SVG-containing power grid stability adaptive control method and system under complex working conditions

Abstract

The invention discloses an SVG-containing power grid stability adaptive control method and system under a complex working condition, and relates to the technical field of power system stability control, and the method comprises the steps: analyzing a key operation working condition with the worst system stability; on the basis of the change condition of the operation condition, simplifying the worst key operation condition small interference robust stability problem, and obtaining an SVG control parameter set; based on the SVG reactive current collected in real time, the system adaptively selects SVG control parameters to control the stability of the power grid. According to the method, by adaptively adjusting the control parameters of the SVG, it can be ensured that the CSCR of the system is always reduced under the key operation condition of the SVG, and therefore the stability of the system is improved; the offline calculation of the control parameter set and the online adjustment of the control parameters are combined, so that the reasonability of the control parameters is ensured, and the response speed of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system stability control, and in particular to a method and system for adaptively controlling the stability of a power grid containing SVG under complex working conditions. Background Art

[0002] As the proportion of new energy sources such as wind power and photovoltaic power generation in the power system increases year by year, the access of a high proportion of grid-connected inverters has changed the dynamic characteristics of modern power systems, posing new challenges to the safe and stable operation of the power grid.

[0003] Renewable energy equipment is typically equipped with a certain proportion of reactive power compensation devices (SVG devices) to maintain node voltage stability. However, SVG devices can interact with the IBRs of renewable energy equipment, even further exacerbating oscillations between the IBRs and the power grid. To suppress IBR-induced oscillations, numerous studies have proposed improved control strategies, which can be categorized into two categories: IBR control design and control design for additional equipment (such as SVG). However, IBRs are typically black-box models, making it difficult to modify IBR control in practice. In contrast, SVGs are white-box models, making it easier to modify SVG control. However, existing research focuses primarily on specific operating conditions and cannot guarantee robust stability to small disturbances under diverse operating conditions.

[0004] In addition, existing control methods for improving the stability of renewable energy power grids containing SVG usually adopt fixed-parameter control strategies, which cannot adapt to changes in the dynamic characteristics of the system under complex working conditions, resulting in system instability under certain working conditions. For example, when the reactive current output of SVG changes, the small-disturbance stability of the system may be seriously affected, and the fixed-parameter control strategy cannot adjust the control parameters in time to adapt to such changes.

[0005] Therefore, the existing technology lacks a method that can adaptively adjust SVG control parameters according to changes in operating conditions to ensure the small-disturbance robust stability of the new energy grid containing SVG under different operating conditions. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problems solved by the present invention are: the existing power system stability control methods are unable to adapt to complex operating conditions, cannot guarantee the robust stability of the system under different operating conditions with small disturbances, lack a mechanism for adaptively adjusting SVG control parameters, and how to improve the stability of new energy power grids containing SVG.

[0008] To address the above technical problems, the present invention provides the following technical solutions: a method for adaptively controlling the stability of a power grid containing SVG under complex operating conditions, comprising analyzing the critical operating conditions where the system has the worst stability; simplifying the small-disturbance robust stability problem under the worst critical operating conditions based on changes in the operating conditions to obtain a set of SVG control parameters; and adaptively selecting SVG control parameters based on the real-time collected SVG reactive current to control power grid stability.

[0009] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, the analysis of the key operating condition with the worst system stability includes the premise that the SVG device always reduces the CSCR. The key operating condition with the worst system stability is expressed as:

[0010] IBR equipment output rated active power (ie, S Bi , i=1,...,n), and the reactive current output of the SVG device I qΣ Varies in the range [-1,1].

[0011] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, the simplified worst critical operating condition small-disturbance robust stability problem includes simplifying the small-disturbance robust stability problem of the new energy power grid system under the condition of changing operating conditions into the small-disturbance robust stability problem of n subsystems under the worst critical operating conditions.

[0012] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, wherein: the obtaining of the SVG control parameter set includes dividing the worst critical operating condition into m subintervals, solving m optimization problems, and obtaining the SVG control parameter set {K1, ..., K m}.

[0013] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, wherein: the SVG control parameter set is obtained and the SVG control parameter set is obtained. ∞ Based on the control idea of ​​SVG, the robust design problem of SVG control parameters in the renewable energy grid containing SVG under key operating conditions is described as a minimum-maximum optimization problem, which can be expressed as:

[0014]

[0015] Among them, ||·|| ∞ represents the infinite norm, C i (sI-(A i +B i KC i )) -1 Bi A represents the closed-loop transfer function matrix of subsystem 1,i. i 、B i 、C i They represent the parameter matrices of the open-loop state space model of subsystem 1,i respectively; subsystem, is expressed as:

[0016]

[0017] Among them, x i 、y i 、u i Represent the state vector, algebraic vector, and input vector respectively; when the new energy device is a black box model, the parameter matrix A i 、B i 、C i The specific form of is obtained through identification;

[0018] K in the formula is H of SVG ∞ Controller, when the K matrix is ​​selected as the static gain matrix, and the proportional integral PI parameters in the AC voltage control loop and the phase-locked loop PLL are selected as the elements in the controller K, the matrix K is expressed as:

[0019]

[0020] y i =[U sqi ,x 1i ,(U si -1),x 2i ] T

[0021]

[0022] Among them, U sqi represents the q-axis component of the SVG terminal voltage, x 1i Indicates U sqi The integral, U si Indicates the terminal voltage amplitude of SVG, x 2i Indicates (U si -1), ω pllsi Represents the frequency output of the phase-locked loop PLL, Indicates the q-axis current reference value of SVG, k acps and k acis Respectively represent the AC voltage outer loop proportional and integral coefficients, k pllps and k pllis Represent the proportional and integral coefficients of the phase-locked loop respectively.

[0023] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, wherein: the SVG control parameter set is obtained, and the reactive current I of SVG is taken into consideration in the key operating conditions. qΣ The variation range is [-1,1], which is too large. When the optimization problem has no solution, the key operating condition is divided into multiple sub-operating conditions, namely P e =1p.u., I qΣ The variation intervals are [-1,-1+2 / m), [-1+2 / m,-1+4 / m),..., (-1+2*(m-1) / m, 1], and the corresponding solutions are obtained for each subinterval.

[0024] As a preferred solution of the adaptive control method for power grid stability containing SVG under complex working conditions described in the present invention, wherein: the adaptive selection of SVG control parameters to control power grid stability includes an offline part, establishing subsystems 1,i,..,.subsystems 1,n, dividing the key operating conditions into m sub-operating conditions, constructing an optimization problem of subsystems 1,i,..,.subsystems 1,n for each sub-operating condition, solving it using Matlab solver, and obtaining m controller parameters K i , constitute different I qΣ The SVG control parameter set corresponding to the interval; in the online part, the SVG output reactive current is sampled in real time and the equivalent reactive current I is calculated qΣ , according to the obtained SVG reactive current I qΣ Select the control parameter K corresponding to the SVG parameter set i .

[0025] Another object of the present invention is to provide an adaptive control system for grid stability containing SVG under complex operating conditions. This system can solve the technical problem that current fixed parameter control methods are unable to adapt to changes in operating conditions, resulting in insufficient system stability, by designing adaptive control parameters based on key operating conditions.

[0026] As a preferred solution of the adaptive control system for power grid stability containing SVG under complex operating conditions described in the present invention, it includes: an analysis module, a calculation module, and an adaptive control module; the analysis module is used to analyze the key operating condition with the worst system stability; the calculation module is used to simplify the small-disturbance robust stability problem of the worst key operating condition based on changes in the operating conditions and obtain a set of SVG control parameters; the adaptive control module is used to adaptively select SVG control parameters to control power grid stability based on the real-time collected SVG reactive current.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for adaptively controlling the stability of a power grid containing SVG under complex working conditions.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for adaptively controlling the stability of a power grid containing SVG under complex working conditions.

[0029] Beneficial effects of the present invention: The adaptive control method for power grid stability containing SVG under complex working conditions provided by the present invention can dynamically suppress the subsynchronous vibration problem caused by the interaction between the IBR of new energy equipment and the power grid by real-time acquisition of SVG reactive current and adaptive adjustment of control parameters, thereby improving the robust stability of the system; based on H ∞ The control concept transforms SVG parameter design into a minimum-maximum optimization problem, ensuring that the SVG always reduces the CSCR under critical operating conditions, lowering the critical short-circuit ratio CSCR and optimizing the system stability boundary. Through the offline-online collaborative mechanism, offline calculation divides the critical operating conditions into multiple sub-intervals, and uses the Matlab solver for parallel optimization to generate control parameter sets for different reactive current intervals, avoiding the delay of online calculation. Online adaptation quickly matches the optimal parameter set by monitoring the SVG reactive current in real time, and the response time can be controlled in milliseconds, solving the hysteresis problem of traditional fixed parameter control. By combining the "white box" characteristics of SVG to optimize control parameters, there is no need to modify the internal logic of the IBR, reducing engineering complexity. By adaptively controlling the reactive compensation capability of SVG, the system can maintain voltage stability and suppress oscillations in scenarios with high penetration of new energy, thereby improving the new energy carrying capacity of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 This is an overall flow chart of a method for adaptively controlling power grid stability including SVG under complex working conditions provided by the first embodiment of the present invention.

[0032] Figure 2 This is a system adaptive control flow chart of a method for adaptive control of power grid stability including SVG under complex working conditions provided by the first embodiment of the present invention.

[0033] Figure 3A schematic diagram of the modified IEEE39 node system structure in a simulation verification of a power grid stability adaptive control method containing SVG under complex working conditions provided by the second embodiment of the present invention.

[0034] Figure 4 CSCR diagrams of key subsystems of Cases 1 to 4 under key operating conditions and a reference example of a power grid stability adaptive control method containing SVG under complex working conditions provided by the second embodiment of the present invention.

[0035] Figure 5 This is a time domain response diagram of the active power output of IBR1 in different scenarios in the simulation verification of a grid stability adaptive control method containing SVG under complex working conditions provided by the second embodiment of the present invention.

[0036] Figure 6 This is an overall flow chart of a grid stability adaptive control system containing SVG under complex working conditions provided by the third embodiment of the present invention. DETAILED DESCRIPTION

[0037] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0038] Example 1, with reference to Figure 1-Figure 2 , which is an embodiment of the present invention, provides a method for adaptive control of power grid stability including SVG under complex working conditions, comprising:

[0039] S1: Analyze the critical operating conditions where the system stability is the worst.

[0040] Furthermore, the analysis of the key operating conditions with the worst system stability includes the assumption that the SVG equipment always reduces the CSCR. The key operating conditions with the worst system stability are expressed as follows:

[0041] IBR equipment output rated active power (ie, S Bi , i=1,...,n), and the reactive current output of the SVG device I qΣ Varies in the range [-1,1].

[0042] It should be noted that by analyzing the stability under different operating conditions, the critical operating conditions where the system has the worst stability can be effectively identified, which helps the system identify the conditions under which instability problems are most likely to occur. By analyzing the worst critical operating conditions, a basis can be provided for the subsequent design of SVG control parameters, ensuring that the system can remain stable under the most unfavorable conditions. In addition, during the analysis process, it is assumed that the SVG device always reduces the system's CSCR (critical short-circuit ratio), which effectively simplifies the problem and ensures that the SVG can effectively improve system stability under critical operating conditions.

[0043] It should also be noted that setting the rated active power output of the IBR equipment and the reactive current output of the SVG equipment to vary within a specific range provides a clear range and conditions for the subsequent optimization and control parameter solution of the system.

[0044] S2: Based on the changes in operating conditions, the robust stability problem of small disturbances under the worst key operating conditions is simplified to obtain the SVG control parameter set.

[0045] Furthermore, the problem of robust stability under small disturbances in the worst critical operating conditions is simplified, including the problem of robust stability under small disturbances in the new energy power grid system under changing operating conditions, which is simplified to the problem of robust stability under small disturbances in n subsystems under the worst critical operating conditions.

[0046] It should be noted that obtaining the SVG control parameter set includes dividing the worst critical operating condition into m subintervals, solving m optimization problems, and obtaining the SVG control parameter set {K1,...,K m}.

[0047] It should also be noted that obtaining the SVG control parameter set also includes the following steps based on H ∞ Based on the control idea of ​​SVG, the robust design problem of SVG control parameters in the renewable energy grid containing SVG under key operating conditions is described as a minimum-maximum optimization problem, which can be expressed as:

[0048]

[0049] Among them, ||·|| ∞ represents the infinite norm, C i (sI-(A i +B i KC i )) -1 B i A represents the closed-loop transfer function matrix of subsystem 1,i. i 、B i 、C i They represent the parameter matrices of the open-loop state-space model of subsystem 1,i;

[0050] Subsystem, represented as:

[0051]

[0052] Among them, x i 、y i 、u i Represent the state vector, algebraic vector, and input vector respectively;

[0053] When the new energy equipment is a black box model, the parameter matrix A i 、B i 、C i The specific form of is obtained through identification;

[0054] K in the formula is H of SVG ∞ Controller, when the K matrix is ​​selected as the static gain matrix, and the proportional integral PI parameters in the AC voltage control loop and the phase-locked loop PLL are selected as the elements in the controller K, the matrix K is expressed as:

[0055]

[0056] y i =[U sqi ,x 1i ,(U si -1),x 2i ] T

[0057]

[0058] Among them, U sqi represents the q-axis component of the SVG terminal voltage, x 1i Indicates U sqi The integral, U si Indicates the terminal voltage amplitude of SVG, x 2i Indicates (U si -1), ω pllsi Represents the frequency output of the phase-locked loop PLL, Indicates the q-axis current reference value of SVG, k acps and k acis Respectively represent the AC voltage outer loop proportional and integral coefficients, k pllps and k pllis Represent the proportional and integral coefficients of the phase-locked loop respectively.

[0059] It should also be noted that the SVG control parameter set also includes the consideration of the SVG's reactive current I in key operating conditions. q∑ The variation range is [-1,1], which is too large. When the optimization problem has no solution, the key operating condition is divided into multiple sub-operating conditions, namely P e =1p.u., Iq∑ The variation intervals are [-1,-1+2 / m), [-1+2 / m,-1+4 / m),..., (-1+2*(m-1) / m, 1], and the corresponding solutions are obtained for each subinterval.

[0060] It should also be noted that this step simplifies the small-disturbance robust stability problem of the new energy power grid system into the small-disturbance robust stability problem of n subsystems under the worst critical operating conditions, which reduces the complexity of the analysis and improves the calculation efficiency. ∞ The control idea describes the robust design problem of SVG control parameters as a minimum-maximum optimization problem, which ensures the robustness of the control parameters. Even in the presence of uncertainty or interference in the system, it can effectively suppress subsynchronous oscillations and ensure stable system operation.

[0061] S3: Based on the real-time collected SVG reactive current, the system adaptively selects SVG control parameters to control grid stability.

[0062] Furthermore, adaptively select SVG control parameters to control grid stability including offline part, establish subsystem 1,i,..,.subsystem 1,n, divide the key operating conditions into m sub-operating conditions, and construct subsystem for each sub-operating condition. 1,i ,..,.subsystem 1,n The optimization problem is solved using Matlab solver to obtain m controller parameters K i , constitute different I qΣ The SVG control parameter set corresponding to the interval; in the online part, the SVG output reactive current is sampled in real time and the equivalent reactive current I is calculated qΣ , according to the obtained SVG reactive current I qΣ Select the control parameter K corresponding to the SVG parameter set i .

[0063] It should be noted that this step realizes adaptive adjustment of control parameters by collecting the reactive current of SVG in real time and selecting corresponding control parameters according to the magnitude of the reactive current. This enables timely response to changes in the system operating conditions and ensures that the system remains stable under different conditions.

[0064] It should also be noted that adaptive control can dynamically adjust the control parameters according to the actual operating conditions of the system, which can better suppress subsynchronous oscillations and improve the dynamic performance and stability of the system compared with the control strategy with fixed parameters; the control parameter set within different reactive current output ranges is obtained through offline calculation, and the parameters are selected according to the reactive current collected in real time during online operation, which not only ensures the rationality of the control parameters, but also improves the response speed of the system and realizes fast and effective control.

[0065] Example 2, reference Figure 3-Figure 5 , which is an embodiment of the present invention, provides an adaptive control method for power grid stability containing SVG under complex working conditions. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0066] Create a modified IEEE39 node system in MATLAB / Simulink, such as Figure 3 As shown, nodes 1 to 9 are connected to the new energy equipment IBR through transformers, and node 39 is connected to the external power grid, which is simplified to an infinite bus. Each IBR represents a wind farm. The IBR capacity is shown in Table 1.

[0067] Table 1 Rated capacity of IBR after normalization

[0068] IBR1 IBR2 IBR3 IBR4 IBR5 IBR6 IBR7 IBR8 IBR9 1 2 1 1 2 10 2 2 1

[0069] In practice, wind farms usually install a certain proportion of SVG for voltage support. Therefore, 30% of the capacity of SVG is installed for each IBR on the high voltage side in the modified 39-bus system, as shown in Figure 3 As shown in Table 2, the control parameters of IBR and SVG refer to Table 2 and Table 3.

[0070] Table 2 IBR control parameters

[0071]

[0072] Table 3 SVG control parameters

[0073] DC capacitance Cdcs / pu 0.038 Filter inductor Lfs / pu 0.1 DC voltage outer loop Hdcs(s) proportional and integral parameters 1.0,5 AC voltage outer loop Hacs(s) proportional and integral parameters 1.0,5 Current inner loop His(s) proportional and integral parameters 1.0,10 Phase-locked loop HPLLs(s) proportional and integral parameters 30,7000

[0074] Assuming that the access of SVG reduces the system CSCR, the system is most unstable under critical operating conditions. In addition, the small disturbance stability of the new energy grid containing SVG under different operating conditions is affected by the subsystem 1,1 ~Subsystem 1,n Therefore, under the premise of SVG reducing CSCR, the small disturbance robust stability problem of the new energy grid containing SVG under different operating conditions can be converted into a subsystem 1,i ,..,.subsystem 1,n Robust stability problem with small disturbances under critical operating conditions.

[0075] Six cases are considered, namely: 1. Dividing the critical operating conditions into 20 intervals; 2. Dividing the critical operating conditions into 10 intervals; 3. Dividing the critical operating conditions into 8 intervals; 4. Dividing the critical operating conditions into 4 intervals; 5. Dividing the critical operating conditions into 2 intervals; 6. Not dividing the critical operating conditions.

[0076] The optimization problems for cases 1–6 were solved using a Matlab solver. Solutions existed for cases 1–4, but no solutions existed for cases 5–6. This suggests that it may be difficult to find fixed control parameters for the SVG to ensure robust small-disturbance stability of a renewable energy grid containing SVG under all operating conditions. Therefore, it is necessary to adaptively adjust the control parameters of the SVG, taking into account different operating conditions.

[0077] Figure 4 The CSCR of key subsystems under key operating conditions of cases 1 to 4 is shown, as well as the SVG control parameters as shown in Table 3. However, the PI control parameters of the phase-locked loop (PLL) are set to 22 and 7300 for the control group. In these five cases, the CSCR of the system is expressed as CSCR20, CSCR10, CSCR4, and CSCR0, respectively. In addition, the SCR and CSCR of the 39-bus system without SVG under rated conditions are 1.68 and 1.94, respectively.

[0078] like Figure 4 As shown in Figure 2, for the reference case, CSCR may be greater than (or less than) 1.94, that is, SVG may deteriorate (or improve) the system stability. In addition, I q∑ The increase of may lead to CSCR>SCR=1.68, that is, the system becomes unstable, which proves the necessity of dynamically adjusting the SVG control parameters to ensure the robust stability of the renewable energy grid containing SVG under different operating conditions. q∑ Within the range of [-1, 1], the CSCR of the key subsystem in cases 1 to 4 is always less than 1.5 (less than 1.68 and 1.94). This means that the control parameters obtained for the SVG ensure robust small-disturbance stability of the key subsystem, and that the SVG can reduce the CSCR in the key subsystem under critical operating conditions. This demonstrates that the SVG adaptive control method proposed in this invention can ensure robust small-disturbance stability of the original SVG-containing renewable energy grid under different operating conditions. Furthermore, it can be seen that the red and blue curves in the figure are generally below the brown and gray curves, indicating that when a large number of subintervals are divided, the obtained SVG control parameter set can better improve system stability.

[0079] At the same time, a time domain simulation of the improved 39-bus system was carried out to verify the effectiveness of the proposed SVG adaptive control method. The example considered two operating conditions: 1. All IBRs output rated active power, I q∑ =-0.241pu; 2. All IBR output rated active power, I q∑ =0.19pu.

[0080] For each operating condition, five cases are considered, namely, Case I to Case V. All SVGs use different control parameters, but the PI parameters of the PLL in Case V are set to 22 and 7300 as a control.

[0081] For each case, a voltage drop disturbance of 0.05 pu is applied to the infinite bus at t = 1 s and lasts for 0.05 s. Figure 5 The time domain response of the active power output of the new energy equipment IBR1 under these conditions is given. Figure 5 In (b), since I q∑ = 0.19pu, the control parameters of SVG are the same, so only the responses of the active power output of the new energy equipment IBR1 in Case IV and Case V are provided. Figure 5 It can be seen from the figure that when SVG uses the proposed adaptive control, the system is robust and stable under different operating conditions, but when I q∑ When the SVG control parameters change from -0.241 to 0.19 pu, the system changes from stable to unstable under fixed SVG control parameters. The simulation results are consistent with Figure 4 The analysis is consistent with that of SVG, but it can be seen that the control parameters obtained by adaptive control can meet the assumptions in the previous article: the access of SVG equipment always reduces CSCR (improves system stability), thus verifying the effectiveness of the proposed SVG adaptive control parameter design method.

[0082] In summary, the frequency domain eigenvalue analysis and time domain simulation analysis show that the proposed SVG adaptive control parameter design method is effective. The method of the present invention can ensure the small disturbance robust stability of the new energy grid containing SVG under key operating conditions.

[0083] Example 3, reference Figure 6 , which is an embodiment of the present invention, provides a power grid stability adaptive control system containing SVG under complex working conditions, including an analysis module, a calculation module, and an adaptive control module.

[0084] The analysis module is used to analyze the critical operating conditions with the worst system stability. The calculation module is used to simplify the small-disturbance robust stability problem of the worst critical operating conditions based on changes in operating conditions and to obtain the SVG control parameter set. The adaptive control module is used to adaptively select SVG control parameters to control grid stability based on the real-time acquisition of SVG reactive current.

[0085] If the function 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0086] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0087] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0088] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc. It should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to be limiting. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications should be encompassed by the claims of the present invention.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for adaptive control of power grid stability containing SVG under complex working conditions, characterized in that: include: Analyze the critical operating conditions where the system stability is the worst; Based on the changing operating conditions, the robust stability problem of small disturbances under the worst key operating conditions is simplified to obtain the SVG control parameter set. Based on the real-time acquisition of SVG reactive current, the system adaptively selects SVG control parameters to control grid stability; The method of obtaining the SVG control parameter set further includes: ∞ Based on the control idea of ​​SVG, the robust design problem of SVG control parameters in the renewable energy grid containing SVG under key operating conditions is described as a minimum-maximum optimization problem, which can be expressed as: Among them, ||·|| ∞ represents the infinite norm, C i (sI-(A i +B i KC i )) -1 B i Representation subsystem 1,i The closed-loop transfer function matrix, A i 、B i 、C i Represents subsystems 1,i The parameter matrix of the open-loop state-space model; Subsystem, represented as: Among them, x i 、y i 、u i Represent the state vector, algebraic vector, and input vector respectively; When the new energy equipment is a black box model, the parameter matrix A i 、B i 、C i The specific form of is obtained through identification; K in the formula is H of SVG ∞ Controller, when the K matrix is ​​selected as the static gain matrix, and the proportional integral PI parameters in the AC voltage control loop and the phase-locked loop PLL are selected as the elements in the controller K, the matrix K is expressed as: y i =[U sqi ,x 1i ,(U si -1),x 2i ] T Among them, U sqi represents the q-axis component of the SVG terminal voltage, x 1i Indicates U sqi The integral, U si Indicates the terminal voltage amplitude of SVG, x 2i Indicates (U si -1), ω pllsi Represents the frequency output of the phase-locked loop PLL, Indicates the q-axis current reference value of SVG, k acps and k acis Respectively represent the AC voltage outer loop proportional and integral coefficients, k pllps and k pllis Represent the proportional and integral coefficients of the phase-locked loop respectively.

2. The method for adaptively controlling power grid stability including SVG under complex working conditions according to claim 1, characterized in that: The analysis of the key operating conditions with the worst system stability includes the premise that the SVG equipment always reduces the CSCR. The key operating conditions with the worst system stability are expressed as follows: IBR equipment output rated active power (ie, S Bi , i=1,...,n), and the reactive current output of the SVG device I sqj Varies in the range [-1,1].

3. The adaptive control method for power grid stability containing SVG under complex working conditions according to claim 2, characterized in that: The simplified worst critical operating condition small disturbance robust stability problem includes simplifying the small disturbance robust stability problem of the new energy power grid system under the condition of changing operating conditions into the small disturbance robust stability problem of n subsystems under the worst critical operating conditions.

4. The method for adaptively controlling the stability of a power grid containing SVG under complex working conditions according to claim 3, wherein: The method of obtaining the SVG control parameter set includes dividing the worst critical operating condition into m sub-intervals, solving m optimization problems, and obtaining the SVG control parameter set {K1, ..., K m }.

5. The method for adaptively controlling the stability of a power grid containing SVG under complex working conditions according to claim 4, characterized in that: The method of obtaining the SVG control parameter set also includes taking into account the reactive current I of the SVG in key operating conditions. q∑ The variation range is [-1,1], which is too large. When the optimization problem has no solution, the key operating condition is divided into multiple sub-operating conditions, namely P e =1p.u., I q∑ The variation intervals are [-1,-1+2 / m), [-1+2 / m,-1+4 / m),..., (-1+2*(m-1) / m, 1], and the corresponding solutions are obtained for each subinterval.

6. The adaptive control method for power grid stability containing SVG under complex working conditions according to claim 5, characterized in that: The adaptive selection of SVG control parameters to control grid stability includes offline part, establishment of subsystem 1,i ,...,subsystem 1,n , divide the key operating conditions into m sub-operating conditions, and construct a subsystem for each sub-operating condition 1,i ,...,subsystem 1,n The optimization problem is solved using Matlab solver to obtain m controller parameters K i , constitute different I q∑ The SVG control parameter set corresponding to the interval; In the online part, the SVG output reactive current is sampled in real time and the equivalent reactive current I is calculated. q∑ , according to the obtained SVG reactive current I q∑ Select the control parameter K corresponding to the SVG parameter set i .

7. A system using the adaptive control method for power grid stability containing SVG under complex working conditions according to any one of claims 1 to 6, characterized in that: Including analysis module, calculation module, and adaptive control module; The analysis module is used to analyze the key operating conditions where the system stability is the worst; The calculation module is used to simplify the worst key operating condition small disturbance robust stability problem based on the change of operating conditions and obtain the SVG control parameter set; The adaptive control module is used to adaptively select SVG control parameters based on the real-time collected SVG reactive current to control grid stability.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for adaptively controlling the stability of a power grid containing SVG under complex working conditions according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for adaptively controlling the stability of a power grid containing SVG under complex working conditions according to any one of claims 1 to 7 are implemented.

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