State determination method and device of power system, storage medium and electronic equipment

Abstract

The invention discloses a state determination method and device of a power system, a storage medium and electronic equipment. The method comprises the steps that target change characteristics of target operation parameters of a target power system are determined, and the target change characteristics are used for describing the change track of the target operation parameters along with time; based on the target change characteristics, phase space characteristics of the target power system under the target load power are determined, and the phase space characteristics represent the number and the distribution state of intersection points of the change track and a predetermined phase space cross section; and based on the phase space characteristics, determining a target state of the target power system, the target state being used for indicating whether the target power system is in a chaotic state under the target load power, and the chaotic state indicating that the target operation parameter presents an irregular oscillation change. According to the invention, the technical problem of low accuracy of the state determination result of the power system under the specific load power in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular to a method, device, storage medium, and electronic device for determining the state of a power system. Background Art

[0002] As a typical non-autonomous power system with nonlinear characteristics, the power system will experience chaotic oscillations under certain parameter conditions (such as load power). This causes the system's voltage, current, and the rotor speed of the connected generators, which are related to system stability, to oscillate continuously and irregularly, seriously affecting the stable operation of the power grid. Therefore, if the power system's state under specific parameters cannot be identified, it is impossible to accurately determine whether the system is in a chaotic state under these parameters and issue an early warning. This will threaten the safe and stable operation of the system. In severe cases, it may even cause the interconnected system to disconnect, resulting in large-scale power outages and major safety accidents.

[0003] In related technologies, nonlinear dynamic analysis methods such as bifurcation theory are mainly used to study the state of power systems. Bifurcation theory determines the state of the power system under specific parameter conditions by establishing a nonlinear dynamic model of the power system. However, bifurcation theory is computationally intensive and requires high model accuracy. For complex power systems with high-dimensional characteristics, the mutual coupling between multiple operating parameters makes model establishment more difficult, resulting in large errors when using bifurcation theory to determine the state of the power system. Therefore, related technologies have the technical problem of low accuracy in determining the state of the power system under specific load power.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present application provide a method, device, storage medium and electronic device for determining the state of an electric power system, so as to at least solve the technical problem in the related art of low accuracy of the state determination result of the electric power system under a specific load power.

[0006] According to one aspect of an embodiment of the present application, a method for determining the state of an electric power system is provided, including: determining a target change characteristic of a target operating parameter of a target electric power system, wherein the target change characteristic is used to describe the change trajectory of the target operating parameter over time; based on the target change characteristic, determining a phase space characteristic of the target electric power system under a target load power, wherein the phase space characteristic represents the number and distribution state of intersections of the change trajectory and a predetermined phase space section, and the phase space section is a two-dimensional plane in the phase space; based on the phase space characteristic, determining a target state of the target electric power system, wherein the target state is used to indicate whether the target electric power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameter presenting irregular oscillating changes.

[0007] According to another aspect of an embodiment of the present application, a state determination device for an electric power system is provided, including: a first determination module for determining a target change characteristic of a target operating parameter of a target electric power system, wherein the target change characteristic is used to describe the change trajectory of the target operating parameter over time; a second determination module for determining a phase space characteristic of the target electric power system under a target load power based on the target change characteristic, wherein the phase space characteristic represents the number and distribution state of intersections of the change trajectory and a predetermined phase space section, and the phase space section is a two-dimensional plane in the phase space; a third determination module for determining a target state of the target electric power system based on the phase space characteristic, wherein the target state is used to indicate whether the target electric power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameter presenting irregular oscillating changes.

[0008] According to another aspect of an embodiment of the present application, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executed by any one of the methods for determining the state of a power system.

[0009] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the methods for determining the state of an electric power system.

[0010] In an embodiment of the present application, by determining the target change characteristics of the target operating parameters of the target power system, wherein the target change characteristics are used to describe the change trajectory of the target operating parameters over time; based on the target change characteristics, the phase space characteristics of the target power system at the target load power are determined, wherein the phase space characteristics represent the number and distribution state of the intersections of the change trajectory and the predetermined phase space section, and the phase space section is a two-dimensional plane in the phase space; based on the phase space characteristics, the target state of the target power system is determined, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameters showing irregular oscillation changes. The purpose of determining the phase space characteristics of the target power system under the target load power by analyzing the change characteristics of the target operating parameters of the power system over time and then determining the state of the power system is achieved, thereby achieving the technical effect of improving the accuracy of the state determination result of the power system under a specific load power, thereby solving the technical problem of low accuracy of the state determination result of the power system under a specific load power existing in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0012] Figure 1 is a flow chart of an optional method for determining the state of a power system provided according to an embodiment of the present application;

[0013] Figure 2 This is an optional dual-machine interconnected power system topology diagram provided according to an embodiment of the present application;

[0014] Figure 3 is a block diagram of an optional method for determining the state of a power system provided according to an embodiment of the present application;

[0015] Figure 4 is an optional three-dimensional phase diagram of a power system provided according to an embodiment of the present application;

[0016] Figure 5 is a Poincaré cross-sectional diagram of an optional power system provided according to an embodiment of the present application;

[0017] Figure 6 This is a schematic diagram of an optional power system state determination device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] According to an embodiment of the present application, a method embodiment of a method for determining the state of an electric power system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0021] Figure 1 is a flow chart of an optional method for determining the state of a power system provided according to an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0022] Step S102, determining a target variation characteristic of a target operating parameter of a target power system, wherein the target variation characteristic is used to describe a variation trajectory of the target operating parameter over time;

[0023] It can be understood that the target power system's operating data is collected and, based on this operating data, the power system's topology and operating principles, the target variation characteristics of the target power system's target operating parameters are determined. These variation characteristics are used to describe the trends and patterns of change in the target operating parameters over time. By determining the target variation characteristics of the target power system's target operating parameters, we can gain a deeper understanding of the dynamic variation characteristics of the power system, providing data support and model basis for determining the power system's status.

[0024] In an optional embodiment, determining target change characteristics of target operating parameters of a target power system includes: obtaining operating data of the target power system, wherein the operating data is collected within a predetermined historical time period; based on the operating data, determining initial change characteristics corresponding to multiple initial operating parameters of the target power system; and performing order reduction processing on the initial change characteristics corresponding to the multiple initial operating parameters to obtain target change characteristics.

[0025] It can be understood that the operating data of the target power system within a predetermined historical time period is collected. Based on the collected operating data, data processing and analysis methods are used to determine the initial change characteristics corresponding to the multiple initial operating parameters of the target power system. In order to simplify the analysis, the initial change characteristics corresponding to the multiple initial operating parameters determined above are reduced in order. After the reduction in order, the target change characteristics are obtained from the multiple initial change characteristics. By reducing the order of multiple initial change characteristics to obtain the target change characteristics, the power system model can be simplified, the amount of calculation can be reduced, and the state identification and early warning of the target power system can be made more efficient. At the same time, the target change characteristics obtained by reducing the order only retain the key parameters that have the greatest impact on the stability of the target power system, thereby improving the reliability and accuracy of the analysis results.

[0026] Optionally, the initial change characteristics corresponding to the above-mentioned multiple initial operating parameters can be expressed in the form of differential equations. Figure 2 This is an optional dual-machine interconnected power system topology diagram provided according to an embodiment of the present application, such as Figure 2 G1 and G2 are shown as generators. Figure 2 For the power system shown, the generator rotor phase angle, the generator rotor angular velocity, the generator rotor q-axis potential, and the generator excitation electromotive force can be used as the above-mentioned initial operating parameters. The initial change characteristics of the above-mentioned initial operating parameters expressed in the form of differential equations can be established in the following manner. The initial change characteristics in the form of a group of differential equations can be expressed as follows:

[0027]

[0028] Where, the subscript i represents the i-th generator, i = 1 or 2; δ i is the rotor phase angle of the i-th generator, is δ i The derivative of ω i is the rotor angular velocity of the i-th generator, ω i The derivative of P mi is the input mechanical power of the i-th generator; P Gi is the output electromagnetic power of the i-th generator; D biis the damping coefficient of the i-th generator; H i is the moment of inertia of the i-th generator; E' qi is the q-axis potential of the rotor of the i-th generator, For E' qi The derivative of X di is the d-axis synchronous reactance of the i-th generator; X' di is the d-axis transient reactance of the i-th generator; i di is the d-axis stator current of the i-th generator; i qi is the q-axis stator current of the i-th generator; E fdi is the excitation electromotive force of the i-th generator, For E fdi The derivative of T′ d0i is the d-axis open-circuit transient time constant of the i-th generator; K Ai is the excitation controller gain of the i-th generator; T Ai is the excitation control time constant of the i-th generator; U refi is the reference voltage of the i-th generator; U Gi is the terminal voltage of the i-th generator.

[0029] Optionally, when determining the state of the power system under specific conditions, it is necessary to consider not only the changing characteristics of the operating parameters but also the constraints of the power system. The above constraints can be expressed in the form of algebraic equations. Figure 2 For the power system shown in Figure 1, the constraints of the power system expressed in the form of algebraic equations can be established as follows:

[0030]

[0031] Among them, X qi is the q-axis synchronous reactance of the i-th generator; P L is the load power; P G1 is the output power of the first generator; P G2 is the output power of the second generator; Y l ∠θ l is the transmission line admittance, Y l is the admittance size, θ l is the admittance angle. To simplify the analysis process, let Y l ∠θ l =1∠0°.

[0032] In summary, based on the initial variation characteristic differential equations expressed in the form of differential equations and the constraints expressed in the form of algebraic equations, we can establish the following Figure 2 The nonlinear differential-algebraic equations model of the power system is shown:

[0033]

[0034] The above-mentioned nonlinear differential-algebraic equation model not only includes the initial change characteristics of multiple initial operating parameters of the power system, but also includes the constraints of the power system. By establishing the above-mentioned nonlinear differential-algebraic equation model, we can deeply analyze and understand the operating laws and operating status of the power system.

[0035] In an optional embodiment, when the initial change characteristics corresponding to the multiple initial operating parameters are expressed in the form of a matrix, the initial change characteristics corresponding to the multiple initial operating parameters are reduced in order to obtain target change characteristics, including: based on the initial change characteristics corresponding to the multiple initial operating parameters, using a balance conversion matrix to obtain balance change characteristics corresponding to the multiple initial operating parameters, wherein the balance conversion matrix is ​​used to simplify and standardize the initial change characteristics corresponding to the multiple initial operating parameters; and reducing the balance change characteristics corresponding to the multiple initial operating parameters to obtain target change characteristics.

[0036] It can be understood that in order to improve the efficiency of determining the target state of the target power system, multiple initial change characteristics are reduced in order to obtain simpler and easier-to-analyze target change characteristics. If the initial change characteristics corresponding to multiple initial operating parameters are expressed in matrix form, based on the balance realization theory, the balance change characteristics corresponding to the multiple initial operating parameters are obtained using the balance transformation matrix. The above-mentioned balance transformation matrix is ​​used to simplify and standardize the initial change characteristics corresponding to the multiple initial operating parameters. The balance change characteristics are the balanced form of the initial change characteristics obtained by transforming the initial change characteristics using the balance transformation matrix. The balance change characteristics corresponding to the multiple initial operating parameters are reduced in order to obtain the target change characteristics of the target power system. The order reduction process can reduce the complexity of the power system model, transforming the originally difficult high-dimensional system problem into a relatively simple low-dimensional problem, which is conducive to the rapid identification and analysis of the system state. At the same time, the reduced-order system model focuses more on the dynamic changes of key operating parameters, eliminates the interference of some minor operating parameters, and helps to improve the accuracy of the system state determination results.

[0037] Alternatively, equilibrium realization theory can be used to derive the aforementioned equilibrium variation characteristics. This theory is primarily used to simplify and reduce the order of system models. Its core objective is to find a state-space representation that preserves the system's dynamic behavior, making the model more concise and easier to analyze. In power system stability analysis, equilibrium realization theory can simplify the model and reduce computational costs while maintaining an accurate description of the power system.

[0038] Optionally, in the above Figure 2 Before reducing the order of the multiple initial variation characteristics of the power system shown, the initial variation characteristic equations must be transformed into a balanced form. Using balanced realization theory, the initial variation characteristic equations and the power system constraints can be adaptively modified to obtain balanced variation characteristics and constraints for multiple initial operating parameters.

[0039] First, the initial change characteristics and constraints corresponding to multiple initial operating parameters are converted into the following matrix form:

[0040]

[0041] Where X is the state variable (i.e. the matrix form of the initial operating parameters), and X∈R n , n is the dimension of the state variable; is the derivative of X; Y is the system algebraic variable (the matrix form of the constraint condition), and Y∈R m , m is the dimension of algebraic variables; K is the system control parameter, and K∈R l , l is the dimension of the control variable; A is the system state matrix, and A∈R n×n ; B is the input matrix, and B∈R n×l ; C is the output matrix, and C∈R m×n .

[0042] Secondly, the initial change characteristics in the matrix form are transformed using the balance realization theory and the balance conversion matrix T into a balanced form to obtain the balanced change characteristics. The matrix form of the constraint conditions is then adaptively changed. The balanced change characteristics and the constraint conditions can be expressed as follows:

[0043]

[0044] Among them, T -1 is the inverse matrix of T, For the balance change characteristics, The derivative of is, are the matrices of A, B, and C after being transformed by the balanced transformation matrix T.

[0045] Optionally, after obtaining the balanced change characteristics of multiple initial operating parameters, the balanced residual model reduction method can be used to reduce the balanced change characteristics of multiple initial operating parameters. The balanced residual model reduction method can not only reduce the dimension of the change characteristics while ensuring that the dynamic change characteristics of the power system remain unchanged, thereby reducing the computational cost and complexity, and facilitating the subsequent analysis and determination of the state of the power system. First, the important state variables (i.e., target operating parameters) and secondary state variables in the multiple initial operating parameters are determined, and by ignoring the influence of the secondary parameters, the balanced change characteristics are reduced to obtain the target change characteristics of the target operating parameters. The balanced change characteristics and constraints that distinguish the important state variables (i.e., target operating parameters) and secondary state variables can be expressed as follows:

[0046]

[0047] in, Indicates the target operating parameters, Represents a secondary parameter, is a matrix The block state matrix of ; for The block state matrix of ; for The block state matrix of .

[0048] Optionally, If the value of the balance change characteristic that distinguishes the important state variables (i.e., target operating parameters) from the secondary state variables is approximately 0, the balance change characteristic that distinguishes the important state variables (i.e., target operating parameters) from the secondary state variables can be converted into a target change characteristic, and the constraint conditions that distinguish the important state variables (i.e., target operating parameters) from the secondary state variables can be adaptively changed. The target change characteristic and the constraint conditions can be expressed as follows:

[0049]

[0050] in, for The derivative of for The inverse matrix of .

[0051] Step S104: determining a phase space characteristic of the target power system at the target load power based on the target change characteristic, wherein the phase space characteristic represents the number and distribution of intersections between the change trajectory and a predetermined phase space cross section, and the phase space cross section is a two-dimensional plane in the phase space;

[0052] It can be understood that a two-dimensional plane is predetermined in the phase space as a phase space section. According to the target change characteristics of the target power system, the phase space characteristics of the target power system are determined under the target load power. The above-mentioned phase space characteristics represent the number and distribution of intersections between the change trajectory of the target power system in the phase space and the above-mentioned phase space section. By analyzing the number and distribution of intersections on the phase space section, the analysis of the stability of the power system can be enhanced, especially under the condition of load power changes, which is crucial for preventing large-scale instability of the power system and can significantly improve the safety and reliability of the power grid.

[0053] Optionally, the phase space characteristics of the target power system can be analyzed using a Poincaré cross-section. This method is a dynamical system analysis method primarily used to map the manifold of a high-dimensional dynamical system onto a lower-dimensional manifold. This allows for a more intuitive analysis of the system's long-term behavior, particularly its stability, periodicity, and the presence of chaos. This provides a solid foundation for stable control and fault warning of the power grid.

[0054] In an optional embodiment, before determining the phase space characteristics of the target power system at the target load power based on the target change characteristics, the method also includes: determining a state transition index of the target power system, wherein the state transition index is used to indicate the possibility of the target power system transitioning from a stable state to a chaotic state; and determining the target load power based on the state transition index and a predetermined distance threshold.

[0055] It can be understood that the operating parameters of the target power system are collected and, based on these operating parameters, a state transition indicator is determined, indicating the likelihood that the target power system will transition from a stable state to a chaotic state. Based on this state transition indicator and a predetermined distance threshold, a target load power is determined, facilitating subsequent analysis of the target power system's state under this target load power. Analyzing the power system's state based on this state transition indicator can enhance predictability and control over chaotic states, reduce the likelihood of system instability due to chaotic vibrations, and ensure the continuity and reliability of power supply.

[0056] Alternatively, the target load power can be determined using Melnikov stability theory. Melnikov stability theory can be used to analyze the chaotic behavior of forced nonlinear systems subjected to small-amplitude periodic perturbations. Melnikov stability theory can predict whether a power system exhibits chaotic oscillations by determining the intersection of its stable and unstable manifolds.

[0057] In an optional embodiment, when the target power system includes two generators, determining the state conversion index of the target power system includes: determining the damping coefficient difference between the two generators, the output power difference between the two generators, the moment of inertia difference between the two generators, and the input mechanical power difference between the two generators; and determining the state conversion index based on the damping coefficient difference, the output power difference, the moment of inertia difference, and the input mechanical power difference.

[0058] It can be understood that if the target power system includes two generators, in order to determine the target power system's state transition index, it is first necessary to determine the damping coefficient difference between the two generators, the output power difference between the two generators, the moment of inertia difference between the two generators, and the input mechanical power difference between the two generators. The state transition index is then calculated based on these damping coefficient differences, output power differences, moment of inertia differences, and input mechanical power differences. Determining the state transition index not only effectively assesses the likelihood of the power system transitioning from a stable to chaotic state but also improves the operational safety and efficiency of the power system through early warning mechanisms and resource optimization.

[0059] Alternatively, the distance between the stable manifold and the unstable manifold of the power system (i.e., the state transition index) is first calculated based on the Melnikov stability theory, and the target load power is determined based on the state transition index. The state transition index can be expressed as follows:

[0060]

[0061] in, b=P m / P G , c=P L / P G , P m =P m1 -P m2 (i.e. input mechanical power difference), P G =P G1 -P G2 (i.e. output power difference), D b =D b1 -D b2 (i.e., the difference in damping coefficient), H = H1-H2 (i.e., the difference in moment of inertia), P L cos(αt) represents the load disturbance of the system, α is the frequency of the disturbance, and t represents time.

[0062] Optionally, the target load power is the value solved when the inequality of the state transition index is 1, indicating the critical condition for the power system to transition from an ordered behavior to a chaotic state. The inequality of the state transition index can not only be used to determine the critical conditions for the power system to enter a chaotic state, but also to determine the state of the power system under a specific load power. Substituting the specific load power into the above inequality, when the inequality of the state transition index holds true, it means that the power system meets the conditions for entering a chaotic state. At this time, the power system may enter a chaotic state, that is, the motion state of the power system is no longer limited to a simple periodic orbit, but exhibits complex and unpredictable behavior; when the inequality of the state transition index does not hold true, it means that the power system does not meet the conditions for entering a chaotic state. At this time, the power system may be in a periodic motion state.

[0063] Step S106, determining the target state of the target power system based on the phase space characteristics, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state means that the target operating parameters show irregular oscillation changes.

[0064] It can be understood that the target state of the target power system is determined based on its phase space characteristics, and based on the target state, it is determined whether the target power system is in a chaotic state at the target load power, that is, whether the target operating parameters of the target power system exhibit irregular oscillations. By determining the phase space characteristics of the target power system, the changing patterns of the target operating parameters can be accurately identified, and the target state of the target power system can be determined. This ensures accurate identification of the power system state at a specific load power, providing a scientific basis for establishing an effective early warning mechanism.

[0065] In an optional embodiment, based on the phase space characteristics, the target state of the target power system is determined, including: when the number is less than a preset number threshold, determining that the target state is that the target power system is not in a chaotic state; or when the number is greater than or equal to the number threshold, determining that the target state is that the target power system is in a chaotic state.

[0066] It can be understood that if the phase space characteristics indicate that the number of intersection points in the phase space is less than a preset threshold, it indicates that the target power system is in periodic motion, that is, the target power system is not in a chaotic state; if the phase space characteristics indicate that the number of intersection points in the phase space is greater than or equal to the threshold, it indicates that the target power system is in irregular motion, that is, the target power system is in a chaotic state. By comparing the number of intersection points with the threshold, it is possible to accurately identify whether the power system is in a chaotic state, providing strong support for real-time detection and stability management of the power system.

[0067] In an optional embodiment, the method further includes: when the number is one, the target power system is in a single-cycle motion state; or when the number is not one and is less than a preset number threshold, the target power system is in a multi-cycle motion state.

[0068] It can be understood that if the phase space characteristics indicate that the number of intersection points in the phase space is less than a preset threshold and is equal to one, the target power system is in a single-cycle motion state; if the phase space characteristics indicate that the number of intersection points in the phase space is less than a preset threshold and is not equal to one, the target power system is in a multi-cycle motion state. By accurately determining the state of the power system, unnecessary system adjustments and resource consumption can be avoided, thereby saving operating costs and maintenance resources.

[0069] Optionally, when using the Poincaré section to analyze the phase space characteristics of the power system, the motion state of the system can be identified based on the distribution of discrete points on the Poincaré section: when there is only one intersection point on the Poincaré section, the power system is in single-cycle motion; when there are a small number of discrete intersection points on the Poincaré section, the power system is in multi-cycle motion; when there are a large number of discrete intersection points on the Poincaré section, the power system is in a chaotic motion state.

[0070] Optionally, based on the state transition index and the number and distribution of discrete intersections on the Poincaré section, it is possible to determine whether the power system is in a chaotic state under target conditions (i.e., target load power), and then provide early warning information under the parameter conditions in a timely manner.

[0071] Through the above steps S102 to S106, the purpose of determining the phase space characteristics of the target power system under the target load power by analyzing the change characteristics of the target operating parameters of the power system over time, and then determining the state of the power system can be achieved, thereby achieving the technical effect of improving the accuracy of the state determination results of the power system under a specific load power, and thus solving the technical problem of low accuracy of the state determination results of the power system under a specific load power existing in the related technology.

[0072] Based on the above embodiments and optional embodiments, the present application proposes an optional implementation method of a method for determining the state of an electric power system. This implementation method proposes a method for identifying and warning of chaotic states of an electric power system based on the Melnikov stability theory and the Poincaré section. In this method, the differential-algebraic equation group model of the electric power system (composed of the initial change characteristics of the initial operating parameters and the constraints of the electric power system, wherein the change characteristics are expressed in the form of differential equations and the constraints are expressed in the form of algebraic equations) is reduced in order through the balance realization theory, and then the target state of the electric power system under a specific parameter (i.e., the target load power) is identified and judged based on the Melnikov stability theory and the Poincaré section, that is, whether the electric power system is in a chaotic state. If it is identified that a chaotic phenomenon is about to occur in the electric power system, an early warning signal is issued in a timely manner.

[0073] Figure 3 is a block diagram of an optional method for determining the state of a power system provided in accordance with an embodiment of the present application, such as Figure 3 As shown, the steps of the power system chaotic state identification and early warning method include:

[0074] Step S1: Establish a nonlinear differential-algebraic equation model of the power system.

[0075] The nonlinear differential-algebraic equations model not only includes the initial change characteristics of multiple initial operating parameters of the power system, but also includes the constraints of the power system. By establishing the above nonlinear differential-algebraic equations model, we can deeply analyze and understand the operating laws and operating status of the power system. Figure 2 The power system shown uses the generator rotor phase angle, generator rotor angular velocity, generator rotor q-axis potential, and generator excitation electromotive force as initial operating parameters to establish a nonlinear differential-algebraic equation model. The nonlinear differential-algebraic equation model is established in the same manner as previously described and will not be further elaborated here.

[0076] Step S2: According to the equilibrium realization theory, the nonlinear differential-algebraic equation model is equivalently transformed into an equilibrium form.

[0077] The equilibrium form of the nonlinear differential-algebraic equation model consists of equilibrium change characteristics and constraints.

[0078] First, the nonlinear differential-algebraic equation model is converted into a matrix form, including the matrix form of the initial change characteristics and the matrix form of the constraints. The determination method of the matrix form of the initial change characteristics and the matrix form of the constraints is the same as above and will not be repeated here. Figure 2 In the power system shown, the dimension of state variables is n=4, the dimension of algebraic variables is m=4, and the dimension of control variables is l=3.

[0079] Next, using equilibrium realization theory, we transform the nonlinear differential-algebraic equation model into an equilibrium form. This allows us to determine the equilibrium variation characteristics of multiple initial operating parameters and adaptively modify the matrix form of the constraints. The determination of the equilibrium variation characteristics and constraints is the same as previously described and will not be repeated here.

[0080] Step S3: simplifying and reducing the equilibrium nonlinear differential-algebraic equation model.

[0081] The nonlinear differential-algebraic equation model after order reduction is composed of target variation characteristics and constraint conditions.

[0082] By extracting the important state variables δ and ω from the state variable X, denoted as X1, and the remaining state variables as secondary state variables, denoted as X2, the equilibrium nonlinear differential-algebraic equation model can be transformed into equilibrium change characteristics and constraints that distinguish between important state variables (i.e., target operating parameters) and secondary state variables. The method for determining the equilibrium change characteristics and constraints that distinguish between important state variables (i.e., target operating parameters) and secondary state variables is the same as described above and will not be repeated here.

[0083] Will If the value of the generator saliency effect and the dynamic influence of excitation are approximately zero, the balanced variation characteristics that distinguish important state variables (i.e., target operating parameters) from secondary state variables can be converted into target variation characteristics, and the constraints that distinguish important state variables (i.e., target operating parameters) from secondary state variables can be adaptively changed. The target variation characteristics and constraints are determined in the same way as above and will not be repeated here.

[0084] For Figure 2 In the power system shown in the figure, the change of load power may affect the chaotic state of the power system. Therefore, after the above-mentioned order reduction process, the target change feature including load power is obtained. The target change feature including load power is expressed as follows:

[0085]

[0086] Among them, δ=δ1-δ2, ω=ω1-ω2, P m =P m1 -P m2 , P G =P G1 -P G2 , D b =D b1 -D b2 , H=H1-H2, P Lcos(αt) represents the load disturbance of the system, and α is the frequency of the disturbance.

[0087] Let x = δ, b=P m / P G , c=P L / P G , Convert the above equation into a disturbed Hamiltonian system (for the dynamic behavior of power systems):

[0088]

[0089] Here, γ is a number much smaller than 1.

[0090] Step S4: draw a three-dimensional phase diagram and Poincaré cross section of the system under a certain specific parameter.

[0091] According to the above target change characteristics including load power, the power in the system and load power P are plotted in the three-dimensional phase space. L =64 three-dimensional phase diagram and Poincaré cross section. Figure 4 is an optional three-dimensional phase diagram of a power system provided according to an embodiment of the present application, Figure 4 The three dimensions of the three-dimensional phase diagram are the rotor phase angle δ, the rotor angular velocity ω and the q-axis potential E q , pu represents the per-unit value, and the two-dimensional plane in space is the Poincaré section. As shown in the figure, at P L =64, the three-dimensional phase diagram of the system shows a complex oscillation phenomenon.

[0092] Figure 5 : is a Poincaré cross-sectional diagram of an optional power system provided according to an embodiment of the present application, wherein the horizontal axis represents the rotor angular velocity ω, and the vertical axis represents the rotor phase angle δ. Figure 5 The Poincaré cross section of the system shown in the figure shows a large number of complex discrete intersections, proving that the system is L =64, chaotic oscillation occurs, that is, the system is in a chaotic state.

[0093] Step S5: Based on the Melnikov stability theory and the number and distribution of discrete intersections on the drawn Poincaré cross section, it is identified and determined whether the system is in a chaotic state under the parameter conditions, and early warning information under the parameter conditions is given in a timely manner.

[0094] Based on the Melnikov stability theory, it is determined that in the above P L= 64. The distance between the stable and unstable manifolds of the power system (i.e., the state transition index) is calculated, thereby obtaining an initial judgment result on whether the power system is in a chaotic state. The method for determining the state transition index is the same as described above and will not be repeated here.

[0095] P L =64 is substituted into the above inequality of state transition index, and the inequality holds, indicating that the system may be in chaos at this time. Figure 4 and Figure 5 Further analysis is conducted. Figure 4 It can be seen that in P L = 64, the three-dimensional phase diagram of the system shows a complex oscillation phenomenon. Figure 5 It can be seen that there are a large number of complex discrete intersections on the Poincaré section of the system, proving that the system has a large number of complex discrete intersections on the Poincaré section of the system. L =64, chaotic oscillation occurs, indicating that the system is in a chaotic state. The system should promptly issue a chaos warning signal to remind operators to adjust the load power to avoid system instability.

[0096] This method for identifying and warning of chaotic states in power systems overcomes the problem that Melnikov stability theory is not suitable for analyzing high-dimensional power systems. It also combines the number and distribution of discrete intersection points on a Poincaré cross section to determine whether the power system is in a chaotic state under specific parameters, ensuring the accuracy of warning information issued by the system. By issuing timely and accurate warnings, system operators are reminded to monitor operating parameters, thereby avoiding serious chaotic instability and ensuring the safe and stable operation of the power system.

[0097] The above optional implementation method achieves at least the following effects: the use of order reduction processing can reduce the complexity of the power system model, which is conducive to the rapid identification and analysis of the system state. At the same time, the reduced-order system model focuses more on the dynamic changes of key operating parameters, eliminates the interference of some minor operating parameters, and helps to improve the accuracy of the system state determination results; based on the dual judgment process of Melnikov stability theory and Poincaré section, it can effectively improve the accuracy of the power system state determination results, avoid serious chaotic instability in the system, and improve the operational safety of the power system.

[0098] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0099] In this embodiment, a device for determining the state of a power system is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0100] According to an embodiment of the present application, there is also provided an embodiment of a device for implementing a method for determining a state of a power system. Figure 6 is a schematic diagram of a device for determining the state of a power system according to an embodiment of the present application, such as Figure 6 As shown, the above-mentioned power system state determination device includes a first determination module 602, a second determination module 604, and a third determination module 606. The device is described below.

[0101] A first determining module 602 is configured to determine a target variation characteristic of a target operating parameter of a target power system, wherein the target variation characteristic is used to describe a variation trajectory of the target operating parameter over time;

[0102] a second determining module 604, connected to the first determining module 602, for determining a phase space characteristic of the target power system at the target load power based on the target variation characteristic, wherein the phase space characteristic represents the number and distribution of intersections between the variation trajectory and a predetermined phase space cross section, and the phase space cross section is a two-dimensional plane in the phase space;

[0103] The third determination module 606 is connected to the second determination module 604 and is used to determine the target state of the target power system based on the phase space characteristics, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameters showing irregular oscillation changes.

[0104] In a state determination device for an electric power system provided in an embodiment of the present application, a first determination module 602 is set to determine the target change characteristics of the target operating parameters of the target electric power system, wherein the target change characteristics are used to describe the change trajectory of the target operating parameters over time; a second determination module 604 is connected to the first determination module 602, and is used to determine the phase space characteristics of the target electric power system under the target load power based on the target change characteristics, wherein the phase space characteristics represent the number and distribution state of the intersections of the change trajectory and a predetermined phase space section, and the phase space section is a two-dimensional plane in the phase space; a third determination module 606 is connected to the second determination module 604, and is used to determine the target state of the target electric power system based on the phase space characteristics, wherein the target state is used to indicate whether the target electric power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameters showing irregular oscillation changes. The purpose of determining the phase space characteristics of the target power system under the target load power by analyzing the change characteristics of the target operating parameters of the power system over time and then determining the state of the power system is achieved, thereby achieving the technical effect of improving the accuracy of the state determination results of the power system under a specific load power, and thus solving the technical problem of low accuracy of the state determination results of the power system under a specific load power existing in the related art.

[0105] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0106] It should be noted that the first determination module 602, the second determination module 604, and the third determination module 606 correspond to steps S102 to S106 in the embodiment. The examples and application scenarios implemented by these modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run on a computer terminal.

[0107] It should be noted that the optional or preferred implementation of this embodiment can be found in the relevant description in the embodiment, which will not be repeated here.

[0108] The above-mentioned power system status determination device can also include a processor and a memory. The first determination module 602, the second determination module 604, the third determination module 606, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0109] The processor includes a kernel, which retrieves the corresponding program unit from memory. There can be one or more kernels. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0110] An embodiment of the present application provides a non-volatile storage medium having a program stored thereon, which implements a method for determining the state of a power system when the program is executed by a processor.

[0111] An embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: determining a target change characteristic of a target operating parameter of a target power system, wherein the target change characteristic is used to describe the change trajectory of the target operating parameter over time; based on the target change characteristic, determining a phase space characteristic of the target power system at a target load power, wherein the phase space characteristic represents the number and distribution of intersections of the change trajectory and a predetermined phase space cross section, and the phase space cross section is a two-dimensional plane in the phase space; based on the phase space characteristic, determining a target state of the target power system, wherein the target state is used to indicate whether the target power system is in a chaotic state at the target load power, and the chaotic state refers to the target operating parameter exhibiting irregular oscillating changes. The device in this article can be a server, a PC, etc.

[0112] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: determining a target change characteristic of a target operating parameter of a target power system, wherein the target change characteristic is used to describe the change trajectory of the target operating parameter over time; based on the target change characteristic, determining a phase space characteristic of the target power system under a target load power, wherein the phase space characteristic represents the number and distribution state of intersections of the change trajectory and a predetermined phase space section, and the phase space section is a two-dimensional plane in the phase space; based on the phase space characteristic, determining a target state of the target power system, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state refers to the target operating parameter presenting irregular oscillating changes.

[0113] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0117] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0118] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0119] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0120] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0121] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for determining the state of a power system, characterized in that: include: Determining a target variation characteristic of a target operating parameter of a target power system, wherein the target variation characteristic is used to describe a variation trajectory of the target operating parameter over time; Determining a phase space characteristic of the target power system at a target load power based on the target change characteristic, wherein the phase space characteristic represents the number and distribution of intersections of the change trajectory and a predetermined phase space cross section, and the phase space cross section is a two-dimensional plane in the phase space; Based on the phase space characteristics, the target state of the target power system is determined, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state means that the target operating parameters show irregular oscillating changes.

2. The method according to claim 1, characterized in that Determining target change characteristics of target operating parameters of the target power system includes: Acquiring operating data of the target power system, wherein the operating data is collected within a predetermined historical time period; determining, based on the operating data, initial change characteristics corresponding to a plurality of initial operating parameters of the target power system; The initial change characteristics corresponding to the multiple initial operating parameters are respectively reduced to obtain the target change characteristics.

3. The method according to claim 2, characterized in that In a case where the initial change characteristics respectively corresponding to the multiple initial operating parameters are expressed in the form of a matrix, performing order reduction processing on the initial change characteristics respectively corresponding to the multiple initial operating parameters to obtain the target change characteristics includes: Based on the initial change characteristics respectively corresponding to the multiple initial operating parameters, using a balance conversion matrix, obtaining the balance change characteristics respectively corresponding to the multiple initial operating parameters, wherein the balance conversion matrix is ​​used to simplify and standardize the initial change characteristics respectively corresponding to the multiple initial operating parameters; The balance change characteristics corresponding to the multiple initial operating parameters are respectively reduced to obtain the target change characteristics.

4. The method according to claim 1, wherein Before determining the phase space characteristics of the target power system at the target load power based on the target change characteristics, the method further includes: Determining a state transition index of the target power system, wherein the state transition index is used to indicate the possibility of the target power system transitioning from a stable state to a chaotic state; The target load power is determined based on the state conversion indicator and a predetermined distance threshold.

5. The method according to claim 4, characterized in that In a case where the target power system includes two generators, determining the state conversion index of the target power system includes: Determining a damping coefficient difference between the two generators, an output power difference between the two generators, a moment of inertia difference between the two generators, and an input mechanical power difference between the two generators; The state transition indicator is determined based on the damping coefficient difference, the output power difference, the moment of inertia difference, and the input mechanical power difference.

6. The method according to claim 1, characterized in that Determining the target state of the target power system based on the phase space characteristics includes: In a case where the number is less than a preset number threshold, determining that the target state is that the target power system is not in a chaotic state; or When the number is greater than or equal to the number threshold, it is determined that the target state is that the target power system is in a chaotic state.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: When the number is one, the target power system is in a single-cycle motion state; or When the number is not one and is smaller than a preset number threshold, the target power system is in a multi-cycle motion state.

8. A device for determining the state of a power system, characterized in that: include: A first determining module is configured to determine a target variation characteristic of a target operating parameter of a target power system, wherein the target variation characteristic is used to describe a variation trajectory of the target operating parameter over time; a second determining module, configured to determine a phase space characteristic of the target power system at a target load power based on the target change characteristic, wherein the phase space characteristic represents the number and distribution of intersections between the change trajectory and a predetermined phase space cross section, and the phase space cross section is a two-dimensional plane in the phase space; The third determination module is used to determine the target state of the target power system based on the phase space characteristics, wherein the target state is used to indicate whether the target power system is in a chaotic state under the target load power, and the chaotic state means that the target operating parameters show irregular oscillation changes.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executed by the method for determining the state of the power system according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the state of the power system as described in any one of claims 1 to 7.

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