Power distribution network line potential risk early warning method and system

Abstract

The invention discloses a power distribution network line potential risk early warning method and system. A Batts typhoon model is adopted to establish a mapping relation between a distance and a wind speed; carrying out vulnerability modeling on the power distribution network line, and generating a fault set of the line in the typhoon scene based on the obtained mapping relation between the wind speed and the fault rate of the power distribution line; the generated fault sets of the lines in the typhoon scene are input into a mixed integer programming model of the power distribution system, the load recovery amount is maximized when a multi-line fault occurs, the load flow constraint and the radial topology constraint are met at the same time, and the maximum power supply amount of the power distribution network in each fault set is obtained through statistics; and the maximum power supply quantity of the power distribution network under each fault set is used as data input, line importance ranking is carried out by using a Scioplande method, and an enhancement strategy of the power distribution network line is formed according to a ranking result. Through integration of typhoon disaster modeling and power distribution network reconstruction optimization, dynamic linkage of risk early warning and defense strategies is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system safety planning and operation, and particularly relates to a method and system for early warning of potential risks of distribution network lines. Background Art

[0002] Existing power grid risk assessment methods primarily focus on analyzing grid reliability under conventional weather conditions, with limited consideration given to high-intensity, regionally oriented natural disasters like typhoons. However, the complex operational characteristics of power systems under typhoon conditions present numerous challenges in identifying weak links, such as the combined impact of uncertainties in wind speed, air pressure, and geological conditions on transmission lines and substations. These factors render traditional deterministic line risk assessment models inadequate for typhoon disaster response.

[0003] At this stage, there is an urgent need for a line risk warning method that can comprehensively consider typhoon characteristics, account for multiple uncertainties, and combine actual power grid operation data to simulate the impact of typhoons on the power grid in advance, accurately locate lines with higher risk factors, provide guidance for pre-disaster preparations, and effectively improve the power grid's disaster resistance. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned existing technologies and provide a method and system for warning of potential risks in distribution network lines, which is used to solve the technical problems of unclear risk evolution mechanism of distribution network under typhoon disasters, difficulty in quantifying key risk factors, and delayed identification of weak links. This can be beneficial to enhancing the resilience of the distribution network before the arrival of a typhoon and ensuring its safe and stable operation.

[0005] The present invention adopts the following technical solutions: A method for early warning of potential risks of distribution network lines comprises the following steps: S1. Use the Batts typhoon model to establish a mapping relationship between distance and wind speed; perform vulnerability modeling on distribution network lines, and generate a fault set for the lines under typhoon scenarios based on the mapping relationship between wind speed and distribution line failure rate. S2. Input the line fault set generated in step S1 under the typhoon scenario into the mixed integer programming model of the distribution system, maximize the load restoration amount when multiple line faults occur, and satisfy the power flow constraints and radial topology constraints at the same time, and obtain the maximum power supply of the distribution network under each fault set. S3. Using the maximum power supply of the distribution network under each fault set obtained in step S2 as data input, the Copeland method is used to sort the line importance, and a reinforcement strategy for the distribution network line is formed according to the sorting result.

[0006] Preferably, step S1 is specifically: S101. Use the Batts model to evaluate the wind speed in each area of ​​the distribution network during a typhoon disaster. S102, performing vulnerability modeling on the distribution network lines, constructing a mapping relationship between wind speed and failure rate, and obtaining the distribution network line failure status; S103: Using the Monte Carlo method to simulate the distribution network line fault situation obtained in step S102, to obtain a line fault set under each typhoon scenario.

[0007] Preferably, in step S101, at time t, the wind speed at any point in the study area is calculated as follows:

[0008] in, is the maximum wind speed of the typhoon, is the maximum radius of the typhoon, is the distance between the typhoon center and the study area, is the coefficient related to the attenuation of the typhoon's radial intensity.

[0009] Preferably, in step S102, the line damage probability for:

[0010] in, is the line length, is the damage probability per kilometer of line.

[0011] Preferably, step S2 is specifically: S201, determining the operating power flow constraint and the load recovery constraint according to the Distflow model; S202, determining constraints that satisfy radial topology conditions; S203, using a distribution network reconstruction optimization model to analyze the load weighted recovery amount of the distribution network system under different fault conditions; S204. Use the commercial solver Gurobi to solve the mixed integer linear programming problem established in step S201, step S202, and step S203, and calculate the maximum power supply of the distribution network under each fault set.

[0012] Preferably, the operating power flow constraint is expressed as:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] Load recovery constraints:

[0019]

[0020] in, Node Recovered active and reactive power, Node Active and reactive power output from the upper grid or DG, Node To Node Active and reactive power transmitted; is a node The voltage, It is a slave node To Node The connectivity status of the line, are node sets and line sets respectively; They are respectively the upper grid access node set, the distributed generator node set, and the power outage island set; Represents nodes respectively The parent node set and child node set of is the reference voltage of the distribution network, For nodes The reference voltage of the upper grid or DG, For nodes The upper and lower limits of the voltage at Node The upper and lower limits of active and reactive power generated by the upper power grid or DG; For nodes To Node The maximum capacity of the line between is a sufficiently large number.

[0021] Preferably, the objective function of the distribution network reconstruction optimization model is:

[0022] in, For the j The load recovery amount of each node, Indicates the j The node load point weight is N, and N represents the set of all load points in the system.

[0023] Preferably, step S3 is specifically: S301. In the distribution network risk factor assessment model, the sorting objects are all line sets. , the indicator set is , select As a multidimensional indicator, we can get the index Lower component For components Results of the Copland comparison ; S302, for all features Sum and get the components For components Overall score; S303, components , traverse all components , compare and add the scores to get the component The Copeland score is used to sort the risk factor scores of all lines from high to low. The lines with high scores have high risk factors of the distribution network. S304. Based on the emergency rescue capability of the power sector, select the first q lines with high risk factor scores for reinforcement operations.

[0024] Preferably, the element Copland score for:

[0025] in, For indicators Lower component For components Results of a Copeland comparison.

[0026] In a second aspect, an embodiment of the present invention provides a distribution network line potential risk early warning system, comprising: The collection module uses the Batts typhoon model to establish a mapping relationship between distance and wind speed. It also performs vulnerability modeling on distribution network lines and generates a fault collection for lines under typhoon scenarios based on the mapping relationship between wind speed and distribution line failure rate. The statistical module inputs the generated line fault set under the typhoon scenario into the mixed integer programming model of the distribution system. It maximizes the load restoration amount when multiple line faults occur, while satisfying the power flow constraints and radial topology constraints. The maximum power supply of the distribution network under each fault set is statistically obtained. The sorting module uses the maximum power supply of the distribution network under each fault set as data input, uses the Copeland method to sort the line importance, and forms a reinforcement strategy for the distribution network lines based on the sorting results.

[0027] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for warning of potential risks of distribution network lines when executing the computer program.

[0028] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for warning of potential risks of distribution network lines.

[0029] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for warning of potential risks of distribution network lines when executing the computer program.

[0030] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for warning of potential risks of distribution network lines.

[0031] Compared with the prior art, the present invention has at least the following beneficial effects: A potential risk warning method for distribution network lines was developed. A typical typhoon event simulation and generation method was constructed. Using the Batts typhoon model and line vulnerability modeling, a relationship between typhoon location and line failure rate was established. A Monte Carlo algorithm was used to determine the possible fault set under this scenario. A distribution network line risk warning method based on Copeland ranking was proposed. Based on the fault set generated by typhoon scenario simulation, Copeland ranking was used to assess the risk of new distribution network lines, obtaining a line importance ranking and risk factor scores. Weak links in the system were then strengthened to enhance the distribution network's disaster resilience.

[0032] Furthermore, based on the typhoon's maximum wind speed, radius, and attenuation coefficient, a regional wind speed distribution is generated to reflect the typhoon's dynamic process. Through multiple sampling, a line fault set is generated to cover the randomness of the typhoon's path and intensity, and to quantify the fault probability distribution.

[0033] Furthermore, the spatial distribution of typhoon wind speed is quantified to provide input for failure rate calculation; the nonlinear attenuation characteristics of wind speed from the center to the periphery of the typhoon are reflected and combined with the line vulnerability model to more accurately calculate the failure probability.

[0034] Furthermore, the line damage probability is proportional to the length and unit damage probability, reflecting the degree of line exposure to typhoon risk. Combined with Monte Carlo simulation, the fault set can be quickly generated.

[0035] Furthermore, the fault recovery strategy is optimized through mathematical programming models to balance power supply reliability and economy.

[0036] Furthermore, voltage amplitude and active / reactive power limitations are used to prevent voltage exceeding limits or line overloads; the output of distributed power sources is combined with that of the upper-level power grid to maximize load recovery.

[0037] Furthermore, priority should be given to restoring critical loads and improving the socio-economic benefits of emergency response.

[0038] Furthermore, the impact of lines can be compared through multi-dimensional indicators (such as fault frequency and recovery time) to avoid the limitations of a single indicator. The indicator threshold can be dynamically adjusted based on historical data to adapt to different disaster scenarios.

[0039] Furthermore, the Copeland score aggregates the comparison results of multiple indicators to reflect the average impact of the line under various fault scenarios, ensuring the comprehensiveness of the ranking, quantifying the comprehensive risk of the line, and providing a mathematical basis for priority sorting.

[0040] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0041] In summary, the present invention achieves dynamic early warning and active defense of distribution network risks in typhoon scenarios through a three-stage closed loop of disaster modeling, optimized recovery, and risk ranking. It also combines meteorological models, power system optimization, and data-driven evaluation, taking into account both physical mechanisms and engineering practicality to enhance the disaster resistance of the distribution network.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 This is a flow chart for risk warning of distribution network lines; Figure 2 Schematic diagram of the IEEE-123 node system considering the consumption of new energy; Figure 3 This is the line fault distribution map under typhoon scenario; Figure 4 This is a comparison chart of the average power loss of the Copeland method and the N-1 method; Figure 5 A schematic diagram of a computer device provided in accordance with an embodiment of the present invention; Figure 6The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0045] Among them, 60. Computer device; 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 DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0048] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present 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.

[0049] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0050] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of 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.

[0051] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0052] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0053] The present invention provides a method for early warning of potential risks in distribution network lines, and proposes a risk factor scoring mechanism based on typhoon disaster simulation and the Copeland method. Through steps such as wind speed-failure rate modeling, distribution network reconstruction optimization, and risk factor ranking analysis, it accurately identifies critical lines with higher risks in the distribution network under extreme weather conditions, and proposes targeted early warning plans to enhance the overall resilience and power supply guarantee capabilities of the system.

[0054] Example 1 See also Figure 1 The present invention provides a method for early warning of potential risks of distribution network lines, comprising the following steps: S1. To simulate the impact of typhoons on power lines, it is necessary to establish a relationship between typhoon location and failure rate. The Batts typhoon model is typically used to establish a mapping relationship between distance and wind speed. Vulnerability modeling is also performed on distribution network lines. This model establishes a mapping relationship between wind speed and the failure rate of distribution lines (including overhead lines and towers), enabling a more comprehensive analysis of critical lines within the network. S101. The Batts model was used to assess the wind speed experienced by various areas within the distribution network during a typhoon event. Given that the focus area is relatively small compared to the spatial extent of the strong wind event, the model can be simplified by treating the entire study area as a single point. This assumption ensures that all components within the area experience uniform wind speeds. At time t, the distance between the typhoon center and the study area is The wind speed at any point in this area The maximum wind speed of the typhoon , the maximum radius of the typhoon ,distance To obtain, as: (1) in, is the coefficient related to the attenuation of the typhoon's radial intensity. In this study, it is taken as 0.6.

[0055] S102. Perform vulnerability modeling on the distribution network lines and construct a mapping relationship between wind speed and failure rate.

[0056] In typhoon scenarios, wind speed is the main factor leading to tower failure, which may manifest as collapse, deformation or other structural integrity issues. Therefore, the probability of failure per kilometer of tower is expressed as wind speed and tower critical wind speed The critical wind speed is the threshold wind speed at which the tower will fail; this relationship is expressed as follows: (2) Here, according to the empirical formula In order to simplify the model, we can approximate It is determined by the design strength when the tower is designed and has nothing to do with the service life and design service life of the tower.

[0057] The failure probability of a power line per kilometer can be calculated by the critical wind speed of the line and daily damage to the lines in the absence of wind To determine the relationship between the failure probability of power lines and wind speed, the quadratic expression is: (3) Here, the scaling parameter is generally taken , daily damage probability , critical wind speed for line damage Determined by the maximum critical wind speed of the line design.

[0058] Assuming that line damage and tower damage are independent of each other, the damage probability per kilometer of line is The failure probability of each kilometer of wire can be , tower failure probability per kilometer Jointly determined, expressed as: (4) For a line, the damage probability of each kilometer should be the same and independent. Considering that the failure of each kilometer of line is independent of each other, the damage probability of the line is for: (5) in, is the line length.

[0059] S103: Using the Monte Carlo method to simulate the distribution network line fault situation obtained in step S102, to obtain a line fault set under each typhoon scenario.

[0060] S2. Input the line fault set generated in step S1 under the typhoon scenario into the mixed integer programming model of the distribution system to maximize the load restoration amount when multiple line faults occur while satisfying the power flow constraint and the radial topology constraint; S201. According to the Distflow model, the operating power flow constraint is expressed as: (6) (7) (8) (9) (10) (11) in, Node Recovered active and reactive power, Node Active and reactive power output from the upper grid or DG, Node To Node Active and reactive power transmitted; is a node The voltage, It is a slave node To Node The connectivity status of the line, Indicates line connectivity. Indicates that the line is disconnected. are node sets and line sets respectively; They are respectively the upper grid access node set, the distributed generator node set, and the power outage island set; Represents nodes respectively The parent node set and child node set of is the reference voltage of the distribution network, For nodes The reference voltage of the upper grid or DG, usually , For nodes The upper and lower limits of the voltage at Node The upper and lower limits of active and reactive power generated by the upper power grid or DG; For nodes To Node The maximum capacity of the line between is a sufficiently large number.

[0061] Next, there are load recovery constraints (12) and (13); these two constraints stipulate that the power factor of the load is fixed.

[0062] (12) (13) S202. To satisfy the radial topology condition, the following constraints are given: (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) in, Representing a collection The momentum, is the set of virtual generator nodes in the island, For nodes To Node The virtual flow, For nodes The virtual power generated by the virtual generator, It is the set of faulty lines in a fault.

[0063] S203. The present invention uses a distribution network reconstruction optimization model to analyze the load weighted recovery amount of the distribution network system under different fault conditions. The objective function of the model is: (twenty three) in, For the j The load recovery amount of each node, Indicates the j The node load point weight is N, and N represents the set of all load points in the system.

[0064] S204. Use the commercial solver Gurobi to solve the mixed integer linear programming problem established in step S201, step S202, and step S203, and calculate the maximum power supply of the distribution network under each fault set.

[0065] S3. Using the maximum power supply of the distribution network under each fault set obtained by statistics in step S2 as data input, a risk factor assessment method and system based on Copeland ranking is constructed.

[0066] S301. In the distribution network risk factor assessment model, the entire sorting object is the line set , the indicator set is , that is, select Several percentiles of are used as multidimensional indicators.

[0067] At this time, the indicator Lower component For components The result of the Copeland comparison is (twenty four) When the indicator Lower component Failure will cause the If the load loss is more serious during the fault, 1 point is awarded; if the two are equal, 0.5 points are awarded; The impact of a failure compared to a component If the fault is more minor, no points will be awarded.

[0068] S302, for all features Sum, that is, get the component For components Overall score: (25) S303, components , traverse all components , compare and add the scores to get the component Copeland score (i.e. risk factor score): (26) The risk factor scores of all lines are sorted from high to low, and the lines with higher scores are the links with higher risk factors in the distribution network.

[0069] S304. Based on the emergency rescue capabilities of the power sector, the line risk levels are divided into "high-risk level lines", "medium-risk level lines", and "low-risk level lines", and the resulting high-risk line set is used as risk warning information in disaster scenarios; the first q lines with high risk factor scores are reinforced in sequence to reduce their failure probability, thereby achieving optimal resource allocation scheduling.

[0070] Furthermore, this method can achieve hierarchical and classified dynamic risk warning for key lines in the distribution network by constructing a multi-index fusion risk assessment system based on scenario simulation.

[0071] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Accordingly, various aspects of the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as "circuits," "modules," or "platforms."

[0072] Example 2 The present invention provides a distribution network line potential risk early warning system, which can be used to implement the above-mentioned distribution network line potential risk early warning method. Specifically, the distribution network line potential risk early warning system includes a collection module, a statistics module and a sorting module.

[0073] The collection module uses the Batts typhoon model to establish a mapping relationship between distance and wind speed. It also performs vulnerability modeling on distribution network lines and generates a fault collection for lines under typhoon scenarios based on the mapping relationship between wind speed and distribution line failure rate. The statistical module inputs the generated line fault set under the typhoon scenario into the mixed integer programming model of the distribution system. It maximizes the load restoration amount when multiple line faults occur, while satisfying the power flow constraints and radial topology constraints. The maximum power supply of the distribution network under each fault set is statistically obtained. The sorting module uses the maximum power supply of the distribution network under each fault set as data input, uses the Copeland method to sort the line importance, and forms a reinforcement strategy for the distribution network lines based on the sorting results.

[0074] Example 3 The present invention provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be 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 gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the potential risk warning method of the distribution network line, including: The Batts typhoon model is used to establish a mapping relationship between distance and wind speed; the distribution network lines are modeled for vulnerability, and a fault set of the lines under typhoon scenarios is generated based on the mapping relationship between wind speed and distribution line failure rate. The generated fault set of the lines under typhoon scenarios is input into the mixed integer programming model of the distribution system to maximize the load recovery amount when multiple line faults occur, while satisfying the power flow constraints and radial topology constraints, and statistically obtain the maximum power supply of the distribution network under each fault set. The obtained maximum power supply of the distribution network under each fault set is used as data input, and the Copeland method is used to rank the line importance. According to the ranking results, a reinforcement strategy for the distribution network lines is formed.

[0075] See also Figure 5 The terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable by the processor 61. When the computer program 63 is executed by the processor 61, it implements the method for early warning of potential risks of distribution network lines in the embodiment. To avoid repetition, it is not described in detail here. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the system for early warning of potential risks of distribution network lines in the embodiment. To avoid repetition, it is not described in detail here.

[0076] The computer device 60 may be a desktop computer, a notebook computer, a PDA, a cloud server, or other computing devices. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. It will be understood by those skilled in the art that Figure 5 This is merely an example of the computer device 60 and does not constitute a limitation of the computer device 60 . The computer device 60 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0077] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 60.

[0079] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is about to be output.

[0080] See also Figure 6 The terminal device is an electronic device 600, which is implemented as 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 the storage unit 620 and the processing unit 610), and a display unit 640.

[0081] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0082] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0083] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0084] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0085] The electronic device 600 may also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem). Such communication may occur via an input / output interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network, a wide area network, and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the 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.

[0086] Example 4 The present invention also provides a storage medium, specifically a computer-readable storage medium. The computer-readable storage medium is a memory device in a terminal device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It may 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 being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that more specific examples of the computer-readable storage medium herein include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0087] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program code. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0088] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network or a wide area network, or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0089] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for early warning of potential risks of distribution network lines in the above embodiment; the processor may load and execute the following steps: The Batts typhoon model is used to establish a mapping relationship between distance and wind speed; the distribution network lines are modeled for vulnerability, and a fault set of the lines under typhoon scenarios is generated based on the mapping relationship between wind speed and distribution line failure rate. The generated fault set of the lines under typhoon scenarios is input into the mixed integer programming model of the distribution system to maximize the load recovery amount when multiple line faults occur, while satisfying the power flow constraints and radial topology constraints, and statistically obtain the maximum power supply of the distribution network under each fault set. The obtained maximum power supply of the distribution network under each fault set is used as data input, and the Copeland method is used to rank the line importance. According to the ranking results, a reinforcement strategy for the distribution network lines is formed.

[0090] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0091] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0092] The IEEE 123-node power distribution system is modified appropriately to include multiple types of distributed resources, flexible loads, and V2G sites, and is used as an example. Figure 2 The node parameter information is shown in Table 1, and the energy storage and V2G charging data obtained through simulation are shown in Table 2.

[0093] Table 1 IEEE 123 node parameters

[0094] Table 2 Energy storage and V2G site parameters

[0095] A typhoon attack on the distribution network was simulated. The tower design wind speed was 32 m / s, the critical wind speed for line damage was 30 m / s, and the maximum radius was 20 km. The direction of movement was northwest, and the landing point was 21.7°N / 116.3°E. The typhoon's center was moving at a speed of 15 km / h. At this wind speed, a rectangular coordinate system was established with the typhoon's center as the origin. The distribution network line failure rate heat map is shown below. Figure 3 As shown, wind speeds are highest at the maximum radius, and the failure rate decreases with increasing distance from the maximum radius. The impact of a typhoon's landfall on the failure rate of distribution network lines can be divided into three stages. In the first stage, when the lines are outside the typhoon's maximum radius, the failure rate remains at the daily level, then suddenly increases. In the second stage, when the lines are within the typhoon's maximum radius, the failure rate initially decreases and then increases. In the third stage, as the typhoon moves, the failure rate drops sharply, returning to the daily failure rate. Since the diameter of the distribution network is typically only tens of kilometers, the impact of this typhoon on the lines will only last for a few hours.

[0096] In order to more comprehensively evaluate the system risk and reduce unnecessary redundant calculations, the average power loss of the system is selected Percentile value of , representing the system's ability to cope with frequent small-scale, typical medium-scale, and occasional large-scale high wind scenarios, respectively. Using the Copeland algorithm, we can obtain the risk factor scores for each line, as shown in Table 3.

[0097] Table 3 Line risk factor scores

[0098] Lines <0,1> and <1,7> have high risk factor scores and are weak links in the distribution network that need to be focused on and strengthened. Conversely, lines <68,69> and <40,41> have relatively low risk factor scores and have limited impact on improving the resilience and reliability of the system.

[0099] To sum up, the routes with higher risks can be roughly divided into three categories: (i) The first category is located in the upstream trunk lines of the power system (such as <0,1>, <1,7>). If these lines are damaged, the upstream power station will be unable to supply power to the downstream lines; (ii) The second category is adjacent to important loads (e.g. <31,32>), which usually have great economic value or strategic significance; (iii) The third category is adjacent to generators or energy storage devices (e.g. <27, 33>, <60, 62>). These lines can ensure emergency power supply when the substation functional channel is damaged.

[0100] Lines with lower risk are usually located at the end of the network (such as <40, 41>) or lines with multiple transmission paths (such as <21, 23>, <68, 69>). In this way, even if a line fails due to a fault, the surrounding lines can still obtain power from other transmission paths.

[0101] Before a typhoon arrives, based on weather forecast information, the power sector will usually take wind-proof measures such as strengthening electric poles in advance to reduce load losses after the typhoon and lower the probability of major risks.

[0102] The N-1 method considers the number of single faults and selects the q lines that have the greatest impact on the power system status after the fault as the critical lines. In order to verify the effectiveness of the reinforcement strategy proposed in this paper, this section compares it with the N-1 method. Considering the limited scheduling time before the typhoon disaster occurs, the two methods select the first 1-5 lines for reinforcement respectively. The first 5 lines are shown in Table 4. After reinforcement, it is assumed that the failure probability of the reinforced line is zero. The average power loss is as follows Figure 4 shown.

[0103] Table 4. Reinforcement strategies of the Copeland method and the N-1 method

[0104] Compared with the N-1 method, the key line reinforcement method proposed in this invention takes into account both the power loss distribution and multi-line failures. In the calculation example, the first-ranked lines in the two methods are the same and have comparable performance; although the second-ranked lines are different, their functions are comparable from the perspective of the overall network structure. The third-ranked line <7,8> selected by the Copeland scoring method is located on the necessary path connecting the upper power grid with the mid- and downstream loads, while the line <27,33> selected by the N-1 algorithm can only guarantee power supply to the local microgrid, so the average power loss of the Copeland scoring method is lower. In addition, after reinforcing the three lines, the average power loss of the Copeland scoring method was reduced by more than 60%.

[0105] In summary, considering the characteristics of typhoons being strong and difficult to predict, the present invention provides a method and system for early warning of potential risks of distribution network lines, which effectively reduces the impact of typhoons on the power grid, is conducive to relevant departments making scheduling decisions in advance when manpower and material resources are limited, and improves the power grid's ability to resist typhoons.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0107] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0109] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0112] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for early warning of potential risks in a distribution network, characterized in that: The following steps are involved: S1. Use the Batts typhoon model to establish a mapping relationship between distance and wind speed; The vulnerability of the distribution network lines is modeled, and the fault set of the lines under typhoon scenarios is generated based on the mapping relationship between wind speed and distribution line failure rate. S2. Input the line fault set generated in step S1 under the typhoon scenario into the mixed integer programming model of the distribution system, maximize the load restoration amount when multiple line faults occur, and satisfy the power flow constraints and radial topology constraints at the same time, and obtain the maximum power supply of the distribution network under each fault set. S3. Using the maximum power supply of the distribution network under each fault set obtained in step S2 as data input, the Copeland method is used to sort the line importance, and a reinforcement strategy for the distribution network line is formed according to the sorting result.

2. The method for early warning of potential risks of distribution network lines according to claim 1, characterized in that: Step S1 is specifically as follows: S101. Use the Batts model to evaluate the wind speed in each area of ​​the distribution network during a typhoon disaster. S102, performing vulnerability modeling on the distribution network lines, constructing a mapping relationship between wind speed and failure rate, and obtaining the distribution network line failure status; S103: Using the Monte Carlo method to simulate the distribution network line fault conditions obtained in step S102, to obtain a line fault set under each typhoon scenario.

3. The method for early warning of potential risks of distribution network lines according to claim 2, characterized in that: In step S101, at time t, the wind speed at any point in the study area is calculated as follows: in, is the maximum wind speed of the typhoon, is the maximum radius of the typhoon, is the distance between the typhoon center and the study area, is the coefficient related to the attenuation of the typhoon's radial intensity.

4. The method for early warning of potential risks of distribution network lines according to claim 2, characterized in that: In step S102, the line damage probability for: in, is the line length, is the damage probability per kilometer of line.

5. The method for early warning of potential risks of distribution network lines according to claim 1, characterized in that: Step S2 is specifically as follows: S201, determining the operating power flow constraint and the load recovery constraint according to the Distflow model; S202, determining constraints that satisfy radial topology conditions; S203, using a distribution network reconstruction optimization model to analyze the load weighted recovery amount of the distribution network system under different fault conditions; S204. Use the commercial solver Gurobi to solve the mixed integer linear programming problem established in step S201, step S202, and step S203, and calculate the maximum power supply of the distribution network under each fault set.

6. The method for early warning of potential risks of distribution network lines according to claim 5, characterized in that: The operating power flow constraint is expressed as: Load recovery constraints: in, Node Recovered active and reactive power, Node Active and reactive power output from the upper grid or DG, Node To Node Active and reactive power transmitted; is a node The voltage, It is a slave node To Node The connectivity status of the line, are node sets and line sets respectively; They are respectively the upper grid access node set, the distributed generator node set, and the power outage island set; Represents nodes respectively The parent node set and child node set of is the reference voltage of the distribution network, For nodes The reference voltage of the upper grid or DG, For nodes The upper and lower voltage limits at Node The upper and lower limits of active and reactive power generated by the upper power grid or DG; For nodes To Node The maximum capacity of the line between is a sufficiently large number.

7. The method for early warning of potential risks of distribution network lines according to claim 5, characterized in that: The objective function of the distribution network reconstruction optimization model is: in, For the j The load recovery amount of each node, Indicates the j The node load point weight is N, and N represents the set of all load points in the system.

8. The method for early warning of potential risks of distribution network lines according to claim 1, characterized in that: Step S3 is specifically as follows: S301. In the distribution network risk factor assessment model, the sorting objects are all line sets. , the indicator set is , select As a multidimensional indicator, we can get the index Lower component For components Results of the Copland comparison ; S302, for all features Sum and get the components For components Overall score; S303, components , traverse all components , compare and add the scores to get the component The Copeland score is used to sort the risk factor scores of all lines from high to low. The lines with high scores have high risk factors of the distribution network. S304. Based on the emergency rescue capability of the power sector, select the first q lines with high risk factor scores for reinforcement operations.

9. The method for early warning of potential risks of distribution network lines according to claim 8, characterized in that: element Copland score for: in, For indicators Lower component For components Results of a Copeland comparison.

10. A potential risk early warning system for distribution network lines, characterized in that: include: The collection module uses the Batts typhoon model to establish a mapping relationship between distance and wind speed; The vulnerability of the distribution network lines is modeled, and the fault set of the lines under typhoon scenarios is generated based on the mapping relationship between wind speed and distribution line failure rate. The statistical module inputs the generated line fault set under the typhoon scenario into the mixed integer programming model of the distribution system. It maximizes the load restoration amount when multiple line faults occur, while satisfying the power flow constraints and radial topology constraints. The maximum power supply of the distribution network under each fault set is statistically obtained. The sorting module uses the maximum power supply of the distribution network under each fault set as data input, uses the Copeland method to sort the line importance, and forms a reinforcement strategy for the distribution network lines based on the sorting results.

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