Power transmission system pre-disaster preventive island division method and system based on constraint spectral clustering

Abstract

The invention discloses a power transmission system pre-disaster preventive island division method and system based on constraint spectral clustering. The power transmission system pre-disaster preventive island division method comprises the following steps: acquiring power grid data including grid data, unit parameters, load data and typhoon path parameters; calculating a typhoon maximum wind speed radius and a Holland B parameter by adopting a Holland wind field model based on the acquired power grid data, and then calculating to obtain the wind speed of each node of the power grid; calculating the fault probability of the power transmission line and the tower according to the obtained typhoon maximum wind speed radius and the wind speed of each node of the power grid; simulating cascading faults caused by single-line faults, and generating a high-probability fault scene set; calculating a risk index RI, performing fuzzy classification, and outputting a risk level; and when the risk level exceeds a set threshold value, performing preventive islanding. Through data-driven evaluation-dynamic risk decision-constraint spectral clustering isolation three-level joint control, the technical pain points of pre-disaster defense deficiency and extensive island division in extreme weather are overcome, and a core support is provided for an elastic power grid.

Description

Technical Field

[0001] This invention belongs to the field of power technology, specifically relating to a pre-disaster preventive islanding method and system for power transmission systems using constrained spectral clustering. Background Technology

[0002] With global warming and increasingly frequent extreme weather events, windstorms have become a significant factor threatening power grid safety. In September 2016, a powerful typhoon, accompanied by torrential rain, lightning, and hail, struck South Australia, causing large-scale wind turbine outages and ultimately resulting in a statewide blackout. Power was restored 50 hours after the incident. In May 2023, Super Typhoon Mawar struck northern Guam, causing widespread power outages. In November 2024, Storm Burt brought strong winds, heavy snow, and torrential rain to the UK and Ireland, leading to the closure of multiple railway lines and disruptions to transportation, with Scotland and Wales experiencing particularly severe power outages. Frequent typhoon disasters significantly increase the risk of large-scale power outages, severely impacting the healthy development of the social economy and the normal lives and livelihoods of the people. Meanwhile, with the introduction of the concept of a "resilient grid," scholars both domestically and internationally have conducted extensive research on the grid's ability to cope with extreme weather, using "resilience" indicators to assess the grid's ability to defend against and recover to normal operation in the face of such "low-probability-high-risk" events. To accurately assess the power system's ability to cope with increasingly frequent typhoon disasters, there is an urgent need to deepen research on power system resilience assessment. Current research on power system resilience assessment has made some progress.

[0003] Traditional deterministic assessment criteria, such as N-1 or Nk, are becoming increasingly ineffective in addressing the growing risks to power grids. As extreme weather events can damage multiple components simultaneously, probabilistic methods are gaining increasing attention in the research community. Meanwhile, power infrastructure hardening strategies can be considered the most direct defense against typhoons. For example, Texas has planned to upgrade transmission line design standards to withstand high wind speeds; these measures can improve the reliability of power facilities, but at a high cost. However, hardening strategies as physical actions may not always be sufficient to cope with typhoons. Therefore, they need to be accompanied by appropriate operational resilience enhancement measures.

[0004] In recent years, extensive research has been conducted on defense methods based on appropriate operational strategies. With the U.S. Department of Energy's introduction of the "smart grid" concept, numerous researchers are working to improve the reliability, security, and efficiency of power systems by integrating AI, machine learning, smart meters, and data fusion strategies. The goal is for power systems to operate in a coordinated, efficient, and reliable manner, responding to emergencies through self-healing actions. However, most operational strategies focus on post-event recovery techniques, paying less attention to pre-event preventative controls, particularly preventative islanding strategies, and rarely considering the spatiotemporal characteristics of disasters. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for pre-disaster preventive islanding of power transmission systems based on constrained spectrum clustering, which addresses the shortcomings of the prior art and solves the technical problems of neglecting spatiotemporal dynamic risks, not optimizing islanding accuracy, and lacking preventive triggering mechanisms in the existing pre-disaster control.

[0006] The present invention adopts the following technical solution: A constrained spectral clustering method for pre-disaster preventative islanding in power transmission systems includes the following steps: S1. Obtain power grid data including grid structure data, unit parameters, load data, and typhoon path parameters; S2. Based on the power grid data obtained in step S1, the radius of the typhoon's maximum wind speed is calculated using the Holland wind field model. and Holland B parameters Then the wind speed at each node of the power grid was calculated. ; S3. The radius of the typhoon's maximum wind speed obtained in step S2. and wind speed at each node of the power grid Calculate the failure probability of transmission lines and towers; simulate cascading failures caused by a single line failure to generate a set of high-probability failure scenarios; calculate the risk index RI and perform fuzzy classification to output the risk level; S4. When the risk level exceeds the set threshold At that time, preventative islanding should be implemented.

[0007] Preferably, the wind speed at each node of the power grid for:

[0008] in, Indicates air density, Indicates the distance to the center of the typhoon. Represents the Coriolis force parameter. The angular velocity of Earth's rotation. Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time.

[0009] Preferably, the risk index RI is calculated and fuzzy-classified, and the risk level is output as follows: Based on wind speed at each node of the power grid Calculate the wind load on the conductor and tower wind load Based on the pressure difference between the eye and the outer periphery of the typhoon and the radius of maximum wind speed Correct the bending moment at the base of the tower; determine the segment locations of the transmission line and the structural parameters of the tower based on the grid data, and calculate the fault probability of the transmission line and the tower. ; Based on the fault probability of transmission lines and towers Simulate cascading failures caused by a single line fault; Based on the fault probability of transmission lines and towers Vulnerable branches are identified. All branches with a failure probability higher than a specified failure probability threshold are considered vulnerable branches and used to generate all failure scenarios. The risk index RI of all failure scenarios is calculated. Risk levels are assessed by classifying the risk based on the Risk Index (RI) to obtain risk assessment results.

[0010] Preferably, the fault probability of transmission lines and towers for:

[0011] in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure.

[0012] Preferably, based on the fault probability of transmission lines and towers The simulation of cascading failures caused by a single line fault is as follows: The system's safe operation capability in the short period after an accident is simulated using numerical simulation of emergency ratings. After the corresponding vulnerable branch is tripped in each fault scenario, the power flow distribution of the transmission system is calculated, the branch that violates its short-term emergency rating is identified, the branch is included in the emergency fault set, and the branch in the set is tripped.

[0013] Preferably, the risk level of the power system is assessed, and the risk assessment results are as follows: Identify vulnerable branches at the current time segment and add them to the vulnerable branch set; Branch generation based on the set of fragile branchesK Various failure scenarios; In each fault scenario, trip the vulnerable branch in that fault scenario and calculate the power flow; Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this process until any stopping criterion is met. Repeat the power flow calculation for each failure scenario, record the load loss value for each failure scenario, and calculate the failure probability for each failure scenario. Calculate the RI value based on the failure probability for each failure scenario. The stopping criteria include: no branches are found; the calculated power flow does not converge; the number of program iterations exceeds the set threshold.

[0014] Preferably, the risk index RI is:

[0015] in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This is the set of fault scenarios that are retained after filtering.

[0016] Preferably, when the output risk level exceeds a set threshold, a preventative islanding strategy is implemented, specifically: Construct a weighted graph of the power grid with the apparent power of the branches as the edge weights; use vulnerable buses as constraints and achieve spectral clustering through normalized Laplace matrix eigenvalue decomposition; divide the power grid into a subgraph V1 containing vulnerable buses and a non-vulnerable subgraph V2; perform a disjoint operation on subgraph V1 and control it independently.

[0017] Preferably, the constraints include:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] in, For load nodes i The amount of load reduction, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

[0028] Secondly, embodiments of the present invention provide a pre-disaster preventative islanding system for power transmission systems based on constrained spectral clustering, comprising: The data acquisition module obtains power grid data including grid structure data, unit parameters, load data, and typhoon path parameters. The calculation module, based on the acquired power grid data, uses the Holland wind field model to calculate the radius of the typhoon's maximum wind speed. and Holland B parameters Then, based on the obtained maximum wind speed radius... The wind speed at each node of the power grid was calculated using Holland B parameters. ; The classification module is based on the radius of the typhoon's maximum wind speed. and wind speed at each node of the power grid Calculate the failure probability of transmission lines and towers; simulate cascading failures caused by single-line failures based on the obtained failure probabilities to generate a set of high-probability failure scenarios; calculate the risk index RI of the set of high-probability failure scenarios; and then output the risk level through fuzzy classification. The output module will respond when the risk level exceeds a set threshold. At that time, preventative islanding should be implemented.

[0029] Preferably, the wind speed at each node of the power grid for:

[0030] in, Indicates air density, Indicates the distance to the center of the typhoon. Represents the Coriolis force parameter. The angular velocity of Earth's rotation. Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time.

[0031] Preferably, the risk index RI of the set of high-probability failure scenarios is calculated, and then the risk level is output through fuzzy classification as follows: Based on wind speed at each node of the power grid Calculate the wind load on the conductor and tower wind load Based on the pressure difference between the eye and the outer periphery of the typhoon and the radius of maximum wind speed Correct the bending moment at the base of the tower; determine the segment locations of the transmission line and the structural parameters of the tower based on the grid data, and calculate the fault probability of the transmission line and the tower. ; Based on the fault probability of transmission lines and towers Simulate cascading failures caused by a single line fault; Based on the fault probability of transmission lines and towers Vulnerable branches are identified. All branches with a failure probability higher than a specified failure probability threshold are considered vulnerable branches and used to generate a set of high-probability failure scenarios. The risk index RI of the set of high-probability failure scenarios is calculated. Risk levels are assessed by classifying the risk based on the Risk Index (RI) to obtain risk assessment results.

[0032] Preferably, the fault probability of transmission lines and towers for:

[0033] in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure; Based on the fault probability of transmission lines and towers The simulation of cascading failures caused by a single line fault is as follows: The system's safe operation capability in the short period after an accident is simulated using numerical simulation of emergency ratings. After the corresponding vulnerable branch is tripped in each fault scenario, the power flow distribution of the transmission system is calculated, the branch that violates its short-term emergency rating is identified, the branch is included in the emergency fault set, and the branch in the set is tripped. The risk level of the power system was assessed, and the specific risk assessment results are as follows: Identify vulnerable branches at the current time segment and add them to the vulnerable branch set; Branch generation based on the set of fragile branches K Various failure scenarios; In each fault scenario, trip the vulnerable branch in that fault scenario and calculate the power flow; Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this process until any stopping criterion is met. Repeat the power flow calculation for each failure scenario, record the load loss value for each failure scenario, and calculate the failure probability for each failure scenario. Calculate the RI value based on the failure probability for each failure scenario. The stopping criteria include: no branches are found; the calculated power flow does not converge; the number of program iterations exceeds the set threshold. The risk index RI for a set of high-probability failure scenarios is:

[0034] in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This is the set of fault scenarios that are retained after filtering.

[0035] Preferably, when the output risk level exceeds a set threshold, a preventative islanding strategy is implemented, specifically: Construct a power grid weighted graph with branch apparent power as edge weight; use vulnerable buses as constraints and achieve spectral clustering through normalized Laplace matrix eigenvalue decomposition; divide the power grid into a subgraph V1 containing vulnerable buses and a non-vulnerable subgraph V2; perform a disjoint operation on subgraph V1 and control it independently; The constraints include:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] in, For load nodes i The amount of load reduction, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

[0046] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described pre-disaster preventive islanding method for power transmission systems based on constrained spectral clustering.

[0047] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for pre-disaster preventive islanding of power transmission systems based on constraint spectral clustering.

[0048] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described pre-disaster preventive islanding method for power transmission systems based on constrained spectral clustering.

[0049] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described pre-disaster preventive islanding method for power transmission systems based on constraint spectral clustering.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects: A pre-disaster preventative islanding method for power transmission systems based on constrained spectral clustering quantifies the spatiotemporal characteristics of typhoons using the Holland wind field model and calculates the fault probability of transmission lines and towers using stress-intensity interference theory, accurately identifying vulnerable areas and overcoming the shortcomings of traditional Nk criteria that ignore the spatiotemporal dynamics of extreme weather. It pioneers a fuzzy risk index classification, mapping continuous RI values ​​to five discrete risk levels, enabling dynamic responses of "low-risk warning, high-risk action." Islanding is triggered when the risk level is ≥3, avoiding blind pre-disaster defense. The power grid functional topology is constructed using branch apparent power as edge weights, and high-risk components are forcibly isolated through constrained spectral clustering of vulnerable buses. Recursive optimization ensures that the proportion of vulnerable nodes in subgraph V1 is >80%, reducing the risk of cascading fault propagation. The preventative islanding strategy reduces load loss by 42%~69%, and the RI value drops from 25,921 MWh to 8,023 MWh under a typhoon speed of 45 m / s. After de-isolating, islands achieve 100% self-balancing, improving resilience by 37% and shortening post-disaster recovery time by more than 50%.

[0051] Furthermore, by introducing Coriolis force parameters and dynamic correction of air pressure difference, the problem of traditional static wind speed models ignoring the Earth's rotation effect and the attenuation of air pressure at the typhoon center is solved. This accurately reflects the wind speed gradient distribution around the typhoon, providing high-precision input for fault probability calculation. The wind speed prediction error is less than 5%, avoiding the risk of overly conservative / optimistic misjudgments.

[0052] Furthermore, by integrating wind speed, air pressure difference, and grid data to calculate the probability of failure, simulating cascading failures, and quantifying the impact of load loss, complex typhoon risks are transformed into actionable Level 5 instructions, increasing decision-making response speed by 3 times and avoiding delays in pre-disaster defense.

[0053] Furthermore, the transmission line is abstracted as a conductor-tower series system, quantifying the vulnerability of a single-point fault to cause a complete line outage. Compared to the traditional single-component model, the accuracy of fault probability calculation is improved by 32%, accurately locating highly vulnerable transmission corridors.

[0054] Furthermore, a three-level emergency rated tripping mechanism is introduced to dynamically simulate the step-by-step disconnection process of overloaded branches, realistically reflecting the cascading effect caused by thermal stability limits, avoiding the assumption of manual intervention, and the fault simulation conforms to the actual dispatching logic, with a scenario restoration degree of 92%.

[0055] Furthermore, the triple stop criteria ensure a balance between computational efficiency and accuracy, reducing computation time by 58% in a typhoon scenario of 45 m / s. High-probability scenario sets are generated through Monte Carlo sampling, covering more than 97% of actual fault paths.

[0056] Furthermore, by using a risk index formula, the limitations of traditional methods that only consider single points of failure are addressed, resulting in an 89% improvement in the comprehensiveness of system risk assessment.

[0057] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0058] In summary, this invention overcomes the technical pain points of lack of pre-disaster defense and crude islanding under extreme weather conditions through a three-level joint control of data-driven assessment, dynamic risk decision-making, and constraint spectrum clustering isolation, providing core support for resilient power grids.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] Figure 1 A schematic diagram of the Gaussian membership function; Figure 2 The diagram shows the pollen islands, where (a) is a simplified 29-node GB transmission network considering weather regions, and (b) is a schematic diagram of the island division results. Figure 3 The islanding trend chart shows that (a) is the RI value at lower wind speeds, and (b) is a comparison trend chart of RI values ​​with / without taking preventive islanding strategies at different wind speeds. Figure 4 A trend chart of resilience indicators for adopting / not adopting a preventative islanding strategy; Figure 5 This is a flowchart of the method of the present invention; Figure 6 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 7 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0061] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0064] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0065] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0066] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0067] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0068] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0069] This invention provides a pre-disaster preventive islanding method for power transmission systems based on constrained spectral clustering. It applies constrained spectral clustering to pre-disaster islanding, integrates a typhoon spatiotemporal model (Holland wind field) with a fault probability model of transmission lines and towers, proposes a dynamic risk index triggering mechanism, designs a mandatory isolation strategy for vulnerable busbars, and forms a full-process defense system of "assessment-early warning-isolation".

[0070] Please see Figure 5 This invention discloses a pre-disaster preventative islanding method for power transmission systems based on constrained spectral clustering, comprising the following steps: S1. Acquire data information, including grid structure data, unit parameters, load data, typhoon path and related parameters; S2. Based on the data obtained in step S1, calculate the typhoon model parameters using the Holland empirical wind field model; Typhoon model parameters include two key parameters: the radius of the typhoon's maximum wind speed. And the Holland B parameter, which determines the gustatory strength and intensity of a typhoon, as detailed below:

[0071]

[0072] At the same time, calculate the pressure difference between the typhoon's eye and its outer periphery:

[0073] in, Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time, expressed in hPa. Indicates after the typhoon makes landfall The latitude of the center of time; This indicates the pressure difference between the center and the periphery of a typhoon when it makes landfall, expressed in hPa. It is a roughness parameter related to the terrain.

[0074] Calculate the wind speed at each node of the power grid:

[0075] in, To represent air density, take... ; Indicates the distance to the center of the typhoon; Represents the Coriolis force parameter. , Let be the Earth's rotational angular velocity, and take . , here The unit is Pa.

[0076] S3. Based on the data obtained in step S1 and the typhoon model parameters obtained in step S2, assess the risk of the power transmission system under typhoon disaster; S301. Calculate the wind-induced failure rate of each component based on the data obtained in step S1 and the typhoon model parameters obtained in step S2. Wind load on conductors:

[0077] Tower wind load:

[0078] in, The horizontal span of the conductor (m); The outer diameter of the conductor (m); The wind pressure height variation coefficient is set to 1 in this invention; The wind pressure non-uniformity coefficient for the conductor is taken as 0.61 in this invention; For the wind load shape coefficient of the conductor, when D When <17mm, take =1.2, when D When ≥17mm, take =1.1; The angle between the wind direction and the line; The wind vibration coefficient is set to 1.5 in this invention; The wind load shape coefficient of the tower is taken as 1 in this invention; The projected area of ​​the windward side of the tower ( ).

[0079] Stress on the conductor cross section:

[0080] in, This represents the cross-sectional area of ​​the conductor.

[0081] Pole collapses and breaks caused by typhoons typically occur at the pole's base. The root bending moment experienced by the pole / tower... Bending moment caused by wind load on the pole The bending moment generated by the conductor on the tower vector sum:

[0082]

[0083] in, For the tower height, The height of the lower crossarm from the ground. For the depth of the landfill, This refers to the component load effect of the tower. The length is 12 meters, and the filling depth is generally one-sixth of the length, i.e. Take 2 meters, Take 10 meters) The probability density functions of the tensile strength of conductors and the bending strength of poles typically follow a normal distribution. According to the stress-strength interference theory, when the component strength... Less than the component load effect Component failure. The calculated failure probabilities for the line and tower are:

[0084]

[0085] in, The probability of line / tower failure at the corresponding wind speed; and The tensile strength of the line / tower are respectively The variance and mean are obtained from the actual operation of the project.

[0086] Based on the calculation method of failure probability in the series model in reliability assessment theory, assuming that faults between each transmission line segment are independent, a fault in any transmission line segment can lead to an interruption of power transmission throughout the entire transmission line. Normal operation requires that all poles and conductors on the line are free from faults, therefore the line... In wind speed w Failure rate for:

[0087] in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure.

[0088] S302. Based on the component wind-induced failure probability calculated in step S301, deduce the cascading failures caused by a single line failure. Calculating power flow is crucial for identifying potential cascading failures. Cascading failures result from power flow redistribution, with causal relationships between preceding and subsequent failures. They are influenced by the power grid topology and operating conditions, and their occurrence is dynamic.

[0089] Line overload is one of the most typical fault forms in cascading power outages. Among the many factors affecting the evolution of cascading faults, this invention mainly considers the thermal stability problem caused by overload, analyzing the outage phenomenon of transmission lines and generators caused by the development of cascading faults. It also assumes that no operators take actions such as reducing the load to alleviate line overload and voltage conflicts. Lines with current exceeding the thermal limit are considered overloaded lines and will be tripped in this invention.

[0090] In power systems, line carrying capacity is classified into three categories: Normal rating represents the maximum power transmission capacity of the line under normal operating conditions (unit: MVA). Emergency rating represents the power capacity that a line can withstand for a short period of time; Emergency rating 2 is typically used for more extreme emergency situations.

[0091] Because the analysis of extreme disaster scenarios requires the use of numerical simulation of emergency ratings to assess the system's safe operation capability in the short term after an accident, in the simulation evolution of this invention, after the corresponding vulnerable branch trips in each scenario, the power flow distribution of the entire system is calculated, branches that violate their short-term emergency ratings are identified, and they are included in the emergency fault set. In order to ensure the safe operation of the power system, the branches in this set are tripped, and subsequent calculations are performed.

[0092] S303. Based on the component wind-induced failure probability calculated in step S301, determine the vulnerable branches. All branches with failure probabilities higher than the specified failure probability threshold are considered as vulnerable branches and used to generate all possible failure scenarios to obtain a set of high-probability failure scenarios. If we ignore the order of sudden events and the number of vulnerable branches is N Therefore, the number of possible failure scenarios is 2. NTo improve computational efficiency, K scenarios with probabilities higher than a pre-specified threshold are selected from all generated failure scenarios. The lower the selected failure probability threshold, the more vulnerable branches there are, resulting in more generated failure scenarios and thus more factors to consider. A risk index (RI) is defined by calculating the occurrence probability and impact of the selected failure scenarios.

[0093] in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This refers to the set of fault scenarios that are retained after filtering.

[0094] P k The calculation formula is:

[0095] in, Vulnerable branches in each scenario l , For the scene K The set of vulnerable branches in the middle.

[0096] Defensive islanding is employed to prevent or reduce the cascading effect of power outages. RI (Risk Response Assessment) will be used to determine when to apply preventative islanding. Simultaneously, cascading outages will be considered, and the tripping of branches affected by cascading failures will be included in the risk assessment process.

[0097] The risk assessment process is as follows: Within the timeframe of a specific weather event's impact, the following operations shall be performed: 1) Identify the vulnerable branches at the current time section and include them in the vulnerable branch set.

[0098] 2) Generate the branches based on the vulnerable branch set using the method described above. K Various fault scenarios.

[0099] 3) In each scenario, trip the vulnerable branch in that scenario and calculate the power flow.

[0100] 4) Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this step until one of the following stopping criteria is met: (a) No branch was found in step 3); (b) The case where the power flow does not converge after calculation in step 3) or 4); (c) The number of program iterations exceeds the pre-specified value.

[0101] 5) Repeat steps 3)-4) for each fault scenario, record the load loss value for each scenario, and calculate the fault probability for each scenario.

[0102] 6) Calculate the RI value.

[0103] The contribution of each scenario to the RI value is the load reduction (MWh) to stabilize the system, depending on the stopping criterion. If the procedure terminates due to criterion (b) or (c), a system collapse is assumed, and the impact of the scenario equals the total system power demand. If the procedure terminates due to criterion (a), the load reduction at the time of termination is recorded. During the risk assessment process, isolated islands may form due to wind-induced faults or cascading overload effects. If the calculated RI (MWh) exceeds a pre-specified threshold... To maintain system security, a preventative silo strategy will be activated.

[0104] S304. Risk classification is performed based on the RI value calculated in step S303. The purpose is to quantitatively assess the risk level of the power system, thereby providing a basis for operators' planning and operation decisions.

[0105] First, the risk index RI is fuzzified and then logarithmically transformed to ensure its value falls within the range of 0 to 1:

[0106] The risk levels are divided into 5 levels from lowest to highest, as shown in Table 1.

[0107] Risk levels 1 and 2 are considered low risk, meaning that contingency sets will not trigger many cascading failures and preventative islanding is not required.

[0108] When the risk level exceeds 2, the risk is high, and the system may crash if no available preventative measures are implemented. Therefore, when the risk level exceeds 2, preventative siloing strategies are urgently needed to mitigate the risk.

[0109] Meanwhile, the formula for calculating the elasticity index is:

[0110] in, For network resilience, This is the actual performance curve. This represents the expected performance curve. Clearly, the higher the elasticity index, the greater the system's resilience and the stronger its ability to withstand extreme events.

[0111] Table 1 Risk Level and Whether Preventive Isolation Measures Are Taken

[0112] Based on the division into five risk levels, each risk level has a membership function used to determine the membership degree of the risk, such as... Figure 1 As shown in the figure. Since the five membership functions possess good smoothness and symmetry, this invention assumes they are all Gaussian functions. Substituting RI into the five Gaussian membership functions respectively yields the membership degrees for the five risk levels. According to the maximum membership principle of fuzzy theory, if Then the risk belongs to m The level can determine the risk level of cascading interruptions.

[0113] S4. Implement a preventative islanding strategy based on the risk assessment results obtained in step S3.

[0114] An electrical grid can be naturally represented as a graph: vertices (nodes) represent buses, and edges (links) represent electrical connections. A graph with a set of vertices and edges can be represented as follows: Among them, the set V For vertex set, set E Let be the set of edges.

[0115] In the following sections, we will only consider simple graphs where cycles and multiple edges are not allowed. This assumption does not limit its generality, as multiple edges can be replaced by equivalent single edges.

[0116] Due to the diagram G It is limited and simple, including: ,in N It is the number of vertices (nodes), and ,in Indicates from vertex i To the top j An edge (transmission line or transformer). Since spectral clustering ignores edge orientation, assume all graphs are undirected: If and only if .

[0117] The topology of a graph cannot capture functional information about the power grid. To include this information, edge weights are used. The weight of an edge is a function. And there are: ; ; .

[0118] use ,and This represents the weighted vertex weight.

[0119] To study the functional structure of the power grid, we consider using average apparent power as the edge weight function for analysis, that is:

[0120] in, For nodes i arrive j The actual power flow. It is important to note that power flow is dynamic because it changes according to actual operating conditions.

[0121] Edge weights can be interpreted as a penalty for cutting corresponding lines during clustering, but they can also be interpreted as a measure of connection strength, since strongly connected vertices are more likely to be clustered together. Therefore, admittance-based clustering will reveal the internal structure (electrical distance) of the network, while power flow-based clustering will identify islands. When the network actively disconnects, this method can be used to generate preventative islands in order to minimize load loss and control cascade failures.

[0122] Specifically, considering the need to maximize system functional effectiveness and ensure power supply, optimal AC power flow is used to calculate its power flow distribution. Minimizing load shedding is taken as the objective function to be solved for optimal scheduling. This function needs to consider power flow constraints and network constraints, such as generation and transmission capacity, as well as voltage operating limits. In the calculation of each simulation step, generation capacity is considered as the maximum capacity provided given the current transmission network topology, connectivity, and constraints during the event.

[0123] In the optimal power flow model, through generator scheduling and load reduction, the power flow of each branch of the system returns to the normal range. At this time, the active power output of each generator node is... The power flow in each branch will not exceed the limit. The objective function and the equality and inequality constraints it satisfies are as follows:

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] in, For load nodes i The amount of load reduction, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

[0134] The Laplace matrix has wide applications in graph theory and has a well-defined interpretation in the field of electrical engineering. There are two main types of Laplace matrices that can be applied to an undirected weighted simple graph. The two are related: the normalized Laplace function and the non-normalized Laplace function. Since the normalized Laplace operator is scale-independent, it is more conducive to achieving clustering.

[0135] The standardized Laplace operator is: .in, D It is a diagonal matrix. That is:

[0136] Specifically, the Laplace operator is defined as follows:

[0137] in, I The identity matrix and the diagonal matrix D Midpoint, Vertex degree It lies on its diagonal. The degree of a vertex is the sum of the weights of all its adjacent edges, defined as follows:

[0138] in, Let be the number of vertices. The application of constrained spectrum clustering is to include all vulnerable buses as constraints within the same island. Assume... s Each bus must be isolated. n It is the total number of buses (matrix) D and W (If the sorting also needs to be done in the same way), then a constraint matrix can be used. C Let's introduce constraints:

[0139] Where I is the identity matrix, 0 is a matrix of all zeros, and 1 is a matrix of all ones. Therefore, the matrix C dimension A node that is required to be linked (i.e., a fragile node) can be represented by an equivalent node.

[0140] Then calculate the problem with the generalized features. The eigenvectors corresponding to the two smallest eigenvalues and ,in It is related to the eigenvector Relevant eigenvalues.

[0141] Next, the calculated eigenvectors are normalized to make them so that... With a length of 1, we can obtain:

[0142] vector and Used as a vertex The coordinates in the graph are used to cluster the vertices using hierarchical clustering.

[0143] Ultimately, the power system was divided into two subsets. V 1 and V 2. All fragile buses are relegated to subsets. V In 1, the quality of this solution can be calculated in V In section 1, the total number of all vulnerable busbars accounts for V 1. The proportion of the total number of busbars is used to determine this.

[0144] If this ratio is lower than the preset standard, it will be V Constrained spectral clustering is performed again in step 1. This process will be repeated until the quality of the island partitioning solution reaches a satisfactory level. The final island partitioning scheme adopts the result obtained from the last constrained spectral clustering.

[0145] The quality index of islanding solutions is between 0 and 1. A higher value indicates that the formation of islands is subject to more restrictions, while a lower value may mean that in addition to the vulnerable bus, a large number of non-vulnerable buses have been included in the island to meet the constraints.

[0146] When extreme weather causes branch line failures, the network may be split into multiple islands. Each island will operate as an independent system, and due to the cascading failures, it may trigger unpredictable and severe power outages. In preventative islanding methods, high-risk lines are isolated through proactive disconnection in advance, thereby improving system resilience.

[0147] In another embodiment of the present invention, a pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering is provided. This system can be used to implement the above-mentioned pre-disaster preventive islanding method for power transmission systems based on constrained spectral clustering. Specifically, the pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering includes an acquisition module, a calculation module, a hierarchical module, and an output module.

[0148] The acquisition module obtains power grid data including grid structure data, unit parameters, load data, and typhoon path parameters. The calculation module, based on the acquired power grid data, uses the Holland wind field model to calculate the radius of the typhoon's maximum wind speed. and Holland B parameters Then, based on the obtained maximum wind speed radius... The wind speed at each node of the power grid was calculated using Holland B parameters. ; Wind speed at various nodes of the power grid for:

[0149] in, Indicates air density, Indicates the distance to the center of the typhoon. Represents the Coriolis force parameter. The angular velocity of Earth's rotation. Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time.

[0150] The classification module is based on the radius of the typhoon's maximum wind speed. and wind speed at each node of the power grid Calculate the failure probability of transmission lines and towers; simulate cascading failures caused by single-line failures based on the obtained failure probabilities to generate a set of high-probability failure scenarios; calculate the risk index RI of the set of high-probability failure scenarios; and then output the risk level through fuzzy classification. Calculate the risk index RI and perform fuzzy classification, outputting the specific risk level as follows: Based on wind speed at each node of the power grid Calculate the wind load on the conductor and tower wind load Based on the pressure difference between the eye and the outer periphery of the typhoon and the radius of maximum wind speed Correct the bending moment at the base of the tower; determine the segment locations of the transmission line and the structural parameters of the tower based on the grid data, and calculate the fault probability of the transmission line and the tower. ; Based on the fault probability of transmission lines and towers Simulate cascading failures caused by a single line fault; Based on the fault probability of transmission lines and towers Vulnerable branches are identified. All branches with a failure probability higher than a specified failure probability threshold are considered vulnerable branches and used to generate all failure scenarios. The risk index RI of all failure scenarios is calculated. Risk levels are assessed by classifying the risk based on the Risk Index (RI) to obtain risk assessment results.

[0151] Fault probability of transmission lines and towers for:

[0152] in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure.

[0153] Based on the fault probability of transmission lines and towers The simulation of cascading failures caused by a single line fault is as follows: The system's safe operation capability in the short period after an accident is simulated using numerical simulation of emergency ratings. After the corresponding vulnerable branch is tripped in each fault scenario, the power flow distribution of the transmission system is calculated, the branch that violates its short-term emergency rating is identified, the branch is included in the emergency fault set, and the branch in the set is tripped.

[0154] The risk level of the power system was assessed, and the specific risk assessment results are as follows: Identify vulnerable branches at the current time segment and add them to the vulnerable branch set; Branch generation based on the set of fragile branches K Various failure scenarios; In each fault scenario, trip the vulnerable branch in that fault scenario and calculate the power flow; Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this process until any stopping criterion is met. Repeat the power flow calculation for each failure scenario, record the load loss value for each failure scenario, and calculate the failure probability for each failure scenario. Calculate the RI value based on the failure probability for each failure scenario. The stopping criteria include: no branches are found; the calculated power flow does not converge; the number of program iterations exceeds the set threshold.

[0155] The risk index RI is:

[0156] in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This is the set of fault scenarios that are retained after filtering.

[0157] The output module will respond when the risk level exceeds a set threshold. At that time, preventative islanding should be implemented.

[0158] When the output risk level exceeds a set threshold, a preventative islanding strategy is implemented, specifically: Construct a weighted graph of the power grid with the apparent power of the branches as the edge weights; use vulnerable buses as constraints and achieve spectral clustering through normalized Laplace matrix eigenvalue decomposition; divide the power grid into a subgraph V1 containing vulnerable buses and a non-vulnerable subgraph V2; perform a disjoint operation on subgraph V1 and control it independently.

[0159] The constraints include:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169] in, For load nodes i The amount of load reduction, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

[0170] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for the operation of a pre-disaster preventive islanding method for power transmission systems based on constraint spectral clustering, including: Acquire grid data including grid structure data, generator parameters, load data, and typhoon path parameters; based on the acquired grid data, calculate the radius of the typhoon's maximum wind speed using the Holland wind field model. and Holland B parameters Then, based on the obtained maximum wind speed radius... Holland B parameters are used to calculate wind speeds at various nodes in the power grid. Based on the obtained radius of the typhoon's maximum wind speed and wind speed at each node of the power grid Calculate the fault probability of transmission lines and towers; based on the obtained fault probabilities, simulate cascading faults caused by a single-line fault to generate a set of high-probability fault scenarios; calculate the risk index RI of the high-probability fault scenario set; and then output the risk level through fuzzy classification; when the risk level exceeds a set threshold... At that time, preventative islanding should be implemented.

[0171] Please see Figure 6The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for estimating the concentration of radioactive iodine species in the containment structure after an accident, as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the pre-disaster preventative islanding system of the power transmission system based on constrained spectral clustering, as described in this embodiment. To avoid repetition, these details are not elaborated here.

[0172] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 6 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0173] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0174] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0175] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0176] Please see Figure 7 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0177] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 5 The steps are shown in the figure.

[0178] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0179] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0180] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0181] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0182] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0183] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0184] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0185] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the pre-disaster preventive islanding method for power transmission systems based on constraint spectral clustering in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Acquire grid data including grid structure data, generator parameters, load data, and typhoon path parameters; based on the acquired grid data, calculate the radius of the typhoon's maximum wind speed using the Holland wind field model. and Holland B parameters Then, based on the obtained maximum wind speed radius... Holland B parameters are used to calculate wind speeds at various nodes in the power grid. Based on the obtained radius of the typhoon's maximum wind speed and wind speed at each node of the power grid Calculate the fault probability of transmission lines and towers; based on the obtained fault probabilities, simulate cascading faults caused by a single-line fault to generate a set of high-probability fault scenarios; calculate the risk index RI of the high-probability fault scenario set; and then output the risk level through fuzzy classification; when the risk level exceeds a set threshold... At that time, preventative islanding should be implemented.

[0186] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0187] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0188] The feasibility of the proposed preventive islanding method was then verified using a simplified GB test system. This test system consisted of 29 buses; 98 double-circuit overhead transmission lines and 1 single-circuit transmission line (between nodes 2 and 3); and 65 generators of various types (wind, nuclear, and CCGT) located at 24 nodes, with a total installed capacity of approximately 80 gigawatts. The constructed model was solved using MATLAB.

[0189] Considering network constraints, the corresponding stage in each simulation step schedules generation through AC OPF to meet load demand as much as possible. In the simulation, we consider a sufficient number of cases and scenarios to ensure the accuracy of the method.

[0190] To account for the spatial impact of storms on the power transmission network, the test network was divided into three weather zones, such as... Figure 2 As shown. Furthermore, based on the maximum hourly gust speeds recorded during severe historical storms in the UK, it can be observed that northern England is hit by storms more severely and frequently. Therefore, this analysis focuses on simulation. Figure 2 (a) The storm that occurred in region 1.

[0191] Calculations show that the wind speed fluctuation range in the considered typhoon-affected area 1 is [27 m / s, 45 m / s]. This result is credible because it matches the maximum hourly wind speed recorded in British history and can simulate the most severe typhoon conditions that may threaten network resilience.

[0192] During the simulation, due to the typhoon's path, only region 1 of the system was affected by the storm, while other regions remained unaffected. All branches operated normally with a failure probability of 1%. Long-term operational experience shows that 0.1 is considered the failure threshold for branches during typhoons. Therefore, branches with a failure probability exceeding 0.1 are considered vulnerable and used to generate the studied failure scenarios. Clearly, the failure probability of branches included in the vulnerable branch set is significantly higher than that of branches classified as non-vulnerable. Therefore, by calculating the component failure rate using constantly changing wind speeds at each time point to determine the failure probability of each branch in each simulation step, the spatiotemporal impact of weather events on each component can be systematically quantified, and the overall risk faced by the entire system can be assessed. To analyze a large number of scenarios, various wind speeds were designed and calculated during the risk assessment process, and a failure probability threshold of 1% was set, representing the failure probability of a branch under normal conditions.

[0193] Under normal circumstances, the time to repair (TTR) of the line is assumed to be 10 hours, and the TTR of the tower is 50 hours. However, under the influence of a strong storm, the TTR of each component will inevitably be extended. To simulate this situation, a method is considered: [The text abruptly ends here, so the translation stops as well.] normal With a random number that is uniformly distributed within a predetermined range h Multiplication (i.e.) h obey[ x 1 , x 2] Uniform distribution in the interval U [ x 1 , x 2). Specifically, when the wind speed does not exceed 40 m / s, the set range is [ x 1 , x 2] is [2, 4]; while for wind speeds exceeding 40 m / s, the range is [ x 1 , x 2] is set to [5, 7]. Therefore, it is clear that the repair time for various components of the power system may vary depending on the extent of damage caused by the storm, ranging from a few days to more than a week.

[0194] Defensive islanding is activated once the risk assessment level in the simulation exceeds level 2. At each time interval after implementing the proactive prevention strategy, based on the random number principle in Monte Carlo simulations and the actual failure rate of each branch at that time, the branches that actually tripped due to weather events in this simulation are determined. Next, the grid performance under this network topology and power flow distribution is evaluated, and the evaluation results are analyzed and compared with the post-disaster damage without defensive islanding. Table 2 shows the RI values ​​and risk assessment levels corresponding to different vulnerable branch sets at various wind speeds to decide whether to apply the defensive islanding strategy. The trend of the risk assessment results (RI) is as follows: Figure 3 As shown in the figure, without preventative measures, the rate of increase in RI accelerates with increasing wind speed.

[0195] Table 2 Risk Assessment Results

[0196] Table 3 compares the risk values ​​(RI) under the simulated wind speeds in this test with and without the preventative islanding strategy. It is evident that the RI value is reduced to some extent after implementing this measure, indicating that this strategy can effectively mitigate the risk to the system when facing extreme weather events, better ensure power supply, and reduce system stress. This comparison result is also plotted in [the table / document / etc.]. Figure 3 (b) in.

[0197] Table 3 Comparison of RI values ​​for adopting / not adopting preventive islanding strategies

[0198] As the RI value increases, splitting measures become increasingly effective in mitigating risk, indicating that a higher RI value is more beneficial for network resilience through preventative islanding. Comparative analysis reveals that when wind speeds do not exceed 40 m / s, the system can effectively withstand the pressure of storms. As wind speeds gradually increase to 42 m / s, defensive islanding may be beneficial, but the number of such scenarios is limited. With further increases in wind speed, the tested system struggles to recover from the storm, and the effectiveness of the defensive islanding strategy becomes increasingly significant. Based on the RI value and the definition of resilience, risk and resilience are negatively correlated: the higher the risk value, the worse the system performance and the lower the resilience index.

[0199] Furthermore, to further illustrate the effectiveness of preventative islanding in maintaining system stability, the risk assessment process considering numerous scenarios was abandoned. Instead, only after a weather event occurred and impacted the system were sampled and simulated for various components, and preventative islanding measures were implemented. The results showed that in each of the 1000 simulated scenarios, for different failure conditions, the two islands maintained balanced transmission and reception and stable operation within their respective islands, with almost no load loss. This strongly demonstrates that preventative islanding is an effective and feasible emergency measure in the face of disasters. The final island division results from multiple simulations under different wind speeds are plotted on [the graph / plot]. Figure 2 (b) Table 4 shows several fault scenarios with higher wind speeds and more severe faults in all simulated conditions. It is clear that under this simulation process, the preventative islanding strategy can almost guarantee that the system remains robust.

[0200] Table 4 System load failure under different scenarios

[0201] Select a specific simulation scenario and demonstrate the system resilience indicators for adopting and not adopting preventative islanding, such as... Figure 4 As shown, at t=0, a typhoon strikes, and the system performance begins to decline. Subsequent components are gradually repaired, and the load partially recovers. It is clear that preventative islanding, by preventing the spread of cascading outages, can effectively improve system performance, enabling the system to better withstand extreme disasters.

[0202] In summary, this invention presents a pre-disaster preventative islanding method and system for power transmission systems based on constrained spectral clustering. It quantifies typhoon parameters using the Holland wind field model and calculates the fault probability of transmission lines and towers using stress-intensity interference theory, accurately identifying vulnerable areas. It pioneers a fuzzy risk index (RI) grading mechanism to achieve dynamic decision-making of "low-risk warning, high-risk action," avoiding the static defects of the traditional Nk criterion. It transforms the power grid topology (network data) and operating status (unit / load data) into a weighted graph, using branch apparent power as edge weights. Through a constraint matrix of vulnerable buses driving spectral clustering, it forces the centralized isolation of high-risk components, and recursively optimizes to ensure that the proportion of vulnerable nodes in subgraph V1 is >80%, reducing the risk of cascading spread. The preventative islanding strategy reduces load loss by 42%~69%, and the RI value drops from 25,921 MWh to 8,023 MWh under a typhoon speed of 45 m / s. After de-isolating, the islands achieve 100% self-balancing, improving resilience by 37% and shortening post-disaster recovery time by more than 50%.

[0203] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for pre-disaster preventative islanding in power transmission systems based on constrained spectral clustering, characterized in that, Includes the following steps: Acquire power grid data including grid structure data, unit parameters, load data, and typhoon path parameters; Based on the acquired power grid data, the Holland wind field model was used to calculate the radius of the typhoon's maximum wind speed. and Holland B parameters Then, based on the obtained maximum wind speed radius... Holland B parameters are used to calculate wind speeds at various nodes in the power grid. ; Based on the obtained maximum wind speed radius of the typhoon and wind speed at each node of the power grid Calculate the failure probability of transmission lines and towers; simulate cascading failures caused by single-line failures based on the obtained failure probabilities to generate a set of high-probability failure scenarios; calculate the risk index RI of the set of high-probability failure scenarios; and then output the risk level through fuzzy classification. When the risk level exceeds the set threshold At that time, preventative islanding should be implemented.

2. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 1, characterized in that, Wind speed at various nodes of the power grid for: in, Indicates air density, Indicates the distance to the center of the typhoon. Represents the Coriolis force parameter. This is the Earth's rotational angular velocity. Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time.

3. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 1, characterized in that, The risk index RI of the set of high-probability failure scenarios is calculated, and then the risk level is output through fuzzy classification as follows: Based on wind speed at each node of the power grid Calculate the wind load on the conductor and tower wind load Based on the pressure difference between the eye and the outer periphery of the typhoon and the radius of maximum wind speed Correct the bending moment at the base of the tower; determine the segment locations of the transmission line and the structural parameters of the tower based on the grid data, and calculate the fault probability of the transmission line and the tower. ; Based on the fault probability of transmission lines and towers Simulate cascading failures caused by a single line fault; Based on the fault probability of transmission lines and towers Vulnerable branches are identified. All branches with a failure probability higher than a specified failure probability threshold are considered vulnerable branches and used to generate a set of high-probability failure scenarios. The risk index RI of the set of high-probability failure scenarios is calculated. Risk levels are assessed by classifying the risk based on the Risk Index (RI) to obtain risk assessment results.

4. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 3, characterized in that, Fault probability of transmission lines and towers for: in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure.

5. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 3, characterized in that, Based on the fault probability of transmission lines and towers The simulation of cascading failures caused by a single line fault is as follows: The system's safe operation capability in the short period after an accident is simulated using numerical simulation of emergency ratings. After the corresponding vulnerable branch is tripped in each fault scenario, the power flow distribution of the transmission system is calculated, the branch that violates its short-term emergency rating is identified, the branch is included in the emergency fault set, and the branch in the set is tripped.

6. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 3, characterized in that, The risk level of the power system was assessed, and the specific risk assessment results are as follows: Identify vulnerable branches at the current time segment and add them to the vulnerable branch set; Branch generation based on the set of fragile branches K Various failure scenarios; In each fault scenario, trip the vulnerable branch in that fault scenario and calculate the power flow; Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this process until any stopping criterion is met. Repeat the power flow calculation for each failure scenario, record the load loss value for each failure scenario, and calculate the failure probability for each failure scenario. Calculate the RI value based on the failure probability for each failure scenario. The stopping criteria include: no branches are found; the calculated power flow does not converge; the number of program iterations exceeds the set threshold.

7. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 6, characterized in that, The risk index RI is: in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This is the set of fault scenarios that are retained after filtering.

8. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 1, characterized in that, When the output risk level exceeds a set threshold, a preventative islanding strategy is implemented, specifically: Construct a weighted graph of the power grid with the apparent power of the branches as the edge weights; use vulnerable buses as constraints and achieve spectral clustering through normalized Laplace matrix eigenvalue decomposition; divide the power grid into a subgraph V1 containing vulnerable buses and a non-vulnerable subgraph V2; perform a disjoint operation on subgraph V1 and control it independently.

9. The method for pre-disaster preventative islanding of power transmission systems using constrained spectral clustering according to claim 8, characterized in that, The constraints include: in, For load nodes i Load reduction amount, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

10. A pre-disaster preventative islanding system for power transmission systems using constrained spectral clustering, characterized in that, include: The data acquisition module obtains power grid data including grid structure data, unit parameters, load data, and typhoon path parameters. The calculation module, based on the acquired power grid data, uses the Holland wind field model to calculate the radius of the typhoon's maximum wind speed. and Holland B parameters Then, based on the obtained maximum wind speed radius... Holland B parameters are used to calculate wind speeds at various nodes in the power grid. ; The grading module is based on the radius of the typhoon's maximum wind speed. and wind speed at each node of the power grid Calculate the failure probability of transmission lines and towers; simulate cascading failures caused by single-line failures based on the obtained failure probabilities to generate a set of high-probability failure scenarios; calculate the risk index RI of the set of high-probability failure scenarios; and then output the risk level through fuzzy classification. The output module will respond when the risk level exceeds a set threshold. At that time, preventative islanding should be implemented.

11. The pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering according to claim 10, characterized in that, Wind speed at various nodes of the power grid for: in, Indicates air density, Indicates the distance to the center of the typhoon. Represents the Coriolis force parameter. This is the Earth's rotational angular velocity. Indicates after the typhoon makes landfall The pressure difference between the center and the periphery at any given time.

12. The pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering according to claim 10, characterized in that, The risk index RI of the set of high-probability failure scenarios is calculated, and then the risk level is output through fuzzy classification as follows: Based on wind speed at each node of the power grid Calculate the wind load on the conductor and tower wind load Based on the pressure difference between the eye and the outer periphery of the typhoon and the radius of maximum wind speed Correct the bending moment at the base of the tower; determine the segment locations of the transmission line and the structural parameters of the tower based on the grid data, and calculate the fault probability of the transmission line and the tower. ; Based on the fault probability of transmission lines and towers Simulate cascading failures caused by a single line fault; Based on the fault probability of transmission lines and towers Vulnerable branches are identified. All branches with a failure probability higher than a specified failure probability threshold are considered vulnerable branches and used to generate a set of high-probability failure scenarios. The risk index RI of the set of high-probability failure scenarios is calculated. Risk levels are assessed by classifying the risk based on the Risk Index (RI) to obtain risk assessment results.

13. The pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering according to claim 12, characterized in that, Fault probability of transmission lines and towers for: in, For the line Number of upper conductors / towers; For wires The probability of failure; For towers The probability of failure; Based on the fault probability of transmission lines and towers The simulation of cascading failures caused by a single line fault is as follows: The system's safe operation capability in the short period after an accident is simulated using numerical simulation of emergency ratings. After the corresponding vulnerable branch is tripped in each fault scenario, the power flow distribution of the transmission system is calculated, the branch that violates its short-term emergency rating is identified, the branch is included in the emergency fault set, and the branch in the set is tripped. The risk level of the power system was assessed, and the specific risk assessment results are as follows: Identify vulnerable branches at the current time segment and add them to the vulnerable branch set; Branch generation based on the set of fragile branches K Various failure scenarios; In each fault scenario, trip the vulnerable branch in that fault scenario and calculate the power flow; Determine if a cascading failure occurs, remove the affected branches, and recalculate the power flow. Repeat this process until any stopping criterion is met. Repeat the power flow calculation for each failure scenario, record the load loss value for each failure scenario, and calculate the failure probability for each failure scenario. Calculate the RI value based on the failure probability for each failure scenario. The stopping criteria include: no branches are found; the calculated power flow does not converge; the number of program iterations exceeds the set threshold. The risk index RI for a set of high-probability failure scenarios is: in, P k For the scene k The probability of occurrence, A k For the scene k The impact value, K This is the set of fault scenarios that are retained after filtering.

14. The pre-disaster preventive islanding system for power transmission systems based on constrained spectral clustering according to claim 10, characterized in that, When the output risk level exceeds a set threshold, a preventative islanding strategy is implemented, specifically: Construct a power grid weighted graph with branch apparent power as edge weight; use vulnerable buses as constraints and achieve spectral clustering through normalized Laplace matrix eigenvalue decomposition; divide the power grid into a subgraph V1 containing vulnerable buses and a non-vulnerable subgraph V2; perform a disjoint operation on subgraph V1 and control it independently; The constraints include: in, For load nodes i Load reduction amount, For load nodes i Maximum load capacity, For nodes i The active power of the generator, Let i be the active power demand of node i. , For nodes i , j voltage amplitude, , For nodes i and j The real and imaginary parts of the admittance matrix between nodes. , for nodes ij The phase angle between them and node i The reactive power and reactive power demand of the generator. , For nodes ij The active and reactive power flow between them For the set of all nodes, For the set of all branches, This refers to the set of all generators.

15. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 9.

16. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the method of any one of claims 1 to 9.

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