Coherent generator group identification method and device of power system, terminal equipment and storage medium

Abstract

The invention provides a coherent generator group identification method and device of an electric power system, terminal equipment and a storage medium, and relates to the field of electric power system stability analysis, and the method comprises the steps: obtaining the operation parameters of each generator in the electric power system at a sampling point, and taking the operation parameters of each generator in the faulted electric power system as initial values; integrating the contraction model of the power system, generating a state variable time sequence of the contraction model after the power system has a fault, calculating a first unstable equilibrium point of the contraction model when determining that the power system is subjected to large disturbance, and further obtaining an unstable equilibrium point of an original system model; an original system model is linearized at an unstable equilibrium point to obtain a current feature vector of a generator, and then a coherent cluster is accurately divided by adopting a clustering algorithm, so that the defect that coherent clustering of a power system cannot be accurately realized by a slow coherent algorithm is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system stability analysis, and particularly relates to a power system coherent generator group identification method and device, a terminal equipment and a storage medium. BACKGROUND

[0002] In recent years, with the formation of a large-scale regional interconnected power grid, on the one hand, the energy configuration is greatly optimized, and on the other hand, the risk of cascading accidents in the power system is also brought. Active splitting as the last of the three lines of defense in the power system plays an important role in ensuring the safe operation of the power system. The coherent generator in each island after fault splitting is a necessary condition to ensure the safe and stable operation of the island, so correct coherent generator identification is the premise of active splitting. When a disturbance occurs, it is of great significance to prevent large-scale power outages by quickly and accurately identifying the coherent generator group in the disturbed power system.

[0003] The classical method for coherent generator group identification is the slow coherent algorithm, and the group analysis is based on the linearized state matrix at the stable equilibrium point. The eigenvalues and eigenvectors of the state matrix reflect the dynamic characteristics of the system near the equilibrium point, and then the group division is obtained. However, the traditional slow coherent model is based on an assumption that the grouping mode of the generator is independent of the strength of the fault disturbance. That is, the slow coherent algorithm does not consider the state of the power system after the disturbance, but the large disturbance will make the system deviate from the stable equilibrium point. At this time, the linearized state matrix at the stable equilibrium point cannot accurately reflect the current dynamic characteristics of the system. If the linearized state matrix at the stable equilibrium point is still used to divide the coherent generator group, it will lead to incorrect splitting. Therefore, for the case of a disturbed power system, the slow coherent algorithm cannot accurately achieve coherent grouping of the power system. SUMMARY

[0004] The present application provides a power system coherent generator group identification method, device, terminal equipment and storage medium, which can solve the problem of inaccurate coherent grouping of the power system in the prior art.

[0005] An embodiment of the present application provides a power system coherent generator group identification method, comprising: obtaining the operating parameters of each generator in the power system at a sampling point; wherein the sampling point is a monitoring node in the power system for real-time collection of operating parameters; integrating the reduced model of the power system with the operating parameters as initial values to generate the state variable time series of the reduced model after the fault; wherein the reduced model is a reduced order model with similar properties to the original system model; and the original system model is a mathematical model for characterizing the steady-state and dynamic behavior of the power system; determining a first unstable equilibrium point of the contracted model according to the state variable time series, and determining a second unstable equilibrium point of the original system model according to the first unstable equilibrium point when it is determined that the power system is subjected to a large disturbance; converting the original system model into a linear state equation according to the second unstable equilibrium point, and determining a characteristic vector of each of the generators according to the linear state equation; wherein each of the characteristic vectors is used to represent a dynamic correlation of a corresponding generator with a disturbance state of the power system; performing clustering on the generators according to the characteristic vectors by using a clustering algorithm to generate a plurality of first clustering sets, and dividing the generators in a same first clustering set into a same coherent generator group.

[0006] Further, the determining that the power system is subjected to a large disturbance according to the state variable time series comprises: calculating a system energy function value, a potential energy function value, and a state variable norm of a state variable time series of the power system according to the operating parameters; determining a stable equilibrium point of the original system model according to the original system model, and determining a first energy function value of the second unstable equilibrium point and a second energy function value of the stable equilibrium point; calculating a first difference value between the system energy function value and the first energy function value, and a second difference value between the system energy function value and the second energy function value; determining that the power system is subjected to a large disturbance when the state variable norm presents a change trend of first increasing and then decreasing, the potential energy function value does not have a local maximum value, and an absolute value of the second difference value is greater than an absolute value of the first difference value; determining that the power system is subjected to a large disturbance when the state variable norm presents a change trend of first decreasing and then increasing, and the potential energy function value does not have a local maximum value.

[0007] Further, the coherent generator group identification method of the power system according to the above-mentioned embodiments further comprises: determining that the power system is subjected to a small disturbance when the state variable norm presents a monotonically decreasing change trend; determining that the power system is subjected to a small disturbance when the state variable norm presents a change trend of first increasing and then decreasing, the potential energy function value does not have a local maximum value, and an absolute value of the second difference value is not greater than an absolute value of the first difference value; determining that the power system is in an unstable state when the state variable norm presents a change trend of first increasing and then decreasing, and the potential energy function value has a local maximum value. When the norm of the state variable exhibits a monotonically decreasing trend and the potential energy function value has a local maximum, the power system is determined to be in an unstable state.

[0008] Furthermore, after determining that the power system has been subjected to a small disturbance, the process also includes: Using the slow coherence algorithm, based on the original system model, the generators are subjected to coherence grouping processing, dividing the generators into several coherent generator groups.

[0009] Furthermore, the operating parameters include: rotor angle data; After determining that the power system is in an unstable state, the process also includes: A clustering algorithm is used to cluster the generators based on the rotor angle data, generating several second cluster sets, and the generators in the same second cluster set are divided into the same co-tuning generator group.

[0010] Furthermore, calculating the first unstable equilibrium point of the contraction model includes: The calculation of the first unstable equilibrium point of the contraction model includes: When the norm of the state variable shows a trend of first decreasing and then increasing, identify the minimum value in the norm of the state variable; Based on the operating parameters, a power balance equation is constructed, and the minimum value is used as the initial value. The Newton-Raphson algorithm is used to solve the power balance equation to determine the first unstable equilibrium point.

[0011] Furthermore, calculating the first unstable equilibrium point of the contraction model further includes: When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum, and the absolute value of the second difference is greater than the absolute value of the first difference, the state equation of the generator is constructed based on the operating parameters. Based on the state equation, the fault trajectory under the continuous fault of the power system is determined, and based on the contraction model, the fault trajectory is projected to determine the phase trajectory of the fault trajectory in the contraction model. Based on the phase trajectory, calculate the power deviation equation of the power system after the fault is cleared; Based on the state equation and the power deviation equation, the exit point is determined; the contraction model is integrated with the exit point as the initial value to generate an integral trajectory, and the state variable corresponding to the minimum value in the integral trajectory is taken as the target point; wherein, the exit point is the intersection of the phase trajectory and the stability boundary of the contraction model; Based on the operating parameters, a power balance equation is constructed, and the minimum value is used as the initial value. The Newton-Raphson algorithm is used to solve the power balance equation to determine the first unstable equilibrium point.

[0012] An embodiment of the present invention also provides a power system coordinating generator group identification device, comprising: The parameter acquisition module is used to acquire the operating parameters of each generator in the power system at the sampling points; wherein, the sampling points are the pre-set monitoring nodes in the power system for real-time acquisition of operating parameters. The model integration module is used to integrate the shrinkage model of the power system with the operating parameters as initial values ​​to generate the state variable time series of the shrinkage model after the fault; wherein, the shrinkage model is a reduced-order model with similar properties to the original system model; the original system model is a mathematical model used to characterize the steady-state and dynamic behavior of the power system. The disturbance assessment module is used to calculate the first unstable equilibrium point of the contraction model when the power system is subjected to a large disturbance based on the time series of the state variables, and to determine the second unstable equilibrium point of the original system model based on the first unstable equilibrium point. The linear transformation module is used to convert the original system model into a linear state equation based on the second unstable equilibrium point, and to determine the feature vector of each generator based on the linear state equation; wherein each feature vector is used to characterize the dynamic correlation between the corresponding generator and the disturbance state of the power system. The generator group partitioning module is used to use a clustering algorithm to cluster the generators according to the feature vectors, generate several first cluster sets, and divide the generators under the same first cluster set into the same co-tuning generator group.

[0013] This application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a method for identifying coordinating generator groups in a power system as described in the above embodiments of the invention.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying synchronized generator groups in a power system as described in the above embodiments.

[0015] The following benefits can be obtained by implementing the present invention: This invention provides a method, apparatus, terminal equipment, and storage medium for identifying coherent generator groups in a power system. The method involves acquiring the operating parameters of each generator in the power system at sampling points. Using the operating parameters of each generator in the faulty power system as initial values, the method integrates a contraction model to generate a time series of state variables of the contraction model after the power system fault. This allows for the inference of the power system's state through the contraction model, thereby accurately identifying the disturbance situation in the power system. When a large disturbance is determined to be occurring in the power system, the first unstable equilibrium point of the contraction model is calculated, leading to the second unstable equilibrium point of the original system model. It is understood that under large disturbance conditions, the dynamic evolution of the power system will deviate from the stable operating point and transition to an unstable state. Unstable equilibrium points are critical points. By linearizing the original system model at unstable equilibrium points, the dynamic characteristics of the system near unstable equilibrium points can be reflected, thereby obtaining the characteristic vectors of generators. Based on the core definition that coherent generator groups have similar dynamic responses, the characteristic vectors are used to determine the dynamic correlation of generators under large disturbances, providing a reliable classification basis for clustering algorithms, accurately classifying coherent generator groups, and thus overcoming the defect of slow coherent algorithms in accurately realizing the coherent grouping of power systems. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for identifying synchronized generator groups in a power system according to a certain embodiment of this application; Figure 2 This is a schematic diagram of the structure of a power system coordinating generator group identification device according to a certain embodiment of this application; Figure 3 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of this application; Figure 4 This is a schematic diagram of a type A trajectory provided in a certain embodiment of this application; Figure 5 This is a schematic diagram of a d-shaped trajectory provided in a certain embodiment of this application; Figure 6 This is a schematic diagram of a type b trajectory and a type c trajectory provided in a certain embodiment of this application; Figure 7 This is a schematic diagram of an e-type trajectory and an f-type trajectory provided in a certain embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] See Figure 1 To address the problems in the prior art, an embodiment of the present invention provides a method for identifying synchronized generator groups in a power system, comprising: S1. Obtain the operating parameters of each generator in the power system at the sampling points; wherein, the sampling points are pre-set monitoring nodes in the power system for real-time acquisition of operating parameters. In a preferred embodiment of the present invention, a power system status information is collected using a power measurement unit (PMU), including: active power of the load, reactive power, rotor angle and speed of each generator, etc. In the power system, the PMU collects system status data in real time, approximately once every 0.01 seconds. The algorithm proposed in this invention is executed in real time; that is, each time the PMU samples, the algorithm is executed again based on the data sampled at the current sampling point to obtain the clustering result.

[0026] Furthermore, the contraction model is a simplified analysis model proposed by Chiang et al. in the BCU method. For the system state equations in COI coordinates: ; Its contraction system is defined as: ; When the transfer conductivity is sufficiently small, the original system and the contraction system have the following relationship: (1) For the stable equilibrium point of the contraction system, if and only if This is the stable equilibrium point of the original system.

[0027] (2) For the k-type equilibrium point of the contraction system, if and only if This is the k-type equilibrium point of the original system.

[0028] (3) When the single-parameter cross-section condition is satisfied At the stability boundary of the contraction system Above, if and only if At the stability boundary of the original system superior.

[0029] Of the above properties, (1) and (2) are called static properties between the original system and the contracting system, and (3) is called dynamic property between the original system and the contracting system. These properties indicate that if the dominant unstable equilibrium point of the contracting system can be found through some method... Then the dominant unstable equilibrium point of the original system can be obtained. .

[0030] It should be further explained that the contraction model is a simplified model proposed in the BCU (Boundary of stability region based Controlling Unstable Equilibrium Point) method to simplify analysis and calculation. It can be proven that the contraction model has certain similarities to the original model; therefore, the dominant unstable equilibrium point of the original system can be obtained by calculating the dominant unstable equilibrium point of the contraction model.

[0031] In this invention, in addition to using the above-mentioned properties to calculate the unstable equilibrium point of the original system, the dynamic trajectory characteristics of the original system are approximated by the dynamic trajectory characteristics of the contraction model, thereby obtaining the magnitude of the disturbance of the original system: the disturbance is projected into the contraction model, and the magnitude of the disturbance received by the contraction model is determined according to the dynamic trajectory of the contraction system; since the properties of the original system and the contraction model are related, it can be considered that the magnitude of the disturbance determined in the contraction model is the magnitude of the disturbance received by the original system.

[0032] S2. Using the operating parameters as initial values, integrate the contraction model of the power system to generate the state variable time series of the contraction model after the fault; wherein, the contraction model is a reduced-order model with similar properties to the original system model; the original system model is a mathematical model used to characterize the steady-state and dynamic behavior of the power system. In a preferred embodiment of the present invention, the integration time T and the integration step size are set, and the shrinkage model is integrated using the sampling parameters in S1 as initial values: ; ; ; ; ; Among them, P mi Let P be the mechanical power of the i-th generator in the power system. ei Let P be the electromagnetic power of the i-th generator in the power system, n be the number of generators, and P be the electromagnetic power of the i-th generator. COI This is the cumulative difference between the mechanical power and electromagnetic power of all generators in the power system. , Ei B represents the amplitude of the potential after the transient reactance. ij G ij M represents the corresponding element in the system admittance matrix after the network shrinks to the internal potential node of the generator. i Let T be the inertial time constant of the i-th generator in the system. i The quotient of M and synchronous speed ω0 T For M i The accumulated value, θ i Let be the state variables of the i-th generator; thus, the time series of the state variables of the contraction system is obtained. .

[0033] S3. Based on the time series of the state variables, when the power system is subjected to a large disturbance, calculate the first unstable equilibrium point of the contraction model, and based on the first unstable equilibrium point, determine the second unstable equilibrium point of the original system model. Preferably, determining that the power system is subjected to a large disturbance based on the time series of the state variables includes: S311. Based on the operating parameters, calculate the system energy function value, potential energy function value, and state variable norm of the time series of state variables of the power system; In a preferred embodiment of the present invention, the raw state data obtained by the PMU is converted into the generator state data in the center of inertia (COI) reference coordinate system according to the system parameters: ; ; ; Where, δ i Let ω be the rotor angle of the i-th generator; i M is the rotational speed of the i-th generator; i δ is the quotient of the generator's inertial time constant Ti and the synchronous speed ω0; COI Let ω be the angle center of the system's COI coordinate system. COI Let n be the center of angular velocity in the COI coordinate system, and n be the number of generators. This represents the rotor angle of the original system's generator in the COI coordinate system. Let be the rotational speed of the i-th generator in the original system in the COI coordinate system.

[0034] Based on the state variables of each generator in the COI coordinate system, the energy function of the system is calculated using the following formula: ; ; ; Among them, Pmi Let P be the mechanical power of the i-th generator in the system. ei E represents the electromagnetic power of the i-th generator in the system. i B represents the amplitude of the potential after the transient reactance. ij G ij This refers to the corresponding element in the system admittance matrix after the network shrinks to the internal potential node of the generator. Indicates that generator i is in steady state The value of ; This represents the difference between the state variables i and j of the generator.

[0035] In the energy function, the last three terms are taken as the potential energy function value V of the system. p : .

[0036] S312. Based on the original system model, determine the stable equilibrium point of the original system model, and determine the first energy function value of the second unstable equilibrium point and the second energy function value of the stable equilibrium point; S313. Calculate the first difference between the system energy function value and the first energy function value, and the second difference between the system energy function value and the second energy function value; S314. When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum, and the absolute value of the second difference is greater than the absolute value of the first difference, it is determined that the power system is subjected to a large disturbance. S315. When the norm of the state variable shows a trend of first decreasing and then increasing, and the potential energy function value does not have a local maximum, it is determined that the power system is subjected to a large disturbance.

[0037] Preferably, the method for identifying synchronized generator groups in a power system further includes: S316. When the norm of the state variable shows a monotonically decreasing trend, it is determined that the power system is subjected to a small disturbance; S317. When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum, and the absolute value of the second difference is not greater than the absolute value of the first difference, it is determined that the power system is subjected to a small disturbance. S318. When the norm of the state variable shows a trend of first increasing and then decreasing, and the potential energy function value has a local maximum, it is determined that the power system is in an unstable state. S319. When the norm of the state variable shows a monotonically decreasing trend and the potential energy function value has a local maximum, the power system is determined to be in an unstable state.

[0038] In a preferred embodiment of the present invention, the norm of the state variable is calculated based on the time series of the state variable θ of the contracting system and the equation of the contracting system. , The calculation formula is: ; Based on the changes in the potential energy function before the current state, The magnitude of the disturbance to the system can be determined by the changes in the system's behavior. The relevant principles are as follows: In a system described by a differential equation, the equilibrium point is defined as satisfying The state variable θ, while in other locations in the state space All are greater than zero. Since the state variables change continuously, we can conclude that within the neighborhood of the equilibrium point, on the trajectory closest to the equilibrium point... Decrease, on the trajectory away from the equilibrium point Increase.

[0039] When a system is subjected to disturbances of varying magnitudes, its position in the state space moves to the vicinity of a stable equilibrium point (SEP) or an unstable equilibrium point (UEP). After the disturbance ends, the system state then moves along either a stable or unstable manifold, leading to different... Changes.

[0040] Specifically, when When the system is monotonically decreasing, it is either subjected to a small disturbance (denoted as a-type trajectory) or has become unstable (denoted as a-type trajectory), such as... Figure 4 , Figure 5 As shown; when When the system first decreases and then increases, it experiences a large disturbance (denoted as a type b trajectory and a type c trajectory), such as... Figure 6 As shown; when When the disturbance first increases and then decreases, the system is either subjected to an intermediate level of disturbance (denoted as an e-type trajectory) or has become unstable (denoted as an f-type trajectory), such as... Figure 7 As shown.

[0041] It can be seen that, according to Based on the changes, the system trajectory can be divided into three categories. Figure 4 , 5 As can be seen from points 6 and 7, the difference between the two types of trajectories in this class lies in whether or not they cross the stable manifold of the system. In other words, whether the system is within the stable region. According to the principles of the Potential Energy Boundary Surface Method (PEBS), the system's potential energy reaches a local maximum on the stable boundary. Therefore, if the system's potential energy does not have a local maximum before the current state, the system state is within the stable boundary, corresponding to trajectories of type a, b, and e; if the system's potential energy has a local maximum before the current state, the system state is outside the stable boundary, corresponding to trajectories of type c, d, and f. Thus, we can combine the changes in the potential energy function before the current state with the changes after the current state... By observing changes in the data, we can quickly and accurately determine the type of system trajectory and the magnitude of disturbances.

[0042] Preferably, calculating the first unstable equilibrium point of the contraction model includes: S321. When the norm of the state variable shows a trend of first decreasing and then increasing, and the potential energy function value does not have a local maximum, identify the minimum value in the norm of the state variable. S322. Based on the operating parameters, construct a power balance equation, and use the minimum value as the initial value. Use the Newton-Raphson algorithm to solve the power balance equation and determine the first unstable equilibrium point.

[0043] In a preferred embodiment of the present invention, if the norm of the state variable The existence of a local minimum indicates that the system trajectory is of type b or c, and the system is subjected to a large disturbance. Let θ be the state variable at the local minimum. * . with θ * Using the initial values, solve the following power balance equations using the Newton-Raphson method: ; in, Let be the mechanical power of the i-th generator in the power system after the fault is cleared. Let be the electromagnetic power of the i-th generator in the power system after the fault is cleared. This is the cumulative difference between the mechanical power and electromagnetic power of all generators in the power system after the fault is cleared.

[0044] The unstable equilibrium point is obtained by solving the problem, denoted as θ. u The specific steps are as follows: 1. Construct intermediate functions: ; in, , , , It is a vector. Initial value θ (0) =θ * .

[0045] 2. In the k-th iteration, calculate the Jacobian matrix J: ; 3. The correction amount can be calculated as follows: ; 4. If If ε is the stopping threshold, then update the variable vector: ; Proceed to step 2. If The loop ends, and the unstable equilibrium point θ is obtained. u The unstable equilibrium point of the original system is (θ). u, 0).

[0046] Preferably, calculating the first unstable equilibrium point of the contraction model further includes: S323. When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum and the absolute value of the second difference is greater than the absolute value of the first difference, the state equation of the generator is constructed based on the operating parameters. S324. Based on the state equation, determine the fault trajectory under the continuous fault of the power system, and project the fault trajectory according to the contraction model to determine the phase trajectory of the fault trajectory in the contraction model. S325. Based on the phase trajectory, calculate the power deviation equation of the power system after the fault is cleared; S326. Determine the exit point based on the state equation and the power deviation equation; integrate the contraction model with the exit point as the initial value to generate an integral trajectory, and take the state variable corresponding to the minimum value in the integral trajectory as the target point; wherein, the exit point is the intersection of the phase trajectory and the stability boundary of the contraction model; S327. Based on the operating parameters, construct a power balance equation, take the minimum value as the initial value, and use the Newton-Raphson algorithm to solve the power balance equation to determine the first unstable equilibrium point.

[0047] In a preferred embodiment of the present invention, if the norm of the state variable shows a trend of first increasing and then decreasing, and the potential energy function value does not have a local maximum, the dominant unstable equilibrium point x of the system is calculated using the BCU method. CUEP The steps of the BCU method are as follows: 1. The state equation for a synchronous generator under fault conditions: ; in, Let be the mechanical power of the i-th generator in the power system at the time of the fault. Let be the electromagnetic power of the i-th generator in the power system at the time of the fault. It is the sum of the differences between the mechanical power and electromagnetic power of all generators in the power system at the time of the fault.

[0048] Integrate the faulty system to obtain the system's persistent fault trajectory. Draw the trajectory in the subspace. The projection onto the plane yields the phase trajectory corresponding to the persistent fault trajectory in the contraction system. .

[0049] 2. Based on the phase trajectory in the contraction system Calculate the power deviation equation of the system after fault clearance using the value of . ; in, Let be the mechanical power of the i-th generator in the power system after the fault is cleared. Let be the electromagnetic power of the i-th generator in the power system after the fault is cleared. This is the cumulative difference between the mechanical power and electromagnetic power of all generators in the power system after the fault is cleared. This represents the power deviation value.

[0050] When the power deviation value is multiplied by the speed of the faulty system At that time, that is At that time, the corresponding state variable θ is denoted as the exit point of the reduced-order system. In numerical calculations, it is impossible to find precisely... For a given point, a small positive number ε can be set, which will satisfy... The first point as .

[0051] 3. With As initial conditions, the integral shrinking system is: ; Searching along the integral trajectory The first minimum value corresponds to the state variable. Let the minimum gradient point be denoted as . , that is, the target point.

[0052] 4. Using the initial values, the equation is solved iteratively using the Newton-Raphson method: The dominant unstable equilibrium point of the contraction system is obtained. Then the dominant unstable equilibrium point x of the original system CUEP for .

[0053] S4. Based on the second unstable equilibrium point, the original system model is converted into a linear state equation, and based on the linear state equation, the characteristic vector of each generator is determined; wherein, each characteristic vector is used to characterize the dynamic correlation between the corresponding generator and the disturbance state of the power system. S5. Using a clustering algorithm, the generators are clustered according to the feature vectors to generate several first cluster sets, and the generators in the same first cluster set are divided into the same co-tuning generator group.

[0054] In a preferred embodiment of the present invention, a clustering algorithm under large perturbations is used to obtain the clustering result, and the steps are as follows: 1. Linearize the original model at the second unstable equilibrium point to obtain the linearized state equation: ; 2. Perform eigenvalue analysis on matrix A and calculate the eigenvalues ​​λ in matrix A. i (i = 1,2,…,n), normalized right eigenvector u i (i = 1,2,…,n). Let λ be the value of λ. i The positive eigenvalue is λ1, and its corresponding eigenvector is u1.

[0055] 3. In u1, the i-th element represents the i-th generator in the system. Using clustering algorithms such as k-means, u1 is clustered into two classes, and the generators corresponding to each class are the homology clustering results.

[0056] Preferably, after determining that the power system has been subjected to a small disturbance, the method further includes: S6. Using the slow co-homology algorithm, based on the original system model, the generator is subjected to co-homology grouping processing, and the generator is divided into several co-homology groups.

[0057] In a preferred embodiment of the present invention, the slow cohomology algorithm steps are as follows: 1. At the system's stable equilibrium point x SEP To linearize the system, we obtain the linearized state equations: ; 2. Perform eigenvalue analysis on matrix A and calculate the eigenvalues ​​λ of matrix A. i (i = 1,2,…,n), normalized right eigenvector u i (i = 1,2,…,n).

[0058] 3. Rearrange the eigenvalues ​​in ascending order of their absolute values, denoted as λ1, λ2, ..., λ nThe slowest r patterns are selected as dominant patterns, i.e., the patterns corresponding to the first r smallest eigenvalues ​​are chosen. r is the number of dominant patterns, which can be obtained using the maximum difference method for systems with distinct dual time-scale characteristics. ; Therefore, the dominant mode group can be obtained. The mode matrix composed of its corresponding right eigenvectors .

[0059] 4. For the mode matrix U r By performing Gaussian column pivoting, we can obtain r sets of linearly independent row vectors, and the corresponding generator is the reference generator.

[0060] 5. Rearrange the mode matrix U r Arrange the r row vectors corresponding to the reference generator in the first r rows of the mode matrix, and arrange the remaining row vectors in the last nr rows. Let the rearranged... The first r rows of the submatrix are U1, and the last nr rows of the submatrix are U2, that is: ; Then the grouping matrix L is: ; 6. For the calculated grouping matrix L, if the element in the j-th column of the i-th row of L has the largest absolute value in the row, then generator i is assigned to the coherent machine group with generator j as the reference machine.

[0061] Preferably, the operating parameters include: rotor angle data; S7. After determining that the power system is in an unstable state, the method further includes: A clustering algorithm is used to cluster the generators based on the rotor angle data, generating several second cluster sets, and the generators in the same second cluster set are divided into the same co-tuning generator group.

[0062] In a preferred embodiment of the present invention, if a local maximum exists in the potential energy function value, it indicates that the system trajectory type is f or d, and the system has become unstable. At this time, the power angle differences between co-homogeneous generator groups are large, and a clustering algorithm can be used to directly cluster generators with similar power angles into co-homogeneous generator groups. The k-means algorithm is used to cluster the rotor angle data of each generator obtained in S1 into two classes. The generators corresponding to each class are the co-homogeneous grouping results, and the algorithm ends.

[0063] See Figure 2 This invention provides a power system synchronized generator group identification device according to an embodiment of the present invention, comprising: The parameter acquisition module is used to acquire the operating parameters of each generator in the power system at the sampling points; wherein, the sampling points are the pre-set monitoring nodes in the power system for real-time acquisition of operating parameters. The model integration module is used to integrate the contraction model of the power system with the operating parameters as initial values ​​to generate the time series of state variables of the contraction system after the fault; wherein, the contraction model is a reduced-order model with similar properties to the original system model; the original system model is a mathematical model used to characterize the steady-state and dynamic behavior of the power system. The disturbance assessment module is used to calculate the first unstable equilibrium point of the contraction model when the power system is subjected to a large disturbance based on the time series of the state variables, and to determine the second unstable equilibrium point of the original system model based on the first unstable equilibrium point. The linear transformation module is used to convert the original system model into a linear state equation based on the second unstable equilibrium point, and to determine the feature vector of each generator based on the linear state equation; wherein each feature vector is used to characterize the dynamic correlation between the corresponding generator and the disturbance state of the power system. The generator group partitioning module is used to use a clustering algorithm to cluster the generators according to the feature vectors, generate several first cluster sets, and divide the generators under the same first cluster set into the same co-tuning generator group.

[0064] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the method for identifying coordinating generator groups in a power system provided by any of the above-described method embodiments of the present invention.

[0065] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0066] See Figure 3 One embodiment of this application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a method for identifying coordinating generator groups in a power system as described above.

[0067] The processor controls the overall operation of the terminal device to complete all or part of the steps of the aforementioned method for identifying synchronized generator groups in a power system. The memory stores various types of data to support the operation of the terminal device. This data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0068] In an exemplary embodiment, the terminal device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform a method for identifying coherent generator groups in a power system as described in any of the foregoing embodiments, and to achieve the same technical effects as the methods described above.

[0069] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided. When executed by a processor, the computer program implements the steps of a method for identifying coherent generator groups in a power system as described in any of the foregoing embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program, which may be executed by a processor of a terminal device to complete the method for identifying coherent generator groups in a power system as described in any of the foregoing embodiments, and achieve the same technical effects as the aforementioned method.

[0070] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying synchronized generator groups in a power system, characterized in that, include: The operating parameters of each generator in the power system are obtained at sampling points; wherein, the sampling points are pre-set monitoring nodes in the power system for real-time acquisition of operating parameters. Using the operating parameters as initial values, the contraction model of the power system is integrated to generate the time series of the state variables of the contraction model after the fault; wherein, the contraction model is a reduced-order model with similar properties to the original system model; the original system model is a mathematical model used to characterize the steady-state and dynamic behavior of the power system; Based on the time series of the state variables, when the power system is subjected to a large disturbance, the first unstable equilibrium point of the contraction model is calculated, and based on the first unstable equilibrium point, the second unstable equilibrium point of the original system model is determined. Based on the second unstable equilibrium point, the original system model is converted into a linear state equation, and based on the linear state equation, the characteristic vector of each generator is determined; wherein, each characteristic vector is used to characterize the dynamic correlation between the corresponding generator and the disturbance state of the power system. A clustering algorithm is used to cluster the generators according to the feature vectors, generating several first cluster sets, and the generators in the same first cluster set are divided into the same co-tuning generator group.

2. The method for identifying synchronized generator groups in a power system as described in claim 1, characterized in that, The step of determining that the power system is subjected to a large disturbance based on the time series of the state variables includes: Based on the operating parameters, calculate the system energy function value, potential energy function value, and state variable norm of the time series of state variables of the power system; Based on the original system model, determine the stable equilibrium point of the original system model, and determine the first energy function value of the second unstable equilibrium point and the second energy function value of the stable equilibrium point; Calculate the first difference between the system energy function value and the first energy function value, and the second difference between the system energy function value and the second energy function value; When the norm of the state variable shows a trend of first increasing and then decreasing, the value of the potential energy function does not have a local maximum, and the absolute value of the second difference is greater than the absolute value of the first difference, it is determined that the power system is subjected to a large disturbance. When the norm of the state variable shows a trend of first decreasing and then increasing, and the potential energy function value does not have a local maximum, it is determined that the power system is subjected to a large disturbance.

3. The method for identifying synchronized generator groups in a power system as described in claim 2, characterized in that, Also includes: When the norm of the state variable exhibits a monotonically decreasing trend, it is determined that the power system is subjected to a small disturbance. When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum, and the absolute value of the second difference is not greater than the absolute value of the first difference, it is determined that the power system is subjected to a small disturbance. When the norm of the state variable shows a trend of first increasing and then decreasing, and the potential energy function value has a local maximum, the power system is determined to be in an unstable state. When the norm of the state variable exhibits a monotonically decreasing trend and the potential energy function value has a local maximum, the power system is determined to be in an unstable state.

4. The method for identifying synchronized generator groups in a power system as described in claim 3, characterized in that, After determining that the power system is subjected to a small disturbance, the process also includes: Using the slow coherence algorithm, based on the original system model, the generators are subjected to coherence grouping processing, dividing the generators into several coherent generator groups.

5. The method for identifying synchronized generator groups in a power system as described in claim 4, characterized in that, The operating parameters include: rotor angle data; After determining that the power system is in an unstable state, the process also includes: A clustering algorithm is used to cluster the generators based on the rotor angle data, generating several second cluster sets, and the generators in the same second cluster set are divided into the same co-tuning generator group.

6. The method for identifying synchronized generator groups in a power system as described in claim 5, characterized in that, The calculation of the first unstable equilibrium point of the contraction model includes: When the norm of the state variable shows a trend of first decreasing and then increasing, and the potential energy function value does not have a local maximum, the minimum value in the norm of the state variable is identified. Based on the operating parameters, a power balance equation is constructed, and the minimum value is used as the initial value. The Newton-Raphson algorithm is used to solve the power balance equation to determine the first unstable equilibrium point.

7. The method for identifying synchronized generator groups in a power system as described in claim 6, characterized in that, The calculation of the first unstable equilibrium point of the contraction model further includes: When the norm of the state variable shows a trend of first increasing and then decreasing, the potential energy function value does not have a local maximum, and the absolute value of the second difference is greater than the absolute value of the first difference, the state equation of the generator is constructed based on the operating parameters. Based on the state equation, the fault trajectory under the continuous fault of the power system is determined, and based on the contraction model, the fault trajectory is projected to determine the phase trajectory of the fault trajectory in the contraction model. Based on the phase trajectory, calculate the power deviation equation of the power system after the fault is cleared; Based on the state equation and the power deviation equation, the exit point is determined; the contraction model is integrated with the exit point as the initial value to generate an integral trajectory, and the state variable corresponding to the minimum value in the integral trajectory is taken as the target point; wherein, the exit point is the intersection of the phase trajectory and the stability boundary of the contraction model; Based on the operating parameters, a power balance equation is constructed, and the minimum value is used as the initial value. The Newton-Raphson algorithm is used to solve the power balance equation to determine the first unstable equilibrium point.

8. A device for identifying synchronized generator groups in a power system, characterized in that, include: The parameter acquisition module is used to acquire the operating parameters of each generator in the power system at the sampling points; wherein, the sampling points are the pre-set monitoring nodes in the power system for real-time acquisition of operating parameters. The model integration module is used to integrate the shrinkage model of the power system with the operating parameters as initial values ​​to generate the state variable time series of the shrinkage model after the fault; wherein, the shrinkage model is a reduced-order model with similar properties to the original system model; the original system model is a mathematical model used to characterize the steady-state and dynamic behavior of the power system. The disturbance assessment module is used to calculate the first unstable equilibrium point of the contraction model when the power system is subjected to a large disturbance based on the time series of the state variables, and to determine the second unstable equilibrium point of the original system model based on the first unstable equilibrium point. The linear transformation module is used to convert the original system model into a linear state equation based on the second unstable equilibrium point, and to determine the feature vector of each generator based on the linear state equation; wherein each feature vector is used to characterize the dynamic correlation between the corresponding generator and the disturbance state of the power system. The generator group partitioning module is used to use a clustering algorithm to cluster the generators according to the feature vectors, generate several first cluster sets, and divide the generators under the same first cluster set into the same co-tuning generator group.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a method for identifying coordinating generator groups in a power system as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for identifying synchronized generator groups in a power system as described in any one of claims 1-7.

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