Power grid transmission and distribution cooperation vulnerability assessment method and system

Abstract

The invention discloses a power grid transmission and distribution cooperation vulnerability assessment method and system, and relates to the technical field of electric power engineering.The method comprises the steps that firstly, voltage, current and frequency data of a city-level power grid within target time are collected, cleaned and formatted, and preprocessing completion data are generated; according to the method, the key vulnerability of the power grid is identified by analyzing the vulnerability of the power grid and calculating the vulnerability index of each node and the transmission line. A manager can sort priorities and classify key areas, and targeted enhancement is carried out. In addition, the graph theory is applied to carry out elastic evaluation on key nodes and lines, and node fault influence indexes are obtained, so that the prediction capability and countermeasures for potential faults of the power grid are enhanced. Therefore, the fault diagnosis efficiency is improved, the response strategy when the actual fault occurs is optimized, and the stability and the recovery capability of the power grid facing emergencies are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power engineering, and particularly to a vulnerability assessment method and system for grid transmission and distribution coordination. Background Art

[0002] The technical field of power engineering involves the design, construction, operation, and maintenance of power systems, including power generation, transmission, distribution, and the effective utilization of electric energy. The core purpose of this field is to ensure the stability, security, and economy of power supply. The vulnerability assessment method for grid transmission and distribution coordination is to evaluate the vulnerability of the power system under various potential threats, so as to formulate corresponding enhancement measures to improve the resilience of the system.

[0003] However, the existing power engineering technologies have shortcomings in the systematic assessment and fault prediction of grid vulnerability. Because they focus on post-fault analysis rather than pre-risk assessment, and at the same time lack an early warning system to cope with emergencies such as large-scale power outages caused by system overload. Therefore, improvements are needed. Summary of the Invention

[0004] In view of the above existing problems, the present invention aims to solve the vulnerability problems existing in the current grid during the transmission and distribution process. By collecting and analyzing data such as voltage, current, and frequency of the grid, identifying the key vulnerable points in the grid, and evaluating the node fault impact index, a comprehensive assessment of the network recovery ability and stability can be achieved. This method aims to solve the technical problems of how to accurately identify and evaluate the vulnerability of the grid in a complex grid environment, and how to optimize the grid load distribution according to the evaluation results, improve the anti-interference ability and stability of the grid, and ensure the safe and efficient operation of the grid.

[0005] To solve the above technical problems, a vulnerability assessment method for grid transmission and distribution coordination is proposed, including,

[0006] Collecting the voltage, current, and frequency data of the prefecture-level grid within the target time, cleaning and formatting them to generate preprocessed data; based on the preprocessed data, analyzing the vulnerability of the grid, calculating the vulnerability index of each node and transmission line to obtain the vulnerability index, sorting and classifying the vulnerability index, identifying the key vulnerable points, and generating the key vulnerable point identification result; applying graph theory to the key nodes and lines in the key vulnerable point identification result for resilience assessment to obtain the node fault impact index; based on the node fault impact index, evaluating the recovery ability and stability of the network in each fault situation to generate the network resilience assessment result; using the network resilience assessment result to adjust the load distribution of the grid and applying the load distribution to real-time grid management to obtain the grid optimization implementation result.

[0007] As a preferred embodiment of the vulnerability assessment method for grid transmission and distribution coordination according to the present invention, wherein: the generated preprocessed data includes collecting voltage, current, and frequency data of the power grid, and also includes the periodic changes of the voltage waveform, the peak and steady-state behavior of the current, and the fluctuation of the frequency, to generate the original power grid data;

[0008] Apply high-pass and low-pass filters to the original power grid data to remove noise and non-typical fluctuations in the original power grid data, and correct data errors caused by equipment deviations to obtain the cleaned power grid data;

[0009] Perform formatting processing on the cleaned power grid data, including data encoding and structure adjustment, to generate the preprocessed completed data.

[0010] As a preferred embodiment of the vulnerability assessment method for grid transmission and distribution coordination according to the present invention, wherein: the vulnerability index includes extracting voltage, current, and frequency parameters of each node and transmission line according to the preprocessed completed data to obtain a key parameter data set;

[0011] Based on the key parameter data set, calculate the vulnerability index of each node and transmission line. The calculation formula is:

[0012]

[0013] Wherein, CI i is the vulnerability index of node i, P j is the power load of node j, V i and V j are the voltages of node i and node j respectively, ∈ is a small constant to prevent division by zero, λ is the attenuation coefficient, D ij is the electrical distance between node i and node j, and n is the total number of nodes.

[0014] As a preferred embodiment of the vulnerability assessment method for grid transmission and distribution coordination according to the present invention, wherein: the generation of the key vulnerability point identification result includes summarizing and sorting the obtained vulnerability indices, arranging them in descending order to obtain the sorted vulnerability indices;

[0015] Based on the sorted vulnerability indices, calculate the identification threshold of the key vulnerability points. The calculation formula is:

[0016]

[0017] Wherein, T is the identification threshold of the key vulnerability points, μ is the average value of the sorted vulnerability indices, σ 2 is the variance, k is the threshold coefficient, and θ is the adjustment parameter;

[0018] According to the recognition threshold of the key vulnerability points, identify the nodes and transmission lines that exceed the threshold from the sorted vulnerability indices as key vulnerability points, and form the key vulnerability point recognition result.

[0019] As a preferred solution of a vulnerability assessment method for coordinated power grid transmission and distribution according to the present invention, wherein: the node fault impact index includes extracting key vulnerability points from the key vulnerability point recognition result and constructing a graph theory model, where nodes represent key vulnerability points in the power grid and edges represent transmission lines connecting key vulnerability points, to obtain a graph theory model;

[0020] Based on the graph theory model, calculate the node fault impact index, and the formula is:

[0021]

[0022] wherein, R i is the node fault impact index of node i, N(i) is the set of nodes directly connected to node i, d ij is the distance between node i and node j, b is a small constant, and α is a normalization coefficient.

[0023] As a preferred solution of a vulnerability assessment method for coordinated power grid transmission and distribution according to the present invention, wherein: the network resilience assessment result includes using the node fault impact index to analyze the response time and recovery strength of each key vulnerability point under various fault simulation conditions, and generating a node recovery ability analysis result;

[0024] Based on the node recovery ability analysis result, analyze the stability of each key vulnerability point and connection line during the recovery process, and generate a network stability assessment result;

[0025] Summarize the network stability assessment results to describe the resilience performance of the power grid under various fault conditions, and obtain the power grid resilience assessment result.

[0026] As a preferred solution of a vulnerability assessment method for coordinated power grid transmission and distribution according to the present invention, wherein: the power grid optimization implementation result includes analyzing the performance of each node from the power grid resilience assessment result, identifying nodes with unstable performance or overloading, and obtaining a load adjustment requirement list;

[0027] Based on the load adjustment requirement list, reconfigure and optimize the load distribution in the power grid to generate a new load distribution plan;

[0028] Apply the new load distribution plan to the power grid, monitor the implementation effect of the load distribution, and form the power grid optimization implementation result.

[0029] Another object of the present invention is to provide a vulnerability assessment system for coordinated power grid transmission and distribution. The object of the present invention is to solve the vulnerability problems existing in the operation of the existing power grid, especially the technical problems of vulnerability assessment, fault impact analysis and recovery ability assessment for power grid nodes and transmission lines. By realizing the coordinated work of multiple modules such as data preprocessing, vulnerability analysis, resilience assessment, recovery ability assessment and power grid optimization implementation, the system aims to solve how to accurately identify the key vulnerable points in the power grid, evaluate the impact of node failures on the power grid stability, improve the recovery ability of the power grid under various fault conditions and optimize the power grid load distribution, so as to ensure the safe, stable and efficient operation of the power grid.

[0030] As a preferred embodiment of a vulnerability assessment system for coordinated power grid transmission and distribution according to the present invention, it is characterized in that it includes a data preprocessing module, a vulnerability analysis module, a resilience assessment module, a recovery ability assessment module, and a power grid optimization implementation module;

[0031] The data preprocessing module collects voltage, current and frequency data within the target time, performs data cleaning and formatting, and generates a preprocessed data set;

[0032] The vulnerability analysis module calculates the vulnerability of each node and transmission line of the power grid based on the preprocessed data set, sorts and classifies the vulnerability indexes, identifies the key vulnerable points, and generates the key vulnerable point identification result;

[0033] The resilience assessment module performs graph theory analysis on the nodes and lines in the key vulnerable point identification result to obtain the node fault impact index;

[0034] The recovery ability assessment module evaluates the recovery ability and stability under various fault conditions based on the node fault impact index, and generates a network recovery ability assessment result;

[0035] The power grid optimization implementation module uses the network recovery ability assessment result to adjust the power grid load distribution, applies it to real-time power grid management, and generates a power grid optimization implementation result.

[0036] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of a vulnerability assessment method for coordinated power grid transmission and distribution as described above are implemented.

[0037] A computer-readable storage medium stores a computer program. It is characterized in that when the computer program is executed by a processor, the steps of a vulnerability assessment method for coordinated power grid transmission and distribution as described above are implemented.

[0038] Advantages of the present invention: By analyzing the vulnerability of the power grid and calculating the vulnerability indices of each node and transmission line, the present invention realizes the identification of key vulnerable points in the power grid. This enables managers to prioritize and classify key areas for targeted reinforcement. In addition, graph theory is applied to evaluate the resilience of key nodes and lines, and the node failure impact index is obtained, enhancing the prediction ability of potential power grid failures and response measures. This not only improves the fault diagnosis efficiency but also optimizes the response strategy during actual fault occurrences, ensuring the stability and recovery ability of the power grid in the face of emergencies. Adjusting the power grid load based on this data and applying it to power grid management in real time improves the operation efficiency and flexibility of load management, strengthens the operation safety of the power grid, and has high practicality. Brief Description of the Drawings

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is the overall flowchart of a vulnerability assessment method for power grid transmission and distribution coordination provided by an embodiment of the present invention.

[0041] Figure 2 It is the system scheme module diagram of a vulnerability assessment system for power grid transmission and distribution coordination provided by an embodiment of the present invention.

[0042] In the figure: 10, data preprocessing module; 20, vulnerability analysis module; 30, resilience assessment module; 40, recovery ability assessment module; 50, power grid optimization implementation module. Detailed Embodiments

[0043] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the drawings in the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0044] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0045] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they embodiments that are mutually exclusive of other embodiments individually or selectively.

[0046] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, the three-dimensional spatial dimensions of length, width and depth should be included.

[0047] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0048] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected and coupled" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may also be a mechanical connection, an electrical connection or a direct connection, or may be indirectly connected through an intermediate medium, or may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a vulnerability assessment method for the coordinated power grid transmission and distribution, including:

[0050] S1: Collect the voltage, current and frequency data of the prefecture-level power grid at the target time, clean and format them, and generate the preprocessed data.

[0051] Furthermore, the step of obtaining the preprocessed data is as follows:

[0052] Collect the voltage, current and frequency data of the power grid, including the periodic changes of the voltage waveform, the peak and steady-state behaviors of the current, and the fluctuation conditions of the frequency, and generate the original power grid data;

[0053] Apply high-pass and low-pass filters to the original power grid data, remove the noise and non-typical fluctuations in the original power grid data, correct the data errors caused by equipment deviations, and obtain the cleaned power grid data;

[0054] Format the cleaned power grid data, including data encoding and structural adjustment, to generate preprocessed data.

[0055] Specifically, continuously monitor the power grid, record voltage, current, and frequency data, track the periodic changes of the voltage waveform, the peak and steady-state behavior of the current, and the real-time fluctuations of the frequency, pay attention to the performance of the power grid under different loads and time periods, especially the data during peak power demand periods, and sample the data regularly, recording once every 5 minutes to ensure the timeliness and accuracy of the data. These records form the original dataset, reflecting the real-time operating conditions of the power grid and providing the basic data for further data processing and analysis, generating the original power grid data.

[0056] The collected original power grid data is processed through high-pass and low-pass filtering. High-pass filtering removes low-frequency interference, such as low-frequency fluctuations caused by slow load changes, and low-pass filtering eliminates high-frequency noise generated by electrical switch operations, ensuring that only the reflections of the normal operation of the power grid are retained in the data. Set the high-pass filtering threshold to 0.3 Hz and the low-pass filtering threshold to 50 Hz. These thresholds are set based on the statistical data of the typical operating frequency of the power grid, aiming to minimize data errors. The filtered data is corrected and adjusted. For the errors that may be introduced by the sensors themselves, such as the resistance offset affected by temperature changes, data correction is performed to ensure that the data accurately reflects the power grid state, obtaining the cleaned power grid data.

[0057] The processed and corrected power grid data is formatted. The data encoding is converted to a digital format to suit subsequent analysis and processing, and the data structure is adjusted to optimize the storage format to support time series analysis and pattern recognition. The storage format of the data is adjusted so that each data point contains a timestamp, voltage value, current value, and frequency, facilitating quick query and subsequent analysis. The optimization of these data structures makes the data more standardized and easy to manage, while supporting the efficient retrieval and long-term storage of data, thus providing a structured and easy-to-process data source for the health analysis and prediction of the power grid, generating preprocessed data.

[0058] S2: Based on the preprocessed data, analyze the vulnerability of the power grid, calculate the vulnerability index of each node and transmission line, obtain the vulnerability index, sort and classify the vulnerability index, identify the key vulnerable points, and generate the identification result of the key vulnerable points.

[0059] Furthermore, the steps to obtain the vulnerability index are as follows:

[0060] According to the preprocessed data, extract the voltage, current, and frequency parameters of each node and transmission line to obtain the key parameter dataset;

[0061] Based on the key parameter dataset, calculate the vulnerability index of each node and transmission line. The calculation formula is as follows:

[0062]

[0063] Among them, CI i is the vulnerability index of node i, P j is the power load of node j, V i and V j are the voltages of node i and node j respectively, ∈ is a small constant to prevent division by zero, λ is the attenuation coefficient, D ij is the electrical distance between node i and node j, and n is the total number of nodes;

[0064] Specifically, according to the data extraction steps involved in the file content referred to by the preprocessed data, the voltage values of each node from the cleaned data are periodically collected with a digital multimeter, recorded every 10 seconds, and the results are stored in a data table;

[0065] The current values in the transmission line are measured separately using current sensors. For the frequency, it is read every 10 seconds through a frequency measurement module. The original voltage, current, and frequency data are aligned with the corresponding node and transmission line labels in column order through a data sorting program;

[0066] A mapping table is established for the data of each node in the data with the node number as the index, and the voltage and current values of the transmission line are matched with the corresponding transmission line number using the prepared number comparison table;

[0067] Voltage data exceeding the specified range (when the voltage exceeds 1.10 times the nominal value) are excluded;

[0068] For current data, if it exceeds the specified threshold (this threshold is set by 80% of the full-scale data of the on-site current sensor and determined through multiple calibrations), the data point is marked in the independent record;

[0069] For frequency data, if the fluctuation value exceeds 0.5 Hz, the record is filed separately and the readings of the source data acquisition instrument are rechecked. After the above processing, the data is sorted in ascending order according to the node number to form the key parameter dataset.

[0070] The advantage is that by comprehensively considering the voltage difference between nodes, the load power distribution, and the electrical distance, the vulnerability calculation of each node is based on multiple actual measurement data, so that this index has higher accuracy and pertinence when evaluating the weaknesses of the power grid.

[0071] Specifically, the acquisition steps of each parameter are as follows:

[0072] V i, V j Recorded data from on-site voltage measurement instruments. The instruments record at each node position through a multi-point distributed voltage acquisition unit, and the data is averaged over 10 repeated measurements to obtain the current steady-state voltage value;

[0073] P j Obtained through the node-side power metering device. The device is built with a power metering chip with an accuracy class of 0.5. The power value is recorded once every 1 minute from the 24-hour monitoring data per day, and the average value of the most recent 1 hour is taken as the reference value;

[0074] λ is determined through statistical analysis based on the historical fluctuation characteristic data of the power grid. In this example, λ = 0.1 (dimensionless, obtained through the correlation analysis of the electrical distance and voltage fluctuation in the past 30 days);

[0075] D ij Measured from the line parameters between nodes. The electrical distance is calculated by on-site distance measurement, recording the line length and conductor parameters. Here, D 11 = 0.0, D 12 = 0.03, D 13 = 0.05, D 14 = 0.04, D 15 = 0.02, n = 5 is the total number of nodes, obtained by counting the system topology structure.

[0076] Given parameters, the example calculation process is as follows:

[0077] The node voltage obtained from on-site measurement: V 1 = 1.00, V 2 = 0.98, V 3 = 1.02, V 4 = 1.01, V 5 = 0.99 (per-unit value, obtained by averaging the voltage data recorded by the node voltage measurement instruments 10 times);

[0078] Power value: P 1 = 3.2 kW, P 2 = 4.5 kW, P 3 = 2.8 kW, P 4 = 5.0 kW, P 5 = 3.9 kW (recorded as the average value for 1 hour through the node power metering device);

[0079] Calculate |V 1 - V j | and related terms: |V 1 - V 1 | = 0.00, |V 1 - V 2 | = 0.02, |V1 -V 3 | = 0.02, |V 1 -V 4 | = 0.01, |V 1 -V 5 | = 0.01;

[0080] Substitute the parameters into the formula:

[0081]

[0082] Calculate term by term:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Substitute:

[0089] 3200×1 = 3200;

[0090] 214.2857×0.9970 ≈ 213.642;

[0091] 133.3333×0.9950 ≈ 132.666;

[0092] 454.5454×0.9960 ≈ 452.727;

[0093] 354.5454×0.9980 ≈ 353.636;

[0094] Sum: 3200 + 213.642 + 132.666 + 452.727 + 353.636 = 4352.671;

[0095] Divide by 5: CI 1 = 4352.671 / 5 = 870.5342;

[0096] The results show that for Node 1, its vulnerability index is 870.5342. The larger this value is, the more obvious the vulnerability degree is after the combined action of the differences between nodes, electrical distances, and load characteristics under the current distributed measurement conditions. When the result is much larger than the normal range (exceeding 500), it indicates that the node is in a more disturbance-prone state within the system, and if it is lower than a relatively low value (less than 100), it means the node is relatively stable.

[0097] It should be noted that the steps for obtaining the identification results of key vulnerable points are as follows:

[0098] Summarize and sort the obtained vulnerability indices in descending order to obtain the sorted vulnerability indices;

[0099] Based on the sorted vulnerability indices, calculate the identification threshold of key vulnerable points. The calculation formula is:

[0100]

[0101] where T is the identification threshold of key vulnerable points, μ is the average value of the sorted vulnerability indices, σ 2 is the variance, k is the threshold coefficient, and θ is the adjustment parameter;

[0102] According to the identification threshold of key vulnerable points, identify the nodes and transmission lines whose vulnerability indices exceed the threshold from the sorted vulnerability indices as key vulnerable points, and form the identification results of key vulnerable points.

[0103] Specifically, the original data file referred to for summarizing and sorting the obtained vulnerability indices has noted the corresponding numbers, voltage, current, and frequency value sources of all nodes and transmission lines;

[0104] During the execution process, it is necessary to first establish a corresponding column for node numbers and vulnerability indices in the data record table, and record the previously obtained vulnerability indices one by one in the order of the numbers;

[0105] Rearrange the vulnerability index values of all nodes and transmission lines in descending order. At this time, the corresponding column in the data record table should be set to the floating-point format to ensure that the sorting operation can accurately compare sizes;

[0106] For the situation where the values are concentratedly distributed within a special range, multiple rounds of sorting need to be performed in a numerical comparison method with a precision of 0.001. If there are outliers in some data points, refer to the nominal value range data provided in the node voltage measurement record table, list the vulnerability index data points that are not within the 5% interval above and below the nominal value separately and conduct manual review, and mark the corresponding suspicious record items in the data collation table;

[0107] For the vulnerability index data points that still have anomalies after repeated inspections, store them separately in the outlier table and do not participate in the final sorting operation. After the sorting is completed, record the final results according to the order arranged from high to low in the list.

[0108] The beneficial effect is that by introducing the synergistic effect of the average value, variance, specific threshold coefficient, and adjustment parameter, it is possible to more accurately distinguish high-risk nodes from ordinary nodes when identifying key vulnerable points.

[0109] Specifically, the specific steps for obtaining the μ parameter are as follows:

[0110] First, select all data points from the sorted vulnerability index dataset, sum up all vulnerability index values, and then divide by the total number of data points to obtain the average value μ;

[0111] σ 2 The parameter is obtained by calculating the variance of the data points;

[0112] The k parameter retrieves the reference range applicable to the vulnerability assessment of the power transmission and distribution system from the authoritative standard specification document, and then through the statistical analysis of the node historical data, according to the distribution characteristics of the vulnerability index, a specialized parameter setting personnel selects the k value according to the industry standard;

[0113] The θ parameter first conducts statistics on the data segments with large fluctuations in the historical operation records of the power system, extracts additional deviation coefficients from these segments, determines a numerical range through multiple comparisons, and selects the θ value as the adjustment parameter within this range. This value is determined by comparing the differences between the node fluctuation data and the basic mean value within multiple time periods.

[0114] Given the parameters, the example calculation process is as follows:

[0115] A total of 100 data points of the vulnerability index values of a certain batch of nodes are obtained and recorded in the table. After calculation, μ = 320, σ 2 = 3600, where the variance 3600 is obtained by squaring the differences between all data points and the mean value of 320 and then taking the average. k is taken as 1.5, and θ = 100 is selected as the adjustment parameter in the numerical segment that can improve the resolution through the comparison and classification of the fluctuation coefficients of the historical data for 30 days.

[0116] First, substitute the above parameters into the formula:

[0117]

[0118] Calculate the part inside the square root:

[0119] 3600 + 100 = 3700

[0120] Approximately equal to 60.83 (taking the square root of 3700 to get 60.83);

[0121] Continue to calculate:

[0122] 1.5 × 60.83 = 91.245

[0123] Finally, add the result to 320:

[0124] T = 320 + 91.245 = 411.245

[0125] The result shows that the critical value T = 411.245 calculated here is the threshold for identifying key vulnerable points. When the sorted vulnerability index exceeds 411.245, it indicates that the node or transmission line belongs to the category of key vulnerable points, and when it is less than 411.245, it belongs to the relatively normal node range. Through this threshold, it is convenient to screen and mark the key areas of concern in the system in the follow-up.

[0126] The measurement record description document referred to by the identification threshold of key vulnerable points provides a data classification strategy. The vulnerability indices of each node and transmission line are registered in the record table one by one according to the pre-numbered order. During the implementation process, the calculated identification threshold of key vulnerable points needs to be inserted at the top of the table as a reference value, and then the sorted vulnerability indices in the table are compared row by row to read the difference between the vulnerability index value and the threshold. The data points exceeding the threshold are marked row by row. If it is found that the vulnerability index repeatedly exceeds the threshold at the corresponding position of the marked node, the number of the node will be listed in the independent record list. Finally, all the node numbers exceeding the threshold are summarized in the record list to form the identification result of key vulnerable points.

[0127] S3: Apply graph theory to conduct a resilience assessment on the key nodes and lines in the identification result of key vulnerable points to obtain the node fault impact index.

[0128] Furthermore, the steps to obtain the node fault impact index are as follows:

[0129] Extract the key vulnerable points from the identification result of key vulnerable points to construct a graph theory model, where the nodes represent the key vulnerable points in the power grid, and the edges represent the transmission lines connecting the key vulnerable points, to obtain the graph theory model;

[0130] Based on the graph theory model, calculate the node fault impact index, and the formula is:

[0131]

[0132] where, R i is the node fault impact index of node i, N(i) is the set of nodes directly connected to node i, d ij is the distance between node i and node j, b is a small constant, and α is a normalization coefficient.

[0133] Specifically, sort each node number in the list of key vulnerable points to establish a data record table, where there is a column for node numbers, a column for the geographical location data of each node, and a line connection information table in the record table;

[0134] By comparing the numbers of each key vulnerability item by item with their geographical coordinates in the actual power grid, using a longitude and latitude surveying instrument and the marked points distributed at the power grid site to record the node coordinate data, registering these coordinate data in the node information table in meters, and then reading the length data of the corresponding line segments from the on-site measurement records of the transmission lines, and recording the length values in the length list successively through a rangefinder after on-site measurement;

[0135] According to the corresponding node number, find the transmission line number connected to it in the table. Through the corresponding relationship between the node number and the line segment number, add a column of line length items in the record table, record it in whole meters, and check each node and its connected node numbers item by item through comparison;

[0136] If it is found that the length of this line exceeds 3,000 meters, it is necessary to mark this data point as a special long line in the remarks column of the data table. This 3,000-meter value is taken from the median value of the longest transmission line statistical data obtained from multiple on-site measurements, rather than randomly selected;

[0137] After all the node line data are uniformly registered, form a connection matrix for the connection relationship between nodes through summarization. Each row in the matrix corresponds to a node, each column corresponds to the number of its adjacent nodes, and attach the line length value in the remarks to form a graph theory model that can be used to represent the relationship between key vulnerabilities and their connected lines.

[0138] The beneficial effect is that by introducing the distance information of adjacent nodes and the normalization coefficient, quantitative analysis can be carried out in combination with actual measurement data when evaluating the node fault response.

[0139] Specifically, the acquisition of the α parameter is as follows: Measure the data of several samples in the same type of power grid instances, statistically analyze the line distance distribution, and generate a set of dimensionless data with a concentrated distribution through normalization of multiple groups of distance values to ensure that the calculation results are comparable on the same scale. After the on-site technical personnel normalize the line length statistical data table (the length data of 100 typical lines are all between 0.2 km and 5.0 km), map the distance distribution to the range of 0 to 1 through the linear normalization method, and select a scaling ratio coefficient as α from it. After multiple comparisons, it is determined that α = 0.1 is a more appropriate value;

[0140] The N(i) parameter is the quantification of the set of adjacent nodes of node i. First, obtain the list of directly connected node numbers of node i through the node number comparison table. Each row in this table records a node and its directly connected node number to clarify the specific elements of N(i);

[0141] d ijThe acquisition of parameters is as follows: measure the line lengths between nodes, record the length data of each line in a distance table, and take the average of three independent measurements of the line length connecting node i and node j to obtain d ij .

[0142] Given the parameters, the example calculation process is as follows:

[0143] There is node 1, and its adjacent node set N(1) = 2, 3. According to the record table, the line length d from node 1 to node 2 12 = 0.5 km (average value of multiple measurement results), and the line length d from node 1 to node 3 13 = 1.2 km (average value of multiple measurement results). Substitute b = 0.001 and α = 0.1 into the formula:

[0144]

[0145] Calculate the denominator:

[0146] 0.5 + 0.001 = 0.501

[0147] 1.2 + 0.001 = 1.201

[0148] Calculate the reciprocal of each term:

[0149]

[0150] Sum:

[0151] 1.996 + 0.833 = 2.829

[0152] Multiply by α = 0.1:

[0153] R 1 = 0.1 × 2.829 = 0.2829

[0154] This result indicates that the node fault influence index for node 1 is 0.2829. When the R i value is relatively high (e.g., greater than 0.5), it means the node shows a relatively significant potential for fault diffusion in its connection relationship with other adjacent nodes. When the R i value is relatively low (e.g., less than 0.2), it means the node is relatively weak in terms of fault influence transmission. And the 0.2829 in this result is in the middle region between 0.2 and 0.5, indicating that the fault diffusion potential of node 1 is at a medium level.

[0155] S4: Based on the node fault influence index, evaluate the recovery ability and stability of the network under each fault condition, and generate the network resilience evaluation result.

[0156] It should be noted that the steps to obtain the network resilience evaluation result are:

[0157] Using the node fault impact index, analyze the response time and recovery strength of each key vulnerability under various fault simulation conditions, and generate the analysis results of node recovery ability;

[0158] Based on the analysis results of node recovery ability, analyze the stability of each key vulnerability and connection line during the recovery process, and generate the network stability evaluation results;

[0159] Summarize the network stability evaluation results, describe the resilience performance of the power grid under various fault conditions, and obtain the power grid resilience evaluation results.

[0160] Specifically, using the node fault impact index, first, for each node under the set fault simulation conditions, identify the response time and recovery strength of the node, measure the time from the occurrence of the fault to the node returning to the normal state, and calculate the percentage of the node's recovery to the initial performance state during this period, so as to evaluate the quick response ability and long-term recovery ability of each node. At the same time, record the recovery time of each node under different fault types and compare it with the historical performance data to determine the impact of each type of fault on the node performance;

[0161] According to the analysis results of node recovery ability, continue to evaluate the stability of each node and its connected transmission line during the recovery process, which includes observing the ability of the node to maintain its function without being affected by further faults after recovery, especially its performance after multiple faults, analyzing the performance fluctuations of the node and the transmission line during multiple recovery cycles, and calculating the frequency of the performance recovering to within the safe operation threshold, which is determined based on historical operation data and safety standards, aiming to evaluate the stability and reliability of the network elements after recovery;

[0162] Summarize and analyze the network stability evaluation results. Based on the stability evaluation results of each node and connection line in the face of various fault simulations, including the comprehensive evaluation of the fault recovery time and the stability after recovery, evaluate the overall and local adaptability of the network by comparing the performance in each fault test.

[0163] S5: Using the power grid resilience evaluation results, adjust the load distribution of the power grid and apply the load distribution to real-time power grid management to obtain the power grid optimization implementation results.

[0164] It should also be noted that the steps to obtain the power grid optimization implementation results are as follows:

[0165] Analyze the performance of each node from the power grid resilience evaluation results, identify the nodes with unstable performance or overloading, and obtain the load adjustment requirement list;

[0166] Based on the load adjustment requirement list, reconfigure and optimize the load distribution in the power grid to generate a new load distribution plan;

[0167] Apply the new load distribution scheme to the power grid, monitor the implementation effect of the load distribution, and form the implementation result of power grid optimization.

[0168] Specifically, extract information from the power grid resilience assessment results, analyze the performance of each node in historical load data and load tests, focusing on the maximum carrying capacity and response time of the nodes, identify those nodes whose performance begins to decay when the load reaches 90% of its capacity, and these nodes are marked as high-risk nodes. In addition, examine the fatigue degree of the nodes under continuous operation conditions, determine those nodes that need to reduce the operating frequency to extend the equipment life, and the analysis basis includes the operating temperature, current fluctuation and historical maintenance records of the nodes. Based on this, determine the list of nodes that need to adjust the load preferentially;

[0169] According to the load adjustment requirement list, reconfigure the node loads in the power grid. The specific measures include reducing the power supply of overloaded nodes by 20%, and at the same time enhancing the load capacity of the auxiliary nodes directly connected to them to disperse risks and improve the overall network robustness. During the adjustment process, monitor the current and voltage responses of each node to ensure that the adjustment does not cause unforeseen network fluctuations. In addition, for the connection lines, evaluate their resistance and conductivity, and optimize the line configuration according to the heat capacity and maximum current capacity of the lines to prevent any line from exceeding its maximum safe operating parameters under the new configuration;

[0170] After implementing the new load distribution scheme, continuously monitor the actual operation of the power grid, focusing on monitoring the load balance of the adjusted nodes and the overall stability of the system. The monitoring indicators include the real-time load ratio of the nodes, voltage stability index and fault response time, and these indicators help to evaluate the adaptability and response ability of the power grid. Ensure that the operations of all nodes are within their preset safety ranges.

[0171] Embodiment 2, the second embodiment of the present invention, which is different from the previous embodiment in that:

[0172] If the above-described functions are implemented in the form of 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, in essence, or the part that contributes to the prior art, or a part of this 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 causing a computer device (which may be a personal computer, a server, or a 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 medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0173] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0174] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other appropriate processing, and then stored in a computer memory.

[0175] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0176] Embodiment 3, referring to Figure 2 , is the third embodiment of the present invention. This embodiment provides a vulnerability assessment system for coordinated power grid transmission and distribution, including a data preprocessing module 10, a vulnerability analysis module 20, a resilience assessment module 30, a recovery ability assessment module 40, and a power grid optimization implementation module 50;

[0177] The data preprocessing module 10 collects voltage, current, and frequency data within the target time, performs data cleaning and formatting, and generates a preprocessed data set;

[0178] The vulnerability analysis module 20 calculates the vulnerability of each node and transmission line of the power grid based on the preprocessed data set, sorts and classifies the vulnerability indices, identifies the key vulnerable points, and generates the identification result of the key vulnerable points;

[0179] The resilience assessment module 30 performs graph theory analysis on the nodes and lines in the identification result of the key vulnerable points to obtain the node failure impact index;

[0180] The recovery ability assessment module 40 evaluates the recovery ability and stability under various fault conditions based on the node failure impact index, and generates the network recovery ability assessment result;

[0181] The power grid optimization implementation module 50 uses the network recovery ability assessment result to adjust the load distribution of the power grid, applies it to real-time power grid management, and generates the power grid optimization implementation result.

[0182] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A vulnerability assessment method for power grid transmission and distribution coordination, characterized by: include, Collect the voltage, current and frequency data of the municipal power grid within the target time, clean and format them, and generate pre-processed data; Based on the preprocessed data, the vulnerability of the power grid is analyzed, the vulnerability index of each node and transmission line is calculated, the vulnerability index is obtained, the vulnerability index is sorted and classified, the key vulnerability points are identified, and the key vulnerability point identification results are generated; Apply graph theory to evaluate the resilience of key nodes and lines in the key vulnerability point identification results to obtain the node failure impact index; Based on the node failure impact index, the network's recovery capability and stability under each failure scenario are evaluated to generate network resilience evaluation results; The network elasticity assessment results are used to adjust the load distribution of the power grid, and the load distribution is applied to real-time power grid management to obtain the results of power grid optimization implementation.

2. A method for vulnerability assessment of power grid transmission and distribution coordination according to claim 1, characterized in that: The generating of pre-processed data includes collecting voltage, current and frequency data of the power grid, including periodic changes in voltage waveform, peak value and steady-state behavior of current, and frequency fluctuation, to generate original power grid data; Apply high-pass and low-pass filtering to the raw power grid data to remove noise and atypical fluctuations in the raw power grid data, correct data errors caused by equipment deviations, and obtain cleaned power grid data; The cleaned power grid data is formatted, including data encoding and structure adjustment, to generate pre-processed data.

3. A vulnerability assessment method for power grid transmission and distribution coordination according to claim 2, characterized in that: The vulnerability index includes extracting voltage, current and frequency parameters of each node and transmission line according to the preprocessed data to obtain a key parameter data set; Based on the key parameter data set, the vulnerability index of each node and transmission line is calculated using the following formula: Among them, CI i is the vulnerability index of node i, P j is the power load of node j, V i and V j are the voltages at nodes i and j, ∈ is a small constant to prevent division by zero, λ is the attenuation coefficient, and D ij is the electrical distance between node i and node j, and n is the total number of nodes.

4. A method for vulnerability assessment of power grid transmission and distribution coordination according to claim 3, characterized in that: Generating the key vulnerability point identification result includes summarizing and sorting the obtained vulnerability indexes, arranging them in descending order, and obtaining sorted vulnerability indexes; Based on the ranked vulnerability index, the identification threshold of the key vulnerability point is calculated using the following formula: Among them, T is the identification threshold of key vulnerable points, μ is the average value of vulnerability index after sorting, σ 2 is the variance, k is the threshold coefficient, and θ is the adjustment parameter; According to the identification threshold of the key vulnerable points, nodes and transmission lines exceeding the threshold are identified from the sorted vulnerability indexes as key vulnerable points to form a key vulnerable point identification result.

5. A method for vulnerability assessment of power grid transmission and distribution coordination according to claim 4, characterized in that: The node failure impact index includes extracting key vulnerable points from the key vulnerable point identification results, constructing a graph theory model, wherein nodes represent key vulnerable points in the power grid, and edges represent transmission lines connecting key vulnerable points, to obtain a graph theory model; Based on the graph theory model, the node failure impact index is calculated using the formula: Among them, R i is the node failure impact index of node i, N(i) is the set of nodes directly connected to node i, d ij is the distance between node i and node j, b is a small constant, and α is the normalization coefficient.

6. A method for vulnerability assessment of power grid transmission and distribution coordination according to claim 5, characterized in that: The network resilience assessment results include, using the node failure impact index, analyzing the response time and recovery strength of each key vulnerability point under various failure simulation conditions, and generating node recovery capability analysis results; Based on the node recovery capability analysis results, analyze the stability of each key vulnerability point and connection line during the recovery process to generate a network stability assessment result; The network stability assessment results are summarized to describe the resilience performance of the power grid under various fault conditions, thereby obtaining a power grid resilience assessment result.

7. A method for vulnerability assessment of power grid transmission and distribution coordination according to claim 6, characterized in that: The grid optimization implementation results include analyzing the performance of each node from the grid resilience assessment results, identifying nodes that are unstable or overloaded, and obtaining a list of load adjustment requirements; Based on the load adjustment requirement list, reconfigure and optimize the load distribution in the power grid to generate a new load distribution plan; The new load distribution scheme is applied to the power grid, the implementation effect of the load distribution is monitored, and the optimization implementation result of the power grid is formed.

8. A system using a vulnerability assessment method for power grid transmission and distribution coordination as claimed in any one of claims 1 to 7, characterized in that: It includes data preprocessing module, vulnerability analysis module, resilience assessment module, recovery capacity assessment module and power grid optimization implementation module; The data preprocessing module collects voltage, current and frequency data within a target time, performs data cleaning and formatting, and generates a preprocessing data set; The vulnerability analysis module calculates the vulnerability of each node and transmission line of the power grid based on the preprocessed data set, sorts and classifies the vulnerability index, identifies key vulnerable points, and generates key vulnerable point identification results; The elasticity assessment module performs graph analysis on the nodes and lines in the key vulnerability point identification results to obtain a node failure impact index; The recovery capability evaluation module evaluates the recovery capability and stability under various failure conditions based on the node failure impact index and generates a network recovery capability evaluation result; The power grid optimization implementation module uses the network recovery capability assessment result to adjust the load distribution of the power grid, applies it to real-time power grid management, and generates power grid optimization implementation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for vulnerability assessment of power grid transmission and distribution coordination according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a vulnerability assessment method for power grid transmission and distribution coordination according to any one of claims 1 to 7 are implemented.

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