Power grid high-toughness coordinated operation method under multi-source disturbance scene

Abstract

The invention relates to the technical field of power grid state analysis, in particular to a power grid high-toughness coordinated operation method under a multi-source disturbance scene. According to the method, an inter-frequency influence graph structure is constructed by taking oscillation frequencies as nodes, and a drive influence relationship between oscillation components is analyzed in the graph structure by using a convergence poorer mapping algorithm. Two nodes meeting verification conditions are used as analysis objects, energy transfer between frequencies is represented by means of the significance degree of a peak value, then the edge weight of a graph structure is adjusted, and a verification oscillation image graph is obtained. And obtaining a dominant influence path. And determining the oscillation state of the power grid by analyzing the propagation power on the leading path and the change characteristics of the propagation power and combining the other basic power grid characteristics. According to the method, the power grid state can be identified before the oscillation event occurs by analyzing the propagation power change characteristics of the oscillation frequency on the dominant influence path, the monitoring hysteresis is avoided, and the real-time performance and the decision-making efficiency of power grid coordinated operation decision-making are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid condition analysis technology, and specifically to a method for high-resilience coordinated operation of power grids under multi-source disturbance scenarios. Background Technology

[0002] With the rapid increase in the penetration rate of power electronic interface-type new energy sources, represented by wind power and photovoltaics, in power systems, the dynamic characteristics of the system are shifting from being dominated by traditional synchronous generators to being jointly dominated by numerous grid-connected inverters. Grid-connected inverter technology, due to its ability to simulate the external characteristics of synchronous generators, is considered crucial for supporting the stable operation of systems in future weak grid conditions. When grid-connected inverter units are connected to areas with insufficient grid strength, the complex multi-loop control system within them is highly susceptible to rapid dynamic interaction with the external grid's dynamic characteristics in the subsynchronous / supersynchronous frequency band, inducing millisecond-level electromagnetic oscillations. The fundamental mechanism of this type of oscillation lies in the fact that, under specific operating conditions, the interaction between the grid-connected inverter unit and the grid exhibits an equivalent "negative damping" characteristic. Instead of suppressing the system's inherent oscillation modes, it injects energy into them, causing the oscillations to be rapidly amplified.

[0003] Existing technologies can use online monitoring methods to issue alarms for abnormal fluctuations in electrical quantities such as voltage and power at grid connection points. However, this is essentially a passive response based on oscillation "symptoms." By the time the oscillation phenomenon is clearly measurable, the system is often already in a critical state of instability, leaving a short window for operator intervention. Therefore, direct monitoring methods have a certain lag and cannot effectively analyze the driving characteristics of oscillations, resulting in poor performance in achieving coordinated operation decisions for the power grid. Summary of the Invention

[0004] To address the technical problem that existing monitoring methods suffer from certain lag and cannot effectively analyze the driving characteristics of oscillations, thus leading to poor results in the coordinated operation decision-making of the power grid, the present invention aims to provide a highly resilient coordinated operation method for the power grid under multi-source disturbance scenarios. The specific technical solution adopted is as follows: This invention proposes a method for high-resilience coordinated operation of power grids under multi-source disturbance scenarios, the method comprising: We jointly analyze the complex voltage and complex current signals at the grid connection point of the grid-connected inverter power station to select the set of oscillation frequencies; obtain the injected active power at each oscillation frequency, and obtain the oscillation component of each oscillation frequency in the complex voltage signal; A frequency influence graph structure is constructed using oscillation frequencies as nodes. A convergent cross-mapping algorithm is used to obtain the unidirectional influence coefficient between two oscillation components. The unidirectional influence coefficient is used as the edge weight in the frequency influence graph structure. Each edge in the frequency influence graph structure is traversed. If the frequency difference between the corresponding two nodes satisfies the verification condition, the peak significance of the two nodes in the bispectrum of the complex current signal is obtained. The edge weight is adjusted according to the peak significance to obtain the verification oscillation influence graph. The injected active power of each node is adjusted according to the edge weights in the verification oscillation influence graph to obtain the propagation power; the dominant influence path in the verification oscillation influence graph is found according to the propagation power; the overall propagation power in the dominant influence path is compared with the propagation power at the starting node of the dominant influence path to obtain the propagation path amplification factor. Based on the magnitude of injected active power at the oscillation frequency, the amplification factor of the propagation path, and the structural complexity of the verification oscillation influence diagram, the grid oscillation state is determined, and coordinated operation is carried out according to the grid oscillation state.

[0005] Furthermore, the selection criteria for the oscillation frequency are as follows: Within a preset frequency band, peak searches are performed on both the complex voltage signal and the complex current signal. If a certain frequency corresponds to a peak value in both the complex voltage signal and the complex current signal, and the amplitude of the peak value is greater than a preset energy threshold, then the frequency is taken as the oscillation frequency.

[0006] Furthermore, the formula for obtaining the injected active power includes: ;in The k-th oscillation frequency The injected active power, Re{} represents taking the real part of the complex number, The k-th oscillation frequency The complex spectral values ​​corresponding to the complex voltage signal. The k-th oscillation frequency The complex spectral values ​​corresponding to the complex current signal. This indicates taking the conjugate.

[0007] Furthermore, the method for obtaining the one-way influence coefficient includes: Obtain the correlation coefficient change curve between the predicted value and the true value generated by the two oscillating components in the convergent cross-mapping algorithm; take the average of the last preset number of correlation coefficients on the curve as the one-way influence coefficient.

[0008] Furthermore, the method for obtaining the verification oscillation influence map includes: For two nodes that meet the verification conditions, the significance of the peak value is multiplied by the original edge weight to obtain the adjusted edge weight. For two nodes that do not meet the verification conditions, the adjusted edge weights are set to 0. If the adjusted edge weight is less than the preset edge weight threshold, the corresponding edge is removed to obtain the verification oscillation influence map.

[0009] Furthermore, the method for obtaining the propagation power includes: For a node, if the injected active power is not positive, the propagation power is set to 0; if the injected active power is positive, the product of the injected active power and the edge weight is used as the propagation power.

[0010] Furthermore, the method for obtaining the dominant influence path includes: Starting from any node with non-zero propagation power and ending at any node, traverse all paths and select the path with the largest cumulative propagation power as the dominant influence path.

[0011] Furthermore, the method for obtaining the propagation path amplification factor includes: The propagation path amplification factor is obtained by using the sum of the propagation power of all nodes on the dominant influence path as the numerator and the propagation power of the starting node as the denominator.

[0012] Furthermore, the method for determining the structural complexity includes: If there is more than one dominant influence path, or if the difference between the cumulative propagation power of other paths and the dominant influence path is less than a preset difference threshold, then the structure of the verification oscillation image is determined to be complex.

[0013] Furthermore, the grid oscillation state is determined step-by-step according to a multi-level judgment mechanism, including: The first-level judgment mechanism is used to calculate the cumulative value of all positive injected active power after obtaining the injected active power of each oscillation frequency in the oscillation frequency set as the first judgment value. If the first judgment value is less than the preset safety threshold, the grid oscillation state is judged to be a stable state and the multi-level judgment ends; otherwise, it proceeds to the next level of judgment. The second-level judgment mechanism is used to judge the structural complexity of the oscillation influence diagram. If the structure is complex, the power grid oscillation state is judged to be a systemic coupling conflict state; otherwise, it proceeds to the next level of judgment. The third-level judgment mechanism is used to determine whether the propagation path amplification coefficient is less than the preset coefficient threshold. If it is less, the power grid oscillation state is determined to be a latent source state; otherwise, it is a path-dominated instability state.

[0014] The present invention has the following beneficial effects: To analyze the frequency characteristics of power grid oscillations, this invention uses DC complex signals of current and voltage as the analysis objects. Existing frequency analysis algorithms, such as the Fourier transform algorithm, can be used to filter out the set of oscillation frequencies that generate synchronous oscillations from the two signals. Furthermore, the injected active power reflected by the oscillation frequencies can be used to determine whether it exhibits "negative damping" characteristics. To analyze the driving characteristics between oscillations, this invention constructs a frequency-inter-frequency influence graph structure with oscillation frequencies as nodes. A poor convergence mapping algorithm is used in the graph structure to analyze the driving influence relationship between oscillation components. For power systems, energy transfer occurs between different frequencies. If oscillation linearity is generated, then for a certain frequency, similar sideband components will inevitably be generated in the current. Therefore, this invention further verifies the frequency relationship in the graph structure, using two nodes that meet the verification conditions as the analysis objects. The peak significance level is used to characterize the energy transfer between frequencies, and then the edge weights of the graph structure are adjusted to obtain a verification oscillation image graph. The verification oscillation image graph undergoes unidirectional driving analysis and energy conduction analysis between frequencies, effectively characterizing the frequency driving conditions of oscillation events. Therefore, path analysis is performed on the verification oscillation image graph to obtain the dominant influence path. By analyzing the propagation power and its variation characteristics along the dominant path, combined with other basic grid characteristics, the grid oscillation state can be determined. This invention, through unidirectional drive analysis and energy conduction analysis between oscillation frequencies, obtains the dominant influence path that characterizes the driving trend of oscillation events. Furthermore, analyzing the propagation power variation characteristics of the oscillation frequencies along the dominant influence path allows for the identification of the grid state before oscillation events occur, avoiding monitoring lag and improving the real-time performance and efficiency of grid coordinated operation decisions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a high-resilience coordinated operation method for power grids under multi-source disturbance scenarios proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a highly resilient coordinated operation method for power grids under multi-source disturbance scenarios provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios, provided by an embodiment of the present invention. The method includes: Step S1: Jointly analyze the complex voltage and complex current signals at the grid connection point of the grid-connected inverter power station, and filter out the set of oscillation frequencies; obtain the injected active power of each oscillation frequency, and obtain the oscillation component of each oscillation frequency in the complex voltage signal.

[0021] Because the dynamic interaction between grid-connected inverters and weak power grids occurs on a millisecond-level electromagnetic timescale, its oscillation frequency covers a wide frequency band from subsynchronous to supersynchronous, making it impossible for conventional steady-state measurement systems to capture its complete dynamic process. Therefore, this embodiment of the invention continuously processes the high-frequency electrical quantities at the grid connection point to jointly analyze the voltage and current data at the grid connection point. Considering that the data directly acquired are all AC signals, specifically three-phase AC signals (i.e., the data directly acquired by sensors are three-phase voltage and three-phase current signals), and that three-phase AC signals are not convenient for direct dynamic analysis, the Park transform in power systems can be used to convert the three-phase AC signals into DC signals at the fundamental frequency steady state, namely complex voltage and complex current signals. These two signals contain all the dynamic information in the original three-phase AC signals and can serve as the data basis for spectrum analysis.

[0022] It should be noted that the synchronization reference phase angle required for the Park transformation can be obtained by a phase-locked loop tracking the grid base frequency voltage in real time. The specific transformation method is a technique well-known to those skilled in the art. In this embodiment of the invention, the sampling frequency of the three-phase AC signal is set to 5kHz. The signal in this embodiment is obtained by segmentation using a fixed sliding window with a window length of 2 seconds, i.e., a signal length of 2 seconds. To achieve quasi-online continuous monitoring, adjacent sliding windows overlap. In this embodiment, the overlap is set to 1.5 seconds, i.e., the sliding step size of the window is 0.5 seconds. The processes described subsequently in this embodiment are all implemented within one sliding window, meaning the final grid oscillation is filled with the state within one sliding window. The sub / supersynchronous frequency band of interest in this embodiment is 5Hz to 150Hz.

[0023] For the oscillation frequencies to be selected, the first requirement is that the frequency is in the subsynchronous / supersynchronous band. Secondly, the oscillation frequency must simultaneously affect both voltage and current, and produce a significant impact. This indicates that the selection of oscillation frequencies focuses on the dynamic interaction involving both the inverter and the grid, rather than unilateral internal disturbances. Therefore, this embodiment of the invention can select a set of oscillation frequencies by jointly analyzing the complex voltage and complex current signals at the grid connection point of the grid-connected inverter plant.

[0024] Preferably, in this embodiment of the invention, the selection criteria for the oscillation frequency are: Within a preset frequency band, peak searches are performed on both the complex voltage signal and the complex current signal. If a certain frequency corresponds to a peak value in both the complex voltage signal and the complex current signal, and the amplitude of the peak value is greater than a preset energy threshold, then the frequency is taken as the oscillation frequency. In this embodiment of the invention, the energy threshold can be set according to the specific power grid performance, and this embodiment of the invention does not elaborate or limit it.

[0025] It should be noted that before filtering the oscillation frequencies, a Fast Fourier Transform (FFT) needs to be performed on the complex voltage and complex current signals to obtain the voltage frequency and current spectrum, allowing for filtering of each frequency in the spectrum. Furthermore, to reduce the impact of spectral leakage, embodiments of the present invention can employ FFT with a windowing function (such as a Hamming window) or improved spectral estimation algorithms such as Welch's method.

[0026] In order to measure the damping characteristics of each oscillation frequency, the embodiments of the present invention obtain the injected active power of each oscillation frequency. That is, if the injected active power is positive, it indicates that at the corresponding oscillation frequency, the inverter is injecting energy into the grid in the positive direction and exhibits negative damping characteristics. This oscillation frequency is an energy source of the oscillation event.

[0027] In order to perform causal inference analysis on oscillation events in subsequent steps, it is necessary to separate the dynamic waveforms corresponding to the selected oscillation frequencies from the mixed signal. That is, in this embodiment of the invention, it is necessary to obtain the oscillation component of each oscillation frequency in the complex voltage signal.

[0028] In an embodiment of the present invention, for a certain oscillation frequency A center frequency of can be designed. A narrowband digital bandpass filter can be selected, such as a Butterworth filter or a finite impulse response (FIR) filter; a smaller passband width is further set, which is set to 1Hz in this embodiment. The original complex voltage sequence is used as the input to the filter, and the output is a filter containing only the oscillation frequency. Nearby dynamic, isolated oscillation components.

[0029] Preferably, in this embodiment of the invention, since the acquired signals are all complex signals, the formula for obtaining the injected active power includes the following in order to quantize the power: ;in The k-th oscillation frequency The injected active power, Re{} represents taking the real part of the complex number, The k-th oscillation frequency The complex spectral values ​​corresponding to the complex voltage signal. The k-th oscillation frequency The complex spectral values ​​corresponding to the complex current signal. This indicates taking the conjugate. It should be noted that this formula is an application of the active power formula to complex numbers, a technical feature well-known to those skilled in the art, and will not be elaborated upon or limited here. Where the injected active power is greater than 0, it indicates that at a certain frequency... At this point, the grid connection point outputs active power to the grid, for example, the inverter supplies power to the grid at the fundamental frequency; if the injected active power is less than 0, it indicates that the frequency is below the threshold. In this case, the power grid absorbs active power from the grid connection point. For example, the grid-connected converter absorbs energy from the power grid as a load, or energy flows from the power grid to the grid connection point at a certain harmonic frequency.

[0030] Step S2: Construct an inter-frequency influence graph structure with the oscillation frequency as the node, and use the convergent cross-mapping algorithm to obtain the unidirectional influence coefficient between the two oscillation components. The unidirectional influence coefficient is used as the edge weight in the inter-frequency influence graph structure. Traverse each edge in the inter-frequency influence graph structure. If the frequency difference between the corresponding two nodes satisfies the verification condition, obtain the peak significance of the two nodes in the bispectrum of the complex current signal. Adjust the edge weight according to the peak significance to obtain the verification oscillation influence graph.

[0031] In order to perform causal analysis on oscillation events, this embodiment of the invention employs a graph structure to analyze the oscillation propagation path. Firstly, at the information level, the oscillation propagation path should exhibit a unidirectional driving influence from one oscillation frequency to another. Secondly, at the energy level, this influence must be generated through a nonlinear modulation process of power electronic switches, resulting in verifiable energy transfer between frequencies. Therefore, in step S2, this embodiment of the invention employs both unidirectional driving analysis and energy conduction analysis to construct an effective graph structure for subsequent propagation path analysis.

[0032] First, a unidirectional drive analysis between oscillation frequencies is performed. Since the control systems (such as phase-locked loops, power loops, and current loops) of a grid-connected inverter are cascaded, the dynamics of the outer loop unidirectionally and nonlinearly affect the dynamics of the inner loop. To capture this directional drive relationship, this embodiment employs a convergent cross-mapping method to analyze the unidirectional drive influence between oscillation frequencies. In this embodiment, the oscillation frequency is used as a node. For any two nodes, the unidirectional influence coefficient between the corresponding two oscillation components is obtained through the convergent cross-mapping algorithm, and this coefficient is used as the edge weight between the two nodes.

[0033] It should be noted that the convergent cross-mapping algorithm is a well-known technique in the art, and will not be described in detail here. The embodiments of this invention use an oscillation frequency... To the oscillation frequency Taking the analysis of the one-way influence between two nodes as an example, the oscillation frequency... Corresponding oscillation component and oscillation frequency Corresponding oscillation component Analysis of convergent cross-mapping algorithm: (1) State space reconstruction: because the analysis is of the oscillation frequency To the oscillation frequency Driven by unidirectional influence, if dynamics If there is a causal relationship, then Evolutionary information will be imprinted on In the dynamics, therefore for Perform time-delay embedding to reconstruct its state-space manifold This process is completed by selecting an appropriate embedding dimension and time delay. In this embodiment of the invention, the embedding dimension can be an integer between 3 and 5, and the time delay can be calculated. The autocorrelation function or average mutual information function is used to determine the length. It should be noted that in the convergent cross-mapping algorithm, the reconstruction length of the state-space manifold is increased progressively, eventually reaching the entire... By gradually obtaining the length of the state space manifold, multiple reconstructed lengths are obtained.

[0034] (2) Cross mapping and prediction: using the reconstructed state-space manifold predict The value, to obtain and Predicted sequences of the same length, the predicted sequences are also in There is a corresponding real sequence above.

[0035] (3) Calculation of one-way influence coefficient: Calculate the correlation coefficient between the predicted sequence and the true sequence, and record the curve of the correlation coefficient change as the reconstruction length increases. If the correlation coefficient gradually increases and converges as the reconstruction length increases, a one-way influence coefficient can be quantified. The larger the one-way influence coefficient, the higher the oscillation frequency. To the oscillation frequency The stronger the unidirectional driving relationship between two nodes, the better. In this embodiment of the invention, the correlation coefficient can be the Pearson correlation coefficient, which is a well-known technique among those skilled in the art and will not be described in detail here.

[0036] The frequency influence diagram structure in this embodiment of the invention can be further converted into a matrix form, where the length and width of the matrix are both the number of oscillation frequencies. Each element in the matrix represents the unidirectional influence coefficient between the oscillation frequency represented by the corresponding row and the oscillation frequency represented by the corresponding column.

[0037] Preferably, in this embodiment of the invention, the method for obtaining the one-way influence coefficient includes: Obtain the correlation coefficient change curve between the predicted and actual values ​​generated by the two oscillating components in the convergent cross-mapping algorithm; take the average of the last preset number of correlation coefficients on the curve as the one-way influence coefficient. In this embodiment of the invention, the preset number can be set to 10.

[0038] The frequency influence diagram, which includes the unidirectional driving effect between oscillation frequencies, only reflects the information flow relationship of the time series at the statistical level. In power electronic systems, any control-level influence must ultimately be realized through the nonlinear process of high-frequency switching of power semiconductors, in the form of energy transfer between different frequencies. For example, a low-frequency oscillation... Modulating the fundamental frequency component will inevitably generate a current. The sideband components, of which This is the fundamental frequency of the power grid. Therefore, it is also necessary to perform energy transfer analysis between oscillation frequencies to propose pseudo-transfer relationships without an energy basis.

[0039] This invention employs bispectral analysis as a verification tool, a technique that can effectively detect three-wave coupling phenomena caused by second-order nonlinear effects in signals. Bispectral analysis uses complex current signals as the analysis object, first calculating their bispectrum, which is then plotted at a certain coordinate point. The amplitude directly quantizes the frequency. , and The strength of nonlinear phase coupling occurs between the three frequency components. For each edge in the frequency influence diagram structure, that is, each element after being converted into matrix form in this embodiment of the invention, if the frequency difference between the corresponding two nodes satisfies the verification condition, the peak significance of the two nodes in the bispectrum of the complex current signal is obtained. The larger the peak significance, the more the corresponding two oscillation frequencies satisfy the verification. Therefore, the edge weights can be adjusted according to the peak significance to obtain the verification oscillation influence diagram.

[0040] Specifically, the energy transfer analysis steps in this embodiment of the invention include: (1) Constructing and verifying the hypothesis: For the paths in the frequency influence graph structure The assumption for its energy transfer is: the frequency is The oscillations are related to a known frequency with significant energy in the system. Nonlinear coupling occurs, thereby generating a frequency of The oscillating component. This known frequency is usually the power grid fundamental frequency, typically 50Hz.

[0041] (2) Test the frequency relationship: Under the above verification hypothesis, further test whether there is a frequency relationship. Make and The relationship between summation and difference holds true within a certain tolerance. That is... or The tolerance set in this embodiment of the invention is 0.5Hz, that is... and The summation or difference result is within the range of 0.5Hz. If they are equal, it means that the verification condition is met.

[0042] (3) Verify bispectral evidence: If the above relationship holds, then further obtain the bispectral amplitude at the corresponding position of the current bispectral spectrum. The peak significance of the amplitude is determined. It should be noted that the quantification method for peak significance in this embodiment of the invention can be based on statistical and local background methods, i.e., the peak significance can be accurately quantified by combining the local signal-to-noise ratio with the Z-standard numerator. In this embodiment of the invention, it is further normalized using range standardization, with the value range limited to between 0 and 1.

[0043] It should be noted that the edge weights that need to be adjusted in this invention are edges with non-zero weights in the frequency influence graph structure. Furthermore, if no energy transfer occurs between the two nodes after bispectral verification, the corresponding edge can be eliminated. Specifically, in this embodiment of the invention, the method for obtaining the oscillation influence graph includes: For two nodes that meet the verification conditions, the significance of the peak value is multiplied by the original edge weight to obtain the adjusted edge weight. For two nodes that do not meet the verification conditions, the adjusted edge weights are set to 0. If the adjusted edge weight is less than the preset edge weight threshold, the corresponding edge is removed to obtain the verification oscillation influence map.

[0044] In other words, through the above processing method, edges with an original edge weight of 0 and edges that fail energy verification are removed, ensuring that each edge in the verification oscillation influence graph simultaneously possesses unidirectional driving direction at the information level and transfer evidence at the energy level. In this embodiment of the invention, the edge weight threshold can be set to 0.05.

[0045] Step S3: Adjust the injected active power of each node according to the edge weights in the verification oscillation influence graph to obtain the propagation power; find the dominant influence path in the verification oscillation influence graph based on the propagation power; compare the overall propagation power in the dominant influence path with the propagation power at the starting node of the dominant influence path to obtain the propagation path amplification factor.

[0046] The overall instability of a power grid system is usually caused by an initial energy injection source, which amplifies stepwise along one or more influence paths. Therefore, in order to effectively determine the oscillation state of the power grid, it is necessary to analyze and verify the dominant influence path in the oscillation influence diagram.

[0047] First, it is necessary to assign an explicit power attribute to each node and edge in the verification oscillation influence graph. This involves adjusting the injected active power of each node based on the edge weights in the graph to obtain the propagation power. In other words, each node corresponds to a propagation power. It should be noted that only oscillation frequencies exhibiting negative damping can serve as the starting point of the oscillation propagation chain. Therefore, for oscillation frequencies exhibiting negative injected active power, their propagation power can be directly set to 0.

[0048] Preferably, in this embodiment of the invention, the method for obtaining propagation power includes: For each node, if the injected active power is not positive, the propagation power is set to 0; if the injected active power is positive, the product of the injected active power and the edge weight is used as the propagation power. That is, the propagation power represents the propagation power generated by one node propagating to another, expressed by the formula: ;in For nodes To the node The propagation power of the propagation For nodes The injected active power, For nodes To the node The adjusted edge weights on this edge.

[0049] Based on the propagation power, the dominant influence path in the current verification oscillation influence diagram can be found. That is, on the dominant influence path, a large propagation power is generated through propagation between multiple nodes.

[0050] Preferably, in this embodiment of the invention, the method for obtaining the dominant influence path includes: Starting from any node with non-zero propagation power and ending at any node, traverse all paths and select the path with the largest cumulative propagation power as the dominant influencing path. It should be noted that since the verification oscillation influence graph is a directed acyclic graph (DAG), this process can be solved using dynamic programming algorithms for DAGs, which are well-known techniques in the art and will not be elaborated upon here.

[0051] For the dominant influence path, the propagation power within the dominant influence path can be compared with the propagation power at the starting node of the dominant influence path to obtain the propagation path amplification factor. That is, the larger the path amplification factor, the more serious the oscillation risk.

[0052] Preferably, in this embodiment of the invention, the method for obtaining the propagation path amplification factor includes: The propagation path amplification factor is obtained by using the sum of the propagation power of all nodes on the dominant influence path as the numerator and the propagation power of the starting node as the denominator.

[0053] It should be noted that during the process of identifying the dominant influence path, there may be more than one dominant influence path, or the difference between the cumulative propagation power of other paths and the dominant influence path may be less than a preset difference threshold. This indicates that the structure of the verification oscillation image is too complex, making it impossible to identify a unique dominant path. This situation suggests that the problem is no longer limited to a single source or a clear propagation chain, but rather that a wide-bandwidth, diffuse coupling conflict has occurred between multiple control links, resulting in a fragile system structure and low resilience.

[0054] Step S4: Determine the grid oscillation state based on the magnitude of the injected active power at the oscillation frequency, the propagation path amplification factor, and the structural complexity of the verification oscillation influence diagram, and coordinate operation based on the grid oscillation state.

[0055] This invention aims to improve the resilience of the power grid by effectively identifying grid oscillation states and providing reasonable coordinated decisions. This allows the grid to not only maintain stability in the face of disturbances but also anticipate and suppress potential instability risks. The foundation of this capability lies in a clear understanding of the sources and development mechanisms of risks. Coordinated operation means that when risks occur, control measures should be based on evidence and coordinated actions among multiple entities (such as inverter sites and grid dispatch), rather than a single, isolated response. Currently, when facing oscillations caused by multi-source disturbances, existing power grid operations cannot promptly distinguish whether the problem stems from parameter drift in a single control element, coupling amplification between multiple elements, or systemic structural conflicts. Instead, they can only adopt generalized, tentative adjustment strategies, with unreliable effectiveness. This invention, however, provides a set of qualitative and quantitative identification features for unstable oscillation risks based on the injected active power at the oscillation frequency, the propagation path amplification coefficient, and the structural complexity of the verification oscillation influence diagram. Therefore, the oscillation state of the power grid can be effectively determined based on these features, and coordinated operation can then be implemented according to the grid oscillation state.

[0056] The oscillation state of the power grid is not a single dimension, but is jointly determined by characteristics such as the intensity of the oscillation source, the clarity of the propagation path, and the amplification effect. Therefore, in this embodiment of the invention, the power grid oscillation state is judged step by step according to a multi-level judgment mechanism, specifically including: The first-level judgment mechanism is used to calculate the cumulative value of all positive injected active power after obtaining the injected active power of each oscillation frequency in the oscillation frequency set, and use it as the first judgment value. If the first judgment value is less than the preset safety threshold, the grid oscillation state is judged to be stable and the multi-level judgment ends, indicating that there is no significant negative damping source in the grid system, the internal disturbance can resist the grid system itself, and the grid system has operated to the stable region; otherwise, it proceeds to the next level of judgment. The second-level judgment mechanism is used to judge the structural complexity of the oscillation influence diagram. If the structure is complex, the power grid oscillation state is judged to be a systemic coupling conflict state. This state indicates that the oscillation is no longer limited to a single source or a clear propagation chain, but that a wide-bandwidth, diffuse coupling conflict has occurred between multiple control links. The system structure is fragile and has a low resilience level. Otherwise, it proceeds to the next level of judgment. The third-level judgment mechanism is used to determine whether the propagation path amplification factor is less than a preset threshold. If it is less, the power grid oscillation state is judged to be a latent source state. This state corresponds to the early budding stage of instability risk. A specific control link has begun to exhibit negative damping characteristics, but the unstable energy it generates has not yet found an effective amplification and propagation channel in the system. Otherwise, it is a path-dominated instability state. This state indicates that there is already a clear and significant oscillation propagation path in the power grid system. For example, the oscillation of the phase-locked loop affects the current loop through nonlinear coupling and is eventually amplified.

[0057] In this embodiment of the invention, the preset coefficient threshold can be set to 1.5, and the safety threshold can be specifically set according to parameters such as power grid performance. This embodiment of the invention will not limit or elaborate further.

[0058] The power grid system can implement effective collaborative control strategies based on the power grid state feedback determined above. Specifically, in this embodiment of the invention, these strategies include: (1) If the power grid oscillation is a latent source, it indicates that the oscillation risk source is singular and has not spread. A targeted elimination strategy should be adopted, focusing on the source. The strategy could include suggestions such as "fine-tuning the parameters of the power station." Based on the diagnosis, the oscillation source is... This is related to the dynamic characteristics of the specific control element, such as the phase-locked loop (PLL). It is recommended to adjust the control parameters of this element appropriately (e.g., reduce the PLL bandwidth) to eliminate oscillation sources. This refers to the oscillation frequency corresponding to the starting node of the dominant influence path. This decision only needs to be executed at the power station side and has a relatively small impact on the power grid.

[0059] (2) If the grid oscillation is path-dominant instability, it indicates that the oscillation risk has formed a clear propagation chain. Intervention at both the source and the path is necessary to achieve coordinated control between the power station and the grid, or within multiple links within the power station. Decision-making may include "Recommendation to adopt source-grid coordinated control." Diagnosis reveals that... The dominant power propagation path of the drive. Recommendation: 1. (Site-side - fundamental solution) Adjust the frequency of the oscillation source. 1. Eliminate risk sources by implementing relevant control parameters. 2. (Site-side or grid-side - link breakage) Simultaneously, for frequencies at the end of the path or critical intermediate nodes, implement additional damping control or adjust relevant parameters to block the energy propagation path. For example, if the frequency at the end of the path is related to the current loop, adjust the current loop parameters. (3) If the grid oscillation state is a systemic coupling conflict state, it indicates that the system is on the verge of structural instability, and fine-tuning may fail. A conservative, system-level risk reduction strategy should be adopted to prioritize grid security. The decision content may include "It is recommended to adopt a system-level emergency protection strategy. Diagnosis reveals multi-point or broadband systemic control conflicts. Recommendations: 1. (Site side - mode switching) In order to resolve complex dynamic interactions, immediately switch the main control mode of XX site from grid-forming type (GFM) to grid-following type (GFL). 2. (Grid side - strengthening the grid) If conditions permit, the dispatching side should adjust the grid operation mode (such as putting into operation nearby synchronous generator units) to enhance the grid strength in the area and change the boundary conditions for the oscillation."

[0060] Through the diagnosis of the above-mentioned grid oscillation state and the feedback of collaborative decision-making, the resilience of grid operation was effectively improved in complex scenarios with a high proportion of new energy penetration.

[0061] In summary, this invention uses DC complex signals of current and voltage as the analysis objects, selecting a set of oscillation frequencies that generate synchronous oscillations from these two signals. A frequency-inter-frequency influence graph structure is constructed using the oscillation frequencies as nodes, and a poor convergence mapping algorithm is used within this graph structure to analyze the driving influence relationship between oscillation components. Two nodes meeting the verification conditions are selected as the analysis objects, and the peak significance is used to characterize the energy transfer between frequencies. The edge weights of the graph structure are then adjusted to obtain a verification oscillation image graph. The dominant influence path is then obtained. By analyzing the propagation power and its variation characteristics along the dominant path, combined with other basic grid characteristics, the grid oscillation state can be determined. This invention, through unidirectional driving analysis and energy conduction analysis between oscillation frequencies, obtains the dominant influence path that characterizes the driving trend of oscillation events. Furthermore, analyzing the propagation power variation characteristics of the oscillation frequencies along the dominant influence path allows for the identification of the grid state before oscillation events occur, avoiding monitoring lag and improving the real-time performance and efficiency of grid coordinated operation decisions.

[0062] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for highly resilient coordinated operation of a power grid under multi-source disturbance scenarios, characterized in that, The method includes: We jointly analyze the complex voltage and complex current signals at the grid connection point of the grid-connected inverter power station to select the set of oscillation frequencies; obtain the injected active power at each oscillation frequency, and obtain the oscillation component of each oscillation frequency in the complex voltage signal; A frequency influence graph structure is constructed using oscillation frequencies as nodes. A convergent cross-mapping algorithm is used to obtain the unidirectional influence coefficient between two oscillation components. The unidirectional influence coefficient is used as the edge weight in the frequency influence graph structure. Each edge in the frequency influence graph structure is traversed. If the frequency difference between the corresponding two nodes satisfies the verification condition, the peak significance of the two nodes in the bispectrum of the complex current signal is obtained. The edge weight is adjusted according to the peak significance to obtain the verification oscillation influence graph. The injected active power of each node is adjusted according to the edge weights in the verification oscillation influence graph to obtain the propagation power; the dominant influence path in the verification oscillation influence graph is found according to the propagation power; the overall propagation power in the dominant influence path is compared with the propagation power at the starting node of the dominant influence path to obtain the propagation path amplification factor. Based on the magnitude of injected active power at the oscillation frequency, the amplification factor of the propagation path, and the structural complexity of the verification oscillation influence diagram, the grid oscillation state is determined, and coordinated operation is carried out according to the grid oscillation state.

2. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The selection criteria for the oscillation frequency are as follows: Within a preset frequency band, peak searches are performed on both the complex voltage signal and the complex current signal. If a certain frequency corresponds to a peak value in both the complex voltage signal and the complex current signal, and the amplitude of the peak value is greater than a preset energy threshold, then the frequency is taken as the oscillation frequency.

3. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The formula for obtaining the injected active power includes: ;in The k-th oscillation frequency The injected active power, Re{} represents taking the real part of the complex number, The k-th oscillation frequency The complex spectral values ​​corresponding to the complex voltage signal. The k-th oscillation frequency The complex spectral values ​​corresponding to the complex current signal. This indicates taking the conjugate.

4. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The method for obtaining the one-way influence coefficient includes: Obtain the correlation coefficient change curve between the predicted value and the true value generated by the two oscillating components in the convergent cross-mapping algorithm; take the average of the last preset number of correlation coefficients on the curve as the one-way influence coefficient.

5. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The method for obtaining the verification oscillation influence diagram includes: For two nodes that meet the verification conditions, the significance of the peak value is multiplied by the original edge weight to obtain the adjusted edge weight. For two nodes that do not meet the verification conditions, the adjusted edge weights are set to 0. If the adjusted edge weight is less than the preset edge weight threshold, the corresponding edge is removed to obtain the verification oscillation influence map.

6. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 3, characterized in that, The method for obtaining the propagation power includes: For a node, if the injected active power is not positive, the propagation power is set to 0; if the injected active power is positive, the product of the injected active power and the edge weight is used as the propagation power.

7. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 6, characterized in that, The method for obtaining the dominant influence path includes: Starting from any node with non-zero propagation power and ending at any node, traverse all paths and select the path with the largest cumulative propagation power as the dominant influence path.

8. The method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The method for obtaining the propagation path amplification factor includes: The propagation path amplification factor is obtained by using the sum of the propagation power of all nodes on the dominant influence path as the numerator and the propagation power of the starting node as the denominator.

9. A method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 7, characterized in that, The methods for determining the structural complexity include: If there is more than one dominant influence path, or if the difference between the cumulative propagation power of other paths and the dominant influence path is less than a preset difference threshold, then the structure of the verification oscillation image is determined to be complex.

10. A method for high-resilience coordinated operation of a power grid under multi-source disturbance scenarios according to claim 1, characterized in that, The power grid oscillation state is determined step-by-step according to a multi-level judgment mechanism, including: The first-level judgment mechanism is used to calculate the cumulative value of all positive injected active power after obtaining the injected active power of each oscillation frequency in the oscillation frequency set as the first judgment value. If the first judgment value is less than the preset safety threshold, the grid oscillation state is judged to be a stable state and the multi-level judgment ends; otherwise, it proceeds to the next level of judgment. The second-level judgment mechanism is used to judge the structural complexity of the oscillation influence diagram. If the structure is complex, the power grid oscillation state is judged to be a systemic coupling conflict state; otherwise, it proceeds to the next level of judgment. The third-level judgment mechanism is used to determine whether the propagation path amplification coefficient is less than the preset coefficient threshold. If it is less, the power grid oscillation state is determined to be a latent source state; otherwise, it is a path-dominated instability state.

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