Method, device and equipment for calculating input voltage of transient stability control of power system

Abstract

The present application provides a method, device and equipment for calculating the transient stability control input voltage of an electric power system, the method comprising: establishing a state space equation of the current electric power system, the state space equation comprising a plurality of parameters to be determined; constructing an electric power system control component corresponding to the state space equation, the electric power system control component comprising a learning component and a false identification component; establishing a candidate function corresponding to the state space equation through the learning component, and verifying whether the candidate function meets the preset requirements through the false identification component; if it meets the preset requirements, solving the candidate function to obtain the specific value of the parameter to be determined, bringing the specific value into the state space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, determining that the control input voltage to be verified is the control input voltage of the electric power system. The technical effect of the present application is: used to accurately calculate the control input voltage of the electric power system, thereby ensuring the stability of the electric power system.

Description

Technical Field

[0001] The present application relates to the technical field of electric power grids, and in particular to a method, device and equipment for calculating input voltage of transient stability control of an electric power system. Background Art

[0002] Power system transient stability control is one of the important issues in power system operation. In power systems, the goal of transient stability control is to adjust the control input voltage of the power system so that the system can remain stable in the face of external disturbances or faults, avoiding unstable phenomena such as system collapse or voltage collapse.

[0003] Traditional methods for adjusting the control input voltage of power systems usually rely on empirical rules or the establishment of mathematical models. However, due to the complexity and nonlinear characteristics of power systems, when adjusting the control input voltage of power systems by traditional methods, the final adjusted control input voltage of the power system may be inaccurate and unable to fully meet the actual control requirements, eventually leading to power system collapse or voltage collapse. Summary of the invention

[0004] The present application provides a method, device and equipment for calculating the transient stability control input voltage of an electric power system, which are used to accurately calculate the control input voltage of the electric power system, thereby ensuring the stability of the electric power system.

[0005] In a first aspect, the present application provides a method for calculating a transient stability control input voltage of a power system, the method comprising:

[0006] Establishing a state space equation of the current power system, wherein the state space equation includes a plurality of undetermined parameters;

[0007] Construct an electric power system control component corresponding to the state-space equation, the electric power system control component comprising a learning component and a false identification component; establish a candidate function corresponding to the state-space equation through the learning component, and verify whether the candidate function meets the preset requirements through the false identification component; if it meets the preset requirements, solve the candidate function to obtain the specific value of the undetermined parameter, substitute the specific value into the state-space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, determine that the control input voltage to be verified is the control input voltage of the electric power system.

[0008] By adopting the above technical solution, the state space equation of the current power system is first constructed, and then the candidate function corresponding to the state space equation is established through the learning component, and then the candidate function is verified by the false identification component to see if it meets the requirements; if it meets the requirements, the state space equation is solved through the candidate function to obtain the input voltage to be verified; again, it is judged whether the value of the input voltage to be verified meets the set threshold, if it meets, the control input voltage of the power system is obtained, otherwise, the candidate function corresponding to the state space equation is reconstructed. In summary, the complexity and nonlinear characteristics of the power system can be solved through the state space equation; the control input voltage of the power system can be accurately calculated through layer-by-layer calculation and verification, thereby ensuring the stability of the power system.

[0009] Optionally, the establishing of the state space equation of the current power system includes: establishing a bus dynamic model of the power system and a bus power model of the power system; and establishing the state space equation in combination with the bus dynamic model and the bus power model.

[0010] By adopting the above technical solution, the operation and stability of the power system can be analyzed and evaluated by establishing the bus dynamics of the power system. The status and power information of each bus node of the power system can be provided, which is convenient for the subsequent accurate calculation of the control input voltage of the power system, thereby ensuring the stability of the power system.

[0011] Optionally, the establishing of the bus dynamic model of the power system includes: establishing the ith bus dynamic model as dx i =f(x i ,u i ), y i =c i x i ; where x i =[η i ,θ i , V i ],y i =[θ i , V i ], i∈V, V is the bus set, u i is the control input voltage of bus i, θ i is the phase of bus i, V i is the voltage amplitude of bus i, η i is an auxiliary variable for modeling the bus dynamics of heterogeneous devices, c i is a matrix corresponding to bus i, i is a positive integer greater than 0; the bus dynamic models of each bus are added together to obtain the bus dynamic model of the power system.

[0012] By adopting the above technical solution, by calculating the bus dynamic model of each bus in the power system and then adding the bus dynamic models of each bus, it is convenient to accurately obtain the bus dynamic model of the power system, thereby facilitating the subsequent accurate calculation of the control input voltage of the power system, thereby ensuring the stability of the power system.

[0013] Optionally, the establishing of the bus power model of the power system includes: establishing the i-th bus power model as

[0014]

[0015] Among them, p i is the active power of the ith bus, q i is the reactive power of the ith bus, N j is the set of all adjacent buses leading to bus i, G ii and B ii are the real and imaginary elements of the adjacency matrix, V i is the voltage amplitude of bus i;

[0016] V j is the voltage amplitude of bus j, G ij and B ij are the real and imaginary elements of the adjacency matrix;

[0017] θ ij is the phase angle deviation of adjacent buses, i and j are both positive integers greater than 0;

[0018] The bus power models of the various buses are added together to obtain the bus power model of the power system.

[0019] By adopting the above technical solution, by calculating the bus power model of each bus in the power system and then adding the bus power models of each bus, it is convenient to accurately obtain the bus power model of the power system, thereby facilitating the subsequent accurate calculation of the control input voltage of the power system, thereby ensuring the stability of the power system.

[0020] Optionally, the state space equation is dx=f(x,u), y=cx, u=K(x), wherein f(x,u) is a continuously differentiable function, K(x) is a function related to x, x is the component state of the power system, y is the voltage amplitude of the power system, u is the control input voltage of the power system, x=[η,θ,V], y=[θ,V], θ is the phase of the power system bus, V is the voltage amplitude of the power system bus, η is an auxiliary variable for the power system to dynamically model the bus of heterogeneous devices, and c is a matrix

[0021] By adopting the above technical solution, constructing the state space equation can effectively solve the complexity and nonlinear characteristics of the power system, and can accurately calculate the control input voltage of the power system, thereby ensuring the stability of the power system.

[0022] Optionally, it is characterized in that the establishing of the candidate function corresponding to the state-space equation by the learning component comprises: constructing the candidate function V of the state-space equation by the learning component ε (x) = ρ i+1 (g(x)-g(0)), where V ε (x) is the candidate function, ρ i is a convex monotone non-decreasing nonlinear activation function, g(x) is a convex neural network function

[0023] Among them, z l is a piecewise function, ρ0 is a convex monotone non-decreasing nonlinear activation function, ω0 is the real part of the neural network weight, ω i and U i is the real and positive part of the neural network weights, b0 and b i All are known parameters.

[0024] By adopting the above technical solution, a candidate function is constructed, and the state space equation is accurately solved by the candidate function, so as to accurately obtain the control input voltage.

[0025] Optionally, the checking whether the candidate function meets the preset requirements by the authentication component includes: constructing an authentication formula by the authentication component, wherein the authentication formula is

[0026]

[0027] V ε (x) is a candidate function;

[0028] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0029] Ψ is the corresponding parameter in the feedback law; the false identification formula is used to determine whether the candidate function meets the requirements of the false identification formula; if so, the candidate function is determined to meet the preset requirements; if not, the candidate function is reselected through the learning component.

[0030] By adopting the above technical solution, a false identification formula is constructed through a false identification component, so as to accurately determine whether the candidate function meets the requirements, so that the control input voltage can be accurately calculated through the candidate function in the subsequent process.

[0031] Optionally, the step of substituting the specific value into the state space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than a set threshold, determining that the control input voltage to be verified is the control input voltage of the power system includes:

[0032] Constructing the test function Among them, α and β are known parameters;

[0033] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0034] Ψ is the corresponding parameter in the feedback law;

[0035] V ε (x) is a candidate function; the verification function is used to verify whether the value of the control input voltage is less than the set threshold value. If it is less than the set threshold value, it is determined that the control input voltage meets the final input requirements, and the control input voltage to be verified is the control input voltage of the power system; otherwise, the candidate function is reselected through the learning component.

[0036] By adopting the above technical solution, after determining the control input voltage, the control input voltage is tested through a test function to further ensure that the control input voltage finally input to the power system better meets the requirements, thereby ensuring the stability of the power system.

[0037] In a second aspect, the present application provides a device for calculating the transient stability control input voltage of an electric power system, the device comprising: a first building module, a second building module, a third building module, a judgment module and an output module; wherein the first building module is used to establish a state space equation of the current electric power system, the state space equation comprising multiple parameters to be determined; the second building module is used to construct a power system control component corresponding to the state space equation, the power system control component comprising a learning component and a false identification component; the third building module is used to establish a candidate function corresponding to the state space equation through the learning component, and to verify whether the candidate function meets the preset requirements through the false identification component; the judgment module is used to verify whether the candidate function meets the preset requirements through the false identification component; the output module is used to solve the candidate function if it meets the preset requirements, obtain the specific value of the parameter to be determined, substitute the specific value into the state space equation, obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, the control input voltage to be verified is determined to be the control input voltage of the electric power system.

[0038] By adopting the above technical solution, the state space equation of the current power system is constructed, and then the candidate function corresponding to the state space equation is established through the learning component, and then the candidate function is verified by the false identification component to see if it meets the requirements; if it meets the requirements, the state space equation is solved through the candidate function to obtain the input voltage to be verified; again, it is judged whether the value of the input voltage to be verified meets the set threshold, if it meets, the control input voltage of the power system is obtained, otherwise, the candidate function corresponding to the state space equation is reconstructed. In summary, the complexity and nonlinear characteristics of the power system can be solved through the state space equation; the control input voltage of the power system can be accurately calculated through layer-by-layer calculation and verification, thereby ensuring the stability of the power system.

[0039] In a third aspect, the present application provides an electronic device, adopting the following technical solution: comprising a processor, a memory, a user interface and a network interface, the memory being used to store instructions, the user interface and the network interface being used to communicate with other devices, and the processor being used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned methods for calculating the input voltage of transient stability control of an electric power system.

[0040] In summary, the present application includes at least one of the following beneficial technical effects:

[0041] 1. The complexity and nonlinear characteristics of the power system can be solved through the state space equation; the control input voltage of the power system can be accurately calculated through layer-by-layer calculation and verification, thereby ensuring the stability of the power system;

[0042] 2. After determining the control input voltage, the control input voltage is tested through the test function to further ensure that the control input voltage finally input to the power system is more in line with the requirements, thereby ensuring the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a schematic diagram of a network structure model of a power system provided in an embodiment of the present application;

[0044] Figure 2 It is a flow chart of a method for calculating the input voltage of transient stability control of a power system provided in an embodiment of the present application;

[0045] Figure 3 is a block diagram of a power system model control framework provided in an embodiment of the present application;

[0046] Figure 4 It is a flow chart of a power system transient stability control learning algorithm provided by an embodiment of the present application;

[0047] Figure 5It is a structural schematic diagram of a device for calculating input voltage of transient stability control of a power system provided in an embodiment of the present application;

[0048] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0049] Explanation of the accompanying drawings: 1. First building block; 2. Second building block; 3. Third building block; 4. Judgment module; 5. Output module; 1000. Electronic device; 1001. Processor; 1002. Communication bus; 1003. User interface; 1004. Network interface; 1005. Memory. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0051] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0052] In recent years, with the development of automation technology and electrical technology, the power system has entered an increasingly complex stage. The power generation, transmission, distribution and other equipment and corresponding auxiliary systems together form the power system. Among them, the voltage stabilization module in the power system can be composed of components such as static VAR compensator, negative pressure load shedding and load tap changer.

[0053] The power system will be disturbed during operation, affecting the ability to maintain stable voltage on all buses in the system. Voltage stability depends on the ability of the power system to maintain or restore the balance between load demand and load supply, while voltage instability will cause load loss, transmission line tripping, system cascade blackouts, and generator asynchrony in the power system. Therefore, it is necessary to maintain the output voltage during the operation of the power system, which is of great significance to the normal operation of system components.

[0054] At present, some power systems have designed corresponding voltage stabilization control strategies based on the state space method according to the different components in the system, which can maintain the stability of voltage to a certain extent. However, the power system has the characteristics of fast response, high precision, and strong real-time performance. When the voltage changes, if it cannot be quickly kept within the expected voltage, it may cause disorder in the entire system. Therefore, the present application designs a power system emergency voltage stabilization method with rapid response capability to ensure the stability of the system voltage, that is, to ensure that the voltage can operate within the expected range, which is more in line with the actual requirements of power system construction and operation.

[0055] Below Figure 1 Take this as an example to illustrate Figure 1 As shown, Figure 1 It is a schematic diagram of a network structure model of a power system provided in an embodiment of the present application; it is composed of a physical power system network and multiple bus dynamics. The physical power system network refers to the actual power system composed of power plants, substations, transmission lines, distribution transformers and users, etc. It is the infrastructure for energy transmission and distribution in the power system and the main carrier of power supply; bus dynamics refers to the changes in parameters such as power system bus voltage and frequency caused by load changes, faults or other changes in operating conditions in the power system. Bus dynamics is one of the important issues in the operation of the power system and has an important impact on the stability and reliability of the power system.

[0056] Figure 2 is a flow chart of a method for calculating the input voltage of transient stability control of a power system provided by an embodiment of the present application. It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows; unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps may be executed in other orders; and Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0057] The present application discloses a method for calculating the input voltage of transient stability control of a power system, such as Figure 2 As shown, the method includes S101-S104.

[0058] S101, establishing a state space equation of the current power system, where the state space equation includes a plurality of undetermined parameters.

[0059] In one example, since the power system is a complex system composed of multiple components, and the components of different power systems are different, when analyzing the power system, it is necessary to construct the state space equation corresponding to the current power system according to the component state of the current power system, and establish a unified state space equation including component state, voltage amplitude and control input voltage. The state space equation describes the law of change of system state variables over time. According to the physical characteristics and operation laws of the power system, a state equation can be established. The state equation is usually in the form of a differential equation and can be determined according to the dynamic characteristics and operation model of the power system. The constructed state space equation is usually related to factors such as the phase of the bus, the voltage amplitude of the bus, the active power of the bus and the reactive power of the bus. Therefore, the established space state equation generally includes multiple undetermined parameters. By solving, multiple undetermined parameters can be obtained, so that the control input voltage can be designed through the state space equation, and the designed control input voltage is input into the power system, thereby ensuring the stability of the system voltage, that is, ensuring that the voltage can operate within the expected range, which is more in line with the actual requirements of power system construction and operation.

[0060] Inputting the designed control input voltage into the power system can ensure the stability of the system voltage and meet the actual requirements of power system construction and operation. There are mainly the following reasons:

[0061] 1. Voltage stability is the basis for the normal operation of the power system: The stable operation of the power system requires maintaining an appropriate voltage level. Too high or too low voltage will cause damage to power equipment and user equipment, and even cause power system failure.

[0062] 2. Controlling the input voltage can adjust the power balance of the system: The supply and demand balance in the power system is the key to maintaining the stable operation of the system. By controlling the input voltage, the power balance of the system can be adjusted so that the power supply can meet the needs of users while avoiding overload or power shortage.

[0063] 3. Controlling the input voltage can improve the stability and response speed of the system: The stability of the power system refers to the ability of the system to quickly recover to a stable state when facing external disturbances. By controlling the input voltage, the stability and response speed of the system can be improved, allowing the system to adjust the voltage level more quickly to respond to external changes and disturbances.

[0064] 4. Controlling the input voltage can achieve the regulation and optimization of the power system: By controlling the input voltage, the power system can be regulated and optimized. For example, when the load of the power system changes greatly, the input voltage can be adjusted to achieve load balancing and power distribution, so as to improve the efficiency and economy of the system.

[0065] Establishing the state space equation of the current power system specifically includes: establishing a bus dynamic model of the power system and a bus power model of the power system; and establishing the state space equation by combining the bus dynamic model and the bus power model.

[0066] In one example, a bus dynamic model of a power system is established, including:

[0067] Establish the dynamic model of the i-th bus as dx i =f(x i ,u i ), y i =c i x i ; where x i =[η i ,θ i , V i ],y i =[θ i , V i ], i∈V, V is the bus set, u i is the control input voltage of bus i, θ i is the phase of bus i, V i is the voltage amplitude of bus i, η i is an auxiliary variable for modeling the bus dynamics of heterogeneous devices, c i is the matrix corresponding to bus i, i is a positive integer greater than 0;

[0068] The bus dynamic models of each bus are added together to obtain the bus dynamic model of the power system.

[0069] Establish a bus power model of the power system, including:

[0070] The power model of the i-th bus is established as

[0071]

[0072]

[0073] Among them, p i is the active power of the ith bus, q i is the reactive power of the ith bus, N j is the set of all adjacent buses leading to bus i, G ii and B ii are the real and imaginary elements of the adjacency matrix, V i is the voltage amplitude of bus i;

[0074] V j is the voltage amplitude of bus j, G ij and B ijare the real and imaginary elements of the adjacency matrix;

[0075] θ ij is the phase angle deviation of adjacent buses, i and j are both positive integers greater than 0;

[0076] The bus power models of the various buses are added together to obtain the bus power model of the power system.

[0077] Finally, the state space equation of the current power system is obtained by combining the bus power model of the power system and the bus dynamic model of the power system. The state space equation is dx = f(x, u), y = cx, u = K(x), where f(x, u) is a continuous differentiable function, K(x) is a function related to x, where x is the state vector of the system, containing all state variables of the system; u is the input vector, representing the external input or control signal; y is the output vector, representing the output of the system; f(x, u) is the state equation of the system, describing the change law of the state variables over time; c is the output matrix, used to map the state vector to the output vector; K(x) is the controller, which calculates the input vector according to the state of the system.

[0078] Specifically in the power system, x can be the component state of the power system, y can be the voltage amplitude of the power system, and u can be the control input voltage of the power system. x = [η, θ, V], y = [θ, V], θ is the phase of the power system bus, V is the voltage amplitude of the power system bus, η is the auxiliary variable of the power system for dynamic modeling of the bus of heterogeneous devices, and c is the matrix

[0079] S102, constructing a power system control component corresponding to the state space equation, wherein the power system control component includes a learning component and a false identification component.

[0080] In one example, if Figure 3 As shown, Figure 3 is a block diagram of a power system model control framework provided in an embodiment of the present application; Figure 3 It can be understood as a solution process, solving the parameters in the state space equations to obtain the control input voltage. The entire solution process can be understood as a black box approximation method. The black box approximation method is a method used to establish a mathematical model of an unknown system or function. It is usually used for systems that cannot be described by known physical equations or mathematical models, and is also called a non-parametric modeling method. The control input and system input here can be understood as inferring the internal structure and behavior of the system by observing and sampling the input and output data of the system. The black box approximation method does not require any assumptions or constraints on the system in advance, so it can be applied to various complex systems.

[0081] Common black-box approximation methods include: Linear regression: Linear regression is a common black-box approximation method that builds a model by fitting the linear relationship between input and output data. It can be used to build a model of a linear system.

[0082] Neural network: Neural network is a mathematical model that imitates the human brain's neural network. The model is established by connecting multiple layers of neurons and adjusting weights. Neural network can approximate any nonlinear function, and the weights and structure of the network can be optimized through training.

[0083] Support vector machine: Support vector machine is a machine learning method for classification and regression analysis. It builds a model by finding an optimal hyperplane. It can be used to approximate nonlinear functions and has good generalization ability.

[0084] Genetic Algorithm: Genetic Algorithm is an optimization algorithm that simulates the natural evolution process and searches for the optimal solution through operations such as selection, crossover and mutation. It can be used to build models of complex systems and can handle multi-variable and multi-objective problems.

[0085] and Figure 3 The learning component in the example is the training function. Through continuous training, a function is constructed to solve the parameters of the state-space equation, thereby obtaining the control input voltage of the power system. The false identification component here can be understood as the verification effect. The false identification component is used to check whether the function constructed by the learning component meets the requirements and verify the effect of the function constructed by the learning component.

[0086] S103, establishing a candidate function corresponding to the state space equation through a learning component, and verifying whether the candidate function meets the preset requirements through a false identification component.

[0087] like Figure 4 As shown, Figure 4 This is a flow chart of a learning algorithm for transient stability control of a power system provided in an embodiment of the present application. Find a candidate function through a learning component, and determine whether the candidate function exists. If not, end directly until the candidate function exists and execute the next step; if the candidate function exists, verify the validity of the candidate function through a false identification component. If the candidate function is valid, solve the unknown parameters of the state space equation through the candidate function, thereby solving the control input voltage; if the candidate function is invalid, continue to build it through the learning component until a valid candidate function is built. The specific process of building a function and checking whether the function meets the requirements is as follows:

[0088] The candidate function V of constructing the state space equation through the learned components ε (x) = ρ i+1 (g(x)-g(0)), where V ε(x) is the candidate function, ρ i is a convex monotone non-decreasing nonlinear activation function, g(x) is a convex neural network function

[0089]

[0090] Among them, z l is a piecewise function, ρ0 is a convex monotone non-decreasing nonlinear activation function, ω0 is the real part of the neural network weight, ω i and U i is the real and positive part of the neural network weights, b0 and b i All are known parameters. The learning component can be considered as a black box approximation method, which is used to approximate or estimate unknown parameters and finally obtain feasible values ​​of the parameters.

[0091] The entire learning component can be understood as constructing a candidate function through the learning component to solve the state space equation and obtain the control input voltage. i and ρ0 can be understood as a value that changes over time and can be defined artificially. Similarly, ω0 and ω i , b0 and b i It is also defined by humans and changes over time. The learning component constructs a candidate function to solve multiple unknown parameters included in the state space equation to obtain the control input voltage.

[0092] The role of the false identification component is to verify whether the candidate function constructed by the learning component meets the requirements. The false identification formula is constructed through the false identification component. The false identification formula is

[0093]

[0094] V ε (x) is a candidate function;

[0095] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0096] Ψ is the corresponding parameter in the feedback law; the false identification formula is used to determine whether the candidate function meets the requirements of the false identification formula; if so, the candidate function is determined to meet the preset requirements; if not, the candidate function is reselected through the learning component.

[0097] S104, if it meets the preset requirements, solve the candidate function to obtain the specific value of the parameter to be determined, substitute the specific value into the state space equation to obtain the control input voltage to be verified, if the value of the control input voltage to be verified is less than the set threshold, then the control input voltage to be verified is determined to be the control input voltage of the power system.

[0098] In one example, if the current candidate function meets the requirements through the false identification component, the candidate function is solved to obtain the specific value of the parameter to be determined, and the specific value is substituted into the state space equation to obtain the control input voltage to be verified. The control input voltage to be verified here is not necessarily the voltage finally input into the power system, and it is necessary to construct a verification function

[0099] Among them, α and β are known parameters;

[0100] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0101] Ψ is the corresponding parameter in the feedback law;

[0102] V ε (x) is a candidate function;

[0103] The test function is used to check whether the value of the control input voltage is less than the set threshold. The set threshold here is set according to the actual situation. Generally speaking, the smaller the set threshold, the better. The smaller the threshold, the more stable it is. If it is less than the set threshold, it is determined that the control input voltage meets the final input requirements, and the control input voltage to be verified is the control input voltage that is finally input to the power system; otherwise, the candidate function is reselected through the learning component.

[0104] Based on the above method, the present application also discloses a device for calculating the input voltage of the transient stability control of the power system, such as Figure 5 As shown, Figure 5 It is a structural schematic diagram of a device for calculating the transient stability control input voltage of a power system provided in an embodiment of the present application.

[0105] A device for calculating the transient stability control input voltage of an electric power system comprises: a first construction module 1, a second construction module 2, a third construction module 3, a judgment module 4 and an output module 5; wherein the first construction module 1 is used to establish a state space equation of the current electric power system, the state space equation comprising a plurality of undetermined parameters; the second construction module 2 is used to construct an electric power system control component corresponding to the state space equation, the electric power system control component comprising a learning component group and a false identification component; the third construction module 3 is used to establish a candidate function corresponding to the state space equation through the learning component, and to verify whether the candidate function meets the preset requirements through the false identification component; the judgment module 4 is used to verify whether the candidate function meets the preset requirements through the false identification component; the output module 5 is used to solve the candidate function if it meets the preset requirements, obtain the specific value of the undetermined parameter, bring the specific value into the state space equation, obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold value, the control input voltage to be verified is determined to be the control input voltage of the electric power system.

[0106] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0107] In one example, the above device is also used to establish a bus dynamic model of the power system and a bus power model of the power system; the bus dynamic model and the bus power model are combined to establish a state space equation.

[0108] In one example, the above device is also used to establish the i-th bus dynamic model as dx i =f(x i ,u i ), y i =c i x i ; where x i =[η i ,θ i , V i ],y i =[θ i , V i ], i∈V, V is the bus set, u i is the control input voltage of bus i, θ i is the phase of bus i, V i is the voltage amplitude of bus i, η i is an auxiliary variable for modeling the bus dynamics of heterogeneous devices, c i is the matrix corresponding to bus i, i is a positive integer greater than 0; the bus dynamic models of each bus are added together to obtain the bus dynamic model of the power system.

[0109] In one example, the above device is also used to establish the i-th bus power model as

[0110]

[0111]

[0112] Among them, p i is the active power of the ith bus, q i is the reactive power of the ith bus, N j is the set of all adjacent buses leading to bus i, G ii and B ii are the real and imaginary elements of the adjacency matrix, Vi is the voltage amplitude of bus i;

[0113] V j is the voltage amplitude of bus j, G ij and B ij are the real and imaginary elements of the adjacency matrix;

[0114] θ ij is the phase angle deviation of adjacent buses, i and j are both positive integers greater than 0;

[0115] The bus power models of each bus are added together to obtain the bus power model of the power system.

[0116] In one example, the above-mentioned device is also used to construct a state space equation of dx=f(x,u), y=cx, u=K(x), wherein f(x,u) is a continuously differentiable function, K(x) is a function related to x, x is the component state of the power system, y is the voltage amplitude of the power system, u is the control input voltage of the power system, x=[η,θ,V], y=[θ,V], θ is the phase of the power system bus, V is the voltage amplitude of the power system bus, η is an auxiliary variable for the power system to dynamically model the bus of heterogeneous devices, and c is a matrix

[0117] In one example, the apparatus is further configured to construct a candidate function V of the state space equation by a learning component: ε (x) = ρ i+1 (g(x)-g(0)), where V ε (x) is the candidate function, ρ i is a convex monotone non-decreasing nonlinear activation function, g(x) is a convex neural network function

[0118]

[0119] Among them, z l is a piecewise function, ρ0 is a convex monotone non-decreasing nonlinear activation function, ω0 is the real part of the neural network weight, ω i and U i is the real and positive part of the neural network weights, b0 and b i All are known parameters.

[0120] In one example, the above device is also used to construct a counterfeit identification formula through the counterfeit identification component, and the counterfeit identification formula is

[0121] V ε (x) is a candidate function;

[0122] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0123] Ψ is the corresponding parameter in the feedback law;

[0124] Determine whether the candidate function meets the requirements of the false identification formula through the false identification formula;

[0125] If it is satisfied, the candidate function is determined to meet the preset requirements. If it is not satisfied, the candidate function is reselected through the learning component.

[0126] In one example, the above device is also used to construct a test function Among them, α and β are known parameters;

[0127] is the gradient of the function f(x, ψ) with respect to the variables x and ψ, where x is any value greater than 0;

[0128] Ψ is the corresponding parameter in the feedback law;

[0129] V ε (x) is a candidate function;

[0130] The test function is used to check whether the value of the control input voltage is less than the set threshold. If it is less than the set threshold, it is determined that the control input voltage meets the final input requirements, and the control input voltage is verified to be the control input voltage of the power system; otherwise, the candidate function is reselected through the learning component.

[0131] See also Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0132] The communication bus 1002 is used to realize the connection and communication between these components.

[0133] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0134] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0135] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0136] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 6 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for calculating an input voltage of a transient stability control of a power system.

[0137] exist Figure 6In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 for a method for calculating the input voltage of transient stability control of a power system. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0138] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0139] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0140] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0145] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for calculating a transient stability control input voltage of a power system, characterized in that: The method comprises: Establishing a state space equation of the current power system, wherein the state space equation includes a plurality of undetermined parameters; Constructing a power system control component corresponding to the state-space equation, wherein the power system control component includes a learning component and a false identification component; Establishing a candidate function corresponding to the state-space equation by the learning component, wherein establishing a candidate function corresponding to the state-space equation by the learning component comprises: The candidate function V of the state space equation is constructed by the learning component ε (x) = ρ k+1 (g(x)-g(0)), where V ε (x) is the candidate function, ρ k is a convex monotone non-decreasing nonlinear activation function, g(x) is a convex neural network function Among them, z l is a piecewise function, ρ0 is a convex monotone non-decreasing nonlinear activation function, ω0 is the real part of the neural network weight, ω k and U k is the real and positive part of the neural network weights, b0 and b k are all known parameters; and verifying whether the candidate function meets the preset requirements through the false identification component, wherein the verifying whether the candidate function meets the preset requirements through the false identification component includes: constructing a false identification formula through the false identification component, wherein the false identification formula is V ε (x) is a candidate function; is the gradient of the function f(x, ψ) with respect to the variables x and ψ, x is any value greater than 0; Ψ is the corresponding parameter in the feedback law; judging whether the candidate function meets the requirements of the false identification formula by the false identification formula; if so, judging that the candidate function meets the preset requirements; if not, reselecting the candidate function by the learning component; If the preset requirement is met, the candidate function is solved to obtain a specific value of the undetermined parameter, the specific value is substituted into the state space equation to obtain a control input voltage to be verified, and if the value of the control input voltage to be verified is less than a set threshold, the control input voltage to be verified is determined to be the control input voltage of the power system; the specific value is substituted into the state space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, the control input voltage to be verified is determined to be the control input voltage of the power system, including: Constructing the test function Among them, α and β are known parameters; is the gradient of function f(x, ψ) with respect to variables x and ψ, where x is any value greater than 0; Ψ is the corresponding parameter in the feedback law; V ε (x) is a candidate function; the verification function is used to verify whether the value of the control input voltage is less than the set threshold value. If it is less than the set threshold value, it is determined that the control input voltage meets the final input requirements, and the control input voltage to be verified is the control input voltage of the power system; otherwise, the candidate function is reselected through the learning component.

2. The method for calculating the input voltage of transient stability control of a power system according to claim 1, characterized in that: The state space equation of the current power system is established, including: Establish bus dynamic model and bus power model of power system; The state space equation is established by combining the bus dynamic model and the bus power model.

3. The method for calculating the input voltage of transient stability control of a power system according to claim 2, characterized in that: The method of establishing a bus dynamic model of the power system includes: Establish the dynamic model of the i-th bus as dx i =f(x i ,u i ), y i =c i x i ; where x i =[η i ,θ i , V i ],y i =[θ i , V i ], i∈V, V is the bus set, u i is the control input voltage of bus i, θ i is the phase of bus i, V i is the voltage amplitude of bus i, η i is an auxiliary variable for modeling the bus dynamics of heterogeneous devices, c i is the matrix corresponding to bus i, i is a positive integer greater than 0; The bus dynamic models of the various buses are added together to obtain the bus dynamic model of the power system.

4. The method for calculating the input voltage of transient stability control of a power system according to claim 2, characterized in that: The establishing of the bus power model of the power system comprises: The power model of the i-th bus is established as Among them, p i is the active power of the ith bus, q i is the reactive power of the ith bus, N j is the set of all adjacent buses leading to bus i, G ii and B ii are the real and imaginary elements of the adjacency matrix, V i is the voltage amplitude of bus i; V j is the voltage amplitude of bus j, G ij and B ij are the real and imaginary elements of the adjacency matrix; θ ij is the phase angle deviation of adjacent buses, i and j are both positive integers greater than 0; The bus power models of the various buses are added together to obtain the bus power model of the power system.

5. The method for calculating the input voltage of transient stability control of a power system according to claim 1, characterized in that: The state space equation is dx=f(x,u), y=cx, u=K(x), wherein f(x,u) is a continuously differentiable function, K(x) is a function related to x, x is the component state of the power system, y is the voltage amplitude of the power system, u is the control input voltage of the power system, x=[η,θ,V], y=[θ,V], θ is the phase of the power system bus, V is the voltage amplitude of the power system bus, η is an auxiliary variable for the power system to dynamically model the bus of heterogeneous devices, and c is a matrix 6. A device for calculating input voltage of transient stability control of power system, characterized in that: The device comprises: a first building module (1), a second building module (2), a third building module (3), a judgment module (4) and an output module (5); wherein the first building module (1) is used to establish a state space equation of the current power system, and the state space equation includes a plurality of undetermined parameters; The second construction module (2) is used to construct a power system control component corresponding to the state space equation, and the power system control component includes a learning component and a false identification component; The third building block (3) is used to establish a candidate function corresponding to the state-space equation through the learning component, and the establishing of the candidate function corresponding to the state-space equation through the learning component includes: The candidate function V of the state space equation is constructed by the learning component ε (x) = ρ k+1 (g(x)-g(0)), where V ε (x) is the candidate function, ρ k is a convex monotone non-decreasing nonlinear activation function, g(x) is a convex neural network function Among them, z l is a piecewise function, ρ0 is a convex monotone non-decreasing nonlinear activation function, ω0 is the real part of the neural network weight, ω k and U k is the real and positive part of the neural network weights, b0 and b k are all known parameters, and the candidate function is checked by the false identification component to see whether it meets the preset requirements, and the false identification component is used to check whether the candidate function meets the preset requirements, including: constructing a false identification formula by the false identification component, and the false identification formula is V ε (x) is a candidate function; is the gradient of the function f(x, ψ) with respect to the variables x and ψ, x is any value greater than 0; Ψ is the corresponding parameter in the feedback law; judging whether the candidate function meets the requirements of the false identification formula by the false identification formula; if so, judging that the candidate function meets the preset requirements; if not, reselecting the candidate function by the learning component; The judgment module (4) is used to check whether the candidate function meets the preset requirements through the authentication component; The output module (5) is used to solve the candidate function if it meets the preset requirements, obtain the specific value of the parameter to be determined, substitute the specific value into the state space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, then determine that the control input voltage to be verified is the control input voltage of the power system, and substitute the specific value into the state space equation to obtain the control input voltage to be verified, and if the value of the control input voltage to be verified is less than the set threshold, then determine that the control input voltage to be verified is the control input voltage of the power system, including: constructing a test function Among them, α and β are known parameters; is the gradient of function f(x, ψ) with respect to variables x and ψ, where x is any value greater than 0; Ψ is the corresponding parameter in the feedback law; V ε (x) is a candidate function; the verification function is used to verify whether the value of the control input voltage is less than the set threshold value. If it is less than the set threshold value, it is determined that the control input voltage meets the final input requirements, and the control input voltage to be verified is the control input voltage of the power system; otherwise, the candidate function is reselected through the learning component.

7. An electronic device, characterized in that: The electronic device comprises a processor (1001), a memory (1005), a user interface (1003) and a network interface (1004), wherein the memory (1005) is used to store instructions, the user interface (1003) and the network interface (1004) are used to communicate with other devices, and the processor (1001) is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 5.

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