Power system security assessment method and device based on steady-state security boundary

Abstract

The invention discloses a power system security assessment method and device based on a steady-state security boundary. The method comprises the steps of obtaining power grid parameters of a power system, and determining a first coefficient of a linear power flow equation according to the power grid parameters in combination with a preset power flow model; the method comprises the following steps: acquiring historical operation data of a power system, and determining a linear relation between node power injection and branch power errors through partial least square regression according to the historical operation data; according to the first coefficient and the linear relation, obtaining a static thermal safety area boundary; and determining the safety state of the power system according to the power injection of each node of the current power system in combination with the static thermal safety region boundary. According to the method, the accuracy of the static thermal safety area boundary can be improved, so that the accuracy of safety evaluation of the power system is improved, and the safety of the power system is improved.

Description

Technical Field

[0001] The present application relates to the field of power safety, and in particular to a method and device for evaluating power system safety based on a steady-state safety boundary. Background Art

[0002] As the scale of power systems expands and the operating status becomes more complex, ensuring the safe and stable operation of power systems has become an important goal of the power industry. The definition and calculation of steady-state safety areas are the basis for power system safety analysis, especially in scenarios such as power flow control, power generation scheduling, and fault recovery. Accurate safety boundaries can significantly improve the operational reliability of the system.

[0003] The existing technology usually obtains the safety margin through a DC power flow model or an AC power flow model. The method based on the DC power flow model ignores the influence of reactive power and voltage amplitude. Under high load conditions, the error is large and it cannot accurately describe the complex system operation state. Although the AC power flow model is more accurate, its complexity leads to long calculation time and is not suitable for real-time application scenarios.

[0004] Therefore, how to ensure the accuracy of the physical model while improving the efficiency and accuracy of safety assessment is a technical problem that needs to be solved in the current power system safety assessment problem. Summary of the invention

[0005] The present application provides a method and device for power system safety assessment based on steady-state safety margin to solve the technical problem of how to ensure the accuracy of the physical model while improving the efficiency and accuracy of safety assessment.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a power system security assessment method based on a steady-state safety margin, comprising:

[0007] Acquire grid parameters of the power system, and determine a first coefficient of a linear power flow equation according to the grid parameters combined with a preset power flow model;

[0008] Collecting historical operation data of the power system, and determining the linear relationship between node power injection and branch power error through partial least squares regression based on the historical operation data;

[0009] Obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship;

[0010] The safety state of the power system is determined according to the power injection of each node of the current power system in combination with the static thermal safety area boundary.

[0011] Compared with the prior art, the embodiments of the present application have the following beneficial effects: through a data-driven method, the linear relationship between node power injection and branch power error is fitted; the actual physical model is further linearized, and the first coefficient of the linear power flow equation is determined according to the grid parameters of the power system, thereby ensuring that inherent information such as line information in the power system is retained; finally, based on the data-driven optimization fitting capability, the error of the linear power flow equation is corrected. Since the error correction term contains more dimensional global information, the accuracy of the final static thermal safety area boundary is improved, and at the same time, problems such as calculation complexity caused by complex physical models are solved.

[0012] In some embodiments of the first aspect of the present application, determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes:

[0013] The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form;

[0014] The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by simplifying the complexity of branch admittance and angle in the power flow model, the branch admittance and angle are approximated into a linear form, thereby reducing the complexity of the calculation. This not only improves the calculation speed, but also retains the key information of the power system topology and branch admittance, ensuring the accuracy of the model.

[0016] In some embodiments of the first aspect of the present application, determining the linear relationship between the node power injection and the branch power error by partial least squares regression according to the historical operation data includes:

[0017] Taking the node power injection as an independent variable and the branch power error as a dependent variable;

[0018] According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

[0019] Compared with the prior art, the embodiment of the present application has the following beneficial effects: by fitting the linear relationship between node power injection and branch power error through the data-driven partial least squares regression method, the global information of the system can be more effectively reflected. Compared with the traditional method, the correction ability of the actual power error is improved, thereby improving the accuracy of the safety assessment of the power system.

[0020] In some embodiments of the first aspect of the present application, obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes:

[0021] Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch;

[0022] The static thermal safety region boundary is obtained according to the power injection of all nodes combined with the boundary coefficient of each branch.

[0023] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by combining the power flow upper limit of the branch, and based on the linearization of the physical equation and the data-driven error linearization, the boundary coefficient of each branch is determined, so that the definition of the static thermal safety area boundary is more accurate. This method ensures that under complex load conditions, the power system can make more accurate safety judgments based on boundary conditions to prevent the occurrence of system overload or unsafe conditions.

[0024] In some embodiments of the first aspect of the present application, the step of obtaining the static thermal safety region boundary according to the power injection of all nodes in combination with the boundary coefficient of each branch includes:

[0025] The boundary coefficient is specifically:

[0026]

[0027] The static thermal safety area boundary is specifically:

[0028]

[0029] Among them, α l,i is the boundary coefficient; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, indicating the bias of the power system; D1 and D2 are the coefficient matrices in the linear relationship; is the upper limit of the power flow of the branch; P i is the power injection of node i; n is the total number of nodes in the power system.

[0030] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by introducing a detailed linear power flow equation coefficient matrix, combined with a data-driven error linearization relationship and a power flow upper limit, the relationship between power injection and power flow can be described more accurately, thereby ensuring that the power injection at each node of the power system meets safe operating conditions and improving the safety and stability of the overall system.

[0031] In a second aspect, an embodiment of the present application further provides a power system safety assessment device based on a steady-state safety boundary, comprising: a first coefficient acquisition module, a linear relationship acquisition module, a safety area boundary acquisition module, and a safety state judgment module;

[0032] The first coefficient acquisition module is used to acquire the grid parameters of the power system, and determine the first coefficient of the linear power flow equation according to the grid parameters combined with a preset power flow model;

[0033] The linear relationship acquisition module is used to collect historical operation data of the power system, and determine the linear relationship between the node power injection and the branch power error through partial least squares regression based on the historical operation data;

[0034] The safety area boundary acquisition module is used to acquire a static thermal safety area boundary according to the first coefficient and the linear relationship;

[0035] The safety status judgment module is used to determine the safety status of the power system according to the power injection of each node in the current power system in combination with the static thermal safety area boundary.

[0036] In some embodiments of the second aspect of the present application, determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes:

[0037] The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form;

[0038] The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

[0039] In some embodiments of the second aspect of the present application, determining the linear relationship between the node power injection and the branch power error by partial least squares regression according to the historical operation data includes:

[0040] Taking the node power injection as an independent variable and the branch power error as a dependent variable;

[0041] According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

[0042] In some embodiments of the second aspect of the present application, obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes:

[0043] Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch;

[0044] The static thermal safety area boundary is constructed according to the power injection of all nodes and the boundary coefficients of each branch.

[0045] In some embodiments of the second aspect of the present application, constructing the static thermal safety area boundary according to the power injection of all nodes and the boundary coefficient of each branch includes:

[0046] The boundary coefficient is specifically:

[0047]

[0048] The static thermal safety area boundary is specifically:

[0049]

[0050] Among them, α l,i is the boundary coefficient; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, indicating the bias of the power system; D1 and D2 are the coefficient matrices in the linear relationship; is the upper limit of the power flow of the branch; P i is the power injection of node i; n is the total number of nodes in the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic flow chart of a method for evaluating power system safety with a steady-state safety margin provided in some embodiments of the present application;

[0052] Figure 2 A schematic flow chart of a method for obtaining a static thermal safety area boundary provided in some embodiments of the present application;

[0053] Figure 3 A schematic diagram of the structure of a power system safety assessment device with a steady-state safety margin provided in some embodiments of the present application. DETAILED DESCRIPTION

[0054] The existing technology usually obtains the safety margin through a DC power flow model or an AC power flow model. The method based on the DC power flow model ignores the influence of reactive power and voltage amplitude. Under high load conditions, the error is large and it cannot accurately describe the complex system operation state. Although the AC power flow model is more accurate, its complexity leads to long calculation time and is not suitable for real-time application scenarios.

[0055] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , a power system security assessment method based on a steady-state safety margin provided in an embodiment of the present application, including S10 to S40, specifically:

[0058] S10: Obtain grid parameters of the power system, and determine a first coefficient of a linear power flow equation according to the grid parameters in combination with a preset power flow model.

[0059] Further, in some embodiments of the present application, determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes:

[0060] The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form;

[0061] The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

[0062] By simplifying the complexity of branch admittance and angle in the power flow model and approximating them into linear form, the calculation complexity is reduced. This not only improves the calculation speed, but also retains the key information of the power system topology and branch admittance, ensuring the accuracy of the model.

[0063] Preferably, in some embodiments of the present application, the linear power flow equation can be obtained and the first coefficient can be determined by the following preferred method:

[0064] S11: Determine the AC power flow model using the following formula:

[0065] P ij =t ij G ij V i 2 -V i V j (G ij cosθ ij +B ij sinθ ij )

[0066] Q ij =-V i V j (G ij sinθ ij -B ij cosθ ij )+(B ij -b ij0 )V i 2

[0067] Among them, P ij and Q ij are active and reactive branch power flows respectively; V i and V j represents the voltage amplitude of busbars i and j; t ij is the ratio coefficient of the line transformer, which is used to adjust the voltage amplitude and power flow distribution of the line; b ij0 is the self-susceptance of the branch, which is used to represent the line charging effect; θ ij is the branch angle from node i to node j; G ij and B ij are the real and imaginary parts of the admittance matrix, respectively.

[0068] S12: In some cases, the admittance G of the branch ij ≈0, branch angle θ ij ≈0, so it can be approximated as cosθ ij ≈1, sinθ ij ≈0, based on this simplified condition, the power system can be represented by the following DC power flow model:

[0069]

[0070] Among them, P i is the power injection into node i, θ j is the voltage phase angle of node j; S N It is a set of designated balancing nodes in the power grid, including the reference node.

[0071] S13: Based on the DC power flow model obtained in S12, further simplify it to obtain a linear power flow equation as shown in the following formula:

[0072] P l =M1·P+M2

[0073] Among them, P lis the power flow of branch l; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, representing the bias of the power system; P is the injected power matrix.

[0074] S14: Obtain a safety margin based on the physical model, so as to correct the safety margin based on the physical model according to the linear relationship between the node power injection and the branch power error obtained based on the data drive later:

[0075] S141: The thermal safety area of ​​the power system refers to the power injection combination allowed under the premise of meeting the branch power flow limit. For each branch, the limit condition can be described as:

[0076]

[0077] in, is the upper limit of the power flow in the branch.

[0078] S142: Thermal safety areas are represented by the following set:

[0079]

[0080] Where h is the network topology of the power system; R n is a vector containing n branch power flows; S B Indicates a branch set.

[0081] S143: Calculate the impact of power flow changes on system status through power flow sensitivity analysis, and obtain the hyperplane coefficient of the safety domain boundary:

[0082]

[0083] S144: Obtain the coefficients of the safety boundary equation according to the hyperplane coefficients obtained in S143:

[0084]

[0085] Among them, α l,k is the safety region hyperplane coefficient, which is used to determine the relationship between power injection and safety margin; S l,k is the sensitivity of node k to the power flow of branch l, indicating the impact of node power injection changes on branch power flow; P k is the power injected at node k; x ki 、x kj 、x ij is the reactance of the branch between the two nodes.

[0086] S20: Collecting historical operation data of the power system, and determining the linear relationship between node power injection and branch power error through partial least squares regression based on the historical operation data.

[0087] Further, in some embodiments of the present application, determining the linear relationship between the node power injection and the branch power error by partial least squares regression according to the historical operation data includes:

[0088] Taking the node power injection as an independent variable and the branch power error as a dependent variable;

[0089] According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

[0090] By using the data-driven partial least squares regression method to fit the linear relationship between node power injection and branch power error, the global information of the system can be reflected more effectively. Compared with the traditional method, the correction ability of the actual power error is improved, thereby improving the accuracy of the power system security assessment.

[0091] Preferably, in some embodiments of the present application, the linear relationship between the node power injection and the branch power error can be determined by the following preferred method:

[0092] S21: Obtain fitting data through Monte Carlo simulation or sensor acquisition, specifically:

[0093] S211: Run Monte Carlo simulation on different bus systems to generate different power injection data by adjusting the random factors of the load and obtain fitting data;

[0094] S212: Extract data from historical measurement data of the power system and collect data through modern measurement technologies such as synchronized phasor measurement units (PMUs);

[0095] S22: Fitting the linear relationship between the dependent variable and the independent variable by partial least squares regression, where the independent variable is the power injection of each node (such as generator and load point) in the power system, and the dependent variable is the branch power flow error set, which represents the error between the calculated power flow and the actual measured power flow under specific conditions;

[0096] S23: Obtain the following linear error model through S22:

[0097] ΔP l =D1·P+D2

[0098] Where D1 and D2 are the coefficient matrices in the linear relationship; ΔP lis the power flow error of branch l.

[0099] S30: Obtaining a static thermal safety area boundary according to the first coefficient and the linear relationship.

[0100] Further, in some embodiments of the present application, obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes:

[0101] Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch;

[0102] The static thermal safety region boundary is obtained according to the power injection of all nodes combined with the boundary coefficient of each branch.

[0103] By combining the power flow upper limit of the branch, and based on the linearization of the physical equation and the data-driven error linearization, the boundary coefficients of each branch are determined, making the definition of the static thermal safety area boundary more accurate. This method ensures that under complex load conditions, the power system can make more accurate safety judgments based on boundary conditions to prevent system overload or unsafe conditions.

[0104] Preferably, in some embodiments of the present application, the static thermal safety area boundary can be obtained by the following preferred implementations:

[0105] S31: Based on the linear power flow equation obtained in S13 and the linear error model obtained in S23, the coefficients of the safety boundary equation in S144 are corrected to obtain the following boundary coefficients:

[0106]

[0107] S32: According to the boundary coefficient obtained in S31, the following static thermal safety area boundary is obtained:

[0108]

[0109] Among them, α l,i is the boundary coefficient.

[0110] Preferably, reference Figure 2 , is a flow chart of a method for obtaining a static thermal safety area boundary provided in some embodiments of the present application, comprising:

[0111] S31′: obtaining data such as the topological structure, line parameters, generator operation status, node load, etc. of the power system, and constructing a physical model based on the data;

[0112] S32′: establishing a node matrix, a generator matrix and a branch matrix according to the constructed physical model;

[0113] S33′: Determine the static thermal safety region boundary by Monte Carlo simulation or public test data combined with partial least squares and the matrix obtained in S32′.

[0114] S40: Determine the safety state of the power system according to the power injection of each node of the current power system in combination with the static thermal safety area boundary.

[0115] Preferably, in some embodiments of the present application, whether the power system is in a safe state can be determined by the following methods:

[0116] S41: collecting power injection of each node in the current power system;

[0117] S42: Substitute the power injection data collected by S41 into the static thermal safety area boundary of S32 to determine whether the formula is valid. If so, the power system is in a safe state, otherwise, the power system is in an unsafe state.

[0118] In summary, it can be seen that the power system security assessment method based on steady-state safety margin provided in the embodiment of the present application has the following beneficial effects:

[0119] This application overcomes the problem of large calculation errors of steady-state safety boundaries in traditional physical models such as DC power flow models, and significantly improves the calculation accuracy, especially under high load and complex working conditions. At the same time, the effective combination of physical models and data-driven methods not only retains the interpretability and stability of physical models, but also makes full use of historical data to correct physical models, achieving accurate assessment of static thermal safety area boundaries, further improving the accuracy of the final safety assessment results, while also reducing the computational complexity and improving the real-time nature of safety assessments.

[0120] Embodiment 2

[0121] refer to Figure 3 , a power system safety assessment device based on a steady-state safety boundary provided in an embodiment of the present application, includes: a first coefficient acquisition module 11, a linear relationship acquisition module 12, a safety area boundary acquisition module 13 and a safety state judgment module 14.

[0122] Furthermore, in some embodiments of the present application, the first coefficient acquisition module 11 is used to acquire the grid parameters of the power system, and determine the first coefficient of the linear power flow equation according to the grid parameters in combination with a preset power flow model; the linear relationship acquisition module 12 is used to collect historical operation data of the power system, and determine the linear relationship between node power injection and branch power error through partial least squares regression according to the historical operation data; the safety area boundary acquisition module 13 is used to acquire the static thermal safety area boundary according to the first coefficient and the linear relationship; the safety state judgment module 14 is used to determine the safety state of the power system according to the power injection of each node of the current power system in combination with the static thermal safety area boundary.

[0123] Further, in some embodiments of the present application, determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes:

[0124] The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form;

[0125] The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

[0126] Further, in some embodiments of the present application, determining the linear relationship between the node power injection and the branch power error by partial least squares regression according to the historical operation data includes:

[0127] Taking the node power injection as an independent variable and the branch power error as a dependent variable;

[0128] According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

[0129] Further, in some embodiments of the present application, obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes:

[0130] Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch;

[0131] The static thermal safety area boundary is constructed according to the power injection of all nodes and the boundary coefficients of each branch.

[0132] Further, in some embodiments of the present application, the static thermal safety area boundary is constructed based on the power injection of all nodes and the boundary coefficient of each branch, including:

[0133] The boundary coefficient is specifically:

[0134]

[0135] The static thermal safety area boundary is specifically:

[0136]

[0137] Among them, α l,i is the boundary coefficient; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, indicating the bias of the power system; D1 and D2 are the coefficient matrices in the linear relationship; is the upper limit of the power flow of the branch; P i is the power injection of node i; n is the total number of nodes in the power system.

[0138] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. The embodiment of the present invention provides a power system security assessment device based on a steady-state safety boundary, which can implement any method item embodiment of the present invention, that is, the power system security assessment method based on a steady-state safety boundary provided in Embodiment 1.

[0139] In summary, it can be seen that the power system safety assessment device based on steady-state safety margin provided in the embodiment of the present application has the following beneficial effects:

[0140] This application overcomes the problem of large calculation errors of steady-state safety boundaries in traditional physical models such as DC power flow models, and significantly improves the calculation accuracy, especially under high load and complex working conditions. At the same time, the effective combination of physical models and data-driven methods not only retains the interpretability and stability of physical models, but also makes full use of historical data to correct physical models, achieving accurate assessment of static thermal safety area boundaries, further improving the accuracy of the final safety assessment results, while also reducing the computational complexity and improving the real-time nature of safety assessments.

[0141] Embodiment 3

[0142] Based on the above-mentioned embodiment of the power system security assessment method based on steady-state safety boundary, another embodiment of the present application provides a power system security assessment terminal device based on steady-state safety boundary. The power system security assessment terminal device based on steady-state safety boundary includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the power system security assessment method based on steady-state safety boundary of any embodiment of the present application is implemented.

[0143] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the power system security assessment device based on steady-state security margin.

[0144] The power system security assessment device based on steady-state security boundary can be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The power system security assessment terminal device based on steady-state security boundary can include, but is not limited to, a processor and a memory.

[0145] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the power system security assessment device based on the steady-state safety boundary, and uses various interfaces and lines to connect the various parts of the power system security assessment device based on the steady-state safety boundary. The memory may be used to store the computer program and / or module, and the processor implements various functions of the power system security assessment device based on the steady-state safety boundary by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0146] Embodiment 4

[0147] Based on the above-mentioned embodiment of the power system security assessment method based on steady-state safety boundary, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the power system security assessment method based on steady-state safety boundary of any embodiment of the present application.

[0148] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0149] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A power system security assessment method based on steady-state security margin, characterized in that: include: Acquire grid parameters of the power system, and determine a first coefficient of a linear power flow equation according to the grid parameters combined with a preset power flow model; Collecting historical operation data of the power system, and determining the linear relationship between node power injection and branch power error through partial least squares regression based on the historical operation data; Obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship; The safety state of the power system is determined according to the power injection of each node of the current power system in combination with the static thermal safety area boundary.

2. A power system security assessment method based on steady-state safety margin as claimed in claim 1, characterized in that: Determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes: The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form; The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

3. A method for power system security assessment based on steady-state safety margin as claimed in claim 1, characterized in that: Determining the linear relationship between node power injection and branch power error by partial least squares regression according to the historical operation data includes: Taking the node power injection as an independent variable and the branch power error as a dependent variable; According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

4. A power system security assessment method based on steady-state safety margin as claimed in claim 2 or 3, characterized in that: The step of obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes: Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch; The static thermal safety region boundary is obtained according to the power injection of all nodes combined with the boundary coefficient of each branch.

5. A method for power system security assessment based on steady-state safety margin as claimed in claim 4, characterized in that: The step of obtaining the static thermal safety region boundary based on the power injection of all nodes and the boundary coefficient of each branch includes: The boundary coefficient is specifically: The static thermal safety area boundary is specifically: Among them, α l,i is the boundary coefficient; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, indicating the bias of the power system; D1 and D2 are the coefficient matrices in the linear relationship; is the upper limit of the power flow of the branch; P i is the power injection of node i; n is the total number of nodes in the power system.

6. A power system safety assessment device based on steady-state safety margin, characterized in that: include: A first coefficient acquisition module, a linear relationship acquisition module, a safety area boundary acquisition module, and a safety state judgment module; The first coefficient acquisition module is used to acquire the grid parameters of the power system, and determine the first coefficient of the linear power flow equation according to the grid parameters combined with a preset power flow model; The linear relationship acquisition module is used to collect historical operation data of the power system, and determine the linear relationship between the node power injection and the branch power error through partial least squares regression based on the historical operation data; The safety area boundary acquisition module is used to acquire a static thermal safety area boundary according to the first coefficient and the linear relationship; The safety status judgment module is used to determine the safety status of the power system according to the power injection of each node in the current power system in combination with the static thermal safety area boundary.

7. A power system safety assessment device based on steady-state safety margin as claimed in claim 6, characterized in that: Determining the first coefficient of the linear power flow equation according to the power grid parameter in combination with a preset power flow model includes: The linear power flow equation is obtained by simplifying the admittance and angle of the branches in the power flow model and approximating the power flow model into a linear form; The first coefficient of the linear power flow equation is determined according to the grid parameters of the power system; the grid parameters include: the topological structure of the power system and the admittance of each branch.

8. The power system safety assessment device based on steady-state safety margin according to claim 6, characterized in that: Determining the linear relationship between node power injection and branch power error by partial least squares regression according to the historical operation data includes: Taking the node power injection as an independent variable and the branch power error as a dependent variable; According to the historical operation data, the linear relationship between the independent variable and the dependent variable is fitted by partial least squares regression.

9. A power system safety assessment device based on steady-state safety margin as claimed in claim 7 or 8, characterized in that: The step of obtaining a static thermal safety region boundary according to the first coefficient and the linear relationship includes: Determine a boundary coefficient of each branch according to the first coefficient and the linear relationship and in combination with an upper limit of a power flow of the branch; The static thermal safety area boundary is constructed according to the power injection of all nodes and the boundary coefficients of each branch.

10. The power system safety assessment device based on steady-state safety margin according to claim 9, characterized in that: The static thermal safety area boundary is constructed based on the power injection of all nodes and the boundary coefficients of each branch, including: The boundary coefficient is specifically: The static thermal safety area boundary is specifically: Among them, α l,i is the boundary coefficient; M1 and M2 are the first coefficients, where M1 is the coefficient matrix of the linear power flow equation, reflecting the linear relationship between power injection and power flow, and M2 is the constant term in the linear power flow equation, indicating the bias of the power system; D1 and D2 are the coefficient matrices in the linear relationship; is the upper limit of the power flow of the branch; P i is the power injection of node i; n is the total number of nodes in the power system.

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