Lightning activity analysis method and system for power transmission lines, and device and storage medium

Abstract

The present invention relates to the technical field of lightning protection for power systems. Disclosed are a lightning activity analysis method and system for power transmission lines, and a device and a storage medium. The method comprises: acquiring characteristic parameters of towers of a power transmission line, and discretizing the characteristic parameters to obtain a tower attribute table; performing data mining on data items in the tower attribute table on the basis of an FP-Growth algorithm, so as to obtain frequent 1-itemsets and a frequent pattern tree, establishing a hash linked list on the basis of the frequent 1-itemsets and the frequent pattern tree, and performing association rule mining on the basis of the hash linked list, so as to obtain frequent multi-itemsets; selecting, from the frequent multi-itemsets, association rules using lightning trips as rule consequents, and analyzing the association rules to obtain a lightning activity analysis result; and on the basis of the lightning activity analysis result, establishing a lightning protection strategy for the power transmission line. The present invention improves the applicability of correlations from data analysis and the efficiency of pattern mining, thereby providing data support for lightning protection work on power transmission lines in electric power departments.

Description

Method, system, device and storage medium for analyzing lightning activity of power transmission line TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning protection of power systems, and in particular to a method, system, device and storage medium for analyzing lightning activity of a power transmission line. BACKGROUND

[0002] With the development of power grid power transmission towards high-voltage and high-power, important progress has been made in lightning protection through years of research and practice, and the overall lightning fault rate of the power grid has been continuously reduced. However, due to the characteristics of lightning, such as instantaneous high voltage, large current, strong electromagnetic radiation and random occurrence, lightning is still the first cause of line faults. Soil conductivity is an important variable for calculating the space electromagnetic field around the lightning channel. Many scholars have analyzed and discussed the influence of soil conductivity on lightning electromagnetic wave propagation, and found that soil conductivity is an important factor that cannot be ignored in the analysis of lightning activity.

[0003] In order to further study and analyze the law of lightning activity, most of the existing researches are based on mathematical statistics method, decision tree model and clustering algorithm to analyze the characteristics of lightning activity from the influencing factors such as altitude, ground cover type, slope, slope direction, precipitation, average temperature and other factors in the tower circle domain, to obtain the correlation rules between lightning activity and terrain and meteorological properties. However, most of the databases established by the experiments are not perfect enough, the number of samples needs to be improved, the subjective comparison is more in the data analysis and lacks objectivity, the correlation obtained by the research has certain uncertainty and inadaptability, and in addition, in the current research on lightning activity characteristic correlation information mining, there is no research on soil conductivity as an influencing factor. Soil conductivity is considered to be an important factor affecting the distribution of lightning horizontal electric field and the propagation of lightning electromagnetic wave, and its influence on lightning activity cannot be ignored. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a method, system, device and storage medium for analyzing lightning activity of a power transmission line, which adds soil conductivity in lightning activity correlation information mining to improve the accuracy of the mined information, provides data support for lightning protection work of the power transmission line of the power department, and realizes the construction of differentiated lightning protection strategy to improve the safety and stability of the power system operation.

[0005] In the first aspect, the present application provides a method for analyzing lightning activity of a power transmission line, which comprises:

[0006] Obtaining characteristic parameters of each tower of the power transmission line in a preset area, and discretizing the characteristic parameters to obtain a tower attribute table, wherein the data items in the tower attribute table include lightning parameters and lightning trip, and the lightning parameters include soil conductivity, lightning density and lightning intensity.

[0007] Based on the FP-Growth algorithm, data mining is performed on the data items in the tower attribute table to generate a frequent 1-item set and a frequent pattern tree, a hash linked list is established according to the frequent 1-item set and the frequent pattern tree, and based on the hash linked list, association rule mining is performed to obtain a frequent multi-item set;

[0008] From the frequent multi-item set, an association rule with a lightning trip as a rule consequent is screened out, and the association rule is analyzed to obtain a lightning activity analysis result of a preset area;

[0009] According to the lightning activity analysis result, a lightning protection strategy for a power transmission line is established.

[0010] Further, the step of generating a frequent 1-item set and a frequent pattern tree based on the FP-Growth algorithm and data mining on the data items in the tower attribute table comprises:

[0011] The lightning parameters and the lightning trip are taken as a transaction antecedent and a transaction consequent, respectively, and the support degree of each data item in the tower attribute table is calculated;

[0012] It is judged whether the support degree of each data item is less than a minimum support degree, if yes, the data item is added to a non-frequent item set, otherwise, the data item is added to a frequent 1-item set;

[0013] The data items in the tower attribute table are sorted according to the support degree, and the non-frequent item set is deleted to obtain an updated tower attribute table;

[0014] According to the FP-Growth algorithm and the data items in the updated tower attribute table, a frequent pattern tree is established.

[0015] Further, the step of establishing a hash linked list according to the frequent 1-item set and the frequent pattern tree comprises:

[0016] Each data item in the frequent 1-item set is taken as a first data item, a head node of the hash linked list is established according to the first data item, and the head node comprises a first data item name, a support degree of the first data item, a first pointer, and a second pointer;

[0017] Other data items in a path containing the first data item in the frequent pattern tree are taken as second data items, a subsequent node of the hash linked list is established according to the second data items, and the subsequent node comprises a second data item name, a third pointer, and a number of simultaneous occurrences of the corresponding first data item.

[0018] Further, the step of establishing a subsequent node of the hash linked list according to the second data items comprises:

[0019] The second data item is inserted into the hash chain table as a subsequent node of the corresponding head node in descending order of support degree and based on an open chain method.

[0020] Further, the step of performing association rule mining based on the hash chain table to obtain a frequent multi-item set comprises:

[0021] The hash chain table is traversed to obtain a frequent 2-item set, and it is determined whether two adjacent nodes in the subsequent nodes corresponding to each head node are a frequent 2-item set, if yes, the two adjacent nodes are merged to obtain an updated hash chain table.

[0022] The updated hash chain table is traversed to obtain a frequent multi-item set.

[0023] Further, the step of screening an association rule with lightning trip-out as a rule consequent from the frequent multi-item set, and analyzing the association rule to obtain a lightning activity analysis result of a preset area comprises:

[0024] The association rule with lightning trip-out as a rule consequent is screened from the frequent multi-item set, and the lightning parameters and the lightning trip-out are taken as a transaction antecedent and a transaction consequent respectively, and the confidence of the association rule is calculated.

[0025] The association rule with the confidence meeting a minimum confidence requirement is analyzed to obtain the lightning parameters related to the lightning trip-out in the preset area.

[0026] Further, the step of establishing a lightning protection strategy of a power transmission line according to the lightning activity analysis result comprises:

[0027] The correlation coefficient between the lightning trip-out and the lightning parameters is calculated by using a grey correlation analysis method.

[0028] The lightning activity analysis result is verified according to the correlation coefficient, and a lightning protection strategy of a power transmission line is established according to the lightning activity analysis result that passes the verification.

[0029] In a second aspect, the application provides a lightning activity analysis system of a power transmission line, which comprises:

[0030] A data processing module is configured to acquire characteristic parameters of each tower of a power transmission line in a preset area, and to discretize the characteristic parameters to obtain a tower attribute table, wherein the data items in the tower attribute table include lightning parameters and lightning trip-outs, and the lightning parameters include soil conductivity, ground flash density and ground flash intensity.

[0031] The association mining module is configured to perform data mining on data items in the tower attribute table based on an FP-Growth algorithm, to generate a frequent 1-item set and a frequent pattern tree, to establish a hash linked list according to the frequent 1-item set and the frequent pattern tree, and to perform association rule mining based on the hash linked list to obtain a frequent multi-item set.

[0032] The rule analysis module is configured to filter out association rules with lightning trip as a rule consequent from the frequent multi-item set, and to analyze the association rules to obtain a lightning activity analysis result of a preset area.

[0033] The strategy construction module is configured to establish a lightning protection strategy for the power transmission line according to the lightning activity analysis result.

[0034] In a third aspect, an embodiment of the present application further provides a computer device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executable on a processor to implement the steps of the above method.

[0036] The present application provides a lightning activity analysis method, system, device and storage medium for a power transmission line. In the lightning activity association information mining method, the soil conductivity is considered, and the FP-growth data association algorithm is used to mine the association rules related to lightning trip. The present application not only improves the applicability of data analysis correlation and the efficiency of rule mining, but also provides a solid data basis for the lightning protection work of the power transmission line, and provides theoretical support for the differentiated lightning protection and new lightning protection measures of the power transmission line. The differentiated lightning protection strategy established based on the mined rules can effectively improve the safety and stability of the power system operation. BRIEF DESCRIPTION OF DRAWINGS

[0037] Fig. 1 is a flowchart of a lightning activity analysis method for a power transmission line according to an embodiment of the present application;

[0038] Fig. 2 is a structural diagram of a lightning activity analysis system for a power transmission line according to an embodiment of the present application;

[0039] Fig. 3 is an internal structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0041] Referring to FIG. 1, a lightning activity analysis method for a power transmission line according to a first embodiment of the present application is provided, which comprises steps S10-S40.

[0042] In step S10, characteristic parameters of each tower of the power transmission line in a preset area are acquired, and the characteristic parameters are discretized to obtain a tower attribute table, wherein data items in the tower attribute table include lightning parameters and lightning trip-out, and the lightning parameters include soil conductivity, ground flash density and ground flash intensity.

[0043] In step S20, data mining is performed on the data items in the tower attribute table based on an FP-Growth algorithm to generate a frequent 1-item set and a frequent pattern tree, a hash linked list is established according to the frequent 1-item set and the frequent pattern tree, and association rule mining is performed based on the hash linked list to obtain a frequent multi-item set.

[0044] In step S30, association rules with lightning trip-out as a consequent are screened out from the frequent multi-item set, and the association rules are analyzed to obtain a lightning activity analysis result of the preset area.

[0045] In step S40, a lightning protection strategy for the power transmission line is established according to the lightning activity analysis result.

[0046] In the present application, the soil conductivity factor is added in the lightning activity correlation analysis of the power system, the influence of lightning on the power transmission line, especially on the tower, is analyzed through lightning activity data and soil data, and a lightning activity analysis database of the area to be analyzed is first constructed to facilitate subsequent data analysis. Specifically, the soil conductivity data of the preset area are acquired according to the data of the world soil database, the soil conductivity, lightning activity data and corresponding lightning trip-out of all towers on the power transmission line in the area are extracted, and thus the lightning activity analysis database is established, wherein the lightning activity data mainly include the ground flash density and the ground flash intensity.

[0047] In order to improve the accuracy and reliability of data, reduce noise and error, the present application needs to discretize the continuous characteristic parameters in the database, and in order to minimize the loss of information, the continuous attributes are converted into a small number of finite intervals, and the subsequent association rule mining is not split into sub-rules and loses some attributes, the embodiment adopts the discrete intervals shown in Tables 1, 2 and 3 to discretize the soil conductivity, ground flash density and ground flash intensity three characteristic parameters respectively:

[0048] Table 1 Soil conductivity discrete interval table

[0049] Table 2 Ground flash density discrete interval table

[0050] Table 3 Ground flash intensity discrete interval table

[0051] After discretizing the soil data and lightning activity data based on the above tables, for each tower lightning trip, L1 represents that the tower has lightning trip, L2 represents that the tower has no lightning trip, then the attribute value of each tower is counted, and it is assumed that the obtained tower data table is shown in Table 4:

[0052] Table 4 Tower attribute table

[0053] For the tower attribute information, the embodiment adopts the FP-Growth algorithm to construct the frequent pattern tree to mine the association information of the tower attribute information, and the specific steps include:

[0054] The lightning parameters and the lightning trip are taken as the transaction prefix and the transaction suffix respectively, and the support degree of each data item in the tower attribute table is calculated;

[0055] It is judged whether the support degree of each data item is less than the minimum support degree, if yes, the data item is added to the non-frequent item set, otherwise, the data item is added to the frequent 1 item set;

[0056] The data items in the tower attribute table are sorted according to the support degree, and the non-frequent item set is deleted to obtain an updated tower attribute table;

[0057] According to the FP-Growth algorithm and the data items in the updated tower attribute table, a frequent pattern tree is established.

[0058] The embodiment uses support and confidence as the association rules to quantify the association relationship between the soil conductivity and the lightning activity characteristic parameters of the line and the line lightning fault accidents. The association rule quantification method is to find the association relationship between different transaction item sets. The commonly used measurement of the association rule is support and confidence. The support represents the accidental degree of the transaction item. The smaller the value is, the greater the accidental degree is. The calculation method of the support is as follows:

[0059] In the formula, σ (N∪L) represents the transaction number of N and L, and T is the total transaction set, that is, the total number of towers participating in the association information mining calculation.

[0060] The confidence represents the dependence degree of the transaction post item L on the transaction pre-item N. The greater the confidence is, the greater the dependence degree is. The calculation method of the confidence is as follows:

[0061] In the formula, σ (N) represents the transaction number containing N.

[0062] In the embodiment, N is the pre-item of the association rule, representing the corresponding soil properties and lightning activity properties of the tower of the transmission line, that is, the soil conductivity, the ground flash density and the ground flash intensity. L is the post item of the association rule, representing whether the lightning trip occurs in the tower unit.

[0063] Then, the minimum support (min_sup) and the minimum confidence (min_conf) are set. The min_sup and the min_conf are selected according to the size of the data set and the data amount. If the support and the confidence of an association rule do not satisfy the min_sup and the min_conf, the association rule is considered to be not the rule of interest. Preferably, the min_sup is set to 0.4, and the min_conf is set to 0.7. The support is used for the construction of the frequent pattern tree, and the use of the confidence will be described in the subsequent steps.

[0064] After the tower attribute table is constructed through the above steps, the support calculation formula is used to count the support of each data item in the tower attribute table. The data item whose support does not satisfy the minimum support or the support is less than the minimum support is regarded as a non-frequent item set, and the data item satisfying the minimum support is regarded as a frequent 1-item set. Then, the data items are reordered in the tower attribute table in the order of first vertical and then horizontal according to the support count of the data items, and the non-frequent item set is deleted from the reordered tower attribute table. For example, B1 and C1 are obtained by statistics to be the non-frequent item set. The non-frequent item set is removed from the tower attribute table, so that the updated tower attribute table is obtained, as shown in Table 5.

[0065] Table 5 Updated tower attribute table

[0066] Then the frequent pattern tree can be created based on the data items in Table 5, and specifically, the data items are inserted into the frequent pattern tree (Fp-Tree) according to the Insert_tree method in the FP-Growth algorithm. The Fp-Tree contains a unique empty root node, and each node contains a data item name, a data item count, and two pointers, one of which points to the same data item, and the other points to the data item by the item header table, which is constructed from the frequent 1-item set. Each element in the item header table contains a data item name, a support degree corresponding to the data item, and a data item pointer. The data items are arranged in descending order of support degree, and the data item pointer points to the first node with the same data item name in the constructed Fp-Tree.

[0067] After the frequent pattern tree is constructed, the embodiment does not directly perform association data mining on the frequent pattern tree, but continues to construct a hash linked list based on the frequent pattern tree, and performs association information mining based on the hash linked list. The specific steps of constructing the hash linked list include:

[0068] Each data item in the frequent 1-item set is taken as a first data item, and a head node of the hash linked list is established according to the first data item, the head node including a first data item name, a support degree of the first data item, a first pointer, and a second pointer.

[0069] Other data items in the path containing the first data item in the frequent pattern tree are taken as second data items, and subsequent nodes of the hash linked list are established according to the second data items, the subsequent nodes including a second data item name, a third pointer, and a number of times of simultaneous occurrence with the corresponding first data item.

[0070] In the embodiment, each head node of the created hash linked list is each data item in the frequent 1-item set. Specifically, the head nodes are arranged in the order of the data items in the frequent 1-item table, and the head nodes contain a data item name, a support degree corresponding to the data item, and two data item pointers, i.e., a first pointer and a second pointer. The first pointer points to the first data item on the prefix path corresponding to the head node, and the second pointer points to the next data item in the item header table.

[0071] The subsequent node connected with each head node in the hash chain table is other data items in the path containing the data item corresponding to the head node in the frequent model tree. Specifically, according to the support degree of the data items, the data items are inserted into the hash chain corresponding to the head node in descending order. For the data items with hash conflicts, that is, the items with the same support degree, the open chain method is used for processing. The open chain method refers to directly inserting the data item under the previous node when hash conflict occurs. The subsequent node of the hash chain table contains the data item name, a data item pointer, that is, the third pointer, and the number of times of simultaneous occurrence of the data item corresponding to the head node. The third pointer points to the next subsequent node in the hash chain table.

[0072] After the hash chain table is constructed, the association rule mining is realized by traversing the hash chain table. The specific steps include:

[0073] The hash chain table is traversed to obtain the frequent 2-item set, and it is judged whether two adjacent nodes in the subsequent node corresponding to each head node are a frequent 2-item set. If yes, the two adjacent nodes are merged to obtain an updated hash chain table.

[0074] The updated hash chain table is traversed to obtain the frequent multi-item set.

[0075] In this embodiment, the frequent 2-item set can be directly obtained by traversing the node link corresponding to each head node in the hash chain table. Then, it is judged whether two adjacent nodes in the link corresponding to each head node are a frequent 2-item set. If the two nodes are a frequent 2-item set, the two nodes are merged to obtain an updated hash chain table. Then, the frequent multi-item set of more than 2 items can be directly obtained by traversing the updated hash chain table.

[0076] After the frequent item set is obtained by the association rule mining through the above steps, the association rule can be extracted based on the frequent item set and analyzed to obtain the lightning activity analysis result of the corresponding area. The specific steps include:

[0077] The association rule with lightning trip as the rule consequent is screened out from the frequent multi-item set. The lightning parameter and the lightning trip are taken as the transaction antecedent and the transaction consequent, respectively, and the confidence of the association rule is calculated.

[0078] The association rule with the confidence meeting the minimum confidence requirement is analyzed to obtain the lightning parameter related to the lightning trip in the preset area.

[0079] In the embodiment, the characteristic parameters with high correlation degree to lightning trip-out of the transmission line tower are focused on, and therefore the association rules with the lightning trip-out L1 of the line as the rule consequent are selected from the frequent item sets, the association rules are sorted according to the confidence degree of the rules, the association rules meeting the minimum confidence requirement are selected, and the association rules are analyzed and interpreted accordingly. Assuming that the confidence degree of the selected association rule (A3, B4, C4→L1) reaches 70%, it indicates that the region has the characteristics of high soil conductivity, strong lightning area and extra strong ground flash intensity, and 70% of the tower samples in the region have occurred lightning trip-out events, that is, the soil conductivity, ground flash intensity and ground flash density have strong correlation with the lightning trip-out. In addition, the characteristic factor with the strongest correlation with the lightning trip-out event can be determined according to the confidence degrees of the frequent 1-item sets such as C(A3→L1), C(B4→L1) and C(C4→L1). The present application realizes the mining and exploration of the correlation between the soil conductivity and lightning activity by analyzing the characteristic factors with strong correlation with the lightning trip-out event, provides a solid data basis for the lightning protection work of the power department, and based on the lightning activity analysis result, the lightning protection strategy of the transmission line in the region is constructed in a targeted manner, so that the differential treatment of the lightning protection strategy of the transmission line is realized, thereby effectively improving the safety and stability of the power system operation in the region.

[0080] In order to ensure the accuracy of the mined association information, in a preferred embodiment, the following steps are adopted to construct the lightning protection strategy of the transmission line:

[0081] The correlation coefficient between the lightning trip-out and the lightning parameters is calculated by using the grey correlation analysis method;

[0082] The lightning activity analysis result is verified according to the correlation coefficient, and the lightning protection strategy of the transmission line is established according to the lightning activity analysis result that passes the verification.

[0083] In the embodiment, the grey correlation analysis method is used to calculate the correlation coefficient between the soil conductivity, ground flash intensity and ground flash density and the lightning trip-out event, then the characteristic factors with strong correlation with the lightning trip-out event are obtained based on the correlation coefficient, and it is determined whether the characteristic factors obtained based on the grey correlation analysis are consistent with the characteristic factors obtained based on the FP-Growth algorithm, that is, the result of the association information mining is further verified by using the grey correlation analysis method, thereby improving the accuracy of the mined information. The lightning protection strategy of the transmission line is established based on the verified association information, which can make the established lightning protection strategy more reasonable, thereby further ensuring the safe and stable operation of the power system.

[0084] The embodiment provides a lightning activity analysis method of a power transmission line.

[0085] Referring to FIG. 2, based on the same inventive concept, a lightning activity analysis system of a power transmission line is provided in the second embodiment of the present application, and the system comprises:

[0086] The data processing module 10 is configured to acquire characteristic parameters of each tower of the power transmission line in a preset area, and discretize the characteristic parameters to obtain a tower attribute table, wherein the data items in the tower attribute table comprise lightning parameters and lightning trip-out, and the lightning parameters comprise soil conductivity, ground flash density and ground flash intensity.

[0087] The association mining module 20 is configured to perform data mining on the data items in the tower attribute table based on the FP-Growth algorithm, generate a frequent 1-item set and a frequent pattern tree, establish a hash linked list according to the frequent 1-item set and the frequent pattern tree, and perform association rule mining based on the hash linked list to obtain a frequent multi-item set.

[0088] The rule analysis module 30 is configured to filter out the association rules with lightning trip-out as a rule consequent from the frequent multi-item set, analyze the association rules, and obtain a lightning activity analysis result of the preset area.

[0089] The strategy construction module 40 is configured to establish a lightning protection strategy of the power transmission line according to the lightning activity analysis result.

[0090] The technical features and technical effects of the lightning activity analysis system of the power transmission line are the same as those of the method, and will not be repeated here. The modules in the lightning activity analysis system of the power transmission line can be realized by software, hardware and combinations thereof in whole or in part. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0091] In addition, the embodiment of the present application further provides a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the steps of the above method.

[0092] Referring to FIG. 3, an internal structure diagram of a computer device in an embodiment is shown, which can be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the method for analyzing lightning activities of a power transmission line. The display of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer overlaid on the display, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0093] Those skilled in the art can understand that the structure shown in FIG. 3 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0094] In addition, the embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon. The computer program is executed by the processor to implement the steps of the above method.

[0095] In summary, the power transmission line lightning activity analysis method, system, device and storage medium provided by the embodiment of the present application, the method obtains the characteristic parameters of each tower of the power transmission line in the preset area, and discretizes the characteristic parameters to obtain a tower attribute table, the data items in the tower attribute table include lightning parameters and lightning trip-out, the lightning parameters include soil conductivity, ground flash density and ground flash intensity; based on the FP-Growth algorithm, the data items in the tower attribute table are data mined to generate a frequent 1-item set and a frequent pattern tree, according to the frequent 1-item set and the frequent pattern tree, a hash linked list is established, and based on the hash linked list, association rule mining is performed to obtain a frequent multi-item set; the association rules with lightning trip-out as the rule consequent are screened out from the frequent multi-item set, and the association rules are analyzed to obtain the lightning activity analysis result of the preset area; according to the lightning activity analysis result, a lightning protection strategy of the power transmission line is established. The present application studies the influence of different soil conductivity on lightning activities such as ground flash intensity and ground flash density, and mines the association rules related to lightning trip-out based on the FP-growth data association algorithm, which not only improves the applicability of data analysis correlation and the efficiency of rule mining, but also provides data support for constructing differentiated lightning protection strategies of power transmission lines, thereby effectively improving the safety and stability of the power system operation.

[0096] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment. It should be noted that, each technical feature of the above-mentioned embodiments can be combined arbitrarily, in order to make the description simple, not all possible combinations of the technical features in the above-mentioned embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the specification.

[0097] The above-described embodiments only express several preferred embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the patent. It should be noted that, for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and replacements can be made, and these improvements and replacements should be considered as the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the protection scope of the claims.

Claims

1. A method for analyzing lightning activity in transmission lines, characterized in that, include: The characteristic parameters of each tower of the transmission line within the preset area are obtained, and the characteristic parameters are discretized to obtain the tower attribute table. The data items in the tower attribute table include lightning parameters and lightning tripping. The lightning parameters include soil conductivity, ground flash density and ground flash intensity. Data mining is performed on the data items in the pole attribute table based on the FP-Growth algorithm to generate frequent one-itemsets and frequent pattern trees. A hash chain is established based on the frequent one-itemsets and frequent pattern trees, and association rule mining is performed based on the hash chain to obtain frequent multisets. From the frequent multiple set, association rules with lightning tripping as the rule consequence are selected, and the association rules are analyzed to obtain the lightning activity analysis results of the preset area. Based on the lightning activity analysis results, a lightning protection strategy for transmission lines is established.

2. The method for analyzing lightning activity of transmission lines according to claim 1, characterized in that, The steps of performing data mining on the data items in the tower attribute table based on the FP-Growth algorithm to generate frequent one-itemsets and frequent pattern trees include: Using the lightning parameters and the lightning trip as the preceding and following terms of the transaction, respectively, calculate the support of each data item in the tower attribute table; Determine whether the support of each data item is less than the minimum support. If so, add the data item to the infrequent itemset; otherwise, add the data item to the frequent itemset. The data items in the pole attribute table are sorted according to the support, and infrequent itemsets are deleted to obtain an updated pole attribute table. A frequent pattern tree is built based on the FP-Growth algorithm and the data items in the updated tower attribute table.

3. The method for analyzing lightning activity of transmission lines according to claim 2, characterized in that, The steps for constructing a hash linked list based on frequent 1-itemsets and frequent pattern trees include: Take each data item in the frequent 1 item set as the first data item, and build the head node of the hash chain based on the first data item. The head node includes the name of the first data item, the support of the first data item, the first pointer and the second pointer. Other data items in the path containing the first data item in the frequent pattern tree are taken as the second data item. Subsequent nodes of the hash chain are established based on the second data item. The subsequent nodes include the name of the second data item, a third pointer, and the number of times it appears simultaneously with the corresponding first data item.

4. The method for analyzing lightning activity of transmission lines according to claim 3, characterized in that, The step of establishing subsequent nodes of the hash chain based on the second data item includes: Sort the data in descending order of support, and insert the second data item into the hash chain as the successor node of the corresponding head node, based on the open chaining method.

5. The method for analyzing lightning activity of transmission lines according to claim 1, characterized in that, The step of mining association rules based on the hash chain to obtain frequent multisets includes: Traverse the hash chain to obtain frequent 2-itemsets, and determine whether the two adjacent nodes in the subsequent nodes corresponding to each head node are frequent 2-itemsets. If so, merge the two adjacent nodes to obtain the updated hash chain. Traverse the updated hash list to obtain frequent multisets.

6. The method for analyzing lightning activity of transmission lines according to claim 1, characterized in that, The steps of filtering association rules with lightning tripping as a rule consequence from the frequent multiple set and analyzing the association rules to obtain the lightning activity analysis results for the preset area include: From the frequent multivariate set, association rules with lightning tripping as the rule consequent are selected, and the lightning parameters and the lightning tripping are respectively used as the transaction antecedent and transaction consequent, and the confidence of the association rules is calculated. The association rules that satisfy the minimum confidence requirement are analyzed to obtain the lightning parameters related to the lightning trip within a preset area.

7. The method for analyzing lightning activity of transmission lines according to claim 1, characterized in that, The steps for establishing a lightning protection strategy for transmission lines based on the lightning activity analysis results include: The correlation coefficient between the lightning trip and the lightning parameters was calculated using grey relational analysis. The lightning activity analysis results are verified based on the correlation coefficient, and a lightning protection strategy for transmission lines is established based on the verified lightning activity analysis results.

8. A lightning activity analysis system for transmission lines, characterized in that, include: The data processing module is used to acquire the characteristic parameters of each tower of the transmission line within a preset area, and to discretize the characteristic parameters to obtain a tower attribute table. The data items in the tower attribute table include lightning parameters and lightning tripping. The lightning parameters include soil conductivity, ground flash density and ground flash intensity. The association mining module is used to perform data mining on the data items in the pole attribute table based on the FP-Growth algorithm, generate frequent one-itemsets and frequent pattern trees, establish a hash chain list based on the frequent one-itemsets and frequent pattern trees, and perform association rule mining based on the hash chain list to obtain frequent multisets. The rule analysis module is used to filter out association rules with lightning tripping as the rule consequence from the frequent multiple set, and analyze the association rules to obtain the lightning activity analysis results of the preset area. The strategy construction module is used to establish lightning protection strategies for transmission lines based on the lightning activity analysis results.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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