Power grid evaluation index system construction method and device, electronic equipment, computer readable storage medium and program product

Abstract

The embodiment of the invention provides a power grid evaluation index system construction method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of power grid monitoring. According to the method, a multi-dimensional index system covering safety coverage, risk weighting and safety toughness is constructed by obtaining a long-time-sequence safety statistical table and operation parameters, and the stable operation capacity of a power grid under the long-time scale can be comprehensively reflected; a safety coverage index is used for evaluating basic safety, a risk weighting index is combined with a fault weight and an operation parameter to quantify risk strength, a safety toughness index is used for describing continuity and aggregation of an unsafe state, and the three indexes cooperate to form a power grid evaluation index system. Under the condition that manual intervention is not needed, systematic, differentiated and dynamic evaluation of the safety level of the power grid is automatically achieved, and a technical basis with higher guiding value is provided for operation optimization, planning decision making and emergency response.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, and more specifically, to a method, apparatus, electronic device, computer-readable storage medium, and program product for constructing a power grid assessment index system. Background Technology

[0002] Existing technologies for N-1 analysis and assessment in power systems, energy networks, and other fields over long time periods and under various scenarios generally employ a single assessment scheme of "typical moment sampling + limited fault scenarios + binary safety judgment". This assessment scheme firstly extracts only a few representative moments from the annual time period (such as the 12 monthly peak load moments and the 4 quarterly off-peak moments), ignoring atypical moments such as random power output fluctuations during seasonal transitions and special loads during holidays; secondly, the fault scenario setting is limited to N-1 faults on main lines, failing to cover fault types with actual impact such as N-1 faults on branch lines, N-1 faults and N-2 faults on hub transformers, resulting in an incomplete assessment scope.

[0003] In terms of safety assessment, only a binary judgment standard of "safe / unsafe" is used for each "typical moment-limited fault" combination (safe is recorded as 1, unsafe is recorded as 0), without distinguishing the intensity of risk; finally, only the "safe percentage of limited faults under typical moment" (such as "number of safe faults of main line N-1 in 12 peak moments / 12") is counted as the assessment conclusion of the annual safety level of the power grid system, which lacks a hierarchical structure and in-depth risk quantification logic.

[0004] The above technical solutions have the following unavoidable defects: First, the time-series coverage is incomplete. Because only typical moments are sampled, safety risks at atypical moments are easily missed (such as voltage overruns caused by a sudden drop in renewable energy output). Second, the fault scenarios are not comprehensive and lack weight differentiation, failing to identify the risk priority of high-impact faults (such as trunk line faults affecting the entire network while branch line faults only affect a local area). Third, the accuracy of risk quantification is insufficient. All unsafe scenarios are treated equally, making it impossible to identify high-risk scenarios that need to be prioritized for rectification. Fourth, there is a lack of time-series correlation assessment, making it impossible to identify patterns that reflect system resilience, such as "continuous unsafety for multiple hours" or "concentrated unsafety of a certain type of fault during a certain period". Fifth, the assessment results lack an application loop, only outputting a single safety percentage, which cannot guide subsequent risk management and operational optimization, resulting in poor engineering practicality. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method, apparatus, electronic device, computer-readable storage medium and program product for constructing a power grid assessment index system, which can improve the completeness, accuracy and quantifiability of power grid assessment indicators.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows: In a first aspect, the present invention provides a method for constructing a power grid evaluation index system, the method comprising: Obtain long-time-series safety statistics and operating parameters; the long-time-series safety statistics table records the safety label for each time slice in each fault type, and the safety label is used to characterize whether the power grid is safe; A security coverage index is constructed based on the long-time series security statistics table; the security coverage index is used to assess the power grid's ability to operate stably. A risk-weighted index is constructed based on the long-term security statistics table, the fault weights of each fault type, and the operating parameters; the risk-weighted index is used to assess the intensity and concentration of risks occurring in the power grid. A safety resilience index is constructed based on the long-term security statistics table; the safety resilience index is used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment index system composed of the safety coverage index, the safety resilience index and the risk-weighted index is used for power grid security assessment.

[0007] In an optional implementation, the security coverage indicators include long-term time-series security rate, fault-safe pass rate, and critical fault-safe assurance rate; the step of constructing security coverage indicators based on the long-term time-series security statistics table includes: The number of secure time slices in the long-time-series security statistics table where all security tags represent security for the same time slice is counted, and the long-time-series security rate is determined based on the number of secure time slices and the total number of time slices. The fail-safe pass rate is determined based on the number of safety tags representing safety in the long-term safety statistics table and the total number of safety tags in the long-term safety statistics table; The critical fault safety labels are selected from the long-term safety statistics table according to the critical fault type, and the critical fault safety assurance rate is determined based on the number of critical fault safety labels that characterize safety and the total number of critical fault safety labels.

[0008] In an optional implementation, the operating parameters include rated data, maximum deviation data, and simulated data for each time slice; the construction of a risk-weighted index based on the long-series safety statistics table, the fault weights of each fault type, and the operating parameters includes: The time slice and fault type corresponding to each security label representing insecurity in the long-term security statistics table are determined as a risk combination; The risk value of the risk combination is determined based on the simulation data, rated data, maximum deviation data, and fault weight of the fault type corresponding to the risk combination. The risk-weighted index is constructed based on the fault weights of the fault types corresponding to the security labels in the long-term security statistics table and the risk values ​​of the risk combinations.

[0009] In an optional implementation, the simulated data includes simulated voltage and simulated power, the rated data includes rated voltage and rated power, and the maximum deviation data includes maximum voltage deviation and maximum overload deviation; determining the risk value of the risk combination based on the simulated data, rated data, maximum deviation data, and fault weights of the fault types corresponding to the risk combination includes: For each risk combination, the voltage over-limit ratio is determined based on the simulated voltage, the rated voltage, and the maximum voltage deviation; The overload limit ratio is determined based on the simulated power, the rated power, and the maximum overload deviation. The over-limit weight is determined based on the voltage over-limit ratio and the overload over-limit ratio; The risk value of the risk combination is determined based on the fault weight of the fault type corresponding to the risk combination and the over-limit weight.

[0010] In an optional implementation, the risk-weighted index includes insecurity rate, high-risk percentage, and risk peak; the step of constructing the risk-weighted index based on the fault weights of the fault types corresponding to the security labels in the long-term security statistics table and the risk values ​​of the risk combinations includes: The unsafety rate is determined by summing all the risk values ​​and the fault weights of the fault types corresponding to the safety labels in the long-term safety statistics table. Risk values ​​exceeding a high-risk threshold are defined as high-risk values, and the high-risk percentage is determined based on the number of high-risk values ​​and the number of risk combinations. The entire time slice is divided into multiple time periods according to the preset time period division rules, and the risk peak value is determined based on the sum of all the risk values ​​in each time period.

[0011] In an optional implementation, the security resilience index includes the maximum unsafe duration and the maximum unsafe clustering degree; the step of constructing the security resilience index based on the long-term security statistics table includes: A time slice with at least one security label indicating insecurity is identified as an insecure time slice. The maximum number of consecutive unsafe time slices is determined as the maximum unsafe duration; Based on the fault types in each preset cluster, the risk combinations are traversed to count the number of combinations belonging to the cluster. The maximum unsafe clustering degree is determined by the maximum number of combinations belonging to the cluster and the number of risky combinations.

[0012] Secondly, the present invention provides a device for constructing a power grid evaluation index system, the device comprising: The processing module is used to acquire long-time-series safety statistics tables and operating parameters; the long-time-series safety statistics tables record the safety labels of each time slice for each fault type, and the safety labels are used to characterize whether the power grid is safe. A construction module is used to construct security coverage indicators based on the long-term security statistics table; the security coverage indicators are used to assess the ability of the power grid to operate stably. The construction module is also used to construct a risk-weighted index based on the long-term security statistics table, the fault weights of each fault type, and the operating parameters; the risk-weighted index is used to assess the intensity and concentration of risks occurring in the power grid; The construction module is also used to construct a security resilience index based on the long-term security statistics table; the security resilience index is used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment index system composed of the security coverage index, the security resilience index and the risk-weighted index is used for power grid security assessment.

[0013] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the power grid evaluation index system construction method described in any of the foregoing embodiments.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a power grid evaluation index system as described in any of the foregoing embodiments.

[0015] Fifthly, the present invention provides a program product, which, when executed by a processor, implements the method for constructing a power grid evaluation index system as described in any of the foregoing embodiments.

[0016] Compared to existing technologies, the power grid assessment index system construction method, device, electronic equipment, computer-readable storage medium, and program product provided in this invention, by acquiring long-term time-series security statistics tables and operating parameters, constructs a multi-dimensional index system covering security coverage, risk weighting, and security resilience, which can comprehensively reflect the stable operation capability of the power grid over a long time scale. The security coverage index assesses basic security, the risk weighting index combines fault weights and operating parameters to quantify risk intensity, and the security resilience index characterizes the persistence and clustering of unsafe states. These three elements work together to constitute the power grid assessment index system, automatically achieving a systematic, differentiated, and dynamic assessment of the power grid's security level without human intervention, providing more valuable technical guidance for operation optimization, planning decisions, and emergency response.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This paper illustrates a flowchart of a method for constructing a power grid evaluation index system provided by an embodiment of the present invention.

[0020] Figure 2 This paper illustrates another flowchart of the method for constructing a power grid evaluation index system provided in an embodiment of the present invention.

[0021] Figure 3 A block diagram of a device for constructing a power grid evaluation index system provided in an embodiment of the present invention is shown.

[0022] Figure 4 A block diagram of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] Please refer to Figure 1 , Figure 1 This paper illustrates a flowchart of a method for constructing a power grid evaluation index system according to an embodiment of the present invention. The method includes the following steps: Step S10: Obtain the long-time-series safety statistics table and operating parameters; the long-time-series safety statistics table records the safety labels for each time slice in each fault type, and the safety labels are used to characterize whether the power grid is safe.

[0028] In this embodiment of the invention, the long-term safety statistics table and operating parameters are obtained by simulating and testing long-term power grid operation data (e.g., 8760 hours corresponding to 365 days) or predicted data to determine whether the power grid is safe in each fault scenario corresponding to each fault type in each time slice (e.g., 1 hour). The operating parameters are dynamic data and benchmark information generated during the simulation test.

[0029] It should be understood that long-term safety statistics tables and operating parameters are usually derived from the static safety analysis results of the power system simulation platform under an 8760-hour annual time series. These constitute the basic input for the calculation of subsequent indicators, ensuring that subsequent analysis can cover all time periods and all fault scenarios, and can intuitively reflect the basic safety level of the power grid.

[0030] The long-time-series safety statistics table records the safety labels for each time slot (first column) under various preset fault types (first row). Each safety label indicates whether the power grid meets the safety operation criteria under the corresponding combination of time slot and fault type. As shown in Table 1, a safety label of 1 indicates safety, and 0 indicates insecurity. A 0 in the second row and second column of the long-time-series safety statistics table indicates that the power grid is insecure when a mainline N-1 fault occurs in the first hour.

[0031] Table 1

[0032] Step S20: Construct a security coverage index based on the long-term security statistics table; the security coverage index is used to assess the power grid's ability to operate stably.

[0033] In this embodiment of the invention, addressing the core deficiencies of existing technologies, a long-term power grid assessment index system that is "hierarchical, quantifiable, and strongly correlated" is constructed. The assessment hierarchy of the power grid assessment index system includes a basic layer, an advanced layer, and a decision-making layer. Specifically, the basic layer constructs security coverage indicators, the advanced layer constructs risk-weighted indicators, and the decision-making layer constructs security resilience indicators.

[0034] The safety coverage index aims to measure the power grid's ability to maintain stable operation over a long time scale. Its construction process relies on the statistics and integration of safety labels in long-term safety statistics tables. By summarizing the safety status from multiple perspectives in terms of time slice and fault type dimensions, a quantitative index that can reflect the basic safety level is formed.

[0035] Step S30: Construct a risk-weighted index based on the long-term safety statistics table, the fault weights of each fault type, and operating parameters; the risk-weighted index is used to assess the intensity and concentration of risks occurring in the power grid.

[0036] In this embodiment of the invention, fault weight refers to a pre-set value based on the impact range and severity of different faults on the power grid system. It distinguishes the importance of faults and reflects the difference between high-impact faults such as main line N-1 and hub transformer N-1, and localized faults such as branch line N-1. For example, if the fault type is main line N-1 or hub transformer N-1, affecting the entire power supply and with severe consequences, the fault weight can be set to 1. If the fault type is branch line N-1 or distribution transformer, affecting local users and with less severe consequences, the fault weight can be set to 0.3. If the fault type is critical component N-2 (e.g., simultaneous faults in two main lines), with more severe consequences than main line N-1, the fault weight can be set to 1.5. The fault weight can be greater than 1.

[0037] It should be understood that by using fault weights and operating parameters together to construct indicators, the problem of "all unsafe scenarios are equivalent" in traditional methods can be addressed, thereby achieving differentiated assessment of risk intensity and concentration.

[0038] Step S40: Construct a safety resilience index based on a long-term security statistics table; the safety resilience index is used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment index system, consisting of the safety coverage index, the safety resilience index, and the risk-weighted index, is used for power grid security assessment.

[0039] In this embodiment of the invention, the safety resilience index differs from the static safety coverage index, focusing instead on the power grid's ability to withstand continuous or clustered unsafe events. By identifying the duration of consecutive unsafe events and the spatial clustering trend of high-risk fault combinations, it is possible to reveal the cumulative pressure or structural weaknesses that the power grid system may face during long-term operation.

[0040] It should be noted that this consideration of temporal correlation is usually ignored in previous evaluation frameworks, while the embodiments of the present invention fill the technical gap in the quantitative evaluation of the dynamic disturbance rejection capability of the power grid system by introducing a security resilience index.

[0041] Ultimately, a complete power grid assessment index system is formed by safety coverage indicators, risk-weighted indicators, and safety resilience indicators, and is used for power grid safety assessment. This system is not a simple stacking of multiple independent indicators, but rather the result of a structured organization based on different levels of safety connotations.

[0042] Among them, the security coverage indicator reflects basic protection capabilities, the risk-weighted indicator reflects the ability to differentiate risk levels, and the security resilience indicator reveals the robustness of the system over time. By introducing this multi-layered assessment framework, the aim is to answer deeper questions such as "Is it safe?", "Why is it unsafe?", "How serious is it?", and "Will it continue to deteriorate?".

[0043] It should be noted that the power grid assessment index system constructed in this embodiment of the invention can be widely applied to power grid planning, operation monitoring, and risk early warning scenarios. It is presented to users in the form of multi-dimensional charts through a visualization platform: basic level indicators can be presented through annual heat maps or trend curves to help users intuitively grasp the overall safety level of the system; advanced level indicators can be displayed through fault type pie charts and risk contribution value sorted bar charts to highlight the risk weight and severity distribution of different faults and assist users in identifying key risk sources; decision level indicators combine spatiotemporal clustering maps and time period aggregation analysis charts to accurately locate high-risk periods and spatial clustering areas of "continuous unsafe hours" or "concentrated occurrence of faults in a certain region during a certain season".

[0044] Users can view detailed "time-fault" combinations through the interactive interface, quickly identify the occurrence patterns and causes of high-impact events such as main line N-1 overload and hub transformer failure, and then formulate targeted operation optimization, equipment reinforcement or scheduling adjustment strategies to achieve closed-loop management from "passive response" to "proactive prevention and control".

[0045] In summary, the power grid assessment index system construction method provided by this invention, by acquiring long-term time-series security statistics and operating parameters, constructs a multi-dimensional index system covering security coverage, risk weighting, and security resilience, which can comprehensively reflect the stable operation capability of the power grid over a long time scale. The security coverage index assesses basic security, the risk weighting index combines fault weights and operating parameters to quantify risk intensity, and the security resilience index characterizes the persistence and clustering of unsafe states. These three elements work together to constitute the power grid assessment index system, automatically achieving a systematic, differentiated, and dynamic assessment of the power grid's security level without human intervention, providing more valuable technical guidance for operation optimization, planning decisions, and emergency response.

[0046] Optionally, security coverage metrics include long-term time-to-time security rate, fault-tolerant pass rate, and critical fault-tolerant assurance rate. The following provides a possible implementation method for constructing security coverage metrics. Please refer to... Figure 2 , Figure 1 The sub-steps of step S20 may include: Step S200: Count the number of secure time slices in the long-term security statistics table where all security labels represent security for the same time slice, and determine the long-term security rate based on the number of secure time slices and the total number of time slices.

[0047] In this embodiment of the invention, a set of complementary safety coverage indicators are constructed to characterize the safety performance of the power grid under all fault scenarios throughout the year from three perspectives: "time continuity", "fault coverage rate" and "critical fault protection capability". This breaks through the limitations of traditional methods that rely only on typical moments or main line fault analysis.

[0048] Specifically, the long-sequence time safety rate is used to measure the basic safety assurance capability of the power grid system across the entire time range. It reflects the proportion of time that the power grid system can maintain safe operation when any preset fault occurs, and embodies the time-series coverage capability of the power grid under any fault. It can solve the defects of incomplete time-series coverage and high risk omission rate in the existing technology.

[0049] First, safe time slices are identified. A time slice is considered safe only if all safety tags corresponding to all fault types within that time slice indicate safety. For example, in Table 1, if the safety tags of the row containing time slice 8760 are all 1, then time slice 8760 is identified as a safe time slice.

[0050] Next, the total number of time slices in the long-series security statistics table is counted to obtain the total number of time slices. The ratio of the number of secure time slices (i.e., the number of secure time slices) to the total number of time slices is determined as the long-series time security rate. The formula for calculating the long-series time security rate is:

[0051] in, For long-series time safety, To ensure the number of safe time slices, This represents the total number of time slices.

[0052] Step S210: Determine the fail-safe pass rate based on the number of safety tags representing safety in the long-term safety statistics table and the total number of safety tags in the long-term safety statistics table.

[0053] In this embodiment of the invention, the fail-safe pass rate focuses on the proportion of all "time slice-fault type" combinations that are judged as safe in the long-term safety statistics table, covering the "long-term × all fault" dimension, and reflecting the overall safety coverage of fault scenarios in the long-term.

[0054] Specifically, the fail-safe pass rate is determined by the ratio of the number of safety tags representing safety in the long-term safety statistics table to the total number of safety tags in the long-term safety statistics table. The formula for calculating the fail-safe pass rate is:

[0055] in, To ensure fail-safe pass rate, The number of security labels used to characterize safety. This represents the total number of security labels.

[0056] Step S220: Select critical fault safety tags from the long-term safety statistics table according to the critical fault type, and determine the critical fault safety assurance rate based on the number of critical fault safety tags that characterize safety and the total number of critical fault safety tags.

[0057] In this embodiment of the invention, critical fault types refer to core fault scenarios that have a significant impact on power grid operation and have serious consequences, such as main line N-1, hub transformer N-1, and key components N-2. For core fault scenarios, the safety percentage of the "time slice-fault type" combination is statistically analyzed to focus on the safety assurance capabilities for high-impact faults.

[0058] Specifically, safety tags corresponding to critical fault types are selected from a long-term safety statistics table to obtain critical fault safety tags. The ratio of the number of critical fault safety tags representing safety to the total number of critical fault safety tags is determined as the critical fault safety assurance rate. This critical fault safety assurance rate is then used to focus on core faults. The formula for calculating the critical fault safety assurance rate is:

[0059] in, To ensure critical failure safety assurance rate, The number of safety tags representing safety corresponding to critical fault types. This represents the total number of safety tags corresponding to critical fault types.

[0060] As can be seen, the embodiments of the present invention construct a set of complementary security coverage indicators, which describe the security performance of the power grid in long-term, full-fault scenarios from three perspectives: time continuity, fault coverage rate, and critical fault protection capability. This aims to achieve a three-dimensional description of the basic security status of the power grid and avoid evaluation bias caused by a single indicator.

[0061] Optionally, the operating parameters include rated data, maximum deviation data, and simulated data for each time slice. A possible implementation for constructing the risk-weighted index is provided below. Please refer to... Figure 2 , Figure 1 The sub-steps of step S30 may include: Step S300: Determine the time slice and fault type corresponding to each security label representing insecurity in the long-term security statistics table as a risk combination.

[0062] In this embodiment of the invention, based on the existing security judgment results (i.e., security labels) in the long-term security statistics table, all entries marked as "unsafe" are filtered out, and their corresponding time points are bound to specific fault types to form independent data units, i.e., risk combinations. For example, time slice 1 and trunk line N-1 in Table 1 are a risk combination.

[0063] Step S310: Determine the risk value of the risk combination based on the simulation data, rated data, maximum deviation data, and fault weight of the fault type corresponding to the risk combination.

[0064] In this embodiment of the invention, the simulation data reflects the actual operating state of the power grid components in that time slice, the rated data is used to characterize the design benchmark of the power grid components, and the maximum deviation data defines the allowable safe fluctuation range.

[0065] Step S320: Construct a risk-weighted index based on the fault weights of the fault types corresponding to the safety labels in the long-term safety statistics table and the risk values ​​of the risk combinations.

[0066] In this embodiment of the invention, a comparable comprehensive risk measurement result is generated by combining the weight distribution of each fault type in the overall assessment and the risk value of the risk combination, which can accurately depict the real risk level faced by the power grid in a complex operating environment.

[0067] Optionally, the simulation data includes simulation voltage and simulation power, the rated data includes rated voltage and rated power, and the maximum deviation data includes maximum voltage deviation and maximum overload deviation. Regarding how to calculate the risk value of the risk combination, one possible implementation is provided below. Figure 2 The sub-steps of step S310 may include: Step S310-1: For each risk combination, determine the voltage over-limit ratio based on the analog voltage, rated voltage, and maximum voltage deviation.

[0068] In this embodiment of the invention, the voltage deviation ratio is determined based on the analog voltage and the rated voltage, and then the ratio of the voltage deviation ratio to the maximum voltage deviation is determined as the voltage over-limit ratio. The formula for calculating the voltage over-limit ratio is:

[0069] in, The voltage over-limit ratio, It is the voltage deviation ratio (i.e., the percentage deviation between the analog voltage and the rated voltage). This represents the maximum voltage deviation, which is typically taken as ±5%.

[0070] Step S310-2: Determine the overload limit ratio based on the simulated power, rated power, and maximum overload deviation.

[0071] In this embodiment of the invention, the overload ratio is determined based on the simulated power and the rated power, and the ratio of the overload ratio to the maximum overload deviation is determined as the overload limit ratio. The formula for calculating the overload limit ratio is:

[0072] in, For overload limit ratio, It is the overload ratio (i.e., the overload percentage of the simulated power to the rated power). The maximum overload deviation is typically taken as 10%.

[0073] Step S310-3: Determine the over-limit weight based on the voltage over-limit ratio and the overload over-limit ratio.

[0074] In this embodiment of the invention, if multiple over-limit situations exist in a risk combination, the over-limit weight is determined according to a preset rule based on the voltage over-limit ratio and the overload over-limit ratio. For example, the sum of the voltage over-limit value and the overload over-limit value can be determined as the over-limit weight, or the maximum value of the voltage over-limit ratio or the overload over-limit ratio can be determined as the over-limit weight. The method for determining the over-limit weight can be set according to the actual application scenario; this invention does not limit it, but it is recommended to take the maximum value to highlight the most severe risk.

[0075] Step S310-4: Determine the risk value of the risk combination based on the fault weight and over-limit weight of the fault type corresponding to the risk combination.

[0076] In this embodiment of the invention, the risk value represents the quantified risk of a single time slice combined with a fault type. It is only calculated when the combination is a risky combination; the risk value for a safe combination is 0 by default and is the core foundation for all indicators at the advanced level. The risk value of the risk combination is determined by multiplying the over-limit weight of the risk combination by the fault weight of the fault type within the risk combination. This allows the risk value to reflect the sum of the severity of the fault's impact and the deviation from the operational state, improving the accuracy and differentiated expression of risk assessment, thus addressing the deficiency of existing technologies that treat "all unsafe scenarios" as equivalent. The formula for calculating the risk value is:

[0077] in, This is the risk value. For fault weights, This is for exceeding the limit weight.

[0078] Optionally, risk-weighted indicators include insecurity rate, high-risk percentage, and risk peak. One possible implementation method for constructing risk-weighted indicators is provided below. Figure 2 The sub-steps of step S320 may include: Step S320-1: Determine the unsafety rate by summing the sum of all risk values ​​and the sum of the fault weights of the fault types corresponding to the safety labels in the long-term safety statistics table.

[0079] In this embodiment of the invention, the sum of the risk values ​​of all risk combinations is calculated to obtain the total risk value. Then, the fault weights corresponding to the fault types in all combinations of "time slices and fault types" in the long-term security statistics table are summed to obtain the total fault weight. Finally, the ratio of the total risk value to the total fault weight is determined as the insecurity rate. The lower the insecurity rate, the smaller the overall risk of the power grid system. This avoids ignoring risk differences by simply using the "proportion of insecure combinations," and quantifies risk differences in conjunction with over-limit weighting. The formula for calculating the insecurity rate is:

[0080] in, For the unsafe rate, To sum the risk values ​​of the risk portfolio, This involves summing the fault weights of all fault types in the combinations of "time slice and fault type" in the long-term safety statistics table.

[0081] Step S320-2: Risk values ​​exceeding the high-risk threshold are identified as high-risk values, and the proportion of high-risk values ​​is determined based on the number of high-risk values ​​and the number of risk combinations.

[0082] In this embodiment of the invention, risk values ​​exceeding a preset high-risk threshold (usually set to 1) in a statistical risk combination are used to obtain the high-risk threshold, which represents a combination of high-impact faults and relatively serious violations, thus identifying key risk scenarios requiring priority rectification. The ratio of the number of high-risk values ​​to the number of risk combinations is determined as the high-risk percentage. The formula for calculating the high-risk percentage is:

[0083] in, As a high-risk percentage, The number of high-risk portfolios, The number of risk portfolios.

[0084] Step S320-3: Divide all time slices into multiple time periods according to the preset time period division rules, and determine the risk peak based on the sum of all risk values ​​in each time period.

[0085] In this embodiment of the invention, all time slices are divided into multiple time periods according to a preset time period division rule (such as daily, monthly, or quarterly). Assuming a total of 8760 time slices, i.e., one hour constitutes one time slice, the 8760 time slices are divided into four time periods according to quarters. The sum of the risk values ​​of all risk combinations within each time period is calculated, and the maximum sum of the risk values ​​of all risk combinations within a time period is determined as the risk peak, thus identifying the time period with the highest risk across the entire scenario. The formula for calculating the risk peak is:

[0086] in, This represents the peak of the risk. This represents the sum of risk values ​​within the i-th time period, where n is the total number of time periods, such as 12 if divided by month.

[0087] Optionally, the safety resilience index includes the maximum duration of insecurity and the maximum clustering degree of insecurity, which can assess temporal correlation. A possible implementation method for constructing the safety resilience index is provided below. Please refer to... Figure 2 , Figure 1 The sub-steps of step S40 may include: Step S400: The time slice corresponding to at least one security label that indicates insecurity is identified as an insecure time slice.

[0088] In this embodiment of the invention, the safety resilience index focuses on the continuity over a long time series (e.g., 8760 hours), and assesses the power grid system's ability to withstand continuous or cascading risks (i.e., safety resilience) through time series correlation analysis. If all safety tags corresponding to the same time slice represent safety, then the time slice is determined to be a safe time slice. If at least one safety tag representing insecurity exists among all the safety tags corresponding to the same time slice, then the time slice is determined to be an unsafe time slice.

[0089] Step S410: The maximum number of consecutive unsafe time slices is determined as the maximum unsafe duration.

[0090] In this embodiment of the invention, the number of consecutive unsafe time slices in a long time series is counted. For example, if time slices from 10 to 20 are all unsafe time slices, then the number of unsafe time slices is 11. If time slices from 8000 to 8800 are all unsafe time slices, then the number of unsafe time slices is 801. Therefore, the maximum unsafe duration is 801. The shorter the maximum unsafe duration, the stronger the resilience of the power grid system. The formula for calculating the maximum unsafe duration is:

[0091] in, For the maximum unsafe duration, Let m be the number of time slices within the i-th consecutive unsafe time slice (consecutive unsafe time slices), and m be the number of consecutive unsafe time slices.

[0092] Step S420: Based on the fault types in each preset cluster, traverse the risk combinations and count the number of combinations belonging to the cluster.

[0093] In this embodiment of the invention, a clustering algorithm (such as DBSCAN) is used to cluster the risk combinations of the entire scenario to obtain the fault types in each preset cluster. For example, if cluster A contains two fault types, trunk line N-1 and hub transformer N-1, then the risk combination of fault type trunk line N-1 or hub transformer N-1 is counted to obtain the number of combinations contained in cluster A.

[0094] Step S430: Determine the maximum unsafe clustering degree based on the maximum number of combinations belonging to the cluster and the number of risky combinations.

[0095] In this embodiment of the invention, the ratio of the maximum number of combinations contained in each cluster to the number of risky combinations is determined as the maximum insecure clustering degree. This ratio is used to quantify the degree of clustering of risky combinations, thereby locating high-risk clustering regions that include both time and fault dimensions. The formula for calculating the maximum insecure clustering degree is:

[0096] in, To achieve the maximum insecure clustering degree, Let be the number of risk combinations contained in the i-th cluster.

[0097] Compared with existing technologies, the above embodiments of the present invention achieve four core improvements in power grid N-1 analysis and evaluation: 1. The integrity of time series coverage is improved by about 100%. By upgrading from "sampling at typical moments" to "long time series coverage", a comprehensive assessment of 8,760 hours throughout the year is achieved, avoiding the omission of risks at atypical moments and fully reflecting the long-term security level of the system.

[0098] 2. Risk quantification accuracy is improved by more than 80%. By introducing a dual-weighting mechanism of fault weight and over-limit weight, "unsafe" scenarios are subdivided into different risk values. It also supports classifying unsafe scenarios into different risk levels of high, medium and low based on the risk value. It can accurately identify that "main line N-1 overload 30%" (risk contribution value 3.0) is 8.3 times the risk of "branch line N-1 voltage over-limit 6%" (risk contribution value 0.36), overcoming the "one-size-fits-all" judgment defect of the existing technology.

[0099] 3. For the first time, resilience assessment capability has been achieved. By incorporating time-series correlation into the assessment system, it can identify persistent and clustered risk patterns such as "multiple faults and insecurity caused by 10 consecutive hours of peak load" and "concentrated insecurity of N-1 faults on the main line from 14:00 to 16:00 in summer", providing quantitative support for the long-term stable operation of the system.

[0100] 4. The engineering guidance value is significantly enhanced. It constructs a three-level application logic closed loop: "basic layer (including safety coverage indicators such as long-sequence time safety rate, fault safety pass rate, and critical fault safety assurance rate) - advanced layer (including risk-weighted indicators such as insecurity rate, high risk ratio, and risk peak) - decision layer (including safety resilience indicators such as maximum insecurity duration and maximum insecurity clustering degree)". It is no longer limited to outputting a single safety ratio, but supports risk positioning and optimization decision-making, and has the ability to directly guide engineering practice.

[0101] Based on the same inventive concept, the basic principle and technical effects of the power grid evaluation index system construction device provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments.

[0102] Please refer to Figure 3 , Figure 3 This is a block diagram of a power grid assessment index system construction device 400 provided in an embodiment of the present invention. The power grid assessment index system construction device 400 includes a processing module 410 and a construction module 420.

[0103] The processing module 410 is used to obtain long-time safety statistics table and operating parameters; the long-time safety statistics table records the safety label of each time slice for each fault type, and the safety label is used to characterize whether the power grid is safe.

[0104] Module 420 is used to construct security coverage indicators based on long-term security statistics tables; security coverage indicators are used to assess the ability of the power grid to operate stably. Module 420 is also used to construct risk-weighted indicators based on long-term safety statistics tables, fault weights of each fault type, and operating parameters; the risk-weighted indicators are used to assess the intensity and concentration of risks occurring in the power grid.

[0105] Module 420 is also used to construct safety resilience indicators based on long-term security statistics tables; the safety resilience indicators are used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment indicator system, consisting of safety coverage indicators, safety resilience indicators and risk-weighted indicators, is used for power grid security assessment.

[0106] In summary, the power grid assessment index system construction device provided in this embodiment of the invention, by acquiring long-term time-series security statistics and operating parameters, constructs a multi-dimensional index system covering security coverage, risk weighting, and security resilience, which can comprehensively reflect the stable operation capability of the power grid over a long time scale. The security coverage index assesses basic security, the risk weighting index combines fault weights and operating parameters to quantify risk intensity, and the security resilience index characterizes the persistence and clustering of unsafe states. These three elements work together to constitute the power grid assessment index system, automatically achieving a systematic, differentiated, and dynamic assessment of the power grid's security level without human intervention, providing more valuable technical guidance for operation optimization, planning decisions, and emergency response.

[0107] Optionally, the security coverage indicators include long-term time security rate, fault-safe pass rate, and critical fault security assurance rate. The construction module 420 is specifically used to count the number of safe time slices in the long-term security statistics table where all security tags corresponding to the same time slice represent security, and to determine the long-term time security rate based on the number of safe time slices and the total number of time slices; to determine the fault-safe pass rate based on the number of security tags representing security in the long-term security statistics table and the total number of security tags in the long-term security statistics table; and to select critical fault safety tags from the long-term security statistics table based on critical fault types, and to determine the critical fault security assurance rate based on the number of critical fault safety tags representing security and the total number of critical fault safety tags.

[0108] Optionally, the operating parameters include rated data, maximum deviation data, and simulated data for each time slice; the construction module 420 is specifically used to determine the time slice and fault type corresponding to each safety label representing unsafety in the long-term safety statistics table as a risk combination; determine the risk value of the risk combination based on the simulated data, rated data, maximum deviation data, and fault weight of the fault type corresponding to the risk combination; and construct a risk weighted index based on the fault weight of the fault type corresponding to the safety label in the long-term safety statistics table and the risk value of the risk combination.

[0109] Optionally, the simulated data includes simulated voltage and simulated power, the rated data includes rated voltage and rated power, and the maximum deviation data includes maximum voltage deviation and maximum overload deviation; the construction module 420 is specifically used to determine the voltage over-limit ratio based on the simulated voltage, rated voltage, and maximum voltage deviation for each risk combination; determine the overload over-limit ratio based on the simulated power, rated power, and maximum overload deviation; determine the over-limit weight based on the voltage over-limit ratio and the overload over-limit ratio; and determine the risk value of the risk combination based on the fault weight and over-limit weight of the fault type corresponding to the risk combination.

[0110] Optionally, the risk weighting indicators include the insecurity rate, the proportion of high-risk values, and the risk peak. The construction module 420 is specifically used to determine the insecurity rate based on the sum of all risk values ​​and the sum of the fault weights of the fault types corresponding to the safety labels in the long-term safety statistics table; to determine the risk values ​​that exceed the high-risk threshold as high-risk values, and to determine the proportion of high-risk values ​​based on the number of high-risk values ​​and the number of risk combinations; to divide all time slices into multiple time periods according to the preset time period division rules, and to determine the risk peak based on the sum of all risk values ​​in each time period.

[0111] Optionally, the safety resilience index includes the maximum unsafe duration and the maximum unsafe clustering degree; the construction module 420 is specifically used to identify time slices corresponding to safety labels that represent at least one unsafety as unsafe time slices; to determine the maximum number of consecutive unsafe time slices as the maximum unsafe duration; to traverse risk combinations according to the fault types in each preset cluster and count the number of combinations belonging to the cluster; and to determine the maximum unsafe clustering degree based on the maximum number of combinations belonging to the cluster and the number of risk combinations.

[0112] Please refer to Figure 4This is a block diagram illustrating an electronic device 500 provided in an embodiment of the present invention. The electronic device 500 includes, but is not limited to, a personal computer (PC), a personal digital assistant (PDA), a laptop computer, a tablet computer, and a server. The electronic device 500 includes a memory 510, a processor 520, and a communication module 530. The memory 510, processor 520, and communication module 530 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0113] The memory 510 is used to store programs or data. The memory 510 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0114] The processor 520 is used to read / write data or programs stored in the memory 510 and perform corresponding functions. For example, when a computer program stored in the memory 510 is executed by the processor 520, the method for constructing a power grid evaluation index system disclosed in the above embodiments can be implemented.

[0115] The communication module 530 is used to establish a communication connection between the electronic device 500 and other communication terminals via a network, and to send and receive data via the network.

[0116] It should be understood that, Figure 4 The structure shown is only a schematic diagram of the electronic device 500. The electronic device 500 may also include components that are larger than those shown. Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown. Figure 4 The components shown can be implemented using hardware, software, or a combination thereof.

[0117] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor 520, implements the method for constructing a power grid evaluation index system disclosed in the above embodiments.

[0118] This invention also provides a program product that, when executed by processor 520, implements the power grid evaluation index system construction method disclosed in the above embodiments.

[0119] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0120] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0121] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a power grid evaluation index system, characterized in that, The method includes: Obtain long-time-series safety statistics and operating parameters; the long-time-series safety statistics table records the safety label for each time slice in each fault type, and the safety label is used to characterize whether the power grid is safe; A security coverage index is constructed based on the long-time series security statistics table; the security coverage index is used to assess the power grid's ability to operate stably. A risk-weighted index is constructed based on the long-term security statistics table, the fault weights of each fault type, and the operating parameters; the risk-weighted index is used to assess the intensity and concentration of risks occurring in the power grid. A safety resilience index is constructed based on the long-term security statistics table; the safety resilience index is used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment index system composed of the safety coverage index, the safety resilience index and the risk-weighted index is used for power grid security assessment.

2. The method for constructing a power grid evaluation index system according to claim 1, characterized in that, The security coverage metrics include long-term time-series security rate, fault-tolerant pass rate, and critical fault-tolerant pass rate; the construction of security coverage metrics based on the long-term time-series security statistics table includes: The number of secure time slices in the long-time-series security statistics table where all security tags represent security for the same time slice is counted, and the long-time-series security rate is determined based on the number of secure time slices and the total number of time slices. The fail-safe pass rate is determined based on the number of safety tags representing safety in the long-term safety statistics table and the total number of safety tags in the long-term safety statistics table; The critical fault safety labels are selected from the long-term safety statistics table according to the critical fault type, and the critical fault safety assurance rate is determined based on the number of critical fault safety labels that characterize safety and the total number of critical fault safety labels.

3. The method for constructing a power grid evaluation index system according to claim 1, characterized in that, The operating parameters include rated data, maximum deviation data, and simulated data for each time slice; The construction of a risk-weighted index based on the long-term safety statistics table, the fault weights of each fault type, and the operating parameters includes: The time slice and fault type corresponding to each security label representing insecurity in the long-term security statistics table are determined as a risk combination; The risk value of the risk combination is determined based on the simulation data, rated data, maximum deviation data, and fault weight of the fault type corresponding to the risk combination. The risk-weighted index is constructed based on the fault weights of the fault types corresponding to the security labels in the long-term security statistics table and the risk values ​​of the risk combinations.

4. The method for constructing a power grid evaluation index system according to claim 3, characterized in that, The simulated data includes simulated voltage and simulated power, the rated data includes rated voltage and rated power, and the maximum deviation data includes maximum voltage deviation and maximum overload deviation; determining the risk value of the risk combination based on the simulated data, rated data, maximum deviation data, and fault weights of the fault types corresponding to the risk combination includes: For each risk combination, the voltage over-limit ratio is determined based on the simulated voltage, the rated voltage, and the maximum voltage deviation; The overload limit ratio is determined based on the simulated power, the rated power, and the maximum overload deviation. The over-limit weight is determined based on the voltage over-limit ratio and the overload over-limit ratio; The risk value of the risk combination is determined based on the fault weight of the fault type corresponding to the risk combination and the over-limit weight.

5. The method for constructing a power grid evaluation index system according to claim 3, characterized in that, The risk-weighted indicators include the insecurity rate, the proportion of high-risk areas, and the peak risk level. The step of constructing the risk-weighted index based on the fault weights of the fault types corresponding to the security labels in the long-term security statistics table and the risk values ​​of the risk combinations includes: The unsafety rate is determined by summing all the risk values ​​and the fault weights of the fault types corresponding to the safety labels in the long-term safety statistics table. Risk values ​​exceeding a high-risk threshold are defined as high-risk values, and the high-risk percentage is determined based on the number of high-risk values ​​and the number of risk combinations. The entire time slice is divided into multiple time periods according to the preset time period division rules, and the risk peak value is determined based on the sum of all the risk values ​​in each time period.

6. The method for constructing a power grid evaluation index system according to claim 3, characterized in that, The security resilience index includes the maximum duration of insecurity and the maximum clustering degree of insecurity; the construction of the security resilience index based on the long-term security statistics table includes: A time slice with at least one security label indicating insecurity is identified as an insecure time slice. The maximum number of consecutive unsafe time slices is determined as the maximum unsafe duration; Based on the fault types in each preset cluster, the risk combinations are traversed to count the number of combinations belonging to the cluster. The maximum unsafe clustering degree is determined by the maximum number of combinations belonging to the cluster and the number of risky combinations.

7. A device for constructing a power grid evaluation index system, characterized in that, The device includes: The processing module is used to acquire long-time-series safety statistics tables and operating parameters; the long-time-series safety statistics tables record the safety labels of each time slice for each fault type, and the safety labels are used to characterize whether the power grid is safe. A construction module is used to construct security coverage indicators based on the long-term security statistics table; the security coverage indicators are used to assess the ability of the power grid to operate stably. The construction module is also used to construct a risk-weighted index based on the long-term security statistics table, the fault weights of each fault type, and the operating parameters; the risk-weighted index is used to assess the intensity and concentration of risks occurring in the power grid; The construction module is also used to construct a security resilience index based on the long-term security statistics table; the security resilience index is used to assess the duration of the power grid being in an unsafe state and the clustering of fault types; the power grid assessment index system composed of the security coverage index, the security resilience index and the risk-weighted index is used for power grid security assessment.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that can be executed by the processor, the processor being able to execute the computer program to implement the power grid evaluation index system construction method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for constructing a power grid evaluation index system as described in any one of claims 1-6.

10. A program product, characterized in that, When the program product is executed by the processor, it implements the method for constructing a power grid evaluation index system as described in any one of claims 1-6.

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