Urban power grid scene probability risk assessment method and system under influence of natural disasters

Abstract

The invention relates to the technical field of power system management, and provides an urban power grid scene probability risk assessment method and system under the influence of natural disasters. The method comprises the following steps: carrying out operation trend analysis on collected power grid data, and identifying and extracting key factors of urban power grid risks; according to the key factors, qualitatively building a PICE model for urban power grid risk assessment; and according to the collected power grid data, on the basis of a PICE model for urban power grid risk assessment, carrying out quantitative analysis on the situation evolution of the urban power grid under the influence of natural disasters, and carrying out risk prediction on the urban power grid according to an analysis result. Therefore, through quantitative analysis of the PICE model, main problems and main event evolution possibly faced by the urban power grid under the influence of various natural disasters are predicted, the key aspects of power grid operation are covered, the overall consideration of the stability of the urban power grid is combined, and it is ensured that the potential influence of the natural disasters on the urban power grid can be accurately reflected.

Description

Technical Field

[0001] The present application relates to the technical field of power system management, and in particular to a method and system for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters. Background Art

[0002] With the acceleration of urbanization, urban power grids, as the lifeline of modern society, play an increasingly important role. The complexity of urban power grids comes not only from their large and diverse structures, but also from the high requirements for reliability and safety. Especially in the face of natural disasters (such as storms, extreme temperatures), the stability and emergency response capabilities of the power grid system are particularly critical.

[0003] Although the current urban power grid management technology has made some progress in ensuring the stable operation of the power grid, it still has significant deficiencies in dealing with complex and dynamic environments. In terms of ensuring the stable operation of the power grid, the traditional risk assessment method based on local analysis or static models is currently mainly used. However, risk assessment based on static models is difficult to accurately predict and respond to rapidly changing environmental conditions, especially in extreme events such as natural disasters. Risk assessment based on local analysis only focuses on specific local areas, ignoring other variables in the overall operation of the power grid, lacking global linkage analysis, and having difficulty in dealing with changes in dynamic factors. The ability to handle complex events is limited, resulting in the results of risk assessment being incomplete and inaccurate.

[0004] Therefore, there is an urgent need to provide a technical solution to the above-mentioned deficiencies in the prior art. Summary of the invention

[0005] The purpose of this application is to provide a method and system for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters, so as to solve or alleviate the problems existing in the above-mentioned prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The present application provides a method for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters, including: step S101, analyzing the operating trends of the collected power grid data, identifying and extracting the key factors of the urban power grid risk; step S102, qualitatively building a PICE model for risk evaluation of the urban power grid based on the key factors; step S103, based on the collected power grid data and the PICE model for risk evaluation of the urban power grid, quantitatively analyzing the scenario evolution of the urban power grid under the influence of natural disasters, and predicting the risk of the urban power grid based on the analysis results.

[0008] Preferably, in step S101, the collected power grid data is cleaned to obtain standard format data with continuity and consistency in time series; based on the standard format data, a graphical analysis is performed on the load, equipment and abnormal status of the urban power grid; based on the results of the graphical analysis, the power grid load fluctuations, equipment failure rates and extreme weather events of the urban power grid are identified and extracted.

[0009] Preferably, the collected power grid data is cleaned to obtain standard format data with continuity and consistency in time series, including: problem data; wherein the problem data includes: missing values, abnormal values ​​and repeated values; based on predetermined data cleaning rules, the problem data is processed; wherein the missing values ​​are processed based on interpolation, the abnormal values ​​are processed based on predetermined statistical rules, and the repeated values ​​are deleted; based on a predetermined data format standard, the data format of the power grid data after the problem data is processed is standardized; the power grid data after the data format is standardized is sequentially checked for time series continuity and data consistency to obtain standard format data with continuity and consistency in time series.

[0010] Preferably, the time series continuity check and data consistency check of the power grid data after data format standardization include: based on preset business rules, interpolating or correcting the breakpoint data or jump data in the power grid data after data format standardization to determine that the power grid data after data format standardization has continuity in the time series; performing consistency correction on the power grid data after data format standardization to determine that the power grid data from different sources or different fields after data format standardization has consistency.

[0011] Preferably, step S102 includes: determining the initial event node of the urban power grid risk assessment PICE model based on the identified key factors; determining all possible conditional paths of each of the initial event nodes; wherein the conditional paths include: direct causal relationships, indirect causal relationships, and multi-step conditional sequences; determining all possible conditional transitions of each of the conditional paths of the urban power grid under the influence of natural disasters; determining the subsequent event node corresponding to the initial event node according to the initial event node, all possible conditional paths of the initial event node, and the conditional transitions of each of the conditional paths; and constructing the urban power grid risk assessment PICE model based on the initial event node, the conditional paths, the conditional transitions, and the subsequent event nodes.

[0012] Preferably, the conditional conversion includes: binary conversion and complex conversion; the binary conversion represents that there are only two relatively different event states on the corresponding conditional path; wherein the event state represents the state of the urban power grid when facing natural disasters; the complex conversion includes: stage transition conversion, probabilistic conversion, conditional dependent conversion and fuzzy conversion; wherein the stage transition conversion represents the conversion of the event state in a specific stage sequence; the probabilistic conversion represents the conversion of the event state based on the corresponding event occurrence probability; the conditional dependent conversion represents the conversion of the event state based on the corresponding event occurrence condition; the fuzzy conversion represents the conversion of the event state based on the corresponding event fuzzy logic.

[0013] Preferably, the urban power grid risk assessment PICE model is constructed based on the initial event node, the conditional path, the conditional conversion and the subsequent event node, specifically: the identified initial event node, the conditional path, the conditional conversion and the subsequent event node are integrated into a unified model framework, and visualized as model elements to obtain the urban power grid risk assessment PICE model; wherein, the visual expression of the model elements includes: node representation, path connection and direction arrow marking.

[0014] Preferably, in step S103, based on the PICE model for risk assessment of the urban power grid, the probability of occurrence of scenario events of the urban power grid under the influence of natural disasters is calculated according to the collected power grid data; based on the probability of occurrence of the scenario events, the scenario evolution of the urban power grid under the influence of natural disasters is evaluated to predict the risks of the urban power grid.

[0015] Preferably, according to the formula:

[0016]

[0017] Calculate the probability of occurrence of scenario events of the urban power grid under the influence of natural disasters;

[0018] Wherein, P(A|B) represents the probability of scenario event A occurring under the condition that scenario event B occurs in the urban power grid, P(B|A) represents the probability of scenario event B occurring under the condition that scenario event A occurs in the urban power grid, P(A) and P(B) represent the probability of scenario event A occurring alone and the probability of scenario event B occurring alone, respectively.

[0019] The embodiment of the present application also provides a system for probabilistic risk assessment of urban power grid scenarios under the influence of natural disasters, including: an identification unit, configured to perform operation trend analysis on the collected power grid data, identify and extract key factors of the urban power grid risk; a model building unit, configured to qualitatively build a PICE model for risk assessment of the urban power grid based on the key factors; an analysis and prediction unit, configured to quantitatively analyze the scenario evolution of the urban power grid under the influence of natural disasters based on the collected power grid data and the PICE model for risk assessment of the urban power grid, and predict the risk of the urban power grid based on the analysis results.

[0020] Beneficial effects:

[0021] In the scenario probabilistic risk assessment method for urban power grids under the influence of natural disasters provided in the embodiment of the present application, first, the operation trend analysis of the collected power grid data is performed to identify and extract the key factors of urban power grid risks; then, based on the key factors, the PICE model for urban power grid risk assessment is qualitatively constructed; finally, based on the collected power grid data and the PICE model for urban power grid risk assessment, the scenario evolution of the urban power grid under the influence of natural disasters is quantitatively analyzed, and the urban power grid is risk assessed based on the analysis results. Thus, through the quantitative analysis of the PICE model, the main problems and major event evolution that the urban power grid may face under the influence of various natural disasters are predicted, which not only covers the key aspects of power grid operation, but also combines the overall consideration of urban power grid stability to ensure that the potential impact of natural disasters on urban power grids can be accurately reflected. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0023] Figure 1 A schematic flow chart of a method for evaluating the probabilistic risk of a city power grid scenario under the influence of natural disasters provided according to some embodiments of the present application;

[0024] Figure 2 A logical schematic diagram of a scenario probability risk assessment of a city power grid under the influence of a natural disaster provided according to some embodiments of the present application;

[0025] Figure 3 A schematic diagram of a time series of a load trend analysis of a city power grid in summer provided according to an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a box plot of failure rate distribution under high temperature conditions of a city power grid provided according to an embodiment of the present application;

[0027] Figure 5 A schematic diagram of the PICE model structure for the impact of high temperatures in summer on a city power grid according to an embodiment of the present application;

[0028] Figure 6 A schematic diagram of a PICE model integrating the impact of high summer temperatures on a city power grid provided according to an embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of the structure of a system for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0030] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the embodiments of the present invention should belong to the scope of protection of the embodiments of the present invention.

[0031] The operational efficiency and fault response speed of urban power grids also directly affect urban functions and residents' lives. As a whole, the power system needs to effectively build a comprehensive and multi-dimensional probabilistic analysis model for urban power grids, which requires solving the problem of how to achieve comprehensive and dynamic consideration of the entire power grid system while maintaining the granularity of the analysis.

[0032] At present, in the risk management of urban power grids, risk assessment methods based on static models or local analysis are usually adopted. However, these two methods have great deficiencies in data integration and fail to make full use of real-time data and multivariate data for comprehensive analysis, which limits the timely identification and effective response of power grid operators to potential risks.

[0033] Among them, risk assessment based on static models is difficult to accurately predict and respond to rapidly changing environmental conditions, especially in extreme events such as natural disasters. Risk assessment based on local analysis only focuses on specific parts or specific levels, such as a single device, a certain area or a specific type of failure. The field of vision is relatively narrow and ignores other factors or variables in the overall operation of the power grid. For highly interconnected urban power grid systems, there are complex interactions and dependencies between the various parts. Risk assessment based on local analysis often cannot cope with the global linkage effect of urban power grids. During analysis, the lack of global linkage analysis may lead to the neglect or underestimation of some potential risks.

[0034] In addition, the operating environment of urban power grids is complex and dynamically changing, including changes in load, equipment status, external environment, etc., and local analysis risk assessment is difficult to track and adapt to these changes in real time, resulting in deviations between the assessment results and the actual operating conditions of the power grid. Moreover, when responding to extreme events such as natural disasters, the operating status of urban power grids may change dramatically, involving multiple levels of mutual influence and chain reactions, and local analysis risk assessment is difficult to effectively deal with this complexity and uncertainty, resulting in a lack of accuracy and reliability in the risk assessment structure.

[0035] Based on this, this application proposes a scenario probabilistic risk assessment method for urban power grids under the influence of natural disasters to meet the risk management needs of power systems in complex environments, improve the understanding and prediction capabilities of urban power grids' behavior and reactions under natural disasters, and comprehensively enhance the risk management and emergency response capabilities of urban power grids under the influence of natural disasters.

[0036] like Figure 1 , Figure 2 As shown in the figure, the scenario probability risk assessment method for urban power grid under the influence of natural disasters includes:

[0037] Step S101: Analyze the operation trend of the collected power grid data to identify and extract key factors of urban power grid risks.

[0038] In this application, urban power grid data will be collected, such as urban power grid structure network, urban power grid operation data, urban power grid historical accident data, especially accident data caused by power grid isolation due to natural disasters, etc., and standardized data processing will be carried out in software (such as: Statistical Roduct and Service Solutions, referred to as SPSS), and the statistical function of the software will be applied to use the collected real-time and multivariate data to analyze the operation trend of the urban power grid, identify and extract key factors that may affect the stability and safety of the urban power grid.

[0039] Before analyzing the collected data, the collected power grid data is first cleaned to process missing values ​​and abnormal values ​​in the data to ensure the continuity and consistency of the data in the time series. That is, the collected power grid data is cleaned to obtain standard format data with continuity and consistency in the time series.

[0040] In a specific example, data cleaning of power grid data includes: first, data collection and integration, that is, collecting power grid data from different sources (such as sensors, monitoring, databases, etc.), and integrating these data from different sources into a unified database for subsequent processing and analysis. Second, preliminary inspection of data, identifying possible quality problems in the data, such as missing values, outliers, duplicate values, etc., and determining the standards and rules for data cleaning according to the characteristics of the data and business rules. Third, processing missing values, that is, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) to estimate missing values; if the missing of a data point has little effect on the overall analysis, the data point can be directly deleted; in addition, missing values ​​can be filled according to historical data or business rules. Fourth, processing outliers, that is, by setting thresholds, using statistical methods (such as standard deviation method, interquartile range method, etc.) or machine learning algorithms; for identified outliers, they can be corrected, deleted or marked; Fifth, data format standardization, that is, converting data into a unified format and unit for subsequent analysis and comparison, such as date and time formatting, numerical standardization, etc. Sixth, time series continuity check, that is, check the continuity of data in the time series to ensure that there are no breakpoints or jumps; for discontinuous data, interpolation or correction can be performed according to business rules. Seventh, data consistency check, that is, check the consistency of data between different sources or different fields; for inconsistent data, investigation and correction are required to ensure the accuracy of the data. Eighth, data quality check and verification, that is, after completing data cleaning, the data quality is checked and verified to ensure that the cleaned data meets the analysis requirements; the distribution, trend and correlation of the data can also be checked through visualization tools or statistical methods. Ninth, document record and backup, that is, record the process and results of data cleaning to facilitate subsequent data tracing and problem troubleshooting, and back up the cleaned data to prevent data loss or damage. Tenth, feedback and iteration, that is, according to the results of data analysis and business needs, feedback and iteration are performed on the process and rules of data cleaning to continuously optimize data quality.

[0041] After obtaining the data in standard format, the data trend is observed and analyzed graphically through graphical tools such as time series graphs and box plots. In other words, based on the data in standard format, the load, equipment and abnormal status of the urban power grid are graphically analyzed, and based on the results of the graphical analysis, the grid load fluctuations, equipment failure rates and extreme weather events of the urban power grid are identified and extracted.

[0042] For example, based on the time series graph of the urban power grid load changing over time, we can know the load fluctuation during the high temperature period in summer (such as Figure 3 ); Analyze the distribution characteristics of the failure rate based on the box plot, especially the changes in high temperature weather, such as Figure 4 As shown in the figure, a 30-day moving average is applied to the grid load to smooth short-term fluctuations and reveal long-term trends more clearly. Based on the historical data of the power grid, an ARMA model is constructed to predict the trend of grid load and failure rate in the coming summer, and the load trend and failure rate of the urban power grid under the influence of natural disasters are obtained based on data analysis, thereby identifying key factors that may affect the stability and safety of the power grid, including grid load fluctuations, equipment failure rates, extreme weather events, etc.

[0043] In a specific example, it was determined that high temperatures in summer had a significant impact on the power grid, especially in terms of load increase and equipment efficiency. A load index of 0.7 reflects a stable urban power grid state that is not affected by high temperatures. By analyzing the trend of the collected power grid data, "high temperature weather" was identified as the main external condition (i.e., the key factor).

[0044] Step S102: qualitatively construct a PICE model for urban power grid risk assessment based on key factors.

[0045] In this application, by constructing a time series model, the future short-term or long-term urban power grid trends are predicted. Specifically, first, based on the identified key factors, the initial event node of the urban power grid risk assessment PICE model (i.e., the urban power grid risk assessment model under the influence of natural disasters based on probability conduction and conditional deduction, Probability Induced Conditional Event, referred to as PICE model) is determined. In other words, based on the identified key factors (grid load fluctuations, equipment failure rates, extreme weather events), the initial event nodes are defined, covering the physical components, operating conditions, and external environmental factors of the urban power grid.

[0046] Next, all possible conditional paths between each initial event node are analyzed in detail to determine all possible conditional paths for each initial event node. Specifically, the conditional paths include: direct causal relationships, indirect causal relationships, and multi-step conditional sequences to ensure that the PICE model can capture the complex interactions within the urban power grid.

[0047] Then, a specific conversion direction is defined for each conditional path in the PICE model, and all possible conditional conversions of each conditional path of the urban power grid under the influence of natural disasters are determined, including but not limited to binary conversions (switching between normal state and fault state) and complex conversions (such as phased transitions, probabilistic conversions, condition-dependent conversions, and fuzzy conversions), fully describing the behavior patterns of the power grid under different scenarios. Among them, the binary conversion represents that there are only two relatively different event states on the corresponding conditional path (representing the state of the urban power grid when facing natural disasters, such as normal state and fault state).

[0048] Phased transitions characterize the transition of time states in a specific sequence of phases. This transition does not jump from one extreme state to another at once, but gradually evolves through a series of intermediate stages. For example, the load of the power grid may go through multiple stages from normal to overload, such as slight overload, moderate overload, and finally severe overload.

[0049] Probabilistic transitions represent the transition of event states based on the corresponding probability of event occurrence. In other words, the event state transitions involved in probabilistic transitions are uncertain, but have a certain probability of occurrence. This transition is based on the possibility of occurrence under specific conditions. For example, under extreme weather conditions, the probability of transmission line failure will increase, but it is not inevitable.

[0050] Condition-dependent transitions characterize the transition of event states based on the corresponding event occurrence conditions, that is, the state transition of an event depends on a specific condition or a set of conditions. For example, the fault state of a power grid may depend on multiple factors, such as weather conditions, equipment aging, and maintenance strategies.

[0051] Fuzzy transformation represents the transformation of event states based on the corresponding event fuzzy logic. In other words, the state transition involved in fuzzy transformation is not clear and unambiguous, but has fuzzy boundaries. This transformation usually involves fuzzy logic, in which the boundaries between states are not absolute. For example, the operating efficiency of the power grid system may be affected to varying degrees by multiple factors, such as weather changes, equipment performance, etc., and its state definition has a certain degree of ambiguity.

[0052] After determining the initial event and the analysis of the conditional path, determine a series of subsequent event nodes that may be triggered. That is, based on the initial event node, all possible conditional paths of the initial event node, and the conditional conversion of each conditional path, determine the subsequent event nodes corresponding to the initial event node, such as local overload or increased failure rate that may be caused by increased grid load.

[0053] Finally, based on the initial event nodes, conditional paths, conditional conversions, and subsequent event nodes, the PICE model for urban power grid risk assessment is constructed. That is, the identified initial event nodes, conditional paths, conversion directions (conditional conversions), and subsequent event nodes are integrated into a unified model framework, and graphical tools are used to visualize the model elements, represent the nodes, connect the conditional paths, and mark the arrows of the conversion directions to complete the drawing of the PICE model structure diagram.

[0054] In a specific example, it is determined that high temperatures in summer have a significant impact on the power grid, especially in terms of load increase and equipment efficiency. Therefore, "normal operation of the power grid" is selected as the initial event node A to reflect the baseline state of the power grid without extreme weather conditions, and a load index of 0.7 is used to reflect a stable urban power grid state that is not affected by high temperatures. By analyzing the operating trend of the collected power grid data, "high temperature weather" is identified as the main external condition. This choice is driven by the significant impact of summer weather on power grid operation. Therefore, "high temperature weather (temperature exceeding 35°C)" is selected as the conditional path A1, and based on the impact of high temperature weather on power grid load obtained by analysis, the load index is increased from 0.7 to 0.9. Therefore, the "normal operation of the power grid" state is converted to the "increased power grid load" state, and the conversion direction A2 is obtained.

[0055] At the same time, "grid load increase" reappears as a subsequent event in the next analysis, so "grid load increase" is the subsequent event B. Subsequent event B has a certain probability of causing the urban power grid to convert into local overload and increased failure rate under high load, that is, to determine the probability conversion B1 from "grid load increase" to "local overload" and the probability conversion B2 from "grid load increase" to "increased failure rate". According to the typical behavior pattern of the power grid under different load conditions, the "local overload" and "increased failure rate" caused by "grid load increase" are derived. Therefore, these two probability conversion directions (probability conversion B1, probability conversion B2) are also the subsequent event node C and subsequent event node D.

[0056] According to the analysis of system operation data, the different stages of the subsequent event node E "system stability reduction" state are obtained; based on the dynamic characteristics of power grid operation, the reduction of system stability is a reflection of the increasing load and failure rate; the phased transition from slight fluctuations in E1 to severe instability in E2 indicates that the problem is gradually aggravating, and finally the following is established: Figure 5 The PICE model structure diagram of the urban power grid facing high temperature in summer is shown.

[0057] Step S103: Based on the collected power grid data and the PICE model for urban power grid risk assessment, a quantitative analysis is performed on the scenario evolution of the urban power grid under the influence of natural disasters, and risk prediction of the urban power grid is performed based on the analysis results.

[0058] In this application, firstly, based on the PICE model of urban power grid risk assessment, the probability of occurrence of scenario events of urban power grid under the influence of natural disasters is calculated according to the collected power grid data. Then, according to the probability of occurrence of scenario events, the scenario evolution of urban power grid under the influence of natural disasters is evaluated to predict the risk of urban power grid.

[0059] Specifically, based on the collected power grid data, scenario judgment is performed by comparing it with the drawn PICE model structure, the data is matched to the model structure, input into the model and probability calculation is performed.

[0060] Among them, according to the formula:

[0061]

[0062] In a specific example, the conversion direction A1 is: "normal operation of the grid" to "increased grid load"; the probability of increased grid load in high temperature weather, the total probability of high temperature weather (i.e. 30% of the weather in summer is high temperature); the total probability of increased grid load, the conversion direction A1 is calculated to be: the probability of "normal operation of the grid" to "increased grid load", that is, the probability of changing from "normal operation of the grid" to "increased grid load" is 60%.

[0063] For subsequent event nodes and transitions, such as "local power outage" or "equipment failure" caused by "local overload", and "increased maintenance demand" or "reduced system stability" caused by "increased failure rate", the transition probability (i.e., the probability of occurrence of scenario events) is calculated by combining historical data. For transitions under complex conditions, such as "increased maintenance demand" and "reduced system stability" occurring at the same time leading to "reduced grid operation efficiency", the above method is also used to calculate the joint probability and individual occurrence probability, which will not be repeated here.

[0064] By calculating the probability of occurrence of scenario events, the occurrence probability of each event node in the urban power grid under the influence of natural disasters is quantitatively predicted. Through the interaction and chain reaction of each event node, the accident risk probability of the urban power grid under the influence of natural disasters is systematically analyzed, and then the main risk path of the urban power grid under the influence of natural disasters is determined, so as to take effective measures in time to optimize and adjust the power grid.

[0065] In a specific example, in hot weather, the use of air conditioning and cooling equipment increases significantly, resulting in an increase in the overall grid load. The PICE model predicts that the probability of grid load increase in this case is 60%, and the probability of grid load increase leading to local overload is 40%, which may cause local power outages with a probability of 50% and equipment failure with a probability of 30%. Therefore, local overload poses a direct threat to grid stability and needs to be reduced by optimizing load management strategies.

[0066] At the same time, continued high temperatures will increase the failure rate of power grid equipment, with a predicted probability of 30% for the failure rate to rise. The increase in failure rate may lead to a 45% probability of increased maintenance demand, which in turn affects the efficiency of power grid operation. The increase in failure rate and increased maintenance demand may lead to reduced grid stability (25% probability), which may further lead to a decrease in grid operation efficiency (35% probability) under cumulative effects. Considering factors such as local overload, equipment failure, and reduced stability, it can be seen that the power grid may face overall operation risks under high temperature conditions.

[0067] In this application, the visualization of probabilistic induced conditional events of urban power grids under the influence of natural disasters can also be completed by integrating the qualitative construction and quantitative analysis results into the PICE model, such as Figure 6 As shown, at the same time, the main problems and main event evolution paths that urban power grids may face under various natural disasters are predicted, so as to dynamically evaluate and predict the impact of natural disasters (such as storms, extreme high temperatures, etc.) on the stability and security of urban power grids.

[0068] The present application also provides a system for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters. Figure 7 As shown, the system includes:

[0069] The identification unit 701 is configured to perform operation trend analysis on the collected power grid data, identify and extract key factors of urban power grid risks;

[0070] A model building unit 702 is configured to qualitatively build a PICE model for urban power grid risk assessment based on key factors;

[0071] The analysis and prediction unit 703 is configured to perform quantitative analysis on the scenario evolution of the urban power grid under the influence of natural disasters based on the collected power grid data and the PICE model for urban power grid risk assessment, and to perform risk prediction on the urban power grid based on the analysis results.

[0072] The system for probabilistic risk assessment of urban power grid scenarios under the influence of natural disasters provided in the embodiments of the present application can implement the steps and processes of the probabilistic risk assessment method of urban power grid scenarios under the influence of natural disasters described in any of the above embodiments, and achieve the same technical effects, which will not be repeated here one by one.

[0073] In the description of the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0074] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters, characterized in that: include: Step S101, analyzing the operation trend of the collected power grid data, identifying and extracting the key factors of the urban power grid risk; Step S102: qualitatively constructing the PICE model for risk assessment of the urban power grid according to the key factors; Step S103: According to the collected power grid data and based on the PICE model for risk assessment of the urban power grid, a quantitative analysis is performed on the scenario evolution of the urban power grid under the influence of natural disasters, and risk prediction of the urban power grid is performed according to the analysis results.

2. The method for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: In step S101, Cleaning the collected power grid data to obtain data in a standard format that is continuous and consistent in time series; Based on the standard format data, graphically analyzing the load, equipment and abnormal status of the urban power grid; Based on the results of the graphical analysis, grid load fluctuations, equipment failure rates, and extreme weather events of the urban power grid are identified and extracted.

3. The method for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: The data cleaning of the collected power grid data to obtain standard format data with continuity and consistency in time series includes: Integrate and identify power grid data from different sources to determine problematic data in the power grid data; wherein the problematic data includes: missing values, abnormal values, and duplicate values; Based on predetermined data cleaning rules, the problematic data is processed; wherein the missing values ​​are processed based on interpolation, the abnormal values ​​are processed based on predetermined statistical rules, and the duplicate values ​​are deleted; Based on a predetermined data format standard, standardizing the data format of the power grid data after the problem data is processed; The time series continuity check and data consistency check are carried out on the power grid data after data format standardization in turn to obtain standard format data with continuity and consistency in time series.

4. The method for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: The time series continuity check and data consistency check of the power grid data after data format standardization include: Based on preset business rules, interpolate or correct the breakpoint data or jump data in the power grid data after the data format is standardized, so as to ensure that the power grid data after the data format is standardized has continuity in the time series; The power grid data after data format standardization is corrected for consistency to ensure that the power grid data from different sources or different fields after data format standardization are consistent.

5. The method for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: Step S102 includes: Based on the identified key factors, determining the initial event node of the urban power grid risk assessment PICE model; Determine all possible conditional paths for each of the initial event nodes; wherein the conditional paths include: direct causal relationships, indirect causal relationships, and multi-step conditional sequences; Determine all possible condition conversions of each of the conditional paths of the urban power grid under the influence of natural disasters; Determine a subsequent event node corresponding to the initial event node according to the initial event node, all possible conditional paths of the initial event node, and conditional conversions of each of the conditional paths; The urban power grid risk assessment PICE model is constructed based on the initial event node, the conditional path, the conditional conversion and the subsequent event node.

6. According to the method for evaluating the scenario probability risk of urban power grid under the influence of natural disasters in claim 5, the condition conversion comprises: binary and complex transformations; The binary conversion represents that there are only two different event states on the corresponding conditional path; wherein the event state represents the state of the urban power grid when facing a natural disaster; The complex transformations include: phase transition transformation, probabilistic transformation, conditional dependent transformation and fuzzy transformation; wherein, the phase transition transformation represents the transformation of the event state in a specific phase sequence; the probabilistic transformation represents the transformation of the event state based on the corresponding event occurrence probability; the conditional dependent transformation represents the transformation of the event state based on the corresponding event occurrence condition; the fuzzy transformation represents the transformation of the event state based on the corresponding event fuzzy logic.

7. The method for evaluating the probability risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: The urban power grid risk assessment PICE model is constructed based on the initial event node, the conditional path, the conditional conversion and the subsequent event node, specifically: the identified initial event node, the conditional path, the conditional conversion and the subsequent event node are integrated into a unified model framework, and are visualized as model elements to obtain the urban power grid risk assessment PICE model; wherein, the visual expression of the model elements includes: node representation, path connection and direction arrow marking.

8. The method for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters according to claim 1 is characterized in that: In step S103, Based on the PICE model for risk assessment of the urban power grid, the probability of occurrence of scenario events of the urban power grid under the influence of natural disasters is calculated according to the collected power grid data; According to the occurrence probability of the scenario events, the scenario evolution of the urban power grid under the influence of natural disasters is evaluated to perform risk prediction for the urban power grid.

9. The method for evaluating the probabilistic risk of urban power grid scenarios under the influence of natural disasters according to claim 8 is characterized in that: According to the formula: Calculate the probability of occurrence of scenario events of the urban power grid under the influence of natural disasters; Wherein, P(A|B) represents the probability of scenario event A occurring under the condition that scenario event B occurs in the urban power grid, P(B|A) represents the probability of scenario event B occurring under the condition that scenario event A occurs in the urban power grid, P(A) and P(B) represent the probability of scenario event A occurring alone and the probability of scenario event B occurring alone, respectively.

10. A scenario probability risk assessment system for urban power grids under the influence of natural disasters, characterized in that: include: an identification unit configured to perform operation trend analysis on the collected power grid data, identify and extract key factors of the urban power grid risk; A model building unit, configured to qualitatively build a PICE model for risk assessment of the urban power grid according to the key factors; The analysis and prediction unit is configured to perform quantitative analysis on the scenario evolution of the urban power grid under the influence of natural disasters based on the collected power grid data and the PICE model for risk assessment of the urban power grid, and to perform risk prediction on the urban power grid based on the analysis results.

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