Risk nonlinear quantification method and device for power grid coupling disaster

Abstract

The invention relates to the technical field of electric power safety, and particularly provides a risk nonlinear quantification method and device for power grid coupling disasters, and the method comprises the steps: dividing an electric power system into a plurality of regional subnets, and carrying out the permutation and combination of the plurality of regional subnets, and obtaining a plurality of coupling regions; respectively acquiring disaster scene risk probabilities and disaster scene lambda fuzzy measures of the plurality of coupling regions; and determining a risk nonlinear quantification result of the power system based on the disaster scene risk probabilities of the plurality of coupling regions and the disaster scene lambda fuzzy measure. According to the technical scheme provided by the invention, the complex power system can be divided into a plurality of regional subnets for hierarchical and multi-stage application, so that the multi-disaster coupling disaster accident risk quantitative assessment of the complex power system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power safety, and particularly to a risk non-linear quantification method and device for grid-coupled disasters. Background Art

[0002] With the rapid development of the power system, high-density functional facilities have made the operation process of the power system potentially carry major risks. A variety of key functional facilities of the power system are threatened by natural disasters, including transmission and distribution lines and towers, substations, power plants, etc. Natural disasters trigger power safety accidents by causing physical damage to the components of functional facilities. Accidents of a single facility are easily transmitted and extended to other facilities through geographical and functional correlation relationships, triggering large-scale accidents, and further causing functional losses at the system level.

[0003] For the management of the risk of natural disasters inducing power system accidents, a key link is how to quantify the risk caused by disaster-causing factors to key facilities of the power system. If the risks can be directly superimposed, they can be expressed as the direct summation of independent risks caused by different accidents; however, in reality, both links of risk quantification - the calculation of probability and consequence are typical non-linear processes, which leads to the inability to accurately quantify many accident risks. Summary of the Invention

[0004] In order to overcome the above defects, the present invention proposes a risk non-linear quantification method and device for grid-coupled disasters.

[0005] In a first aspect, a risk non-linear quantification method for grid-coupled disasters is provided. The risk non-linear quantification method for grid-coupled disasters includes:

[0006] Dividing the power system into multiple regional subnets, arranging and combining the multiple regional subnets to obtain multiple coupled regions;

[0007] Respectively obtaining the risk probability of the disaster scenario and the λ fuzzy measure of the disaster scenario of the multiple coupled regions;

[0008] Determining the risk non-linear quantification result of the power system based on the risk probability of the disaster scenario and the λ fuzzy measure of the disaster scenario of the multiple coupled regions.

[0009] Preferably, the risk probability of the disaster scenario of the coupled region is as follows:

[0010]

[0011] In the above formula, r multi is the risk probability of the disaster scenario of the coupled region, n is the number of regional subnets in the coupled region, and m i is the number of accident scenarios of the regional subnet i in the coupled region. is the new occurrence frequency of the jth accident scenario of the regional subnet i in the coupling area, ρ ij is the specified parameter of the analysis object in the jth accident scenario of the regional subnet i in the coupling area.

[0012] Furthermore, the new occurrence frequency of the jth accident scenario of the regional subnet i in the coupling area is as follows:

[0013]

[0014] In the above formula, is the original occurrence frequency of the jth accident scenario of the regional subnet i in the coupling area, is the original occurrence frequency of the accidents of the regional subnet i in the coupling area, is the new occurrence frequency of the accidents of the regional subnet i in the coupling area,

[0015] Furthermore, the specified parameter includes at least one of the following: load loss probability, expected load loss, expected energy loss.

[0016] Preferably, the fuzzy measure of the disaster scenario λ of the multiple coupling areas is as follows:

[0017]

[0018] In the above formula, μ p is the fuzzy measure of the disaster scenario λ of the coupling area p, Q is the number of preset risk parameters, β q is the weight of the preset risk parameter q, is the scoring standard value of the coupling area p for the preset risk parameter q.

[0019] Furthermore, the scoring standard value of the coupling area p for the preset risk parameter q is as follows:

[0020]

[0021] In the above formula, P is the total number of coupling areas, τ p,q is the score of the coupling area p for the preset risk parameter q, and the score of the coupling area p for the preset risk parameter q is obtained by the expert scoring method.

[0022] Furthermore, the score of the coupling area p for the preset risk parameter q is as follows:

[0023]

[0024] In the above formula, K is the total number of experts, α k is the weight corresponding to the expert k, τ p,q,kThe score given by expert k for coupling region p with respect to the preset risk parameter q.

[0025] Preferably, the risk non - linear quantization result of the power system is as follows:

[0026]

[0027] In the above formula, CR is the risk non - linear quantization result of the power system, y is the total number of regional sub - networks, is from the 1st to the 2nd y -1 coupling regions corresponding coupling region sorting set sorted in ascending order of the risk probability of the disaster scenario, f(x′ y ) is the risk probability of the disaster scenario of the y - th coupling region x′ in the coupling region sorting set y , f(x′ y-1 ) is the risk probability of the disaster scenario of the (y - 1)-th coupling region x′ in the coupling region sorting set y , is the λ - fuzzy measure of the disaster scenario of the coupling region .

[0028] Second, a risk non - linear quantization device for power grid coupling disasters is provided. The risk non - linear quantization device for power grid coupling disasters includes:

[0029] A combination module, used to divide the power system into multiple regional sub - networks, perform permutation and combination on the multiple regional sub - networks to obtain multiple coupling regions;

[0030] An acquisition module, used to respectively acquire the risk probability of the disaster scenario and the λ - fuzzy measure of the disaster scenario of the multiple coupling regions;

[0031] A determination module, used to determine the risk non - linear quantization result of the power system based on the risk probability of the disaster scenario and the λ - fuzzy measure of the disaster scenario of the multiple coupling regions.

[0032] Preferably, the risk probability of the disaster scenario of the coupling region is as follows:

[0033]

[0034] In the above formula, r multi is the risk probability of the disaster scenario of the coupling region, n is the number of regional sub - networks in the coupling region, m i is the number of accident scenarios of regional sub - network i in the coupling region, is the new occurrence frequency of the j - th accident scenario of regional sub - network i in the coupling region, ρ ij is the specified parameter of the analysis object in the j - th accident scenario of regional sub - network i in the coupling region.

[0035] Furthermore, the new occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area is as follows:

[0036]

[0037] In the above formula, is the original occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, is the original occurrence frequency of the accidents of the regional subnet i in the coupling area, is the new occurrence frequency of the accidents of the regional subnet i in the coupling area,

[0038] Furthermore, the specified parameter includes at least one of the following: load loss probability, expected load loss, expected energy loss.

[0039] Preferably, the fuzzy measure of the disaster scenario λ of the multiple coupling areas is as follows:

[0040]

[0041] In the above formula, μ p is the fuzzy measure of the disaster scenario λ of the coupling area p, Q is the number of preset risk parameters, and β q is the weight of the preset risk parameter q, is the scoring standard value of the coupling area p for the preset risk parameter q.

[0042] Furthermore, the scoring standard value of the coupling area p for the preset risk parameter q is as follows:

[0043]

[0044] In the above formula, P is the total number of coupling areas, and τ p,q is the score of the coupling area p for the preset risk parameter q, and the score of the coupling area p for the preset risk parameter q is obtained by the expert scoring method.

[0045] Furthermore, the score of the coupling area p for the preset risk parameter q is as follows:

[0046]

[0047] In the above formula, K is the total number of experts, and α k is the weight corresponding to the expert k, and τ p,q,k is the score given by the expert k for the coupling area p for the preset risk parameter q.

[0048] Preferably, the risk non-linear quantization result of the power system is as follows:

[0049]

[0050] In the above formula, CR is the non - linear quantitative result of the risk of the power system, y is the total number of regional sub - networks, from the 1st to the 2nd y -1 coupled regions is the sorted set of coupled regions corresponding to the coupled regions sorted in ascending order of the risk probability of the disaster scenario, f(x′ y ) is the risk probability of the disaster scenario of the y - th coupled region x′ in the sorted set of coupled regions y of the sorted set of coupled regions, f(x′ y-1 ) is the risk probability of the disaster scenario of the (y - 1)-th coupled region x′ y in the sorted set of coupled regions, is the λ - fuzzy measure of the disaster scenario of the coupled region .

[0051] Thirdly, a computer device is provided, including: one or more processors;

[0052] The processor is used to store one or more programs;

[0053] When the one or more programs are executed by the one or more processors, the non - linear quantitative method for the risk of grid - coupled disasters described above is implemented.

[0054] Fourthly, a computer - readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the non - linear quantitative method for the risk of grid - coupled disasters described above is implemented.

[0055] One or more of the above - mentioned technical solutions of the present invention have at least one or more of the following beneficial effects:

[0056] The present invention provides a non - linear quantitative method and device for the risk of grid - coupled disasters, including: dividing a power system into multiple regional sub - networks, arranging and combining the multiple regional sub - networks to obtain multiple coupled regions; respectively obtaining the risk probability of the disaster scenario and the λ - fuzzy measure of the disaster scenario of the multiple coupled regions; determining the non - linear quantitative result of the risk of the power system based on the risk probability of the disaster scenario and the λ - fuzzy measure of the disaster scenario of the multiple coupled regions. The technical solution provided by the present invention, through the comprehensive application of precise risk analysis and fuzzy subjective evaluation, avoids the complexity and uncertainty introduced by the influence of the quantification of the coupling effect, thereby improving the applicability and operability of the method and maintaining the balance between accuracy and applicability; further, the technical solution provided by the present invention can divide a complex power system into multiple regional sub - networks for hierarchical and multi - level applications, and realize the quantitative assessment of the risk of multi - disaster - type coupled disaster accidents in a complex power system. Description of the Drawings

[0057] Figure 1 It is a schematic diagram of the main step process of the risk non-linear quantification method for grid-coupled disasters in the embodiments of the present invention. Detailed implementation manners

[0058] The following further elaborates on the detailed implementation manners of the present invention in conjunction with the accompanying drawings.

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0060] As disclosed in the background art, with the rapid development of the power system, the high-density functional facilities have hidden major risks in the operation process of the power system. A variety of key functional facilities of the power system are threatened by natural disasters, including transmission and distribution lines and towers, substations, power plants, etc. Natural disasters trigger power safety accidents by causing physical damage to the components of functional facilities. The accidents of a single facility are easily transmitted and extended to other facilities through geographical and functional correlation relationships, triggering large-scale accidents, and then causing functional losses at the system level.

[0061] For the management of the risks of natural disaster-induced power system accidents, the key link is how to quantify the risks caused by disaster-causing factors to the key facilities of the power system. If the risks can be directly superimposed, they can be expressed as the direct sum of the independent risks caused by different accidents. However, in reality, both of the two links of risk quantification - the calculation of probability and consequence are typical non-linear processes, which leads to the inability to accurately quantify many accident risks.

[0062] To address the above problems, the present invention provides a risk non - linear quantification method and device for grid - coupled disasters, including: dividing the power system into multiple regional sub - networks, arranging and combining the multiple regional sub - networks to obtain multiple coupled regions; respectively obtaining the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions; and determining the risk non - linear quantification result of the power system based on the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions. The technical solution provided by the present invention, through the comprehensive application of precise risk analysis and fuzzy subjective evaluation, avoids the complexity and uncertainty introduced by the influence of the quantification coupling effect, thereby improving the applicability and operability of the method and maintaining the balance between accuracy and applicability; further, the technical solution provided by the present invention can divide the complex power system into multiple regional sub - networks for hierarchical and multi - level applications, realizing the quantitative assessment of the risks of multi - disaster - coupled accidents in the complex power system. The above solution will be elaborated in detail below.

[0063] Embodiment 1

[0064] Refer to the appendix Figure 1 , Figure 1 which is a schematic diagram of the main steps of the risk non - linear quantification method for grid - coupled disasters according to an embodiment of the present invention. As Figure 1 shown, the risk non - linear quantification method for grid - coupled disasters in the embodiment of the present invention mainly includes the following steps:

[0065] Step S101: Divide the power system into multiple regional sub - networks, arrange and combine the multiple regional sub - networks to obtain multiple coupled regions;

[0066] Step S102: Respectively obtain the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions;

[0067] Step S103: Determine the risk non - linear quantification result of the power system based on the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions.

[0068] In this embodiment, the disaster scenario risk probability of the coupled region is as follows:

[0069]

[0070] In the above formula, r multi is the disaster scenario risk probability of the coupled region, n is the number of regional sub - networks in the coupled region, m i is the number of accident scenarios of the regional sub - network i in the coupled region, is the new occurrence frequency of the j - th accident scenario of the regional sub - network i in the coupled region, and ρ ij is the specified parameter of the analysis object in the j - th accident scenario of the regional sub - network i in the coupled region.

[0071] In one embodiment, the new occurrence frequency of the jth accident scenario of the regional subnet i in the coupling region is as follows:

[0072]

[0073] In the above formula, is the original occurrence frequency of the jth accident scenario of the regional subnet i in the coupling region, is the original occurrence frequency of the accidents of the regional subnet i in the coupling region, is the new occurrence frequency of the accidents of the regional subnet i in the coupling region,

[0074] In one embodiment, the specified parameter includes at least one of the following: load loss probability, expected load loss, expected energy loss.

[0075] In this embodiment, the fuzzy measure of the disaster scenario λ of the multiple coupling regions is as follows:

[0076]

[0077] In the above formula, μ p is the fuzzy measure of the disaster scenario λ of the coupling region p, Q is the number of preset risk parameters, β q is the weight of the preset risk parameter q, is the scoring standard value of the coupling region p for the preset risk parameter q.

[0078] In one embodiment, the scoring standard value of the coupling region p for the preset risk parameter q is as follows:

[0079]

[0080] In the above formula, P is the total number of coupling regions, τ p,q is the score of the coupling region p for the preset risk parameter q, and the score of the coupling region p for the preset risk parameter q is obtained by the expert scoring method.

[0081] In one embodiment, the score of the coupling region p for the preset risk parameter q is as follows:

[0082]

[0083] In the above formula, K is the total number of experts, α k is the weight corresponding to the expert k, τ p,q,k is the score given by the expert k for the coupling region p for the preset risk parameter q.

[0084] In one embodiment, the Fine-Kinney evaluation index is applied to the risk assessment of the power system facing natural disasters, and the grading criteria for three risk parameters, namely accident risk probability (P), exposure frequency (E), and consequence (C), are obtained. The corresponding descriptions are shown in Tables 1, 2, and 3 as follows:

[0085] Table 1

[0086]

[0087] Table 2

[0088]

[0089]

[0090] Table 3

[0091]

[0092] In this embodiment, the non-linear quantization result of the risk of the power system is as follows:

[0093]

[0094] In the above formula, CR is the non-linear quantization result of the risk of the power system, y is the total number of regional subnets, is the first to the second y -1 coupled regions is the set of sorted coupling regions corresponding to the sorted disaster scenario risk probabilities from small to large, f(x′ y ) is the disaster scenario risk probability of the y-th coupling region x′ y in the set of sorted coupling regions, f(x′ y-1 ) is the disaster scenario risk probability of the (y - 1)-th coupling region x′ y in the set of sorted coupling regions, is the λ fuzzy measure of the disaster scenario of the coupling region .

[0095] In a specific embodiment, taking the risk quantification of a transmission line under the "strong wind - heavy rain" compound disaster as an example, for the risk impact under the coupled influence of "strong wind - heavy rain", the transmission line to be analyzed is divided into multiple sub - units. First, based on the Bayesian network, the independent risks of each sub - unit on the transmission line under the influence of three types of disasters, namely "strong wind", "heavy rain", and "strong wind + heavy rain", are calculated respectively. Secondly, based on the analysis of the worst - credible accident scenario, the coupled risk of the transmission line under the three types of disasters is calculated. Then, according to the risk parameter grading standard, each item in the set of possible disaster scenarios is scored for risk, and the scoring weight is calculated through the analytic hierarchy process to obtain the λ - fuzzy measure. Finally, the independent risk and the coupled risk on the transmission line are non - linearly summed to obtain the risk value of the transmission line under the coupled influence of "strong wind - heavy rain".

[0096] Embodiment 2

[0097] Based on the same inventive concept, the present invention also provides a non - linear risk quantification device for grid - coupled disasters. The non - linear risk quantification device for grid - coupled disasters includes:

[0098] A combination module, configured to divide the power system into multiple regional sub - networks, arrange and combine the multiple regional sub - networks to obtain multiple coupled regions;

[0099] An acquisition module, configured to respectively acquire the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions;

[0100] A determination module, configured to determine the non - linear risk quantification result of the power system based on the disaster scenario risk probability and the disaster scenario λ - fuzzy measure of the multiple coupled regions.

[0101] Preferably, the disaster scenario risk probability of the coupled region is as follows:

[0102]

[0103] In the above formula, r multi is the disaster scenario risk probability of the coupled region, n is the number of regional sub - networks in the coupled region, m i is the number of accident scenarios of the regional sub - network i in the coupled region, is the new occurrence frequency of the j - th accident scenario of the regional sub - network i in the coupled region, ρ ij is the specified parameter of the analysis object in the j - th accident scenario of the regional sub - network i in the coupled region.

[0104] Furthermore, the new occurrence frequency of the j - th accident scenario of the regional sub - network i in the coupled region is as follows:

[0105]

[0106] In the above formula, is the original occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, is the original occurrence frequency of the accident of the regional subnet i in the coupling area, is the new occurrence frequency of the accident of the regional subnet i in the coupling area,

[0107] Furthermore, the specified parameter includes at least one of the following: load loss probability, expected load loss, expected energy loss.

[0108] Preferably, the fuzzy measure of the disaster scenario λ of the multiple coupling areas is as follows:

[0109]

[0110] In the above formula, μ p is the fuzzy measure of the disaster scenario λ of the coupling area p, Q is the number of preset risk parameters, β q is the weight of the preset risk parameter q, is the scoring standard value of the coupling area p for the preset risk parameter q.

[0111] Furthermore, the scoring standard value of the coupling area p for the preset risk parameter q is as follows:

[0112]

[0113] In the above formula, P is the total number of coupling areas, τ p,q is the score of the coupling area p for the preset risk parameter q, and the score of the coupling area p for the preset risk parameter q is obtained by the expert scoring method.

[0114] Furthermore, the score of the coupling area p for the preset risk parameter q is as follows:

[0115]

[0116] In the above formula, K is the total number of experts, α k is the weight corresponding to the expert k, τ p,q,k is the score of the expert k for the coupling area p for the preset risk parameter q.

[0117] Preferably, the risk non-linear quantization result of the power system is as follows:

[0118]

[0119] In the above formula, CR is the risk non-linear quantization result of the power system, y is the total number of regional subnets, is from the 1st to the 2nd y -1 coupling areas Sorted set of coupling regions corresponding to the sorted disaster scenario risk probabilities from smallest to largest, f(x′ y ) is the disaster scenario risk probability of the y-th coupling region x′ in the sorted set of coupling regions y . f(x′ y-1 ) is the disaster scenario risk probability of the (y - 1)-th coupling region x′ in the sorted set of coupling regions y . is the λ fuzzy measure of the disaster scenario of the coupling region .

[0120] Embodiment 3

[0121] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a risk non-linear quantification method for grid coupling disasters in the above embodiment.

[0122] Embodiment 4

[0123] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this 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. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of a method for non-linear quantification of risks for grid-coupled disasters in the above embodiments.

[0124] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0125] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0126] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and this instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one or more processes and / or boxes. Figure 1 One process or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A non-linear quantization method for risks facing grid-coupled disasters, characterized in that, The method includes: Dividing the power system into multiple regional subnets, arranging and combining the multiple regional subnets to obtain multiple coupled regions; Respectively obtaining the disaster scenario risk probability and the disaster scenario λ fuzzy measure of the multiple coupled regions; Determining the risk non-linear quantization result of the power system based on the disaster scenario risk probability and the disaster scenario λ fuzzy measure of the multiple coupled regions.

2. The method according to claim 1, characterized in that The disaster scenario risk probability of the coupled region is as follows: In the above formula, r multi is the disaster scenario risk probability of the coupling area, n is the number of regional subnets in the coupling area, and m i is the number of accident scenarios of the regional subnet i in the coupling area. is the new occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, and ρ ij is the specified parameter of the analysis object in the j-th accident scenario of the regional subnet i in the coupling area.

3. The method according to claim 2, wherein The new occurrence frequency of the j-th accident scenario of the regional subnet i in the coupled region is as follows: In the above formula, is the original occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, is the original occurrence frequency of the accident of the regional subnet i in the coupling area, is the new occurrence frequency of the accident of the regional subnet i in the coupling area, 4. The method according to claim 2, wherein The specified parameter includes at least one of the following: load loss probability, load expected loss, energy loss expectation.

5. The method according to claim 1, wherein The disaster scenario λ fuzzy measure of the multiple coupled regions is as follows: In the above formula, μ p is the fuzzy measure of the disaster scenario λ in the coupling area p, Q is the number of preset risk parameters, and β q is the weight of the preset risk parameter q, is the standard value of the score for the coupling area p for the preset risk parameter q.

6. The method according to claim 5, wherein The scoring standard value of the coupled region p for the preset risk parameter q is as follows: In the above formula, P is the total number of coupling regions, and τ p,q is the score of the coupling region p for the preset risk parameter q, and the score of the coupling region p for the preset risk parameter q is obtained by the expert scoring method.

7. The method according to claim 6, wherein The score of the coupled region p for the preset risk parameter q is as follows: In the above formula, K is the total number of experts, and α k is the weight corresponding to expert k, and τ p,q,k is the score given by expert k for the preset risk parameter q with respect to the coupling region p.

8. The method according to claim 1, characterized in that, The risk non-linear quantization result of the power system is as follows: In the above formula, CR is the non-linear quantitative result of the risk of the power system, and y is the total number of regional subnets. from the 1st to the 2nd y -1 coupled regions is the sorted set of coupled regions corresponding to the sorted order of the risk probabilities of disaster scenarios from small to large, and f(x′ y ) is the risk probability of the disaster scenario of the y-th coupled region x′ in the sorted set of coupled regions y , and f(x′ y-1 ) is the risk probability of the disaster scenario of the (y - 1)-th coupled region x′ in the sorted set of coupled regions y . is the λ fuzzy measure of the disaster scenario of the coupled region .

9. A risk non-linear quantification device for grid-coupled disasters, characterized in that, The device includes: A combination module, configured to divide the power system into multiple regional subnets, arrange and combine the multiple regional subnets to obtain multiple coupled regions; An acquisition module, configured to respectively obtain the disaster scenario risk probability and the disaster scenario λ fuzzy measure of the multiple coupled regions; A determination module, configured to determine the risk non-linear quantization result of the power system based on the disaster scenario risk probability and the disaster scenario λ fuzzy measure of the multiple coupled regions.

10. The device according to claim 9, characterized in that, The disaster scenario risk probability of the coupled region is as follows: In the above formula, r nulti is the risk probability of the disaster scenario in the coupling area, n is the number of regional subnets in the coupling area, m i is the number of accident scenarios of the regional subnet i in the coupling area, is the new occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, ρ ij is the specified parameter of the analysis object in the j-th accident scenario of the regional subnet i in the coupling area.

11. The device according to claim 10, wherein, The new occurrence frequency of the j-th accident scenario of the regional subnet i in the coupled region is as follows: In the above formula, is the original occurrence frequency of the j-th accident scenario of the regional subnet i in the coupling area, is the original occurrence frequency of the accident of the regional subnet i in the coupling area, is the new occurrence frequency of the accident of the regional subnet i in the coupling area, 12. The device according to claim 10, wherein The specified parameter includes at least one of the following: load loss probability, load expected loss, energy loss expectation.

13. The device according to claim 9, characterized in that The disaster scenario λ fuzzy measure of the multiple coupled regions is as follows: In the above formula, μ p is the fuzzy measure of the disaster scenario λ in the coupling region p, Q is the number of preset risk parameters, and β q is the weight of the preset risk parameter q, is the standard value of the score for the coupling region p with respect to the preset risk parameter q.

14. The device according to claim 13, wherein The scoring standard value of the coupled region p for the preset risk parameter q is as follows: In the above formula, P is the total number of coupling regions, and τ p,q is the score of the coupling region p for the preset risk parameter q, and the score of the coupling region p for the preset risk parameter q is obtained by the expert scoring method.

15. The device according to claim 14, characterized in that, The score of the coupled region p for the preset risk parameter q is as follows: In the above formula, K is the total number of experts, and α k is the weight corresponding to expert k, and τ p,q,k is the score given by expert k for the preset risk parameter q for the coupling region p.

16. The device according to claim 9, characterized in that The risk non-linear quantization result of the power system is as follows: In the above formula, CR is the risk non-linear quantification result of the power system, y is the total number of regional subnets, from the 1st to the 2nd y -1 coupled regions is the sorted set of coupled regions corresponding to the sorted risk probabilities of disaster scenarios from small to large, f(x′ y ) is the risk probability of the disaster scenario of the y-th coupled region x′ in the sorted set of coupled regions y , f(x′ y-1 ) is the risk probability of the disaster scenario of the (y - 1)-th coupled region x′ in the sorted set of coupled regions y , is the λ fuzzy measure of the disaster scenario of the coupled region .

17. A computer device, characterized in that, Including: One or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the risk non-linear quantization method for grid-coupled disasters as described in any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed, the risk non-linear quantization method for grid-coupled disasters as described in any one of claims 1 to 8 is implemented.

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