8. A converter transformer behavior model reduction device, characterized in that: The device comprises a simulation unit, a parameter acquisition unit and a model order reduction unit; The simulation unit is used to establish a simulation physical model of the converter transformer; The parameter acquisition unit is used to obtain the state parameter time series of the converter transformer based on the simulation physical model, and determine whether the state parameter time series has chaotic characteristics; The model order reduction unit is used to reduce the order of the simulation physical model based on the Lyapunov method when the state parameter time series has chaotic characteristics, so as to obtain a simplified simulation physical model after the order reduction.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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