Optimization method suitable for PSS and VPSS parameter coordination setting

Abstract

The invention belongs to the technical field of power electronic inverter technology / power system low-frequency oscillation, and particularly relates to a low-frequency oscillation suppression method suitable for cooperation of a PSS and a VPSS. Comprising the steps of building a PSS and VPSS parallel control mathematical model; triggering low-frequency oscillation in the established multi-machine system, acquiring system oscillation data by using a phasor measurement unit (PMU), performing system oscillation mode identification on the oscillation data by using a dynamic mode decomposition method, and analyzing a damping ratio of a dominant oscillation mode; comparing whether the damping ratio of the dominant oscillation mode meets a set damping ratio requirement for low-frequency oscillation suppression or not; assigning new PSS and VPSS configuration parameters to the multi-machine model, triggering low-frequency oscillation, and repeating the previous step until all dominant oscillation modal damping ratio data meet requirements; and outputting the final optimized configuration parameters, and completing PSS and VPSS collaborative low-frequency oscillation suppression. According to the invention, when oscillation occurs, flexible parameter optimization can be carried out on the PSS and the VPSS in real time.

Description

Technical Field

[0001] The invention belongs to the field of power electronic inverter technology / power system low-frequency oscillation technology, and in particular relates to an optimization method suitable for coordinated setting of PSS and VPSS parameters. Background Art

[0002] The large-scale application of renewable energy in the power grid has become an industry trend. Taking the installed capacity of State Power Investment Corporation at the end of 2022 as an example, the proportion of new energy units is getting higher and higher, which also means that the traditional power grid is gradually moving towards the direction of power electronic grid. With the improvement of the degree of power electronicization of the power grid, some new problems have also been brought to the power grid, such as low inertia, weak damping and insufficient voltage support. In addition, the randomness and volatility of renewable energy generation have brought serious challenges to the reliable operation and frequency stability of the power grid.

[0003] The oscillation frequency of low-frequency oscillation is generally between 0.1-2.5Hz, which is a typical problem that affects the safe and stable operation of the power grid. In order to weaken the impact of low-frequency oscillation on the power grid, power system stabilizers are often used to suppress low-frequency oscillations in the system. Due to its significant ability to suppress low-frequency oscillations, it is widely used in various fields. However, in new power grids with a high proportion of new energy penetration, traditional power system stabilizers are difficult to meet the damping requirements of the power system. In order to better cope with the intermittent and random nature of new energy units, the power system stabilizer technology is introduced into the new energy units, and a virtual power system stabilizer is constructed to further improve the system damping, which can further weaken the low-frequency oscillations of the system and improve the system stability.

[0004] In new power systems with a high penetration of new energy, the VPSS system used in new energy units and the PSS device in traditional generator sets often exist at the same time. The two types of equipment need to be coordinated and the parameters need to be adjusted reasonably to exert their suppression capabilities.

[0005] Among them, PSS (Power System Stabilizer) is the power system stabilizer; VPSS (Virtual Power System Stabilizer) is the virtual power system stabilizer. Summary of the invention

[0006] In view of the deficiencies existing in the above-mentioned prior art, the present invention provides an optimization method suitable for the coordinated setting of PSS and VPSS parameters. Based on the dynamic mode decomposition algorithm, the dominant oscillation mode is extracted from the mixed oscillation mode, and then the PSS and VPSS parameters are updated using the sparrow search optimization algorithm, thereby achieving the purpose of the invention of improving the damping characteristics of the power system and effectively suppressing oscillations.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0008] An optimization method suitable for coordinated setting of PSS and VPSS parameters includes the following steps:

[0009] Step 1. Build a mathematical model of PSS and VPSS parallel control;

[0010] Step 2. Trigger low-frequency oscillation in the constructed multi-machine system, use the phasor measurement unit PMU to collect voltage, current and phase angle data as system oscillation data, use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data, and analyze the damping ratio of the dominant oscillation mode;

[0011] Step 3. Compare whether the damping ratio of the dominant oscillation mode can meet the set damping ratio requirement for low-frequency oscillation suppression;

[0012] If the requirements are met, the current PSS and VPSS parameter configurations are the optimization results;

[0013] If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain a new set of PSS and VPSS configuration parameters;

[0014] Step 4. Assign the new PSS and VPSS configuration parameters to the built multi-machine model, trigger the low-frequency oscillation again, and repeat the analysis in step 3 until the damping ratio data of all dominant oscillation modes meet the requirements;

[0015] Step 5. Output the final optimized PSS and VPSS configuration parameters to complete the coordinated low-frequency oscillation suppression of PSS and VPSS.

[0016] Furthermore, the system oscillation data includes voltage, current and phase angle data.

[0017] Furthermore, the method triggers low-frequency oscillation in the constructed multi-machine system, collects system oscillation data using a phasor measurement unit (PMU), and uses a dynamic mode decomposition method to identify and analyze the system oscillation mode of the collected oscillation data to obtain the damping ratio of the dominant oscillation mode, including:

[0018] The phasor measurement unit (PMU) is used to discretize the continuous quantity in the power system into a time series discrete quantity, namely Δx k =Δx(t k ), each Δx k The state time series matrix of the form (1) exists, and the two time series matrices have the mapping relationship (2).

[0019]

[0020] Xm+1 =A d X m (2)

[0021] In the above formula, A d is the discrete system state matrix, A d It is a high-dimensional matrix. On the basis of retaining the key electromechanical characteristics of the system, the matrix A d Perform dimensionality reduction to obtain its low-dimensional approximate state matrix For the original data matrix X with rank r m , perform singular value decomposition on it and get mode U; X m+1 The PMU collects data at time m+1, X m is the PMU collected data at time m, △x1 is the first type of data that the PMU can collect, △x2 is the second type of data that the PMU can collect, △x3 is the third type of data that the PMU can collect, and △x m is the mth type of data that can be collected by PMU, △x m+1 This is the m+1th type of data that can be collected by the PMU;

[0022] X m =UΣV * (3)

[0023] Among them, U and V are left and right singular value vectors, respectively, both are unitary matrices, and satisfy U * U=I,V * V=I;V * is the complex conjugate matrix of the V matrix; where U contains the spatial structure of the sampled data sequence, V contains the time domain structure of the sampled data sequence, I is the unit matrix, and the first r orders of U are taken to truncate the data, Σ is a singular value diagonal matrix containing non-zero diagonal elements Right now:

[0024]

[0025] In the above formula, For X m The first singular value obtained after singular value decomposition, For X m The second singular value obtained after singular value decomposition, For X m The rth singular value obtained after singular value decomposition; the system matrix A d The low-dimensional matrix approximation to the system By X m The POD modality U is associated with:

[0026]

[0027] In the above formula, U * is the complex conjugate matrix of POD mode U, for The calculation of is regarded as the minimization problem shown in equations (3) and (14) to be solved, where is the Frobenius norm:

[0028]

[0029] In the above formula, V * is the complex conjugate matrix of the V matrix;

[0030] Combining equations (3), (5) and (6), we can obtain:

[0031]

[0032] In the above formula, Σ -1 is the inverse matrix of the Σ matrix in formula (4);

[0033] matrix As the system matrix A d The low-dimensional approximate matrix of the system is retained, and the electromechanical small disturbance characteristic information of the original system is retained to achieve effective dimensionality reduction of the high-dimensional state matrix of the original system. Perform eigenvalue decomposition and get the following formula:

[0034]

[0035] Where W is The eigenvector matrix of W, Λ is the eigenvalue matrix corresponding to the W matrix, W -1 is the inverse matrix of W matrix; i and ω i are the right eigenvector and eigenvalue matrix of the low-dimensional approximation matrix of the discrete system, respectively. The discrete eigenvalues ​​are converted into eigenvalues ​​under the continuous system, that is:

[0036] λ i =lg(ω i ) / Δt (9)

[0037] In the above formula, λ i is the characteristic value of the continuous system, Δt is the sampling interval, the oscillation frequency and damping ratio of the electromechanical oscillation are calculated, and the oscillation mode identification is completed, as shown in the following formula:

[0038]

[0039] In the above formula, f i is the oscillation frequency of the i-th oscillation mode, σ i is the damping of the i-th oscillation mode, Im is calculated by taking the imaginary part, and Re is calculated by taking the real part.

[0040] Furthermore, the PSS and VPSS parameters are optimized by the sparrow search optimization algorithm to obtain a set of new PSS and VPSS configuration parameters, as follows:

[0041] Run the built multi-machine model, and use the dynamic mode decomposition method to analyze the oscillation mode under the current PSS and VPSS parameter configurations. Then calculate whether the damping ratio meets the requirements. If not, use the sparrow optimization algorithm to update the PSS and VPSS parameters.

[0042] Furthermore, the new PSS and VPSS configuration parameters are assigned to the constructed multi-machine model, low-frequency oscillation is triggered again, and the analysis of step 3 is repeated until the damping ratio data of all dominant oscillation modes meet the requirements; it means using the dynamic mode decomposition method to analyze the oscillation mode under the new parameter configuration, analyzing whether the damping ratio meets the requirements, repeating the above optimization operation until the damping ratio meets the requirements, and the obtained PSS and VPSS parameters are the optimization results.

[0043] Furthermore, in the optimization process, the damping ratio is defined as the optimization objective function, and in the process of adjusting the parameters, the damping ratio of the system under the dominant oscillation mode is maximized as the optimization objective, which is defined as:

[0044]

[0045] In the above formula, ξ i is the damping ratio of the ith oscillation mode, σ i is the damping factor of the i-th oscillation mode, ω i is the oscillation angular frequency of the i-th oscillation mode;

[0046] The eigenvalue of each oscillation mode is expressed in complex form as λ i =σ i ±jω i , where i represents the serial number of different low-frequency oscillation modes, σ i is the damping factor, which determines the speed of attenuation of the system response, ω i It represents the oscillation angular frequency of different modes; among them, the damping ratio ξ i It is an indicator of the damping size in the oscillation mode. The larger the damping ratio, the shorter the time to decay to the steady state. In the power system, all E i Greater than 0.05;

[0047] J=min{ξ i ,i∈S} (12)

[0048] Where J is the minimum value of all electromechanical oscillation modes, ξ iis the damping ratio of the i-th electromechanical oscillation mode, S is the set of electromechanical oscillation modes; after the parameters to be optimized are selected, a parameter variation range is given, and the final optimization goal is to maximize the damping ratio of the system under the dominant oscillation mode through PSS and VPSS parameter optimization;

[0049] The parameter optimization range and optimization objective function are shown in formula (13), T G1i , T G2i , T VSG1i and T VSG2i They are the PSS optimization parameters of generator 1 and generator 2 and the VPSS optimization parameters of VSG1 and VSG2 respectively;

[0050]

[0051] In the above formula, st is the optimization condition constraint Subject to, T G1i is the lead-lag link parameter of the PSS device in the synchronous generator G1, T G2i is the lead-lag link parameter of the PSS device in the synchronous generator G2, T VSG1i is the lead-lag link parameter of the VPSS device in the energy storage VSG1, T VSG2i It is the lead-lag link parameter of the VPSS device in the energy storage VSG2.

[0052] An optimization device suitable for coordinated setting of PSS and VPSS parameters, comprising:

[0053] Building module for building mathematical model of PSS and VPSS parallel control;

[0054] The acquisition and analysis module is used to trigger low-frequency oscillation in the built multi-machine system, collect system oscillation data using the phasor measurement unit PMU, and use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data to analyze the damping ratio of the dominant oscillation mode;

[0055] A comparison module, used to compare whether the damping ratio of the dominant oscillation mode meets the set damping ratio requirement for low-frequency oscillation suppression;

[0056] If the requirements are met, the current PSS and VPSS parameters are configured as the optimization results;

[0057] If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain new PSS and VPSS configuration parameters;

[0058] The trigger module is used to assign the new PSS and VPSS configuration parameters to the built multi-machine model, trigger the low-frequency oscillation again, and repeat the previous step until the damping ratio data of all dominant oscillation modes meet the requirements;

[0059] The output module is used to output the final optimized PSS and VPSS configuration parameters to complete the low-frequency oscillation suppression of the PSS and VPSS collaboration.

[0060] Furthermore, the device is used to implement the steps of an optimization method suitable for coordinated setting of PSS and VPSS parameters as described in any one of claims 1-6.

[0061] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the optimization methods for coordinated adjustment of PSS and VPSS parameters are implemented.

[0062] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the optimization methods described in any one of the above for coordinated setting of PSS and VPSS parameters.

[0063] The present invention has the following beneficial effects and advantages:

[0064] The present invention applies the collaborative optimization method of the traditional PSS to a new parallel system of PSS and VPSS, which can perform modal analysis on low-frequency oscillations caused by small disturbances, and at the same time achieve suppression of the dominant oscillation mode by optimizing the parameters of PSS and VPSS. Compared with the traditional power system that uses a single PSS to suppress oscillations, the present invention adds a VPSS link to the energy storage unit, thereby providing additional oscillation damping, and has a better oscillation suppression effect in the new power system with a high proportion of new energy participation.

[0065] Compared with the traditional off-line optimization algorithm, the present invention can flexibly optimize the parameters of PSS and VPSS in real time when oscillation occurs based on the online system oscillation mode analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0067] Figure 1 It is a structural diagram of the PSS model of the present invention;

[0068] Figure 2 It is a structural diagram of the VPSS model of the present invention;

[0069] Figure 3 It is a model diagram of the PSS and VPSS parallel control system of the present invention. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0072] Refer to the following Figure 1-Figure 3 The technical solutions of some embodiments of the present invention are described.

[0073] Example 1

[0074] The present invention provides an embodiment, which is an optimization method suitable for coordinated setting of PSS and VPSS parameters. Figure 1 As shown, Figure 1 It is the PSS model structure of the present invention.

[0075] An optimization method suitable for coordinated setting of PSS and VPSS parameters includes the following:

[0076] Step 1. Build a mathematical model of PSS and VPSS parallel control;

[0077] Build a mathematical model of PSS and VPSS parallel control, that is, build a multi-machine system. PSS and VPSS parallel control includes: synchronous generator PSS model and virtual synchronous generator VPSS model, such as Figure 3 As shown, Figure 3 This is the model diagram of the PSS and VPSS parallel control system of the present invention. In the figure, all components are connected using overhead lines of corresponding voltage levels. The voltage levels are given in the figure, where L7, L8, C7, and C8 have a total of 2734kw active load and 200Mvar reactive load.

[0078] Step 2. Trigger low-frequency oscillation in the constructed multi-machine system, use the phasor measurement unit PMU to collect voltage, current and phase angle data as system oscillation data, use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data, and analyze the damping ratio of the dominant oscillation mode;

[0079] Step 3: Compare whether the damping ratio of the dominant oscillation mode can meet the set damping ratio requirement for low-frequency oscillation suppression.

[0080] If the requirements are met, the current PSS and VPSS parameter configurations are the optimization results;

[0081] If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain a new set of PSS and VPSS configuration parameters;

[0082] Step 4. Assign the new PSS and VPSS configuration parameters to the constructed multi-machine model, trigger the low-frequency oscillation again, and repeat the analysis in step 3 until the damping ratio data of all dominant oscillation modes meet the requirements;

[0083] Step 5. Output the final optimized PSS and VPSS configuration parameters to complete the coordinated low-frequency oscillation suppression of PSS and VPSS.

[0084] As described in step 2 of the present invention, low-frequency oscillation is triggered in the system, and voltage, current and phase angle data are collected as system oscillation data using a phasor measurement unit PMU. The system oscillation mode is identified using a dynamic mode decomposition algorithm, and the damping ratio of the dominant oscillation mode is analyzed, including:

[0085] The phasor measurement unit (PMU) is used to discretize the continuous quantity in the power system into a time series discrete quantity, namely Δx k =Δx(t k ), each Δx k The state time series matrix of the form (1) exists. And there is a mapping relationship (2) between the two time series matrices.

[0086]

[0087] X m+1 =A d X m (2)

[0088] In formula (2), A d A is the discrete system state matrix. d It is usually a high-dimensional matrix. In order to improve the analysis efficiency, it is necessary to modify the matrix A while retaining the key electromechanical characteristics of the system. d Perform dimensionality reduction, that is, obtain its low-dimensional approximate state matrix For the original data matrix X with rank r m , it can be subjected to singular value decomposition to obtain mode U; X m+1 The PMU collects data at time m+1, X m is the PMU collected data at time m, △x1 is the first type of data that the PMU can collect, △x2 is the second type of data that the PMU can collect, △x3 is the third type of data that the PMU can collect, △X m is the mth type of data that can be collected by PMU, △x m+1 This is the m+1th type of data that can be collected by the PMU;

[0089] X m =UΣV * (3)

[0090] Among them, U and V are left and right singular value vectors, respectively, both are unitary matrices, and satisfy U * U=I,V * V=I;V * is the complex conjugate matrix of the V matrix. Among them, U contains the spatial structure of the sampled data sequence, V contains the time domain structure of the sampled data sequence, I is the unit matrix, and then the first r orders of U are taken to truncate the data, Σ is a singular value diagonal matrix, containing non-zero diagonal elements Right now:

[0091]

[0092] In the above formula, For X m The first singular value obtained after singular value decomposition, For X m The second singular value obtained after singular value decomposition, For X m The rth singular value obtained after singular value decomposition.

[0093] System Matrix A d The low-dimensional matrix approximation to the system Available through X m The POD modality U is associated with:

[0094]

[0095] In the above formula, U * is the complex conjugate matrix of the POD mode U. The calculation of can be regarded as the minimization problem shown in equations (3) and (14), where is the Frobenius norm:

[0096]

[0097] In the above formula, V * is the complex conjugate matrix of the V matrix;

[0098] Combining equations (3), (5) and (6), we can obtain:

[0099]

[0100] In the above formula, Σ -1 is the inverse matrix of the Σ matrix in equation (4).

[0101] matrix As the system matrix A d The low-dimensional approximate matrix of the system is obtained, which effectively reduces the dimension of the high-dimensional state matrix of the original system while retaining the electromechanical small disturbance characteristic information of the original system. Perform eigenvalue decomposition and get the following formula:

[0102]

[0103] Where W is The eigenvector matrix of W, Λ is the eigenvalue matrix corresponding to the W matrix, W -1 is the inverse matrix of W matrix. i and ω i are the right eigenvector and eigenvalue matrix of the low-dimensional approximation matrix of the discrete system respectively. The discrete eigenvalue is converted into the eigenvalue of the continuous system, that is:

[0104] λ i =lg(ω i ) / Δt (9)

[0105] In the above formula, λ i That is, the characteristic value of the continuous system; Δt is the sampling interval. Then, the oscillation frequency and damping ratio of the electromechanical oscillation can be calculated by the following formula to complete the oscillation mode identification, as shown in the following formula:

[0106]

[0107] In the above formula, f i is the oscillation frequency of the i-th oscillation mode, σ i is the damping of the i-th oscillation mode, Im is calculated by taking the imaginary part, and Re is calculated by taking the real part.

[0108] As described in step 3, the PSS and VPSS parameters are optimized by the sparrow search optimization algorithm to obtain a new set of PSS and VPSS configuration parameters, as follows:

[0109] The sparrow search algorithm is applied in the coordinated optimization of PSS and VPSS parameters. The dynamic mode decomposition method is used to analyze the oscillation mode. After extracting the dominant oscillation mode, the sparrow optimization algorithm is used to adjust the parameters of PSS and VPSS. The overall process is as follows:

[0110] Run the attached Figure 3The multi-machine model built in the paper is used, and the dynamic mode decomposition method is used to analyze the oscillation mode under the current PSS and VPSS parameter configuration; then it is calculated whether the damping ratio meets the requirements. If not, the sparrow optimization algorithm is used to update the parameters of PSS and VPSS, and then the dynamic mode decomposition method is used to analyze the oscillation mode under the new parameter configuration to analyze whether the damping ratio meets the requirements; the above optimization operation is repeated until the damping ratio meets the requirements, and the PSS and VPSS parameters at this time are the optimization results.

[0111] In the above specific optimization operation process, the damping ratio is defined as the optimization objective function, and the optimization objective is to maximize the damping ratio of the system under the dominant oscillation mode during the parameter adjustment process. The specific definition is:

[0112]

[0113] In the above formula, ξ i is the damping ratio of the ith oscillation mode, σ i is the damping factor of the i-th oscillation mode, ω i is the oscillation angular frequency of the i-th oscillation mode.

[0114] The eigenvalue of each oscillation mode is expressed in complex form as λ i =σ i ±jω i , where i represents the serial number of different low-frequency oscillation modes, σ i is the damping factor, which determines the speed of attenuation of the system response, ω i It represents the oscillation angular frequency of different modes. Among them, the damping ratio ξ i It is an indicator of the damping size in the oscillation mode. The larger the damping ratio, the shorter the time to decay to steady state. In the power system, it is required that all ξ i Greater than 0.05.

[0115] J=min{ξ i ,i∈S} (12)

[0116] Where J is the minimum value of all electromechanical oscillation modes, ξ i is the damping ratio of the i-th electromechanical oscillation mode; S is the set of electromechanical oscillation modes. After the parameters to be optimized are selected, a parameter variation range is given. The ultimate optimization goal is to maximize the damping ratio of the system in the dominant oscillation mode through PSS and VPSS parameter optimization.

[0117] The parameter optimization range and optimization objective function are shown in formula (13). G1i 、T G2i 、T VSG1i and T VSG2iThey are the PSS optimization parameters of generator 1 and generator 2, and the VPSS optimization parameters of VSG1 and VSG2. The parameter definitions can be seen in Figure 1 and Figure 2 , Figure 1 It is the PSS model structure diagram of the present invention, Figure 2 It is a structural diagram of the VPSS model of the present invention.

[0118]

[0119] In the above formula, st is the optimization condition restriction (Subject to), T G1i is the lead-lag link parameter of the PSS device in the synchronous generator G1, see Figure 1 and Figure 3 , T G2i is the lead-lag link parameter of the PSS device in the synchronous generator G2, see Figure 1 and Figure 3 , T VSG1i is the lead-lag link parameter of the VPSS device in the energy storage VSG1, see Figure 2 and Figure 3 , T VSG2i is the lead-lag link parameter of the VPSS device in the energy storage VSG2, see Figure 2 and Figure 3 .

[0120] Example 2

[0121] The present invention further provides an embodiment, which is an optimization method suitable for coordinated setting of PSS and VPSS parameters.

[0122] like Figure 1 As shown, Figure 1 It is the PSS model structure of the present invention.

[0123] A low-frequency oscillation suppression method suitable for PSS and VPSS collaboration includes the following contents:

[0124] Step 1. Build a mathematical model of PSS and VPSS parallel control;

[0125] PSS and VPSS parallel control includes: synchronous generator PSS model and virtual synchronous generator VPSS model, such as Figure 3 As shown, Figure 3 It is a model diagram of the PSS and VPSS parallel control system of the present invention.

[0126] Step 2. Figure 3The low-frequency oscillation is triggered in the multi-machine system built in the paper, and the voltage, current and phase angle data are collected by the phasor measurement unit PMU as the system oscillation data. The dynamic mode decomposition method is used to identify the system oscillation mode of the collected oscillation data, and the damping ratio of the dominant oscillation mode and the corresponding participating units are analyzed;

[0127] Step 3: Compare whether the damping ratio of the dominant oscillation mode can meet the set damping ratio requirement for low-frequency oscillation suppression.

[0128] If the requirements are met, the current PSS and VPSS parameter configurations are the optimization results;

[0129] If the requirements are not met, the PSS and VPSS parameters of the units participating in this dominant oscillation are optimized through the sparrow search optimization algorithm to obtain a new set of PSS and VPSS configuration parameters;

[0130] Step 4. Assign the new PSS and VPSS configuration parameters to the attached Figure 3 The PSS and VPSS of the units participating in the dominant oscillation in the multi-machine model built in the present embodiment are used to trigger the low-frequency oscillation again, and the analysis of step 3 is repeated until the damping ratio data of all dominant oscillation modes meet the requirements;

[0131] Step 5. Output the final optimized PSS and VPSS configuration parameters to complete the coordinated low-frequency oscillation suppression of PSS and VPSS.

[0132] Compared with Example 1, this example introduces the concept of participation factor of the leading oscillating unit. According to the left and right singular value vectors U and the element u in V in formula (3), ki and v ki , define the kth state quantity x k and the i-th dynamic mode λ i The participation factor p ki for:

[0133] p ki =u ki v ki (14)

[0134] According to formula (14), the participation matrix P can be formed

[0135]

[0136] Among them, p ki The larger the value is, the greater the degree of participation of the k-th generator in the ith oscillation mode, and the PSS and VPSS parameters of the units with large participation are optimized separately.

[0137] The parameter optimization range and optimization objective function are shown in formula (16). The definition of J is shown in formula (12), T opGki and T opVSGki They are the PSS optimization parameters of the generator k that participates in the largest degree of dominant oscillation and the VPSS optimization parameters of the VSGk that participates in the largest degree of dominant oscillation. The parameter definitions can be seen in Figure 1 and Figure 2 , Figure 1 It is the PSS model structure diagram of the present invention, Figure 2 It is a structural diagram of the VPSS model of the present invention.

[0138]

[0139] In the above formula, st is the optimization condition restriction (Subject to), T G1i is the lead-lag link parameter of the PSS device in the synchronous generator G1, refer to Figure 1 and Figure 3 , T G2i is the lead-lag link parameter of the PSS device in the synchronous generator G2, refer to Figure 1 and Figure 3 , T VSG1i The lead-lag parameters of the VPSS device in the energy storage VSG1 are shown in Table 1. Figure 2 and Figure 3 , T VSG2i The lead-lag parameters of the VPSS device in the energy storage VSG2 are shown in Table 1. Figure 2 and Figure 3 .

[0140] Example 3

[0141] The present invention further provides an embodiment, which is an optimization device suitable for coordinated setting of PSS and VPSS parameters, comprising:

[0142] Building module for building mathematical model of PSS and VPSS parallel control;

[0143] The acquisition and analysis module is used to trigger low-frequency oscillation in the constructed multi-machine system, collect system oscillation data using the phasor measurement unit PMU, and use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data to analyze the damping ratio of the dominant oscillation mode;

[0144] A comparison module, used to compare whether the damping ratio of the dominant oscillation mode meets the set damping ratio requirement for low-frequency oscillation suppression;

[0145] If the requirements are met, the current PSS and VPSS parameters are configured as the optimization results;

[0146] If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain new PSS and VPSS configuration parameters;

[0147] The trigger module is used to assign the new PSS and VPSS configuration parameters to the constructed multi-machine model, trigger the low-frequency oscillation again, and repeat step 3 until the damping ratio data of all dominant oscillation modes meet the requirements;

[0148] The output module is used to output the final optimized PSS and VPSS configuration parameters to complete the low-frequency oscillation suppression of the PSS and VPSS collaboration.

[0149] The optimization device suitable for coordinated setting of PSS and VPSS parameters described in the present invention is used to implement the steps of the optimization method suitable for coordinated setting of PSS and VPSS parameters described in Example 1 or 2.

[0150] Example 4

[0151] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any one of the optimization methods for coordinated setting of PSS and VPSS parameters described in Embodiment 1 or 2 are implemented.

[0152] Example 5

[0153] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the optimization methods described in Embodiment 1 or 2 for coordinated adjustment of PSS and VPSS parameters are implemented.

[0154] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An optimization method suitable for coordinated setting of PSS and VPSS parameters, characterized by: The following steps are involved: Step 1. Build a mathematical model of PSS and VPSS parallel control; Step 2. Trigger low-frequency oscillation in the constructed multi-machine system, use the phasor measurement unit PMU to collect voltage, current and phase angle data as system oscillation data, use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data, and analyze the damping ratio of the dominant oscillation mode; Step 3. Compare whether the damping ratio of the dominant oscillation mode can meet the set damping ratio requirement for low-frequency oscillation suppression; If the requirements are met, the current PSS and VPSS parameter configurations are the optimization results; If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain a new set of PSS and VPSS configuration parameters; Step 4. Assign the new PSS and VPSS configuration parameters to the built multi-machine model, trigger the low-frequency oscillation again, and repeat the analysis in step 3 until the damping ratio data of all dominant oscillation modes meet the requirements; Step 5. Output the final optimized PSS and VPSS configuration parameters to complete the coordinated low-frequency oscillation suppression of PSS and VPSS.

2. The optimization method for coordinated setting of PSS and VPSS parameters according to claim 1, characterized in that: The system oscillation data includes voltage, current and phase angle data.

3. The optimization method for coordinated setting of PSS and VPSS parameters according to claim 1, characterized in that: The method triggers low-frequency oscillation in the constructed multi-machine system, collects system oscillation data using a phasor measurement unit (PMU), and uses a dynamic mode decomposition method to identify the collected oscillation mode of the system to analyze the damping ratio of the dominant oscillation mode, including: The phasor measurement unit (PMU) is used to discretize the continuous quantity in the power system into a time series discrete quantity, namely Δx k =Δx(t k ), each Δx k The state time series matrix of the form (1) exists, and the two time series matrices have the mapping relationship (2). X m+1 =A d X m (2) In the above formula, A d is the discrete system state matrix, A d It is a high-dimensional matrix. On the basis of retaining the key electromechanical characteristics of the system, the matrix A d Perform dimensionality reduction to obtain its low-dimensional approximate state matrix For the original data matrix X with rank r m , perform singular value decomposition on it and get mode U; X m+1 The PMU collects data at time m+1, X m is the PMU collected data at time m, △x1 is the first type of data that the PMU can collect, △x2 is the second type of data that the PMU can collect, △x3 is the third type of data that the PMU can collect, and △x m is the mth type of data that can be collected by PMU, △x m+1 This is the m+1th type of data that can be collected by the PMU; X m =UΣV * (3) Among them, U and V are left and right singular value vectors, respectively, both are unitary matrices, and satisfy U * U=I,V * V=I;V * is the complex conjugate matrix of the V matrix; where U contains the spatial structure of the sampled data sequence, V contains the time domain structure of the sampled data sequence, I is the unit matrix, and the first r orders of U are taken to truncate the data, Σ is a singular value diagonal matrix containing non-zero diagonal elements Right now: In the above formula, For X m The first singular value obtained after singular value decomposition, For X m The second singular value obtained after singular value decomposition, For X m The rth singular value obtained after singular value decomposition; the system matrix A d The low-dimensional matrix approximation to the system By X m The POD modality U is associated with: In the above formula, U* is the complex conjugate matrix of POD mode U. The calculation of is regarded as the minimization problem shown in equations (3) and (14) to be solved, where is the Frobenius norm: In the above formula, V* is the complex conjugate matrix of the V matrix; Combining equations (3), (5) and (6), we can obtain: In the above formula, Σ -1 is the inverse matrix of the Σ matrix in formula (4); matrix As the low-dimensional approximate matrix of the system matrix Ad, the original system electromechanical small disturbance characteristic information is retained to achieve effective dimensionality reduction of the original system high-dimensional state matrix. Perform eigenvalue decomposition and get the following formula: Where W is The eigenvector matrix of W, Λ is the eigenvalue matrix corresponding to the W matrix, W -1 is the inverse matrix of W matrix; i and ω i are the right eigenvector and eigenvalue matrix of the low-dimensional approximation matrix of the discrete system, respectively. The discrete eigenvalues ​​are converted into eigenvalues ​​under the continuous system, that is: l i =lg(ω i ) / Δt (9) In the above formula, λ i is the characteristic value of the continuous system, Δt is the sampling interval, the oscillation frequency and damping ratio of the electromechanical oscillation are calculated, and the oscillation mode identification is completed, as shown in the following formula: In the above formula, f i is the oscillation frequency of the i-th oscillation mode, σ i is the damping of the i-th oscillation mode, Im is calculated by taking the imaginary part, and Re is calculated by taking the real part.

4. The optimization method for coordinated setting of PSS and VPSS parameters according to claim 1, characterized in that: The PSS and VPSS parameters are optimized by the sparrow search optimization algorithm to obtain a set of new PSS and VPSS configuration parameters, as follows: Run the built multi-machine model, and use the dynamic mode decomposition method to analyze the oscillation mode under the current PSS and VPSS parameter configurations. Then calculate whether the damping ratio meets the requirements. If not, use the sparrow optimization algorithm to update the PSS and VPSS parameters.

5. The optimization method for coordinated setting of PSS and VPSS parameters according to claim 1, characterized in that: The new PSS and VPSS configuration parameters are assigned to the constructed multi-machine model, low-frequency oscillation is triggered again, and the analysis of step 3 is repeated until the damping ratio data of all dominant oscillation modes meet the requirements; it means using the dynamic mode decomposition method to analyze the oscillation mode under the new parameter configuration, analyzing whether the damping ratio meets the requirements, repeating the above optimization operation until the damping ratio meets the requirements, and the obtained PSS and VPSS parameters are the optimization results.

6. The optimization method for coordinated setting of PSS and VPSS parameters according to claim 5, characterized in that: In the optimization process, the damping ratio is defined as the optimization objective function. In the process of adjusting the parameters, the damping ratio of the system under the dominant oscillation mode is maximized as the optimization objective, which is defined as: In the above formula, ξ i is the damping ratio of the ith oscillation mode, σ i is the damping factor of the i-th oscillation mode, ω i is the oscillation angular frequency of the i-th oscillation mode; The eigenvalue of each oscillation mode is expressed in complex form as λ i =σ i ±jω i , where i represents the serial number of different low-frequency oscillation modes, σ i is the damping factor, which determines the speed of attenuation of the system response, ω i It represents the oscillation angular frequency of different modes; among them, the damping ratio ξ i It is an indicator of the damping size in the oscillation mode. The larger the damping ratio, the shorter the decay time to steady state. In the power system, all ξ i Greater than 0.05; J=min{ξ i ,i∈S} (12) Where J is the minimum value of all electromechanical oscillation modes, ξ i is the damping ratio of the i-th electromechanical oscillation mode, S is the set of electromechanical oscillation modes; after the parameters to be optimized are selected, a parameter variation range is given, and the final optimization goal is to maximize the damping ratio of the system under the dominant oscillation mode through PSS and VPSS parameter optimization; The parameter optimization range and optimization objective function are shown in formula (13), T G1i , T G2i , T VSG1i and T VSG2i They are the PSS optimization parameters of generator 1 and generator 2 and the VPSS optimization parameters of VSG1 and VSG2 respectively; In the above formula, st is the optimization condition restriction Subject to, T G1i is the lead-lag link parameter of the PSS device in the synchronous generator G1, T G2i is the lead-lag link parameter of the PSS device in the synchronous generator G2, T VSG1i is the lead-lag link parameter of the VPSS device in the energy storage VSG1, T VSG2i It is the lead-lag link parameter of the VPSS device in the energy storage VSG2.

7. An optimization device suitable for coordinated setting of PSS and VPSS parameters, characterized by: include: Building module for building mathematical model of PSS and VPSS parallel control; The acquisition and analysis module is used to trigger low-frequency oscillation in the built multi-machine system, collect system oscillation data using the phasor measurement unit PMU, and use the dynamic mode decomposition method to identify the system oscillation mode of the collected oscillation data to analyze the damping ratio of the dominant oscillation mode; A comparison module, used to compare whether the damping ratio of the dominant oscillation mode meets the set damping ratio requirement for low-frequency oscillation suppression; If the requirements are met, the current PSS and VPSS parameters are configured as the optimization results; If the requirements are not met, the PSS and VPSS parameters are optimized through the sparrow search optimization algorithm to obtain new PSS and VPSS configuration parameters; The trigger module is used to assign the new PSS and VPSS configuration parameters to the built multi-machine model, trigger the low-frequency oscillation again, and repeat the previous step until the damping ratio data of all dominant oscillation modes meet the requirements; The output module is used to output the final optimized PSS and VPSS configuration parameters to complete the low-frequency oscillation suppression of the PSS and VPSS collaboration.

8. The low-frequency oscillation suppression device suitable for PSS and VPSS collaboration according to claim 1, characterized in that: The device is used to implement the steps of an optimization method suitable for coordinated setting of PSS and VPSS parameters as described in any one of claims 1-6.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of an optimization method suitable for coordinated adjustment of PSS and VPSS parameters as described in any one of claims 1 to 6.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of an optimization method for coordinated adjustment of PSS and VPSS parameters as described in any one of claims 1 to 6.

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