Real-time operation monitoring method and system for virtual-real fusion of large power grid

Abstract

The invention discloses a large power grid virtual-real fusion real-time operation monitoring method and system, and relates to the warehouse management scheduling related technical field, and the method comprises the steps: determining a first grid-connected device in a target power grid, and carrying out the intermittent modulation of the first grid-connected device, and constructing an impedance twinborn body; embedding the impedance twinbody into a virtual processing unit, triggering a decision along with the updating of the impedance twinbody, determining a stability margin matrix of the target power grid, executing oscillation mode analysis based on a power grid admittance matrix in parallel, and determining an oscillation matrix; and obtaining a power grid topological structure of the target power grid, executing power grid node positioning based on the stability margin matrix and the oscillation matrix, and performing power grid risk operation and maintenance management. The technical problems that in the prior art, real-time performance and accuracy of operation monitoring are insufficient, dynamic characteristics are difficult to capture, and model updating and decision response are lagged are solved, and the technical effects that the real-time performance and accuracy of power grid operation monitoring are improved, and efficient risk operation and maintenance management is achieved are achieved.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, specifically to a real-time operation monitoring method and system for large power grids that integrates virtual and physical data. Background Technology

[0002] With the continuous expansion of power system scale, the operating characteristics of modern large power grids are becoming increasingly complex, posing challenges to their safe and stable operation. Traditional power grid operation monitoring relies on data acquisition and monitoring and control systems. When dealing with the high-dimensional, nonlinear, and strongly coupled characteristics of new power systems, these systems often suffer from problems such as data update lag, insufficient analysis depth, and slow dynamic response. In particular, in real-time operation monitoring, it is difficult to quickly and accurately capture key operating states such as system stability margin and oscillation modes, which limits the real-time performance and effectiveness of operation and maintenance decisions. In addition, existing virtual mapping of power grids lacks a detailed characterization of the dynamic characteristics of the power grid, and the model update frequency does not match the real-time decision-making requirements, thus affecting the real-time performance and accuracy of power grid operation monitoring.

[0003] Therefore, current technologies suffer from technical problems such as insufficient real-time performance and accuracy in operation monitoring, difficulty in capturing dynamic characteristics, and lag in model updates and decision-making responses. Summary of the Invention

[0004] This application provides a real-time operation monitoring method and system for large power grids that integrates virtual and physical data. This solves the technical problems of insufficient real-time performance and accuracy of operation monitoring, difficulty in capturing dynamic characteristics, and lag in model updates and decision response in existing technologies. It achieves the technical effect of improving the real-time performance and accuracy of power grid operation monitoring and realizing efficient risk operation and maintenance management.

[0005] This application provides a real-time operation monitoring method for a large power grid integrating virtual and real systems. The method includes: identifying a first grid-connected device in the target power grid; constructing an impedance twin by intermittently modulating the first grid-connected device, wherein the grid connection point impedance, geographical location, and electrical connection relationship are used as an impedance node constituting the impedance twin; the impedance twin is updated intermittently; embedding the impedance twin into a virtual processing unit; triggering decisions as the impedance twin is updated; determining the stability margin matrix of the target power grid; performing oscillation mode analysis based on the power grid admittance matrix in parallel to determine the oscillation matrix; acquiring the power grid topology of the target power grid; performing power grid node location based on the stability margin matrix and the oscillation matrix; and performing power grid risk operation and maintenance management.

[0006] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid also performs the following processing: taking the grid connection point of the first grid-connected device in the target power grid as the monitoring grid connection point, wherein the type of the first grid-connected device is at least a grid-connected inverter, and the first grid-connected device is distributedly connected in the target power grid; for each monitoring grid connection point, the control mode of the first grid-connected device is intermittently modulated, and impedance detection under the modulation output is performed.

[0007] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid also performs the following processing: determining the output power frequency current of the first grid-connected device; setting a probe signal, wherein the probe signal is a low-amplitude, frequency-sweepable sensing signal; and superimposing the probe signal onto the output power frequency current as the modulation output of the first grid-connected device.

[0008] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid also performs the following processing: receiving the monitoring cycle of the target power grid and determining the detection interval time zone based on the monitoring cycle; and intermittently modulating and triggering the first grid-connected equipment using the detection interval time zone.

[0009] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid further performs the following processing: determining a first detection time node, triggering power frequency current modulation based on each first grid-connected device, and determining a first modulation output; the probe signal superimposed in the first modulation output propagates with the power grid and is captured by the detection response of the grid-connected device under power flow, wherein the detection response includes at least harmonic changes in voltage and current; performing impedance calculation based on the relationship analysis of probe signal-detection response, and determining real-time impedance data, wherein the real-time impedance data characterizes the impedance at the grid connection point; and updating the impedance twin based on the real-time impedance data.

[0010] In a possible implementation, the real-time operation monitoring method for the virtual-real fusion of the large power grid further performs the following processing: for each impedance node in the impedance twin, the power grid loop gain is determined by the ratio of the power grid impedance to the node impedance; a trajectory curve based on the power grid loop gain is plotted, wherein the trajectory curve represents the relationship between the power grid loop gain and frequency on the complex plane; based on the trajectory curve, the phase margin and amplitude margin of the target power grid are determined as stability margin data for each impedance node, forming a stability margin matrix.

[0011] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid also performs the following processing: using the first grid-connected device as a node, constructing a power grid admittance matrix based on the target power grid; performing eigenvalue decomposition on the power grid admittance matrix to determine an oscillation matrix based on the oscillation mode, wherein the matrix elements of the oscillation matrix include at least the oscillation frequency and the damping strength.

[0012] In a possible implementation, the real-time operation monitoring method for the virtual-real fusion of the large power grid also performs the following processing: mapping the stability margin matrix and the oscillation matrix to determine multiple mapping groups, wherein each mapping group includes a stability margin term and an oscillation term, and the multiple mapping groups correspond one-to-one with the first grid-connected equipment; obtaining the power grid topology of the target power grid, locating and identifying the topology nodes of the multiple mapping groups, and determining the power grid operation map.

[0013] In a possible implementation, the real-time operation monitoring method for the virtual-real integration of the large power grid also performs the following processing: scanning the power grid operation map, identifying the power grid risk map through operation risk identification, wherein stable weak locations and high-risk oscillation modes are used as identification targets; and performing power grid operation and maintenance management based on the power grid risk map.

[0014] This application also provides a real-time operation monitoring system for a large power grid integrating virtual and real data. The system includes: an impedance twin construction module, used to determine a first grid-connected device in the target power grid, and construct an impedance twin by intermittently modulating the first grid-connected device, wherein the grid connection point impedance, geographical location, and electrical connection relationship are used as an impedance node constituting the impedance twin, and the impedance twin is updated intermittently; an oscillation mode analysis module, used to embed the impedance twin into a virtual processing unit, triggering decisions as the impedance twin is updated, determining the stability margin matrix of the target power grid, and performing oscillation mode analysis based on the power grid admittance matrix in parallel to determine the oscillation matrix; and a power grid node positioning module, used to obtain the power grid topology of the target power grid, perform power grid node positioning based on the stability margin matrix and the oscillation matrix, and perform power grid risk operation and maintenance management.

[0015] This application proposes a real-time operation monitoring method and system for large power grids that integrates virtual and real-world data. The method identifies the first grid-connected device in the target power grid, constructs an impedance twin by intermittently modulating the first device, embeds the impedance twin into a virtual processing unit, and triggers decisions based on the impedance twin's updates to determine the stability margin matrix of the target power grid. Simultaneously, it performs oscillation mode analysis based on the power grid admittance matrix to determine the oscillation matrix. Finally, it acquires the power grid topology, performs grid node location based on the stability margin matrix and oscillation matrix, and conducts power grid risk operation and maintenance management. This method solves the technical problems of insufficient real-time performance and accuracy in operation monitoring, difficulty in capturing dynamic characteristics, and lag in model updates and decision responses in existing technologies. It achieves the technical effect of improving the real-time performance and accuracy of power grid operation monitoring and realizing efficient risk operation and maintenance management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic diagram of the real-time operation monitoring method for virtual-real integration of a large power grid provided in this application embodiment.

[0018] Figure 2 A schematic diagram of the structure of a real-time operation monitoring system for a large power grid that integrates virtual and real data, provided in an embodiment of this application.

[0019] Figure labeling: Impedance twin construction module 10, oscillation mode analysis module 20, power grid node location module 30. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structure, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This application provides a method for real-time operation monitoring of a large power grid that integrates virtual and real systems, such as... Figure 1 As shown, the method includes: Step S100: Determine the first grid-connected device in the target power grid, and construct an impedance twin by intermittently modulating the first grid-connected device. The impedance twin is constructed by using the grid connection point impedance, geographical location, and electrical connection relationship as an impedance node in the impedance twin. The impedance twin is updated intermittently.

[0022] Preferably, from the target power grid to be monitored, a grid-connected device is selected as the first grid-connected device for operation and monitoring. This could be a distributed energy source such as a photovoltaic inverter or wind power converter, or a device connected to the grid via power electronic equipment. By intermittently modulating the first grid-connected device—that is, temporarily changing its control signal or operating state at specific, discontinuous time points or time periods—it outputs a signal for detection. This focuses on key impedance characteristics of power grid stability. A lightweight and dynamically updated impedance twin is constructed based on digital twin technology to reflect the equivalent impedance characteristics, spatial layout, and topological connections of the power grid at the grid connection point. Among these, the impedance at the grid connection point, geographical location, and electrical connections are considered. As an impedance node constituting an impedance twin, the grid-connected point impedance is the real-time impedance value of that point determined through modulation and measurement calculations. Geographical location refers to the physical coordinates or topological location information of the first grid-connected device. Electrical connection relationship refers to the connection method between this node and lines, transformers and other nodes in the target power grid, thereby enabling the impedance twin to perform electrical calculations and accurately locate itself in the power grid structure. The intermittent update of the impedance twin refers to the periodic or specific event-triggered re-execution of intermittent modulation-measurement-calculation of all its constituent impedance nodes, and updating the impedance twin with updated real-time impedance data, geographical location and electrical connection relationship, ensuring that it tracks changes in the state of the target power grid in real time.

[0023] Furthermore, step S100 also includes step S101, taking the grid connection point of the first grid-connected device in the target power grid as the monitoring grid connection point, wherein the type of the first grid-connected device is at least a grid-connected inverter, and the first grid-connected device is distributedly connected in the target power grid; step S102, for each monitoring grid connection point, intermittently modulate the control mode of the first grid-connected device and perform impedance detection under modulation output.

[0024] Preferably, the grid connection point of the first grid-connected device in the target power grid is used as the monitoring grid connection point to determine the physical location of the measurement. The grid connection point is the interface between the device and the target power grid, and can directly monitor electrical parameters such as voltage, current, and impedance. The first grid-connected device is at least a grid-connected inverter, and the first grid-connected device is distributed in the target power grid. Specifically, the grid-connected inverter is the core interface device for distributed energy such as photovoltaic, wind power, and energy storage to access the power grid. Its control mode directly determines the output impedance characteristics, thereby affecting the stability of the target power grid. Distributed access means that the grid-connected devices are geographically dispersed and numerous, making centralized monitoring difficult to handle. Then, for each monitored grid-connected point, the control mode of the first grid-connected device is intermittently modulated, that is, the internal control parameter signals of the grid-connected inverter device are periodically modified. Specifically, the grid-connected inverter is determined by its PQ control, VF control, and internal current loop and phase-locked loop parameters, etc. The intermittent modulation control mode can change the output impedance of the grid-connected device to reduce interference to the normal power generation of the device and reduce the overall computing and communication burden. Finally, impedance detection under modulation output is performed, that is, during the intermittent modulation period, a known detection signal is injected into the grid and its voltage and current response at the monitored grid-connected point is measured. The equivalent impedance of the monitored grid-connected point is calculated by the injected current disturbance and the measured voltage change, and finally the real-time impedance data of each monitored grid-connected point is obtained.

[0025] Furthermore, step S102 also includes step S102a, determining the output power frequency current of the first grid-connected device; step S102b, setting a probe signal, wherein the probe signal is a low-amplitude, frequency-swept sensing signal; and step S102c, superimposing the probe signal onto the output power frequency current as the modulation output of the first grid-connected device.

[0026] Preferably, the reference value of the fundamental current to be output by the first grid-connected device is acquired in real time as the output power frequency current. This may be a sinusoidal signal, representing the basic current for power transmission to the grid by the grid-connected inverter under normal operating conditions. A low-amplitude, frequency-sweepable sensing signal is set as the probe signal, including a signal amplitude much smaller than the amplitude of the output power frequency current, to minimize interference with the power quality of the grid and the normal operation of the device itself. At the same time, the probe signal frequency can be scanned and varied within a preset frequency range to obtain wideband impedance information. By injecting the probe signal sequentially at different frequency points for measurement, the impedance spectrum of the first grid-connected device in the corresponding frequency band can be plotted. To assess stability, the probe signal is then superimposed on the output power frequency current. This involves algebraically adding the set probe signal and the output power frequency current to generate a composite current command signal, which serves as the modulation output of the first grid-connected device. The power circuit of the grid-connected inverter executes this composite current command signal, ensuring that its actual output current includes both the power frequency current and the weak probe current with frequency variations. This generates a corresponding voltage response at the monitoring grid connection point. By precisely measuring the total voltage and current at the monitoring grid connection point and extracting the voltage response component and current injection component at the probe frequency through Fourier signal analysis, the real-time impedance of the monitoring grid connection point at that frequency is finally precisely calculated.

[0027] Furthermore, step S102 also includes receiving the monitoring cycle of the target power grid, determining the detection interval time zone based on the monitoring cycle, and intermittently modulating and triggering the first grid-connected device using the detection interval time zone.

[0028] Preferably, the monitoring cycle configured in the target power grid is received, which is the length of time for the entire target power grid to perform a complete state monitoring and update. This cycle may be set to 5 minutes, 15 minutes, or 1 hour. Then, based on the monitoring cycle, multiple short time windows are planned as detection interval time zones for performing impedance detection tasks. Then, the first grid-connected device is intermittently modulated and triggered using the detection interval time zone. Specifically, when the real-time clock enters the predetermined detection interval time zone, a trigger command is sent to the local controller of the first grid-connected device to perform intermittent modulation and triggering, including generating probe signals, superimposing them on the power frequency current, and outputting them. This ensures that interference with the power quality of the power grid and the device itself, as well as resource consumption, are reduced, and the real-time performance of power grid monitoring is guaranteed.

[0029] Furthermore, step S100 also includes step S110, determining a first detection time node, triggering power frequency current modulation based on each first grid-connected device, and determining a first modulation output; step S120, the probe signal superimposed in the first modulation output propagates with the power grid and the grid-connected device performs detection response capture under power flow, wherein the detection response includes at least harmonic changes in voltage and current; step S130, performing impedance calculation based on the relationship analysis of probe signal-detection response, and determining real-time impedance data, wherein the real-time impedance data characterizes the impedance at the grid connection point; step S140, updating the impedance twin based on the real-time impedance data.

[0030] Preferably, the start time of the detection interval time zone is determined as the first detection time node. When the real-time clock reaches this detection time node, a modulation command is sent to each first grid-connected device and power frequency current modulation is performed. After receiving the modulation command, each grid-connected device obtains its own power frequency current and then superimposes a probe signal of a specific frequency. The superimposed composite current signal is used as the first modulation output, which is then converted into actual current injected into the grid by the power switch of the grid-connected inverter. The probe signal superimposed in the first modulation output propagates with the grid, that is, it propagates in the grid according to Kirchhoff's circuit laws, affecting the voltage and current on its connecting lines. Then, through the downstream grid-connected device in the target grid located at the injection point electrical relationship, the high-precision measurement unit is activated to synchronously capture the detection response at its own monitoring grid-connected point. The detection response is then transmitted to the grid-connected device at the injection point electrical relationship. The signal contains minimal harmonic variations in voltage and current, specifically the voltage and current components generated at a specific frequency of the probe signal. Fourier transform can be used for spectral analysis to accurately extract the amplitude and phase of the voltage and current response at that frequency. Then, impedance calculation is performed based on the relationship between the probe signal and the detection response. Specifically, the injected probe signal is compared with the measured voltage response signal, and the impedance at that frequency is directly calculated using Ohm's law to obtain real-time impedance data, which is used to characterize the grid connection point impedance. Furthermore, an impedance spectrum for the corresponding frequency band is obtained through frequency sweeping, thus comprehensively characterizing the impedance characteristics of the monitored grid connection point. Finally, the impedance twin is updated based on the real-time impedance data to synchronously reflect the actual impedance state of the target power grid at the first detection time node, thereby ensuring high-precision real-time monitoring and stability analysis.

[0031] Step S200: The impedance twin is embedded in the virtual processing unit. The decision is triggered as the impedance twin is updated to determine the stability margin matrix of the target power grid. In parallel, the oscillation mode analysis based on the power grid admittance matrix is ​​performed to determine the oscillation matrix.

[0032] Preferably, the impedance twin is embedded in a virtual processing unit for high-performance computing, and a logical rule is set to trigger decision-making as the impedance twin is updated. That is, whenever the impedance twin completes a data update, the analysis decision is automatically triggered to ensure that the stability analysis is synchronized with the latest grid status, realizing real-time linkage from perception to analysis. Then, based on the latest updated impedance twin, the analysis and calculation of the target grid are performed. Specifically, the loop gain is determined by calculating the ratio of the impedance of each impedance node in the impedance twin to the grid impedance. Then, the trajectory of the loop gain changing with frequency is plotted, and two key stability indicators, phase margin and magnitude margin, are extracted from the trajectory. The key stability indicators of all impedance nodes are organized into a stability margin matrix, with each row or column of the matrix corresponding to an impedance node. This allows for the evaluation of the small disturbance stability of the target grid, thereby locating weak nodes in the grid. Parallel oscillation mode analysis based on the grid admittance matrix is ​​performed. The grid admittance matrix is ​​derived from the node impedance, geographical location, and electrical connection data in the impedance twin. Admittance is the reciprocal of impedance. Eigenvalue decomposition is performed on the admittance matrix to obtain multiple eigenvalues, each of which corresponds to a potential grid oscillation mode. Each grid oscillation mode is described by oscillation frequency and damping strength. The oscillation frequency refers to the speed at which the oscillation occurs, and the damping strength refers to the speed at which the oscillation decays. Then, multiple grid oscillation modes and their correlation with grid nodes are organized to determine the oscillation matrix, which is used to evaluate the oscillation stability of the target grid, thereby revealing the inherent dynamic behavior pattern of the target grid.

[0033] Furthermore, step S200 also includes step S210, determining the grid loop gain for each impedance node in the impedance twin using the ratio of grid impedance to node impedance; step S220, plotting a trajectory curve based on the grid loop gain, wherein the trajectory curve characterizes the relationship between the grid loop gain and frequency on the complex plane; step S230, determining the phase margin and magnitude margin of the target grid based on the trajectory curve, using them as stability margin data for each impedance node, and constructing a stability margin matrix.

[0034] Preferably, for each impedance node in the impedance twin, i.e. each monitored grid-connected inverter, the output impedance of the grid-connected inverter itself is obtained, and the grid impedance is calculated and determined. The ratio of the grid impedance to the node impedance is determined as the grid loop gain, which is used to describe the dynamic interaction strength between the distributed generation unit and the grid. On the complex plane with the real part as the horizontal axis and the imaginary part as the vertical axis, the frequency is scanned from a lower value to a higher value. The grid loop gain at each frequency is calculated and all points are connected to draw the trajectory curve of the grid loop gain, i.e., the Nyquist plot, which is used to visualize the relationship between the grid loop gain and the frequency, and contains all the amplitude and phase information required to evaluate stability. Next, the phase margin and gain margin of the target power grid are determined based on the trajectory curve. Specifically, the intersection point of the trajectory curve and the unit circle centered at the origin is determined, called the gain boundary frequency point. The loop gain at this point is 1. The angle between the line connecting the origin and this point and the negative real axis is the phase margin, representing the additional phase delay tolerated before reaching critical oscillation. The intersection point of the trajectory curve and the negative real axis is also determined, called the phase boundary frequency point. The distance from this intersection point to the origin is measured as the loop gain amplitude, and then the gain is calculated according to the formula... The gain margin is calculated to characterize the increase in loop gain before reaching critical oscillation. Then, the phase margin and gain margin of each impedance node are used as stability margin data, and the stability margin data of all impedance nodes are combined into a structured matrix to determine the stability margin matrix. The rows or columns of the stability margin matrix correspond one-to-one with the grid-connected equipment of the target power grid, thereby achieving accurate location of stability risks.

[0035] Furthermore, step S200 also includes step S240, constructing a grid admittance matrix based on the target grid using the first grid-connected device as a node; step S250, performing eigenvalue decomposition on the grid admittance matrix to determine an oscillation matrix based on the oscillation mode, wherein the matrix elements of the oscillation matrix include at least the oscillation frequency and the damping strength.

[0036] Preferably, each grid-connected device is treated as an electrical node. Using real-time impedance data, electrical connections, and the grid topology provided by impedance twins, the admittance of each electrical node is determined through circuit network theory. This includes self-admittance and mutual admittance elements. Self-admittance is the sum of the admittances of the electrical node and each connected grid branch, while mutual admittance is the negative admittance of the connecting branch between any two electrical nodes. This constructs a grid admittance matrix describing the electrical characteristics of the target grid, including the self-admittance of all grid-connected devices and the mutual admittance between devices. Each element represents the electrical relationship between nodes. Then, eigenvalue decomposition is performed on the grid admittance matrix to obtain eigenvalues ​​and eigenvectors. The inherent dynamic behavior patterns of the target power grid are then determined. Each characteristic value corresponds to a potential oscillation mode. The oscillation frequency and damping strength are then calculated for each characteristic value. The oscillation frequency represents the number of times the oscillation mode swings back and forth per second, and the damping strength, or damping ratio, represents how quickly the oscillation mode decays after being disturbed. If the damping strength is positive, it indicates that the oscillation is gradually decaying and the power grid is stable. Conversely, if the damping strength is negative, it indicates that the oscillation is gradually amplifying and the power grid is unstable. Finally, the oscillation frequencies and damping strengths of all oscillation modes are organized into a structured oscillation matrix to reveal the oscillation risk of the target power grid, thereby ensuring an accurate assessment of power grid stability.

[0037] Step S300: Obtain the grid topology of the target grid, perform grid node location based on the stability margin matrix and oscillation matrix, and carry out grid risk operation and maintenance management.

[0038] Step S300 further includes step S310, mapping the stability margin matrix and the oscillation matrix to determine multiple mapping groups, wherein each mapping group includes a stability margin term and an oscillation term, and the multiple mapping groups correspond one-to-one with the first grid-connected device; step S320, obtaining the grid topology of the target grid, locating and identifying the topology nodes of the multiple mapping groups, and determining the grid operation map.

[0039] Preferably, the topology of the target power grid is obtained, and grid node location is performed based on the stability margin matrix and oscillation matrix. Specifically, the phase margin and amplitude margin of each node in the stability margin matrix are associated and mapped with the oscillation mode of the corresponding node in the oscillation matrix, forming multiple mapping groups that correspond one-to-one with multiple grid-connected devices. Each mapping group contains at least one stability margin term and one oscillation term. Then, the topology of the target power grid, including all substations, lines, transformers, and grid-connected devices and their connection relationships, is retrieved from the power grid graph database. The topology nodes of the multiple mapping groups are then located and identified. Each identified mapping group is precisely labeled with the physical device location on the power grid topology using its grid-connected device ID or geographical location information. At the same time, key data in the mapping group is displayed in a visual way, such as using color depth to represent phase margin, green to indicate high margin and red to indicate low margin. A small spectrum diagram is also displayed next to the device to show the oscillation frequency and damping intensity of its oscillation mode. This enables the binding of stability margin terms, oscillation terms, and the spatial location and electrical connection relationship of the power grid, and finally generates a power grid operation map to visually present the physical structure of the power grid, the stability state of grid-connected devices, and the spatial distribution of the target power grid oscillation mode.

[0040] Furthermore, step S300 also includes step S330, scanning the power grid operation map, identifying the power grid risk map through operation risk identification, wherein stable weak locations and high-risk oscillation modes are used as identification targets; step S340, performing power grid operation and maintenance management based on the power grid risk map.

[0041] Preferably, all data in the power grid operation map is scanned and identified for operational risks, with stable weak points and high-risk oscillation modes as the identification targets. Stable weak points refer to locating all grid-connected equipment nodes with stability margins below a preset safety threshold on the power grid operation map, such as marking all grid-connected equipment with a phase margin below 30°. High-risk oscillation modes refer to locating all oscillation modes with damping strength below a preset safety threshold on the power grid operation map, such as marking all oscillation modes with a damping ratio below 3%. Through feature vector analysis, the grid-connected equipment nodes with the highest degree of participation in the high-risk oscillation mode are located. Then, all the identified operational risk points, i.e., weak equipment, high-risk oscillation modes, and their main participating equipment, are highlighted and marked with severity levels on the power grid operation map to generate a power grid risk map. Finally, grid operation and maintenance management is based on the grid risk map. This includes automatically generating risk alarm information and notifying operation and maintenance personnel through monitoring screens, work order systems, etc. The operation and maintenance personnel formulate and execute adjustment instructions, such as slightly adjusting the active / reactive power output of weak nodes, disconnecting units or some loads that cause high-risk oscillations. At the same time, the identified oscillation frequency is sent to the damping controller for effective suppression. It can also be sent to the corresponding grid-connected inverter, which dynamically adjusts its own control parameters, such as current loop gain and phase-locked loop bandwidth, to reshape its output impedance in real time, so that it actively avoids the resonance point with the grid impedance, thereby achieving oscillation suppression. This improves the real-time and accuracy of grid operation monitoring, thereby ensuring the safe and stable operation of the large power grid.

[0042] In the above text, refer to Figure 1 This paper describes in detail a real-time operation monitoring method for large power grid virtual-real integration according to an embodiment of the present invention. Next, we will refer to... Figure 2 A real-time operation monitoring system for virtual and real-world integration of a large power grid, according to an embodiment of the present invention, is described.

[0043] The real-time operation monitoring system for large power grids integrating virtual and real-world data, according to embodiments of the present invention, addresses the technical problems in existing technologies, such as insufficient real-time performance and accuracy of operation monitoring, difficulty in capturing dynamic characteristics, and lag in model updates and decision-making responses. It achieves the technical effect of improving the real-time performance and accuracy of power grid operation monitoring and realizing efficient risk-based operation and maintenance management. Figure 2 As shown, the real-time operation monitoring system for the integration of virtual and real systems in a large power grid includes: an impedance twin construction module 10, an oscillation mode analysis module 20, and a power grid node location module 30.

[0044] Impedance twin construction module 10 is used to identify the first grid-connected device in the target power grid and construct an impedance twin by intermittently modulating the first grid-connected device. The grid connection point impedance, geographical location, and electrical connection relationship are used as an impedance node in the impedance twin. The impedance twin is updated intermittently. Oscillation mode analysis module 20 is used to embed the impedance twin in a virtual processing unit. The update of the impedance twin triggers a decision to determine the stability margin matrix of the target power grid and performs oscillation mode analysis based on the power grid admittance matrix in parallel to determine the oscillation matrix. Power grid node positioning module 30 is used to obtain the power grid topology of the target power grid, perform power grid node positioning based on the stability margin matrix and the oscillation matrix, and perform power grid risk operation and maintenance management.

[0045] The specific configuration of the impedance twin construction module 10 will be described in detail below. The impedance twin construction module 10 further includes: using the grid connection point of the first grid-connected device in the target power grid as the monitoring grid connection point, wherein the type of the first grid-connected device is at least a grid-connected inverter, and the first grid-connected device is distributedly connected in the target power grid; for each monitoring grid connection point, the control mode of the first grid-connected device is intermittently modulated, and impedance detection is performed under the modulated output.

[0046] The specific configuration of the impedance twin construction module 10 will be described in detail below. The impedance twin construction module 10 further includes: determining the output power frequency current of the first grid-connected device; setting a probe signal, wherein the probe signal is a low-amplitude, frequency-swept sensing signal; and superimposing the probe signal onto the output power frequency current as the modulation output of the first grid-connected device.

[0047] The specific configuration of the impedance twin construction module 10 will be described in detail below. The impedance twin construction module 10 further includes: receiving the monitoring period of the target power grid, determining the detection interval time zone based on the monitoring period, and intermittently modulating and triggering the first grid-connected device using the detection interval time zone.

[0048] The specific configuration of the impedance twin construction module 10 will be described in detail below. The impedance twin construction module 10 further includes: determining a first detection time node, triggering power frequency current modulation based on each first grid-connected device, and determining a first modulation output; the probe signal superimposed in the first modulation output propagates along the power grid and is captured by the detection response of the grid-connected device under power flow conditions, wherein the detection response includes at least harmonic changes in voltage and current; analyzing the relationship between the probe signal and the detection response to perform impedance calculation and determine real-time impedance data, wherein the real-time impedance data characterizes the impedance at the grid connection point; and updating the impedance twin based on the real-time impedance data.

[0049] The specific configuration of the oscillation mode analysis module 20 will be described in detail below. The oscillation mode analysis module 20 further includes: determining the grid loop gain for each impedance node within the impedance twin using the ratio of grid impedance to node impedance; plotting a trajectory curve based on the grid loop gain, wherein the trajectory curve characterizes the relationship between the grid loop gain and frequency in the complex plane; and determining the phase margin and amplitude margin of the target grid based on the trajectory curve, using these as stability margin data for each impedance node to form a stability margin matrix.

[0050] The specific configuration of the oscillation mode analysis module 20 will be described in detail below. The oscillation mode analysis module 20 further includes: constructing a grid admittance matrix based on the target grid, using the first grid-connected device as a node; performing eigenvalue decomposition on the grid admittance matrix to determine an oscillation matrix based on the oscillation mode, wherein the matrix elements of the oscillation matrix include at least the oscillation frequency and damping strength.

[0051] The specific configuration of the power grid node positioning module 30 will be described in detail below. The power grid node positioning module 30 further includes: mapping the stability margin matrix and the oscillation matrix to determine multiple mapping groups, wherein each mapping group includes a stability margin term and an oscillation term, and the multiple mapping groups correspond one-to-one with the first grid-connected equipment; obtaining the power grid topology of the target power grid; performing topology node positioning and identification on the multiple mapping groups; and determining the power grid operation map.

[0052] The specific configuration of the power grid node positioning module 30 will be described in detail below. The power grid node positioning module 30 further includes: scanning the power grid operation map, identifying a power grid risk map through operational risk identification, wherein stable and vulnerable locations and high-risk oscillation patterns are used as identification targets; and performing power grid operation and maintenance management based on the power grid risk map.

[0053] The real-time operation monitoring system for virtual-real integration of large power grids provided in this embodiment of the invention can execute the real-time operation monitoring method for virtual-real integration of large power grids provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution. The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A real-time operation monitoring method for large power grids integrating virtual and real systems, characterized in that, The method includes: In the target power grid, a first grid-connected device is identified. An impedance twin is constructed by intermittently modulating the first grid-connected device. The impedance twin is constructed by using the grid connection point impedance, geographical location, and electrical connection relationship as an impedance node in the impedance twin. The impedance twin is updated intermittently. The impedance twin is embedded in a virtual processing unit. Decisions are triggered as the impedance twin is updated to determine the stability margin matrix of the target power grid. In parallel, oscillation mode analysis based on the power grid admittance matrix is ​​performed to determine the oscillation matrix. Obtain the grid topology of the target power grid, perform grid node location based on the stability margin matrix and oscillation matrix, and carry out grid risk operation and maintenance management.

2. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 1, characterized in that, Before constructing the impedance twin, the following steps are included: The grid connection point of the first grid-connected device in the target power grid is used as the monitoring grid connection point, wherein the first grid-connected device is at least a grid-connected inverter, and the first grid-connected device is distributedly connected in the target power grid; For each monitoring grid connection point, the control mode of the first grid-connected device is intermittently modulated, and impedance detection is performed under the modulated output.

3. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 2, characterized in that, The control mode of the first grid-connected device is intermittently modulated, and the intermittent modulation method includes: Determine the output power frequency current of the first grid-connected device; A probe signal is set, wherein the probe signal is a low-amplitude, frequency-swept sensing signal; The probe signal is superimposed on the output power frequency current to serve as the modulation output of the first grid-connected device.

4. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 3, characterized in that, Before the control mode of the first grid-connected device performs intermittent modulation, it includes: Receive the monitoring cycle of the target power grid and determine the detection interval time zone based on the monitoring cycle; The first grid-connected device is intermittently modulated and triggered according to the detection interval time zone.

5. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 1, characterized in that, Constructing impedance twins includes: Determine the first detection time point, trigger the power frequency current modulation based on each first grid-connected device, and determine the first modulation output; The probe signal superimposed in the first modulation output propagates with the power grid and is captured by the grid-connected equipment under power flow conditions. The detection response includes at least harmonic changes in voltage and current. Impedance is calculated by analyzing the relationship between probe signal and detection response to determine real-time impedance data, wherein the real-time impedance data characterizes the impedance at the grid connection point. The impedance twin is updated based on the real-time impedance data.

6. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 1, characterized in that, Determine the stability margin matrix of the target power grid, including: For each impedance node in the impedance twin, the power grid loop gain is determined by the ratio of the power grid impedance to the node impedance. Plot a trajectory curve based on the power grid loop gain, wherein the trajectory curve characterizes the relationship between the power grid loop gain and frequency in the complex plane; Based on the trajectory curve, the phase margin and magnitude margin of the target power grid are determined, which serve as the stability margin data for each impedance node, forming a stability margin matrix.

7. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 1, characterized in that, Perform oscillation mode analysis based on the grid admittance matrix to determine the oscillation matrix, including: Using the first grid-connected device as a node, construct a grid admittance matrix based on the target power grid; The power grid admittance matrix is ​​decomposed by eigenvalue to determine the oscillation matrix based on the oscillation mode, wherein the matrix elements of the oscillation matrix include at least the oscillation frequency and the damping strength.

8. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 1, characterized in that, Perform grid node location based on the stability margin matrix and oscillation matrix, and conduct grid risk operation and maintenance management, including: The stability margin matrix and the oscillation matrix are mapped to determine multiple mapping groups, wherein each mapping group includes a stability margin term and an oscillation term, and the multiple mapping groups correspond one-to-one with the first grid-connected device; Obtain the power grid topology of the target power grid, locate and identify the topology nodes of the multiple mapping groups, and determine the power grid operation map.

9. The real-time operation monitoring method for large power grid virtual-real integration as described in claim 8, characterized in that, After determining the power grid operation diagram, the following is included: Scan the power grid operation map and identify the power grid risk map through operation risk identification, with stable weak points and high-risk oscillation modes as identification targets; Power grid operation and maintenance management is carried out based on the aforementioned power grid risk map.

10. A real-time operation monitoring system for large power grids integrating virtual and physical elements, characterized in that: The system is used to implement the real-time operation monitoring method for virtual-real integration of large power grids as described in any one of claims 1 to 9, and the system includes: An impedance twin construction module is used to determine the first grid-connected device in the target power grid and construct an impedance twin by intermittently modulating the first grid-connected device. The grid connection point impedance, geographical location, and electrical connection relationship are used as an impedance node in the impedance twin. The impedance twin is updated intermittently. The oscillation mode analysis module is used to embed the impedance twin into the virtual processing unit, trigger decision-making as the impedance twin is updated, determine the stability margin matrix of the target power grid, and perform oscillation mode analysis based on the power grid admittance matrix in parallel to determine the oscillation matrix. The power grid node location module is used to obtain the power grid topology of the target power grid, perform power grid node location based on the stability margin matrix and oscillation matrix, and carry out power grid risk operation and maintenance management.

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