A power grid multi-time-space risk assessment method and system considering tidal current evolution

Abstract

The application discloses a kind of power grid multi-time-space risk assessment method and system considering tidal flow evolution, it is related to power grid risk assessment technical field, including the following steps: obtaining the continuous power grid operation section in target evaluation window, determine the tidal flow state and operating margin of each power grid object, and generate non-fault continuous tidal flow evolution segment by screening;Risk mitigation object and risk enhancement object are identified in tidal flow evolution segment;Risk bearing switching relationship is generated based on the topological relationship between the two and the corresponding relationship of tidal flow increase and decrease;Risk bearing evolution chain is formed according to risk bearing switching relationship, and multi-time-space risk assessment result is output.The application can identify the switching process of risk bearing object in non-fault continuous operation process, avoid misjudging local risk mitigation as overall risk elimination.

Description

Technical Field

[0001] This invention relates to the field of power grid risk assessment technology, and more specifically, to a multi-temporal and spatial risk assessment method and system for power grids that takes into account power flow evolution. Background Technology

[0002] Existing power grid operation risk assessment methods typically calculate risk indicators such as line overload, main transformer overload, bus voltage exceeding limits, and load loss based on a single operating section, a post-fault section, or an N-1 verification scenario, and output risk levels or risk warning results accordingly. These methods can identify exceeding-limit risks at a specific moment and can also identify potentially dangerous lines or cascading overloaded equipment in post-fault power flow redistribution scenarios.

[0003] However, during non-fault continuous operation processes such as fluctuations in renewable energy output, load ramp-up, energy storage charging and discharging switching, and tie switch adjustments, power flow gradually redistributes between adjacent time periods. At this time, a decrease in risk at a particular line, main transformer, or bus does not necessarily indicate that the overall risk has been eliminated; rather, it may mean that the risk-bearing object has shifted from that grid object to another topologically related object. Existing methods typically lack the ability to identify the correlation between risk-mitigating and risk-enhancing objects between adjacent operating sections, making it difficult to determine whether the risk-bearing object has shifted with the evolution of power flow. This leads operators to easily misjudge local risk mitigation as overall risk mitigation.

[0004] The above-disclosed technical solutions have at least the following technical problems: existing power grid risk assessment methods are mostly based on the risk value of a single section or the parallel comparison of risk values ​​of multiple sections. It is difficult to identify the process of risk-bearing objects switching with the power flow during non-fault continuous operation. It is easy to misjudge the local risk mitigation of a certain power grid object as the elimination of the overall risk of the target power grid. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for multi-temporal risk assessment of power grids that takes into account power flow evolution. By identifying the risk-bearing switching relationship between risk mitigation objects and risk enhancement objects within non-fault-continuous power flow evolution segments, the present invention addresses the problems in existing power grid risk assessments, such as the difficulty in identifying the switching of risk-bearing objects with power flow evolution and the tendency to misjudge local risk mitigation as overall risk elimination.

[0006] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, a multi-temporal risk assessment method for power grids that considers power flow evolution includes the following steps: obtaining continuous power grid operation sections within the target assessment window, determining the power flow status and operating margin of each power grid object, and generating power flow evolution segments based on non-fault continuity; within the power flow evolution segments, determining risk mitigation objects and risk enhancement objects based on the changes in the operating margin of power grid objects between adjacent sections; generating risk carrying switching relationships based on the topological association relationship and the corresponding relationship of power flow increase and decrease between risk mitigation objects and risk enhancement objects; forming a risk carrying evolution chain based on the risk carrying switching relationship, and outputting multi-temporal risk assessment results based on the risk carrying evolution chain.

[0007] In a preferred embodiment, the step of generating power flow evolution segments based on non-fault continuity screening includes: acquiring operational event information between adjacent power grid operating sections, determining whether there is a fault event that causes the interruption of the correspondence between power grid objects; if there is, then the time corresponding to the fault event is used as the segment boundary; if there is no fault event, and the power grid objects between adjacent power grid operating sections can establish a correspondence, then the adjacent power grid operating sections are assigned to the same power flow evolution segment.

[0008] In a preferred embodiment, determining risk mitigation targets and risk enhancement targets based on the changes in the operating margin of the power grid target between adjacent sections includes: simulating the operating margin according to the constraint type of the power grid target to generate a risk proximity score; determining the risk carrying capacity based on the risk proximity score and its relative distribution within the power flow influence range; determining candidate mitigation targets and candidate enhancement targets based on the changes in risk carrying capacity between adjacent sections; verifying the candidate mitigation targets and candidate enhancement targets, and determining the candidate targets that pass the verification as risk mitigation targets or risk enhancement targets.

[0009] In a preferred embodiment, the step of unifying the operating margin according to the constraint type of the power grid object to generate a risk proximity score includes: determining the corresponding constraint boundary according to the constraint type of the power grid object, and converting the operating margin under different constraint types into a risk proximity score in a unified direction according to the rule that the closer the current operating value is to the corresponding constraint boundary, the greater the risk proximity score; when the power grid object corresponds to multiple constraint boundaries at the same time, the constraint boundary with the greatest proximity score is selected to generate the risk proximity score.

[0010] In a preferred embodiment, determining the risk carrying capacity by combining the risk proximity and its relative distribution within the power flow influence range includes: determining the power flow influence range corresponding to the power grid object based on the power grid topology and power flow direction; statistically analyzing the risk proximity distribution of each power grid object within the power flow influence range; and determining the risk carrying capacity of the target power grid object in the corresponding power grid operating section based on the risk proximity of the target power grid object and its relative proportion in the risk proximity distribution.

[0011] In a preferred embodiment, the verification of candidate mitigation targets and candidate enhancement targets includes: calculating the overall risk pressure change within the tidal current influence range of the candidate target, and combining the changes in operating limits and fluctuation range to exclude candidate mitigation targets caused by the overall risk pressure decrease, candidate enhancement targets caused by the adjustment of operating limits, and candidate targets caused by the measurement or calculation of fluctuations, and determining the remaining candidate targets as risk mitigation targets or risk enhancement targets.

[0012] In a preferred embodiment, generating a risk-bearing switching relationship based on the topological association and tidal flow increase / decrease correspondence between the risk mitigation object and the risk enhancement object includes: constructing a directed tidal flow influence map based on first data within a tidal flow evolution segment; the first data includes topological connectivity, switching status, and tidal flow direction; determining the reachable tidal flow influence path from the risk mitigation object to the risk enhancement object in the directed tidal flow influence map; performing pairing verification based on the reachable tidal flow influence path, tidal flow increase / decrease matching degree, and risk type compatibility relationship; if the verification passes, generating a risk-bearing switching relationship from the risk mitigation object to the risk enhancement object.

[0013] In a preferred embodiment, the topological connection relationship is specifically obtained as follows: taking the risk mitigation object as the preceding bearing object, search for the reachable power flow influence path from it to the risk enhancement object in the directed power flow influence map; if there is a reachable power flow influence path, and the connection relationship in the reachable power flow influence path is in an effective operating state within adjacent sections, then it is determined that the risk mitigation object and the risk enhancement object have a topological association relationship for risk bearing switching judgment; if there is no reachable power flow influence path, or the reachable power flow influence path is blocked by a non-operating connection relationship, then it is determined that the two do not have a topological association relationship for risk bearing switching judgment.

[0014] In a preferred embodiment, the step of outputting multi-temporal risk assessment results based on the risk bearer evolution chain includes: sorting the risk bearer switching relationships according to the switching occurrence sequence, and merging adjacent risk bearer switching relationships in a chain to form a risk bearer evolution chain; and outputting multi-temporal risk assessment results based on the changes in the bearer objects, the impact paths, and the link strength represented by the risk bearer evolution chain.

[0015] On the other hand, a multi-temporal risk assessment system for power grids that considers power flow evolution includes the following modules: a continuity verification module, used to acquire continuous power grid operating sections within the target assessment window, determine the power flow status and operating margin of each power grid object, and generate power flow evolution segments based on non-fault continuity screening; an object confirmation module, used to determine risk mitigation objects and risk enhancement objects within the power flow evolution segments based on the changes in the operating margin of power grid objects between adjacent sections; a switching relationship identification module, used to generate risk-bearing switching relationships based on the topological association relationship and the corresponding relationship of power flow increase and decrease between risk mitigation objects and risk enhancement objects; and a result output module, used to form a risk-bearing evolution chain based on the risk-bearing switching relationship, and output multi-temporal risk assessment results based on the risk-bearing evolution chain.

[0016] The technical effects and advantages of the present invention, which proposes a multi-temporal and spatial risk assessment method and system for power grids that takes into account power flow evolution, are as follows: This invention performs non-fault continuity screening on continuous power grid operation sections within the target assessment window. It first eliminates interference from sudden scenarios such as fault tripping and fault reconfiguration on risk assessment. Then, it identifies risk mitigation and risk enhancement targets within the same power flow evolution segment. Combining the topological relationships and power flow increase / decrease correspondences between these targets, it generates risk-bearing switching relationships, further forming a risk-bearing evolution chain. Therefore, this invention can identify the process of risk pressure shifting from one power grid object to another during non-fault continuous operation, moving beyond simply comparing risk values ​​at a single section or multiple sections. This avoids misjudging a localized risk decrease in a line, transformer, or bus as an elimination of overall power grid risk. The risk assessment results reflect the temporal continuity, spatial transferability, and current bearing location of the risk, providing dispatchers with a more accurate basis for determining subsequent key targets. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a multi-temporal and spatial risk assessment method for power grids that takes into account power flow evolution, according to the present invention. Figure 2 This is a schematic diagram of the structure of a power grid multi-temporal risk assessment system that takes into account power flow evolution according to the present invention; Figure 3 A schematic diagram illustrating the process of identifying risk-bearing switching relationships; Figure 4 This is a diagram showing the variation in risk bearing capacity between adjacent cross sections. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, Figure 1 This invention presents a multi-temporal and spatial risk assessment method for power grids that takes into account power flow evolution, comprising the following steps: S1, obtain the continuous power grid operation section within the target evaluation window, determine the power flow status and operating margin of each power grid object, and generate power flow evolution segments based on non-fault continuity. In this embodiment, the continuous power grid operation section refers to a set of multiple power grid operation states formed within the target evaluation window according to the acquisition time, prediction time, or scheduling plan execution time. Adjacent power grid operation sections have corresponding power grid object identifiers, traceable topological connection relationships, and comparable operational constraint calibers. Each power grid operation section is used to characterize the topological connection state, electrical operation state, and operational constraint state of the target power grid at the corresponding time.

[0020] The target evaluation window can be a historical evaluation window that traces back from the current moment, a predictive evaluation window that extrapolates from the current moment, or a hybrid evaluation window that includes historical sections, current sections, and predictive sections.

[0021] Each power grid operation section includes at least one of the following: power grid topology information, power grid object operation values, power grid object operation limits, switch status, dispatch operation records, protection action records, load change information, new energy output information, and energy storage charging and discharging information. The power grid objects include at least one of the following: lines, main transformers, busbars, feeder sections, interconnection channels, distributed power generation grid connection points, energy storage access points, and load aggregation points.

[0022] The power flow status of each power grid object is determined as follows: Based on the active power, reactive power, current, voltage phase angle, switch status, and topological connection relationships in each power grid operating section, determine the power flow direction, power flow amplitude, and power flow variation of each power grid object in the corresponding section.

[0023] The power flow direction of lines, feeder sections, and interconnection channels is determined based on the power flow direction or phase angle relationship of their two end nodes; the power flow direction of the main transformer is determined based on the power flow direction between the high-voltage side and the low-voltage side; the power flow state of the bus is determined based on the injected power and outflow power of the branch connected to the bus; and the power flow state of the distributed power grid connection point and energy storage access point is determined based on its injected power or absorbed power.

[0024] Specifically, for power grid object i, the power flow change between section k and section k+1 for:

[0025] in, For tidal changes Let i be the power flow amplitude of the power grid object i in section k. Let be the power flow amplitude of power grid object i in section k+1. A value greater than 0 indicates an increase in power flow for the power grid object; if A value less than 0 indicates a decrease in power flow for that power grid object.

[0026] The operational margins of each power grid object are determined as follows: Obtain the operating value and corresponding operating limit of the power grid object in the current power grid operating section, and determine the operating margin based on the difference between the operating limit and the operating value.

[0027] For lines, main transformers, feeder sections, and interconnection channels, the operating margin is determined based on the difference between the load limit and the current load value; for buses, the operating margin is determined based on the distance between the current voltage and the upper or lower voltage limit; for distributed power grid connection points or energy storage access points, the operating margin is determined based on grid-connected power constraints, reverse power constraints, or charging and discharging power constraints.

[0028] Specifically, for power grid objects with upper limit constraints, their operational margin... for:

[0029] For power grid objects with upper and lower limit constraints, their operating margin for:

[0030] in, The operating margin of power grid object i in section k. The operating limits corresponding to power grid object i. This represents the operating value of power grid object i in section k. This is the lower bound of the operation of power grid object i. This represents the upper limit of operation for grid object i. The operating value is determined based on the constraint type of the grid object and includes at least one of the following: line current, line active power, main transformer load rate, bus voltage, tie-line power, grid connection point reverse power, or energy storage charging and discharging power.

[0031] In this embodiment, the power flow evolution segment refers to a segment selected from continuous power grid operation segments that has not experienced fault-related operational interruptions between adjacent segments and whose power flow changes have a continuous evolutionary relationship.

[0032] The power flow evolution segment is used to characterize the power flow gradual change, power flow reversal, power flow convergence, or power flow dispersion processes of the target power grid during non-fault continuous operation. Each power flow evolution segment includes at least two adjacent power grid operating sections, and there is no operational state interruption caused by fault isolation, protection tripping, post-fault reconfiguration, or N-1 simulation triggering between adjacent power grid operating sections within the power flow evolution segment.

[0033] The generation of power flow evolution segments based on non-fault continuity screening includes: Obtain operational event information between adjacent power grid operating sections. The operational event information includes at least one of the following: protection action information, fault alarm information, switch change information, dispatch operation record, N-1 simulation marker, load change information, new energy output change information, and energy storage charging and discharging status change information. Determine whether a fault-related breakage event has occurred between adjacent power grid operating sections based on operational event information; When there are protection tripping, fault isolation, post-fault reconstruction, accident circuit breaker, N-1 simulation triggering, or faulty equipment out of operation between adjacent power grid operating sections, it is determined that the non-fault continuity between adjacent power grid operating sections is not satisfied, and the adjacent power grid operating sections are divided into different power flow evolution segments. When the changes between adjacent power grid operating sections are caused by natural load fluctuations, changes in new energy output, or switching of energy storage charging and discharging, and no protection action, fault alarm, or fault isolation event is detected, it is determined that the adjacent power grid operating sections meet the non-fault continuity requirement, and the adjacent power grid operating sections are kept in the same power flow evolution segment. When there are planned scheduling operations, faultless switch adjustments, or interconnection and transfer operations between adjacent power grid operating sections, if the power grid objects before and after the operation can establish a corresponding relationship through equipment identification, topology connection relationship, or transfer path, and no protection action, fault alarm, or fault isolation event is detected, then it is determined that the adjacent power grid operating sections meet the non-fault continuity requirement, and the adjacent power grid operating sections are retained in the same power flow evolution segment; if the power grid objects before and after the operation cannot establish a corresponding relationship, then the operation time is taken as the segment boundary of the power flow evolution segment. Based on the above judgment results, the continuous power grid operation section within the target evaluation window is segmented to generate one or more power flow evolution segments.

[0034] Furthermore, when the adjustment or transfer operation of the tie switch is accompanied by a fault alarm, protection action, or fault isolation marker, it is treated as a fault-related disconnection event; when the adjustment or transfer operation of the tie switch corresponds to a planned operation record and is not accompanied by a fault alarm, protection action, or fault isolation marker, it is treated as a non-fault continuous operation event.

[0035] Existing multi-section risk assessment methods typically calculate risk values ​​for multiple operating sections in chronological order, or analyze the risk of heavy overload caused by power flow redistribution in sections after a fault. While these methods can obtain the risk level of each section or the dangerous objects after a fault, they do not distinguish whether the power flow changes between adjacent sections belong to the same non-fault continuous operation process. If sections after a fault trip, N-1 simulated sections, reconstructed sections after fault isolation, and normally operating sections are directly compared continuously, it is easy to mistake the sudden change caused by the fault for a risk-bearing switch in normal operation; if the relationship between adjacent sections is not compared at all, it is impossible to identify the changes in risk-bearing objects caused by new energy fluctuations, load ramping, or energy storage switching during non-fault operation.

[0036] In this embodiment, the sudden changes in cross-section caused by post-fault reconstruction, protection actions, and N-1 simulation triggering are distinguished from the power flow evolution during continuous operation without faults. Subsequent risk mitigation and risk enhancement targets are identified only within power flow evolution segments that satisfy non-fault continuity. Thus, the subsequently generated risk-bearing switching relationship is neither a post-fault power flow transfer relationship nor an isolated cross-section risk value change, but rather a risk-bearing change relationship caused by gradual power flow changes or planned adjustments during the same non-fault operation.

[0037] S2, within the power flow evolution segment, based on the changes in the operating margin of the power grid object between adjacent sections, determine the risk mitigation objects and risk enhancement objects; In this embodiment, the risk mitigation target refers to a power grid object that, within a power flow evolution segment, was already in a risk-bearing state in the previous power grid operation section, and whose risk-bearing intensity in the subsequent power grid operation section is effectively reduced compared to the previous power grid operation section. The risk mitigation target is used to characterize the reduced risk-bearing capacity of the power grid object to the original operational risks, but does not indicate that the overall risk of the target power grid has been eliminated. The risk-bearing state refers to the state where the risk proximity of the power grid object is greater than a preset proximity threshold, or the risk-bearing intensity is greater than a preset carrying intensity threshold.

[0038] The risk-enhanced object refers to a power grid object whose risk-bearing intensity in a later power grid operating section is effectively increased relative to the previous power grid operating section within a power flow evolution segment. The risk-enhanced object is used to characterize that the power grid object is generating new risk pressures or may absorb risk pressures released by other power grid objects.

[0039] The process of determining risk mitigation targets and risk enhancement targets based on the changes in the operational margin of power grid objects between adjacent sections includes: Based on the constraint type of the power grid object, the operating margin of the power grid object in each power grid operating section is homogenized to generate a risk proximity degree; wherein, the homogenization process is used to ensure that power grid objects under different constraint types are expressed in the direction of higher risk and greater risk proximity degree. Based on the risk proximity, and combined with the risk proximity of other power grid objects within the power flow influence range of the power grid object, the risk bearing capacity of the power grid object in the corresponding section is determined; Compare the risk carrying capacity of the same power grid object between adjacent power grid operating sections to generate the risk carrying capacity change. Based on the change in risk carrying capacity, power grid objects with reduced risk carrying capacity are marked as candidate mitigation targets, and power grid objects with increased risk carrying capacity are marked as candidate enhancement targets. Based on the changes in overall risk and pressure, changes in operational limits, and the range of measured fluctuations within the current evolution segment, the effectiveness of candidate mitigation and enhancement targets is verified. Candidate mitigation targets that pass the validity verification are identified as risk mitigation targets, and candidate enhancement targets that pass the validity verification are identified as risk enhancement targets.

[0040] The process of aligning the operational margins of power grid objects across different power grid operating sections based on their constraint types to generate risk proximity includes: For power grid objects with upper limit constraints, the risk proximity is determined based on how close the current operating value of the power grid object is to the operating upper limit. For a power grid object with a lower limit constraint, the risk proximity is determined based on how close the current operating value of the power grid object is to the lower operating limit. For a power grid object that has both upper limit constraints and lower limit constraints, calculate its degree of approach to the upper limit and the degree of approach to the lower limit respectively, and take the larger one as the risk proximity. For grid objects with reverse power flow constraints, charge / discharge constraints, or grid-connected power constraints, the risk proximity is determined based on the proximity of the corresponding constraint boundary under the current power flow direction of the grid object.

[0041] The specific formula for calculating the risk proximity is as follows:

[0042] in, Let i be the risk proximity of the power grid object i in section k. The operating margin of power grid object i in section k. This represents the attention margin threshold corresponding to grid object i. The smaller the operating margin, the greater the risk proximity; when the operating margin is greater than or equal to the attention margin threshold, the risk proximity is 0.

[0043] The attention margin threshold is used to determine whether a power grid object falls within the scope of risk attention. The attention margin threshold is determined based on the power grid object's operating limits, early warning attention boundaries, historical normal fluctuation range, and minimum regulation resolution.

[0044] For power grid objects with only upper limit constraints, focus on the margin threshold. for:

[0045] For power grid objects with only lower bound constraints, focus on the margin threshold. for:

[0046] in, This represents the upper limit of the operation of power grid object i. This represents the early warning focus boundary corresponding to power grid object i. For the measurement error or historical stability fluctuation amplitude of power grid object i, This represents the minimum adjustment resolution corresponding to power grid object i. This is the lower limit of operation for power grid object i.

[0047] For a power grid object that has both upper limit constraints and lower limit constraints, determine the upper limit concern margin threshold and the lower limit concern margin threshold respectively, and select the corresponding concern margin threshold based on the constraint boundary that the current operating value is close to.

[0048] The determination of the risk bearing capacity of the power grid object in the corresponding cross section based on risk proximity, combined with the risk proximity of other power grid objects within the power flow influence range of the power grid object, includes: Determine the power flow influence range of the power grid object based on the power grid topology and power flow direction; The distribution of risk proximity among various power grid objects within the statistical power flow influence range; The risk bearing capacity of the target power grid object is determined based on its risk proximity and its relative proportion within the power flow influence range.

[0049] The specific formula for calculating the risk bearing capacity is as follows:

[0050] in, Let i be the risk bearing capacity of the power grid object i in section k. Let i be the risk proximity of the power grid object i in section k. The risk-bearing weight of power grid object i within its power flow influence range is determined based on the load ratio, power flow change contribution, topological location, and relative proportion of risk proximity of the power grid object.

[0051] For power grid object i, the change in risk bearing capacity between adjacent cross sections k and k+1 for:

[0052] in, This represents the change in risk bearing capacity between adjacent cross sections k and k+1. Let i be the risk bearing capacity of power grid object i in section k+1. Let be the risk bearing capacity of power grid object i in section k. When When the load release threshold is less than negative, the power grid object is marked as a candidate for mitigation; when... If the load is greater than the load enhancement threshold, the power grid object is marked as a candidate enhancement object.

[0053] The load release threshold and load enhancement threshold are used to distinguish between effective changes and normal fluctuations in risk load intensity. They are determined based on the load intensity fluctuations, measurement errors, power flow calculation errors, and minimum identifiable changes of the power grid object in historical non-fault stable operation segments. The load enhancement threshold is less than or equal to the load release threshold so that the risk enhancement identification has higher early warning sensitivity.

[0054] The effectiveness verification of candidate mitigation and enhancement targets is based on changes in overall risk and pressure, operational limit changes, and measured fluctuation range within the current evolution segment, including: Calculate the overall risk and pressure changes within the corresponding tidal influence range of a tidal evolution segment; When the risk bearing intensity of a candidate mitigation object decreases, and the overall risk pressure of the tidal current's influence range decreases simultaneously without any candidate enhancement objects appearing, the candidate mitigation object is identified as an object with overall risk reduction and is not included in the set of risk mitigation objects used to generate risk bearing switching relationships. When the risk bearing capacity of a candidate enhancement object increases, but its corresponding operating limit is tightened between adjacent sections, and the power flow state does not change accordingly, the candidate enhancement object is identified as a pseudo enhancement object caused by the limit change. When the change in risk bearing of a candidate mitigation object or candidate enhancement object does not exceed the corresponding measurement fluctuation range or calculation error range, it is identified as a fluctuation object. After removing objects that reduce overall risk, pseudo-enhancement objects, and volatile objects, the remaining candidate mitigation objects are determined as risk mitigation objects, and the remaining candidate enhancement objects are determined as risk enhancement objects.

[0055] In this embodiment, instead of simply determining risk objects based on increases or decreases in the operational margin of the power grid, the operational margin under different constraint types is first converted into a risk proximity in a unified direction. Then, the risk carrying capacity is determined by combining the relative risk distribution within the power flow influence range. Furthermore, spurious changes in risk pressure, operational limit changes, and measured fluctuation ranges are used to eliminate objects experiencing false changes. Therefore, the identified risk mitigation and risk enhancement objects more accurately reflect the risk carrying capacity release and increase during the same non-faulty power flow evolution process, providing a reliable basis for subsequent identification of risk carrying capacity switching relationships.

[0056] Figure 4 This graph illustrates the changes in risk-bearing intensity of different power grid objects in adjacent operating sections. The horizontal axis represents the time sequence of the operating sections, k, k+1, and k+2 respectively; the vertical axis represents the normalized value of risk-bearing intensity, with higher values ​​indicating greater risk pressure borne by the object. As shown in the graph, the risk-bearing intensity of line L1 decreased from 1.60 to 1.15, and further to 0.80, indicating a decreasing trend in the local risk of line L1. However, simultaneously, the risk-bearing intensity of main transformer T2 increased from 0.60 to 1.20, and further to 1.50, while the risk-bearing intensity of feeder F3 also increased from 0.40 to 0.60, and further to 1.10. This change indicates that although the risk of line L1 decreased, the risk pressure was not eliminated entirely, but rather gradually shifted to be borne by main transformer T2 and feeder F3. This figure can intuitively demonstrate the necessity of identifying the risk-bearing evolution chain in this invention. That is, the overall risk of the power grid cannot be judged to be alleviated simply based on the risk reduction of a certain object. Instead, the risk-bearing changes of multiple power grid objects in adjacent sections should be combined to identify the current risk-bearing object and the subsequent warning object.

[0057] S3, based on the topological relationship and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects, generate risk bearing switching relationship; In this embodiment, the topological association refers to the electrical influence relationship formed between a risk mitigation object and a risk enhancement object within the same power flow evolution segment, based on the grid topology connection, switch status, and power flow direction. The topological association includes direct and indirect topological associations. A direct topological association refers to a direct connection between a risk mitigation object and a risk enhancement object via the same busbar, main transformer, feeder section, tie switch, or power transfer channel. An indirect topological association refers to a location between a risk mitigation object and a risk enhancement object within the same power flow influence path, the same power transfer path, the same upstream power supply path, the same tie power supply range, or the same power flow convergence / dispersion area, and a traceable electrical connection path exists between them.

[0058] The topological association is used to limit whether there is a spatial basis for risk-bearing switching between risk mitigation objects and risk enhancement objects. Power grid objects that are only located in the same administrative region or the same substation statistical range but do not have a power flow influence path are not considered to have a topological association.

[0059] In this embodiment, the power flow increase / decrease correspondence refers to the correspondence between the decrease in power flow carrying capacity of a risk mitigation object and the increase in power flow carrying capacity of a risk enhancement object within the same power flow evolution segment, in terms of the direction of change, the time of change, the magnitude of change, and the power flow path. This power flow increase / decrease correspondence is used to characterize whether the decrease in power flow or the release of risk pressure in a risk mitigation object and the increase in power flow or the increase in risk pressure in a risk enhancement object belong to the same power flow redistribution process. If the decrease in power flow in a risk mitigation object and the increase in power flow in a risk enhancement object only occur simultaneously in numerical values, but there is no topological influence path between them, or if the risk enhancement of the enhancement object is independently caused by changes in local load, changes in grid-connected output, or limit adjustments, then a power flow increase / decrease correspondence is not considered to exist.

[0060] In this embodiment, the risk-bearing switching relationship refers to the directed association relationship in which the risk pressure originally borne by a risk-mitigation object is transferred to a risk-enhancing object after the current flow redistribution within the same power flow evolution segment. The risk-bearing switching relationship includes the preceding risk-bearing object, the following risk-bearing object, the switching time period, the topological association path, the current flow increase / decrease matching degree, and the risk-bearing switching intensity; wherein, the preceding risk-bearing object is the risk-mitigation object, and the following risk-enhancing object is the risk-enhancing object.

[0061] The generation of risk-bearing switching relationships based on the topological association and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects includes: Based on the topological connection relationship, switch status and power flow direction of each power grid operating section within the power flow evolution segment, a directed power flow influence diagram between adjacent sections is constructed. In the directed current influence graph, the risk mitigation object is taken as the starting object and the risk enhancement object is taken as the candidate receiving object. The current influence path between the two is searched, and the topological relationship between the risk mitigation object and the risk enhancement object is determined according to the current influence path. Based on the power flow change of the risk mitigation object, the risk carrying release, the power flow change of the risk enhancement object, and the risk carrying increase, calculate the power flow increase / decrease matching degree between the risk mitigation object and the risk enhancement object; Based on topological relationships, power flow increase / decrease matching degree, and risk type compatibility, risk mitigation objects and risk enhancement objects are paired and verified. When the pairing verification passes, a risk bearer switching relationship is generated, pointing from the risk mitigation object to the risk enhancement object.

[0062] Furthermore, the directed power flow influence diagram is used to characterize the direction and range of power flow changes that may be transmitted between adjacent sections. The directed power flow influence diagram uses power grid objects as graph nodes or graph edges, and uses closed switches, bus connections, main transformer connections, feeder connections, tie channel connections, and transfer paths as connection relationships, and determines the direction of the connection relationships based on the changes in power flow direction between adjacent sections.

[0063] When a connection is closed or in an effective power supply state between adjacent sections, and the power flow direction is consistent with the influence direction from the risk mitigation object to the risk enhancement object, the connection is regarded as an effective power flow influence edge; when the connection is in an open state, or when the connection corresponds to fault isolation, protection tripping, or an unreachable path, it is not regarded as an effective power flow influence edge.

[0064] The determination of the topological association between risk mitigation objects and risk enhancement objects includes: Using the risk mitigation object as the forward carrier object, search the directional power flow influence graph for power flow influence paths that can reach the risk enhancement object; When there is a direct connection path, a common bus connection path, a main transformer upper and lower level connection path, a feeder transfer path, a tie channel path, or a common power flow convergence path between the risk mitigation object and the risk enhancement object, it is determined that the two have a topological relationship. When there is no reachable power flow path between the risk mitigation object and the risk enhancement object, or when the path between them is blocked by a disconnect switch, fault isolation device, or non-operable device, it is determined that the two do not have a topological relationship for risk bearing switching judgment.

[0065] Specifically, for risk mitigation object a and risk enhancement object b, between adjacent cross sections k and k+1, the risk carrying capacity release of risk mitigation object a is... for:

[0066] Increase in risk tolerance of risk-enhanced object b for:

[0067] in, The risk-bearing capacity release amount for risk mitigation object a. The risk bearing capacity of the risk mitigation object a in section k. The risk bearing capacity of the risk mitigation object a in section k+1, The increase in risk tolerance for risk-enhancing object b. To determine the risk bearing capacity of the risk-enhanced object b at section k+1, The risk bearing capacity of the risk-enhancing object b in section k.

[0068] Furthermore, determine the effective power flow release amount for risk mitigation target a. And the effective power flow carrying capacity of risk-enhanced object b Wherein, the effective power flow release amount The effective power flow carrying capacity is determined based on the power flow reduction of risk mitigation target a between adjacent cross sections after deducting the effects of local load changes, new energy output changes, and energy storage charging and discharging changes. The increase in power flow is determined based on the risk enhancement object b after deducting the effects of local load changes, new energy output changes, and energy storage charging and discharging changes between adjacent cross sections.

[0069] The specific formula for calculating the power flow increase / decrease matching degree is as follows:

[0070] in, The power flow increase / decrease matching degree between risk mitigation object a and risk enhancement object b. The effective power flow release for risk mitigation target a. The effective power flow carrying capacity for risk-enhancing object b. and The closer they get, The larger the difference between the two, the better. The smaller.

[0071] when and When the difference between them does not exceed the allowable loss or calculation error corresponding to the power flow influence path, it is determined that risk mitigation object a and risk enhancement object b meet the corresponding conditions for power flow increase or decrease.

[0072] The process of pairing and verifying risk mitigation objects and risk enhancement objects based on topological relationships, power flow increase / decrease matching degrees, and risk type compatibility includes: A topology reachability check is performed on the risk mitigation object and the risk enhancement object to determine whether there is a reachable power flow influence path from the risk mitigation object to the risk enhancement object, and whether the connection relationship in the reachable power flow influence path is in an effective operating state within the adjacent cross section; if there is no reachable power flow influence path, or the reachable power flow influence path is blocked by a non-operating connection relationship, the pairing check is determined to fail. Perform a power flow direction consistency check on the risk mitigation object and the risk enhancement object to determine whether the power flow decreasing direction of the risk mitigation object, the power flow increasing direction of the risk enhancement object, and the direction of the reachable power flow influence path are consistent; if the power flow decreasing of the risk mitigation object and the power flow increasing of the risk enhancement object cannot be explained along the same reachable power flow influence path, then the pairing check is deemed to have failed. A power flow increase / decrease matching verification is performed on the risk mitigation object and the risk enhancement object to determine whether the effective power flow release of the risk mitigation object and the effective power flow reception of the risk enhancement object meet the preset matching conditions; if the difference between the two exceeds the allowable loss or calculation error corresponding to the power flow influence path, or the power flow increase / decrease matching degree is lower than the preset matching threshold, the pairing verification is determined to fail. A risk type compatibility check is performed on the risk mitigation object and the risk enhancement object to determine whether the risk type corresponding to the risk mitigation object and the risk type corresponding to the risk enhancement object can be explained by the redistribution of current, reversal of current, convergence of current or dispersion of current along the same accessible current influence path; if there is no causal relationship between the current evolution of the two risk types, the pairing check is deemed to have failed. When the risk mitigation object and the risk enhancement object pass the topology reachability verification, power flow direction consistency verification, power flow increase / decrease matching verification, and risk type compatibility verification, the pairing verification is deemed successful, and a risk bearer switching relationship is generated from the risk mitigation object to the risk enhancement object.

[0073] Table 1

[0074] As shown in Table 1, the decrease in risk-bearing intensity during the cross-sectional change of line L1 does not necessarily indicate the elimination of overall risk in the target power grid. For the candidate pairing "line L1 → main transformer T2", there is an accessible power flow influence path between the two, and the effective power flow release and effective power flow absorption are close. The risk type can also be explained by the same power flow redistribution process, thus generating a risk-bearing switching relationship. Conversely, for the candidate pairing "line L1 → bus B3", although the two show a decrease and an increase in value, there is no accessible power flow influence path between them. It cannot be proven that the risk is transferred from line L1 to bus B3, therefore, no risk-bearing switching relationship is generated. Through this pairing verification process, this embodiment can eliminate superficial risk changes between irrelevant objects and identify the risk-bearing object switching that is truly caused by power flow evolution, thereby avoiding misjudging local risk mitigation as overall risk elimination.

[0075] In this embodiment, risk transfer is not determined solely by whether the risk mitigation target and the risk enhancement target appear simultaneously. Instead, a directed power flow impact map is first constructed, limiting risk changes to occur within an interpretable power flow impact path. Then, by deducting the effective power flow release and effective power flow carrying capacity after accounting for local load changes, new energy output changes, and the effects of energy storage charging and discharging, it is determined whether the two belong to the same power flow redistribution process. Finally, a risk-bearing switching relationship is generated by combining risk type compatibility relationships. This avoids misinterpreting risk changes in two unrelated devices as risk switching and distinguishes it from post-fault power flow transfer path search methods, enabling accurate identification of risk-bearing target switching during non-fault continuous operation.

[0076] Figure 2 This section explains how to identify the switching relationship of risk-bearing objects within the same non-faulty power flow evolution segment. The diagram first associates the continuously operating section k→k+1 with the non-faulty continuity screening results. Only sections that pass the non-faulty continuity screening enter the subsequent risk-bearing switching identification; faulty or N-1 sections are separately segmented and do not participate in the risk switching identification under normal power flow evolution, thus avoiding misjudging fault abrupt changes as normal risk transfers. In the screened power flow evolution segments, line L1 shows a decrease in risk bearing, while main transformer T2 shows an increase in risk bearing. A reachable power flow influence path is formed between the two through bus B1. Furthermore, the system calculates the effective power flow release of line L1 separately. and the effective power flow capacity of the main transformer T2 The system performs power flow increase / decrease matching and risk type compatibility judgment on the two. If the topology is reachable, the direction is consistent, the power flow increase / decrease is matched, and the risk type is compatible, then the risk-bearing switching relationship "L1→T2" is output. This diagram shows that the present invention does not simply judge risk transfer based on the decrease in risk of one object and the increase in risk of another object, but rather comprehensively identifies it by combining non-fault continuity, topology path, power flow release / acceptance relationship, and risk type compatibility.

[0077] S4 forms a risk-bearing evolution chain based on the risk-bearing switching relationship, and outputs multi-temporal and spatial risk assessment results based on the risk-bearing evolution chain.

[0078] In this embodiment, the risk-bearing evolution chain refers to the directed risk evolution result formed by connecting multiple risk-bearing switching relationships according to time sequence, object connection, and power flow path continuity within the same power flow evolution segment. The risk-bearing evolution chain is used to characterize the switching process of risk-bearing objects between different power grid operation sections, including at least one of the following: risk initiation object, risk traversed object, current bearing object, switching occurrence time, topological association path, risk duration, and risk-bearing switching intensity.

[0079] The risk-bearing evolution chain differs from the risk level results of a single cross-section and also from the fault propagation path. The risk level of a single cross-section only represents the risk level of a certain object at a certain moment, and the fault propagation path usually starts with the faulty equipment or protection action; while the risk-bearing evolution chain is based on the risk-bearing switching relationship during non-fault continuous operation, and is used to characterize the risk pressure change process between different power grid objects as the power flow evolves.

[0080] The process of forming a risk-bearing evolution chain based on the risk-bearing switching relationship includes: Obtain multiple risk-bearing switching relationships within the same power flow evolution segment, and determine the preceding and following bearer objects, switching time period, topological association path, power flow increase / decrease matching degree, and risk-bearing switching intensity corresponding to each risk-bearing switching relationship; Multiple risk bearer handover relationships are sorted according to the handover time period to generate a risk bearer handover sequence; Based on the object connection relationship, temporal adjacency relationship and path continuity relationship between adjacent risk bearer switching relationships, the risk bearer switching sequence is chained and merged. When the preceding carrier object of the subsequent risk-bearing switching relationship is the same as the following carrier object of the previous risk-bearing switching relationship, or when the two are in the same tidal current influence range and the topological association path is continuous, the two risk-bearing switching relationships will be merged into the same risk-bearing evolution chain. When there is no object connection relationship, temporal adjacency relationship or path continuity relationship between adjacent risk-bearing switching relationships, they are assigned to different risk-bearing evolution chains; By summarizing multiple risk-bearing switching relationships in the same risk-bearing evolution chain, the risk initiation object, risk-passing object, current bearing object, risk duration, spatial impact range, and overall link strength of the risk-bearing evolution chain can be determined.

[0081] The object connection relationship refers to the continuity of the bearing objects between two adjacent risk bearing switching relationships. When the subsequent bearing object of the previous risk bearing switching relationship is the same as the preceding bearing object of the subsequent risk bearing switching relationship, or when the two belong to the same power grid object group, the same interconnection channel, the same feeder section, or the same power flow influence range, the object connection relationship is determined to be satisfied.

[0082] The temporal adjacency relationship refers to the interval between the occurrence time periods of two adjacent risk bearer switching relationships not exceeding a preset link interval threshold; the preset link interval threshold is determined based on the cross-section sampling interval, power flow adjustment response time, and scheduling operation duration.

[0083] The path continuity relationship refers to the existence of overlapping paths, hierarchical connection paths, common bus connection paths, common link paths, or the same power flow convergence / dispersion path between two adjacent risk-bearing switching relationships. When two risk-bearing switching relationships are only temporally adjacent but their topological connection paths are independent of each other, they are not merged into the same risk-bearing evolution chain.

[0084] The specific calculation formula for the risk-bearing switching strength is as follows:

[0085] The specific formula for calculating the overall link strength is as follows:

[0086] in, The risk bearing capacity switching intensity between risk mitigation object a and risk enhancement object b at section k to section k+1. Adjusting the match to suit different trends The risk-bearing capacity release amount for risk mitigation object a. The increase in risk tolerance for risk-enhancing object b. This is the topological distance correction ratio. The topological distance between risk mitigation object a and risk enhancement object b. The overall strength of the risk-bearing evolution chain L.

[0087] Furthermore, the multi-temporal risk assessment results output based on the risk-bearing evolution chain include: Based on the risk initiation object and the switching period in the risk-bearing evolution chain, determine the risk initiation position and risk initiation time; Based on the current risk-bearing object in the risk-bearing evolution chain, determine the current risk concentration object; The spatial impact range of the risk is determined based on the topological association path and the objects through which the risk passes in the risk-bearing evolution chain; Based on the timing of each risk-bearing switching relationship in the risk-bearing evolution chain, determine the duration of risk and the direction of risk migration; Based on the overall link strength of the risk-bearing evolution chain, the risk-bearing strength of the current bearing object, and the subsequent reachable power flow impact path, determine the subsequent early warning objects; The output includes a multi-temporal risk assessment result that includes the risk originating object, the current bearing object, the objects through which the risk passes, the scope of the risk's impact, the duration of the risk, the direction of the risk's migration, the subsequent warning objects, and the overall strength of the link.

[0088] In this embodiment, the risk impact range is determined based on the union of the topological association paths corresponding to each risk carrying switching relationship in the risk carrying evolution chain; when the topological association paths of multiple risk carrying switching relationships overlap, the power grid objects corresponding to the overlapping paths are identified as key risk channels; when the current carrying object at the end of the risk carrying evolution chain is still in a risk enhancement state, the current carrying object and its adjacent power grid objects reachable along the power flow direction are identified as subsequent early warning objects.

[0089] In this embodiment, instead of simply arranging the risk results of each section into a time series, the risk-bearing evolution chain is formed based on the object connection, temporal adjacency, and path continuity between risk-bearing switching relationships. This allows for the output of which power grid object the risk originates from, which objects it passes through, which object currently bears the risk, how long it has lasted, and which objects might be affected subsequently. This enables dispatchers to identify the evolution of risk-bearing objects during non-faulty continuous operation, avoiding the misjudgment of local risk mitigation as overall risk elimination.

[0090] Example 2, Figure 2 A multi-temporal risk assessment system for power grids that takes into account power flow evolution is presented, including the following modules: The continuity verification module is used to obtain the continuous power grid operation section within the target evaluation window, determine the power flow status and operating margin of each power grid object, and generate power flow evolution segments based on non-fault continuity screening. The object identification module is used to identify risk mitigation objects and risk enhancement objects within the power flow evolution segment, based on the changes in the operating margin of power grid objects between adjacent sections. The switching relationship identification module is used to generate risk bearing switching relationships based on the topological association and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects; The results output module is used to form a risk bearing evolution chain based on the risk bearing switching relationship, and output multi-temporal risk assessment results based on the risk bearing evolution chain.

[0091] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0093] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0096] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the multi-temporal and spatial risks of a power grid considering power flow evolution, characterized in that, Includes the following steps: Obtain continuous power grid operation sections within the target evaluation window, determine the power flow status and operating margin of each power grid object, and generate power flow evolution segments based on non-fault continuity. Within the power flow evolution segment, risk mitigation targets and risk enhancement targets are determined based on the changes in the operating margin of the power grid objects between adjacent sections; Based on the topological relationship and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects, a risk bearing switching relationship is generated. A risk-bearing evolution chain is formed based on the risk-bearing switching relationship, and multi-temporal risk assessment results are output based on the risk-bearing evolution chain.

2. The power grid multi-temporal risk assessment method considering power flow evolution according to claim 1, characterized in that, The generation of power flow evolution segments based on non-fault continuity screening includes: Obtain operational event information between adjacent power grid operating sections to determine whether there are any fault events that could cause the interruption of the correspondence between power grid objects; If it exists, the time corresponding to the failure event shall be used as the segment boundary; If no such relationship exists, and a corresponding relationship can be established between the power grid objects of adjacent power grid operating sections, then the adjacent power grid operating sections will be classified into the same power flow evolution segment.

3. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 2, characterized in that, The process of determining risk mitigation targets and risk enhancement targets based on the changes in the operational margin of power grid objects between adjacent sections includes: The operational margin is aligned according to the constraint type of the power grid object to generate a risk proximity degree. The risk bearing capacity is determined by combining the proximity of the risk and its relative distribution within the influence range of the tidal current; Candidate mitigation and enhancement targets are determined based on the changes in risk bearing capacity between adjacent cross sections; The candidate mitigation targets and candidate enhancement targets are verified, and the candidate targets that pass the verification are determined as risk mitigation targets or risk enhancement targets.

4. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 3, characterized in that, The process of aligning the operating margin according to the constraint type of the power grid object to generate a risk proximity degree includes: The corresponding constraint boundary is determined according to the constraint type of the power grid object, and the operating margin under different constraint types is converted into a risk proximity in a unified direction according to the rule that the closer the current operating value is to the corresponding constraint boundary, the greater the risk proximity. When a power grid object corresponds to multiple constraint boundaries, the constraint boundary with the highest degree of proximity is selected to generate the risk proximity score.

5. The power grid multi-temporal risk assessment method considering power flow evolution according to claim 4, characterized in that, The determination of risk bearing capacity by combining the proximity of the risk and its relative distribution within the tidal current's influence range includes: Determine the power flow influence range of the corresponding power grid object based on the power grid topology and power flow direction; Within the influence range of the power flow, the risk proximity distribution of each power grid object is statistically analyzed; The risk bearing intensity of the target power grid object in the corresponding power grid operation section is determined based on the risk proximity of the target power grid object and its relative proportion in the risk proximity distribution.

6. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 5, characterized in that, The verification of candidate mitigation targets and candidate enhancement targets includes: Calculate the overall risk pressure change within the tidal current influence range of the candidate object, and combine it with the changes in operating limits and fluctuation range to exclude candidate objects that are mitigated by the overall risk pressure decrease, candidate objects that are enhanced by the operating limit adjustment, and candidate objects that are affected by the measured or calculated fluctuations. The remaining candidate objects are then identified as risk mitigation objects or risk enhancement objects.

7. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 6, characterized in that, The generation of risk-bearing switching relationships based on the topological association and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects includes: A directed current influence graph is constructed based on the first data within the current evolution segment; the first data includes topological connectivity, switching status, and current direction. In the directed tidal current influence map, determine the reachable tidal current influence path from the risk mitigation object to the risk enhancement object; The pairing verification is performed based on the reachable tidal current impact path, tidal current increase / decrease matching degree, and risk type compatibility relationship. If the verification passes, a risk bearing switching relationship is generated from the risk mitigation object to the risk enhancement object.

8. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 7, characterized in that, The specific method for obtaining the topological connection relationship is as follows: Using the risk mitigation object as the preceding carrier object, search for the reachable power flow path from it to the risk enhancement object in the directed power flow influence graph; If there is an accessible tidal current influence path, and the connection relationship in the accessible tidal current influence path is in an effective operating state within adjacent sections, then it is determined that the risk mitigation object and the risk enhancement object have a topological association relationship for risk bearing switching judgment. If there is no reachable power flow path, or if the reachable power flow path is blocked by a non-operational connection, then it is determined that the two do not have a topological relationship for risk-bearing switching judgment.

9. The multi-temporal risk assessment method for power grids considering power flow evolution according to claim 8, characterized in that, The multi-temporal risk assessment results based on the risk-bearing evolution chain include: The risk-bearing switching relationships are sorted according to the timing of the switching, and adjacent risk-bearing switching relationships are chained together to form a risk-bearing evolution chain. Based on the changes in the carrier objects, the impact paths, and the link strength represented by the risk-bearing evolution chain, multi-temporal and spatial risk assessment results are output.

10. A power grid multi-temporal risk assessment system considering power flow evolution, used in the power grid multi-temporal risk assessment method considering power flow evolution as described in any one of claims 1-9, characterized in that, Includes the following modules: The continuity verification module is used to obtain the continuous power grid operation section within the target evaluation window, determine the power flow status and operating margin of each power grid object, and generate power flow evolution segments based on non-fault continuity screening. The object identification module is used to identify risk mitigation objects and risk enhancement objects within the power flow evolution segment, based on the changes in the operating margin of power grid objects between adjacent sections. The switching relationship identification module is used to generate risk bearing switching relationships based on the topological association and power flow increase / decrease correspondence between risk mitigation objects and risk enhancement objects; The results output module is used to form a risk bearing evolution chain based on the risk bearing switching relationship, and output multi-temporal risk assessment results based on the risk bearing evolution chain.

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