A calculation method for security assessment indicators of power grid multi-agent large model

Abstract

The present invention provides a method for calculating safety assessment indicators of a large-scale multi-agent model of a power grid, which relates to the field of intelligent control technology. The method includes: obtaining the voltage change rate and frequency change rate, dividing the disturbance into four stages according to the disturbance starting point and extreme value point, pairing the curve trends, judging the delay and synchronization characteristics, extracting the continuous change structure, and generating a disturbance segment evolution structure; the present invention realizes dynamic structural reconstruction and trend mapping of the disturbance process through staged analysis of node voltage and frequency change trends, combining trend consistency judgment and abnormal segment identification, trend boundary aggregation and frequency classification to improve the consistency and accuracy of indicator response state identification, coupling direction and delay analysis of multi-parameter changes between nodes to enhance collaborative characteristic identification, regulating response sorting to clarify node adjustment priority, and constructing a regulation path under multi-dimensional linkage, thereby improving the refined management and control capabilities of disturbance response and the pertinence of control decision-making.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a method for calculating safety assessment indicators of a large-scale multi-agent model of a power grid. Background Art

[0002] The field of intelligent control technology encompasses a technical system that leverages control theory, artificial intelligence, and system modeling to dynamically adjust and control state parameters in complex systems. The core of this technology is the realization of autonomous regulation and efficient control of systems in dynamic environments. This technology primarily encompasses coordinated control of multi-agent systems, distributed decision-making and computing, and state prediction and optimization modeling. It is widely used in scenarios such as power systems, autonomous driving, industrial automation, and energy management. It involves modeling methods, control strategies, and evaluation mechanisms. By integrating multi-source data and learning mechanisms, it enables refined management and control of the target system's operating status.

[0003] Among them, the calculation method of safety assessment indicators of large-scale multi-agent models of power grids refers to the introduction of multi-agent systems and large-scale models in the power system. By setting multiple safety assessment indicators and utilizing the interactive information between agents, a numerical quantitative assessment of each operating state is performed based on preset state information sampling rules and indicator function calculation methods. Specifically, it covers the extraction method of stability assessment indicators based on the calculation of node topology attributes, the voltage over-limit judgment logic constructed based on operating state parameters, and the generation method of global risk measurement indicators formed by the analysis of multi-agent interaction patterns. This method completes the hierarchical calculation and combination generation of various indicators through rule definition, logical function construction and data mapping.

[0004] Existing technologies lack the ability to identify parameter change trends in a temporal manner during the evolution of dynamic disturbances. This makes it difficult to visualize the indicator response paths caused by disturbances, affecting the continuity and structure of state identification. In trend correlation analysis, a unified standard for determining trend consistency among multiple parameters has not yet been established, which can easily overlook short-term non-cooperative behavior between trends, making it difficult to effectively locate trend breakpoints and abnormal segments. A composite judgment mechanism combining temporal distribution and trend frequency has not yet been established for trend change processes, resulting in a lack of precision in determining the triggering conditions for regulatory actions and difficulty adapting to the response classification requirements of multiple types of indicators in real-world scenarios. The relationship between parameter changes between nodes lacks the ability to identify delays and directions, making it difficult to form coupled paths for highly interconnected state changes, further limiting the analysis of group behavior impacts. In regulation logic, ambiguity between boundary responses and sorting rules leads to conflicts in node response order and a lack of a clear hierarchical priority framework for regulation paths. For example, in power systems, node regulation measures often rely on fixed thresholds, ignoring the impact of differences between nodes. This can easily lead to cumulative regulation errors, impacting overall system stability and regulation efficiency. Summary of the Invention

[0005] To address the technical problem of the prior art in processing dynamic disturbance evolution, which lacks time-phased identification of parameter change trends, the present invention provides a method and apparatus for calculating security assessment indicators for a large-scale multi-agent model of a power grid. The technical solution is as follows:

[0006] In one aspect, a method for calculating safety assessment indicators for a large multi-agent model of a power grid is provided. The method is implemented by a safety assessment indicator calculation device and includes:

[0007] S1: Obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the disturbance start and extreme value points, pair the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate the disturbance segmented evolution structure;

[0008] S2: According to the disturbance segment evolution structure, extract three types of trend parameters, judge trend consistency, locate abnormal segments and turning points, organize trend transfer paths, and generate trend correlation pattern sequences;

[0009] S3: Call the trend association pattern sequence, extract the trend change segments of the node in adjacent time periods, record the change frequency and determine whether it exceeds the indicator response range, perform boundary aggregation on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify the differentiated response path characteristics, and obtain the indicator participation change category;

[0010] S4: Call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify the combination according to the collaborative frequency, and obtain the parameter coupling distribution type;

[0011] S5: Based on the parameter coupling distribution type, extract the combination response interval, determine the boundary adjustment direction, sort the combination priority by impact degree, and generate multi-indicator linkage sorting rules.

[0012] On the other hand, a device for calculating a security assessment index of a large multi-agent model of a power grid is provided. The device is applied to a method for calculating a security assessment index of a large multi-agent model of a power grid. The device includes:

[0013] The disturbance segmented evolution structure generation module is used to obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the onset and extreme value points, pair the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate the disturbance segmented evolution structure;

[0014] The trend association pattern sequence generation module is used to extract three types of trend parameters based on the disturbance segment evolution structure, determine trend consistency, locate abnormal segments and turning points, organize trend transfer paths, and generate trend association pattern sequences;

[0015] The indicator response path feature recognition module is used to call the trend association pattern sequence, extract the trend change segments of adjacent time periods of the node, record the change frequency and determine whether it exceeds the indicator response range, perform boundary clustering on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify differentiated response path features, and obtain the indicator participation change category;

[0016] The parameter coupling distribution type acquisition module is used to call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify and combine according to the coordination frequency, and obtain the parameter coupling distribution type;

[0017] The multi-indicator linkage sorting rule generation module is used to extract the combination response interval based on the parameter coupling distribution type, determine the boundary adjustment direction, sort the combination priority according to the degree of influence, and generate the multi-indicator linkage sorting rule.

[0018] On the other hand, a safety assessment index calculation device is provided, which includes: a processor; a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned methods for calculating the safety assessment index of the large-scale multi-agent model of the power grid is implemented.

[0019] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for calculating security assessment indicators of a large multi-agent model of a power grid.

[0020] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0021] The present invention proposes a method for calculating safety assessment indicators of a large-scale multi-agent model of a power grid. By analyzing the node voltage and frequency change trends in a phased manner, combined with trend consistency judgment and abnormal segment identification, dynamic structural reconstruction and trend mapping of the disturbance process are realized. Trend boundary aggregation and frequency classification improve the consistency and accuracy of indicator response state identification. The coupling direction and delay analysis of multi-parameter changes between nodes enhance the identification of collaborative characteristics. The response sorting is regulated to clarify the node adjustment priority, and an adjustment path under multi-dimensional linkage is constructed to improve the refined management and control capabilities of disturbance responses and the pertinence of control decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a method for calculating security assessment indicators of a power grid multi-agent large model provided by an embodiment of the present invention;

[0024] Figure 2 This is a block diagram of a device for calculating security assessment indicators of a large-scale multi-agent model of a power grid provided by an embodiment of the present invention;

[0025] Figure 3 It is a structural diagram of a security assessment index calculation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0027] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0028] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0029] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0030] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0031] The embodiment of the present invention provides a method for calculating the security evaluation index of a large model of a multi-agent power grid. The method can be implemented by a security evaluation index calculation device, which can be a terminal or a server. Figure 1 The flowchart of the method for calculating the security assessment index of the power grid multi-agent large model is shown. The processing flow of this method may include the following steps:

[0032] S1: Obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the disturbance onset and extreme value points, pair the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate the disturbance segmented evolution structure.

[0033] The disturbance segment evolution structure includes voltage change trend characteristics, frequency change trend characteristics, parameter response relationship within the stage and structural stage arrangement pattern;

[0034] The trend association pattern sequence includes abnormal trend identification points, trend transfer time series, node trend arrangement structure and trend direction consistency mark;

[0035] The categories of indicator participation changes include trend change frequency statistics, trend drift boundary distribution, response duration classification, and regulation indicator response type;

[0036] Parameter coupling distribution types include parameter change coupling combinations, synergy relationship types, response delay characteristics, and propagation path patterns;

[0037] The multi-indicator linkage sorting rules include the control response interval set, boundary matching priority, node influence sorting and adjustment operation records.

[0038] Optionally, obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the disturbance start and extreme value points, pair the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate a disturbance segmented evolution structure, including:

[0039] S101: Obtain the voltage change rate and frequency change rate of the nodes in the power grid, segment the time axis according to the disturbance starting point and extreme point sequence, mark the sequence of four time intervals, and generate a stage division interval sequence.

[0040] In a feasible implementation method, time series sampling is performed based on actual power grid operation data. For example, taking a node in a 220kV substation as an example, the frequency and voltage data from 10 seconds before the disturbance to 20 seconds after the disturbance are extracted in the system operation monitoring platform. The sampling period is set to 0.1 seconds, and a total of 300 data points are collected. The difference between the adjacent voltage values ​​and frequency values ​​between each two consecutive data points is calculated and divided by the time interval to form a voltage change rate sequence and a frequency change rate sequence. Preliminary disturbance identification operations are performed on the two sequences respectively.

[0041] For the identification of the starting point of the disturbance, the section where the change rate of three consecutive sampling points is greater than the change amplitude threshold of 0.05 is searched for the first time in the voltage change rate sequence as the initial disturbance time point. For example, in a certain disturbance, it is found that the voltage change rate exceeds 0.05 for three consecutive points at 5.2 seconds. This moment is determined to be the starting point of the disturbance. Then, by calculating the change trend of the voltage and frequency change rate sequence in each time period after the disturbance, the point where the absolute value of the change rate continues to increase over time and forms a peak in the local time period is found as the extreme point. The recognition logic is: the difference between the maximum and minimum values ​​of five consecutive points exceeds the change amplitude threshold of 0.1, and the trend is obviously reversed after this point. For example, at 7.8 seconds, the voltage change rate gradually decreases from a positive value to a negative value, and the disturbance is determined. is an extreme point. Finally, the disturbance starting point and multiple extreme points are combined to divide the overall disturbance process into four stages in chronological order. The first stage is the initial response stage of the disturbance, the second stage is the response peak stage, the third stage is the system fallback stage, and the fourth stage is the gradual recovery stage. Taking a certain disturbance data as an example, the disturbance starting point is 5.2 seconds, the extreme points are 6.1 seconds, 8.3 seconds and 12.0 seconds respectively, and the system recovery stable point is 20.0 seconds. Finally, four time segments are formed: [5.2 seconds, 6.1 seconds], [6.1 seconds, 8.3 seconds], [8.3 seconds, 12.0 seconds] and [12.0 seconds, 20.0 seconds]. Each segment is marked with a number for subsequent analysis and processing. The final output stage division interval sequence is four sequential intervals.

[0042] S102: Extract the voltage and frequency change rate curves according to the stage-divided interval sequence, determine the starting time difference between the rising and falling segments of the two curves within the stage, select the segments with offset values ​​less than the disturbance detection threshold as the synchronization interval, and combine the voltage direction change corresponding to the frequency peak to obtain the synchronization interval delay value set.

[0043] In a feasible implementation, corresponding segments of the voltage change rate sequence and the frequency change rate sequence are extracted in each time period to form a voltage change rate curve and a frequency change rate curve within the stage. A characteristic trend analysis is performed on each curve to determine whether there is an obvious rising or falling segment. The judgment method is to observe whether the change rates of five consecutive sampling points increase or decrease in the same direction, and whether the average change rate is greater than 0.03, which is a judgment threshold for distinguishing disturbance change trends. For example, in the second stage, the voltage change rate continues to rise from 6.3 seconds to 6.8 seconds, and the frequency change rate begins to rise in the same trend at 6.4 seconds. The starting points of the rising segments of the two are recorded as 6.3 seconds and 6.4 seconds respectively, and the difference in starting time between the two is calculated to be 0.1 seconds. This time difference is less than the disturbance detection threshold of 0.3 seconds set by the system. Therefore, this time period is determined to be a synchronous change interval of voltage and frequency.

[0044] In the synchronization interval delay value extraction stage, the time point corresponding to the extreme point of the frequency change rate curve in the synchronization interval is extracted as the peak moment. For example, the frequency reaches its maximum value at 6.9 seconds. At the same time, the trend direction of the voltage change rate is analyzed within 0.2 seconds before and after this time point. If the voltage change rate changes from a positive value to a negative value during this period, the direction changes from rising to falling, and the direction is marked as "falling". At this time, the frequency peak time point 6.9 seconds and the voltage change direction are recorded, and the time interval between the two is calculated as the synchronization delay value, that is, 0.3 seconds. This process is repeated in multiple synchronization segments to form multiple synchronization delay values ​​with direction information. For example, the delay values ​​in three typical segments are 0.1 seconds, 0.2 seconds, and 0.3 seconds, respectively, and the directions are "rising", "falling", and "rising", respectively. This forms the final synchronization interval delay value set for the next stage of disturbance structure analysis.

[0045] S103: Based on the synchronization interval delay value set, the voltage and frequency change trends are sorted, the inflection point segment direction sequence is called, the structure arrangement type is identified, and the disturbance segment evolution structure is generated.

[0046] In a feasible implementation, the delay values ​​are arranged from small to large, and the delay values ​​are sorted as 0.1 seconds, 0.2 seconds and 0.3 seconds respectively, while the corresponding voltage change direction is kept in the order of "rising", "falling" and "rising", and the average change rate of the voltage change rate and the frequency change rate in each stage is counted. The average change rate is calculated as the difference between the starting value and the ending value of the curve of the stage divided by the stage time length. For example, in the second stage, the voltage change rate changes from 0.05 to 0.20, the stage length is 2.2 seconds, and the average change rate is 0.068. The frequency change rate changes from 0.02 to 0.18, and the average change rate is 0.073. After obtaining the change trend rate set of multiple stages, combined with the inflection point information of each direction change in the aforementioned synchronization interval, the stage where the voltage or frequency changes from an upward trend to a downward trend or from a downward trend to an upward trend is marked, and an inflection point segment direction sequence is constructed, such as "rising→falling", "falling→rising", etc.

[0047] The sorted direction sequence is compared with the inflection point segment direction sequence to determine whether the overall disturbance process constitutes a specific disturbance structure type. For example, if the direction sequence is "rise, fall, rise" and the inflection point segment direction is "rise→fall, fall→rise", it indicates that the overall disturbance process presents a symmetrical structure with low pressure in the middle and high pressure at both ends. Alternatively, if the delay time is uneven and the direction changes are chaotic, it is determined to be a multi-level step-type disturbance process. Ultimately, a clearly structured disturbance segmented evolution structure identifier is formed to support subsequent system response modeling or power equipment status prediction analysis.

[0048] S2: According to the disturbance segmented evolution structure, three types of trend parameters are extracted, trend consistency is judged, abnormal segments and turning points are located, trend transfer paths are sorted out, and trend association pattern sequences are generated.

[0049] Optionally, based on the disturbance segmented evolution structure, three types of trend parameters are extracted to determine trend consistency, locate abnormal segments and turning points, organize trend transfer paths, and generate trend association pattern sequences, including:

[0050] S201: Obtain the node data of each disturbance stage in the disturbance segmented evolution structure, extract the load change rate, voltage fluctuation amplitude and frequency offset direction of the node in the same stage, and judge the directional consistency state between the three parameters based on the change slope signs of the three parameters in the current time period. If the directions of the three slopes are inconsistent, the corresponding node and time period are recorded as abnormal segments, and an abnormal trend segment interval set is generated.

[0051] In one feasible implementation, sampling data of each key node in the power grid is located for each defined stage. The node data must include at least real-time load values, voltage values, and frequency values. Taking node A of a 220kV substation as an example, during a certain disturbance, the first stage is [5.2s, 6.1s]. The load value, voltage value, and frequency value every 0.1s in this time period are extracted, and a total of 9 sets of time series data are collected.

[0052] The rate of change is calculated for each parameter sequence, that is, the change between every two adjacent data points is divided by the time interval of 0.1s to obtain the three sequences of load change rate, voltage fluctuation rate and frequency change rate. Then the slope sign of each change rate sequence is determined. A positive slope sign indicates an upward trend, and a negative slope sign indicates a downward trend. Then, the values ​​of the slope signs greater than zero or less than zero in the three sequences within the time period are taken to mark the trend direction respectively. If at least two of the three parameters have positive slopes and one is negative, or two are negative and one is positive, then the three slope directions are determined to be inconsistent. Taking node A in this stage as an example, the load change rate is Positive growth, the rate of change of voltage fluctuation amplitude decreases, and the rate of change of frequency shows a downward trend. At this time, only the load is positive among the three, and the other two are negative. It is judged that the direction is inconsistent. If the slope direction of node A remains consistent in other stages, no record is made. The system traverses all nodes and all stages, and performs the above direction judgment operation on each node in each stage. At the same time, all nodes with inconsistent slope directions and their corresponding time periods are recorded. Finally, this information is summarized to form an abnormal trend segment interval set. Each record in the set includes the node number, abnormal stage number, time period start and end time, and the slope direction values ​​of the three parameters.

[0053] S202: Call the abnormal trend segment interval set to record the node abnormal segment time interval, locate the inflection point time of the load change rate curve in each abnormal segment, match the starting position of the slope change before the inflection point as the trend starting point, summarize the time range between the trend starting point and the corresponding inflection point, arrange the segment trend transfer relationship in chronological order, and generate a trend transfer structure sequence.

[0054] In a feasible implementation, an inflection point location operation is performed on the load change rate curve in each record.

[0055] Starting from the starting position of the current abnormal time period, observe the changing trend of the load change rate value point by point. When multiple consecutive points change from rising to falling or from falling to rising, record the trend reversal time point as the inflection point. For example, in a certain abnormal segment [6.1s, 8.3s] of node B, the load change rate continues to increase from 6.1 to 6.7 seconds, and the change rate gradually decreases from 6.8 seconds. The inflection point time is recorded as 6.8 seconds. At the same time, trace back from the inflection point time to determine the time point when the first continuous slope change (positive or negative) occurs as the trend starting point. For example, a continuous growth trend begins at 6.3 seconds, then the trend starting point is 6.3 seconds, and the trend change segment within this stage is [6.3s, 6.8s]. The system records the time range and marks the direction as rising to falling. The system performs the above operation on each abnormal segment, extracts the trend starting point and inflection point time pairs, generates trend transfer record entries, and arranges all trend transfer entries in chronological order to ensure that adjacent trend segments can form a coherent evolution sequence. For example, in the three abnormal segments of node C, three trend transfer segments appear at [6.5s, 7.1s], [7.2s, 7.8s], and [7.9s, 8.4s]. The direction changes are recorded as "rising → falling", "falling → rising", and "rising → falling", respectively, forming a complete trend transfer structure sequence. This sequence reflects the transfer logical relationship of the load change rate in multiple trend turning points and is output in chronological order.

[0056] S203: According to the trend transfer structure sequence, extract the trend sequence combination of the three parameters in each segment, sort the differentiated combination sequence according to the time axis and merge the repeated segments with the same structure, count the number of occurrences and sequential positions of the trend repeated structure, establish the trend structure arrangement sequence corresponding to the node, and generate the trend association pattern sequence.

[0057] In a feasible implementation, in each trend transfer segment, the voltage fluctuation amplitude change rate and frequency offset direction change rate sequences corresponding to its time period are extracted, and the trend directions of the three parameters are arranged and combined to form a trend sequence combination label for the segment. For example, in the trend segment [6.3s, 6.8s], if the load change rate is increasing, the voltage fluctuation amplitude is decreasing, and the frequency offset direction is increasing, then the combination of the segment is recorded as "load↑-voltage↓-frequency↑". The trend sequence combinations of all segments are collected in chronological order, and trend segments with exactly the same combination form are merged into the same structure. For example, if the combination "load↑-voltage↓-frequency↑" is used, the load change rate is increased, the voltage fluctuation amplitude is decreased, and the frequency offset direction is increased. "Rate↑" appears 3 times in a certain node, occurring at [6.3s, 6.8s], [7.5s, 8.0s], and [9.2s, 9.8s] respectively. The number of occurrences of this combination structure is recorded as 3, and the sequence positions are the 1st, 4th, and 6th segments. At the same time, the node numbers and combination labels are matched one by one. The order in which all trend combinations appear in the node is counted to form the trend structure arrangement sequence of the node. Then, each node is used as an index to output the corresponding trend structure arrangement sequence set. Finally, by traversing all node trend combination sequences, the structural units of repeated combinations are collected to form the trend structure type index and trend association pattern sequence.

[0058] Among them, the frequency, sorting and distribution of each combination label in the overall node set can be merged and mapped accordingly to obtain a complete trend evolution type structure.

[0059] S3: Call the trend association pattern sequence, extract the trend change segments of the adjacent time periods of the node, record the change frequency and determine whether it exceeds the indicator response range, perform boundary aggregation on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify the differentiated response path characteristics, and obtain the indicator participation change category.

[0060] Optionally, call the trend association pattern sequence, extract the trend change segments of the node in adjacent time periods, record the change frequency and determine whether it exceeds the indicator response range, perform boundary aggregation on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify the differentiated response path characteristics, and obtain the indicator participation change category, including:

[0061] S301: Call the trend sequence of the node in the trend association pattern sequence, extract the trend change starting point and trend maintenance end point corresponding to the adjacent time periods, mark the change location and maintenance interval according to the trend direction change position and the duration length in the adjacent segments, and generate a trend transformation structure information group.

[0062] In a feasible implementation method, the time information between two adjacent segments in the trend sequence of each node is extracted, and the end time of the previous trend is recorded as the trend holding end point, and the starting time of the next trend is recorded as the trend change starting point, thereby forming the trend connection boundary information. Taking node A as an example, its trend sequence segments are [5.2s, 6.3s], [6.3s, 6.9s], and [6.9s, 7.5s], then the trend change starting points are recorded as 6.3s and 6.9s, and the corresponding trend holding end points are 6.3s and 6.9s. In the process of structure generation, the direction of each trend segment needs to be identified. The direction is based on the change trend of the three parameters of load, voltage, and frequency. If the three parameters in the previous segment are The numbers are all rising, and there are at least two direction changes in the latter part, for example, the voltage and frequency change from rising to falling, then the position is marked as the position where the trend change occurs, and the holding duration is calculated for the duration of the previous trend. For example, the trend direction of the segment [5.2s, 6.3s] is consistent, and the duration is 1.1 seconds, forming a trend holding segment, and the corresponding change position 6.3s is marked as the trend change point. If there are direction changes in three consecutive trend segments and the time periods are 1.1 seconds, 0.6 seconds, and 0.9 seconds respectively, then three trend transformation structure units are recorded in sequence, which respectively include the change starting point, the holding end point, the trend duration, and the change direction combination, and a trend transformation structure information group is generated for subsequent trend change frequency analysis.

[0063] S302: Calculate the node's change frequency index within the trend direction change segment based on the trend direction change position and time interval recorded in the trend transformation structure information group. Use the upper limit of the node's index response range as a judgment threshold. Classify trend segments with a change frequency exceeding the threshold to obtain a drift trend segment interval set.

[0064] The calculation formula for the node change frequency index in the trend direction change segment is as follows (1):

[0065] (1);

[0066] in, Representative Node In the trend segment The trend change frequency indicator within Representative trend segment length of time, Representative Node In the The frequency change direction slope of the sampling point, Representative Node In the The voltage change direction slope of each sampling point, Representative Node In the The slope of the load change direction at each sampling point, 、 、 Representing the The weight factor of the direction slope of frequency, voltage and load changes at each sampling point, For trend segment The number of sampling points within .

[0067] In one possible implementation, formula (1) is intended to calculate the node The average frequency of changes within a trend change segment, where For trend segment The total length of time, is the number of sampling points in this time period. The three main components in the formula are related to the frequency change ( ), voltage change ( ) and load changes ( ). For each sampling point , the slopes of the change directions of these three parameters will be calculated Multiply by its corresponding weight .

[0068] Set the specific example data as follows: In the trend segment , assuming the time length hours (i.e. 3600 seconds). This trend segment contains 360 sampling points, i.e. Assume that at a typical sampling point , the frequency change slope Hz / s, voltage change slope % / s, load change slope % / s. In order to adjust the effect of parameters on the results, set the weight , , , which reflects that in this monitoring, the change of frequency has a greater impact on system stability, followed by voltage, while the impact of load is relatively small.

[0069] Substitute the above values ​​into the formula for calculation:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] The results show that, over the entire trend segment, there are an average of 0.0013 changes per second, indicating the frequency of node trend changes during this period. If the threshold is set to 0.002 changes per second, this means that the current node's change frequency does not exceed this threshold and is therefore not classified as a drift trend segment. This provides important guidance for further trend analysis and node behavior classification.

[0076] S303: Based on the starting point and ending point of the trend segment in the drift trend segment interval set, the boundary position of each segment is aggregated by node, the number of boundary segments corresponding to each node is counted and the corresponding time label is extracted, and the boundary distribution position is classified and sorted according to the time series to obtain the trend boundary distribution structure.

[0077] In a feasible implementation, the starting points and end points of all drift segments of each node are extracted as boundary position data, and the corresponding time tags are recorded. For example, the drift segments of node D are [5.5s, 6.2s], [7.1s, 7.8s] and [9.0s, 9.7s], then the number of boundary segments of node D is 3, and the corresponding boundary points are 6 time points at 5.5s, 6.2s, 7.1s, 7.8s, 9.0s and 9.7s. These boundary points are arranged in time series to obtain a boundary distribution order sequence.

[0078] After executing this operation on all nodes, a statistical table of the number of boundaries of each node and a corresponding time label set are constructed. Then, all time points are classified according to the global time axis order, and the number of node boundaries concentrated in the same time window is counted. If there are trend boundaries of more than 5 nodes in a certain time period such as [6.0s, 6.5s], this segment is marked as a trend boundary concentrated segment. After time aggregation and distribution statistics of all concentrated segments, the output is a trend boundary distribution structure. This structure is arranged in combination according to time label, node number, boundary number and density, which is convenient for subsequent trend feature classification and state identification.

[0079] S304: Call the trend repetition range and residence time in the trend boundary distribution structure, perform combined classification based on the residence time interval and the trend segment appearance range, divide the node's control response state according to the differentiated trend distribution characteristics, and generate the indicator participation change category.

[0080] In one feasible implementation, the duration of each node's stay in a certain repetitive trend segment is counted, that is, the continuous time during which the trend direction does not change. For example, if node E continuously presents the combination of "load drop-voltage rise-frequency drop" in the segment [6.0s, 7.0s], the stay time is 1.0s. The repetition range refers to the total duration of multiple occurrences of the same trend combination at different nodes or in different time periods. If the total repetition time of the same trend at multiple nodes exceeds 5 seconds, the trend combination is recorded as a high-repetition trend.

[0081] Different dwell time intervals are set for division, such as short dwell (<1.5s), medium dwell (1.5s-3.5s), and long dwell (>3.5s). Each trend segment is classified into the corresponding interval, and its type is determined based on its repetition range. For example, if node E has a long dwell in the [6.0s, 7.0s] segment and belongs to a high repetition segment, it is marked as a "stable response state". If node F has a changeable trend in the [7.0s, 7.6s] segment but each duration is less than 1.0s and the repetition range is dispersed, it is classified as a "fast drift type". Finally, the trend response behavior classified by each node is combined to form a unified classification label for nodes with similar dwell time characteristics and trend distribution characteristics, and the output is a set of indicator participation change categories.

[0082] S4: Call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify the combination according to the collaborative frequency, and obtain the parameter coupling distribution type.

[0083] Optionally, call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify and combine according to the collaborative frequency, and obtain the parameter coupling distribution type, including:

[0084] S401: Call the node classification results in the indicator participation change category, identify the node combination in which the frequency drift rate, voltage fluctuation amplitude and load change rate of the same category nodes change simultaneously within the same time period, extract the node combination list and the corresponding time index, and generate a synchronous change combination set.

[0085] In a feasible implementation, nodes are clustered and grouped by category, and the set of nodes under the same category is used as the operation object. The change status of the three parameters of frequency drift rate, voltage fluctuation amplitude and load change rate of each group of nodes in the same time period is traversed, and the trend direction of each parameter in each time slice is extracted. The trend direction is judged as rising, falling or unchanged based on the difference between the current time point and the previous time point. If all three parameters change in the time slice, that is, the change signs of at least two directions of the three differences are not zero, and the third is not in the "maintain" state, it is judged as "simultaneous change".

[0086] Combine the node numbers that meet the conditions and record the time index corresponding to the combination. For example, if the three parameters of nodes A, B, and C all change direction at [6.5s], then mark the combination as {A, B, C}, and the time is 6.5s. Repeat the above operation to extract all synchronous change points of each group of classified nodes within the entire cycle, and form a corresponding node combination list. At the same time, mark their respective corresponding time indexes. Each record in the combination list contains the node set, the change direction of the three parameters at that moment, and the time index information. Finally, a synchronous change combination set is generated as the basic input for subsequent analysis of collaborative trends.

[0087] S402: Based on the node combinations recorded in the synchronous change combination set, calculate the change starting point time difference index between the three parameters of the node, determine the order of change response, extract the delay time difference and direction relationship between the parameters, select the forward and reverse collaborative segments based on the delay direction relationship, mark the intersection time interval of the consistent change of the three parameters in each group, and obtain the collaborative response structure sequence.

[0088] Among them, the calculation formula of the starting time difference index of the change of the three parameters of the node is as follows (2):

[0089] (2);

[0090] in, Represents a node combination The average time difference between the start time of the change of the three parameters of medium frequency, voltage and load, Represents a node combination The starting time of the change of the medium frequency parameters, Indicates the starting time of the change of voltage parameters, Indicates the starting time of load parameter change.

[0091] In one possible implementation, formula (2) is used to calculate any node combination The time difference between the start of changes in the three parameters of medium frequency, voltage, and load during the disturbance response process is used to quantify the degree of temporal consistency of the three responses. All parameters are determined using actual response data collected by monitoring equipment.

[0092] Assume that the monitoring object is a combination of three key nodes in a 110 kV substation. The frequency monitoring device (based on PMU) collects data at 50 millisecond intervals, and the voltage and load monitoring devices have a sampling period of 100 milliseconds. The collection time is uniformly aligned with the reference time of the disturbance. According to the data records, during a typical disturbance, the starting points of the three parameter changes of the node combination m are: frequency starting time Seconds, calculated based on the trend detection result of the frequency decreasing from 50.00Hz to 49.96Hz and continuing to decrease, the change threshold is set at ±0.02Hz, and the data comes from the frequency curve slope analysis collected by the synchronous measurement device; voltage starting time Seconds, based on the voltage fluctuation from 220kV to 217.3kV, the change range exceeds the stable deviation range of ±1.2%, and the monitoring device confirms the continuous decline of the slope point by point; the load starting time Seconds, based on the rapid increase of load from 86.5MW to 92.0MW and maintaining at a high level, the slope analysis confirmed that the slope was continuously positive and the amplitude exceeded 0.5MW / s.

[0093] Substitute the above values ​​into the formula for calculation:

[0094] ;

[0095] ;

[0096] The results show that in this node combination, the average difference in the change response time of the three parameters is 0.1133 seconds, indicating that there is a certain degree of asynchronous offset in the change response process. The smaller the value, the more synchronous the response, and the larger the value, the stronger the degree of separation of the change sequence. In this step, this time difference indicator is used to determine the closeness of the parameter response sequence within the collaborative segment, and combined with the directional relationship to further distinguish between positive and reverse collaborative structures, providing a time dimension basis for the accurate construction of the subsequent collaborative response structure sequence.

[0097] S403: Call the combined collaborative records in the collaborative response structure sequence, count the number of occurrences of the same collaborative structure in the combination, screen the combination relationships with repetition frequency higher than the collaborative structure judgment threshold, sequentially summarize the delay paths in the collaborative chain in each type of combination, record the parameter change propagation path in node order, and generate the parameter coupling distribution type.

[0098] In one feasible implementation, the parameter arrangement, directional consistency, and delay time difference characteristics of the collaborative structure in each record are identified and marked. First, a unique code is performed according to the direction combination and order of the three parameters in the combination. For example, when frequency → voltage → load are all increasing, it is encoded as "F↑-V↑-L↑". The frequency of occurrence of this structure code in all collaborative records is counted. If the frequency of occurrence of a certain type of structure code is greater than the collaborative structure judgment threshold, for example, the threshold is set to 3 times, if the structure "F↑-V↑-L↑" appears 5 times, 4 times, and 6 times in nodes E, F, and G respectively, then the structure meets the screening conditions and is retained as a typical structure.

[0099] For the combination relationships that meet the conditions, the delay paths of the three parameters in their collaborative chain are extracted, that is, the starting order time series of parameter changes is recorded. Taking node F as an example, the starting point of the frequency change is 7.10s, the voltage is 7.15s, and the load is 7.25s. The sequential path is frequency → voltage → load. For all combination relationships that meet the conditions, they are aggregated according to the node number. For each node, the sequential path of parameter changes in its collaborative structure is recorded, and the corresponding structure type code and occurrence position are marked. Finally, the parameter coupling distribution type of each node is summarized. For each type, its frequency of occurrence in the network, the number of nodes involved, and the collaborative path sequence are recorded.

[0100] S5: Based on the parameter coupling distribution type, extract the combination response interval, determine the boundary adjustment direction, sort the combination priority by impact degree, and generate multi-indicator linkage sorting rules.

[0101] Optionally, based on the parameter coupling distribution type, extract the combined response interval, determine the boundary adjustment direction, sort the combined priorities by impact, and generate multi-indicator linkage sorting rules, including:

[0102] S501: Based on the parameter coupling distribution type, extract the control response interval of nodes of the same type during the disturbance process, calculate the length of the effective response time interval, and compare the interval boundary value with the currently set response threshold range to determine whether there is any boundary exceedance, identify the interval direction change state, and generate a response boundary matching result.

[0103] The calculation formula for the effective response time interval length is as follows (3):

[0104] (3);

[0105] in, Representation node Upper parameters The length of the effective response time interval, Indicates the parameter response starting time, Indicates the parameter response end time, Representation node In the Parameters at sampling points The amount of change, represents the sampling period, Representation parameters The corresponding response judgment threshold.

[0106] In one feasible implementation, formula (3) is used to calculate a node During the disturbance response process, the parameters The effective change time interval length. This interval is determined by the absolute value of the change rate continuously exceeding the response threshold. The time interval between sampling points is , the response interval is from the starting point To the end constitute.

[0107] Taking the frequency parameters of a 110 kV node as an example, the data is collected by the power grid monitoring system using a PMU device, and the sampling period is set to Seconds. The national standard stipulates that the normal fluctuation range of the power grid frequency is ±0.2Hz, and the minimum effective change slope threshold of this parameter is set to Hz / s, used to identify the real disturbance change section.

[0108] During the disturbance, the frequency sampling of this node is recorded as follows, in Hz: the sampling time is from 6.00 seconds to 6.80 seconds, with a total of 17 sampling points (0.05 second intervals);

[0109] The data segments are: 50.00, 50.01, 50.03, 50.06, 50.10, 50.15, 50.19, 50.22, 50.23, 50.22, 50.18, 50.12, 50.05, 49.96, 49.89, 49.85, 49.84;

[0110] Calculate the slope of the rate of change for each point (difference divided by interval):

[0111] The slope of the second point = (50.0150.00) / 0.05 = 0.20Hz / s;

[0112] The slope of point 3 = (50.0350.01) / 0.05 = 0.40 Hz / s;

[0113] The slope of point 4 = (50.0650.03) / 0.05 = 0.60Hz / s;

[0114] ...;

[0115] The slope of point 17 = (49.8449.85) / 0.05 = 0.20 Hz / s;

[0116] For each of the above points, determine whether its absolute slope is greater than , the satisfied ones are marked as 1, and the unsatisfied ones are marked as 0.

[0117] Among them, the indexes of the points that meet the conditions are points 2 to 17, and a total of 16 consecutive points meet the valid response conditions. The corresponding time interval starts at 6.05 seconds and ends at 6.80 seconds. The length of the response time interval is calculated as:

[0118] ;

[0119] This result indicates that node k's effective frequency response duration during this disturbance was 0.80 seconds. This value is used to determine whether the response has reached the control intervention threshold and provides a basic time marker for subsequent boundary state determination. In subsequent steps, this start and end interval will be combined with the threshold exceeding state to determine whether it constitutes a boundary abnormality state and used for response boundary matching.

[0120] S502: Normalize the node change amplitude values ​​according to the direction state recorded in the response boundary matching result, extract the impact degree score corresponding to each node during the disturbance process, sort them from high to low according to the score, and assign adjustment sequence numbers to the sorted nodes in turn to establish the node adjustment sorting result.

[0121] In a feasible implementation, the change amplitude value of each node is normalized. First, the maximum change amplitude of the three parameters of frequency, voltage and load in the response section is calculated, and normalization is performed based on the absolute value of the amplitude. The normalization value is the current change value divided by the maximum change reference value of the corresponding parameter. The reference value is taken from the maximum change record of this type of node in the disturbance influence area. For example, the maximum change reference value of frequency is set to 0.3Hz, the voltage is 8%, and the load change rate is 15%. If the frequency change of node B is 0.18Hz, the normalization value is 0.6, and the three normalizations are The node impact score is obtained by weighted average of the single values. The weights can be set to 0.4, 0.3, and 0.3 for frequency, voltage, and load, respectively. The nodes are sorted from high to low according to their score values. For example, if node C scores 0.82, node A scores 0.76, and node F scores 0.71, then node C is ranked first, A second, and F third. The sorted nodes are assigned adjustment numbers in sequence, namely #1, #2, and #3. The scores and adjustment numbers of all nodes are uniformly stored in the sorting result list as the basis for the subsequent regulation linkage, and the node adjustment sorting results are output.

[0122] S503: Call the node sequence number in the node adjustment sorting result, match the interval position in the response boundary matching result, mark the boundary change direction of the sorting node in the self-response interval, and extract the response boundary continuity paragraph, arrange the adjustment order according to the priority of the continuous interval, and generate a multi-index linkage sorting rule.

[0123] In a feasible implementation, the node adjustment sorting result is located, and the boundary status mark information of its control section is searched node by node in the corresponding interval position in the response boundary matching result. For example, the three response sections of node #1 are respectively recorded as "frequency rises out of bounds", "voltage rises out of bounds", and "load drops normally", which are marked as having multi-parameter positive boundary change characteristics.

[0124] Identify whether there are continuous sections in the response segment of the node, that is, whether the time interval between the end point of the previous response segment and the starting point of the next segment is less than the set continuity judgment threshold. The continuity threshold is set according to the minimum adjustment period of the control response. For example, it is set to 0.5 seconds. If the two sections of node #1 are [6.0s, 6.8s] and [6.9s, 7.4s], the interval is 0.1s less than 0.5s, and it is judged to be continuous. If the length of the continuous section exceeds 2 sections, the adjustment order is arranged according to the priority principle of continuous sections. After performing the same processing on all sorted nodes, the nodes with continuous response areas are placed at the front of the priority response list, and the others are arranged backward according to the score sorting position. Finally, a multi-indicator linkage sorting rule structure is established. In this structure, each node is accompanied by its adjustment sequence number, the number of continuous sections, the boundary direction status and the time position to support the formulation of the response adjustment plan.

[0125] The present invention proposes a method for calculating safety assessment indicators of a large-scale multi-agent model of a power grid. By analyzing the node voltage and frequency change trends in a phased manner, combined with trend consistency judgment and abnormal segment identification, dynamic structural reconstruction and trend mapping of the disturbance process are realized. Trend boundary aggregation and frequency classification improve the consistency and accuracy of indicator response state identification. The coupling direction and delay analysis of multi-parameter changes between nodes enhance the identification of collaborative characteristics. The response sorting is regulated to clarify the node adjustment priority, and an adjustment path under multi-dimensional linkage is constructed to improve the refined management and control capabilities of disturbance responses and the pertinence of control decisions.

[0126] Figure 2 This is a block diagram of a device for calculating a security evaluation index of a large multi-agent model of a power grid according to an exemplary embodiment. The device is used in a method for calculating a security evaluation index of a large multi-agent model of a power grid. Figure 2 The device includes a disturbance segmented evolution structure generation module 210, a trend association pattern sequence generation module 220, an indicator response path feature recognition module 230, a parameter coupling distribution type acquisition module 240, and a multi-indicator linkage sorting rule generation module 250. Among them:

[0127] The disturbance segmented evolution structure generation module 210 is used to obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the disturbance start and extreme value points, match the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate the disturbance segmented evolution structure;

[0128] The trend association pattern sequence generation module 220 is used to extract three types of trend parameters based on the disturbance segment evolution structure, determine trend consistency, locate abnormal segments and turning points, organize trend transfer paths, and generate trend association pattern sequences;

[0129] The indicator response path feature identification module 230 is used to call the trend association pattern sequence, extract the trend change segments of the node in adjacent time periods, record the change frequency and determine whether it exceeds the indicator response range, perform boundary clustering on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify differentiated response path features, and obtain the indicator participation change category;

[0130] The parameter coupling distribution type acquisition module 240 is used to call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify and combine according to the coordination frequency, and obtain the parameter coupling distribution type;

[0131] The multi-index linkage sorting rule generation module 250 is used to extract the combination response interval based on the parameter coupling distribution type, determine the boundary adjustment direction, sort the combination priority according to the impact degree, and generate the multi-index linkage sorting rule.

[0132] The disturbance segment evolution structure includes voltage change trend characteristics, frequency change trend characteristics, parameter response relationship within the stage and structural stage arrangement pattern;

[0133] The trend association pattern sequence includes abnormal trend identification points, trend transfer time series, node trend arrangement structure and trend direction consistency mark;

[0134] The categories of indicator participation changes include trend change frequency statistics, trend drift boundary distribution, response duration classification, and regulation indicator response type;

[0135] Parameter coupling distribution types include parameter change coupling combinations, synergy relationship types, response delay characteristics, and propagation path patterns;

[0136] The multi-indicator linkage sorting rules include the control response interval set, boundary matching priority, node influence sorting and adjustment operation records.

[0137] Optionally, the disturbance segmented evolution structure generation module 210 is further configured to:

[0138] S101: Obtain the voltage change rate and frequency change rate of the nodes in the power grid, segment the time axis according to the disturbance starting point and extreme point sequence, mark the sequence of four time intervals, and generate a stage division interval sequence;

[0139] S102: Extracting voltage and frequency rate of change curves based on the phase-divided interval sequence, determining the start time difference between the rising and falling segments of the two curves within the phase, selecting segments with offset values ​​less than the disturbance detection threshold as synchronization intervals, and obtaining a set of synchronization interval delay values ​​based on the voltage direction change corresponding to the frequency peak.

[0140] S103: Based on the synchronization interval delay value set, the voltage and frequency change trends are sorted, the inflection point segment direction sequence is called, the structure arrangement type is identified, and the disturbance segment evolution structure is generated.

[0141] Optionally, the trend association pattern sequence generating module 220 is further configured to:

[0142] S201: Obtain node data for each disturbance stage in the disturbance segmented evolution structure, extract the load change rate, voltage fluctuation amplitude, and frequency offset direction of the node in the same stage, and determine the directional consistency between the three parameters based on the signs of the slope changes of the three parameters in the current time period. If the directions of the three slopes are inconsistent, record the corresponding node and time period as an abnormal segment, and generate an abnormal trend segment interval set.

[0143] S202: Calling the abnormal trend segment interval set to record the node abnormal segment time interval, locating the inflection point time of the load change rate curve in each abnormal segment, matching the starting position of the slope change before the inflection point as the trend starting point, summarizing the time range between the trend starting point and the corresponding inflection point, arranging the segment trend transfer relationship in chronological order, and generating a trend transfer structure sequence;

[0144] S203: According to the trend transfer structure sequence, extract the trend sequence combination of the three parameters in each segment, sort the differentiated combination sequence according to the time axis and merge the repeated segments with the same structure, count the number of occurrences and sequential positions of the trend repeated structure, establish the trend structure arrangement sequence corresponding to the node, and generate the trend association pattern sequence.

[0145] Optionally, the indicator response path feature identification module 230 is further configured to:

[0146] S301: Call the trend sequence of the nodes in the trend association pattern sequence, extract the trend change starting point and trend maintenance end point corresponding to the adjacent time periods, mark the change location and maintenance interval according to the trend direction change position and the duration length in the adjacent segments, and generate a trend transformation structure information group;

[0147] S302: Calculate the node's change frequency index within the trend direction change segment based on the trend direction change position and time interval recorded in the trend transformation structure information group. Use the upper limit of the node's index response range as a judgment threshold. Classify trend segments with a change frequency exceeding the threshold to obtain a drift trend segment interval set.

[0148] S303: Based on the starting and ending points of the trend segments in the drift trend segment interval set, the boundary positions of each segment are aggregated by node, the number of boundary segments corresponding to each node is counted and the corresponding time tags are extracted, and the boundary distribution positions are classified and sorted according to the time series to obtain the trend boundary distribution structure;

[0149] S304: Call the trend repetition range and residence time in the trend boundary distribution structure, perform combined classification based on the residence time interval and the trend segment appearance range, divide the node's control response state according to the differentiated trend distribution characteristics, and generate the indicator participation change category.

[0150] The calculation formula for the node change frequency index in the trend direction change segment is as follows (1):

[0151] (1);

[0152] in, Representative Node In the trend segment The trend change frequency indicator within Representative trend segment length of time, Representative Node In the The frequency change direction slope of the sampling point, Representative Node In the The voltage change direction slope of each sampling point, Representative Node In the The slope of the load change direction at each sampling point, 、 、 Representing the The weight factor of the direction slope of frequency, voltage and load changes at each sampling point, For trend segment The number of sampling points within .

[0153] Optionally, the parameter coupling distribution type acquisition module 240 is further configured to:

[0154] S401: Calling the node classification results in the indicator participation change category, identifying node combinations in the same category whose frequency drift rate, voltage fluctuation amplitude, and load change rate parameters change simultaneously within the same time period, extracting the node combination list and the corresponding time index, and generating a synchronous change combination set;

[0155] S402: Based on the node combinations recorded in the synchronous change combination set, calculate the change starting point time difference index between the three parameters of the node, determine the order of change response, extract the delay time difference and direction relationship between the parameters, select the forward and reverse collaborative segments based on the delay direction relationship, mark the intersection time interval of the consistent change of the three parameters in each group, and obtain the collaborative response structure sequence;

[0156] S403: Call the combined collaborative records in the collaborative response structure sequence, count the number of occurrences of the same collaborative structure in the combination, screen the combination relationships with repetition frequency higher than the collaborative structure judgment threshold, sequentially summarize the delay paths in the collaborative chain in each type of combination, record the parameter change propagation path in node order, and generate the parameter coupling distribution type.

[0157] Among them, the calculation formula of the starting time difference index of the change of the three parameters of the node is as follows (2):

[0158] (2);

[0159] in, Represents a node combination The average time difference between the start time of the change of the three parameters of medium frequency, voltage and load, Represents a node combination The starting time of the change of the medium frequency parameters, Indicates the starting time of the change of voltage parameters, Indicates the starting time of load parameter change.

[0160] Optionally, the multi-index linkage sorting rule generation module 250 is further configured to:

[0161] S501: Based on the parameter coupling distribution type, extract the control response interval of nodes of the same type during the disturbance process, calculate the length of the effective response time interval, and compare the interval boundary value with the currently set response threshold range to determine whether the boundary is exceeded, identify the interval direction change state, and generate a response boundary matching result;

[0162] S502: Based on the direction state recorded in the response boundary matching result, the node change amplitude value is normalized, and the impact degree score corresponding to each node during the disturbance process is extracted. The nodes are sorted from high to low according to the score, and the sorted nodes are assigned adjustment sequence numbers in sequence to establish the node adjustment sorting result;

[0163] S503: Call the node sequence number in the node adjustment sorting result, match the interval position in the response boundary matching result, mark the boundary change direction of the sorting node in the self-response interval, and extract the response boundary continuity paragraph, arrange the adjustment order according to the priority of the continuous interval, and generate a multi-index linkage sorting rule.

[0164] The calculation formula for the effective response time interval length is as follows (3):

[0165] (3);

[0166] in, Representation node Upper parameters The length of the effective response time interval, Indicates the parameter response starting time, Indicates the parameter response end time, Representation node In the Parameters at sampling points The amount of change, represents the sampling period, Representation parameters The corresponding response judgment threshold.

[0167] The present invention proposes a method for calculating safety assessment indicators of a large-scale multi-agent model of a power grid. By analyzing the node voltage and frequency change trends in a phased manner, combined with trend consistency judgment and abnormal segment identification, dynamic structural reconstruction and trend mapping of the disturbance process are realized. Trend boundary aggregation and frequency classification improve the consistency and accuracy of indicator response state identification. The coupling direction and delay analysis of multi-parameter changes between nodes enhance the identification of collaborative characteristics. The response sorting is regulated to clarify the node adjustment priority, and an adjustment path under multi-dimensional linkage is constructed to improve the refined management and control capabilities of disturbance responses and the pertinence of control decisions.

[0168] Figure 3 is a schematic diagram of the structure of a security assessment index calculation device provided by an embodiment of the present invention, such as Figure 3 As shown, the safety evaluation index calculation device may include the above Figure 2 Optionally, the safety assessment index calculation device 310 may include a first processor 2001 .

[0169] Optionally, the safety assessment index calculation device 310 may further include a memory 2002 and a transceiver 2003 .

[0170] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0171] The following combination Figure 3 The components of the security assessment index calculation device 310 are described in detail:

[0172] The first processor 2001 is the control center of the security assessment index calculation device 310 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0173] Optionally, the first processor 2001 may execute various functions of the security assessment index calculation device 310 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0174] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0175] In a specific implementation, as an embodiment, the security assessment index calculation device 310 may also include multiple processors, such as Figure 3 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0176] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0177] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be accessed through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0178] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0179] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0180] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be connected to the first processor 2001 through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0181] It should be noted that Figure 3 The structure of the security assessment index calculation device 310 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0182] In addition, the technical effects of the safety assessment index calculation device 310 can refer to the technical effects of the power grid multi-agent large model safety assessment index calculation method described in the above method embodiment, and will not be repeated here.

[0183] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0184] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0185] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), 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. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0186] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0187] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0188] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0189] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0190] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0191] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0192] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0193] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0194] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0195] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for calculating security assessment indicators of a large multi-agent model of a power grid, characterized by: The method comprises: S1: Obtain the voltage change rate and frequency change rate, divide the disturbance into four stages according to the disturbance start and extreme value points, pair the curve trends, determine the delay and synchronization characteristics, extract the continuous change structure, and generate the disturbance segmented evolution structure; S2: According to the disturbance segment evolution structure, extract three types of trend parameters, judge trend consistency, locate abnormal segments and turning points, organize trend transfer paths, and generate trend correlation pattern sequences; S3: Call the trend association pattern sequence, extract the trend change segments of the node in adjacent time periods, record the change frequency and determine whether it exceeds the indicator response range, perform boundary aggregation on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify the differentiated response path characteristics, and obtain the indicator participation change category; S4: Call the indicator participation change category, screen the three-parameter synchronization combination, identify the impact direction and response order, classify the combination according to the collaborative frequency, and obtain the parameter coupling distribution type; S5: Based on the parameter coupling distribution type, extract the combination response interval, determine the boundary adjustment direction, sort the combination priority by impact degree, and generate multi-indicator linkage sorting rules.

2. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The disturbance segmented evolution structure includes voltage change trend characteristics, frequency change trend characteristics, intra-stage parameter response relationship and structural stage arrangement pattern; The trend association pattern sequence includes abnormal trend identification points, trend transfer time series, node trend arrangement structure and trend direction consistency mark; The indicator participation change categories include trend change frequency statistics, trend drift boundary distribution, response duration classification and regulation indicator response type; The parameter coupling distribution type includes parameter change coupling combination, synergy relationship type, response delay characteristics and propagation path mode; The multi-index linkage sorting rules include a set of control response intervals, boundary matching priorities, node influence sorting, and adjustment operation records.

3. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The method of obtaining the voltage change rate and frequency change rate, dividing the disturbance into four stages according to the disturbance starting point and extreme value point, pairing the curve trends, judging the delay and synchronization characteristics, extracting the continuous change structure, and generating the disturbance segmented evolution structure includes: S101: Obtain the voltage change rate and frequency change rate of the nodes in the power grid, segment the time axis according to the disturbance starting point and extreme point sequence, mark the sequence of four time intervals, and generate a stage division interval sequence; S102: Extracting voltage and frequency rate of change curves based on the phase-divided interval sequence, determining the start time difference between the rising and falling segments of the two curves within the phase, selecting segments with offset values ​​less than the disturbance detection threshold as synchronization intervals, and obtaining a set of synchronization interval delay values ​​based on the voltage direction change corresponding to the frequency peak. S103: Based on the synchronization interval delay value set, the voltage and frequency change trends are sorted, the inflection point segment direction sequence is called, the structure arrangement type is identified, and the disturbance segment evolution structure is generated.

4. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The method extracts three types of trend parameters based on the disturbance segment evolution structure, determines trend consistency, locates abnormal segments and turning points, organizes trend transfer paths, and generates trend association pattern sequences, including: S201: Obtain node data for each disturbance stage in the disturbance segmented evolution structure, extract the load change rate, voltage fluctuation amplitude, and frequency offset direction of the node in the same stage, and determine the directional consistency between the three parameters based on the signs of the slope changes of the three parameters in the current time period. If the directions of the three slopes are inconsistent, record the corresponding node and time period as an abnormal segment, and generate an abnormal trend segment interval set. S202: Calling the abnormal trend segment interval set to record the node abnormal segment time interval, locating the inflection point time of the load change rate curve in each abnormal segment, matching the starting position of the slope change before the inflection point as the trend starting point, summarizing the time range between the trend starting point and the corresponding inflection point, arranging the segment trend transfer relationship in chronological order, and generating a trend transfer structure sequence; S203: According to the trend transfer structure sequence, extract the trend sequence combination of the three parameters in each segment, sort the differentiated combination sequence according to the time axis and merge the repeated segments with the same structure, count the number of occurrences and sequential positions of the trend repeated structure, establish the trend structure arrangement sequence corresponding to the node, and generate the trend association pattern sequence.

5. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The trend association pattern sequence is called to extract the trend transformation segments of the node in adjacent time periods, record the change frequency and determine whether it exceeds the indicator response range, perform boundary aggregation on the start and end positions of the drift segment, classify the node status based on the number of boundary distributions and the duration of the trend, identify the differentiated response path characteristics, and obtain the indicator participation change category, including: S301: Call the trend sequence of the nodes in the trend association pattern sequence, extract the trend change starting point and trend maintenance end point corresponding to the adjacent time periods, mark the change location and maintenance interval according to the trend direction change position and the duration length in the adjacent segments, and generate a trend transformation structure information group; S302: Calculate the node's change frequency index within the trend direction change segment based on the trend direction change position and time interval recorded in the trend transformation structure information group. Use the upper limit of the node's index response range as a judgment threshold. Classify trend segments with a change frequency exceeding the threshold to obtain a drift trend segment interval set. S303: Based on the starting and ending points of the trend segments in the drift trend segment interval set, the boundary positions of each segment are aggregated by node, the number of boundary segments corresponding to each node is counted and the corresponding time tags are extracted, and the boundary distribution positions are classified and sorted according to the time series to obtain the trend boundary distribution structure; S304: Call the trend repetition range and residence time in the trend boundary distribution structure, perform combined classification based on the residence time interval and the trend segment appearance range, divide the node's control response state according to the differentiated trend distribution characteristics, and generate the indicator participation change category.

6. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 5 is characterized in that: The calculation formula for the node change frequency index within the trend direction change segment is as follows (1): （1）； in, Representative Node In the trend segment The trend change frequency indicator within Representative trend segment length of time, Representative Node In the The frequency change direction slope of the sampling point, Representative Node In the The voltage change direction slope of each sampling point, Representative Node In the The slope of the load change direction at each sampling point, 、 、 Representing the The weight factor of the direction slope of frequency, voltage and load changes at each sampling point, For trend segment The number of sampling points within .

7. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The calling indicator participates in the change category, screens the three-parameter synchronization combination, identifies the impact direction and response sequence, and classifies and combines according to the collaborative frequency to obtain the parameter coupling distribution type, including: S401: Calling the node classification results in the indicator participation change category, identifying node combinations in the same category whose frequency drift rate, voltage fluctuation amplitude, and load change rate parameters change simultaneously within the same time period, extracting the node combination list and the corresponding time index, and generating a synchronous change combination set; S402: Based on the node combinations recorded in the synchronous change combination set, calculate the change starting point time difference index between the three parameters of the node, determine the order of change response, extract the delay time difference and direction relationship between the parameters, select the forward and reverse collaborative segments based on the delay direction relationship, mark the intersection time interval of the consistent change of the three parameters in each group, and obtain the collaborative response structure sequence; S403: Call the combined collaborative records in the collaborative response structure sequence, count the number of occurrences of the same collaborative structure in the combination, screen the combination relationships with repetition frequency higher than the collaborative structure judgment threshold, sequentially summarize the delay paths in the collaborative chain in each type of combination, record the parameter change propagation path in node order, and generate the parameter coupling distribution type.

8. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 7 is characterized in that: The calculation formula for the starting time difference index of the change between the three parameters of the node is as follows (2): （2）； in, Represents a node combination The average time difference between the start time of the change of the three parameters of medium frequency, voltage and load, Represents a node combination The starting time of the change of the medium frequency parameters, Indicates the starting time of the change of voltage parameters, Indicates the starting time of load parameter change.

9. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 1 is characterized in that: The method of extracting the combined response interval based on the parameter coupling distribution type, determining the boundary adjustment direction, sorting the combined priority by the degree of influence, and generating the multi-indicator linkage sorting rules includes: S501: Based on the parameter coupling distribution type, extract the control response interval of nodes of the same type during the disturbance process, calculate the length of the effective response time interval, and compare the interval boundary value with the currently set response threshold range to determine whether the boundary is exceeded, identify the interval direction change state, and generate a response boundary matching result; S502: Based on the direction state recorded in the response boundary matching result, the node change amplitude value is normalized, and the impact degree score corresponding to each node during the disturbance process is extracted. The nodes are sorted from high to low according to the score, and the sorted nodes are assigned adjustment sequence numbers in sequence to establish the node adjustment sorting result; S503: Call the node sequence number in the node adjustment sorting result, match the interval position in the response boundary matching result, mark the boundary change direction of the sorting node in the self-response interval, and extract the response boundary continuity paragraph, arrange the adjustment order according to the priority of the continuous interval, and generate a multi-index linkage sorting rule.

10. The method for calculating security assessment indicators of a power grid multi-agent large model according to claim 9, characterized in that: The calculation formula of the effective response time interval length is as follows (3): （3）； in, Representation node Upper parameters The length of the effective response time interval, Indicates the parameter response starting time, Indicates the parameter response end time, Representation node In the Parameters at sampling points The amount of change, represents the sampling period, Representation parameters The corresponding response judgment threshold.

Images