Method for calculating participation factors of repetitive and similar oscillation modes and related equipment

Abstract

The invention belongs to the technical field of stability evaluation of a new energy station grid-connected system, and discloses a participation factor calculation method of repeated and similar oscillation modes and related equipment, and the method comprises the steps: obtaining a linear state space model of a new energy station system; acquiring a linearized state matrix according to the linearized state space model of the new energy station system; calculating a characteristic value of the new energy station system and a characteristic vector corresponding to the characteristic value according to the linearized state matrix, and respectively establishing a modal matrix according to the characteristic value and the characteristic vector; identifying a repeated oscillation mode or a similar oscillation mode of the new energy station system, and selecting a key repeated oscillation mode or a similar oscillation mode; based on a key repeated oscillation mode or a similar oscillation mode, a group participation factor of the repeated oscillation mode or the similar oscillation mode is calculated according to a modal matrix of a characteristic value and a characteristic vector, only one summation operation is involved for calculation of the group participation factor, and the calculation process is simple and efficient.

Description

Technical Field

[0001] This invention belongs to the field of stability assessment technology for grid-connected systems of new energy power plants, specifically a method and related equipment for calculating participation factors of repetitive and similar oscillation modes. Background Technology

[0002] Traditional thermal power generation is unsustainable due to limited fossil fuel reserves and environmental pollution. New energy sources such as wind and solar power, being clean and environmentally friendly, are gaining increasing attention and application. These new energy power generation devices generally require power electronic equipment as interfaces to connect to the power grid. The power electronic equipment used is typically a voltage source converter, which can be broadly classified into grid-connected converters and grid-connected converters. A high proportion of new energy integration and a high proportion of power electronic equipment application constitute the core characteristics of the new power system. On the one hand, they offer advantages such as significant environmental benefits, promoting low-carbon sustainable development; improving system controllability and flexibility, thereby increasing energy efficiency; promoting diversified and distributed energy structure development, and enhancing the reliability of local power grids. However, on the other hand, the dynamic characteristics of the new power system have also undergone profound changes. New energy sources such as wind and solar power exhibit strong randomness, volatility, and uncertainty, while power electronic equipment, dominated by multi-timescale control algorithms, has low physical inertia, leading to more complex stability issues across a wider frequency range. Therefore, effective stability analysis and evaluation of new energy power plant grid-connected systems have extremely important research value and engineering significance.

[0003] In existing technologies, the state-space method is a commonly used approach for modeling and evaluating the stability of grid-connected renewable energy power plants. This method establishes a state-space model of the entire system to obtain a state matrix describing the system's dynamics. Then, it calculates the eigenvalues ​​and participation factors of the state matrix. Eigenvalues, also known as modes, reflect the system's stability based on their sign. Participation factors reflect the degree of participation of each state variable in a specific mode. Through participation factor analysis, the key links with the greatest impact on system stability can be located, allowing for targeted oscillation suppression. However, renewable energy power plant grid-connected systems typically contain a large number of isomorphic or even identical power generation units, leading to densely distributed oscillation modes, which can be further subdivided into repetitive oscillation modes and similar oscillation modes. For repetitive oscillation modes, the corresponding eigenspace may be multidimensional, meaning the same oscillation mode corresponds to multiple linearly independent eigenvectors. This results in participation factors calculated based on eigenvectors no longer being unique, exhibiting multi-valuedness, rendering participation factor analysis completely ineffective and unable to provide the unique reference value needed to locate key links. For similar oscillation modes, although the corresponding participation factors are unique, these factors are highly sensitive to small perturbations in system parameters, such as sudden changes in wind speed or illumination. This sensitivity makes the calculated participation factors unreliable and unreliable under actual system operating conditions, severely diminishing their practical value in oscillation source tracing and suppression strategy design. Summary of the Invention

[0004] This invention provides a method and related equipment for calculating participation factors in repetitive and similar oscillation modes, which solves the problems that repetitive oscillation modes in new energy power plant grid-connected systems cannot provide a unique reference value for locating key links, and that participation factors calculated by similar oscillation modes are not very reliable and have poor reference value under actual system operating conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for calculating the participation factor of repetitive and similar oscillation modes includes: Obtain the linearized state-space model of the new energy power station system; Obtain the linearized state matrix based on the linearized state-space model of the new energy power station system; The eigenvalues ​​and corresponding eigenvectors of the new energy power station system are calculated based on the linearized state matrix, and the modal matrix is ​​established based on the eigenvalues ​​and eigenvectors respectively. Identify repetitive or similar oscillation patterns in the new energy power station system, and select key repetitive or similar oscillation patterns. Based on key repetitive or similar oscillation modes, the group participation factor of the repetitive or similar oscillation modes is calculated according to the mode matrix of eigenvalues ​​and eigenvectors.

[0006] Preferably, the steps for obtaining the linearized state-space model of the new energy power station system are as follows: Linearized state-space models of the power generation equipment and power grid of new energy power plants are established separately. Obtain the interconnection feedback relationship of the linearized state-space model of the power generation equipment and power grid of new energy power plants; A linearized state-space model of the new energy power station system is obtained by establishing interconnected feedback relationships.

[0007] Preferably, the linearized state-space model of the new energy power station system is as follows:

[0008] In the formula: express Linearized state matrix of order 1, express 3D linearized state vector, Indicates time.

[0009] Preferably, the feature vector includes a left feature vector and a right feature vector corresponding to the feature value.

[0010] Preferably, the method for calculating the eigenvalues ​​and corresponding eigenvectors of the new energy power station system based on the linearized state matrix is ​​as follows:

[0011]

[0012] In the formula: The system's first 1 eigenvalue, Indicates corresponding to The right eigenvector, Indicates corresponding to The left eigenvector, express Linearized state matrix of order.

[0013] Preferably, the key repetitive oscillation mode or similar oscillation mode is the oscillation mode located on the rightmost side of the complex plane.

[0014] Preferably, the method for calculating the group participation factor of repetitive oscillation modes or similar oscillation modes based on the mode matrix of eigenvalues ​​and eigenvectors is as follows:

[0015] In the formula: This indicates the number of selected repetitive or similar oscillation patterns. Represents the linearized state matrix The first in diagonal elements, This represents the modality matrix composed of the left eigenvectors. express The Middle Line number Column elements, This represents the modality matrix composed of the right eigenvectors. express The Middle Line number Column elements, As a group participation factor, The system's first Each feature value.

[0016] A participation factor calculation system for repetitive and similar oscillation modes includes: Model acquisition module: used to acquire the linearized state-space model of the new energy power station system; Matrix acquisition module: used to obtain the linearized state matrix based on the linearized state-space model of the new energy power station system; Matrix establishment module: used to calculate the eigenvalues ​​and corresponding eigenvectors of the new energy power station system based on the linearized state matrix, and to establish the modal matrix based on the eigenvalues ​​and eigenvectors respectively; Selection module: Used to identify repetitive or similar oscillation modes in new energy power station systems, and to select key repetitive or similar oscillation modes; Calculation module: Used to calculate the group participation factor of repetitive or similar oscillation modes based on the modal matrix of eigenvalues ​​and eigenvectors, according to the key repetitive or similar oscillation modes.

[0017] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a method for calculating participation factors of repetitive and similar oscillation modes.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for calculating participation factors in repetitive and similar oscillation modes.

[0019] Compared with existing technologies, this invention has the following advantages: This invention provides a method for calculating participation factors for repetitive and similar oscillation modes. By obtaining the linearized state-space model and state matrix of a new energy power station, it can accurately calculate eigenvalues, eigenvectors, and establish a mode matrix. Based on this, key repetitive or similar oscillation modes are identified, and the collective contribution of key equipment groups in this mode is clarified through group participation factor calculation. By calculating the group participation factor, the problem that repetitive oscillation modes in the grid-connected system of new energy power stations cannot provide a unique reference value for locating key links, and that the participation factors calculated for similar oscillation modes are not very reliable and have poor reference value under actual system operating conditions, are solved.

[0020] Furthermore, through the mathematical construction of the group participation factor calculation formula, for repetitive oscillation modes, the multi-valued participation factors of the multidimensional eigenspace are uniformly transformed into single-valued outputs by summing the partial derivatives of the entire set of eigenvalues. This fundamentally eliminates the analysis failure problem caused by the non-uniqueness of the traditional eigenvector method, ensuring that the calculation results have unique physical meaning. For similar oscillation modes, this summing operation significantly suppresses the sensitivity of single-mode participation factors to small perturbations of system parameters through mathematical smoothing, so that the analysis results remain stable under actual operating conditions and improve engineering reliability. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for calculating the participation factor of repetitive and similar oscillation modes according to the present invention.

[0022] Figure 2 This is a circuit diagram of a grid-connected system of three parallel grid-type converters in an embodiment of the participation factor calculation method proposed in this invention.

[0023] Figure 3 This is a closed-loop pole distribution diagram of Example 1 in the embodiment of the participation factor calculation method proposed in this invention.

[0024] Figure 4 This is the group participation factor analysis corresponding to the key repetitive oscillation mode in Example 1 of the embodiment of the participation factor calculation method proposed in this invention.

[0025] Figure 5 This is a closed-loop pole distribution diagram of Example 2 in the embodiment of the participation factor calculation method proposed in this invention.

[0026] Figure 6 This is the group participation factor analysis corresponding to the key similar oscillation modes in Example 2 of the embodiment of the participation factor calculation method proposed in this invention.

[0027] Figure 7 This is a block diagram of a participation factor calculation system for repetitive and similar oscillation modes according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0031] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] like Figure 1 As shown, this invention provides a method for calculating the participation factor of repetitive and similar oscillation modes, including: S1: Obtain the linearized state-space model of the new energy power station system; S2: Obtain the linearized state matrix based on the linearized state-space model of the new energy power station system; S3: Calculate the eigenvalues ​​and corresponding eigenvectors of the new energy power station system based on the linearized state matrix, and establish the modal matrix based on the eigenvalues ​​and eigenvectors respectively; S4: Identify repetitive or similar oscillation patterns in the new energy power station system, and select key repetitive or similar oscillation patterns. S5: Based on the key repetitive oscillation modes or similar oscillation modes, calculate the group participation factor of the repetitive oscillation modes or similar oscillation modes according to the modal matrix of eigenvalues ​​and eigenvectors.

[0033] By acquiring the linearized state-space model and state matrix of a renewable energy power station, eigenvalues, eigenvectors, and modal matrices can be accurately calculated. Based on this, key repetitive or similar oscillation modes can be identified, and the collective contribution of key equipment groups in this mode can be clarified by calculating the group participation factor. The calculation of the group participation factor solves the problem that repetitive oscillation modes in renewable energy power station grid-connected systems cannot provide a unique reference value for locating key links, and the participation factors calculated for similar oscillation modes are not very reliable and have poor reference value under actual system operating conditions.

[0034] The detailed steps are as follows: First, establish linearized state-space models for the power generation equipment and the power grid of the new energy power plant. Then, through their interconnection and feedback relationships, establish the linearized state-space model of the entire system, represented as:

[0035] In the formula: express Linearized state matrix of order 1, express 3D linearized state vector, Indicates time.

[0036] Based on the linearized state matrix Calculate the eigenvalues ​​and corresponding eigenvectors of the entire system, satisfying the equation:

[0037] In the formula: The system's first The eigenvalue, or the th eigenvalue One pattern, Indicates corresponding to The right eigenvector is a column vector. Indicates corresponding to The left eigenvector is a row vector.

[0038] In the entire system Among the various patterns, repetitive or similar oscillation patterns are identified, and the key repetitive or similar oscillation patterns are selected. The key oscillation pattern referred to here is the oscillation pattern located on the far right of the complex plane, which can determine the stability of the system to the greatest extent.

[0039] The participation factor for repetitive or similar oscillation modes can be calculated using the following formula.

[0040]

[0041] In the formula: This indicates the number of selected repetitive or similar oscillation patterns. Represents the linearized state matrix The first in diagonal elements, This represents the modality matrix composed of the left eigenvectors. express The Middle Line number Column elements, This represents the modality matrix composed of the right eigenvectors. express The Middle Line number Column elements, As a group participation factor, The system's first Each feature value.

[0042] The participation factor calculated using the above formula Essentially, it is a response to the first The summation of the participation factors of the _th state variable over the entire set of repetitive oscillation modes or similar oscillation modes reflects the _th The degree to which each state variable participates in the group of oscillation modes as a whole. Therefore, this participation factor is named the group participation factor.

[0043] The method proposed in this invention involves only a summation operation for calculating the group participation factor, making the calculation process concise and efficient. The calculation results characterize the overall participation of state variables in the entire set of repetitive oscillatory modes or similar oscillatory modes, possessing clear physical meaning and being intuitive and reasonable. The participation factor of the repetitive oscillatory modes calculated by the method of this invention is unique and effective; and for similar oscillatory modes, the sensitivity of their participation factor to system parameter perturbations is significantly reduced, exhibiting good robustness.

[0044] The effectiveness of the proposed participation factor calculation method was verified on a grid-connected system with three parallel grid-connected converters. Figure 2 As shown, three grid-connected converters are connected to nodes 2, 3, and 4 respectively, converging at the common coupling point node 5, and then connected to node 1 of the external power grid. The outer loop of the grid-connected converter's control section employs power droop control with a low-pass filter, while the inner loop uses dual voltage and current closed-loop control. Different specific cases were constructed by setting different parameters.

[0045] Case 1: When the structure and parameters of the three grid-connected converters are exactly the same, and the line impedance connected to node 5 is also exactly the same, the entire grid-connected system has multiple sets of repetitive oscillation modes. Figure 3A closed-loop pole distribution diagram of the entire grid-connected system is presented, from which a set of key repetitive oscillation modes, approximately -1.5 to +11.5i Hz, located on the rightmost side of the complex plane, are extracted. Participation factor analysis is performed on this mode using the calculation method proposed in this invention to obtain the corresponding group participation factor. Figure 4 Pie charts and bar charts are shown to represent the swarm participation factors corresponding to key repetitive oscillatory modes. The pie charts represent the percentage contribution of each state variable to the swarm participation factor of the key repetitive oscillatory mode, while the bar charts more intuitively display the values ​​of the real and imaginary parts of each state variable's contribution to the swarm participation factor. Figure 4 It can be seen that the power angle of the three grid-type converters angular frequency and line current Axial components The three grid-type converters contribute significantly to the key repetitive oscillation mode, and their contributions are completely equal. This means that the synchronization control loop and line impedance dynamics of the three grid-type converters are the dominant factors in the key repetitive oscillation mode.

[0046] Case 2: When the three grid-connected converters have the same structure, but their active power output values ​​and the line impedance connected to node 5 are different to a certain extent, the entire grid-connected system has a variety of similar oscillation modes. Figure 5 The closed-loop pole distribution diagram of the entire grid-connected system is presented. From this diagram, a set of key similar oscillation modes located on the rightmost side of the complex plane are extracted, with approximate values ​​of -1.4 +11.8i Hz and -1.6 +11.3i Hz. Participation factor analysis is performed on these modes using the calculation method proposed in this invention to obtain the corresponding group participation factors. Figure 6 Pie charts and bar charts are shown to represent the swarm participation factors corresponding to key similar oscillation modes. The pie charts represent the percentage contribution of each state variable to the swarm participation factor of the key similar oscillation modes, while the bar charts more intuitively display the values ​​of the real and imaginary parts of each state variable's contribution to the swarm participation factor. Figure 6 It can be seen that the power angle of the three grid-type converters angular frequency and line current Axial components The three grid-type converters make significant contributions to the key similar oscillation modes, and their overall contributions are very similar. This means that the synchronization control loop and line impedance dynamics of the three grid-type converters are the dominant factors in determining the key similar oscillation modes. These conclusions also align with intuitive understanding and empirical knowledge.

[0047] The present invention also provides a participation factor calculation system for repetitive and similar oscillation modes, comprising: Model acquisition module: used to acquire the linearized state-space model of the new energy power station system; Matrix acquisition module: used to obtain the linearized state matrix based on the linearized state-space model of the new energy power station system; Matrix establishment module: used to calculate the eigenvalues ​​and corresponding eigenvectors of the new energy power station system based on the linearized state matrix, and to establish the modal matrix based on the eigenvalues ​​and eigenvectors respectively; Selection module: Used to identify repetitive or similar oscillation modes in new energy power station systems, and to select key repetitive or similar oscillation modes; Calculation module: Used to calculate the group participation factor of repetitive or similar oscillation modes based on the modal matrix of eigenvalues ​​and eigenvectors, according to the key repetitive or similar oscillation modes.

[0048] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0049] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0050] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0051] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0052] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0053] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0054] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art, guided by the specification, can make many other modifications without departing from the scope of the claims of the present invention, and all of these modifications are within the scope of protection of the present invention.

Claims

1. A method of calculating a participation factor of a repetitive and nearly oscillation mode, characterized by, The method comprises the following steps: obtaining a linear state space model of a new energy station system; obtaining a linear state matrix according to the linear state space model of the new energy station system; calculating eigenvalues and eigenvectors corresponding to the eigenvalues of the new energy station system according to the linear state matrix, and establishing a modal matrix according to the eigenvalues and the eigenvectors respectively; identifying repeated oscillation modes or similar oscillation modes of the new energy station system, and selecting key repeated oscillation modes or similar oscillation modes; calculating group participation factors of the repeated oscillation modes or the similar oscillation modes according to the modal matrix of the eigenvalues and the eigenvectors based on the key repeated oscillation modes or the similar oscillation modes.

2. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The step of obtaining the linear state space model of the new energy station system comprises the following steps: respectively establishing linear state space models of new energy station power generation equipment and a power network; obtaining an interconnection feedback relationship of the linear state space models of the new energy station power generation equipment and the power network; establishing the linear state space model of the new energy station system through the interconnection feedback relationship.

3. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The linear state space model of the new energy station system is as follows: In the formula: represents linearized state matrix, represents linearized state vector, represents time.

4. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The eigenvectors comprise left eigenvectors and right eigenvectors corresponding to the eigenvalues.

5. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The method for calculating the eigenvalues and the eigenvectors corresponding to the eigenvalues of the new energy station system according to the linear state matrix comprises the following steps: where: represents the i-th eigenvalue of the system, represents the right eigenvector corresponding to represents the left eigenvector corresponding to represents the order linearized state matrix.​​​ 6. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The key repeated oscillation modes or the similar oscillation modes are oscillation modes on the most right side in a complex plane.

7. The method for calculating the participation factor of repetitive and similar oscillation modes according to claim 1, characterized in that, The method for calculating the group participation factors of the repeated oscillation modes or the similar oscillation modes according to the modal matrix of the eigenvalues and the eigenvectors comprises the following steps: In the formula: This indicates the number of selected repetitive or similar oscillation patterns. Represents the linearized state matrix The first in diagonal elements, This represents the modality matrix composed of the left eigenvectors. express The Middle Line 1 Column elements, This represents the modality matrix composed of the right eigenvectors. express The Middle Line 1 Column elements, As a group participation factor, Represents the system's first Each feature value.

8. A system for calculating a participation factor of a repetitive and proximate oscillation pattern, characterized by The method comprises the following steps: a model obtaining module for obtaining a linear state space model of a new energy station system; a matrix obtaining module for obtaining a linear state matrix according to the linear state space model of the new energy station system; a matrix establishing module for calculating eigenvalues and eigenvectors corresponding to the eigenvalues of the new energy station system according to the linear state matrix, and establishing a modal matrix according to the eigenvalues and the eigenvectors respectively; a selection module for identifying repeated oscillation modes or similar oscillation modes of the new energy station system, and selecting key repeated oscillation modes or similar oscillation modes; a calculation module for calculating group participation factors of the repeated oscillation modes or the similar oscillation modes according to the modal matrix of the eigenvalues and the eigenvectors based on the key repeated oscillation modes or the similar oscillation modes.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the repeated and similar oscillation mode participation factor calculation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is executed by the processor to realize the steps of the repeated and similar oscillation mode participation factor calculation method according to any one of claims 1 to 7.

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