New energy grid-connected system modeling method and system

Abstract

The invention discloses a new energy grid-connected system modeling method and system, and relates to the technical field of electric power grids, and the method comprises the steps: obtaining a topological structure of a new energy grid-connected system, constructing a small signal state space model of the topological structure, and carrying out the matrix operation of the small signal state space model based on the topological structure, obtaining a node voltage observability matrix corresponding to the new energy grid-connected system, and performing dominant oscillation modal analysis on the node voltage observability matrix by adopting each oscillation modal in the small-signal state space model to obtain a node voltage distribution coefficient corresponding to each node in the topological structure; and constructing an equivalent simplified model corresponding to the new energy grid-connected system according to the voltage distribution coefficient of each node. The technical problem that the accuracy of the simulation analysis result of the new energy grid-connected system is reduced due to the fact that an equivalent model cannot accurately and effectively analyze the broadband oscillation in the system because the dynamic characteristics of the system are affected due to the interaction among the existing multiple new energy stations is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical system modeling and simulation, and particularly to a modeling method and system for a new energy grid-connected system. Background Art

[0002] In recent years, renewable energy power generation technologies such as wind energy and solar energy have developed rapidly and have been widely developed and utilized in the power grid. However, broadband oscillation problems often occur in new energy grid-connected systems, which have become important factors threatening the safe and stable operation of the power system. In order to study broadband oscillation problems, it is necessary to construct a new energy grid-connected system model for theoretical analysis and simulation operation. However, since the new energy grid-connected system involves a large number of electrical components, if a detailed electromagnetic transient modeling and simulation of the entire system is carried out, it is difficult to apply in practice due to system scale and calculation speed limitations, and it is difficult to conduct a very detailed analysis of its dynamic characteristics. Therefore, an equivalent model is usually used to study the dynamic characteristics of the new energy grid-connected system.

[0003] Currently, the existing equivalent models mainly perform system equivalence at the multi-station centralized connection point to retain the dominant oscillation characteristics of the system. However, in the scenario of multiple new energy power stations accessing the main grid, due to the interaction between multiple new energy power stations, which affects the system dynamic characteristics, the equivalent model cannot accurately and effectively analyze the broadband oscillation in the system, reducing the reliability of the operation of the new energy grid-connected system. Summary of the Invention

[0004] The present invention provides a modeling method and system for a new energy grid-connected system, which solves the technical problem that in the scenario of multiple new energy power stations accessing the main grid, due to the interaction between multiple new energy power stations, which affects the system dynamic characteristics, the traditional equivalent method is prone to ignoring the influence of the characteristics of adjacent power grids on the dominant oscillation mode of the system, resulting in the equivalent model being unable to accurately and effectively analyze the broadband oscillation in the system and reducing the accuracy of the analysis results.

[0005] A modeling method for a new energy grid-connected system provided by the first aspect of the present invention includes:

[0006] Obtain the topological structure of the new energy grid-connected system and construct a small-signal state space model of the topological structure;

[0007] Based on the topological structure, perform matrix operations on the small-signal state space model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system;

[0008] Use each oscillation mode in the small-signal state space model to perform dominant oscillation mode analysis on the node voltage observability matrix to obtain the node voltage distribution coefficients corresponding to each node in the topological structure;

[0009] Construct an equivalent simplified model corresponding to the new energy grid-connected system according to each of the node voltage distribution coefficients.

[0010] Optionally, the step of constructing the small-signal state space model of the topological structure includes:

[0011] Obtain the power output combination parameters of the topological structure;

[0012] Based on the state space representation method, construct a state space model according to the topological structure and the power output combination parameters;

[0013] Perform linearization processing on the state space model to obtain a small-signal state space model.

[0014] Optionally, the step of performing matrix operations on the small-signal state space model based on the topological structure to obtain the node voltage observability matrix corresponding to the new energy grid-connected system includes:

[0015] Diagonalize the state space matrix of the small-signal state space model to obtain a state diagonal matrix and a right eigenvector matrix;

[0016] Perform a linear transformation on the right eigenvector matrix to obtain modal quantities and a system state vector;

[0017] Perform discrete equivalent processing on the topological structure according to the system state vector to obtain a system node voltage model;

[0018] Input the modal quantities into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0019] Optionally, the step of performing dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small-signal state space model to obtain the node voltage distribution coefficients corresponding to each node in the topological structure includes:

[0020] Extract the node voltage vectors corresponding to each oscillation mode from the node voltage observability matrix and solve the node eigenvalues of each node voltage vector;

[0021] Respectively extract the real parts of each node eigenvalue as the node damping, and select the node voltage vector associated with the minimum value among each node damping as the dominant oscillation vector;

[0022] According to the dominant oscillation vector and a preset voltage oscillation component function, obtain the node voltage distribution coefficients corresponding to each node in the topological structure.

[0023] Optionally, the step of obtaining a node voltage distribution coefficient corresponding to each node in the topological structure according to the dominant oscillation vector and a preset voltage oscillation component function comprises:

[0024] Inputting the dominant oscillation vector into a preset voltage oscillation component function to obtain voltage oscillation components corresponding to each node in the topological structure;

[0025] Performing module value processing on each of the voltage oscillation components respectively to obtain a first module value corresponding to each of the nodes;

[0026] Performing mean processing on all the first modulus values ​​to obtain a first mean value;

[0027] Ratio processing is performed on each of the first modulus values ​​and the first average value to obtain a node voltage distribution coefficient corresponding to each node.

[0028] Optionally, the step of constructing an equivalent simplified model corresponding to the new energy grid-connected system according to each of the node voltage distribution coefficients includes:

[0029] Determining whether the voltage distribution coefficient of each node is less than a preset risk threshold;

[0030] If the node voltage distribution coefficient is less than the risk threshold, simplifying and equalizing the region associated with the node voltage distribution coefficient to obtain an equivalent node model;

[0031] If the node voltage distribution coefficient is greater than or equal to the risk threshold, a region associated with the node voltage distribution coefficient is subjected to detailed modeling processing to obtain a target node model;

[0032] Each of the equivalent node models and each of the target node models are combined to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

[0033] A new energy grid-connected system modeling system provided in a second aspect of the present invention includes:

[0034] An acquisition module is used to obtain the topological structure of the new energy grid-connected system and construct a small signal state space model of the topological structure;

[0035] A mode conversion module, used for performing matrix operations on the small signal state space model based on the topological structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system;

[0036] An analysis module, used to perform dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small signal state space model to obtain a node voltage distribution coefficient corresponding to each node in the topological structure;

[0037] A building block for constructing an equivalent simplified model corresponding to the new energy grid-connected system according to the respective node voltage distribution coefficients.

[0038] An electronic device provided in the third aspect of the present invention includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor is caused to execute the steps of the new energy grid-connected system modeling method as described in any one of the above.

[0039] A computer-readable storage medium provided in the fourth aspect of the present invention has a computer program stored thereon. When the computer program is executed, the new energy grid-connected system modeling method as described in any one of the above is implemented.

[0040] A computer program product provided in the fifth aspect of the present invention includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the new energy grid-connected system modeling method as described in any one of the above.

[0041] It can be seen from the above technical solutions that the present invention has the following advantages:

[0042] By obtaining the topological structure of the new energy grid-connected system, constructing a small-signal state space model of the topological structure, and then performing oscillation mode analysis on the small-signal state space model, the node voltage distribution coefficients corresponding to each node in the dominant oscillation mode during dominant oscillation are determined. Thus, the construction of the equivalent model of the new energy grid-connected system is guided by the node voltage distribution coefficients, enabling the equivalent model to accurately reflect the interaction between devices, overcoming the technical problem that the interaction between existing multiple new energy power stations affects the dynamic characteristics of the system, resulting in the inability of the equivalent model to effectively analyze the broadband oscillation in the system accurately, and reducing the accuracy of the operation analysis conclusion of the new energy grid-connected system. Compared with the traditional equivalent simplification method, the present invention retains the dynamic characteristics of the system, enables the equivalent model to effectively analyze the broadband oscillation in the system accurately, and improves the reliability of the operation of the new energy grid-connected system. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1The flowchart of steps of a new energy grid-connected system modeling method provided by Embodiment 1 of the present invention;

[0045] Figure 2 The flowchart of steps of a new energy grid-connected system modeling method provided by Embodiment 2 of the present invention;

[0046] Figure 3 The structural schematic diagram of the new energy grid-connected system provided by Embodiment 2 of the present invention;

[0047] Figure 4 The histogram of node voltage distribution coefficients of multi-wind farm interval different-mode oscillation modes provided by Embodiment 2 of the present invention;

[0048] Figure 5 The structural schematic diagram of the equivalent simplified model provided by Embodiment 2 of the present invention;

[0049] Figure 6 The simulation waveform diagram of the equivalent simplified model provided by Embodiment 2 of the present invention;

[0050] Figure 7 The compass diagram of the output power oscillation component in the equivalent simplified model provided by Embodiment 2 of the present invention;

[0051] Figure 8 The structural block diagram of a new energy grid-connected system modeling system provided by Embodiment 3 of the present invention;

[0052] Figure 9 The structural block diagram of an electronic device provided by Embodiment 4 of the present invention. Detailed implementation manners

[0053] The embodiments of the present invention provide a new energy grid-connected system modeling method and system, which are used to solve the technical problem that in the scenario of multiple new energy power stations accessing the main grid, due to the interaction between multiple new energy power stations, the dynamic characteristics of the system are affected, and the traditional equivalent method is prone to ignoring the influence of the characteristics of adjacent power grids on the dominant oscillation mode of the system, resulting in the equivalent model being unable to accurately analyze the broadband oscillation in the system effectively and reducing the accuracy of the analysis results.

[0054] To make the invention purpose, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] Please refer to Figure 1 , Figure 1It is a step flowchart of a new energy grid - connected system modeling method provided in Embodiment 1 of the present invention.

[0056] A new energy grid - connected system modeling method provided by the present invention includes:

[0057] Step 101: Obtain the topological structure of the new energy grid - connected system and construct a small - signal state - space model of the topological structure;

[0058] In an embodiment of the present invention, the topological structure of the new energy grid - connected system is obtained, and a small - signal state - space model of the topological structure is constructed according to the small - disturbance analysis method.

[0059] Step 102: Based on the topological structure, perform matrix operations on the small - signal state - space model to obtain the node - voltage observability matrix corresponding to the new energy grid - connected system;

[0060] In an embodiment of the present invention, the state - space matrix of the small - signal state - space model is input into a preset system discrete state - space function to obtain modal quantities and a system state vector. The topological structure is discretely equivalent processed according to the system state vector to obtain a system node - voltage model. The modal quantities are input into the system node - voltage model to obtain the node - voltage observability matrix corresponding to the new energy grid - connected system.

[0061] It should be noted that the \(i\) - th column in the node - voltage observability matrix represents the response of the \(i\) - th mode in each node voltage.

[0062] Step 103: Use each oscillation mode in the small - signal state - space model to perform dominant oscillation mode analysis on the node - voltage observability matrix to obtain the node - voltage distribution coefficients corresponding to each node in the topological structure;

[0063] The oscillation mode refers to the type of broadband oscillation that occurs in the new energy grid - connected system.

[0064] In an embodiment of the present invention, according to each oscillation mode in the small - signal state - space model, the node - voltage vectors corresponding to each oscillation mode are extracted from the node - voltage observability matrix. The eigenvalues of each node - voltage vector are respectively solved, and the real part of each eigenvalue is taken as the damping. The node - voltage vector with the largest damping among them is selected as the dominant oscillation vector. According to a preset voltage oscillation component function, the dominant oscillation vector is solved to obtain the node - voltage distribution coefficients corresponding to each node in the topological structure under the dominant vibration mode.

[0065] Step 104: Construct an equivalent simplified model corresponding to the new energy grid - connected system according to each node - voltage distribution coefficient.

[0066] In the embodiment of the present invention, the node voltage distribution coefficients are sorted from large to small, and the node voltage distribution coefficients of the top preset modeling quantity threshold are selected as the target distribution coefficients, and the remaining node voltage distribution coefficients are used as the equivalent distribution coefficients. The intervals associated with the respective target distribution coefficients are modeled in detail to obtain a target region model, and the regions associated with the respective equivalent distribution coefficients are equivalently processed to obtain an equivalent region model. The target region model and the equivalent region model are coupled to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

[0067] In the embodiment of the present invention, by obtaining the topological structure of the new energy grid-connected system, constructing a small-signal state space model of the topological structure, and then performing an oscillation mode analysis on the small-signal state space model, the node voltage distribution coefficients corresponding to each node during the dominant oscillation are determined, so as to guide the construction of the equivalent model of the new energy grid-connected system through the node voltage distribution coefficients, enabling the equivalent model to accurately reflect the interaction between devices, overcoming the technical problem that there is an interaction between existing multiple new energy power stations, affecting the dynamic characteristics of the system, resulting in the equivalent model being unable to effectively analyze the broadband oscillation in the system accurately, and reducing the accuracy of the operation analysis conclusion of the new energy grid-connected system. Compared with the traditional equivalent simplification method, the present invention retains the dynamic characteristics of the system, enabling the equivalent model to effectively analyze the broadband oscillation in the system accurately and improving the reliability of the operation of the new energy grid-connected system.

[0068] Please refer to Figure 2 , Figure 2 which is the step flowchart of a new energy grid-connected system modeling method provided in the second embodiment of the present invention.

[0069] A new energy grid-connected system modeling method provided by the present invention includes:

[0070] Step 201, obtain the topological structure of the new energy grid-connected system and construct a small-signal state space model of the topological structure;

[0071] Further, step 201 includes the following sub-steps:

[0072] S11, obtain the power output combination parameters of the topological structure;

[0073] The power output combination parameters refer to the capacity and power output parameters of different types of power sources in each regional power grid of the new energy grid-connected system.

[0074] In the embodiment of the present invention, the capacity and power output parameters of different types of power sources in each region of the topological structure are obtained.

[0075] S12, based on the state space representation method, construct a state space model according to the topological structure and the power output combination parameters;

[0076] In an embodiment of the present invention, based on the state - space representation method and the power output combination parameters, a state - space model of the topological structure is constructed.

[0077] It is worth mentioning that the state - space representation method can be used to describe various types of dynamic systems, including linear and non - linear systems, time - invariant and time - varying systems, single - input single - output (SISO) and multi - input multi - output (MIMO) systems, etc. For example, whether it is a simple circuit system, a mechanical system, or a complex aerospace control system.

[0078] S13. Linearize the state - space model to obtain a small - signal state - space model.

[0079] In an embodiment of the present invention, the state - space model is linearized to obtain a small - signal state - space model.

[0080] Step 202. Based on the topological structure, perform matrix operations on the small - signal state - space model to obtain the node - voltage observability matrix corresponding to the new - energy grid - connected system;

[0081] Furthermore, step 202 includes the following sub - steps:

[0082] S21. Diagonalize the state - space matrix of the small - signal state - space model to obtain a state diagonal matrix and a right - eigenvector matrix;

[0083] In an embodiment of the present invention, the state - space matrix of the small - signal state - space model is input into a preset diagonalization decomposition function to obtain a state diagonal matrix and a right - eigenvector matrix.

[0084] It should be noted that the diagonalization decomposition function is specifically:

[0085]

[0086] V T W = I

[0087] where V is the left - eigenvector matrix corresponding to the discrete eigenvalues, W is the right - eigenvector matrix corresponding to the discrete eigenvalues, is, is the state - space matrix, T is the transpose of the matrix, and I is the identity matrix with the same dimension as the state - space matrix.

[0088] S22. Linearly transform the right - eigenvector matrix to obtain modal quantities and the system state vector;

[0089] The modal quantity refers to the system state variable.

[0090] In an embodiment of the present invention, the right - eigenvector matrix is input into a preset linear - transformation function to obtain modal quantities and the system state vector.

[0091] It should be noted that the linear transformation function is specifically:

[0092]

[0093] in, is the system state vector, is the modal quantity.

[0094] S23, performing discrete equivalent processing on the topological structure according to the system state vector to obtain a system node voltage model;

[0095] In an embodiment of the present invention, a node voltage function is constructed according to the topological structure, an injection current function is constructed using a branch-node association matrix and historical current terms in discrete models of each component, the node voltage function and the injection current function are combined to obtain an initial system node voltage model, and the system state vector is input into the initial system node voltage model to obtain a system node voltage model.

[0096] It is worth mentioning that since the AC system can be represented in the discrete domain as a discrete equivalent circuit network consisting of equivalent conductance of components and historical current sources, the network can be described by the node voltage equation (i.e., node voltage function). According to the interconnection structure between the components in the system, the node injection current in the node voltage equation can be expressed as the product of the branch-node association matrix and the historical current term in the discrete model of each component (i.e., injection current function).

[0097] It should be noted that the node voltage function is specifically:

[0098]

[0099] in, is the equivalent conductance of the element, Inject current into the node, is the system node voltage.

[0100] It should be noted that the injection current function is specifically:

[0101]

[0102] in, is a diagonal block matrix composed of the coefficient matrices in the historical current sources of each component, T is the transpose of the matrix, is the coefficient matrix in the historical current source of each component, It is the product of the diagonal block matrix composed of the coefficient matrix in the historical current source of each component and the system state vector.

[0103] It should be noted that the system node voltage model is specifically:

[0104]

[0105] Among them, is the node voltage observability matrix.

[0106] S24. Input the modal quantity into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0107] In the embodiment of the present invention, the modal quantity is used as the input of the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

[0108] Step 203. Extract the node voltage vectors corresponding to each oscillation mode from the node voltage observability matrix, and solve the node eigenvalues of each node voltage vector;

[0109] In the embodiment of the present invention, the node voltage observability matrix is divided into multiple node voltage vectors according to each oscillation mode, and each node voltage vector is solved to obtain the node eigenvalues of each node voltage vector.

[0110] It is worth mentioning that since the modal quantities of the system represented by Z(t) are decoupled from each other, the i-th column of the matrix S represents the response of the i-th mode in the node voltages. Since the node voltages and branch currents are converted to the unified xy rotation coordinate system of the whole network during the full-system state space modeling, for an n-node system, the node voltage vector in the injection current function should be of order 2n×1 (including the x / y axis components of the voltage). Therefore, in order to obtain the total response of the oscillation components in each node voltage, the x-axis component and the y-axis component need to be superimposed.

[0111] Step 204. Respectively extract the real parts of each node eigenvalue as the node damping, and select the node voltage vector associated with the minimum value among each node damping as the dominant oscillation vector;

[0112] In the embodiment of the present invention, the real parts of each node eigenvalue are respectively extracted as the node damping, and the node voltage vector associated with the minimum value among all the node dampings is selected as the dominant oscillation vector.

[0113] Step 205. According to the dominant oscillation vector and the preset voltage oscillation component function, obtain the node voltage distribution coefficients corresponding to each node in the topological structure.

[0114] Further, step 205 includes the following sub-steps:

[0115] S31. Input the dominant oscillation vector into the preset voltage oscillation component function to obtain the voltage oscillation components corresponding to each node in the topological structure;

[0116] In the embodiment of the present invention, the dominant vibration line is used as the input of a preset voltage oscillation component function to obtain the voltage oscillation components corresponding to each node in the topological structure.

[0117] It should be noted that the voltage oscillation component function is specifically:

[0118]

[0119] Wherein, is the voltage oscillation component of the p-th node in the i-th mode, p is the oscillation mode number, and i is the node number. is the imaginary unit.

[0120] S32. Respectively perform modulus value processing on each voltage oscillation component to obtain the first modulus value corresponding to each node;

[0121] In the embodiment of the present invention, each voltage oscillation component is respectively input into a preset modulus value function to obtain the first modulus value corresponding to each node.

[0122] S33. Perform mean value processing on all the first modulus values to obtain the first mean value;

[0123] S34. Respectively perform ratio processing on each first modulus value and the first mean value to obtain the node voltage distribution coefficient corresponding to each node.

[0124] In the embodiment of the present invention, each voltage oscillation component is input into a preset node voltage distribution coefficient function to obtain the node voltage distribution coefficient corresponding to each node.

[0125] It should be noted that the node voltage distribution coefficient function is specifically:

[0126]

[0127] Wherein, is the node voltage distribution coefficient of the j-th node in the i-th mode, j is the node number in the i-th mode, and n is the total number of nodes in the i-th mode.

[0128] Step 206. Construct an equivalent simplified model corresponding to the new energy grid-connected system according to each node voltage distribution coefficient.

[0129] Further, step 206 includes the following sub-steps:

[0130] S41. Determine whether each node voltage distribution coefficient is less than a preset risk threshold;

[0131] It should be noted that each node voltage distribution coefficient is sorted, and the Q-th node distribution coefficient is selected as the risk threshold.

[0132] S42, if the node voltage distribution coefficient is less than the risk threshold, simplifying and equalizing the region associated with the node voltage distribution coefficient to obtain an equivalent node model;

[0133] In an embodiment of the present invention, it is determined whether the voltage distribution coefficient of each node is less than a preset risk threshold. When the node voltage distribution coefficient is less than the risk threshold, the area associated with the node voltage distribution coefficient is simplified and equivalently processed to obtain an equivalent node model (i.e., the area with a smaller node voltage distribution coefficient is simplified and equivalently processed).

[0134] S43, if the node voltage distribution coefficient is greater than or equal to the risk threshold, a region associated with the node voltage distribution coefficient is subjected to detailed modeling processing to obtain a target node model;

[0135] In an embodiment of the present invention, when the node voltage distribution coefficient is greater than or equal to the risk threshold, the region associated with the node voltage distribution coefficient is modeled in detail to obtain a target node model (ie, the region with a larger node voltage distribution coefficient is modeled in detail).

[0136] S44, combining each equivalent node model with each target node model to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

[0137] In the embodiment of the present invention, each equivalent node model is coupled with each target node model to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

[0138] In another embodiment, see Figure 3As shown in the figure, the topological structure and multiple oscillation modes of the new energy grid-connected system are obtained. 1. The new energy grid-connected system is based on the 500 kV main grid and is mainly divided into five regions. Among them, regions 1 and 4, and regions 2 and 3 are interconnected by back-to-back flexible DC 1 (VSC-HVDC1) and back-to-back flexible DC 2 (VSC-HVDC2) respectively, and other regions are interconnected by a 500 kV AC large loop represented by a red line. 2. A large-capacity equivalent thermal power unit is used in each regional power grid to simulate the conventional synchronous machine power supply, and only the 500 kV lines between the main grid represented by the equivalent machine and each external connection node are retained within the region. 3. The example contains 6 conventional DCs and 1 flexible DC feed-in. The capacity and connection location of each DC are LCC-HVDC1 (±800 kV, 5000 MW, connected to region 1), LCC-HVDC2 (±500 kV, 3000 MW, connected to region 2), LCC-HVDC3 (±500 kV, 6400 MW, double-circuit, connected to region 2), LCC-HVDC4 (±500 kV, 3000 MW, connected to region 3), LCC-HVDC5 (±800 kV, 5000 MW, connected to region 3), LCC-HVDC6 (±800 kV, 5000 MW, connected to region 4), and VSC-HVDC3 (±400 kV, 5000 MW, connected to region 3). 4. The example contains 4 direct-drive wind farms and 2 onshore photovoltaic power stations. Among them, wind farm 2 (4000 MW) and wind farm 3 (3000 MW) are connected to region 1, wind farm 1 (6000 MW) and photovoltaic 1 (3000 MW) are connected to region 2, wind farm 4 (4000 MW) is connected to region 4, and photovoltaic 2 (2000 MW) is connected to region 3; 20% of the dynamic reactive power compensation device SVG is arranged for each new energy power station according to the regulations.

[0139] First, it is necessary to construct the small-signal state space model of the example system and set the capacity and output of different types of power sources in each regional power grid as shown in Table 1.

[0140] Table 1

[0141]

[0142] In the above scenario, the proportion of non-synchronous machine power sources (including new energy power generation and conventional / flexible DC feed-in) in the total installed capacity of all power sources in the system is 64%, and the output of non-synchronous machine power sources accounts for 72% of the total load capacity. After establishing the small-signal state space model of the example system in this operating scenario, the eigenvalues of the state space matrix are solved. The damping calculation results of some nodes are shown in Table 2, and the dynamic characteristics of the system will be dominated by different node damping modes.

[0143] Table 2

[0144]

[0145] By analyzing the participation factors and component participation degrees of each eigenvalue in Table 2, it can be seen that the modes λ 1,2 , λ 9,10 , λ 11,12 , λ 13,14 are the electromechanical oscillation modes corresponding to the synchronous machine, and the modes λ 3,4 , λ 7,8 , λ 15,16 are the sub-synchronous oscillation modes related to the current loop control of the direct-drive wind farm. The mode λ 17,18 is the super-synchronous oscillation mode related to the control of the receiving converters of VSC-HVDC1 and VSC-HVDC3. This result shows that when the proportion of power electronic devices is relatively high, Figure 3 the system shown may face three typical oscillation instability risks, namely, synchronous machine-dominated electromechanical oscillation, wind farm-dominated sub-synchronous oscillation, and flexible DC-dominated super-synchronous oscillation.

[0146] However, when the sub-synchronous oscillation modes with relatively small positive damping ratios of 3 pairs of nodes related to the direct-drive wind farm encounter faults, the system will become weaker. The damping ratios of these mode nodes may become negative, resulting in oscillation problems in the system. Subsequently, by recalculating the eigenvalue results corresponding to the two operating modes respectively in the full connection, N-1, and N-2 modes (N-1 means disconnecting one of the double-circuit outgoing lines of Wind Farm 1 (i.e., Figure 2 the line between bus B6 and B326 in ), N-2 means further disconnecting one of the double-circuit outgoing lines of Wind Farm 2 (i.e., the line B2-B327) on the basis of N-1), the 3 pairs of oscillation modes related to the direct-drive wind farm are listed in Table 3.

[0147] Table 3

[0148]

[0149] In order to enable the simplified equivalent model of the power grid to still accurately reflect the dynamic characteristics of the dominant oscillation modes in the complete system, the key lies in retaining the power grid network structure that has a greater impact on the dominant oscillation modes. In the present invention, by sorting the node voltage distribution coefficients from large to small, the node voltage distribution coefficients of the first preset modeling quantity threshold are selected as the target distribution coefficients, and the remaining node voltage distribution coefficients are used as the equivalent distribution coefficients. The intervals associated with each target distribution coefficient are respectively detailedly modeled to obtain the target region model, and the regions associated with each equivalent distribution coefficient are respectively equivalent processed to obtain the equivalent region model. The target region model and the equivalent region model are coupled to obtain the equivalent simplified model corresponding to the new energy grid-connected system.

[0150] Since in the N-2 mode, there are three dominant instability forms, namely, inter-region heteromorphic oscillation, intra-region heteromorphic oscillation, and common-mode oscillation, among multiple wind farms. Taking the inter-region heteromorphic mode λ with the largest real part3,4 Taking (6.38±j35.3×2π) as an example, calculate its distribution coefficients among the voltages of all the system nodes, and sort the results in descending order. As Figure 4 shown, the two nodes with the largest distribution coefficients in the figure are B299 and B300. Taking 1 / 10 of the largest distribution coefficient as the limit value (i.e., the red line marked in the enlarged part of the figure), it is considered that the nodes with distribution coefficients greater than this limit value have a greater impact on the dynamics of the modes λ3,4 and should be retained during modeling.

[0151] According to Figure 4 the results in, there are a total of 20 nodes with distribution coefficients exceeding 1 / 10 of the maximum value. List them in Table 4. Similarly, calculate the node voltage distribution coefficients of the other two oscillation instability modes respectively, and also take 1 / 10 of the maximum value as the boundary to find the nodes that have a greater impact on the two modes and list them in Table 4.

[0152] Table 4

[0153]

[0154] Compare the key nodes in Table 4 with the Figure 3 system node numbers in. It can be found that although the key nodes corresponding to different modes are different, their common feature is that they are all distributed in both Region 1 and Region 2 power grids simultaneously, rather than only in the vicinity of a single wind farm. This result indicates that for the three modes dominated by the wind farms, due to the influence of the grid structure characteristics and the interaction between wind farms, there is coupling between Region 1 and Region 2 power grids. Therefore, the conventional engineering experience of extending three nodes further outward at the location where new energy is connected to the grid and simplifying and equivalenting the two regional power grids separately is inappropriate in itself.

[0155] To obtain a simplified grid model that can retain the oscillation characteristics of the above three modes simultaneously, take the union of the nodes in Table 4, and model in detail these nodes and the grid scope they cover, and the Figure 5 shown simplified model can be obtained. Compared with the Figure 3 complete system model in, this simplified model retains the grid structures of the two regions where Wind Farm 1 / 2 / 3 are connected, and equivalentizes the remaining system at the AC external connection node B9 and the AC receiving end node of the flexible DC respectively.

[0156] Keep the equipment parameters and system power flow in the above simplified model consistent with the detailed model, and still take the N-2 mode as an example to calculate the eigenvalues of the simplified system. The three pairs of oscillation modes related to the wind farms are shown in Table 5.

[0157] Table 5

[0158]

[0159] Comparing with the eigenvalue results obtained based on the detailed system model in Table 3, the two are basically the same, indicating that using the Figure 5 simplified model in it can still accurately analyze the multimodal oscillation problem caused by the complex interaction between multiple wind farms. And through the analysis of the oscillation modes of each mode, it shows that the oscillation between multiple wind farms in the simplified model is still mainly in the heterogenous mode, which is consistent with the relevant analysis conclusions in the detailed model.

[0160] To verify the effectiveness of the simplified equivalent method, a detailed electromagnetic transient simulation model of this example system was established in PSCAD / EMTDC. One outgoing line of each of Wind Farm 1 / 2 was disconnected at 2 s, and the output power waveforms of the three wind farms and their spectrum analysis results are as Figure 6 shown. It can be seen that obvious oscillations occurred in all three wind farms, and the waveforms contained three oscillation frequency components, which were 31.5 Hz, 32.7 Hz, and 35.1 Hz respectively. The amplitudes and phases of the oscillation components of each wind farm were drawn in the form of a compass diagram, as Figure 7 shown. The simulation results show that for the 35.1 Hz component, the oscillation phases of Wind Farm 1 and Wind Farm 2 / 3 are opposite, corresponding to the heterogenous oscillation mode λ 3,4 in the corresponding interval; for the 32.7 Hz component, the amplitudes of Wind Farm 2 / 3 are relatively large and the phases are opposite, corresponding to the heterogenous oscillation mode λ 7,8 in the corresponding area; for the 31.5 Hz component, the oscillation phases of Wind Farm 1 / 2 / 3 are basically the same, corresponding to the common mode oscillation mode λ 15,16 . The simulation results are consistent with the modal analysis results.

[0161] In summary, when analyzing the system multimodal oscillation problem in the scenario of multiple wind farms connected to the main grid, the method of determining the grid equivalent range based on the node voltage distribution coefficient can effectively retain the dominant oscillation characteristics of the system. The key lies in retaining the grid structure that has a greater impact on the dominant instability mode and the interaction paths between the related wind farms.

[0162] In the embodiment of the present invention, by obtaining the topological structure of the new energy grid-connected system, constructing a small-signal state space model of the topological structure, and then performing oscillation modal analysis on the small-signal state space model, the node voltage distribution coefficients corresponding to each node during dominant oscillation are determined, so as to guide the construction of the equivalent model of the new energy grid-connected system through the node voltage distribution coefficients, enabling the equivalent model to accurately reflect the interaction between devices, overcoming the technical problems that there are interactions between existing multiple new energy stations, affecting the dynamic characteristics of the system, resulting in the equivalent model being unable to accurately analyze the broadband oscillation in the system effectively, and reducing the accuracy of the operation analysis conclusions of the new energy grid-connected system. Compared with the traditional equivalent simplification method, the present invention retains the dynamic characteristics of the system, enables the equivalent model to accurately analyze the broadband oscillation in the system effectively, and improves the reliability of the operation of the new energy grid-connected system.

[0163] Please refer to Figure 8 , Figure 8 which is the structural block diagram of a new energy grid-connected system modeling system provided in Embodiment 3 of the present invention.

[0164] A new energy grid-connected system modeling system provided by the present invention includes:[[]]

[0165] An acquisition module 301, configured to obtain the topological structure of the new energy grid-connected system and construct a small-signal state space model of the topological structure;

[0166] A modal conversion module 302, configured to perform matrix operations on the small-signal state space model based on the topological structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system;

[0167] An analysis module 303, configured to perform dominant oscillation mode analysis on the node voltage observability matrix by using each oscillation mode in the small-signal state space model to obtain node voltage distribution coefficients corresponding to each node in the topological structure;

[0168] A construction module 304, configured to construct an equivalent simplified model corresponding to the new energy grid-connected system according to each node voltage distribution coefficient.

[0169] Further, the acquisition module 301 includes:

[0170] An acquisition sub-module, configured to obtain the power output combination parameters of the topological structure;

[0171] A construction sub-module, configured to construct a state space model based on the state space representation method according to the topological structure and the power output combination parameters;

[0172] A linearization sub-module, configured to perform linearization processing on the state space model to obtain a small-signal state space model.

[0173] Further, the modal conversion module 302 includes:

[0174] A decomposition sub-module, configured to perform diagonalization decomposition on the state space matrix of the small-signal state space model to obtain a state diagonal matrix and a right eigenvector matrix;

[0175] A linear transformation sub-module, configured to perform linear transformation on the right eigenvector matrix to obtain modal quantities and a system state vector;

[0176] A discrete equivalent sub-module, configured to perform discrete equivalent processing on the topological structure according to the system state vector to obtain a system node voltage model;

[0177] A second configuration sub-module, configured to input the modal quantities into the system node voltage model to obtain a node voltage observability matrix corresponding to the new energy grid-connected system.

[0178] Furthermore, the analysis module 303 includes:

[0179] A feature extraction submodule is used to extract the node voltage vector corresponding to each oscillation mode from the node voltage observability matrix and solve the node eigenvalue of each node voltage vector;

[0180] The damping submodule is used to extract the real part of each node eigenvalue as the node damping, and select the node voltage vector associated with the minimum value in each node damping as the dominant oscillation vector;

[0181] The oscillation analysis submodule is used to obtain the node voltage distribution coefficient corresponding to each node in the topological structure according to the dominant oscillation vector and the preset voltage oscillation component function.

[0182] Furthermore, the oscillation analysis submodule includes:

[0183] An analysis unit, used for inputting a dominant oscillation vector into a preset voltage oscillation component function to obtain voltage oscillation components corresponding to each node in the topological structure;

[0184] A modulus value unit, used to perform modulus value processing on each voltage oscillation component to obtain a first modulus value corresponding to each node;

[0185] A mean value unit, used for performing mean processing on all first modulus values ​​to obtain a first mean value;

[0186] The ratio unit is used to perform ratio processing on each first modulus value and the first mean value respectively to obtain a node voltage distribution coefficient corresponding to each node.

[0187] Further, the construction module 304 includes:

[0188] The first analysis submodule is used to determine whether the voltage distribution coefficient of each node is less than a preset risk threshold;

[0189] If the node voltage distribution coefficient is less than the risk threshold, the region associated with the node voltage distribution coefficient is simplified and equivalently processed to obtain an equivalent node model;

[0190] If the node voltage distribution coefficient is greater than or equal to the risk threshold, the area associated with the node voltage distribution coefficient is modeled in detail to obtain the target node model;

[0191] The combined submodule is used to combine each equivalent node model and each target node model to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

[0192] See also Figure 9 , Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0193] An electronic device according to an embodiment of the present invention, the electronic device includes: a memory 401 and a processor 402, and a computer program is stored in the memory 402; when the computer program is executed by the processor 402, the processor 402 is caused to execute the new energy grid-connected system modeling method according to any of the above embodiments.

[0194] The memory 401 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. The memory 401 has a storage space 403 for program code 413 for executing any method steps in the above methods. For example, the storage space 403 for program code may include respective program codes 413 for implementing various steps in the above methods. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed in an appropriate form, for example. When these codes are run by a computing processing device, the computing processing device is caused to execute each of the steps in the methods described above.

[0195] Embodiment 5 of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the new energy grid-connected system modeling method according to any of the above embodiments is implemented.

[0196] Embodiment 6 of the present invention further provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the new energy grid-connected system modeling method according to any of the above embodiments.

[0197] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0198] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0199] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0201] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, and other media that can store program codes.

[0202] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A new energy grid-connected system modeling method, characterized in that: include: Obtaining a topological structure of a new energy grid-connected system and constructing a small signal state space model of the topological structure; Based on the topological structure, matrix operations are performed on the small signal state space model to obtain a node voltage observability matrix corresponding to the new energy grid-connected system; Using each oscillation mode in the small signal state space model to perform dominant oscillation mode analysis on the node voltage observability matrix, and obtain node voltage distribution coefficients corresponding to each node in the topological structure; An equivalent simplified model corresponding to the new energy grid-connected system is constructed according to the voltage distribution coefficients of each node.

2. The new energy grid-connected system modeling method according to claim 1 is characterized in that: The step of constructing the small signal state space model of the topological structure comprises: Obtaining power output combination parameters of the topology structure; Based on the state space representation method, a state space model is constructed according to the topological structure and the power output combination parameters; The state space model is linearized to obtain a small signal state space model.

3. The new energy grid-connected system modeling method according to claim 1 is characterized in that: The step of performing matrix operations on the small signal state space model based on the topological structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system includes: Decomposing the state space matrix of the small signal state space model diagonally to obtain a state diagonal matrix and a right eigenvector matrix; Performing a linear transformation on the right eigenvector matrix to obtain a modal quantity and a system state vector; Performing discrete equivalent processing on the topological structure according to the system state vector to obtain a system node voltage model; The modal quantity is input into the system node voltage model to obtain the node voltage observability matrix corresponding to the new energy grid-connected system.

4. The new energy grid-connected system modeling method according to claim 1, characterized in that: The step of using each oscillation mode in the small signal state space model to perform dominant oscillation mode analysis on the node voltage observability matrix to obtain the node voltage distribution coefficient corresponding to each node in the topological structure includes: Extracting the node voltage vector corresponding to each of the oscillation modes from the node voltage observability matrix, and solving the node eigenvalue of each of the node voltage vectors; Extracting the real part of each node characteristic value as the node damping, and selecting the node voltage vector associated with the minimum value in each node damping as the dominant oscillation vector; According to the dominant oscillation vector and a preset voltage oscillation component function, a node voltage distribution coefficient corresponding to each node in the topological structure is obtained.

5. The new energy grid-connected system modeling method according to claim 4 is characterized in that: The step of obtaining the node voltage distribution coefficient corresponding to each node in the topological structure according to the dominant oscillation vector and the preset voltage oscillation component function comprises: Inputting the dominant oscillation vector into a preset voltage oscillation component function to obtain voltage oscillation components corresponding to each node in the topological structure; Performing module value processing on each of the voltage oscillation components respectively to obtain a first module value corresponding to each of the nodes; Performing mean processing on all the first modulus values ​​to obtain a first mean value; Ratio processing is performed on each of the first modulus values ​​and the first average value to obtain a node voltage distribution coefficient corresponding to each node.

6. The new energy grid-connected system modeling method according to claim 1, characterized in that: The step of constructing an equivalent simplified model corresponding to the new energy grid-connected system according to each of the node voltage distribution coefficients includes: Determining whether the voltage distribution coefficient of each node is less than a preset risk threshold; If the node voltage distribution coefficient is less than the risk threshold, simplifying and equalizing the region associated with the node voltage distribution coefficient to obtain an equivalent node model; If the node voltage distribution coefficient is greater than or equal to the risk threshold, a region associated with the node voltage distribution coefficient is subjected to detailed modeling processing to obtain a target node model; Each of the equivalent node models and each of the target node models are combined to obtain an equivalent simplified model corresponding to the new energy grid-connected system.

7. A new energy grid-connected system modeling system, characterized in that: include: An acquisition module is used to obtain the topological structure of the new energy grid-connected system and construct a small signal state space model of the topological structure; A mode conversion module, used for performing matrix operations on the small signal state space model based on the topological structure to obtain a node voltage observability matrix corresponding to the new energy grid-connected system; An analysis module, used to perform dominant oscillation mode analysis on the node voltage observability matrix using each oscillation mode in the small signal state space model to obtain a node voltage distribution coefficient corresponding to each node in the topological structure; A construction module is used to construct an equivalent simplified model corresponding to the new energy grid-connected system according to each of the node voltage distribution coefficients.

8. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the new energy grid-connected system modeling method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the new energy grid-connected system modeling method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the new energy grid-connected system modeling method as described in any one of claims 1-6.

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