A distributed fault location method and system for transmission lines

Abstract

The present invention discloses a distributed fault location method and system for transmission lines, which belongs to the field of line fault technology. The method includes: generating monitoring nodes through distributed intelligent sensing devices, dynamically dividing line sections and establishing dynamic coverage relationships between monitoring nodes and sections; constructing a digital twin platform for transmission lines, mapping sections and nodes and generating multi-source operating parameter feature clusters; calculating parameter correlation based on the error accumulation boundary function, dynamically quantifying the correlation coefficient, and screening strongly correlated sections as fault priority location objects. The system includes an intelligent sensing device cluster module, a digital twin mapping module, a data processing module and a fault location module, which realizes real-time monitoring of the operating status of the transmission line and efficient fault location. The present invention improves the accuracy and efficiency of fault location through dynamic coverage relationship update, multi-source data fusion and quantitative analysis, and is suitable for distributed fault diagnosis of complex transmission networks.

Description

Technical Field

[0001] The present invention relates to the technical field of line faults, and in particular to a distributed fault location method and system for a power transmission line. Background Art

[0002] During transmission line operation, fault location is a key technology for ensuring grid security and stability. Traditional fault location methods rely on centralized monitoring systems and have the following shortcomings:

[0003] Inflexible monitoring coverage: The fixed segment division method cannot adapt to the dynamic changes in the transmission line operation status, and monitoring node failures can easily lead to coverage blind spots;

[0004] Insufficient multi-source data fusion: Lack of comprehensive analysis of multiple electrical parameters such as voltage, current, and temperature makes it difficult to accurately identify fault characteristics;

[0005] Lack of prioritization: No mechanism for assessing the urgency of fault sections has been established, resulting in low efficiency in allocating O&M resources.

[0006] Poor dynamic adaptability: The existing system relies on static configuration and cannot update the association between monitoring nodes and line sections in real time, resulting in reduced positioning accuracy.

[0007] With the development of smart grids, the complexity of transmission lines continues to increase. There is an urgent need for a distributed fault location technology with dynamic monitoring, multi-source data fusion and intelligent analysis to address the shortcomings of traditional methods in flexibility, accuracy and efficiency. Summary of the Invention

[0008] The object of the present invention is to provide a distributed fault location method and system for a transmission line to solve the problems raised in the above background technology.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] A distributed fault location system for power transmission lines, comprising: an intelligent sensing device cluster module, a digital twin mapping module, a data processing module, and a fault location module;

[0011] The intelligent sensing device cluster module is distributedly deployed on the transmission line to generate monitoring nodes and collect multi-source operating parameters of the transmission line in real time;

[0012] The digital twin mapping module is used to build a digital twin platform for transmission lines, establish a dynamic coverage relationship between monitoring nodes and line sections, and map line section sets and monitoring node sets;

[0013] The data processing module is used to generate actual operating parameter feature clusters and observed operating parameter feature clusters, and calculate parameter correlation based on the error accumulation boundary function;

[0014] The fault location module is used to dynamically quantify the correlation coefficient, select strongly correlated line sections as priority fault location objects, and output the fault urgency ranking result.

[0015] Furthermore, the intelligent sensing device cluster includes a plurality of intelligent sensing devices, each intelligent sensing device corresponds to a monitoring node, and the monitoring node dynamically updates the line section within the coverage range according to the real-time connection status.

[0016] Furthermore, the digital twin mapping module includes a dynamic coverage relationship representation unit and a feature classification unit;

[0017] The dynamic coverage relationship representation unit is used to establish a digital twin platform for transmission lines, map line sections and monitoring nodes, form a line section set and a monitoring node set, and establish a dynamic coverage relationship;

[0018] The feature classification unit collects multi-source operating parameters of the line section through monitoring nodes, the multi-source operating parameters including several types of electrical state parameters, and generates dynamic feature clusters, the dynamic feature clusters including actual operating parameter feature clusters and observed operating parameter feature clusters.

[0019] Furthermore, the data processing module includes an error boundary processing unit and a correlation analysis unit;

[0020] The error boundary processing unit is used to construct an error accumulation boundary function and calculate the error accumulation cost;

[0021] The correlation analysis unit evaluates the correlation between the line sections and the monitoring nodes with respect to different types of electrical status parameters based on an error accumulation boundary function.

[0022] Furthermore, the fault location module includes a target screening unit and an emergency sorting unit;

[0023] The target screening unit is used to dynamically quantify the correlation coefficient within the monitoring coverage of the monitoring node and screen out the line sections that are strongly correlated with the monitoring node;

[0024] The emergency sorting unit is used to evaluate the fault urgency of the line section and arrange the faults in descending order and send them to the operation and maintenance backend.

[0025] A distributed fault location method for a transmission line, comprising the following steps:

[0026] S1: Multiple monitoring nodes are generated by distributing intelligent sensing devices on the transmission lines. Each monitoring node corresponds to an intelligent sensing device and is used to monitor the operating status of the transmission lines within its coverage area in real time. The line sections are dynamically divided according to the coverage of the monitoring nodes, and dynamic coverage relationships are established between the monitoring nodes and the line sections.

[0027] S2: Build a digital twin platform for transmission lines, map line sections and monitoring nodes, collect multi-source operating parameters of line sections, and generate feature clusters of actual operating parameters and observed operating parameters;

[0028] S3: Based on the actual operating parameter feature cluster and the observed operating parameter feature cluster, an error accumulation boundary function is constructed to calculate the error accumulation cost and evaluate the parameter correlation between the line section and the monitoring node;

[0029] S4: Within the monitoring coverage of the monitoring node, the correlation coefficient of the dynamic coverage relationship that changes with time is dynamically quantified to achieve distributed fault location of the transmission line and output the fault urgency ranking result.

[0030] Furthermore, the specific implementation process of S1 includes:

[0031] Generate monitoring nodes through distributed intelligent sensing devices deployed on the transmission lines, wherein one intelligent sensing device generates one monitoring node corresponding to the intelligent sensing device, and the monitoring node is used to monitor the operating status of the transmission lines within the coverage area;

[0032] The line segments are divided by the monitoring coverage of the monitoring nodes, so that there is a dynamic coverage relationship between the monitoring nodes and the line segments, and one monitoring node corresponds to at least one line segment. The dynamic coverage relationship is characterized by the real-time connection status of the monitoring nodes during the operation of the transmission line;

[0033] In the above method, the line sections are dynamically divided through distributed intelligent sensing devices, and a dynamic coverage relationship between monitoring nodes and sections is established, which solves the monitoring blind spot problem caused by traditional fixed section division. It enables the system to adapt to changes in the operating status of the transmission line in real time, flexibly adjust the monitoring coverage range, and improve the comprehensiveness and flexibility of monitoring.

[0034] Furthermore, the specific implementation process of S2 includes:

[0035] Establish a digital twin platform for transmission lines, map line sections and monitoring nodes, and form a line section set. and monitoring node sets , and establish dynamic coverage relationships ,in, represents the nth line section, represents the mth monitoring node, N and M represent the total number of line sections and monitoring nodes respectively, is the correlation coefficient of the dynamic coverage relationship that changes with time t;

[0036] The multi-source operating parameters of the line section are collected by monitoring nodes. The multi-source operating parameters include several types of electrical state parameters and generate dynamic feature clusters. The dynamic feature clusters include actual operating parameter feature clusters and observed operating parameter feature clusters. The line section generated by the change of the i-th type of electrical state parameter with time t is The actual operating parameter feature cluster is recorded as , the monitoring nodes generated by the change of the electrical state parameters of the i-th type with time t The observed operating parameter characteristic cluster is recorded as ;

[0037] In the above method, a digital twin platform for transmission lines is constructed, and multi-source electrical parameters such as voltage, current, and temperature are integrated to generate feature clusters of actual and observed operating parameters. This enables comprehensive analysis of multi-source data. Compared with the traditional method of single parameter analysis, it can more accurately identify fault characteristics and improve the accuracy of fault location.

[0038] Furthermore, the specific implementation process of S3 includes:

[0039] The actual operating parameter feature cluster The xth electrical state parameter is recorded as , the observed operating parameter feature cluster The yth electrical state parameter is recorded as ;

[0040] Construct the error accumulation boundary function and calculate the error accumulation cost :

[0041] ;

[0042] Evaluate line sections based on error accumulation boundary function With monitoring node Correlation between the electrical state parameters of type i , where max{} and min{} are the maximum value function and the minimum value function respectively;

[0043] In the above method, the parameter correlation is calculated based on the error accumulation boundary function, the correlation coefficient is dynamically quantified, and strongly correlated sections are screened as priority fault location objects. The fault urgency is evaluated and ranked. This mechanism establishes a priority evaluation system for fault sections, solves the problem of lack of priority sorting in traditional methods, enables efficient allocation of operation and maintenance resources, and improves fault handling efficiency. The greater the error accumulation cost, the greater the correlation between the electrical status parameters between the line section and the monitoring node.

[0044] Furthermore, the specific implementation process of S4 includes:

[0045] At the monitoring node Within the monitoring coverage, the correlation coefficient Perform dynamic quantization ;

[0046] Where I represents the total number of classes of electrical state parameters;

[0047] Configure the duration range of time t and regularly check the correlation coefficient Update, filter and monitor nodes Line sections with strong correlation , within the regular range, if the monitoring node If a fault event occurs within the monitoring coverage area, the line section As the priority fault location object;

[0048] In the kth periodic range, if the line section is screened out for the rth time For the fault priority location object, the generated distributed label is recorded as ,and , evaluate line sections Fault urgency , in the formula, R represents the secondary number;

[0049] According to the urgency of the fault Arrange them in descending order and send them to the operation and maintenance backend;

[0050] In the above method, the dynamic coverage relationship between monitoring nodes and line sections is characterized based on real-time connection status, and the correlation coefficient is updated regularly. This frees the system from the limitations of traditional static configuration and enables real-time updating of the correlation between monitoring nodes and line sections. This effectively addresses the challenges brought about by the increased complexity of transmission lines and enhances the system's dynamic adaptability and positioning accuracy in complex transmission networks. The fault urgency reflects the continuous fluctuation of strong correlation. The larger the value of The smaller the value of , the greater the fault urgency, reflecting that the line section is marked as a fault priority object multiple times in a short period of time (with a small time difference), reflecting that the frequency of faults in this section is high or the urgency is strong.

[0051] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a distributed fault location method and system for a transmission line provided by the present invention, monitoring nodes are generated through distributed intelligent sensing devices, line sections are dynamically divided, and a dynamic coverage relationship between monitoring nodes and sections is established; a digital twin platform for transmission lines is constructed, sections and nodes are mapped, and multi-source operating parameter feature clusters are generated; parameter correlation is calculated based on the error accumulation boundary function, correlation coefficients are dynamically quantified, and strongly correlated sections are screened as priority fault location objects. The system includes an intelligent sensing device cluster module, a digital twin mapping module, a data processing module, and a fault location module, which realize real-time monitoring of the operating status of the transmission line and efficient fault location. The present invention improves the accuracy and efficiency of fault location through dynamic coverage relationship updates, multi-source data fusion, and quantitative analysis, and is suitable for distributed fault diagnosis of complex transmission networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0053] Figure 1 It is a schematic diagram of the steps of a distributed fault location method for a transmission line according to the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] In this embodiment 1: a distributed fault location system for a power transmission line is provided, the system comprising: an intelligent sensing device cluster module, a digital twin mapping module, a data processing module and a fault location module;

[0056] The intelligent sensing device cluster module is distributedly deployed on the transmission line to generate monitoring nodes and collect multi-source operating parameters of the transmission line in real time;

[0057] The intelligent sensing device cluster includes a plurality of intelligent sensing devices, each of which corresponds to a monitoring node, and the monitoring node dynamically updates the line section within the coverage area according to the real-time connection status.

[0058] The digital twin mapping module is used to build a digital twin platform for transmission lines, establish a dynamic coverage relationship between monitoring nodes and line sections, and map line section sets and monitoring node sets;

[0059] Wherein, the digital twin mapping module includes a dynamic coverage relationship representation unit and a feature classification unit;

[0060] The dynamic coverage relationship representation unit is used to establish a digital twin platform for transmission lines, map line sections and monitoring nodes, form a line section set and a monitoring node set, and establish a dynamic coverage relationship;

[0061] The feature classification unit collects multi-source operating parameters of the line section through monitoring nodes, the multi-source operating parameters including several types of electrical state parameters, and generates dynamic feature clusters, the dynamic feature clusters including actual operating parameter feature clusters and observed operating parameter feature clusters.

[0062] The data processing module is used to generate actual operating parameter feature clusters and observed operating parameter feature clusters, and calculate parameter correlation based on the error accumulation boundary function;

[0063] Wherein, the data processing module includes an error boundary processing unit and a correlation analysis unit;

[0064] The error boundary processing unit is used to construct an error accumulation boundary function and calculate the error accumulation cost;

[0065] The correlation analysis unit evaluates the correlation between the line sections and the monitoring nodes with respect to different types of electrical status parameters based on an error accumulation boundary function.

[0066] The fault location module is used to dynamically quantify the correlation coefficient, select strongly correlated line sections as priority fault location objects, and output the fault urgency ranking results;

[0067] Wherein, the fault location module includes a target screening unit and an emergency sorting unit;

[0068] The target screening unit is used to dynamically quantify the correlation coefficient within the monitoring coverage of the monitoring node and screen out the line sections that are strongly correlated with the monitoring node;

[0069] The emergency sorting unit is used to evaluate the fault urgency of the line section and arrange the faults in descending order and send them to the operation and maintenance backend.

[0070] See also Figure 1 In the second embodiment, a method for locating a distributed fault on a power transmission line is provided, which is applicable to the first embodiment. The method includes the following steps:

[0071] S1: Multiple monitoring nodes are generated by distributing intelligent sensing devices on the transmission lines. Each monitoring node corresponds to an intelligent sensing device and is used to monitor the operating status of the transmission lines within its coverage area in real time. The line sections are dynamically divided according to the coverage of the monitoring nodes, and dynamic coverage relationships are established between the monitoring nodes and the line sections.

[0072] Exemplarily, monitoring nodes are generated by distributed intelligent sensing devices deployed on transmission lines, wherein one intelligent sensing device generates one monitoring node correspondingly, and the monitoring node is used to monitor the operating status of the transmission lines within the coverage area;

[0073] The line sections are divided by the monitoring coverage of the monitoring nodes, so that there is a dynamic coverage relationship between the monitoring nodes and the line sections, and one monitoring node corresponds to at least one line section. The dynamic coverage relationship is characterized based on the real-time connection status of the monitoring nodes during the operation of the transmission line.

[0074] S2: Build a digital twin platform for transmission lines, map line sections and monitoring nodes, collect multi-source operating parameters of line sections, and generate feature clusters of actual operating parameters and observed operating parameters;

[0075] For example, a digital twin platform for transmission lines is established to map line sections and monitoring nodes to form a line section set. and monitoring node sets , and establish dynamic coverage relationships ,in, represents the nth line section, represents the mth monitoring node, N and M represent the total number of line sections and monitoring nodes respectively, is the correlation coefficient of the dynamic coverage relationship that changes with time t;

[0076] The multi-source operating parameters of the line section are collected by monitoring nodes. The multi-source operating parameters include several types of electrical state parameters and generate dynamic feature clusters. The dynamic feature clusters include actual operating parameter feature clusters and observed operating parameter feature clusters. The line section generated by the change of the i-th type of electrical state parameter with time t is The actual operating parameter feature cluster is recorded as , the monitoring nodes generated by the change of the electrical state parameters of the i-th type with time t The observed operating parameter characteristic cluster is recorded as .

[0077] S3: Based on the actual operating parameter feature cluster and the observed operating parameter feature cluster, an error accumulation boundary function is constructed to calculate the error accumulation cost and evaluate the parameter correlation between the line section and the monitoring node;

[0078] For example, the actual operating parameter feature cluster The xth electrical state parameter is recorded as , the observed operating parameter feature cluster The yth electrical state parameter is recorded as ;

[0079] Construct the error accumulation boundary function and calculate the error accumulation cost :

[0080] ;

[0081] Evaluate line sections based on error accumulation boundary function With monitoring node Correlation between the electrical state parameters of type i , where max{} and min{} are the maximum value function and the minimum value function respectively.

[0082] S4: Dynamically quantify the correlation coefficient of the dynamic coverage relationship that changes over time within the monitoring coverage of the monitoring node to achieve distributed fault location of the transmission line and output the fault urgency ranking result;

[0083] For example, at the monitoring node Within the monitoring coverage, the correlation coefficient Perform dynamic quantization ;

[0084] Where I represents the total number of classes of electrical state parameters;

[0085] Configure the duration range of time t and regularly check the correlation coefficient Update, filter and monitor nodes Line sections with strong correlation , within the regular range, if the monitoring node If a fault event occurs within the monitoring coverage area, the line section As the priority fault location object;

[0086] In the kth periodic range, if the line section is screened out for the rth time For the fault priority location object, the generated distributed label is recorded as ,and , evaluate line sections Fault urgency , in the formula, R represents the secondary number;

[0087] According to the urgency of the fault Arrange them in descending order and send them to the operation and maintenance backend.

[0088] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0089] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A distributed fault location method for a transmission line, characterized in that: The method comprises the following steps: S1: Multiple monitoring nodes are generated by distributing intelligent sensing devices on the transmission lines. Each monitoring node corresponds to an intelligent sensing device and is used to monitor the operating status of the transmission lines within its coverage area in real time. The line sections are dynamically divided according to the coverage of the monitoring nodes, and dynamic coverage relationships are established between the monitoring nodes and the line sections. S2: Build a digital twin platform for transmission lines, map line sections and monitoring nodes, collect multi-source operating parameters of line sections, and generate feature clusters of actual operating parameters and observed operating parameters; S3: Based on the actual operating parameter feature cluster and the observed operating parameter feature cluster, an error accumulation boundary function is constructed to calculate the error accumulation cost and evaluate the parameter correlation between the line section and the monitoring node; S4: Dynamically quantify the correlation coefficient of the dynamic coverage relationship that changes over time within the monitoring coverage of the monitoring node to achieve distributed fault location of the transmission line and output the fault urgency ranking result; The specific implementation process of S3 includes: The actual operating parameter feature cluster S n，i The xth electrical state parameter in (t) is recorded as The observed operating parameter feature cluster L m，i The yth electrical state parameter in (t) is recorded as Construct the error accumulation boundary function and calculate the error accumulation cost D i (x, y): Based on the error accumulation boundary function, the line section S is evaluated. n With monitoring node L m Correlation between the electrical state parameters of type i Where max{} and min{} are the maximum value function and the minimum value function respectively, n is the serial number of the line section, m is the serial number of the monitoring node, i is the type serial number of the electrical state parameter, and t is the time variable; The specific implementation process of S4 includes: At the monitoring node L m Within the monitoring coverage, the correlation coefficient B n，m (t) Dynamic quantization Where I represents the total number of electrical state parameter classes, and N represents the total number of line sections. Configure the duration range of time t and regularly adjust the correlation coefficient B n，m (t) Update and filter out the monitoring nodes L m There is a strongly associated line section S n =argmax n {B n，m (t)}, within the periodic range, if the monitoring node L m If a fault event occurs within the monitoring coverage area, the line section S n As the priority fault location object; In the kth periodic range, if the line section S is screened out for the rth time n For the fault priority location object, the generated distributed label is recorded as k r (S n ), and k=k r (S n ), evaluate line section S n Fault urgency In the formula, R represents the secondary number; According to the fault urgency U(S n ) are sorted in descending order and sent to the operation and maintenance backend.

2. A distributed fault location method for a transmission line according to claim 1, characterized in that: The specific implementation process of S1 includes: Generate monitoring nodes through distributed intelligent sensing devices deployed on the transmission lines, wherein one intelligent sensing device generates one monitoring node corresponding to the intelligent sensing device, and the monitoring node is used to monitor the operating status of the transmission lines within the coverage area; The line sections are divided by the monitoring coverage of the monitoring nodes, so that there is a dynamic coverage relationship between the monitoring nodes and the line sections, and one monitoring node corresponds to at least one line section. The dynamic coverage relationship is characterized based on the real-time connection status of the monitoring nodes during the operation of the transmission line.

3. A distributed fault location method for a transmission line according to claim 2, characterized in that: The specific implementation process of S2 includes: Establish a digital twin platform for transmission lines, map line sections and monitoring nodes, and form a line section set {S n |n∈[1,N]} and the monitoring node set {L m |m∈[1,M]}, and establish a dynamic covering relationship S n →L m :B n，m (t), where S n Indicates the nth line section, L m represents the mth monitoring node, N and M represent the total number of line sections and monitoring nodes respectively, and B n，m (t) is the correlation coefficient of the dynamic coverage relationship changing with time t; The multi-source operating parameters of the line section are collected by monitoring nodes. The multi-source operating parameters include several types of electrical state parameters and generate dynamic feature clusters. The dynamic feature clusters include actual operating parameter feature clusters and observed operating parameter feature clusters. The line section S generated by the change of the i-th type of electrical state parameter with time t is n The actual operating parameter characteristic cluster is recorded as S n，i (t), the monitoring node L generated by the change of the electrical state parameter of the i-th type with time t m The characteristic cluster of observed operating parameters is recorded as L m，i (t).

4. A distributed fault location system for a power transmission line, executing the fault location method according to any one of claims 1 to 3, characterized in that: The system includes: an intelligent sensing device cluster module, a digital twin mapping module, a data processing module and a fault location module; The intelligent sensing device cluster module is distributedly deployed on the transmission line to generate monitoring nodes and collect multi-source operating parameters of the transmission line in real time; The digital twin mapping module is used to build a digital twin platform for transmission lines, establish a dynamic coverage relationship between monitoring nodes and line sections, and map line section sets and monitoring node sets; The data processing module is used to generate actual operating parameter feature clusters and observed operating parameter feature clusters, and calculate parameter correlation based on the error accumulation boundary function; The fault location module is used to dynamically quantify the correlation coefficient, select strongly correlated line sections as priority fault location objects, and output the fault urgency ranking result.

5. A distributed fault location system for power transmission lines according to claim 4, characterized in that: The intelligent sensing device cluster includes a plurality of intelligent sensing devices, each of which corresponds to a monitoring node. The monitoring node dynamically updates the line sections within the coverage area according to the real-time connection status.

6. A distributed fault location system for power transmission lines according to claim 4, characterized in that: The digital twin mapping module includes a dynamic coverage relationship representation unit and a feature classification unit; The dynamic coverage relationship representation unit is used to establish a digital twin platform for transmission lines, map line sections and monitoring nodes, form a line section set and a monitoring node set, and establish a dynamic coverage relationship; The feature classification unit collects multi-source operating parameters of the line section through monitoring nodes, the multi-source operating parameters including several types of electrical state parameters, and generates dynamic feature clusters, the dynamic feature clusters including actual operating parameter feature clusters and observed operating parameter feature clusters.

7. A distributed fault location system for power transmission lines according to claim 4, characterized in that: The data processing module includes an error boundary processing unit and a correlation analysis unit; The error boundary processing unit is used to construct an error accumulation boundary function and calculate the error accumulation cost; The correlation analysis unit evaluates the correlation between the line sections and the monitoring nodes with respect to different types of electrical status parameters based on an error accumulation boundary function.

8. A distributed fault location system for power transmission lines according to claim 4, characterized in that: The fault location module includes a target screening unit and an emergency sorting unit; The target screening unit is used to dynamically quantify the correlation coefficient within the monitoring coverage of the monitoring node and screen out the line sections that are strongly correlated with the monitoring node; The emergency sorting unit is used to evaluate the fault urgency of the line section and arrange the faults in descending order and send them to the operation and maintenance backend.

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