A method and system for identifying high-risk areas of a power distribution network under extreme weather conditions

Abstract

The application belongs to the technical field of power system monitoring, and more particularly to a high-risk area identification method and system for distribution networks under extreme weather, which obtains the failure probability of the distribution line by the failure probability of each power transmission tower, the failure probability of the distribution pole and the failure probability of the conductor segment in the target forecast wind speed area of the distribution network; constructs a target discrete Markov chain through the transition probability between the state intervals corresponding to the SOC of each photovoltaic power station, and then accurately obtains the photovoltaic energy storage failure supply probability of each photovoltaic power station by using the steady-state probability distribution property of the discrete Markov chain; optimizes the total disaster risk coefficient of the distribution network and the total active power change value of each load node by constructing a double-layer probability AD model, and then solves the AD model by combining the constraint function set for constraining the operation state of the distribution network, so as to accurately obtain the distribution line and photovoltaic power station that have the greatest impact on the safe and stable operation of the distribution network area under typhoon environment and take them as high-risk areas.

Description

Technical Field

[0001] This application belongs to the field of power system monitoring technology, and more specifically, relates to a method and system for identifying high-risk areas of a distribution network under extreme weather conditions. Background Technology

[0002] In recent years, with global warming, extreme weather events such as typhoons, torrential rains, and floods have become more frequent, posing a serious threat to the safe and stable operation of power systems. As the final link in the power system directly connecting to user terminals, the distribution network has a vast network structure exposed to the outdoors. Its disaster resistance under extreme typhoon weather is relatively weak, making it highly susceptible to physical damage such as line breaks and pole collapses, leading to large-scale power outages. Therefore, to improve the distribution network's adaptability to extreme environments, identification technologies for key high-risk and vulnerable areas of the distribution network have become a research hotspot in the field of power system security.

[0003] With the rapid development of distributed energy, a large number of photovoltaic power stations equipped with energy storage devices have been connected to modern distribution networks. However, existing methods for assessing high-risk areas and screening fault scenarios in distribution networks often only focus on the probability of damage and circuit breakage of the physical lines (distribution lines and photovoltaic power stations) due to extreme typhoon weather, and use this to establish a high-risk area identification model for distribution networks. However, in actual operation scenarios, due to cloud cover or continuous heavy rain during typhoons, some photovoltaic power stations, even if their grid-connected physical lines are not damaged, may still have extremely low daytime power generation due to insufficient sunlight. After the distributed power generation's energy storage system continuously consumes the original power, it cannot receive effective replenishment from the photovoltaic power (i.e., the state of charge (SOC) is depleted). This results in the photovoltaic power station being unable to effectively support the load area of ​​the distribution network, thus making the photovoltaic power station in the distribution network a high-risk area. This significantly reduces the accuracy of existing high-risk area identification models for distribution networks. Summary of the Invention

[0004] To address the aforementioned deficiencies in existing technologies, this application provides a method and system for identifying high-risk areas in power distribution networks under extreme weather conditions. This aims to solve the problem that the identification results of high-risk area identification models that only consider the risk of physical line outages in power distribution networks are seriously deviated from the actual situation due to the difficulty in accurately quantifying the impact of extreme typhoon weather on the power generation efficiency of photovoltaic power plants.

[0005] Firstly, this application provides a method for identifying high-risk areas of a power distribution network under extreme weather conditions, including: S1. Based on the historical typhoon data of the distribution network area within the acquired historical time period, obtain the target predicted wind speed of the distribution network area during the target time period, and obtain the failure probability of the distribution line based on the target predicted wind speed. S2. Divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and obtain the transition probability between each target state interval based on the changes in the target state interval of SOC in the historical time period. S3. Based on the transition probability between each target state interval, obtain the photovoltaic energy storage failure probability of each photovoltaic power station, and construct a two-layer probabilistic AD model based on the distribution line failure probability, the photovoltaic energy storage failure probability of each photovoltaic power station and the active power of each load node in the distribution network area. S4. Based on the constraint relationships between active power, reactive power, power flow, voltage, and protection status among photovoltaic power stations, energy storage devices, power distribution lines, and load nodes corresponding to the power distribution lines in the distribution network area, a constraint function set is constructed, and the two-layer probabilistic AD model is solved based on the constraint function set to obtain the high-risk area identification results of the distribution network area.

[0006] Furthermore, the target predicted wind speed for the distribution network area during the target time period is obtained, including: Obtain the typhoon eye path, the maximum sustained wind speed of the typhoon eye, the inner and outer ring influence radii of the maximum sustained wind speed from historical typhoon data, and obtain the first distance between the center of the distribution network area and the typhoon eye based on the location information of the distribution network area and the typhoon eye path during the target time period. Based on the parameter coupling relationship between the first distance, the inner ring influence radius, the outer ring influence radius, and the maximum continuous wind speed, the target predicted wind speed of the distribution network area during the target time period is obtained.

[0007] The calculation methods for wind speed within the inner and outer ring influence radii of a typhoon's eye differ. The inner and outer ring influence radii can be pre-fitted using the wind speed kernel density from historical typhoon heatmaps, combined with the maximum sustained wind speed of the typhoon's eye. By determining the location of the power distribution network area within the typhoon's influence radius using the first distance, a suitable formula can be used to accurately calculate the target predicted wind speed for the power distribution network area during the target time period.

[0008] Furthermore, the probability of power line failure is obtained based on the target predicted wind speed, including: Based on the target predicted wind speed, the failure probabilities of each transmission tower, distribution pole, and conductor segment in the distribution network are obtained. Based on the failure probabilities of each transmission tower, distribution pole, and conductor segment, the failure probability of the distribution line is obtained.

[0009] Furthermore, the formula for calculating the failure probability of power distribution lines is as follows:

[0010] in, This indicates the probability of power distribution line failure. Indicates the first The failure probability of a conductor segment. Indicates the first The failure probability of a single power distribution pole. Indicates the first The failure probability of a transmission tower express t Target predicted wind speed for the specified time period.

[0011] Furthermore, based on the transition probabilities between each target state interval, the photovoltaic energy storage failure probability of each photovoltaic power station is obtained, including: Construct a target discrete Markov chain based on the transition probabilities between each target state interval, and take the target state interval in the lowest state as the disabling interval; Based on the target discrete Markov chain, construct the probability transition matrix and steady-state probability distribution vector for each target state interval, and construct the target steady-state equation system based on the probability transition matrix and steady-state probability distribution vector; The target steady-state equations are solved using the least squares method to obtain the target steady-state probability of the power failure interval within the steady-state probability distribution vector, and the target steady-state probability is used as the photovoltaic energy storage power failure probability.

[0012] Furthermore, the upper and lower layers of the two-layer probabilistic AD model are used to minimize the total disaster risk coefficient corresponding to the probability of photovoltaic energy storage failure and the probability of power distribution line failure, respectively, and to maximize the total active power change value of each load node.

[0013] Furthermore, auxiliary variables are introduced to perform log-linearization on the two-level probabilistic AD model to obtain the target two-level probabilistic AD model. Dual variables are then introduced to perform dual transformation on the constraint function set and the target two-level probabilistic AD model to obtain the target dual constraint function set and the dual condition function between the upper and lower linear models of the target two-level probabilistic AD model. Based on the objective solution algorithm, the objective dual constraint function set and dual condition function are combined to solve the objective two-layer probabilistic AD model, and the objective protection status of each photovoltaic power station and distribution line is obtained. The representation result of the objective protection status is whether risk protection is required or not.

[0014] Furthermore, the objective solution algorithm is the Gurobi solver.

[0015] Secondly, this application also provides a high-risk area identification system for power distribution networks under extreme weather conditions, for performing any of the methods in the first aspect, including: The power distribution line failure probability acquisition module is used to obtain the target predicted wind speed of the power distribution network area in the target time period based on the historical typhoon data of the power distribution network area within the acquired historical time period, and to obtain the power distribution line failure probability based on the target predicted wind speed. The SOC transition probability acquisition module is used to divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and to obtain the transition probability between each target state interval based on the changes in the target state interval of SOC in the historical time period. The model building module is used to obtain the photovoltaic energy storage failure probability of each photovoltaic power station based on the transition probability between each target state interval, and to build a two-layer probabilistic AD model based on the distribution line failure probability, the photovoltaic energy storage failure probability of each photovoltaic power station and the active power of each load node in the distribution network area. The constraint function set construction module is used to construct a constraint function set based on the constraint relationships between active power, reactive power, power flow, voltage, and protection status between photovoltaic power stations, energy storage devices, power distribution lines, and load nodes corresponding to power distribution lines in the distribution network area. The model solving module is used to solve the two-layer probabilistic AD model based on the constraint function set to obtain the high-risk area identification results of the distribution network area.

[0016] Thirdly, this application also provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0017] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: This application provides a method and system for identifying high-risk areas of a power distribution network under extreme weather conditions. It obtains the failure probability of power distribution lines by predicting the failure probabilities of transmission towers, distribution poles, and conductor segments in the power distribution network area under target wind speeds. A target discrete Markov chain is constructed using the transition probabilities between state intervals corresponding to the State of Charge (SOC) of each photovoltaic (PV) power station. The steady-state probability distribution properties of the discrete Markov chain are then used to accurately obtain the photovoltaic energy storage failure probability of each PV power station. A two-layer probabilistic AD model is constructed using the failure probabilities of power distribution lines, the photovoltaic energy storage failure probability of each PV power station, and the active power of each load node in the power distribution network area. This model simultaneously optimizes the total disaster risk coefficient of the power distribution network and the total active power change value of each load node. Furthermore, by combining the constraint function set constraining the operating state of the power distribution network, the two-layer probabilistic AD model is solved, thereby accurately identifying the power distribution lines (transmission towers, distribution poles, and conductor segments) and PV power stations that have the greatest impact on the safe and stable operation of the power distribution network area under typhoon conditions, and designating these as high-risk areas. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a high-risk area identification method provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the power distribution network structure provided in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the structure of the target discrete Markov chain provided in the embodiments of this application.

[0022] Figure 4 This is a schematic diagram of the structure of the high-risk area identification system provided in the embodiments of this application.

[0023] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0025] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0026] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0027] Figure 1 This is a flowchart illustrating the high-risk area identification method for power distribution networks under extreme weather conditions provided in this application embodiment. Figure 1 As shown, the method includes at least the following steps: S1. Based on the historical typhoon data of the distribution network area within the acquired historical time period, obtain the target predicted wind speed of the distribution network area during the target time period, and obtain the failure probability of the distribution line based on the target predicted wind speed.

[0028] In one possible implementation, obtaining the target predicted wind speed for the distribution network area during the target time period includes: Obtain the typhoon eye path, the maximum sustained wind speed of the typhoon eye, the inner and outer ring influence radii of the maximum sustained wind speed from historical typhoon data, and obtain the first distance between the center of the distribution network area and the typhoon eye based on the location information of the distribution network area and the typhoon eye path during the target time period. Based on the parameter coupling relationship between the first distance, the inner ring influence radius, the outer ring influence radius, and the maximum continuous wind speed, the target predicted wind speed of the distribution network area during the target time period is obtained.

[0029] In this embodiment of the application, the implementing entity can be the central controller of the power distribution network monitoring device. Different power distribution network areas Different typhoon eye paths tr The first distance from the eye of the typhoon Target predicted wind speed The calculation formula is as follows: Radius of influence from the eye of the storm to the inner ring:

[0030] Radius of influence from the eye of the storm to the outer ring:

[0031] in, Path of the typhoon's eye tr exist t The maximum sustained wind speed during the period, Path of the typhoon's eye tr exist t The radius of influence of the inner ring during the time period. Path of the typhoon's eye tr exist t The outer radius of influence during the time period , The boundary coefficients of the inner and outer rings are greater than 1, respectively controlling the rise curvature of the inner ring and the attenuation amplitude of the outer ring.

[0032] In one possible implementation, the probability of power line failure is obtained based on the target predicted wind speed, including: Based on the target predicted wind speed, the failure probabilities of each transmission tower, distribution pole, and conductor segment in the distribution network are obtained. Based on the failure probabilities of each transmission tower, distribution pole, and conductor segment, the failure probability of the distribution line is obtained.

[0033] In this embodiment, the power distribution network includes transmission towers, distribution poles, and conductor segments between poles. Therefore, it is necessary to integrate the failure probabilities of each component to obtain the overall failure probability of the power distribution network. For example, the functional expression for the failure probability of a single transmission tower under typhoon conditions is:

[0034] in, Indicates the path of the typhoon's eye. tr exist t The target predicted wind speed for a given period of time can be... t The average wind speed predicted over the time period is used to reduce the amount of subsequent calculations. The formula represents the failure probability of a single transmission tower. The coefficients of each fixed constant can be adjusted according to the installation method and physical structure of the transmission tower. This application only provides an example of a laboratory simulation result.

[0035] The functional expression for the failure probability of a power distribution pole is:

[0036] in, Indicates the path of the typhoon's eye. tr existt The target predicted wind speed for a given time period, and the coefficients of each fixed constant in the probability function of pole failure are adjusted according to the pole's installation method and physical structure.

[0037] The functional expression for the failure probability of a conductor segment is:

[0038]

[0039] in, Indicates the first The failure probability of a conductor segment. f u The term represents the burial rate of the conductor segment, and the constant α reflects the impact of "tree fall / felling / pruning level" on conductor damage. For the end of the wire k Direct wind-induced damage, F wind,k This refers to the wind load on the end of this conductor section. F force,k This is the maximum normal force that the line segment can withstand. Damage caused by wind-blown fallen trees, and , These are specific parameters for conductor segments. It is a conductor segment The wind intensity received, This is the wind intensity adjustment coefficient.

[0040] Therefore, the formula for calculating the failure probability of a power distribution line composed of conductor segments, distribution poles, and transmission towers is as follows:

[0041] in, This indicates the probability of power distribution line failure. Indicates the first The failure probability of a conductor segment. Indicates the first The failure probability of a single power distribution pole. Indicates the first The failure probability of a transmission tower express t Target predicted wind speed for the specified time period.

[0042] S2. Divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and obtain the transition probability between each target state interval based on the changes in the target state interval of SOC in the historical time period.

[0043] In the embodiments of this application, such as Figure 2As shown, in addition to standby energy storage devices, the built-in SOC (State of Charge) of each photovoltaic (PV) power station also plays a role in supplying power to various nodes of the distribution network under extreme weather conditions. Even if the physical lines connecting some PV power stations to the grid are not damaged, severe lack of sunlight can lead to extremely low daytime power generation. After the distributed generation's energy storage system continuously consumes the existing power, it cannot receive effective replenishment from the PV power (i.e., SOC depletion). This results in the PV power station being unable to effectively support the load areas of the distribution network, thus making the PV power station in the distribution network a high-risk area. Therefore, it is necessary to obtain the probability of PV energy storage failure in PV power stations.

[0044] S3. Based on the transition probabilities between each target state interval, obtain the photovoltaic energy storage failure probability of each photovoltaic power station. Based on the distribution line failure probability, the photovoltaic energy storage failure probability of each photovoltaic power station, and the active power of each load node in the distribution network area, construct a two-layer probabilistic AD model. The upper and lower layers of the two-layer probabilistic AD model are used to minimize the total disaster risk coefficient corresponding to each photovoltaic energy storage failure probability and the distribution line failure probability, and to maximize the total active power change value of each load node, respectively.

[0045] In one possible implementation, the photovoltaic energy storage failure probability of each photovoltaic power station is obtained based on the transition probability between each target state interval, including: Construct a target discrete Markov chain based on the transition probabilities between each target state interval, and take the target state interval in the lowest state as the disabling interval; Based on the target discrete Markov chain, construct the probability transition matrix and steady-state probability distribution vector for each target state interval, and construct the target steady-state equation system based on the probability transition matrix and steady-state probability distribution vector; The target steady-state equations are solved using the least squares method to obtain the target steady-state probability of the power failure interval within the steady-state probability distribution vector, and the target steady-state probability is used as the photovoltaic energy storage power failure probability.

[0046] In the embodiments of this application, such as Figure 3 As shown, the State of Charge (SOC) is first divided into several target state intervals, corresponding to the SOC intervals M to M in the diagram. For example, M1 corresponds to an SOC > 80%, M2 corresponds to an SOC between 60% and 80%, M3 corresponds to an SOC between 40% and 60%, M4 corresponds to an SOC between 20% and 40%, and M5 corresponds to an SOC less than 20%. Based on the changes in the target state intervals of the SOC within historical time periods, the transition probabilities between each target state interval can be obtained, and thus a target discrete Markov chain can be constructed. Figure 3 middle p ij This can reflect the firsti The target state interval is transitioned to the first... j The transition probabilities of each target state interval can be integrated to obtain the probability transformation matrix. P Based on the steady-state probability distribution properties of discrete Markov chains, a steady-state probability distribution vector is constructed. π= ,because This leads to the target steady-state equations. ， Thus, the target steady-state probability of the disabled interval among the m target state intervals can be obtained by the least squares method.

[0047] Furthermore, the upper and lower layers of the two-layer probabilistic AD (Attacker-Defender) model are used to minimize the total disaster risk coefficients corresponding to the probabilities of photovoltaic energy storage failure and distribution line failure, respectively, and to maximize the total active power change value of each load node. The formula for the two-layer probabilistic AD model is expressed as follows:

[0048] in, Indicates power distribution lines l The corresponding probability of power distribution line failure; Indicates photovoltaic power station g The probability of photovoltaic energy storage failure. PD d,t for t Time-of-use load nodes d The active power, the target time period of this application is composed of multiple equal lengths t The time period is defined, therefore the total change in active power is the sum of the values ​​of each time period. t The total disaster risk coefficient is the sum of the active power changes of each load node during a given period. It is the product of the probability of each photovoltaic energy storage failure and the probability of each distribution line failure.

[0049] S4. Based on the constraint relationships between active power, reactive power, power flow, voltage, and protection status among photovoltaic power stations, energy storage devices, power distribution lines, and load nodes corresponding to the power distribution lines in the distribution network area, a constraint function set is constructed, and the two-layer probabilistic AD model is solved based on the constraint function set to obtain the high-risk area identification results of the distribution network area.

[0050] In this embodiment of the application, the constraint function set is used to ensure the normal operation of photovoltaic power stations, energy storage devices, and distribution lines (transmission towers, distribution poles, and conductor segments) within the distribution network area. The formula for the constraint function set is as follows:

[0051] in, n ,l , g , d , s, k These represent the serial numbers of nodes, power distribution lines, photovoltaic power stations with batteries, load nodes, energy storage devices, and the power distribution lines (towers, power supply towers and distribution poles) connected to each node, respectively. BL , BG , BS , BD These are the node-line correlation matrix, the node-photovoltaic power plant correlation matrix, the node-energy storage correlation matrix, and the node-load correlation matrix, respectively. PD d,t and QD d,t for t Load nodes during the time period d The active and reactive power values; r l and x l The lines are respectively l Resistance and reactance, V nmax , PG gmax , QG gmax , PS smax , QS smax , Pf lmax , Qf lmax and PG gmin , QG gmin These are the upper bounds for load node voltage, photovoltaic active power, reactive power, energy storage active power, energy storage reactive power, line active capacity, and line reactive capacity, and the lower bounds for photovoltaic power active power and reactive power. E s The upper limit of energy for each energy storage device. η dis The discharge efficiency of the energy storage device; variable PG g,t , QG g,t , PS s,t , QS s,t Δ PD d,t 、 ΔQ D d,t , Pf l,t , Qfl,t They are respectively t Active power of photovoltaic power station during time period, reactive power of photovoltaic power station, active power of energy storage device, reactive power of energy storage device, load node d Active power change value, load node d The reactive power change value, active power flow of the line, and reactive power flow of the line. z l , z g , a l , a g , a n All are binary variables of 0-1, representing power distribution lines. l The state of being protected ( z l =1 means the line is protected), the photovoltaic power station is in a state of pending protection. z g =1 means the line is protected), power distribution line l Will it be destroyed? a l =1 means the line will be damaged), whether the photovoltaic power station has been damaged ( a g =1 means the photovoltaic power station will be destroyed.

[0052] In one possible implementation, the two-level probabilistic AD model is solved based on a set of constraint functions, including: Auxiliary variables are introduced to perform log-linearization on the two-level probabilistic AD model to obtain the target two-level probabilistic AD model. Dual variables are then introduced to perform dual transformation on the constraint function set and the target two-level probabilistic AD model to obtain the target dual constraint function set and the dual condition function between the upper and lower linear models of the target two-level probabilistic AD model. Based on the objective solution algorithm, the objective dual constraint function set and dual condition function are combined to solve the objective two-layer probabilistic AD model, and the objective protection status of each photovoltaic power station and distribution line is obtained. The representation result of the objective protection status is whether risk protection is required or not.

[0053] In this embodiment, because the Gurobi solver has built-in branch and bound, cutting plane, and multiple heuristic algorithms, it can solve large dual equation systems at high speed. Therefore, the objective solution algorithm can be the Gurobi solver, but is not limited to it. This application performs logarithmic linearization and dual transformation on the two-layer probabilistic AD model to convert the two-layer model, which involves a large number of multiplications, into a single-layer hybrid linear model, thereby facilitating fast and accurate solving by the solver. The final solution result of the objective two-layer probabilistic AD model includes the target protection status of each photovoltaic power station and distribution line, that is, it shows which distribution lines and photovoltaic power stations, when protected, simultaneously meet the requirements of the lowest total disaster risk coefficient and the largest change in total active power at each load node. Therefore, the corresponding distribution lines and photovoltaic power stations can be considered as high-risk areas of the distribution network.

[0054] The results of processing the two-layer probabilistic AD model are as follows:

[0055]

[0056]

[0057] in, Indicates power distribution lines l The corresponding probability of power distribution line failure; Indicates photovoltaic power station g The probability of photovoltaic energy storage failure. PD d,t for t Time-of-use load nodes d The active power. For the introduction of characterization t Time-of-use load nodes d The active power fluctuation value, This represents the weighting coefficients allocated during the 0-t time period.

[0058]

[0059] in, Indicates power distribution lines l The corresponding probability of power distribution line failure; Indicates photovoltaic power station g The probability of photovoltaic energy storage failure. PD d,t for t Time-of-use load nodes d active power, Indicates each of the target time periods t The sum of the changes in active power at each load node during the time period.

[0060] The result of the dual transformation of the constraint function set (dual constraint function set) is as follows:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] The dual condition function is:

[0069] in, All of these are dual variables corresponding to the original constraint function set. The specific dual transformation process is existing technology, so it will not be elaborated on in more detail.

[0070] Figure 4 This is a schematic diagram of the structure of a high-risk area identification system for a power distribution network under extreme weather conditions provided in an embodiment of this application, as shown below. Figure 4 As shown, the system includes at least: The power distribution line failure probability acquisition module is used to obtain the target predicted wind speed of the power distribution network area in the target time period based on the historical typhoon data of the power distribution network area within the acquired historical time period, and to obtain the power distribution line failure probability based on the target predicted wind speed. The SOC transition probability acquisition module is used to divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and to obtain the transition probability between each target state interval based on the changes in the target state interval of SOC in the historical time period. The model building module is used to obtain the photovoltaic energy storage failure probability of each photovoltaic power station based on the transition probability between each target state interval, and to build a two-layer probabilistic AD model based on the distribution line failure probability, the photovoltaic energy storage failure probability of each photovoltaic power station and the active power of each load node in the distribution network area. The constraint function set construction module is used to construct a constraint function set based on the constraint relationships between active power, reactive power, power flow, voltage, and protection status between photovoltaic power stations, energy storage devices, power distribution lines, and load nodes corresponding to power distribution lines in the distribution network area. The model solving module is used to solve the two-layer probabilistic AD model based on the constraint function set to obtain the high-risk area identification results of the distribution network area.

[0071] like Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: a processor 501, a communications interface 502, a memory 503, and a communication bus 504. The processor 501, communications interface 502, and memory 503 communicate with each other via the communication bus 504. The processor 501 can call software instructions in the memory 503 to execute the methods described in the above embodiments.

[0072] Furthermore, the logical instructions in the aforementioned memory 503 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0073] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0074] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0075] It is understood that the processor in the embodiments of this application can be a CPU (Central Processing Unit), or other general-purpose processors, DSPs (Digital Signal Processors), ASICs (Application Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0076] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, ROM (Read-only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), EEPROM (Electrically Erasable EPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0077] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line DSL) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD (Solid State Disk)).

[0078] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0079] Those skilled in the art will readily understand that the above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for identifying high-risk areas of a power distribution network under extreme weather conditions, characterized in that, include: S1. Based on the historical typhoon data of the distribution network area within the acquired historical time period, obtain the target predicted wind speed of the distribution network area during the target time period, and obtain the failure probability of the distribution line based on the target predicted wind speed. S2. Divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and obtain the transition probability between each target state interval based on the changes in the target state interval of the SOC in the historical time period. S3. Based on the transition probability between each of the target state intervals, obtain the photovoltaic energy storage failure probability of each of the photovoltaic power stations, and construct a two-layer probability AD model based on the power distribution line failure probability, the photovoltaic energy storage failure probability of each of the photovoltaic power stations, and the active power of each load node in the power distribution network area. S4. Based on the constraint relationships between the photovoltaic power stations, energy storage devices, power distribution lines, and the load nodes corresponding to the power distribution lines in the distribution network area, a constraint function set is constructed, and the two-layer probabilistic AD model is solved based on the constraint function set to obtain the high-risk area identification result of the distribution network area in the target time period.

2. The high-risk area identification method according to claim 1, characterized in that, The acquisition of the target predicted wind speed for the distribution network area during the target time period includes: The system obtains the typhoon eye path, the maximum sustained wind speed of the typhoon eye, the inner and outer influence radii of the maximum sustained wind speed from the historical typhoon data, and obtains the first distance between the center of the power distribution network area and the typhoon eye path based on the location information of the power distribution network area and the typhoon eye path during the target time period. Based on the parameter coupling relationship between the first distance, the inner ring influence radius, the outer ring influence radius, and the maximum continuous wind speed, the target predicted wind speed of the power distribution network area during the target time period is obtained.

3. The high-risk area identification method according to claim 1, characterized in that, The step of obtaining the power line failure probability based on the target predicted wind speed includes: Based on the target predicted wind speed, the failure probability of each transmission tower, distribution pole, and conductor segment of the distribution network under the target predicted wind speed is obtained, and the failure probability of the distribution line is obtained based on the failure probability of each transmission tower, distribution pole, and conductor segment.

4. The high-risk area identification method according to claim 1, characterized in that, The formula for calculating the failure probability of the power distribution line is: in, This indicates the probability of power distribution line failure. Indicates the first The failure probability of a conductor segment. Indicates the first The failure probability of a single power distribution pole. Indicates the first The failure probability of a transmission tower express t Target predicted wind speed for the specified time period.

5. The high-risk area identification method according to claim 1, characterized in that, The step of obtaining the photovoltaic energy storage failure probability of each photovoltaic power station based on the transition probability between each of the target state intervals includes: A target discrete Markov chain is constructed based on the transition probabilities between each target state interval, and the target state interval in the lowest state is taken as the disabling interval. Based on the target discrete Markov chain, construct the probability transition matrix and steady-state probability distribution vector for each target state interval, and construct the target steady-state equation system based on the probability transition matrix and steady-state probability distribution vector. The target steady-state equations are solved using the least squares method to obtain the target steady-state probability of the power failure interval within the steady-state probability distribution vector, and the target steady-state probability is used as the photovoltaic energy storage power failure probability.

6. The high-risk area identification method according to claim 1, characterized in that, The upper and lower layers of the dual-layer probabilistic AD model are used to minimize the total disaster risk coefficient corresponding to the probability of photovoltaic energy storage failure and the probability of power distribution line failure, respectively, and to maximize the total active power change value of each load node.

7. The high-risk area identification method according to claim 6, characterized in that, Solving the two-layer probabilistic AD model based on the constraint function set includes: Auxiliary variables are introduced to perform log-linearization on the bi-level probabilistic AD model to obtain the target bi-level probabilistic AD model. Dual variables are then introduced to perform dual transformation on the constraint function set and the target bi-level probabilistic AD model to obtain the target dual constraint function set and the dual condition function between the upper and lower linear models of the target bi-level probabilistic AD model. The target bilayer probabilistic AD model is solved based on the target solution algorithm combined with the target dual constraint function set and dual condition function to obtain the target protection status of each photovoltaic power station and distribution line. The characterization result of the target protection status is whether risk protection is required or not.

8. The high-risk area identification method according to claim 7, characterized in that, The target solution algorithm is the Gurobi solver.

9. A high-risk area identification system for a power distribution network under extreme weather conditions, used to perform the method as described in any one of claims 1-8, characterized in that, include: The power distribution line failure probability acquisition module is used to acquire the target predicted wind speed of the power distribution network area in the target time period based on the historical typhoon data of the power distribution network area within the acquired historical time period, and to acquire the power distribution line failure probability based on the target predicted wind speed. The SOC transition probability acquisition module is used to divide the SOC of each photovoltaic power station in the distribution network area into multiple target state intervals from low to high, and to acquire the transition probability between each target state interval based on the changes in the target state interval of the SOC within the historical time period. The model building module is used to obtain the photovoltaic energy storage failure probability of each photovoltaic power station based on the transition probability between each of the target state intervals, and to build a two-layer probabilistic AD model based on the failure probability of the distribution line, the photovoltaic energy storage failure probability of each of the photovoltaic power stations and the active power of each load node in the distribution network area. The constraint function set construction module is used to construct a constraint function set based on the constraint relationships between active power, reactive power, power flow, voltage, and protection status between photovoltaic power stations, energy storage devices, power distribution lines, and the corresponding load nodes in the power distribution network area. The model solving module is used to solve the two-layer probabilistic AD model based on the constraint function set to obtain the high-risk area identification results of the power distribution network area.

10. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-8.

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