Power system restoring force evaluation method and system considering uncertainty

Abstract

The invention discloses a power system resilience evaluation method and system considering uncertainty. The method comprises the following steps: acquiring various data of a power system after an extreme disaster; constructing an element fault rate model dynamically associated with the power system recovery decision based on the decision dependence uncertainty; based on the acquired data and an element fault rate model, considering uncertain states of part of elements to generate a power failure scene; establishing an incidence matrix between the state uncertainty and the fault element based on a grey correlation theory, calculating the damage confidence of the state uncertainty element, and screening out a typical power failure scene by using system information entropy; a multi-dimensional power system dynamic restoring force evaluation index system is constructed in combination with renewable energy output uncertainty; and calculating an evaluation index for restoring force evaluation. According to the method, decision dependence uncertainty, element state uncertainty and renewable energy output uncertainty are integrated, so that an evaluation result is closer to the complexity of an actual post-disaster scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system resilience assessment, and in particular relates to a power system resilience assessment method and system taking uncertainty into account. Background Art

[0002] The statements herein merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In the context of the world's energy transformation, the penetration rate of renewable energy in the power system has gradually increased, and has gradually developed into the core of the new generation of energy systems. However, with the frequent occurrence of global climate change and extreme weather events, the power system is facing more and more challenges. As a key infrastructure in modern society, the safe and stable operation of the power system is of great significance to the national economy and social development. Once a large-scale power outage occurs, it will not only affect people's daily lives, but also have a serious impact on industrial production, transportation, communication systems, etc., and even endanger national security.

[0004] The resilience of the power system, also known as toughness or elasticity, refers to its ability to resist, adapt to, and quickly recover from low-probability, high-loss extreme events. By evaluating the resilience of the power system, we can comprehensively reflect the overall resilience of the system, thereby optimizing the system's preventive measures and recovery strategies in the face of extreme natural disasters, thereby reducing the losses caused by disasters.

[0005] At present, there are two main types of research on power system resilience assessment: resilience assessment based on load loss and resilience assessment considering the system performance change process. These methods mostly reflect the system's resistance or the performance of the entire system, and do not consider the impact of component state uncertainty on power system resilience in post-disaster power outage scenarios. Traditional methods basically assume that the component state is completely known at the time of assessment, while the component state in the actual power outage scenario may be in an uncertain state due to monitoring delays or communication interruptions, resulting in deviations between the assessment results and the actual recovery capacity.

[0006] In addition, decision-dependent uncertainty is rarely used in power system resilience assessment, and its mechanism related to the resilience of power systems after extreme disasters has not been studied. In addition, current system resilience assessment methods are mainly designed around fixed indicators and static scenarios, and the modeling of dynamic recovery processes is insufficient. The dynamic impact of the uncertainty of renewable energy connected to the power grid on resilience should also be considered. Summary of the invention

[0007] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art and to provide a method and system for evaluating the resilience of a power system taking into account uncertainty. On the basis of considering the uncertainty of restoration decision dependence, the uncertainty of component status and the uncertainty of output of renewable energy, a power system resilience evaluation index system is constructed to dynamically evaluate the resilience of the system, which can provide a more comprehensive understanding of the overall resilience of the system, thereby helping the power system to take corresponding measures to improve resilience and reduce losses caused by extreme disasters.

[0008] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the technical solution of the present invention provides a method for assessing the resilience of a power system taking into account uncertainty, comprising: Obtain various data on the power system after experiencing extreme disasters; Construct a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; Based on the acquired data and the component failure rate model, a power outage scenario is generated considering the uncertainty of some component states; Based on the grey correlation theory, the correlation matrix between state uncertainty and faulty components is established, the damage confidence of state uncertainty components is calculated, and the typical power outage scenarios are screened out using system information entropy. Combined with the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment index system is constructed; Calculate evaluation indicators for resilience assessment.

[0009] In at least one embodiment, the various types of data include historical disaster data, power grid failure data, and renewable energy output time series data.

[0010] In at least one embodiment, generating a power outage scenario is specifically as follows: based on the various types of data obtained on the power system and the component failure rate model, the states of some components are randomly set to be uncertain, and on the basis of considering the uncertainty of the states of some components, the Monte Carlo algorithm is used to simulate the random generation of the fault set of the power system under extreme events, thereby obtaining the power outage scenario of the power system under extreme events.

[0011] In at least one embodiment, the damage confidence of the uncertain state component is calculated specifically as follows: using the grey correlation analysis theory, the grey correlation between the uncertain state component and the faulty component is calculated, and a correlation matrix between the components is further obtained; the correlation matrix is ​​decomposed into eight categories according to the physical distance of the components, the type of components, and the functional level, and an uncertainty reasoning Petri network is established based on the decomposed correlation matrix, and the damage confidence of each uncertain state component is quickly obtained through matrix calculation.

[0012] In at least one embodiment, the method of using system information entropy to screen out typical power outage scenarios is specifically: using the system information entropy method to quantify the uncertainty of the power system, calculating the corresponding entropy value of each power outage scenario, screening out typical and reasonable power outage scenarios according to the entropy value constraint range, and determining the scale of the fault.

[0013] In at least one embodiment, the power system dynamic resilience assessment index system is constructed from three dimensions: robustness, rapidity, and coupling, and each dimension has different secondary indicators to quantify the power system resilience.

[0014] In at least one embodiment, the secondary indicators of robustness include output uncertainty, average load loss rate, key load recovery rate, and component uncertainty impact factor.

[0015] In at least one embodiment, the secondary indicators of rapidity include dynamic recovery time, load loss speed, and critical load recovery speed.

[0016] In at least one embodiment, the secondary indicator of coupling is an uncertainty-fault coupling coefficient.

[0017] In a second aspect, the technical solution of the present invention further provides a power system resilience assessment system taking uncertainty into account, comprising: The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster; A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states; The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and screen out typical power outage scenarios using system information entropy; The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system; The resilience assessment module is configured to: calculate an assessment index to perform resilience assessment.

[0018] The beneficial effects of the technical solution of the present invention are as follows: 1) The uncertainty-based power system resilience assessment method of the present invention integrates three types of uncertainty factors, including decision-making dependency uncertainty, component state uncertainty and renewable energy output uncertainty. This realistic perspective makes the assessment results no longer limited to the theoretical level, but comprehensively considers the complex conditions and uncertainty factors in many actual scenarios and incorporates them into the assessment system, which can truly reflect the system's resilience in a complex and changing environment, and provides a more practical and guiding basis for the planning, operation and maintenance of the power system.

[0019] 2) The power system resilience assessment method taking uncertainty into account in the present invention innovatively proposes a "decision-dependent uncertainty" modeling method, which dynamically links the system recovery decision with the component failure rate, and can more accurately reflect the actual recovery capability of the power system in the face of failures, thereby significantly improving the accuracy and reliability of resilience assessment.

[0020] 3) The uncertainty-based power system resilience assessment method of the present invention takes into account the actual possibility that the status of some components is uncertain due to information interruption and other reasons after a disaster, and incorporates this status uncertainty into the assessment framework. A method based on grey correlation theory and Petri network is proposed to calculate the damage confidence of components with uncertain status, so that the assessment results are closer to the actual risks, greatly enhancing the practicality and credibility of the assessment results.

[0021] 4) The uncertainty-based power system resilience assessment method of the present invention constructs a comprehensive dynamic assessment index system from the three dimensions of robustness, rapidity and coupling, taking into account the uncertainty of renewable energy output, and proposes an "uncertainty-fault coupling coefficient" index to quantify the synergistic effect between faults caused by extreme events and the uncertainty of renewable energy output. The constructed index system overcomes the limitations of traditional fixed static indicators, and can more comprehensively and flexibly evaluate the resilience of the power system under different working conditions, providing a more scientific and accurate decision-making basis for the planning and operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 It is an overall schematic diagram of a power system resilience assessment method taking uncertainty into account according to the present invention; Figure 2 It is a flow chart of a method for assessing the resilience of a power system taking uncertainty into account according to the present invention; Figure 3It is a schematic diagram of decision-dependent uncertainty in a power system resilience assessment method taking uncertainty into account according to the present invention; Figure 4 It is a Petri network diagram for calculating component damage confidence in a power system resilience assessment method taking uncertainty into account in the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] As introduced in the background technology, the purpose of the present invention is to overcome the shortcomings existing in the above-mentioned prior art and to provide a method and system for evaluating the resilience of a power system taking into account uncertainty. On the basis of considering the uncertainty of restoration decision dependence, the uncertainty of component status and the uncertainty of renewable energy output, a power system resilience evaluation index system is constructed to dynamically evaluate the resilience of the system, which can provide a more comprehensive understanding of the overall resilience of the system, thereby helping the power system to take corresponding measures to improve resilience and reduce losses caused by extreme disasters.

[0026] Example 1 In a typical embodiment of the present invention, Figure 1 As shown, this embodiment discloses a method for assessing the resilience of a power system taking uncertainty into account, including: S100. Obtain various data of the power system after experiencing extreme disasters; S200. Constructing a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; S300. Based on the acquired data and the component failure rate model, a power outage scenario is generated taking into account the uncertainty of the status of some components; S400. Establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and use system information entropy to screen out typical power outage scenarios; S500. Combined with the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment index system is constructed; S600. Calculate evaluation indicators to perform resilience evaluation.

[0027] Figure 2 The implementation process of the evaluation method is demonstrated. The above-mentioned power system resilience evaluation method taking uncertainty into account is described in detail below in conjunction with specific embodiments.

[0028] S100. Obtain various data on the power system after experiencing extreme disasters.

[0029] In this embodiment, the various data obtained after the power system experiences extreme disasters include historical disaster data, power grid fault data, and renewable energy output time series data. Among them, historical disaster data includes extreme event data records, such as historical wind speed, intensity, frequency, etc.; historical power grid fault data includes fault line location, power outage scope, communication interruption frequency, fault diagnosis response time, duration of component uncertainty state, etc.

[0030] S200. Construct a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty.

[0031] Decision-dependent uncertainty reveals that different restoration decisions of the power system will significantly affect the system resilience assessment results. Therefore, this embodiment uses decision-dependent uncertainty for modeling and establishes a component failure rate model that is dynamically associated with the power system restoration decision.

[0032] Component failure rate modeling and load current based on decision-dependent uncertainty I About, while the load current I It is obtained through power flow calculation, and the power flow calculation result is determined by the system recovery decision. It can be seen that the constructed component failure rate model can link the system recovery decision with the component failure rate, such as Figure 3 Component failure rate The formula is defined as: (1); in, (2); (3); (4); In the formula, and Respectively represent the rated current value and the tripping current value, is the initial value of the component failure rate, and They represent the power outage rate and power restoration rate of the components respectively, which can be obtained from historical data.

[0033] S300. Based on the acquired data and the component failure rate model, a power outage scenario is generated taking into account the uncertainty of the status of some components.

[0034] In this embodiment, the power outage scenario is generated in the following way: based on the various data of the power system and the component failure rate model obtained, the status of some components is randomly set to "uncertain", and on the basis of considering the uncertainty of the status of some components, the Monte Carlo algorithm is used to simulate the random generation of the fault set of the power system under extreme events, and then the power outage scenario of the power system under extreme events is obtained.

[0035] Specifically, when using the Monte Carlo algorithm to generate power outage scenarios, based on the acquired post-disaster data and model data, a power outage scenario of the power system under extreme events is randomly generated by simulation. A random number between [0, 1] is generated for each component. , and compared with the component failure rate to obtain the status of each component. The specific formula is as follows: (5); In the formula, For components i The operating status of For components i probability of failure.

[0036] However, in actual post-disaster scenarios, incomplete information caused by communication failures and other reasons makes it impossible to accurately perceive the status of some components, presenting uncertainty. Therefore, during the simulation process, the status of some components is randomly set to “uncertain”.

[0037] S400. Based on the grey correlation theory, the correlation matrix between state uncertainty and faulty components is established, the damage confidence of state uncertainty components is calculated, and the typical power outage scenarios are screened out using system information entropy.

[0038] To calculate the damage confidence of the uncertain state component, it is necessary to first obtain the component state correlation. This embodiment uses the grey correlation analysis theory to calculate the grey correlation between the uncertain state component and the known state component. i The attribute sequence is , then the calculation formula of the correlation coefficient and grey correlation degree between the two elements is as follows, where the correlation degree is the average value of the correlation coefficient.

[0039] (6); (7); In the formula, Display element i and components j The correlation coefficient of Display element i and components j Grey relational degree of is the resolution coefficient, usually taken as 0.5.

[0040] After obtaining the grey correlation degree between the state uncertain components and the faulty components, the correlation matrix is ​​constructed R , the formula is as follows: (8); In the formula, the components i and components j The grey relational degree is abbreviated as .

[0041] The incidence matrix R According to the physical distance of components, component type and functional level, it can be decomposed into , , , , , , as well as Eight categories, among which The association matrix represents close physical distance, same component type and same functional level; The correlation matrix represents long physical distance, same component type and same functional level; The association matrix represents close physical distance, different component types and the same functional level; The association matrix represents close physical distance, same component type and different functional levels; The correlation matrix representing long physical distances, different component types, and the same functional level; The correlation matrix representing physically distant components, components of the same type, and different functional levels; Association matrices representing close physical distances, different component types, and different functional levels; The correlation matrix representing the long physical distance, different component types, and different functional levels. Then, uncertainty reasoning is established based on the decomposed correlation matrix. Petri Network, such as Figure 4 As shown, the library P1-P 10 It represents the component state association matrix, and transition T1-T3 is the calculation process of damage confidence. The damage confidence of each component with uncertain state is quickly obtained through matrix calculation.

[0042] The core of the method of system information entropy is to screen out typical and reasonable power outage scenarios by quantifying the uncertainty of the power system. The method of system information entropy is used to quantify the uncertainty of the power system, calculate the corresponding entropy value of each power outage scenario, and screen out typical and reasonable power outage scenarios according to the entropy constraint range to determine the scale of the fault. This method screens and optimizes system status scenarios based on the probability distribution of a single event. Entropy, as an important indicator to measure system uncertainty, has important application value in the dynamic and uncertain system of the power system. The system may fail at any time, and its corresponding entropy value W It can be described by the following formula: (9); in, It is a collection of power system components; T It is the time from the beginning to the end of the extreme disaster experienced by the system; For components i exist t Failure rate at any time; Display element i exist t Whether a fault occurs at the moment, if a fault occurs, it is 1, otherwise it is 0; each power outage scenario corresponds to a , which can describe whether a system component fails and when the failure occurs t , and then the entropy value of the system when it is in the power outage scenario can be calculated.

[0043] The entropy value in the power system blackout scenario reflects the uncertainty of whether the system fails. From the actual blackout scenario, the entropy value W The value of cannot be too large or too small, so the entropy value of each reasonable scenario should be within a certain range, that is: (10); in, and They represent the maximum and minimum values ​​of the information entropy of the power system respectively. According to the constraint of formula (10), a reasonable set of power system blackout scenarios can be screened out.

[0044] S500. Combined with the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment index system is constructed.

[0045] Taking into account the uncertainty of renewable energy output, the dynamic resilience evaluation index system of the power system is mainly constructed from three dimensions: robustness, rapidity and coupling, and each dimension has different secondary indicators to quantify the resilience of the power system.

[0046] The robustness of the power system can be understood as the ability of the system to maintain its basic functions and performance stability and ensure reliable power supply when it is disturbed or faced with uncertain factors. Therefore, the secondary indicators of the robustness dimension are output uncertainty, average load loss rate, key load recovery rate and component uncertainty influencing factors, as follows: (1) Output uncertainty Output uncertainty The uncertainty range of renewable energy output that the system can bear is quantified, taking into account the uncertainty of renewable energy output during system recovery, and also reflecting the regulation capacity of the system. The formula is defined as: (11); In the formula, Represents the set of nodes connected to renewable energy; Representation Node j The standard deviation of renewable energy output; Representation Node j The average load power.

[0047] (2) Average load loss rate Load loss refers to the phenomenon that after the system suffers an extreme disaster, it is difficult to effectively resist the impact of the disaster, resulting in partial load power outage. Average load loss rate To a certain extent, it can quantify the impact of disasters on the power system, and also reflect its ability to resist damage under extreme conditions. Specifically, the smaller the value, the stronger the system's ability to maintain normal power supply during the disaster, and also lays the foundation for improving the subsequent system's resilience. The formula is defined as: (12); In the formula, n Indicates the number of system nodes; Representation Node j The load loss in Representation Node j The load amount.

[0048] (3) Critical load recovery rate Critical load recovery rate It refers to the ratio of key loads that have been effectively restored to power during the emergency recovery phase of the system. Loads that cannot be restored in a short period of time will gradually restore power in the subsequent recovery phase. The key load recovery rate reflects the stability of the system to a certain extent. The formula is defined as: (13); In the formula, Indicates the number of nodes containing critical loads; Representation Node jThe critical load lost in Representation Node j The critical load in.

[0049] (4) Component uncertainty impact factor Component uncertainty influence factor The role of system resilience assessment is to measure the stability of the system in the face of uncertainty. A high value indicates that the node is subject to greater uncertainty and may face more instability risks during system recovery. The formula is defined as: (14); In the formula, Representation Node j Uncertain influence factors of components; Representation and Node j A collection of associated components; Display element i Confidence level of damage; Display element i The probability of failure; Representation Node j The load amount.

[0050] The rapidity of power system recovery directly reflects the strength of system resilience. In this embodiment, the secondary indicators of rapidity are measured by three indicators: dynamic recovery time, load loss speed, and key load recovery speed.

[0051] (1) Dynamic recovery time Dynamic recovery time It represents the cumulative time that the actual output power of renewable energy does not reach the rated capacity, reflecting the uncertainty and dynamic performance of renewable energy in the recovery process, and further demonstrating the recovery capacity of the entire system in a more comprehensive way. The formula is defined as: (15); In the formula, T It indicates the time from the end of the disaster to the time when the system fully restores its power supply capacity; express t Time Node j The actual output of renewable energy in China; Representation Node j Rated capacity of renewable energy sources.

[0052] (2) Load loss speed Load loss speed It refers to the average speed at which part of the load loses power supply from before the power system suffers an extreme disaster to after the disaster ends. The faster the system loses load, the faster the system can adjust the load after the disaster occurs, creating conditions for restoring power supply. The formula is defined as: (16); In the formula, It indicates the duration of an extreme disaster from the beginning to the end. Representation Node j The load lost in the

[0053] (3) Critical load recovery speed Critical load recovery speed It refers to the critical load quantity that can be effectively restored to power per unit time during the emergency recovery phase of the system. Restoring critical loads can greatly improve the recovery efficiency of the system, and its recovery speed directly reflects the rapidity of system recovery. The formula is defined as: (17); In the formula, It indicates the time from the end of the disaster to the completion of the emergency recovery experience of the system; Indicates the number of nodes containing critical loads; express t Time Node j The critical load to be restored.

[0054] Secondary index selection of power system coupling: uncertainty-fault coupling coefficient This indicator is used to quantify the synergistic effect between the failure caused by extreme events in the power system and the uncertainty of renewable energy output. Its core meaning is to determine whether there is a statistical correlation between the occurrence of failures and the change of uncertainty. Close to 1, it means that the two are highly positively correlated. At this time, the system has a higher risk after the disaster and a weaker recovery ability. This indicator can reflect the interactive impact of multi-dimensional uncertainty through a single coefficient. The formula is defined as: (18); (19); (20); In the formula, Indicates the load loss rate; It represents the maximum uncertainty range of renewable energy output; represents covariance; and express and The standard deviation of .

[0055] S600. Calculate evaluation indicators to perform resilience evaluation.

[0056] After the resilience assessment index system is constructed, the resilience assessment of the system can be carried out. First, the data required for each indicator is collected, which can be obtained from the historical operation records of the power system, real-time monitoring data, etc. After obtaining the data, the indicator calculation stage begins. According to the pre-set calculation method and formula, each indicator is accurately calculated to obtain the specific value of each indicator. These data provide a solid foundation for subsequent analysis and decision-making. Finally, the scores of each evaluation indicator are deeply interpreted and analyzed, and the level of resilience of the system in various aspects is analyzed to clearly reveal the resilience status of the power system, and then targeted planning and operation optimization of different aspects of the system are carried out to improve the scientificity and practicality of the resilience assessment of the power system.

[0057] Example 2 In a typical implementation of the present invention, this embodiment discloses a power system resilience assessment system taking uncertainty into account, including: The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster; A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states; The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and screen out typical power outage scenarios using system information entropy; The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system; The resilience assessment module is configured to: calculate the assessment index to perform resilience assessment. The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for assessing the resilience of a power system taking uncertainty into account, characterized in that: include: Obtain various data on the power system after experiencing extreme disasters; Construct a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; Based on the acquired data and the component failure rate model, a power outage scenario is generated considering the uncertainty of some component states; Based on the grey correlation theory, the correlation matrix between state uncertainty and faulty components is established, the damage confidence of state uncertainty components is calculated, and the typical power outage scenarios are screened out using system information entropy. Combined with the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment index system is constructed; Calculate evaluation indicators for resilience assessment.

2. A method for assessing the resilience of a power system taking into account uncertainty according to claim 1, characterized in that: The various types of data include historical disaster data, power grid failure data, and renewable energy output time series data.

3. A method for assessing the resilience of a power system taking into account uncertainty according to claim 1, characterized in that: The specific steps of generating a power outage scenario are as follows: based on the various data of the power system and the component failure rate model obtained, the states of some components are randomly set to be uncertain. On the basis of considering the uncertainty of the states of some components, the Monte Carlo algorithm is used to simulate the random generation of the fault set of the power system under extreme events, and then the power outage scenario of the power system under extreme events is obtained.

4. A method for assessing the resilience of a power system taking into account uncertainty according to claim 1, characterized in that: The damage confidence of the uncertain state component is calculated specifically as follows: the grey correlation analysis theory is used to calculate the grey correlation between the uncertain state component and the faulty component, and a correlation matrix between the components is further obtained; the correlation matrix is ​​decomposed into eight categories according to the physical distance of the components, the component type and the functional level, and an uncertainty reasoning Petri network is established based on the decomposed correlation matrix, and the damage confidence of each uncertain state component is quickly obtained through matrix calculation.

5. A method for assessing the resilience of a power system taking uncertainty into account according to claim 1, characterized in that: The method of using system information entropy to screen out typical power outage scenarios specifically includes: using the system information entropy method to quantify the uncertainty of the power system, calculating the corresponding entropy value of each power outage scenario, screening out typical and reasonable power outage scenarios according to the entropy value constraint range, and determining the fault scale.

6. A method for assessing the resilience of a power system taking uncertainty into account according to claim 1, characterized in that: The power system dynamic resilience assessment index system is constructed from three dimensions: robustness, rapidity and coupling. Each dimension has different secondary indicators to quantify the power system resilience.

7. A method for assessing the resilience of a power system taking into account uncertainty according to claim 6, characterized in that: The secondary indicators of robustness include output uncertainty, average load loss rate, key load recovery rate and component uncertainty impact factor.

8. A method for assessing the resilience of a power system taking uncertainty into account according to claim 6, characterized in that: The secondary indicators of rapidity include dynamic recovery time, load loss speed and key load recovery speed.

9. A method for assessing the resilience of a power system taking into account uncertainty according to claim 6, characterized in that: The secondary indicator of coupling is the uncertainty-fault coupling coefficient.

10. A power system resilience assessment system taking uncertainty into account, characterized in that: include: The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster; A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states; The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and screen out typical power outage scenarios using system information entropy; The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system; The resilience assessment module is configured to: calculate an assessment index to perform resilience assessment.

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