Power grid high-risk scene discrimination method and system based on subjective and objective comprehensive weighting

Abstract

The invention discloses a power grid high-risk scene discrimination method and system based on subjective and objective comprehensive weighting, and belongs to the technical field of large power grid regulation and control safety and stability analysis. The method comprises the following steps: selecting various actual historical operation data of a regional power grid within a period of time, and determining a key rule representing a stable operation boundary of the power grid based on the various actual historical operation data; based on the key rule, carrying out preliminary screening on massive operation scenes of the target power grid to obtain a risk power grid scene; selecting a preset index for the risk power grid scene, and calculating to obtain a severity evaluation index value of the risk of the risk power grid scene according to the preset index by adopting a subjective and objective comprehensive weighting method; and sorting the severity evaluation index values, and screening out a high-risk scene from the risky power grid scene. According to the method, the high-risk operation scene is identified through comprehensive index sorting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of large power grid regulation safety and stability analysis, and more particularly, to a power grid high-risk scenario discrimination method and system based on subjective and objective comprehensive weighting. BACKGROUND

[0002] With the rapid development of UHV AC-DC hybrid large power grid, the correlation between AC and DC power grids, between sending and receiving end power grids, and between power grids of different voltage levels is becoming increasingly complex. The rapid growth of new energy installed capacity brings challenges of intermittent and fluctuating output, making the power grid operation risk present complex characteristics of multi-dimensional interweaving and dynamic evolution, and bringing new pressure to personnel at all levels of dispatching agencies. Especially, serious faults such as high-power DC blocking and commutation failure have a huge impact on power grid safety and stability, and it is necessary to quickly evaluate and judge the power grid operation risk under various complex faults. The traditional risk assessment method based on historical data and static model has been difficult to adapt to the characteristics of dynamic correlation of risk factors and rapid alternation of scenarios in new power systems, and it is urgent to build a high-risk scenario evaluation system that can integrate multi-source information and dynamically reflect system state, to provide accurate support for power grid safety warning and dispatching decision.

[0003] Existing power grid risk scenario evaluation and identification focus on a single risk type. When building a risk scenario, it often relies only on a single data source, such as a certain risk indicator after a fault, while ignoring the dynamic influence of external factors such as meteorological warnings or natural disasters on the operation mode. Moreover, when building the risk evaluation index system, the subjectivity and objectivity of index weight allocation are imbalanced. Subjective weighting method relies on expert experience and is easily affected by individual cognitive bias, and has high computational complexity (such as consistency check). Objective weighting method (such as entropy weight method) is based on data-driven, but relies too much on data quality, which may ignore domain knowledge (such as device operation experience). SUMMARY

[0004] To adapt to the rapid development of current UHV AC-DC interconnected large power grid, facilitate the on-duty personnel of dispatching agencies to effectively respond to various power grid risks, provide decision basis for power grid real-time warning and operation mode adjustment, and improve the efficiency of dispatchers in judging the risk state of power grid, the present application proposes a power grid high-risk scenario discrimination method based on subjective and objective comprehensive weighting, which comprises:

[0005] selecting various types of actual historical operation data of a regional power grid within a period of time, and determining key rules representing the stable operation boundary of the power grid based on the various types of actual historical operation data;

[0006] based on the key rules, preliminarily screening a large number of operation scenarios of the target power grid to obtain power grid scenarios with risks;

[0007] For the risky power grid scene, preset indexes are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risk of the risky power grid scene according to the preset indexes.

[0008] The severity evaluation index value is sorted, and a high-risk scene is screened out in the risky power grid scene.

[0009] Optionally, actual historical operation data of each type of a certain regional power grid within a period of time is selected, and based on the actual historical operation data of each type, key rules representing the stable operation boundary of the power grid are determined, including:

[0010] Actual historical operation data of each type of a certain regional power grid within a period of time is selected, and based on the actual historical operation data of each type, key rules representing the stable operation boundary of the power grid are determined, including:

[0011] Optionally, the impact factor includes at least one of the following:

[0012] The starting condition of the generator, the new energy penetration rate, the regional maximum load level, the branch topology, and the stable section load rate.

[0013] Optionally, based on the key rules, the massive operation scenes of the target power grid are preliminarily screened to obtain the risky power grid scene, including:

[0014] For the massive operation scenes of the target power grid, according to the key rules, the rule set is matched, the scenes not meeting the key operation mode rule boundary are preliminarily screened, and the risky power grid scene is screened.

[0015] Optionally, the preset index includes at least one of the following:

[0016] Short-circuit current, DC short-circuit ratio, new energy short-circuit ratio, inertia ratio, disturbance power, and frequency change rate.

[0017] Optionally, for the risky power grid scene, preset indexes are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risk of the risky power grid scene according to the preset indexes, including:

[0018] For the risky power grid scene, preset indexes are selected, and after the preset indexes are normalized, a subjective and objective comprehensive weighting method is used to weight the weight coefficients of the preset indexes, and based on the weighted weight coefficients, the severity evaluation index value of the risk of the risky power grid scene is calculated.

[0019] Optionally, the higher the severity evaluation index value is sorted, the higher the severity of the power grid fault is.

[0020] In still another aspect, the application further provides a power grid high-risk scenario discrimination system based on subjective and objective comprehensive weighting, comprising:

[0021] An initial unit is configured to select various types of actual historical operation data of a certain regional power grid within a period of time, and determine key rules representing the stable operation boundary of the power grid based on the various types of actual historical operation data;

[0022] A preliminary screening unit is configured to preliminarily screen a large number of operation scenarios of a target power grid based on the key rules to obtain power grid scenarios at risk;

[0023] A calculation unit is configured to select preset indexes for the power grid scenarios at risk, and calculate the severity evaluation index value of the risk of the power grid scenarios at risk according to the preset indexes by using a subjective and objective comprehensive weighting method;

[0024] An output unit is configured to sort the severity evaluation index value and screen out high-risk scenarios from the power grid scenarios at risk.

[0025] Optionally, the various types of actual historical operation data of a certain regional power grid within a period of time are selected, and the key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data, comprising:

[0026] The various types of actual historical operation data of a certain regional power grid within a period of time are selected, and the influencing factors of the high risk of the power grid are extracted based on the various types of actual historical operation data, and the key rules representing the stable operation boundary of the power grid are determined according to the influencing factors and the safety and stability constraints of the power grid.

[0027] Optionally, the influencing factors include at least one of the following:

[0028] The starting condition of the generator, the penetration rate of new energy, the maximum load level of the region, the branch topology, and the load rate of the stable section.

[0029] Optionally, the large number of operation scenarios of the target power grid are preliminarily screened based on the key rules to obtain the power grid scenarios at risk, comprising:

[0030] For the large number of operation scenarios of the target power grid, the rule set is matched according to the key rules, the scenarios not meeting the key operation mode rule boundary are preliminarily screened, and the power grid scenarios possibly at risk are screened out.

[0031] Optionally, the preset indexes include at least one of the following:

[0032] The short-circuit current, the DC short-circuit ratio, the new energy short-circuit ratio, the inertia ratio, the disturbance power, and the frequency change rate.

[0033] Optionally, for the risky power grid scene, preset indexes are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risky power grid scene risk according to the preset indexes, including:

[0034] For the risky power grid scene, preset indexes are selected, and after the preset indexes are normalized, a subjective and objective comprehensive weighting method is used to weight the preset indexes with weight coefficients, and based on the weight coefficients, the severity evaluation index value of the risky power grid scene risk is calculated.

[0035] Optionally, the higher the severity evaluation index value is sorted, the higher the severity of the power grid fault is.

[0036] In another aspect, the application also provides a computing device, comprising: one or more processors;

[0037] The processor is used for executing one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the method as described above is realized.

[0039] In another aspect, the application also provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed to realize the method as described above.

[0040] Compared with the prior art, the application has the following beneficial effects:

[0041] The application provides a power grid high-risk scene discrimination method based on subjective and objective comprehensive weighting, including: selecting various types of actual historical operation data of a regional power grid in a period of time, and determining key rules representing the stable operation boundary of the power grid based on the various types of actual historical operation data; based on the key rules, performing preliminary screening on a large number of operation scenes of a target power grid to obtain risky power grid scenes; for the risky power grid scenes, preset indexes are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risky power grid scene risk according to the preset indexes; and the severity evaluation index value is sorted to screen out high-risk scenes from the risky power grid scenes. The application identifies the high-risk operation scene by comprehensive index sorting. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The flowchart of the method of the application is shown;

[0043] Figure 2 The flowchart of the embodiment of the method of the application is shown;

[0044] Figure 3 The structural diagram of the system of the application is shown. Detailed Implementation

[0045] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0046] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0047] Example 1:

[0048] This invention proposes a method for identifying high-risk power grid scenarios based on a combination of subjective and objective weighting, such as... Figure 1 As shown, it includes:

[0049] Step 1: Select various types of actual historical operating data of a power grid in a certain area over a period of time, and determine the key rules characterizing the stable operation boundary of the power grid based on the various types of actual historical operating data;

[0050] Step 2: Based on key rules, conduct a preliminary screening of the massive number of operation scenarios of the target power grid to obtain the power grid scenarios with risks;

[0051] Step 3: For risky power grid scenarios, select preset indicators and use a subjective and objective comprehensive weighting method to calculate the severity evaluation index value of the risk of the risky power grid scenario based on the preset indicators.

[0052] Step 4: Sort the severity evaluation index values ​​and select high-risk scenarios from the risky power grid scenarios.

[0053] This involves selecting various types of actual historical operating data of a power grid in a specific region over a certain period, and based on these data, determining key rules characterizing the stable operating boundary of the power grid, including:

[0054] Select various types of actual historical operation data of a power grid in a certain region over a period of time, extract the influencing factors of high risk to the power grid based on the various types of actual historical operation data, and determine the key rules characterizing the boundary of stable operation of the power grid according to the influencing factors and the power grid safety and stability constraints.

[0055] Among them, the influencing factors include at least one of the following:

[0056] Generator start-up status, new energy penetration rate, maximum regional load level, branch topology, and stable section load rate.

[0057] Among these, based on key rules, a preliminary screening of the massive number of operational scenarios of the target power grid is conducted to identify risky power grid scenarios, including:

[0058] For the massive number of operating scenarios of the target power grid, the rule set is matched according to the key rules, and the scenarios that do not meet the boundary of the key operating mode rules are initially screened to identify potentially risky power grid scenarios.

[0059] The preset indicators include at least one of the following:

[0060] Short-circuit current, DC short-circuit ratio, new energy short-circuit ratio, inertia ratio, disturbance power, and frequency change rate.

[0061] Specifically, for risky power grid scenarios, preset indicators are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risk in the risky power grid scenario based on the preset indicators, including:

[0062] For risky power grid scenarios, preset indicators are selected and normalized. Then, a subjective and objective comprehensive weighting method is used to assign weight coefficients to the preset indicators. Based on the weighted weight coefficients, the severity evaluation index value of the risk in the risky power grid scenario is calculated.

[0063] Among them, the severity evaluation index values ​​are ranked, and the higher the ranking, the higher the severity of the power grid fault.

[0064] The invention will be further explained below with reference to specific implementation examples:

[0065] This invention addresses risk assessment for massive power grid scenarios. First, it conducts in-depth analysis of massive historical operating data and the mechanisms affecting safety and stability. Based on the power grid's operating status and safety and stability constraints, it extracts key rules that characterize the stable operating boundary of the power grid from the operating mode level. For massive operating scenarios, it performs preliminary screening through the key operating mode boundaries to identify potentially risky power grid scenarios.

[0066] Furthermore, for the scenarios after initial screening, the severity of the fault is analyzed from the perspective of fault severity. Indicators such as short-circuit current, short-circuit ratio, inertia ratio, disturbance power, and frequency change rate at the fault point are selected to assess the severity of the fault. A comprehensive subjective and objective weighting method is used to comprehensively assess the severity of the scenario risk for each evaluation indicator. The severity comprehensive evaluation index is calculated through weight coefficients. By ranking the indicators, high-risk operating scenarios are identified.

[0067] The specific process is shown in Figure 2 , including:

[0068] (1) Selecting various types of actual historical operation data of a regional power grid in a period of time, analyzing the influencing factors such as regional generator start-up conditions, new energy penetration rate, regional maximum load level, branch topology and stable section load rate, and extracting key rules that can represent the stable operation boundary of the power grid.

[0069] (2) For the power grid operation scene to be identified, through the key rules extracted in step (1), the rule set is matched, the scenes that do not meet the key operation mode rule boundary are preliminarily screened, and the power grid scenes that may have risks are screened out.

[0070] (3) For the power grid scenes screened in step (2), analyze from the fault severity level, select short-circuit current, DC short-circuit ratio, new energy short-circuit ratio, inertia ratio, disturbance power, frequency change rate and other indicators, and use the subjective and objective comprehensive weighting method to evaluate the fault severity and further judge the scene risk severity.

[0071] (4) For the subjective and objective comprehensive weighting method mentioned in step (3), the objective weighting method uses the CRITIC weighting method. The premise of calculating the CRITIC weight is to understand the correlation coefficient. First, the Pearson correlation coefficient is applied to obtain the correlation r between two indicators ij For the jth indicator x j , the standard deviation is S j , and the weight ω j is calculated as follows:

[0072]

[0073]

[0074] Where C j represents the amount of information contained in the jth indicator.

[0075] (5) For the subjective and objective comprehensive weighting method mentioned in step (3), the subjective weighting method uses the order relation analysis method to weight. The specific calculation steps of the order relation analysis method to determine the subjective weight are as follows:

[0076] 1) Determine the importance relationship between indicators. Assume that the indicator set is {x1, x2, …, x m}, according to the influence size, get the indicator set {x1 * , x2 * , …, x m *}.

[0077] 2) For the new index set after determining the importance, give the adjacent index x k-1 * The relative importance r k * between x k (k=m, m-1, …, 2), while the relative importance r k should also meet the limit condition: r k-1 >1 / r k .

[0078] 3) Calculate the subjective weight. If the rational assignment of r k satisfies the limit condition, the sequence relationship index set weight coefficient calculation formula is:

[0079]

[0080] ω k-1 = r k ω k (k=m, m-1, …, 2) (4)

[0081] (6) For the subjective and objective weighting method mentioned in steps (4) and (5), the subjective weighting method is combined with the weight of the objective weighting method to determine the comprehensive weight of the index. The calculation formula of the subjective and objective comprehensive weighting method is as follows:

[0082]

[0083] Wherein, α j , β j are the subjective weight and objective weight of the to-be-evaluated index j.

[0084] (7) Normalize each index mentioned in step (3), and through the subjective and objective comprehensive weighting method in step (6), weight the weight coefficient of each index, and then calculate the comprehensive evaluation index of fault severity, and sort the comprehensive evaluation index. The higher the sorting is, the higher the fault severity is. Further set the threshold value of the comprehensive evaluation index of fault severity, and determine the high-risk operation scene of the power grid for the scene exceeding the index threshold.

[0085] Embodiment 2:

[0086] In still another aspect, the application also proposes a power grid high-risk scene discrimination system 200 based on subjective and objective comprehensive weighting, as shown in Figure 3 , comprising:

[0087] The initial unit 201 is used for selecting various types of actual historical operation data of a certain regional power grid within a period of time, and determining the key rules representing the stable operation boundary of the power grid based on the various types of actual historical operation data.

[0088] The preliminary screening unit 202 is configured to perform preliminary screening on a large number of operation scenarios of a target power grid based on key rules to obtain risky power grid scenarios;

[0089] The calculation unit 203 is configured to select preset indexes for the risky power grid scenarios, and calculate a severity evaluation index value of the risk of the risky power grid scenarios according to the preset indexes by using a subjective and objective comprehensive weighting method.

[0090] The output unit 204 is configured to sort the severity evaluation index value and screen out high-risk scenarios from the risky power grid scenarios.

[0091] The key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data of the regional power grid in a period of time, and include:

[0092] The key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data of the regional power grid in a period of time, and include:

[0093] The influence factors include at least one of the following:

[0094] The influence factors include at least one of the following:

[0095] The key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data of the regional power grid in a period of time, and include:

[0096] The key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data of the regional power grid in a period of time, and include:

[0097] The preset indexes include at least one of the following:

[0098] The preset indexes include at least one of the following:

[0099] The key rules representing the stable operation boundary of the power grid are determined based on the various types of actual historical operation data of the regional power grid in a period of time, and include:

[0100] For the risky power grid scene, preset indexes are selected, and after the preset indexes are normalized, a subjective and objective comprehensive weighting method is used to weight the weight coefficients of the preset indexes, and based on the weighted weight coefficients, the severity evaluation index value of the risky power grid scene risk is calculated.

[0101] The higher the order of the severity evaluation index value is, the higher the severity of the power grid fault is.

[0102] The present application mainly aims at risk identification of complex operation scenarios of large power grids, unlike traditional single discrimination methods, the present application constructs a risk identification framework suitable for complex operation scenarios from two aspects of operation mode boundary and fault severity, first performs risk scene preliminary screening through historical operation boundary matching, further selects different fault severity evaluation indexes, objectively and subjectively weights each evaluation index, constructs a comprehensive evaluation index and calculates it, and sorts the comprehensive index to identify high-risk operation scenarios.

[0103] Embodiment 3:

[0104] Based on the same inventive concept, the present application also provides a computer device, which comprises a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method process or corresponding function, so as to realize the steps of the method in the above-mentioned embodiments.

[0105] Embodiment 4:

[0106] Based on the same inventive concept, the present application also provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, used for storing programs and data. It can be understood that the computer readable storage medium here can include the built-in storage medium in the computer device, and of course can also include the extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium here can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to realize the steps of the method in the above embodiments.

[0107] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0108] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of the flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks.

[0109] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0111] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to cover all such additional variations and modifications as fall within the scope of the present application. What is claimed is:

[0112] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for identifying high-risk power grid scenarios based on a combination of subjective and objective weighting, characterized in that, include: Select various types of actual historical operating data of a power grid in a certain region over a period of time, and based on the various types of actual historical operating data, determine the key rules that characterize the stable operation boundary of the power grid; Based on key rules, a preliminary screening of a large number of operating scenarios of the target power grid is conducted to identify risky power grid scenarios. For risky power grid scenarios, preset indicators are selected, and a subjective and objective comprehensive weighting method is adopted to calculate the severity evaluation index value of the risk of the risky power grid scenario based on the preset indicators. The severity evaluation index values ​​are sorted, and high-risk scenarios are selected from the risky power grid scenarios.

2. The method for identifying high-risk power grid scenarios according to claim 1, characterized in that, The process of selecting various types of actual historical operating data of a power grid in a certain region over a period of time, and determining key rules characterizing the stable operation boundary of the power grid based on these various types of actual historical operating data, includes: Select various types of actual historical operation data of a power grid in a certain region over a period of time, extract the influencing factors of high risk to the power grid based on the various types of actual historical operation data, and determine the key rules characterizing the stable operation boundary of the power grid according to the influencing factors and the power grid safety and stability constraints.

3. The method for identifying high-risk power grid scenarios according to claim 2, characterized in that, The influencing factors include at least one of the following: Generator start-up status, new energy penetration rate, maximum regional load level, branch topology, and stable section load rate.

4. The method for identifying high-risk power grid scenarios according to claim 1, characterized in that, The process of initially screening a large number of operational scenarios of the target power grid based on key rules yields risky power grid scenarios, including: For the massive number of operating scenarios of the target power grid, the rule set is matched according to the key rules, and the scenarios that do not meet the boundary of the key operating mode rules are initially screened to identify potentially risky power grid scenarios.

5. The method for identifying high-risk power grid scenarios according to claim 1, characterized in that, The preset indicators include at least one of the following: Short-circuit current, DC short-circuit ratio, new energy short-circuit ratio, inertia ratio, disturbance power, and frequency change rate.

6. The method for identifying high-risk power grid scenarios according to claim 1, characterized in that, For risky power grid scenarios, preset indicators are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risk in the risky power grid scenario based on the preset indicators, including: For risky power grid scenarios, preset indicators are selected and normalized. Then, a subjective and objective comprehensive weighting method is used to assign weight coefficients to the preset indicators. Based on the weighted weight coefficients, the severity evaluation index value of the risk in the risky power grid scenario is calculated.

7. The method for identifying high-risk power grid scenarios according to claim 1, characterized in that, The severity evaluation index values ​​are ranked, with higher rankings indicating greater severity of the power grid fault.

8. A power grid high-risk scenario identification system based on a combination of subjective and objective weighting, characterized in that, include: The initial unit is used to select various types of actual historical operating data of a power grid in a certain area over a period of time, and based on the various types of actual historical operating data, to determine the key rules characterizing the stable operation boundary of the power grid. The initial screening unit is used to perform initial screening on a large number of operating scenarios of the target power grid based on key rules, and to identify risky power grid scenarios. The calculation unit is used to select preset indicators for risky power grid scenarios and use a subjective and objective comprehensive weighting method to calculate the severity evaluation index value of the risky power grid scenario based on the preset indicators. The output unit is used to sort the severity evaluation index values ​​and filter out high-risk scenarios from the risky power grid scenarios.

9. The power grid high-risk scenario identification system according to claim 8, characterized in that, The process of selecting various types of actual historical operating data of a power grid in a certain region over a period of time, and determining key rules characterizing the stable operation boundary of the power grid based on these various types of actual historical operating data, includes: Select various types of actual historical operation data of a power grid in a certain region over a period of time, extract the influencing factors of high risk to the power grid based on the various types of actual historical operation data, and determine the key rules characterizing the stable operation boundary of the power grid according to the influencing factors and the power grid safety and stability constraints.

10. The power grid high-risk scenario identification system according to claim 9, characterized in that, The influencing factors include at least one of the following: Generator start-up status, new energy penetration rate, maximum regional load level, branch topology, and stable section load rate.

11. The power grid high-risk scenario identification system according to claim 8, characterized in that, The process of initially screening a large number of operational scenarios of the target power grid based on key rules yields risky power grid scenarios, including: For the massive number of operating scenarios of the target power grid, the rule set is matched according to the key rules, and the scenarios that do not meet the boundary of the key operating mode rules are initially screened to identify potentially risky power grid scenarios.

12. The power grid high-risk scenario identification system according to claim 8, characterized in that, The preset indicators include at least one of the following: Short-circuit current, DC short-circuit ratio, new energy short-circuit ratio, inertia ratio, disturbance power, and frequency change rate.

13. The power grid high-risk scenario identification system according to claim 8, characterized in that, For risky power grid scenarios, preset indicators are selected, and a subjective and objective comprehensive weighting method is used to calculate the severity evaluation index value of the risk in the risky power grid scenario based on the preset indicators, including: For risky power grid scenarios, preset indicators are selected and normalized. Then, a subjective and objective comprehensive weighting method is used to assign weight coefficients to the preset indicators. Based on the weighted weight coefficients, the severity evaluation index value of the risk in the risky power grid scenario is calculated.

14. The power grid high-risk scenario identification system according to claim 8, characterized in that, The severity evaluation index values ​​are ranked, with higher rankings indicating greater severity of the power grid fault.

15. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-7 is implemented.

16. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-7.

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