Identification method and device for key line of power transmission network and related product

Abstract

The invention relates to the technical field of power systems, and discloses an identification method and device for a key line of a power transmission network and a related product. The method comprises the following steps: acquiring first power flow data of a to-be-evaluated line after any operation line except the to-be-evaluated line is cut off in different cut-off scenes; calculating a power flow influence entropy index value of the to-be-evaluated line based on the first power flow data and the line capacity data of the to-be-evaluated line; obtaining second power flow data of any target operation line after the to-be-evaluated line is disconnected; calculating a power flow distribution entropy index value of the to-be-evaluated line based on the second power flow data and the line capacity data of the target operation lines; and judging whether the line to be evaluated is a key line based on a comprehensive power flow entropy index value generated according to the power flow influence entropy index value and the power flow distribution entropy index value. According to the method and the device, the criticality of the to-be-evaluated line can be identified more accurately by utilizing the comprehensive indexes which reflect the dual characteristics that the to-be-evaluated line is influenced by other lines and the other lines are influenced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power systems, and in particular to a method and device for identifying key lines of a transmission network and related products. Background Art

[0002] In the field of power systems, line cascading tripping refers to the phenomenon that after a line in the power system trips due to a fault, it causes the redistribution of system power flow, resulting in other lines tripping one after another due to overload, overvoltage or other abnormal conditions. Among them, there are some critical lines that are easily affected by the disconnected lines and are also easily affected by other normally operating lines. These critical lines will accelerate the vicious chain evolution of faults. Therefore, accurately identifying the critical lines in the power grid has important research significance and application value for improving the reliability of the power system.

[0003] The existing key line identification method generally evaluates the importance of the line based on the power grid operation status to identify the key line. However, this method fails to comprehensively consider the impact of various factors on the line identification results, and the accuracy of the identification results still needs to be further improved. Summary of the invention

[0004] In view of the above situation, the embodiments of the present disclosure provide a method, device and related products for identifying key lines of a power transmission network, aiming to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for identifying a key line of a transmission network, the method comprising:

[0006] For any target breaking scenario in the preset breaking scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected is obtained in the target breaking scenario;

[0007] After calculating a plurality of first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated, a first index value of the line to be evaluated under the power flow influence entropy index is calculated based on each of the first load rates; the power flow influence entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated;

[0008] In the target disconnection scenario, respectively obtaining the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected;

[0009] After calculating a plurality of second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines, a second index value of the line to be evaluated under the power flow distribution entropy index is calculated based on each of the second load rates; the power flow distribution entropy index is used to measure the degree of influence of the disconnection of the line to be evaluated on the distribution balance of the load rate of the power grid line;

[0010] Calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight;

[0011] According to the comprehensive power flow entropy index value and a preset threshold, it is judged whether the line to be evaluated is a key line to obtain a judgment result.

[0012] In a second aspect, an embodiment of the present disclosure further provides a device for identifying a key line of a transmission network, the device comprising:

[0013] A first acquisition module is used for acquiring, for any target disconnection scenario in a preset disconnection scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected in the target disconnection scenario;

[0014] A first calculation module is used to calculate a first index value of the line to be evaluated under a power flow influence entropy index based on each of the first load rates after calculating a plurality of first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated; the power flow influence entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated;

[0015] A second acquisition module is used to respectively acquire the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected in the target disconnection scenario;

[0016] A second calculation module is used to calculate a second index value of the line to be evaluated under the power flow distribution entropy index based on each of the second load rates after calculating multiple second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines; the power flow distribution entropy index is used to measure the degree of influence of the disconnection of the line to be evaluated on the balance of the load rate distribution of the power grid line;

[0017] A third calculation module, used to calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight;

[0018] The judgment module is used to judge whether the line to be evaluated is a key line according to the comprehensive power flow entropy index value and a preset threshold value, and obtain a judgment result.

[0019] In a third aspect, an embodiment of the present disclosure further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the steps of the above-mentioned method for identifying key lines of a power transmission network.

[0020] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the steps of the above-mentioned method for identifying key lines of the transmission network.

[0021] In a fifth aspect, an optional implementation of the present disclosure further provides a computer program product, wherein the computer program product carries a program code, and the program code includes instructions that can be used to execute the steps of the method for identifying critical lines of a transmission network as described in the first aspect.

[0022] By means of the above technical scheme, the identification method, device and related products of the key lines of the transmission network provided by the embodiments of the present disclosure, based on the entropy theory, creatively propose a comprehensive power flow entropy index, which is generated based on the power flow impact entropy index and the power flow distribution entropy index in different line disconnection scenarios. Among them, the power flow impact entropy index can reflect the characteristics of the line to be evaluated being affected by other lines, and the power flow distribution entropy index can reflect the characteristics of the line to be evaluated affecting other lines. Therefore, the key line identification scheme provided by this embodiment can take into account the dual characteristics of the line to be evaluated, and more accurately evaluate the criticality of the line to be evaluated at the power flow transfer level, thereby significantly improving the system's cascading failure defense effect.

[0023] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0025] Figure 1 A schematic diagram of multiple fault chains provided by an embodiment of the present disclosure is shown;

[0026] Figure 2 A schematic diagram of a flow chart of a method for identifying a key line of a transmission network provided by an embodiment of the present disclosure is shown;

[0027] Figure 3A schematic diagram of the structure of the IEEE39 node system provided by an embodiment of the present disclosure is shown;

[0028] Figure 4 A bar chart showing the weighted power flow impact entropy index value and the weighted power flow distribution entropy index value of each line in the system;

[0029] Figure 5 A bar chart showing the comprehensive power flow entropy index values ​​of each line in the system;

[0030] Figure 6 A schematic diagram of a solution process of a cascading failure simulation model provided by an embodiment of the present disclosure is shown;

[0031] Figure 7 A comparison diagram of the cumulative probability distribution of load loss rates under different capacity expansion scenarios provided by an embodiment of the present disclosure is shown;

[0032] Figure 8 A comparison diagram of the cumulative probability distribution of load loss rate under different methods provided by the embodiments of the present disclosure is shown;

[0033] Fig. 9 A schematic diagram of the structure of a device for identifying key lines of a power transmission network provided by an embodiment of the present disclosure is shown;

[0034] Fig.10 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the technical solutions of the present disclosure will be clearly and completely described below in combination with the specific embodiments of the present disclosure and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0036] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such usage is interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "including" and its variants are to be interpreted as open-ended terms meaning "including but not limited to".

[0038] As mentioned above, during the line chain tripping process, there are some key lines that are easily affected by the disconnected lines and are also easy to affect other normally operating lines. Figure 1 A schematic diagram of multiple fault chains provided by an embodiment of the present disclosure is shown. Figure 1 The evolution process of three fault chains in the transmission network is shown in FIG. Among them, fault chain 1 starts with a line a fault, which in turn leads to a line b fault; fault chain s starts with a line c fault, which causes a line d fault; and fault chain q is a line e fault that leads to a line f fault. In addition, the faults of line b, line d, and line f will all cause line g faults, and the line g fault will further cause line h, line l, and line r faults. Here, line g can be defined as a critical line. Critical lines will accelerate the vicious chain evolution of faults. Therefore, it is of great significance to accurately identify critical lines in the power grid. Existing critical line identification methods generally evaluate the importance of lines based on the operating status of the power grid to identify critical lines. However, this method fails to comprehensively consider the influence of various factors on the line identification results, and the accuracy of the identification results still has room for further improvement. Based on this, the present invention proposes a method, device, and related products for identifying critical lines in a transmission network. The present disclosure is described in detail below through specific embodiments.

[0039] To facilitate understanding of this embodiment, a method for identifying a key line of a transmission network disclosed in an embodiment of the present disclosure is first introduced in detail. The execution subject of the method for identifying a key line of a transmission network provided in an embodiment of the present disclosure is generally a computer device with a certain computing capability, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, etc. In some possible implementations, the method for identifying a key line of a transmission network may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0040] Figure 2 The flowchart of the method for identifying the key line of the power transmission network provided by the embodiment of the present disclosure is shown. Figure 2 It can be seen that the embodiment of the present disclosure at least includes steps S201-S206:

[0041] S201: For any target breaking scenario in a preset breaking scenario set, first power flow data of the line to be evaluated is obtained after any operating line other than the line to be evaluated in the target power system is disconnected in the target breaking scenario.

[0042] It should be noted that the present disclosure studies power flow overload type cascading faults, and therefore, in the embodiments of the present disclosure, only one line is disconnected in one fault stage.

[0043] In this step, the preset disconnection scenario set is obtained by processing the total number of lines of the target power system and the fault stage of the line to be evaluated based on the principle of permutation and combination. In any target disconnection scenario, the first power flow data of the line to be evaluated can be obtained after any operating line other than the line to be evaluated is disconnected. For example, in addition to the line to be evaluated, there are 5 operating lines in the target power system, then these 5 lines can be disconnected separately to obtain 5 first power flow data. Here, the first power flow data can be current, power, etc. During implementation, for example, the matpower tool in the matlab software can be used to perform fault simulation on the target power system, and the relevant functions can be called to obtain each first power flow data.

[0044] S202: After calculating multiple first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated, calculate the first index value of the line to be evaluated under the power flow impact entropy index based on each of the first load rates; the power flow impact entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated.

[0045] After obtaining a plurality of first power flow data, each first power flow data can be divided by the line capacity data of the line to be evaluated to obtain a plurality of first load rates, that is, the first load rate can be calculated according to the following formula:

[0046]

[0047] in, represents the first power flow data of the line i to be evaluated after the operating line j is disconnected under the target disconnection scenario ω; F i,max Indicates the line capacity data of the line to be evaluated.

[0048] It is understandable that the type of the first power flow data is the same as the type of the line capacity data. For example, if the first power flow data is current, the line capacity data is the line rated current. In specific implementation, for example, the first load rate under each target disconnection scenario can be added to obtain the first index value of the line to be evaluated. Here, the larger the value of the power flow impact entropy index, the more lines in the target power system will cause the line to be evaluated to be affected, and the more important the line; conversely, the less important the line is.

[0049] S203: In the target disconnection scenario, second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected is respectively obtained.

[0050] S204: After calculating multiple second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines, calculate the second index value of the line to be evaluated under the power flow distribution entropy index based on each of the second load rates; the power flow distribution entropy index is used to measure the impact of the disconnection of the line to be evaluated on the balance of the load rate distribution of the power grid line.

[0051] After obtaining a plurality of second power flow data, each second power flow data may be divided by the line capacity data of the corresponding target operation line to obtain a plurality of second load rates. That is, the second load rate is calculated according to the following formula:

[0052]

[0053] in, represents the second power flow data of the target operating line j after the line to be evaluated is disconnected in the target disconnection scenario ω, F j,,ax Indicates the line capacity data of the target operating line j.

[0054] In specific implementation, for example, the second load rate under each target disconnection scenario can be added to obtain the second index value of the line to be evaluated. The power flow distribution entropy index can measure the impact of line disconnection on the balance of load rate distribution of power grid lines. A large power flow distribution entropy indicates that the load rate corresponding to the power flow on each line is evenly distributed in different load rate intervals; a small power flow distribution entropy indicates that the load rate corresponding to the power flow on each line is concentrated, and most lines have similar load rates. The power flow borne by the line is basically positively correlated with the line capacity. If the system is disturbed when the power flow distribution entropy is large, the lines in the high load rate interval are likely to be overloaded, while the line load rate in the low load rate interval may be low, and the line capacity is not fully utilized, resulting in a waste of resources.

[0055] S205: Calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, and the second index value and the preset second weight.

[0056] That is, the comprehensive power flow entropy index value can be calculated according to the following formula:

[0057] H=ω1H x +ω2H y

[0058] Among them, H represents the comprehensive power flow entropy index value, ω1 represents the preset first weight, and H x represents the first index value, ω2 represents the preset second weight, H y The preset first weight and the preset second weight can be set according to actual needs, which is not limited in the embodiment of the present disclosure.

[0059] S206: judging whether the line to be evaluated is a critical line according to the comprehensive power flow entropy index value and a preset threshold, and obtaining a judgment result.

[0060] The preset threshold can be set according to actual needs. For example, the preset threshold is set to 0.9, if the comprehensive power flow entropy index value is greater than or equal to 0.9, the line to be evaluated is judged to be a critical line; otherwise, the line to be evaluated is judged to be a non-critical line.

[0061] from Figure 1 It can be seen from the method shown that the disclosed embodiment, based on the entropy theory, creatively proposes a comprehensive power flow entropy index, which is generated based on the power flow impact entropy index and the power flow distribution entropy index under different line disconnection scenarios. Among them, the power flow impact entropy index can reflect the characteristics of the line to be evaluated being affected by other lines, and the power flow distribution entropy index can reflect the characteristics of the line to be evaluated affecting other lines. Therefore, the key line identification scheme provided by this embodiment can take into account the dual characteristics of the line to be evaluated, and more accurately evaluate the criticality of the line to be evaluated at the power flow transfer level, thereby significantly improving the system's cascading failure defense effect.

[0062] Further, in order to better illustrate the process of the above-mentioned method for identifying the key line of the transmission network, as a refinement and extension of the above-mentioned embodiment, the embodiment of the present invention provides several optional embodiments, but is not limited thereto, which are specifically as follows:

[0063] It has been found through research that entropy is a measure of uncertainty in random events described in information theory, and the size of its value reflects the size of the uncertainty of things. When the power flow of the line is concentrated in the low load rate interval or the high load rate interval, due to the similar concentration of the distribution, their entropy values ​​may appear the same, and the load rate at this time may have certain limitations in reflecting the true characteristics of the line. Based on this, in a possible implementation of the present disclosure, the first index value of the line to be evaluated under the power flow influence entropy index is calculated based on each of the first load rates, including: according to the first preset load rate interval set, each of the first load rates is divided into intervals to obtain a first division result; based on the first division result, the first probability that the load rate of the line to be evaluated falls in each interval; and the first load rate data corresponding to each interval is calculated; the first load rate data is used to measure the overall level of all load rates in the interval; based on each of the first probabilities and each of the first load rate data, the intermediate power flow influence entropy index value of the line to be evaluated is calculated; based on the intermediate power flow influence entropy index value under each of the target breaking scenarios, the first index value is obtained.

[0064] In this embodiment, in order to obtain the entropy index value of the power flow impact, each first load rate can be divided into intervals according to the first preset load rate interval set to obtain a first division result; then, based on the first division result, the first probability that the load rate of the line to be evaluated falls in each interval is calculated; and the first load rate data corresponding to each interval is calculated. Exemplarily, the first preset load rate interval set includes the following load rate intervals: (0, 20], (20, 40], (40, 60], (60, 80], (80, 100]. Multiple first load rates are: load rate 1 (25%), load rate 2 (25%), load rate 3 (45%), load rate 4 (50%), load rate 5 (55%), load rate 6 (80%), load rate 7 (75%), load rate 8 (10%), load rate 9 (59%), load rate 10 (20%). These 10 load rates are divided into intervals to obtain a first division result: load rates 8 and 10 fall in the load rate interval (0, 20]; load rates 1 and 2 fall in the load rate interval (20, 40]; Load rates 3, 4, 5, and 9 fall within the load rate interval (40, 60]; load rates 6 and 7 fall within the load rate interval (60, 80]. According to the first division result, it can be calculated that the first probability that the load rate of the line to be evaluated falls within the load rate interval (0, 20] is 20%; the first probability that the load rate falls within the load rate interval (20, 40] is 20%; the first probability that the load rate falls within the load rate interval (40, 60] is 40%; the first probability that the load rate falls within the load rate interval (60, 80] is 20%; the first probability that the load rate falls within the load rate interval (80, 100] is 0. That is, the first probability that the load rate falls within the load rate interval (Uk, Uk+1) is calculated according to the following formula, where, Indicates the number of load rates within the load rate interval (Uk,Uk+1):

[0065]

[0066] Then, according to the first division result, the first load rate data corresponding to each interval is calculated. In implementation, for any interval A, the median of each load rate in interval A can be used as the first load rate data corresponding to interval A; the mean of each load rate in interval A can also be used as the first load rate data corresponding to interval A, that is, the first load rate data W1(k) is determined according to the following formula:

[0067]

[0068] Then, based on each first probability and each first load rate data, the intermediate power flow impact entropy index value of the line to be evaluated is calculated. Specifically, each first probability and the corresponding first load rate data are multiplied to obtain a plurality of products, and then the plurality of products are added to obtain the intermediate power flow impact entropy index value of the line to be evaluated in the target disconnection scenario.

[0069] Finally, based on the intermediate power flow impact entropy index value under each target disconnection scenario, a power flow impact entropy index value is generated. For example, the intermediate power flow impact entropy index values ​​under each target disconnection scenario can be added to obtain a first index value. For another example, the first index value can be calculated according to the following formula:

[0070]

[0071] Where i represents the line to be evaluated, H i,x represents the first index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W1(k) represents the first load rate data corresponding to the load rate interval (Uk, Uk+1), and P1(k) represents the first probability that the load rate of the line to be evaluated falls in the interval (Uk, Uk+1).

[0072] This embodiment determines the weight of each load interval by calculating the probability of occurrence of each load rate interval, so that the distribution characteristics of the load rate are considered more carefully. The load rate interval with a high weight has a greater impact on the overall result, so that it can more accurately reflect the actual load situation of the line to be evaluated, thereby significantly improving the accuracy of the power flow impact entropy index value.

[0073] In one possible implementation, the second index value of the line to be evaluated under the power distribution entropy index is calculated based on each of the second load rates, including: dividing each of the second load rates into intervals according to a second preset load rate interval set to obtain a second division result; based on the second division result, calculating the second probability that the load rate of the target operating line falls in each interval; and calculating the second load rate data corresponding to each interval; the second load rate data is used to measure the overall level of all load rates in the interval; based on each of the second probabilities and each of the second load rate data, the intermediate power distribution entropy index value of the line to be evaluated is calculated; based on the intermediate power distribution entropy index value under each of the target breaking scenarios, the second index value is obtained.

[0074] In specific implementation, in order to obtain the power flow distribution entropy index value, each second load rate can be divided into intervals according to the second preset load rate interval set to obtain a second division result; based on the second division result, the second probability that the load rate of the target operating line falls in each interval and the second load rate data corresponding to each interval are calculated. Specifically, the second probability P2(z) and the second load rate data W2(z) can be calculated according to the following formula. Wherein, Indicates the total number of load factors in the load factor interval (Uz, Uz+1).

[0075]

[0076] The calculation process of the second probability and the second load rate data in this embodiment is similar to the calculation process of the first probability and the first load rate data described above, and will not be repeated here.

[0077] After obtaining each second probability and each second load rate data, each second probability and the corresponding second load rate data can be multiplied to obtain multiple products, and then the multiple products are added to obtain the intermediate flow distribution entropy index value of the line to be evaluated under the target disconnection scenario.

[0078] Finally, based on the intermediate power flow distribution entropy index value under each target disconnection scenario, a second index value is generated. For example, the intermediate power flow distribution entropy index value under each target disconnection scenario can be added to obtain the second index value. For another example, the second index value can be calculated according to the following formula:

[0079]

[0080] Where i represents the line to be evaluated, H i,y represents the second index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W2(z) represents the second load rate data corresponding to the load rate interval (Uz, Uz+1), and P2(z) represents the second probability that the load rate of the target operating line falls in the interval (Uz, Uz+1).

[0081] Similar to the aforementioned power flow impact entropy index, this embodiment determines the weight of each load interval by calculating the probability of occurrence of each load rate interval, so that the distribution characteristics of the load rate of each target operating line are considered more carefully. The load rate interval with a high weight has a greater impact on the overall result, so that it can more accurately reflect the actual load conditions of each operating line after the line to be evaluated is disconnected, thereby significantly improving the accuracy of the power flow distribution entropy index value.

[0082] In a possible implementation, the method further includes: using an entropy weight method to process the acquired flow impact entropy index value set and flow distribution entropy index value set to obtain the preset first weight and the preset second weight.

[0083] In specific implementation, the corresponding power flow impact entropy index value and power flow distribution entropy index value can be calculated for multiple lines in the target power system, and each power flow impact entropy index value constitutes a power flow impact entropy index value set, and each power flow distribution entropy index value constitutes a power flow distribution entropy index value set. Then, the elements in the two sets are standardized; the proportion of each index value under each index is calculated; the difference coefficient of each index is calculated; finally, the preset first weight and the preset second weight are calculated.

[0084] The weight determination method provided in this embodiment does not rely on people's subjective experience. It uses the entropy weight method to assign weights to the weighted tidal impact entropy index and the weighted tidal distribution entropy index according to the distribution characteristics of the index data and the amount of information contained. The weight allocation is more objective and accurate, thereby improving the credibility of the evaluation results of the comprehensive tidal indicators.

[0085] The embodiment of the present disclosure also provides a method for identifying key lines of a power transmission network, comprising the following steps:

[0086] Step S1: for any target breaking scenario in the preset breaking scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected is obtained in the target breaking scenario.

[0087] Step S2: Calculate and obtain a plurality of first load rates according to the first power flow data and the line capacity data of the line to be evaluated.

[0088] Step S3: According to the first preset load rate interval set, each first load rate is divided into intervals to obtain a first division result.

[0089] Step S4: Based on the first division result, calculate the first probability that the load rate of the line to be evaluated falls within each interval; and calculate the first load rate data corresponding to each interval.

[0090] Step S5: Based on each first probability and each first load rate data, the intermediate power flow impact entropy index value of the line to be evaluated is calculated.

[0091] Step S6: Based on the intermediate power flow impact entropy index value under each target interruption scenario, a first index value is obtained. Specifically, the first index value is calculated according to the following formula:

[0092]

[0093] Step S7: in the target disconnection scenario, the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected is obtained respectively.

[0094] Step S8: Calculate and obtain a plurality of second load rates according to each second power flow data and the line capacity data of each target operating line.

[0095] Step S9: dividing each second load rate into intervals according to the second preset load rate interval set to obtain a second division result.

[0096] Step S10: Based on the second division result, calculate the second probability that the load rate of the target operating line falls within each interval; and calculate the second load rate data corresponding to each interval.

[0097] Step S11: Based on each second probability and each second load rate data, the intermediate power flow distribution entropy index value of the line to be evaluated is calculated.

[0098] Step S12: Based on the intermediate power flow distribution entropy index value in each target breaking scenario, a second index value is obtained. Specifically, the second index value is calculated according to the following formula:

[0099]

[0100] Step S13: using the entropy weight method, the acquired flow influence entropy index value set and flow distribution entropy index value set are processed to obtain a preset first weight and a preset second weight.

[0101] Step S14: Calculate the comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight.

[0102] Step S15: judging whether the line to be evaluated is a key line according to the comprehensive power flow entropy index value and the preset threshold value, and obtaining a judgment result.

[0103] In order to prove the effectiveness of the indicators proposed in the embodiments of the present disclosure, the IEEE39 node system is used to verify the proposed indicators. Figure 3 FIG. 1 shows a schematic diagram of the structure of the IEEE 39 node system provided by the embodiment of the present disclosure. Figure 3 As shown, the system contains 46 branches, 10 generators (G) and 19 loads.

[0104] Since the disconnection of key lines during a cascading failure may cause serious load loss, this verification is carried out from the perspective of load loss risk. Failure of the generator outgoing line or transformer branch will cause great harm to the safe operation of the system. Such branches are already key branches, so they are not taken into account in this verification process. In addition, when a cascading failure occurs, it must be blocked as soon as possible to prevent a large-scale power outage. Therefore, this verification identifies the key lines from the second fault stage.

[0105] Firstly, the calculation method of the power flow influence entropy index and the power flow distribution entropy index mentioned above is used to calculate the index values ​​of all lines except the generator outgoing line or transformer branch in the IEEE39 node system, and the index values ​​are normalized. Figure 4 The bar charts of the weighted entropy index values ​​of the power flow impact and the weighted entropy index values ​​of the power flow distribution of each line in the system are shown. Then, the entropy weight method is used to assign weight values ​​to each index, as shown in Table 1 below.

[0106] Table 1 Index weight values

[0107]

[0108] Based on the normalized index values ​​of the lines and the weight values ​​shown in Table 1, the comprehensive power flow index values ​​of each line are calculated. Figure 5 The bar chart of the comprehensive power flow entropy index value of each line in the system is shown. According to the preset conditions and the comprehensive power flow index value of each line, the more important key lines are selected.

[0109] Then, for the key line identification results, the key lines are expanded, the load loss of the system after expansion is counted, and the cumulative probability distribution is compared to verify the effectiveness of key line identification. Specifically, all lines are first arranged in descending order according to the comprehensive power flow entropy, and the first 9 lines are divided into 3 groups. The grouping is shown in Table 2 below, and each group contains 3 lines. Among them, the first group is ranked from 1 to 3, the second group is ranked from 4 to 6, and the third group is ranked from 7 to 9.

[0110] Table 2 Line grouping

[0111]

[0112] Then, the capacity of each group of lines is expanded by 300MW, and then a cascading failure simulation is performed. For the line capacity expansion method, it can be implemented in the actual system by replacing the line, adding parallel lines, and other measures. Figure 6 FIG. 1 is a schematic diagram showing a solution process of a cascading failure simulation model provided by an embodiment of the present disclosure. Figure 6 As shown in the figure, when solving the model, we can first set the grid parameters (the basic parameters of the system's transmission line network), the system initial state, the number of simulations m = 1, and the convergence threshold of the number of fault chains to δ max; Set random faults in network Nk (k = 1, 2); Update network topology parameters; Determine whether to split into two or more subnets; If so, record the load loss; If not, balance the subnet power (adopt the balancing strategy in document 1, i.e., the paper "An improved OPA model and blackout risk assessment"), and calculate the DC power flow of the power grid; Determine whether the power grid contains overloaded lines; If so, calculate the outage probability of the overloaded line, select the disconnected line by Monte Carlo sampling method, and update the network topology parameters; If not, record the load loss; Determine whether the number of fault chains is less than or equal to the convergence threshold of the number of fault chains; If so, end; If not, return to the first step and re-simulate. Among them, the outage probability of the overloaded line is determined by the following formula (adopting the outage probability calculation method in document 2, i.e., "Large Power Grid Chain Failure Defense Strategy Based on Preventive-Emergency Coordinated Control [J]. Power Automation Equipment"):

[0113]

[0114] in, is the power flow data of line i under scenario ω; p0 is the hidden fault probability when the line power flow is not overloaded; p1 is the probability that the line power flow exceeds the transmission limit value F i,lim The probability of outage at .

[0115] The convergence criterion of the number of fault chains is the variance coefficient corresponding to the load loss, as shown in the following formula:

[0116]

[0117] When the variance coefficient is less than the threshold value δ max When , the load loss of the newly added fault chain has little effect on all the recorded load losses, so it can be considered that the number of fault chains has reached convergence, which is sufficient to analyze the risk of cascading failures. m is the load loss of the mth fault chain; V(ΔP m ) is the variance of load loss of m fault chains; E(ΔP m ) is the mean of the load loss of m fault chains. The cumulative probability distribution function F(x) is expressed by the proportion of the number of times the load loss of a large number of fault chains is not less than ΔP to the total number of fault chains, as shown in the following formula:

[0118]

[0119] Among them, m max is the total number of fault chains; I m (ΔP) is the state where the load loss of the mth fault chain is not less than ΔP. Finally, the cumulative probability distribution of the load loss rate under different expansion scenarios can be statistically analyzed. Figure 7The cumulative probability distribution comparison diagram of the load loss rate under different expansion scenarios provided by the embodiment of the present disclosure is shown. Figure 7 It can be clearly seen that the cumulative probability distribution curves of the load loss rate of the line under different expansion scenarios all show a downward trend, with small loss accidents (load loss rate less than 0.05) accounting for a large proportion, while large loss accidents (load loss rate greater than 0.35) accounting for a small proportion. With the decrease of the comprehensive power flow entropy index value (from Group 1 to Group 3), the risk of load loss under the three expansion scenarios also decreases accordingly, and is smaller than the risk of load loss under the non-expansion scenario. For example, the fault chains with load losses of not less than 15% under the three expansion scenarios account for 1.08%, 2.16% and 2.93% of the total number of fault chains, respectively, which is 2.82%, 1.76% and 0.97% less than 3.90% under the non-expansion scenario, respectively. This is because: lines with larger comprehensive power flow entropy are more critical, and expanding the critical lines can significantly reduce the risk of power outages. In summary, the indicators proposed in the embodiments of the present disclosure are effective, and the importance of the critical lines obtained using the proposed indicators is in line with the actual situation.

[0120] In order to further verify the effectiveness and rationality of the proposed method in the critical line identification task, the proposed method is compared with two different methods proposed in Reference 3 (Identification of critical lines in power grid based on vulnerability to shock and disconnection consequences [J]. Power System Protection and Control, 2020, 48(1): 81-87.) and Reference 4 (Identification of critical lines in power grid based on comprehensive index of entropy weight-analytic hierarchy process [J]. China Electric Power, 2020, 53(05): 39-47+55.). Reference 3 comprehensively considers the vulnerability to shock and disconnection consequences to identify critical lines, while Reference 4 comprehensively considers network structure and physical operation characteristics to identify critical lines. The top 5 critical lines in descending order obtained by the proposed method, Reference 3 and Reference 4 are shown in Table 3 below.

[0121] Table 3 Key line identification results under different methods

[0122] Sorting results Methods Reference 3 Reference 4 1 26-28 6-11 16-19 2 22-23 10-13 15-16 3 26-29 23-24 26-27 4 23-24 16-21 1-2 5 28-29 4-5 21-22

[0123] The critical lines identified by the above three methods were expanded by 300MW respectively and then a cascading failure simulation was performed. The cumulative probability distribution of load loss rate under different expansion scenarios was statistically analyzed. Figure 8 The cumulative probability distribution comparison diagram of the load loss rate under different methods provided by the embodiment of the present disclosure is shown. Figure 8It can be seen that the load loss risk obtained by expanding the critical lines identified by the proposed method is significantly lower than the corresponding load loss risk in References 3 and 4. For example, the fault chains with load loss rates not less than 10% under the three methods account for 4.32%, 8.20% and 7.30% of the total number of fault chains, respectively. The number of fault chains of the proposed method is 40% to 50% less than that of other methods. This is because: the comprehensive power flow entropy index proposed in this paper takes into account the dual characteristics of the line being easily affected and easily affecting the disconnection of other lines. Expanding the line with large comprehensive power flow entropy can largely resist the impact of power flow transfer after the disconnection of other lines, thereby reducing the disconnection and reducing the impact on other lines. It can be seen that expanding the critical lines identified by the proposed method can more effectively reduce the load loss and load loss probability of cascading failures.

[0124] Those skilled in the art will appreciate that, in the above method of the specific implementation manner, the writing order of the steps does not mean a strict execution order, but does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0125] It should be noted that, in practical applications, all possible implementations described above can be combined in any manner to form possible embodiments of the present disclosure, which will not be described one by one here.

[0126] Based on the same concept, the embodiment of the present disclosure also provides a device for identifying key lines of a transmission network. Fig. 9 FIG. 1 shows a schematic diagram of a structure of a device for identifying a key line of a transmission network provided by an embodiment of the present disclosure, see Fig. 9 As shown, the key line identification device 900 of the power transmission network provided by the embodiment of the present disclosure includes:

[0127] The first acquisition module 901 is used for acquiring, for any target disconnection scenario in the preset disconnection scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected in the target disconnection scenario;

[0128] A first calculation module 902 is used to calculate a first index value of the line to be evaluated under a power flow influence entropy index based on each of the first load rates after calculating a plurality of first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated; the power flow influence entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated;

[0129] The second acquisition module 903 is used to respectively acquire the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected in the target disconnection scenario;

[0130] A second calculation module 904 is used to calculate a second index value of the line to be evaluated under the power flow distribution entropy index based on each of the second load rates after calculating multiple second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines; the power flow distribution entropy index is used to measure the degree of influence of the disconnection of the line to be evaluated on the balance of the load rate distribution of the power grid line;

[0131] A third calculation module 905 is used to calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight;

[0132] The judgment module 906 is used to judge whether the line to be evaluated is a key line according to the comprehensive power flow entropy index value and a preset threshold value, and obtain a judgment result.

[0133] In a possible implementation, in the above-mentioned device, the first calculation module 902 is used to: divide each of the first load rates into intervals according to a first preset load rate interval set to obtain a first division result; based on the first division result, calculate the first probability that the load rate of the line to be evaluated falls in each interval; and calculate the first load rate data corresponding to each interval; the first load rate data is used to measure the overall level of all load rates in the interval; based on each of the first probabilities and each of the first load rate data, calculate the intermediate power flow impact entropy index value of the line to be evaluated; based on the intermediate power flow impact entropy index value under each of the target breaking scenarios, obtain the first index value.

[0134] In a possible implementation manner, in the above device, the first calculation module 902 is used to calculate the first indicator value according to the following formula:

[0135]

[0136] Where i represents the line to be evaluated, H i,x represents the first index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W1(k) represents the first load rate data corresponding to the load rate interval (Uk, Uk+1), and P1(k) represents the first probability that the load rate of the line to be evaluated falls in the interval (Uk, Uk+1).

[0137] In one possible implementation, in the above-mentioned device, the second calculation module 904 is used to: divide each of the second load rates into intervals according to a second preset load rate interval set to obtain a second division result; based on the second division result, calculate the second probability that the load rate of the target operating line falls in each interval; and calculate the second load rate data corresponding to each interval; the second load rate data is used to measure the overall level of all load rates in the interval; based on each of the second probabilities and each of the second load rate data, calculate the intermediate power flow distribution entropy index value of the line to be evaluated; based on the intermediate power flow distribution entropy index value under each of the target breaking scenarios, obtain the second index value.

[0138] In a possible implementation manner, in the above device, the second calculation module 904 is used to calculate the second indicator value according to the following formula:

[0139]

[0140] Where i represents the line to be evaluated, H i,y represents the second index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W2(z) represents the second load rate data corresponding to the load rate interval (Uz, Uz+1), and P2(z) represents the second probability that the load rate of the target operating line falls in the interval (Uz, Uz+1).

[0141] In a possible implementation, the device further includes a weight determination module, which is used to: utilize an entropy weight method to process the acquired flow impact entropy index value set and flow distribution entropy index value set to obtain the preset first weight and the preset second weight.

[0142] It should be noted that any of the above-mentioned devices for identifying key lines of a power transmission network can implement the above-mentioned method for identifying key lines of a power transmission network in a one-to-one correspondence, which will not be described in detail here.

[0143] Fig.10 FIG. 1 shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Fig.10 As shown, at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0144] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.10 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0145] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0146] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an identification device for the key line of the transmission network at the logical level. The processor executes the program stored in the memory and is specifically used to execute the above method.

[0147] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0148] The electronic device can execute the identification method of the key line of the power transmission network provided by the multiple embodiments of the present disclosure, and realize the identification device of the key line of the power transmission network. Fig. 9 The functions of the illustrated embodiments will not be described in detail in the embodiments of the present disclosure.

[0149] The embodiments of the present disclosure also propose a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, the electronic device can execute the method for identifying key lines of the transmission network provided by multiple embodiments of the present disclosure.

[0150] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may 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-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0155] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0156] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0157] The embodiments of the present disclosure also provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the steps of the method for identifying key lines of the transmission network described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0158] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0159] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity or device including the element.

[0160] It will be appreciated by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may 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-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The above are only embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the scope of the claims of the present disclosure.

Claims

1. A method for identifying key lines of a transmission network, characterized in that: The method comprises: For any target breaking scenario in the preset breaking scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected is obtained in the target breaking scenario; After calculating a plurality of first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated, a first index value of the line to be evaluated under the power flow influence entropy index is calculated based on each of the first load rates; the power flow influence entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated; In the target disconnection scenario, respectively obtaining the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected; After calculating a plurality of second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines, a second index value of the line to be evaluated under the power flow distribution entropy index is calculated based on each of the second load rates; the power flow distribution entropy index is used to measure the degree of influence of the disconnection of the line to be evaluated on the distribution balance of the load rate of the power grid line; Calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight; According to the comprehensive power flow entropy index value and a preset threshold, it is judged whether the line to be evaluated is a key line to obtain a judgment result.

2. The method according to claim 1, characterized in that The calculating, based on each of the first load rates, a first index value of the line to be evaluated under a power flow influence entropy index comprises: According to a first preset load rate interval set, each of the first load rates is divided into intervals to obtain a first division result; Based on the first division result, calculating the first probability that the load rate of the line to be evaluated falls within each interval; and calculating the first load rate data corresponding to each interval; the first load rate data is used to measure the overall level of all load rates in the interval; Based on each of the first probabilities and each of the first load rate data, calculating an intermediate power flow impact entropy index value of the line to be evaluated; The first index value is obtained based on the intermediate power flow impact entropy index value in each of the target breaking scenarios.

3. The method according to claim 2, characterized in that The first index value is calculated according to the following formula: Wherein, i represents the line to be evaluated, H i,x represents the first index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W1(k) represents the first load rate data corresponding to the load rate interval (Uk, Uk+1), and P1(k) represents the first probability that the load rate of the line to be evaluated falls in the interval (Uk, Uk+1).

4. The method according to claim 1, characterized in that: The calculating, based on each of the second load rates, the second index value of the line to be evaluated under the power flow distribution entropy index comprises: According to a second preset load rate interval set, each of the second load rates is divided into intervals to obtain a second division result; Based on the second division result, calculating the second probability that the load rate of the target operating line falls within each interval; and calculating the second load rate data corresponding to each interval; the second load rate data is used to measure the overall level of all load rates in the interval; Based on each of the second probabilities and each of the second load rate data, calculate and obtain an intermediate power flow distribution entropy index value of the line to be evaluated; The second index value is obtained based on the intermediate power flow distribution entropy index value in each of the target breaking scenarios.

5. The method according to claim 4, characterized in that The second index value is calculated according to the following formula: Wherein, i represents the line to be evaluated, H i,y represents the second index value of the line to be evaluated, N represents the total number of lines in the target power system, m represents the fault stage of the line to be evaluated, ω represents the target breaking scenario, W2(z) represents the second load rate data corresponding to the load rate interval (Uz, Uz+1), and P2(z) represents the second probability that the load rate of the target operating line falls in the interval (Uz, Uz+1).

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The obtained power flow impact entropy index value set and power flow distribution entropy index value set are processed by using the entropy weight method to obtain the preset first weight and the preset second weight.

7. A device for identifying key lines of a transmission network, characterized in that: The device comprises: A first acquisition module is used for acquiring, for any target disconnection scenario in a preset disconnection scenario set, first power flow data of the line to be evaluated after any operating line other than the line to be evaluated in the target power system is disconnected in the target disconnection scenario; A first calculation module is used to calculate a first index value of the line to be evaluated under a power flow influence entropy index based on each of the first load rates after calculating a plurality of first load rates according to each of the first power flow data and the line capacity data of the line to be evaluated; the power flow influence entropy index is used to measure the degree of influence of the disconnection of other lines on the line to be evaluated; A second acquisition module is used to respectively acquire the second power flow data of any target operating line in the target power system after the line to be evaluated is disconnected in the target disconnection scenario; A second calculation module is used to calculate a second index value of the line to be evaluated under the power flow distribution entropy index based on each of the second load rates after calculating multiple second load rates according to each of the second power flow data and the line capacity data of each of the target operating lines; the power flow distribution entropy index is used to measure the degree of influence of the disconnection of the line to be evaluated on the balance of the load rate distribution of the power grid line; A third calculation module, used to calculate a comprehensive power flow entropy index value according to the first index value and the preset first weight, the second index value and the preset second weight; The judgment module is used to judge whether the line to be evaluated is a key line according to the comprehensive power flow entropy index value and a preset threshold value, and obtain a judgment result.

8. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor is caused to perform the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, characterized in that: When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device executes the steps of the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product carries a program code, and the program code includes instructions that can be used to execute the steps of any one of the methods according to claims 1-6.

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