Urban power supply risk assessment method and system under action of natural disasters

Abstract

The invention provides an urban power supply risk assessment method and system under the action of natural disasters. The method comprises the following steps: collecting and verifying power grid basic data in a pre-selected risk assessment area; screening risk assessment model data for urban power supply risk assessment from the power grid basic data according to a preset rule; based on a pre-established urban power grid element outage probability model dominated by disaster-inducing factors under different disasters, taking the risk assessment model data as input to obtain risk assessment index values respectively output by the outage probability model; taking the risk assessment index values, the corresponding disaster-inducing factors and the urban power grid elements which are respectively output by the outage probability model as inputs of a pre-trained incidence relation model, and outputting an urban power supply risk assessment result containing spatial and temporal distribution of the power outage area; wherein a training set of the incidence relation model comprises fusion codes of disaster-inducing factors, urban power grid elements and risk assessment index values and marked power failure area space-time distribution code labels.

Description

Technical Field

[0001] The present application belongs to the technical field of urban power supply risk assessment, and in particular, relates to a method and system for urban power supply risk assessment under the influence of natural disasters. Background Art

[0002] Safe and reliable power supply is an important foundation for economic development and people’s livelihood security.

[0003] The power grid is responsible for urban power supply risk assessment, disaster emergency preparedness and emergency response. Risk assessment is one of the important indicators for evaluating the pros and cons of urban power grid planning schemes. It is particularly important to take precautionary measures in the early stages and conduct urban power supply risk assessment before natural disasters occur.

[0004] The existing emergency risk analysis mainly focuses on the safety risk prediction of local subsystems or equipment of the power supply system, but does not take into account the correlation between disaster events and power outage areas.

[0005] Therefore, how to provide a method and system for assessing urban power supply risks under natural disasters that can comprehensively consider the correlation between disaster events and power outage areas is a technical problem that needs to be solved urgently. Summary of the invention

[0006] The present application provides a method for assessing the risk of urban power supply under the influence of natural disasters, the method comprising: collecting and verifying basic data of power grids in a pre-selected risk assessment area; screening risk assessment model data for urban power supply risk assessment from the basic power grid data according to preset rules; based on a pre-built urban power grid component outage probability model dominated by disaster-causing factors under different disasters, taking the risk assessment model data as input, and obtaining the risk assessment index values ​​output by the outage probability model respectively; taking the risk assessment index values ​​output by the outage probability model respectively and the corresponding disaster-causing factors and urban power grid components as inputs of a pre-trained association model, and outputting an urban power supply risk assessment result including the spatiotemporal distribution of the power outage area; wherein the training set of the association model includes a fusion code of the disaster-causing factors, urban power grid components and risk assessment index values, and annotated spatiotemporal distribution code labels of the power outage area, and the urban power supply risk assessment model is a prediction model based on deep learning.

[0007] By adopting the above scheme, the outage probability model of urban power grid components dominated by disaster-causing factors under different disasters can be integrated to analyze and obtain the risk assessment index values ​​corresponding to different disaster-causing factors, and then learn the correlation between disaster-causing factors, urban power grid components, risk assessment index values ​​and the spatiotemporal distribution of power outage areas based on the pre-trained association model, so as to obtain the urban power supply risk assessment results including the spatiotemporal distribution of power outage areas.

[0008] In some embodiments of the present invention, the risk assessment model data input into the urban power grid component outage probability model includes various disaster-causing factors, urban power grid components, and the operation mode of the components in the power grid; wherein the disaster-causing factors include multiple types of extreme weather, earthquakes, wind and rain, waterlogging, and cyber attacks.

[0009] In some embodiments of the present invention, the type of the urban power grid component outage probability model belongs to the optimal power flow model or the load reduction optimal model, and the urban power grid component outage probability model includes a load loss assessment model. During the training process of the load loss assessment model, a training set is constructed based on a load classification method.

[0010] In some embodiments of the present invention, during the training process of the urban power grid component outage probability model, the method further includes: generating fault scenarios by sampling based on the Monte Carlo method, and encoding the spatiotemporal distribution of the power outage area based on risk assessment model data statistics.

[0011] In some embodiments of the present invention, during the training process of the association relationship model, the method further includes: improving the generalization ability of the model based on ensemble learning, and adjusting the training process using category balance and overfitting suppression.

[0012] In some embodiments of the present invention, for the output of the urban power grid component outage probability model, the method also includes: weighting the risk assessment index values ​​output by each outage probability model based on the power outage loss model, so as to use the weighted risk assessment index values ​​as input of the association model.

[0013] In some embodiments of the present invention, the method also includes: for an association relationship model with a weighted risk assessment index value as input, using fuzzy information entropy evaluation to evaluate the urban power grid risk from multiple dimensions, and outputting an urban power supply risk assessment result that also includes a risk weight.

[0014] Correspondingly, on the other hand, the present invention proposes a system for assessing the risk of urban power supply under the influence of natural disasters, comprising a processor, a memory and a computer program / instructions stored in the memory, wherein the processor is used to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the method described in the above embodiment.

[0015] Correspondingly, another aspect of the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the method described in the above embodiment when the computer program / instruction is executed by a processor.

[0016] Accordingly, another aspect of the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of the method described in the above embodiment when executed by a processor.

[0017] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention can be specifically pointed out and obtained in the specification and the accompanying drawings.

[0018] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present disclosure and constitute a part of the specification. They are used to explain the present disclosure together with the following detailed description, but do not constitute a limitation of the present disclosure.

[0020] In the attached picture:

[0021] Figure 1 This is a flow chart of a method for assessing urban power supply risks under the influence of natural disasters according to an embodiment of the present invention.

[0022] Figure 2 This is a flow chart of a method for assessing urban power supply risks under the influence of natural disasters according to another embodiment of the present invention.

[0023] Figure 3 It is a schematic diagram of the principle of the urban power supply risk assessment under the influence of natural disasters according to the present invention.

[0024] Figure 4 The present invention provides a complete technical roadmap for risk assessment of urban power supply under the influence of natural disasters. DETAILED DESCRIPTION

[0025] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0027] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0028] In order to overcome the problems existing in the prior art, the present invention proposes a method and system for assessing the risk of urban power supply under the influence of natural disasters. The method and system are aimed at the natural disasters and emergencies that the urban distribution network suffers and their disaster-causing mechanisms. By assessing the impact of natural disasters and emergencies on the safe operation of the urban power grid, a spatiotemporal correlation model of disaster-grid-power outage based on deep learning is constructed, which helps to reveal the correlation between major disaster events and power outage areas, and helps to achieve accurate assessment of the risk of power outage in urban power grids under emergencies.

[0029] The general definition of risk is: "the probability of harm or loss to human life or property." In actual engineering evaluation, the widely accepted risk refers to the harm of events with loss consequences that may occur within a certain period of time when humans engage in certain activities. This harm comes from two aspects: possibility (probability P) and severity (consequence C), and the risk value is the product of the two, that is, R = P·C. "Therefore, when conducting risk assessment on an uncertain event, it is necessary to conduct a comprehensive evaluation of both the possibility and severity of the event. We should not only consider the probability of failure events or over-emphasize the impact or loss caused by a failure event while ignoring the possibility of the event itself. Only by comprehensively considering the probability of an event and the severity of its consequences can we truly and comprehensively reflect its risks.

[0030] The risk analysis of power grid emergencies is to conduct quantitative analysis and estimation of the spatiotemporal distribution, intensity, probability of occurrence and possible consequences of emergencies that may affect the power grid infrastructure in a certain area in the future.

[0031] The risk of power grids stems from the complexity and uncertainty of large systems. Taking the urban power grid, which is the focus of this study, as an example, the probabilistic characteristics of equipment behavior in the system are often not accurately predicted, and the occurrence of random failures cannot be completely controlled. How to objectively and reasonably describe the risks of the power system is a very challenging basic task, which should be studied from the following aspects: 1) Analyze the outage model of system components; 2) Select system fault states and calculate their probabilities; 3) Evaluate the impact and consequences of the selected system states; 4) Form quantitative risk indicators.

[0032] The key to risk assessment is to quantitatively give a comprehensive assessment of the possibility and severity of the uncertainties contained in the system, and to construct an assessment model and quantitative assessment indicators for urban power grid risks; that is, when conducting a risk assessment of the power system, it is necessary to consider both the probability of system failure events and the severity of the consequences of these events. Therefore, the general formula for defining the urban power grid risk assessment model is:

[0033] R=P·S (0-1)

[0034] Where: R is the risk level of the urban power grid; P is the probability of a failure event; S is the severity of the consequences of a failure event.

[0035] Since the risk assessment of power system will inevitably involve two related fields, system reliability and system safety, it is necessary to clarify the differences and connections between the three in terms of concepts and research content, and it is not possible to simply assume that the three exist independently without any overlap. In fact, risk, reliability and safety all describe the operating status of the system to a certain extent, but their respective focuses are different. After comprehensive comparison, it can be seen that compared with the research focus of reliability and safety, the concept of risk is richer and more comprehensive, and can better describe the uncertainty of power system behavior from an overall perspective, because it can not only identify the probabilistic characteristics of failure events, but also reflect the severity of the consequences of these events. Due to the special network structure of the urban power grid itself, its risk assessment is different from the traditional risk assessment of the power generation and transmission network. In addition to the fact that their traditional assessment criteria - "deterministic principle" cannot feedback the probabilistic characteristics of urban power grid component failures, load changes and system behavior, they also do not give full consideration to the grid structure of the urban power grid, which makes the risk assessment of the urban power grid particularly special. For this reason, this scheme starts from the topological structure of the network, considers the variable structure characteristics of its grid, conducts a detailed assessment of key equipment, and establishes a risk assessment indicator that is more suitable for the urban power grid.

[0036] One aspect of the present invention provides a method for assessing the risk of urban power supply under the influence of natural disasters.

[0037] Figure 1This is a flow chart of a method for assessing urban power supply risk under natural disasters according to an embodiment of the present invention. The method comprises the following steps:

[0038] Step S110: Collect and verify basic power grid data within a pre-selected risk assessment area.

[0039] Step S120: Filter risk assessment model data for urban power supply risk assessment from the basic power grid data according to preset rules.

[0040] Step S130: Based on a pre-built urban power grid component outage probability model dominated by disaster-causing factors under different disasters, the risk assessment model data is used as input to obtain the risk assessment index values ​​output by the outage probability model.

[0041] Step S140: The risk assessment index values ​​and corresponding disaster-causing factors and urban power grid components respectively output by the outage probability model are used as inputs of the pre-trained association model, and the urban power supply risk assessment results including the spatiotemporal distribution of the power outage area are output.

[0042] Among them, the training set of the association relationship model contains the fusion coding of disaster-causing factors, urban power grid components and risk assessment index values, as well as the marked spatiotemporal distribution coding labels of power outage areas. The urban power supply risk assessment model is a prediction model based on deep learning.

[0043] By adopting the above scheme, the outage probability model of urban power grid components dominated by disaster-causing factors under different disasters can be integrated to analyze and obtain the risk assessment index values ​​corresponding to different disaster-causing factors, and then learn the correlation between disaster-causing factors, urban power grid components, risk assessment index values ​​and the spatiotemporal distribution of power outage areas based on the pre-trained association model, so as to obtain the urban power supply risk assessment results including the spatiotemporal distribution of power outage areas.

[0044] In some embodiments of the present invention, the risk assessment model data input into the urban power grid component outage probability model includes various disaster-causing factors, urban power grid components, and the operation mode of the components in the power grid; wherein the disaster-causing factors include multiple types of extreme weather, earthquakes, wind and rain, waterlogging, and cyber attacks.

[0045] In some embodiments of the present invention, the type of the urban power grid component outage probability model is an optimal power flow model or a load reduction optimal model, and the urban power grid component outage probability model includes a load loss assessment model. During the training process of the load loss assessment model, a training set is constructed based on a load classification method.

[0046] In some embodiments of the present invention, during the training process of the urban power grid component outage probability model, the method further includes: generating fault scenarios by sampling based on the Monte Carlo method, and encoding the spatiotemporal distribution of the power outage area based on risk assessment model data statistics.

[0047] In some embodiments of the present invention, during the training process of the association relationship model, the method further includes: improving the generalization ability of the model based on ensemble learning, and adjusting the training process using category balance and overfitting suppression.

[0048] In some embodiments of the present invention, for the output of the urban power grid component outage probability model, the method also includes: weighting the risk assessment index values ​​output by each outage probability model based on the power outage loss model, so as to use the weighted risk assessment index values ​​as input of the association model.

[0049] In some embodiments of the present invention, the method also includes: for an association relationship model having a weighted risk assessment index value as input, using fuzzy information entropy evaluation to evaluate the urban power grid risk from multiple dimensions, and outputting an urban power supply risk assessment result that also includes a risk weight.

[0050] Before conducting a risk assessment of the urban power supply system, the urban power grid must be layered from top to bottom according to the direction of the power flow, and when evaluating one layer of the power grid, it is assumed that the upper layer directly connected to it is safe and reliable.

[0051] The urban power grid is divided into three main parts according to the topological structure: substation part, distribution trunk line part, and distribution branch feeder part. First, the risks of these three parts are analyzed in detail, and their quantitative evaluation indicators are established. Then, the comprehensive risk indicators are established according to the internal relationship of the risk principle. After analyzing and studying the outage models of typical components in the urban power grid, the urban power grid risk assessment process is given, such as Figure 2 As shown, Figure 2 This is a flow chart of a method for assessing urban power supply risks under the influence of natural disasters according to another embodiment of the present invention.

[0052] Figure 2 The specific steps are as follows:

[0053] Step S210: Select the risk assessment area: Select the power grid area that needs to be assessed for the urban power supply risk under the influence of natural disasters. For example, the division principle of the "Supplementary Explanation of the State Grid Corporation of China on the Electric Power Reliability Evaluation Procedure (Revised Edition)" can be referred to, and the topological results of each node (device, subsystem, etc.) in the power grid area can be set based on this division principle. The assessment scope is mainly selected from representative urban power grids in the city.

[0054] Step S220: Collect and verify the basic data of the power grid in the area: After selecting the evaluation object, collect the equipment data and operation data of the power grid in the area, and verify and screen these data to filter out the data that is "harmful" to the subsequent evaluation work, so that the final evaluation result is objective and effective.

[0055] Step S230: Export risk assessment model data: The filtered and screened reasonable network data is used as the data basis for urban power grid risk assessment and as input data for the established risk assessment model.

[0056] Step S240: Establish risk assessment models for each part and calculate risk assessment indicators: According to the characteristics of the network topology and based on the risk principle, construct a reasonable analysis and assessment model for each part of the selected power grid area to form quantitative assessment indicators; then import the above input data to calculate the risk assessment indicators and obtain the risk assessment indicator value (or risk quantification value).

[0057] Step S250: Introduce a power outage loss model to assess the risk of economic costs: In order to make the values ​​of each risk assessment index more intuitively reflect the risk level of the urban power grid, adapt to the reform of the electricity market, and guide the planning or transformation and upgrading work from an economic perspective, it is not possible to only establish technical indicators to quantify the risk. We should also try to describe the risk from an economic perspective. This paper introduces a power outage loss model to solve this problem.

[0058] Step S260: Introduce the fuzzy information entropy evaluation model to evaluate the urban power grid risks from technical and economic perspectives: According to the operating life cycle of the urban power grid, the risk factors are combined with the fuzzy information entropy evaluation model, and finally the urban power grid risks are evaluated from different technical and economic perspectives. The obtained evaluation results are sorted from large to small, and the key high-risk locations in the urban power grid are found for analysis and optimization.

[0059] In this scheme, risk assessment and feature association mining of urban power supply under various natural disasters and emergencies are carried out. Different types of natural disasters and emergencies have significantly different impacts on urban power supply risks. For example, the power outage area caused by urban flooding is often local and local, while earthquakes may cause the shutdown of distant urban power grids by damaging the transmission grid. By studying the urban power supply risk assessment model under different natural disasters and emergencies, we can not only obtain the power supply capacity indicators of urban power grids, but also find the weak links of urban power grids, providing a technical basis for the corresponding power grid emergency plan.

[0060] Figure 3The schematic diagram of the principle of the urban power supply risk assessment under the influence of natural disasters in the present invention. This scheme comprehensively considers the impact of various emergencies, takes the types of emergencies as labels, and analyzes the spatiotemporal distribution characteristics of urban power supply risks and power outage areas respectively. In the process of analyzing urban power supply risks, the urban power supply risk under emergencies is evaluated, and in the process of analyzing spatiotemporal distribution characteristics, the spatiotemporal correlation between emergencies and power outage areas of the power grid is mined.

[0061] This plan mainly conducts urban power supply risk assessment under the influence of natural disasters from two dimensions. Figure 4 The present invention provides a complete technical roadmap for risk assessment of urban power supply under the influence of natural disasters.

[0062] Dimension 1: Risk assessment model for urban power supply under various natural disasters and emergencies.

[0063] Natural disasters and emergencies will damage urban power grid components, resulting in the destruction of urban power grid connectivity, which in turn will cause large-scale power outages. Urban power supply risk assessment is an important basis for effective pre-disaster emergency preparedness.

[0064] This scheme firstly targets the major natural disasters and emergencies that the urban power grid may suffer. For example, a certain area is vulnerable to sudden natural disasters such as earthquakes and high temperatures, as well as the chain disasters derived from them (such as: earthquakes and rainstorms cause secondary disasters such as landslides, mudslides, and barrier lakes, which in turn derive secondary disasters such as building destruction, traffic interruptions, and communication interruptions). It builds a probability model for the shutdown of urban power grid components dominated by disaster-causing factors under different disasters and emergencies, and proposes a method for generating extreme scenarios for urban power grids. This further analyzes the necessary loads to ensure the basic operation of the city, the important loads with huge impacts on social economy, etc., considers the expected impact of power outages on medical units, water supply units, and urban transportation infrastructure, studies the load loss assessment model under extreme scenarios of urban power grids, and proposes a risk assessment model for urban power grid power supply under natural disasters and emergencies.

[0065] Dimension 2: A method for mining the spatiotemporal association between emergencies and urban power grid blackout areas based on deep learning. The large-scale power outages caused by natural disasters and emergencies are extremely costly but have a very low probability. A large amount of analysis and calculation is required to determine the possible impact on the urban power grid. Efficiently and quickly obtaining possible blackout areas is an important basis for effective emergency response. This scheme first analyzes the key factors affecting the urban distribution network caused by natural disasters and emergencies, the spatiotemporal distribution characteristics of blackout areas under historical emergencies in cities and surrounding areas, etc., studies the characteristics of emergencies, power grid operation modes, and the characteristic coding methods of the spatiotemporal distribution of blackout areas, analyzes the loss characteristics of different types of loads, and constructs a correlation model between urban distribution networks and emergencies. On this basis, a method for mining the association relationship of multi-source heterogeneous data based on deep learning algorithms is studied to obtain the implicit association rules of each relevant factor and quickly obtain the urban power grid blackout areas that may be caused by emergencies.

[0066] Furthermore, in another embodiment of the present invention, constructing a risk assessment model for urban power grid power supply under the influence of various natural disasters and emergencies can reveal the impact of natural disasters and emergencies on the urban power grid, which is beneficial to improving the power supply capacity of the urban power grid.

[0067] The embodiment of the present invention takes the urban area of ​​a certain city as the main research object, collects the power outage data of the historical area of ​​a certain city, analyzes the disaster factors and key elements that affect the safe operation of the urban power grid from the natural disasters and emergencies that the urban power grid mainly suffers and their disaster-causing mechanisms, and analyzes the outage models of different components in the power system under the influence of natural disasters and emergencies, so as to analyze the impact of emergencies on the operation of the power system and the reasons for equipment failure. On this basis, in order to quantify the impact of emergencies on the safe operation of the power system, the probability distribution function of the disaster factors or key elements in the emergencies is established by combining theoretical methods such as probability theory and mathematical statistics, and the outage failure probability model of the urban power grid components under the influence of emergencies is established based on the relationship between the failure conditions of each device and the disaster factors or key elements. Taking the key power protection facilities in a certain city as the main research object, considering different types of loads such as medical units, water supply units and urban transportation, the expected impact after the power outage of each type of load is analyzed, and considering the probability of occurrence of emergencies and the probability of equipment outage, the urban power grid power supply risk assessment model is established by combining the optimal power flow model, the load reduction optimal model and other methods to accurately and effectively assess the operation risk of the power system affected by emergencies. Finally, the scenario method is used to generate sufficient samples and extract the impact association chain of "event-line-distribution network-load"; based on the deep learning method, the fusion coding of natural disasters, emergencies, power grid operation modes, etc. is used as input, and the corresponding coding of the spatiotemporal distribution of power outage areas is used as output. The integrated learning, category balance, overfitting suppression and other methods are integrated to train and establish a model that can characterize the association relationship between emergencies, power grid operation modes and the spatiotemporal distribution of power outage areas, so as to quickly determine the urban power grid blackout areas that may be caused by emergencies.

[0068] Accordingly, an embodiment of the present application provides a system for assessing the risk of power supply to a city under the influence of natural disasters, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the device implements the steps implemented by the above method.

[0069] Accordingly, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned offshore platform safety warning system is implemented.

[0070] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0071] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0072] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0073] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box 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, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0074] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for assessing urban power supply risk under natural disasters, characterized in that: include: Collect and verify basic data of power grid in pre-selected risk assessment area; Filter risk assessment model data for urban power supply risk assessment from basic power grid data according to preset rules; Based on the pre-built urban power grid component outage probability model dominated by disaster-causing factors under different disasters, the risk assessment model data is used as input to obtain the risk assessment index values ​​output by the outage probability model; The risk assessment index values ​​and corresponding disaster-causing factors and urban power grid components output by the outage probability model are used as inputs to the pre-trained association model, and the urban power supply risk assessment results including the spatiotemporal distribution of the outage area are output; Among them, the training set of the association relationship model contains the fusion coding of disaster-causing factors, urban power grid components and risk assessment index values, as well as the marked spatiotemporal distribution coding labels of power outage areas. The urban power supply risk assessment model is a prediction model based on deep learning.

2. The method according to claim 1, characterized in that The risk assessment model data input into the urban power grid component outage probability model includes various disaster-causing factors, urban power grid components and the operation mode of the components in the power grid; wherein the disaster-causing factors include multiple types of extreme weather, earthquakes, wind and rain, waterlogging and cyber attacks.

3. The method according to claim 2, characterized in that The type of the urban power grid component outage probability model belongs to the optimal power flow model or the load reduction optimal model. The urban power grid component outage probability model includes a load loss assessment model. During the training process of the load loss assessment model, a training set is constructed based on load classification.

4. The method according to claim 1, characterized in that During the training process of the urban power grid component outage probability model, the method further includes: generating fault scenarios by sampling based on the Monte Carlo method, and encoding the spatiotemporal distribution of the power outage area based on the risk assessment model data statistics.

5. The method according to claim 1, characterized in that: During the training process of the association relationship model, the method further includes: improving the generalization ability of the model based on an integrated learning approach, and adjusting the training process using a category balance and overfitting suppression approach.

6. The method according to claim 1, characterized in that For the output of the urban power grid component outage probability model, the method further includes: weighting the risk assessment index values ​​respectively output by each outage probability model based on the power outage loss model, so as to use the weighted risk assessment index values ​​as input of the association model.

7. The method according to claim 6, characterized in that The method also includes: for an association relationship model having a weighted risk assessment index value as input, using a fuzzy information entropy assessment method to assess the urban power grid risk from multiple dimensions, and outputting an urban power supply risk assessment result that also includes a risk weight.

8. A system for assessing the risk of urban power supply under natural disasters, comprising a processor, a memory and a computer program / instruction stored in the memory, characterized in that: The processor is used to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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