Power transmission line abnormity early warning and fault positioning system

Abstract

The invention discloses a power transmission line abnormity early warning and fault positioning system, which belongs to the technical field of line detection, and specifically comprises the following steps: dividing early warning equipment in a line into sub-regions according to distribution through a region delimiting module; when a fault occurs, the region positioning module quickly marks the related sub-regions and equipment as undetermined states; the data acquisition module is used for collecting data of the to-be-determined equipment in a set time window before a fault, and preprocessing the data to judge whether the fault is caused by an electrical problem or not; if yes, collecting historical power consumption data of the to-be-determined area; the fault positioning module normalizes historical data, constructs a time sequence model, extracts a current value change sequence, divides the current value change sequence into a plurality of subsequences and then performs clustering analysis; judging the fault reason according to the offset condition of the category cluster where the undetermined current value change sequence is located in the clustering result; if the offset exceeds a preset threshold value, considering that the electric equipment is abnormal; otherwise, the aging of the line insulating material is caused.

Description

Technical Field

[0001] The present invention relates to the technical field of line detection, and in particular to a transmission line abnormality early warning and fault location system. Background Art

[0002] With the continuous development of the power system and the continuous growth of electricity demand, the safe and stable operation of transmission lines is crucial to ensure power supply. However, during the long-term operation of transmission lines, they may be affected by a variety of factors, leading to abnormalities or failures. These factors include abnormal behavior of electrical equipment, aging of line insulation materials, environmental factors (such as lightning strikes, wind and rain, etc.), and aging and failure of the equipment itself. Therefore, timely and accurate warning of abnormal conditions of transmission lines and fault location are of great significance to improving the reliability and operation efficiency of the power system.

[0003] Traditional transmission line fault detection methods mainly rely on manual inspections and simple fault indicators. However, these methods have many limitations. First, manual inspections are inefficient and difficult to detect potential fault hazards in a timely manner; second, simple fault indicators can only provide limited information and cannot accurately locate the fault location or distinguish the specific cause of the fault. In addition, with the increasing complexity of power systems, traditional detection methods have been unable to meet the high-precision requirements of modern power systems for fault warning and location.

[0004] In recent years, with the rapid development of sensor technology, data acquisition technology and data analysis methods, intelligent transmission line fault warning and positioning systems have gradually become a research hotspot. By deploying a variety of early warning devices on the transmission line, the operation data of the line can be collected in real time, including current, voltage, power and other parameters. After preprocessing and analysis, these data can provide strong support for fault warning and positioning.

[0005] However, the existing intelligent fault warning and location systems still have some shortcomings. On the one hand, most systems can only make preliminary judgments on faults and it is difficult to accurately distinguish the specific causes of the faults. On the other hand, in terms of data processing and analysis, the existing systems often lack in-depth mining and utilization of historical data and cannot fully utilize the value of data. Summary of the invention

[0006] The purpose of the present invention is to provide a transmission line abnormality warning and fault location system to solve the following technical problems:

[0007] Most systems can only make preliminary judgments on faults and cannot accurately distinguish the specific causes of faults. Relying only on simple data comparisons and lacking in-depth mining and analysis of historical data and time series, it is difficult to accurately distinguish the causes of faults.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] A transmission line abnormality warning and fault location system, comprising:

[0010] The area demarcation module is used to collect statistics on all warning equipment information in the line and divide the line into several sub-areas according to the distribution of warning equipment. Each warning equipment is associated with a corresponding sub-area.

[0011] The regional positioning module is used to mark the sub-region related to the fault event as a pending area and mark the early warning equipment in the pending area as a pending equipment when a fault event occurs;

[0012] A data acquisition module is used to collect monitoring data of the pending equipment within a set time window t before the fault, and pre-process the monitoring data, and determine whether the fault event is caused by an electrical fault based on the monitoring data. If so, collect historical power consumption data of the pending area before the fault event occurs;

[0013] A fault location module is used to normalize the historical power consumption data and build a time series model to extract the current value change sequence before the fault event occurs, and divide the current value change sequence into a number of sub-current value change sequences with a duration of t, mark the sub-current value change sequence within a set time window as a pending current value change sequence, cluster all the sub-current value change sequences, and check whether the category cluster to which the pending current value change sequence belongs is offset during the clustering process. If the offset exceeds a preset threshold, it is determined that the electrical fault is caused by an abnormality in the electrical equipment; if the offset exceeds the preset threshold, it is determined that the electrical fault is caused by aging of the line insulation material.

[0014] As a further solution of the present invention: the early warning equipment includes an electric energy meter, a circuit protection device, an electrical fault early warning sensor, an intelligent socket, and an electrical fault prevention and control equipment; the monitoring data of the early warning equipment includes the fundamental wave of the current signal, the amplitude of the current signal, the harmonic component, and the voltage.

[0015] As a further solution of the present invention: the early warning device calculates and generates power parameters according to the real-time voltage value and the real-time current value, and the power parameters include apparent power, active power, reactive power, and power factor.

[0016] As a further solution of the present invention: the process of preprocessing the monitoring data by the data acquisition module is:

[0017] Verify the monitoring data collected from each warning device, supplement missing values ​​or outliers, convert date and time fields from strings to a unified format, detect and remove duplicate monitoring data records, remove outliers and invalid characters in monitoring data, and normalize the values ​​of different types of monitoring data.

[0018] As a further solution of the present invention: the process of normalization is:

[0019] ;

[0020] where x i represents the normalized monitoring data, X i represents the original monitoring data, X max Indicates the maximum value in the original monitoring data, X min Indicates the minimum value in the original monitoring data.

[0021] As a further solution of the present invention: the process of the data acquisition module determining whether the fault event is caused by an electrical fault is as follows:

[0022] Get the values ​​of all monitoring data and power parameters within the set time window before the fault event occurs. If the real-time current value amplitude is 0, it is considered that the fault event is not an electrical fault; if the real-time current value amplitude exceeds the preset standard range, it is considered that the fault event is an electrical fault; if the real-time current value amplitude is within the preset standard range, get any real-time monitoring data or power parameter. When the value of any real-time monitoring data or power parameter exceeds the corresponding preset standard range, it is considered that the fault event is an electrical fault.

[0023] As a further solution of the present invention: the process of the fault location module extracting the current value change sequence before the fault event occurs is:

[0024] The initial time series of historical power consumption data is obtained, the length of the initial time series is collected and marked as L, the sliding step of the time series is obtained and marked as S, the length of the current value change sequence is t, and the number of splits of the historical power consumption data is n=L / St.

[0025] As a further solution of the present invention: the process in which the fault location module clusters all sub-current value change sequences and checks whether the category cluster to which the pending current value change sequence belongs is offset during the clustering process is as follows:

[0026] Set the control radius and the minimum similarity number. For the pending current value change sequence, detect the number m of similar sequences within the control radius. If m is less than the minimum similarity number, directly judge that the cause of the electrical fault is the abnormality of the electrical equipment. If m is greater than or equal to the minimum similarity number, put the pending current value change sequence into the category cluster, and put all the sub-current value change value sequences into the corresponding category cluster or mark them as abnormal in turn.

[0027] Repeat the above process, extract the cluster centers of the category clusters after several clusterings, compare the number of cluster centers where the current change value sequence to be determined is located and the difference in the cluster center values. If the cluster center deviates by more than the preset threshold, it is determined that the electrical fault is caused by abnormal electrical equipment; if the deviation exceeds the preset threshold, it is determined that the electrical fault is caused by aging of line insulation materials.

[0028] As a further solution of the present invention: the formula for judging whether the cluster center is offset is:

[0029] ;

[0030] ;

[0031] Among them, S i is a set of data points, |S i ∣ is the number of data points in the class cluster, Δc i represents the change value of the i-th cluster center between the previous and next two iterations; c i,t and c i,t+ 1 represents the value of the i-th cluster center at time t and time t+1 respectively. When Δc i When it is greater than the preset threshold, it indicates that the cluster center has shifted.

[0032] Beneficial effects of the present invention:

[0033] The present invention divides the transmission line into several sub-areas according to the distribution of early warning equipment, collects monitoring data within a set time window before the fault, and analyzes the current value amplitude and other power parameters to quickly determine whether the fault event is caused by an electrical fault, thereby avoiding misjudgment and improving the accuracy and reliability of the early warning. The time series model is constructed using historical electricity consumption data, and the current value change sequence before the fault event occurs is extracted and divided into several sub-sequences for cluster analysis. Through cluster analysis and cluster center offset detection, the system can effectively distinguish whether the electrical fault is caused by abnormal electrical equipment or aging of line insulation materials. This data-driven fault cause analysis method avoids misjudgment caused by lack of experience in traditional methods and provides a clear direction for subsequent repair and maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, the present invention is a transmission line abnormality warning and fault location system, comprising:

[0038] The regional demarcation module is the basic component of the system. Its main function is to collect information about all early warning devices in the transmission line. These early warning devices include energy meters, circuit protection devices, electrical fault early warning sensors, intelligent sockets, and electrical fault prevention and control equipment. According to the distribution of these early warning devices, the module divides the entire transmission line into several sub-areas and ensures that each early warning device is associated with a specific sub-area. This regional division method provides a clear geographical and logical framework for subsequent fault location and data collection.

[0039] The regional positioning module will start quickly when a fault event occurs. It will analyze the relevant information of the fault event and mark the sub-area related to the fault event as a "pending area". At the same time, the module will further mark all warning devices in the pending area as "pending devices". The purpose of this process is to quickly narrow the scope of fault investigation and focus on the areas and devices that may be involved in the fault, thereby improving the efficiency of fault location.

[0040] Data acquisition module, the data acquisition module is the data processing core of the system. It is responsible for collecting monitoring data of the pending equipment within the set time window (denoted as t) before the fault occurs. These monitoring data include key parameters such as the fundamental wave, amplitude, harmonic components, and voltage of the current signal. In order to ensure the availability and accuracy of the data, the module will preprocess the collected monitoring data. The preprocessing process includes verifying the data, supplementing missing values ​​or abnormal values, converting the date and time fields from string format to a unified standard format, detecting and removing duplicate monitoring data records, removing outliers and invalid characters, and normalizing the values ​​of different types of monitoring data. After completing the preprocessing, the module will determine whether the fault event is caused by an electrical fault based on the monitoring data. If it is determined to be an electrical fault, the historical power consumption data of the pending area before the fault event occurs will be further collected to provide more comprehensive data support for subsequent fault location and cause analysis.

[0041] Fault location module, the fault location module is the core functional module of the system, which is used to conduct in-depth analysis of the collected historical power consumption data to achieve accurate fault location and cause judgment. The module first normalizes the historical power consumption data, and then builds a time series model. Through this model, the module extracts the current value change sequence before the fault event occurs, and divides the sequence into several sub-current value change sequences with a duration of t. Among them, the sub-current value change sequence within the set time window is marked as "pending current value change sequence".

[0042] Next, the module performs cluster analysis on all sub-current value change sequences. The purpose of cluster analysis is to classify current value change sequences with similar characteristics into the same category cluster. During the clustering process, the module will pay special attention to whether the category cluster where the pending current value change sequence is located is offset. Specifically, the module will set a control radius and a minimum similarity number. For the pending current value change sequence, the module will detect the number of similar sequences within its control radius (denoted as m). If m is less than the minimum similarity number, it is directly judged that the electrical fault is caused by the abnormal behavior of the electrical equipment; if m is greater than or equal to the minimum similarity number, the pending current value change sequence is placed in the corresponding category cluster, and all sub-current value change sequences are processed in sequence.

[0043] In order to further confirm the cause of the fault, the module will repeat the above clustering process several times and extract the cluster center of the category cluster after each clustering. By comparing the number of cluster centers where the current value change sequence to be determined is located and the difference in the cluster center value, the module determines whether the cluster center has shifted beyond the preset threshold. If the shift exceeds the preset threshold, it is determined that the electrical fault is caused by abnormal electrical equipment; if the shift does not exceed the preset threshold, it is determined that the electrical fault is caused by aging of the line insulation material.

[0044] Workflow:

[0045] 1. Area division and positioning: The system first uses the area demarcation module to count all the early warning equipment information in the transmission line, and divides the line into several sub-areas according to the equipment distribution. When a fault event occurs, the regional positioning module quickly marks the sub-area related to the fault as a pending area, and marks the early warning equipment in the area as a pending device.

[0046] 2. Data collection and preprocessing: The data collection module collects monitoring data of the pending equipment within the set time window before the fault, and preprocesses the data, including verification, supplementation of missing values, format conversion, deduplication, removal of outliers and normalization. The preprocessed data is used to determine whether the fault is caused by an electrical fault. If it is an electrical fault, the module further collects historical electricity consumption data of the pending area.

[0047] 3. Fault location and cause analysis: The fault location module normalizes the historical power consumption data and builds a time series model to extract the current value change sequence before the fault event occurs. The module divides the current value change sequence into several subsequences and performs cluster analysis on these subsequences. By detecting the offset of the category clusters during the clustering process, the module ultimately determines whether the fault is caused by abnormal power equipment or aging of line insulation materials.

[0048] Through the collaborative work of the above modules, the power transmission line abnormality warning and fault location system of the present invention can realize real-time warning, precise location and accurate cause analysis of abnormal conditions of the transmission line, providing strong support for the safe operation and efficient maintenance of the power system.

[0049] In another preferred embodiment of the present invention, the early warning equipment in the system is further clarified and optimized to improve the monitoring capability and data quality of the system. Specifically, the early warning equipment includes the following key equipment:

[0050] Electric energy meter: used to accurately measure the electric energy consumption in the line and provide basic parameters such as current, voltage and power.

[0051] Circuit protection device: used to monitor the current and voltage of the line to ensure that the power supply can be cut off in time under abnormal circumstances to protect the safety of equipment and personnel.

[0052] Electrical fault warning sensor: specially designed to detect early signs of electrical faults, such as partial discharge, overheating, etc., and can issue early warning signals.

[0053] Intelligent socket: As the access point of the terminal device, it can monitor the current, voltage and power of the connected device in real time and upload the data to the system.

[0054] Electrical fault prevention and control equipment: used to actively monitor and intervene in electrical faults to prevent the faults from expanding.

[0055] These early warning devices can collect a variety of monitoring data in real time, including key parameters such as the fundamental wave of the current signal, the amplitude of the current signal, harmonic components, and voltage. These data provide a rich information basis for system fault warning and location.

[0056] Calculation of power parameters

[0057] In a preferred embodiment of the present invention, the early warning device not only collects basic current and voltage data, but also further calculates and generates the following power parameters according to the real-time voltage value and the real-time current value:

[0058] Apparent power: It refers to the total power actually consumed by the equipment during operation, which is the product of current and voltage.

[0059] Active power: refers to the power part of the equipment that actually does work, reflecting the effective use of electrical energy by the equipment.

[0060] Reactive power: refers to the power exchanged between the device and the power grid, reflecting the reactive load of the device on the power grid.

[0061] Power factor: It is the ratio of active power to apparent power, reflecting the effective utilization of electrical energy by the equipment.

[0062] By calculating these power parameters, the system can more comprehensively evaluate the operating status of the equipment and the electrical characteristics of the line, thus providing a more accurate basis for fault diagnosis.

[0063] Preprocessing function of data acquisition module

[0064] In order to ensure the quality and availability of monitoring data, the data acquisition module will perform a series of preprocessing operations after collecting data. The specific steps are as follows:

[0065] Data verification: Verify the monitoring data collected from various early warning devices to ensure the integrity and accuracy of the data.

[0066] Supplement missing values ​​or outliers: For missing data or outliers, the system will reasonably supplement or correct them based on contextual information.

[0067] Format conversion: Convert date and time fields from string format to a unified time format to facilitate subsequent time series analysis.

[0068] Deduplication operation: Detect and remove duplicate monitoring data records to avoid the impact of redundant data on analysis results.

[0069] Remove outliers and invalid characters: Remove outliers and invalid characters in monitoring data through statistical analysis methods to ensure the rationality of the data.

[0070] Normalization processing: Normalize the values ​​of different types of monitoring data to unify the dimensions of the data and improve the accuracy of the analysis.

[0071] Detailed process of normalization

[0072] In a preferred embodiment of this embodiment, the specific formula for normalization processing is as follows:

[0073] ;

[0074] where x i represents the normalized monitoring data, X i represents the original monitoring data, X max Indicates the maximum value in the original monitoring data, X min Indicates the minimum value in the original monitoring data.

[0075] In another preferred embodiment of the present invention, the data acquisition module uses a systematic analysis process to determine whether the fault event is caused by an electrical fault to ensure the accuracy and reliability of the judgment. The specific process is as follows:

[0076] Data acquisition:

[0077] The data acquisition module first obtains all monitoring data and power parameter values ​​within the set time window before the fault event occurs. These data include the fundamental wave, amplitude, harmonic components, voltage of the current signal, and power parameters (such as apparent power, active power, reactive power, power factor, etc.) calculated and generated by the early warning equipment. These data provide a comprehensive information basis for subsequent fault judgment.

[0078] Current amplitude analysis:

[0079] If the real-time current value amplitude is 0: In this case, it indicates that there is no current flowing through the line, which may be due to the device not being started or the line being completely disconnected. Therefore, it can be determined that the fault event is not caused by an electrical fault, but may be caused by equipment shutdown, switch disconnection or other non-electrical factors.

[0080] If the real-time current value amplitude exceeds the preset standard range: This indicates that the current value is abnormal and exceeds the normal operating range. In this case, it can be clearly determined that the fault event is caused by an electrical fault. For example, a current amplitude that is too high may indicate a short circuit, overload, or other electrical abnormality.

[0081] Further monitoring data and power parameter analysis:

[0082] If the real-time current value is within the preset standard range, it means that the current value itself is not obviously abnormal. At this time, the module will further obtain any real-time monitoring data or power parameters for more detailed analysis:

[0083] Monitoring data or power parameters exceed the preset standard range: If the value of any real-time monitoring data (such as voltage, harmonic components) or power parameters (such as apparent power, active power, power factor) exceeds the corresponding preset standard range, the fault event is considered to be an electrical fault. For example, voltage is too high or too low, harmonic components increase abnormally, power factor changes abnormally, etc., which may be signs of electrical faults.

[0084] Monitoring data and power parameters are within the standard range: If all monitoring data and power parameters are within the preset standard range, it can be preliminarily determined that the fault event may not be caused by an electrical fault, but by other factors (such as normal startup, shutdown or external environmental factors of the equipment).

[0085] In another preferred embodiment of the present invention, the process of the fault location module extracting the current value change sequence before the fault event occurs is:

[0086] The initial time series of historical power consumption data is obtained, the length of the initial time series is collected and marked as L, the sliding step of the time series is obtained and marked as S, the length of the current value change sequence is t, and the number of splits of the historical power consumption data is n=L / St.

[0087] In another preferred embodiment of the present invention, the fault location module adopts a method based on cluster analysis to process all sub-current value change sequences, and determines the specific cause of the electrical fault by checking the changes in the category clusters of the pending current value change sequences during the clustering process. The specific implementation steps of this process are as follows:

[0088] 1. Initialization of cluster analysis:

[0089] The fault location module first normalizes the historical power consumption data and builds a time series model. Through this model, the module extracts the current value change sequence before the fault event occurs and divides it into several sub-current value change sequences with a duration of t. These sub-sequences cover the key current change information before the fault occurs, providing basic data for subsequent cluster analysis.

[0090] 2. Similarity detection and category clustering:

[0091] The module sets two key parameters for cluster analysis: control radius and minimum similarity number. The control radius is used to define the search range of the current value change sequence to be determined in the feature space, while the minimum similarity number is the minimum number requirement for judging the sequence similarity.

[0092] For each current value change sequence to be determined, the module detects the number of similar sequences within the control radius, denoted as m;

[0093] If m is less than the minimum similarity number: This indicates that within the control radius, there are not enough sequences similar to the current value change sequence to be determined. In this case, the module directly determines that the cause of the electrical fault is the abnormal behavior of the electrical equipment. This is because the abnormality of the electrical equipment usually leads to the randomness and uniqueness of the current change, making it difficult to match it with other normal sequences.

[0094] If m is greater than or equal to the minimum similarity number: This indicates that the pending current value change sequence has enough similar sequences within the control radius. The module puts the pending current value change sequence into the corresponding category cluster, and processes all sub-current value change sequences in turn, classifying them into the corresponding category cluster or marking them as abnormal. This process aims to classify current change sequences with similar characteristics for subsequent analysis.

[0095] 3. Extraction and comparison of cluster centers:

[0096] In order to further confirm the cause of the fault, the module repeats the above clustering process several times, and extracts the cluster center of the category cluster after each clustering. The cluster center is the characteristic representative of all sequences in the category cluster, reflecting the typical current change pattern of the category cluster.

[0097] Calculation of cluster center shift: By comparing the change in the cluster center between two clustering iterations, it is determined whether the cluster center has shifted.

[0098] Determination of deviation threshold: If the change value of the cluster center exceeds the preset threshold, it is determined that the electrical fault is caused by abnormal electrical equipment. Because abnormal behavior of electrical equipment usually causes a significant deviation in the current change pattern, this deviation is particularly evident in the change of the cluster center.

[0099] Case of no significant deviation: If the change value of the cluster center does not exceed the preset threshold, the electrical fault is determined to be the aging of the line insulation material. The aging of the line insulation material usually causes a slow degradation of the current change pattern, which is manifested as a small deviation value in the change of the cluster center.

[0100] 4. Through the above cluster analysis and the judgment of cluster center offset, the fault location module can accurately distinguish whether the electrical fault is caused by abnormal electrical equipment or aging of line insulation materials. The advantages of this method are:

[0101] Data-driven judgment: Cluster analysis based on the current value change sequence can mine the characteristic patterns of faults from a large amount of historical data, avoiding misjudgment caused by lack of experience in traditional methods.

[0102] Dynamic adaptability: Through multiple clustering and dynamic comparison of cluster centers, the module can adapt to changes in fault characteristics in different scenarios, improving the robustness and reliability of the system.

[0103] Precise positioning and cause analysis: It can not only accurately locate the location of the fault, but also further analyze the specific cause of the fault, providing a clear direction for subsequent repair and maintenance work.

[0104] 5. In practical applications, this fault judgment method based on cluster analysis can effectively cope with complex and changeable transmission line fault scenarios. For example, in the current change sequence of a certain area, if the cluster center shifts significantly, the module can quickly determine that the power equipment is abnormal and prompt the operation and maintenance personnel to check the relevant equipment. For the case where the cluster center shifts less, the module can accurately identify the aging of the line insulation material and remind the operation and maintenance personnel to perform line maintenance and replacement.

[0105] Through this optimized fault location and cause analysis method, the power transmission line abnormality warning and fault location system of the present invention can significantly improve the operating efficiency and reliability of the power system, and provide a strong guarantee for the safe and stable operation of the power system.

[0106] In a preferred case of this embodiment, the formula for determining whether the cluster center is offset is:

[0107] ;

[0108] ;

[0109] Among them, S i is a set of data points, |S i ∣ is the number of data points in the class cluster, Δc i represents the change value of the i-th cluster center between the previous and next two iterations; c i,t and c i,t+ 1 represents the value of the i-th cluster center at time t and time t+1 respectively. i When it is greater than the preset threshold, it indicates that the cluster center has shifted.

[0110] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A power transmission line abnormality warning and fault location system, characterized in that: include: The area demarcation module is used to collect statistics on all warning equipment information in the line and divide the line into several sub-areas according to the distribution of warning equipment. Each warning equipment is associated with a corresponding sub-area. The regional positioning module is used to mark the sub-region related to the fault event as a pending area and mark the early warning equipment in the pending area as a pending equipment when a fault event occurs; A data acquisition module is used to collect monitoring data of the pending equipment within a set time window t before the fault, and pre-process the monitoring data, and determine whether the fault event is caused by an electrical fault based on the monitoring data. If so, collect historical power consumption data of the pending area before the fault event occurs; A fault location module is used to normalize the historical power consumption data and build a time series model to extract the current value change sequence before the fault event occurs, and divide the current value change sequence into a number of sub-current value change sequences with a duration of t, mark the sub-current value change sequence within a set time window as a pending current value change sequence, cluster all the sub-current value change sequences, and check whether the category cluster to which the pending current value change sequence belongs is offset during the clustering process. If the offset exceeds a preset threshold, it is determined that the electrical fault is caused by an abnormality in the electrical equipment; if the offset exceeds the preset threshold, it is determined that the electrical fault is caused by aging of the line insulation material.

2. A power transmission line abnormality warning and fault location system according to claim 1, characterized in that: The early warning equipment includes an electric energy meter, a circuit protection device, an electrical fault early warning sensor, an intelligent socket, and an electrical fault prevention and control device; the monitoring data of the early warning equipment includes the fundamental wave of the current signal, the amplitude of the current signal, the harmonic component, and the voltage.

3. A power transmission line abnormality warning and fault location system according to claim 2, characterized in that: The early warning device calculates and generates power parameters according to the real-time voltage value and the real-time current value. The power parameters include apparent power, active power, reactive power, and power factor.

4. A power transmission line abnormality warning and fault location system according to claim 2, characterized in that: The process of preprocessing the monitoring data by the data acquisition module is as follows: Verify the monitoring data collected from each warning device, supplement missing values ​​or outliers, convert date and time fields from strings to a unified format, detect and remove duplicate monitoring data records, remove outliers and invalid characters in monitoring data, and normalize the values ​​of different types of monitoring data.

5. A power transmission line abnormality warning and fault location system according to claim 3, characterized in that: The normalization process is: ； where x i represents the normalized monitoring data, X i represents the original monitoring data, X max Indicates the maximum value in the original monitoring data, X min Indicates the minimum value in the original monitoring data.

6. A power transmission line abnormality warning and fault location system according to claim 3, characterized in that: The process of the data acquisition module determining whether the fault event is caused by an electrical fault is as follows: Get the values ​​of all monitoring data and power parameters within the set time window before the fault event occurs. If the real-time current value amplitude is 0, it is considered that the fault event is not an electrical fault; if the real-time current value amplitude exceeds the preset standard range, it is considered that the fault event is an electrical fault; if the real-time current value amplitude is within the preset standard range, get any real-time monitoring data or power parameter. When the value of any real-time monitoring data or power parameter exceeds the corresponding preset standard range, it is considered that the fault event is an electrical fault.

7. A power transmission line abnormality warning and fault location system according to claim 1, characterized in that: The process of the fault location module extracting the current value change sequence before the fault event occurs is as follows: The initial time series of historical power consumption data is obtained, the length of the initial time series is collected and marked as L, the sliding step of the time series is obtained and marked as S, the length of the current value change sequence is t, and the number of splits of the historical power consumption data is n=L / St.

8. A power transmission line abnormality warning and fault location system according to claim 7, characterized in that: The process of clustering all sub-current value change sequences and checking whether the category cluster to which the pending current value change sequence belongs is offset during the clustering process is as follows: Set the control radius and the minimum similarity number. For the pending current value change sequence, detect the number m of similar sequences within the control radius. If m is less than the minimum similarity number, directly judge that the cause of the electrical fault is the abnormality of the electrical equipment. If m is greater than or equal to the minimum similarity number, put the pending current value change sequence into the category cluster, and put all the sub-current value change value sequences into the corresponding category cluster or mark them as abnormal in turn. Repeat the above process, extract the cluster centers of the category clusters after several clusterings, compare the number of cluster centers where the current change value sequence to be determined is located and the difference in the cluster center values. If the cluster center deviates by more than the preset threshold, it is determined that the electrical fault is caused by abnormal electrical equipment; if the deviation exceeds the preset threshold, it is determined that the electrical fault is caused by aging of line insulation materials.

9. A power transmission line abnormality warning and fault location system according to claim 8, characterized in that: The formula for determining whether the cluster center is offset is: ； ； Among them, S i is a set of data points, |S i ∣ is the number of data points in the class cluster, Δc i represents the change value of the i-th cluster center between the previous and next two iterations; c i,t and c i,t+ 1 represents the value of the i-th cluster center at time t and time t+1 respectively. i When it is greater than the preset threshold, it indicates that the cluster center has shifted.

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