A multi-sensor cooperative control method for power transmission line state monitoring

Abstract

This invention relates to the field of power supply monitoring technology, and in particular to a multi-sensor collaborative control method for monitoring the status of transmission lines. This invention analyzes strongly correlated faults after a fault occurs. By establishing a strongly correlated region based on the connection relationship between power towers, the monitoring and diagnosis range is precisely limited to the faulty line segment and its directly connected surrounding line segments. This avoids the large-scale and ineffective data scanning and processing of the entire transmission network in traditional methods. Due to the significantly reduced processing range and controllable data volume, the system can complete the collaborative fault detection, abnormal curve comparison, and latent fault identification within the region at a faster speed, greatly improving the real-time performance of fault response and the targeted nature of collaborative monitoring.

Description

Technical Field

[0001] This invention relates to the field of power supply monitoring technology, and in particular to a multi-sensor collaborative control method for monitoring the condition of transmission lines. Background Technology

[0002] As a crucial component of the power system, the real-time monitoring of transmission lines' operational status is of paramount importance for ensuring the safe and stable operation of the power grid. Currently, in the field of transmission line condition monitoring, the common approach is to deploy various sensors (such as current, voltage, temperature, and vibration sensors) on the towers and lines to continuously collect line operating parameters, and then perform data analysis and fault diagnosis through a back-end system.

[0003] However, existing transmission line condition monitoring methods generally suffer from the following shortcomings: When a fault occurs in a certain line segment, the system often needs to perform a comprehensive scan and anomaly analysis of sensor data across the entire network or a large area, lacking an effective area limitation mechanism. This large-scale, indiscriminate data processing method not only leads to a heavy computational burden and slow response speed, but also makes it difficult to accurately locate the fault-related area, easily overlooking hidden faults that are physically topologically related to the faulty line segment. In addition, traditional methods typically only inspect the fault point itself after a fault occurs, lacking coordinated monitoring and correlation analysis of surrounding adjacent line segments, failing to promptly detect cascading hazards caused by fault propagation, thus reducing the comprehensiveness of fault investigation and the targeted nature of operation and maintenance response.

[0004] Therefore, there is an urgent need for a multi-sensor control method that can quickly define the fault-affected area and achieve precise local collaborative monitoring based on the physical topology of transmission lines, so as to improve the efficiency and accuracy of fault detection. Summary of the Invention

[0005] To achieve the above objectives, this invention proposes a multi-sensor collaborative control method for power transmission line condition monitoring, comprising the following steps:

[0006] Step 1: Sensor tag encoding and strong association topology modeling. The power towers, line segments and sensors in the power transmission line network are uniformly tagged and encoded, and a strong association set between line segments is defined based on the connection relationship of the power towers.

[0007] Step 2: Real-time monitoring and maintenance triggering of single-segment faults. The sensor set on each line segment is monitored in real time. When an abnormality is detected, the maintenance process is triggered and an alarm containing the faulty line segment tag is issued.

[0008] Step 3: Regional data integration. Based on the strongly correlated set, a strongly correlated region is defined with the faulty line segment as the center, and all sensor data in the region are subjected to unified anomaly detection and fault propagation analysis.

[0009] Step 4: Power outage collaborative monitoring and latent fault determination. Power outage is performed on all line segments within the strongly correlated area. Under power outage conditions, the actual data sequence collected by the sensors on each line segment within the area is compared point by point with the pre-established normal power outage expected curve for that line segment to identify the line segments with latent faults.

[0010] Step 5: Dynamic area reconstruction and iterative fault screening. When a new line segment with a related fault is identified, the strongly correlated area is redefined with the line segment with the related fault as the center, and power outage collaborative monitoring and latent fault judgment are performed again. This process is iterated until no new abnormal line segments appear, and all abnormal line segments are summarized to generate a collaborative maintenance list.

[0011] Step 6: Small current curve comparison verification and closed-loop confirmation. After completing the repair of the fault point, a small current is uniformly applied to all line segments in the repair point and its strongly correlated area. The measured response curve of the repair point is compared with the unified benchmark curve obtained by integrating the measured data of the healthy line segments in the area to verify the repair effect.

[0012] In one example, in the first step, each power tower in the entire network is assigned a unique label, and the combination of labels of adjacent power towers is used as the label of the line segment connecting the two power towers. All sensors installed on the line segment are treated as a set of sensors with the same label. For any line segment, its strong association set is defined as the line segment itself and all other line segments connected to either of the power towers at both ends of the line segment.

[0013] In one example, the basis for detecting an anomaly in the second step includes: sensor data exceeding a preset safety threshold or sensor data meeting preset mutation characteristics, including temperature rise rate exceeding a first threshold, current rise rate exceeding a second threshold, and voltage drop rate exceeding a third threshold.

[0014] In one example, the fourth step, which compares the actual data sequence with the expected curve of a normal power outage point by point to identify the line segments with hidden faults, also includes: calculating the actual rate of change of sensor data for each line segment, wherein the rate of change includes the rate of temperature drop and the time constant for current and voltage to return to zero.

[0015] The actual rate of change of each line segment is compared with the average rate of change of other line segments in the same area. If the deviation exceeds the set threshold, it is judged as an abnormal rate.

[0016] Calculate the coefficient of variation of the data within the sliding window to assess data stability;

[0017] The three indicators of point-to-point comparison deviation, rate of change deviation, and data variation coefficient are weighted and fused to obtain the anomaly score for each line segment. When the anomaly score exceeds the preset threshold, it is judged as a cascading fault.

[0018] In one example, in the fifth step, the dynamic region reconstruction and iterative fault screening take the initial faulty line segment as the center and delineate the first-level strongly correlated region for screening;

[0019] If a second faulty line segment is found within the first-level strongly correlated region, then the second faulty line segment is taken as the new fault source, and the second-level strongly correlated region is defined with it as the center.

[0020] The second-level strongly correlated area was subjected to power outage coordinated monitoring and latent fault identification again until no new abnormal line segments appeared in a continuous round of screening.

[0021] In one example, the sixth step specifically includes:

[0022] Apply a small current of 5% to 10% of the rated value to all line sections in the central maintenance point and its strongly associated area for 10 to 15 minutes.

[0023] The response data of sensors in each line segment within the collection area during the power-on period are plotted as a response curve with time as the horizontal axis.

[0024] The curves of the same type for healthy line segments in the region, excluding the central maintenance point, are integrated, and the average value of the changes in each time period is taken to form a unified benchmark curve.

[0025] The measured curve at the central repair point is compared point by point with the reference curve. If the overall difference between the measured curve and the reference curve is very small and none of the indicators exceed the system's preset allowable threshold, the repair is deemed valid; otherwise, rework is deemed necessary.

[0026] The multi-sensor collaborative control method for power transmission line condition monitoring proposed in this invention can bring the following beneficial effects:

[0027] 1. This invention analyzes strongly correlated faults after their occurrence. By establishing a strongly correlated region based on the connection relationship between power towers, the monitoring and diagnostic scope is precisely limited to the faulty line segment and its directly connected surrounding line segments. This avoids the large-scale and ineffective data scanning and processing of the entire transmission network as in traditional methods. Due to the significantly reduced processing range and controllable data volume, the system can complete coordinated fault detection, abnormal curve comparison, and latent fault identification within the region at a faster speed, greatly improving the real-time performance of fault response and the targeted nature of coordinated monitoring.

[0028] 2. This invention utilizes the maintenance outage window period to compare actual data with expected curves of normal power outages for each line segment within a strongly correlated area point by point. It also identifies hidden faults through weighted fusion of multiple indicators such as rate of change analysis and coefficient of variation calculation. After maintenance, the maintenance effect is verified by comparing with a small current curve. This forms a full-process control of fault detection, collaborative maintenance, and closed-loop verification, which greatly improves the reliability, safety, and automation level of transmission line operation and maintenance. Attached Figure Description

[0029] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0030] Figure 1 A schematic diagram of the flow structure of a multi-sensor collaborative control method for monitoring the condition of power transmission lines;

[0031] Figure 2 Example diagram of line tags for a multi-sensor collaborative control method for monitoring the condition of power transmission lines. Detailed Implementation

[0032] To more clearly illustrate the overall concept of the present invention, a detailed description will be provided below with reference to the accompanying drawings and examples.

[0033] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0034] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0035] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0036] In this invention, unless otherwise expressly specified and limited, the first feature "on" or "below" the second feature may be in direct contact with the first and second features, or indirect contact through an intermediate medium. In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0037] like Figures 1 to 2 As shown, this invention proposes a multi-sensor collaborative control method for power transmission line condition monitoring, comprising the following steps:

[0038] Step 1: Sensor Tag Encoding and Strong Association Topology Modeling. This step involves uniformly tagging the power towers, line segments, and sensor sets on the transmission line network, and defining strong association sets between line segments based on tower connection relationships. The overall approach is as follows: power towers are the basic nodes, and line segments between adjacent towers are the basic monitoring units. All sensors (including current, voltage, temperature, vibration, etc.) on each line segment are treated as a whole, with their tags matching the tag of that line segment. The strong association range between line segments consists of all line segments connected to the towers at both ends of that segment. The tag naming rules aim to intuitively reflect the physical topology relationships, facilitating rapid querying and automatic reasoning.

[0039] The specific implementation method is as follows: First, assign a unique label to each power tower in the entire network, such as Ai or Aj, where Ai represents the i-th power tower and Aj represents the j-th power tower. Examples include A1, A2, A3, etc. (numbered sequentially according to the line route). Second, for any line segment between two adjacent power towers, use the combination of the labels from the towers at both ends as the label for that line segment. For example, the line segment connecting A1 and A2 is labeled A1A2. It is agreed that the tower numbers in the label are arranged in ascending order to ensure uniqueness. All sensors installed on this line segment (regardless of type or quantity) together form a sensor set. The label of this set is the label for this line segment (e.g., A1A2). No further subdivision and coding is performed on individual sensors. (See reference...) Figure 2 Example.

[0040] After labeling, define strong associations: For any line segment such as A1A2, its strong association set includes A1A2 itself and all other line segments connected to tower A1 or A2, i.e., S = {all line segments with Ai as one end} ∪ {all line segments with Aj as one end}. This set precisely describes the minimum physical range through which a fault directly radiates to adjacent line segments via the towers at both ends when A1A2 fails. Based on this, a strong association table for the entire network and an inverted index from towers to line segments can be established for use in subsequent steps.

[0041] Step 2: Real-time monitoring and maintenance triggering of single-segment faults. This step is responsible for real-time monitoring of the sensor set on each line segment. When abnormal values ​​are detected, an alarm is issued and subsequent maintenance procedures are triggered. The sensor set on each line segment continuously collects key parameters such as current, voltage, and temperature. The system sets safe thresholds for normal operation for each parameter, which can be manually set. For example, the upper limit for current is 1.2 times the rated value, and the lower limit is 0.1 times the rated value; the upper limit for voltage is 1.1 times the rated value, and the lower limit is 0.7 times the rated value; the upper limit for temperature is set according to the allowable temperature of the conductor (typically 85℃).

[0042] Simultaneously, the system also detects abrupt changes in parameters, such as a temperature surge exceeding 5°C / minute, a current surge exceeding 50% of the rated value / second, and a voltage drop exceeding 30% of the rated value / second. When any sensor data exceeds the safety threshold or meets the abrupt change characteristic, the system immediately determines that a fault has occurred in that line segment and issues an alarm. The alarm information includes the label of the faulty line segment (e.g., A1A2), the fault type, and the abnormal value. The fault type is determined based on the anomaly detected by the sensor. For example, if a temperature sensor detects an abnormal temperature, a temperature anomaly alarm will be issued; similarly, fault types exist for parameters such as current and voltage.

[0043] Once the fault is confirmed, the system automatically generates a maintenance work order, notifying maintenance personnel to perform a power outage maintenance on the affected line segment. At this stage, the maintenance scope is temporarily limited to the alarmed line segment itself; subsequent steps will expand the scope of collaborative diagnosis based on strong correlations.

[0044] Step 3: Regional data integration. When a fault is confirmed on a certain line segment and an alarm is triggered, the system delineates a strongly correlated area centered on the faulty line segment based on the strong correlation established in Step 1. Through this regional division, the system incorporates the fault point and all line segments directly connected to it by power towers into the same analysis unit, thereby enabling an overall assessment of the impact of the fault on neighboring line segments and providing a data foundation for collaborative maintenance.

[0045] Once the region is divided, the system can perform unified anomaly detection, curve comparison, and fault propagation analysis on all sensor data within that region.

[0046] Step 4: Power Outage Co-monitoring and Latent Fault Identification. After confirming the faulty line segment and delineating the strongly correlated area, the system arranges maintenance personnel to conduct power outage repairs at the fault point. Before repairs, the faulty line segment and all line segments within its strongly correlated area, i.e., all line segments sharing power towers with the faulty line segment, are simultaneously de-energized. This is to ensure the safety of maintenance personnel and prevent the fault from escalating.

[0047] Even when the power is off, the sensors on each line segment within the strongly correlated area remain operational due to their independent backup power supply, continuously collecting key parameters such as current, voltage, temperature, and vibration.

[0048] The system utilizes this power outage window to perform real-time data monitoring on each strongly correlated line segment. Under normal circumstances, a healthy line will exhibit a clear physical response pattern after a power outage: current and voltage will rapidly drop to near zero within tens of milliseconds; temperature will start from the load temperature before the power outage and slowly decrease according to an exponential decay law, eventually approaching the ambient temperature, with the rate of decrease depending on the line's heat dissipation conditions and initial temperature difference; vibration amplitude will rapidly decay to the level of ambient noise.

[0049] During the initial commissioning or periodic maintenance, the system has established a normal power outage expectation curve library for each line segment through planned power outage tests. This library includes current zero-return curves, voltage zero-return curves, temperature exponential decline curves, and their tolerance threshold bands, such as the allowable deviation range of temperature at each time point.

[0050] During this maintenance power outage, the system will compare the actual data sequence collected by the sensors on each line segment in the strongly correlated area with the expected curve of the corresponding line segment point by point.

[0051] To further improve the sensitivity and reliability of the judgment, the system also needs to analyze the overall rate of change of data for all line segments within the strongly correlated set and identify outliers. Specifically, this involves: first, calculating the first derivative, i.e., the cooling rate, of the temperature drop curve for each line segment. Healthy lines exhibit a higher cooling rate initially after power outages, gradually decreasing later. Lines with latent faults, such as excessive contact resistance or insulation damage, may show abnormally low or even negative cooling rates, indicating a temperature increase.

[0052] The system compares the real-time cooling rate of each line segment with the average cooling rate of other line segments in the same area. If the rate of a certain line segment deviates from the average value by more than the set threshold, it is marked as an abnormal rate.

[0053] Secondly, regarding the zeroing process of current and voltage, the system not only detects the final residual value but also monitors the zeroing time constant, comparing it with the typical zeroing time of a healthy circuit. Significantly delayed zeroing or the presence of slowly decaying residual components indicate an anomaly. Furthermore, the system can use the coefficient of variation within a sliding window to assess data stability. In a healthy circuit, parameters should quickly stabilize to low values ​​after power loss, while data from an abnormal circuit may exhibit continuous fluctuations or slow drift.

[0054] During comprehensive judgment, the system weights and fuses three indicators: point-by-point comparison deviation, rate of change deviation, and coefficient of variation, between the measured value and the standard value of the corresponding time point in the expected curve library. An anomaly score is given for each strongly correlated line segment. The point-by-point comparison deviation is the absolute value of the measured value at each sampling time minus the standard value of the expected normal power outage curve for that line segment at the same time. The rate of change deviation is the difference between the measured cooling rate and the expected cooling rate. The coefficient of variation is the ratio of the standard deviation to the mean within the sliding window. When the anomaly score exceeds a preset threshold, it is determined that the line segment has a cascading fault and requires simultaneous repair. The threshold is determined by statistical modeling of normal power outage response data for similar line segments during the initial commissioning phase and in previous planned power outage tests: the mean of the anomaly scores of historical normal samples plus three times the standard deviation is taken as the static baseline threshold. Simultaneously, dynamic correction based on the current seasonal ambient temperature, line load level, and conductor type is supported. The corrected threshold value is not lower than 80% of the static baseline threshold to ensure detection sensitivity.

[0055] Step 5: Dynamic area reconstruction and iterative fault screening. During the maintenance and power outage monitoring process in Step 4, if the system finds that, in addition to the initial fault point, there is a second line segment whose abnormal score exceeds the preset threshold and is judged as a related fault after analyzing the rate of change and identifying outliers of the data of each line segment in the strongly correlated area, the system will regard the second fault point as a new fault source.

[0056] Since fault propagation may exhibit multi-level diffusion characteristics, a strongly correlated area defined solely by the initial fault point may not fully cover all affected lines. Therefore, it is necessary to re-divide and screen the area based on the newly detected fault point.

[0057] This process can be iterative. Each time a new fault point is discovered, the affected area is redefined and screened again until no new abnormal line segments appear in a continuous round of screening. Finally, the system compiles all line segments marked as abnormal, generating a complete collaborative maintenance list. This dynamic area reconstruction mechanism ensures comprehensive capture of the fault's impact range, avoiding the omission of distant or secondary damaged lines due to fixed area divisions. It is particularly suitable for scenarios where faults spread tier by tier along power towers.

[0058] Step 6: Low-current curve comparison verification and closed-loop confirmation. After completing on-site repairs at the central maintenance point, this step does not involve directly energizing at full voltage. Instead, a small current (approximately 5% to 10% of the rated value) is uniformly applied to all line sections within the central maintenance point and its strongly correlated area, and energized continuously for 10 to 15 minutes. Sensors in each line section within the area simultaneously collect key parameters such as current, voltage, and temperature. The response curves for each line section are plotted with time as the horizontal axis and the sensor data change value as the vertical axis.

[0059] Subsequently, similar curves of each healthy line segment within the strongly correlated area, excluding the central maintenance point, are integrated, and the average value of the changes in each time stage is taken to form a unified benchmark curve representing the normal response of the area.

[0060] Finally, the measured curves at the central repair point are compared point by point with the integrated benchmark curve: if the overall overlap is high and the changes in each time stage are within the allowable threshold (e.g., temperature deviation does not exceed ±5% and the curve shape is consistent), the repair is deemed effective; if there is a significant deviation (e.g., the temperature rise rate is too fast, the decay trend is abnormal, or the value in a certain time stage is consistently higher than the benchmark), the repair is deemed incomplete and rework is required.

[0061] Once the verification is successful, the maintenance lock is released, and the line enters the normal power supply process; if it fails, the line is repaired again and verified again until it is deemed successful, thus forming a closed loop.

[0062] The system architecture applied to the above methods includes a perception layer, a network layer, a platform layer, and an application layer.

[0063] The perception layer is responsible for the acquisition and preliminary processing of on-site data, deployed along the transmission lines on various power towers and line sections. This layer includes a multi-sensor acquisition module, an edge preprocessing module, a backup power supply module, and a tag binding module. The multi-sensor acquisition module integrates current transformers, voltage transformers, temperature sensors, vibration sensors, etc., and is uniformly grouped according to line section tags, reporting data in an aggregate manner. The edge preprocessing module filters, denoises, and standardizes the format of the raw sampled data, performing local rapid judgment of threshold violations to reduce invalid data transmission. The backup power supply module provides independent power supply for sensors and communication equipment, automatically switching to supercapacitors or batteries when the main power supply is interrupted, ensuring continuous operation of power outage monitoring. The tag binding module physically binds the sensor hardware identity to the line section tag, automatically registering it to the system upon power-up.

[0064] Data transmission between the network layer and the platform layer is adapted to the characteristics of long-distance, distributed deployment of power transmission lines. This layer includes a wireless communication module, a fiber optic communication module, a protocol conversion module, and a data buffer module. The wireless communication module uses 4G or 5G public networks, power grid private wireless networks, or BeiDou short message service to achieve sensor data backhaul. The fiber optic communication module constructs a backbone transmission channel along overhead ground wire composite optical cables or all-dielectric self-supporting optical cables to ensure high bandwidth and low latency. The protocol conversion module unifies the communication protocols of sensors from various manufacturers and converts them into the system's internal standard data frame format. The data buffer module locally stores sampled data when the network is interrupted and automatically retransmits it after recovery to ensure data integrity.

[0065] The platform layer is the core computing and data hub of the system, deployed in substations or dispatch center server clusters. This layer includes a tag management module, a real-time data aggregation module, a strong correlation region engine, a fault detection module, a power outage analysis module, a dynamic reconfiguration module, a low-current verification module, a predictive curve library module, and a data storage module. The tag management module maintains the mapping relationship between tower tags, line segment tags, and sensor set tags across the entire network, and manages the strong correlation table and the inverted index from tower to line segment. The real-time data aggregation module receives and parses the reported data from sensor sets of each line segment, storing it in a time-series database according to tag classification. The strong correlation region engine automatically queries the strong correlation set based on the faulty line segment tag, delineates the collaborative analysis region, and outputs a list of region members. The fault detection module performs threshold exceeding judgment and abrupt change feature identification, generating an initial alarm. The power outage analysis module calls the normal power outage predictive curve library, performs point-by-point comparison between actual data and predictive curves, rate of change analysis, coefficient of variation calculation, and outputs anomaly scores. The dynamic reconfiguration module receives information about associated faulty line segments, redefines strongly correlated areas centered on these points, and iteratively performs power outage analysis until no new anomalies appear. The low-current verification module controls the low-current injection process, collects response data from each line segment, plots curves, integrates them to generate a baseline curve, and performs a comparison between the measured curve at the maintenance point and the baseline curve. The expected curve library module stores the expected normal power outage curves and tolerance thresholds for each line segment established through planned power outage tests during the initial commissioning phase and periodic maintenance. The data storage module includes a time-series database storing sensor sampling values, a relational database storing topology and tag mappings, and files storing curve images and closed-loop archives.

[0066] At the application layer, it provides human-machine interaction and business closed-loop capabilities for maintenance personnel and the scheduling system. This layer includes a monitoring and alarm module, a maintenance work order module, a collaborative maintenance checklist module, a mobile terminal module, a closed-loop archive module, and a knowledge accumulation module. The monitoring and alarm module displays the entire network's operational status in the form of a line topology map, highlighting faulty line segments and sending audible, visual, and SMS alarms. The maintenance work order module automatically receives fault confirmation information, generates maintenance work orders containing fault tags, types, and abnormal values, and dispatches them to the maintenance terminal. The collaborative maintenance checklist module summarizes all abnormal line segments after dynamic reconstruction, generating a collaborative maintenance checklist that supports sorting by abnormal score. The mobile terminal module allows on-site maintenance personnel to view comparison results, rate curves, and abnormal score lists, receive work orders, and receive maintenance progress feedback. The closed-loop archive module binds data from the entire fault, maintenance, and verification process, supporting historical archive queries, duplicate fault identification, and longitudinal comparative analysis. The knowledge accumulation module archives verification cases, provides feedback on optimized expected curve library thresholds and judgment parameters, and enables system self-learning.

[0067] The data flow relationships between the modules are as follows: Data collected by the perception layer is transmitted to the platform layer via the network layer for aggregation. The tag management module provides a unified topology query service for the strongly correlated region engine, power outage analysis module, dynamic reconstruction module, and low-current verification module, serving as a fundamental dependency for each analysis engine. The real-time data aggregation module distributes data to each analysis engine. The fault detection module generates initial alarms, the power outage analysis module outputs anomaly scores, the dynamic reconstruction module expands the maintenance scope, and the low-current verification module determines the maintenance effect. The power outage analysis module and the dynamic reconstruction module form an internal loop: after power outage analysis discovers a new anomaly, dynamic reconstruction expands the region, and power outage analysis is performed again in the new region until convergence. The low-current verification module operates independently of the preceding analysis process, starting only after maintenance is completed. Its results directly drive the closed-loop archive module to archive or trigger rework. The expected curve library module is only called by the power outage analysis module. The low-current verification stage does not rely on historical curves but instead uses measured data from healthy lines of the same period to build a dynamic benchmark. Finally, the analysis results are aggregated to the various business modules in the application layer to complete work order dispatch, on-site execution, and closed-loop archiving.

[0068] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0069] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A multi-sensor collaborative control method for monitoring the condition of transmission lines, characterized in that: Includes the following steps: Step 1: Sensor tag encoding and strong association topology modeling. The power towers, line segments and sensors in the power transmission line network are uniformly tagged and encoded, and a strong association set between line segments is defined based on the connection relationship of the power towers. Step 2: Real-time monitoring and maintenance triggering of single-segment faults. The sensor set on each line segment is monitored in real time. When an abnormality is detected, the maintenance process is triggered and an alarm containing the faulty line segment tag is issued. Step 3: Regional data integration. Based on the strongly correlated set, a strongly correlated region is defined with the faulty line segment as the center, and all sensor data in the region are subjected to unified anomaly detection and fault propagation analysis. Step 4: Power outage collaborative monitoring and latent fault determination. Power outage is performed on all line segments within the strongly correlated area. Under power outage conditions, the actual data sequence collected by the sensors on each line segment within the area is compared point by point with the pre-established normal power outage expected curve for that line segment to identify the line segments with latent faults. Step 5: Dynamic area reconstruction and iterative fault screening. When a new line segment with a related fault is identified, the strongly correlated area is redefined with the line segment with the related fault as the center, and power outage collaborative monitoring and latent fault judgment are performed again. This process is iterated until no new abnormal line segments appear, and all abnormal line segments are summarized to generate a collaborative maintenance list. Step 6: Small current curve comparison verification and closed-loop confirmation. After completing the repair of the fault point, a small current is uniformly applied to all line segments in the repair point and its strongly correlated area. The measured response curve of the repair point is compared with the unified benchmark curve obtained by integrating the measured data of the healthy line segments in the area to verify the repair effect.

2. The multi-sensor collaborative control method for transmission line condition monitoring according to claim 1, characterized in that: In the first step, each power tower in the entire network is assigned a unique label. The combination of labels of adjacent power towers is used as the label of the line segment connecting the two power towers. All sensors installed on the line segment are treated as a set of sensors with the same label. For any line segment, its strong association set is defined as the line segment itself and all other line segments connected to either of the power towers at both ends of the line segment.

3. The multi-sensor collaborative control method for transmission line condition monitoring according to claim 1, characterized in that: The basis for detecting anomalies in the second step includes: sensor data exceeding a preset safety threshold or sensor data meeting preset mutation characteristics, including temperature rise rate exceeding a first threshold, current rise rate exceeding a second threshold, and voltage drop rate exceeding a third threshold.

4. The multi-sensor collaborative control method for transmission line condition monitoring according to claim 1, characterized in that: The fourth step involves comparing the actual data sequence with the expected curve of a normal power outage point by point to identify the line segments with hidden faults. It also includes calculating the actual rate of change of sensor data for each line segment, including the rate of temperature drop and the time constant for current and voltage to return to zero. The actual rate of change of each line segment is compared with the average rate of change of other line segments in the same area. If the deviation exceeds the set threshold, it is judged as an abnormal rate. Calculate the coefficient of variation of the data within the sliding window to assess data stability; The three indicators of point-to-point comparison deviation, rate of change deviation, and data variation coefficient are weighted and fused to obtain the anomaly score for each line segment. When the anomaly score exceeds the preset threshold, it is judged as a cascading fault.

5. The multi-sensor collaborative control method for transmission line condition monitoring according to claim 1, characterized in that: In the fifth step, dynamic region reconstruction and iterative fault screening take the initial faulty line segment as the center and delineate the first-level strongly correlated region for screening. If a second faulty line segment is found within the first-level strongly correlated region, then the second faulty line segment is taken as the new fault source, and the second-level strongly correlated region is defined with it as the center. The second-level strongly correlated area was subjected to power outage coordinated monitoring and latent fault identification again until no new abnormal line segments appeared in a continuous round of screening.

6. The multi-sensor collaborative control method for transmission line condition monitoring according to claim 1, characterized in that: The sixth step specifically includes: Apply a small current of 5% to 10% of the rated value to all line sections in the central maintenance point and its strongly associated area for 10 to 15 minutes. The response data of sensors in each line segment within the collection area during the power-on period are plotted as a response curve with time as the horizontal axis. The curves of the same type for healthy line segments in the region, excluding the central maintenance point, are integrated, and the average value of the changes in each time period is taken to form a unified benchmark curve. The measured curves at the central maintenance point were compared point by point with the baseline curve, as detailed below: If the overall difference between the measured curve and the baseline curve and all indicators do not exceed the system's preset allowable thresholds, the repair is deemed effective; otherwise, rework is deemed necessary.

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