Adaptive collaborative protection method and system for arcing horn

Abstract

The invention discloses a self-adaptive collaborative protection method and system for an arcing horn, and belongs to the technical field of lightning protection of a power system. The method comprises the following steps: acquiring multi-source sensing data of a plurality of arcing horns through non-intrusive state sensing; the action event is judged through information fusion processing, and an action event record is generated and uploaded to the data processing platform; establishing and updating an arcing horn digital file containing the health state evaluation value; in response to thunder and lightning activity early warning, generating a collaborative protection strategy for optimizing a thunder and lightning current discharge path through a collaborative protection decision model in combination with the digital file; the control instruction drives the controllable protection device to adjust electrical parameters, and the lightning current is guided to preferentially flow through the arcing horn with a better health state; and calculating a residual life index based on the digital archive and generating maintenance early warning information. According to the invention, accurate perception of the state of the arcing horn is realized, discharge and predictive maintenance are cooperatively optimized, and the problems of lack of perception, insufficient collaboration and maintenance lag of a traditional arcing horn are solved.

Description

Technical Field

[0001] This invention relates to the field of power system lightning protection technology, specifically to an arc angle adaptive collaborative protection method and system. Background Technology

[0002] Transmission lines are a critical component of the power system, and their operational reliability directly affects grid security and power supply quality. Lightning activity is one of the main natural factors threatening the safe and stable operation of transmission lines, and insulator flashover caused by lightning overvoltage is a significant cause of line tripping. To address this threat, arc-guiding angles, as an economical and effective passive lightning protection device, are widely installed at both ends of insulator strings. They guide and fix the electric arc within their pre-set electrode gaps, protecting insulators and other core equipment from arc burns.

[0003] However, existing lightning protection models centered on arc-prone angles have limitations. First, they lack status awareness. Traditional arc-prone angles are purely mechanical and electrical components, lacking any information sensing or recording capabilities. Maintenance personnel cannot obtain key data such as whether the angle activated, the timing of activation, lightning current parameters, and arc energy in real time or after the event, leading to a lack of basis for line lightning strike analysis and lightning protection strategy optimization falling into a data blind spot. Second, protection strategies are isolated and lack coordination. A large number of arc-prone angles in the area respond independently to lightning strikes, with random action selection, failing to proactively optimize the discharge path based on the differences in the health status of each device. This may cause some arc-prone angles to deteriorate faster due to repeated activation, while other devices in good condition are not effectively utilized, resulting in low overall protection resource utilization and suboptimal cluster lifespan. Third, the maintenance model is passive and lagging. Current maintenance relies on periodic inspections or replacement after failure, lacking accurate assessment based on the actual cumulative electrolytic erosion damage of the devices. This model may leave safety hazards due to untimely replacement or waste resources due to over-maintenance.

[0004] While existing technologies attempt to monitor the arc angle status using sensors, most employ invasive installations or rely on single-sensor principles, making them susceptible to interference with the device itself or environmental factors, resulting in insufficient reliability. More importantly, there is currently a lack of a system-level solution capable of aggregating multi-dimensional information, including device status, lightning warnings, and network topology, and making collaborative intelligent decisions, leaving the arc angle monitoring system in an isolated and passive operational phase.

[0005] Therefore, how to overcome the technical bottlenecks of traditional lightning protection, such as lack of arc angle perception, inability to coordinate, and lagging maintenance, and achieve non-intrusive and accurate perception and digital archiving of its working status without affecting its reliability, and on this basis, build a collaborative protection mechanism and predictive maintenance strategy based on global optimization, so as to realize the transformation of lightning protection of transmission lines from passive response to active intelligent management, has become a key technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive collaborative protection method and system for arc angle to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an adaptive collaborative protection method and system for lightning arc angle, which can accurately perceive the state of lightning arc angle, support collaborative optimization of lightning current discharge path, improve lightning protection efficiency, and support predictive maintenance, thereby solving the problems of perception blind spots, lack of collaboration and maintenance lag in the prior art.

[0008] This application provides an adaptive cooperative protection method for arc angle, including the following steps: S1: Perform non-intrusive state sensing on multiple arc angles installed within the preset power supply area to acquire multi-source sensor data related to the arc angle action; S2: Based on the multi-source sensor data, through information fusion processing, determine whether an action event has occurred at the arc angle, and when the determination is yes, generate an action event record containing the identity of the arc angle, the action time and the estimated action parameters; S3: Upload the action event records to the data processing platform to establish and update the digital archives corresponding to each attack arc angle. The digital archives include a set of historical action event records for that attack arc angle and a health status assessment value calculated based on that set. S4: In response to the lightning activity warning information for the preset power supply area, and in combination with the digital files of multiple arc angles stored in the data processing platform, a collaborative protection strategy for optimizing the lightning current discharge path is generated through the collaborative protection decision model. S5: Convert the collaborative protection strategy into a control command and send it to the corresponding controllable protection device in the preset power supply area to adjust the relevant electrical parameters, thereby guiding the lightning current to flow preferentially to the arc angle with a better health status assessment value during lightning activity; S6: Based on the digital files of each arc angle, calculate its remaining life index, and generate maintenance warning information when the remaining life index is lower than a preset threshold.

[0009] Preferably, the non-invasive state sensing in step S1 is achieved by a sensor group deployed on the tower where the arc angle is located. The sensor group includes at least two of the following: a high-frequency electromagnetic sensor for capturing characteristic electromagnetic signals when the arc angle breaks down, an acoustic sensor for monitoring the shock wave signal generated by the arc, and an optical sensor for identifying characteristic spectral signals of the arc.

[0010] Preferably, the step S2, which involves determining whether an action event has occurred at the arc angle through information fusion processing, includes: Time synchronization and feature extraction are performed on multi-source sensing data from the sensor group; The extracted features are matched and analyzed with a pre-stored database of typical action features of the arc angle. When the matching analysis results from different sensors all indicate that a discharge event has occurred within a preset time window and the event location information points to the same arc angle, it is determined that an action event has occurred at that arc angle.

[0011] Preferably, the estimated action parameters in step S2 include at least the lightning current amplitude parameter estimated based on the signal amplitude of the high-frequency electromagnetic sensor and the arc energy parameter estimated based on the signal strength and duration of the optical sensor.

[0012] Preferably, the health status assessment value in step S3 is calculated by a preset weighted cumulative model based on the estimated action parameters in the historical action event record set corresponding to the arc angle. In the weighted cumulative model, recently occurring action events and parameters representing higher intensity in the estimated action parameters are given greater weight.

[0013] Preferably, the logic for generating the collaborative protection strategy by the collaborative protection decision model in step S4 includes: Based on the aforementioned lightning activity warning information, the potentially affected line sections and multiple arc angles are determined; Retrieve the current health status assessment value of the potentially affected arc angle; Based on preset optimization objectives, strategy instructions are generated to indicate that during lightning activity, lightning current should be preferentially guided through the arc angle with the better health status assessment value.

[0014] Preferably, the controllable protection device in step S5 includes a controllable parallel gap or a controllable surge arrester installed near the arc angle, and the adjustment of relevant electrical parameters includes adjusting the breakdown voltage of the controllable parallel gap or pre-activating the controllable surge arrester.

[0015] Preferably, the calculation of its remaining life index in step S6 includes: Obtain the historical cumulative arc energy parameters from the digital archive of the arc angle; Based on the pre-established arc angle electrode material ablation model, the proportion of lifetime already consumed is calculated according to the historical cumulative arc energy parameters. The remaining lifespan index is determined based on the proportion of lifespan already consumed.

[0016] Preferably, the method further includes: S7: Receive confirmation and processing feedback for the maintenance warning information, and update the processing result to the digital file of the corresponding arc angle.

[0017] This application also proposes an arc angle adaptive cooperative protection system for running the above method, including: The sensing module is deployed on multiple towers to perform non-intrusive state sensing of the arc angle on the corresponding towers and acquire multi-source sensor data; An edge processing unit, communicatively connected to the sensing module, is used to perform the information fusion processing to determine the arc angle action event and generate an action event record; The data communication module is used to record and upload the action events. The data processing platform is connected to the data communication module to receive and store the action event records, establish and maintain the arc angle digital archive, and run the collaborative protection decision model to generate collaborative protection strategies and maintain early warning information. The strategy execution module is communicatively connected to the data processing platform and the controllable protection device, and is used to receive the collaborative protection strategy and generate corresponding control commands to drive the controllable protection device to operate.

[0018] Compared with the prior art, the beneficial effects of the present invention are: through non-intrusive state perception, information fusion processing, digital archive establishment, collaborative decision-making and control execution, intelligent protection and maintenance of the arc angle are realized. It has the ability to accurately perceive the state of the arc angle, support collaborative optimization of lightning current discharge path, improve lightning protection efficiency, and support predictive maintenance, thereby solving the problems of perception blind spots, lack of collaboration and maintenance lag in the prior art. Attached Figure Description

[0019] Figure 1 This is a flowchart of the arc angle adaptive collaborative protection method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the connection of the arc angle adaptive cooperative protection system according to an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1-2 An adaptive collaborative protection method for arc angle includes the following steps: S1: Perform non-intrusive state sensing on multiple arc angles installed within the preset power supply area to acquire multi-source sensor data related to the arc angle action; S2: Based on multi-source sensor data, through information fusion processing, determine whether an action event has occurred at the arc angle, and when the determination is yes, generate an action event record containing the identity of the arc angle, the action time and the estimated action parameters; S3: Upload the motion event records to the data processing platform to establish and update the digital archives corresponding to each move arc angle. The digital archives contain a set of historical motion event records for that move arc angle and a health status assessment value calculated based on that set. S4: In response to the early warning information of lightning activity in the preset power supply area, and combined with the digital files of multiple arc angles stored in the data processing platform, a collaborative protection strategy for optimizing the lightning current discharge path is generated through the collaborative protection decision model. S5: Convert the collaborative protection strategy into control commands and send them to the corresponding controllable protection devices in the preset power supply area to adjust the relevant electrical parameters, thereby guiding the lightning current to flow preferentially to the arc angle with a higher health status assessment value during lightning activity; S6: Based on the digital files of each arc angle, calculate its remaining life index, and generate maintenance warning information when the remaining life index is lower than the preset threshold.

[0022] For ease of understanding, the following explains some key terms in this embodiment: Arc-attracting angle: A lightning protection device installed at both ends of an insulator string in a transmission line, operating in a passive response mode. Its core function is to guide and fix the electric arc through its own electrode gap when lightning overvoltage causes flashover of the insulator, thereby protecting the insulator and other core equipment from arc burns.

[0023] Non-invasive state sensing: refers to a method of obtaining information related to the working state of a swivel angle through external sensors or detection technologies without changing the structure of the swivel angle itself or directly contacting its charged parts.

[0024] Multi-source sensor data refers to a collection of data obtained from sensors of different types and principles to describe the working state or environmental conditions of the arc angle, such as electromagnetic signals, acoustic signals, optical signals, temperature, humidity, etc.

[0025] Information fusion processing refers to the process of integrating, analyzing, and interpreting information from different sensors or data sources, aiming to improve the accuracy and reliability of judging the arc angle action event and extract more comprehensive feature information.

[0026] Action event record: refers to a structured data record generated by the system when a discharge action occurs at the arc angle, containing important information about the action event, such as the arc angle's identification identifier, the time point of the action, and the action parameters obtained through data analysis.

[0027] Data processing platform: refers to a centralized system for computing and storage, used to receive, store, process and analyze action event records and other related data from multiple angles, and to make advanced decisions and manage based on this data.

[0028] Digital archives refer to a continuously updated collection of electronic records created for each attacking arc in the data processing platform. These records include all historical action events, health status assessment values, and other relevant operation and maintenance information for that attacking arc.

[0029] Health status assessment value: refers to a quantifiable index calculated by a specific algorithm based on the set of historical action event records in the digital archive of the arc angle, used to characterize the current working performance, aging degree or potential failure risk of the arc angle.

[0030] Lightning activity warning information: refers to a notification issued by meteorological departments or professional lightning monitoring systems that predicts the possibility of lightning activity in a specific area in the near future.

[0031] Collaborative protection decision model: refers to an intelligent set of algorithms or rules running in a data processing platform. It comprehensively considers lightning activity early warning information, digital files of multiple arc angles, and preset optimization targets to generate a lightning current discharge path guidance strategy that achieves optimization on a global scale.

[0032] Collaborative protection strategy: refers to multiple instructions or suggestions generated by the collaborative protection decision model, which guide how to adjust the operating status of controllable protection devices in the area during lightning activity to achieve optimal distribution of lightning current among different arc angles.

[0033] Controllable protection devices refer to electrical equipment installed near transmission lines or arcing angles that have the ability to be controlled or adaptively adjusted remotely, such as controllable parallel gaps or controllable surge arresters, whose electrical parameters can be adjusted according to the coordinated protection strategy.

[0034] Remaining life index: This refers to a quantifiable index calculated by a life prediction model based on historical cumulative damage data in the arc angle digital archive. It is used to characterize the estimated time or number of actions that the arc angle can continue to operate safely under current operating conditions.

[0035] Maintenance warning information: This refers to a notification automatically generated by the system and sent to maintenance personnel when the remaining life index of the arc angle is lower than the preset threshold, indicating that the arc angle may need to be inspected, repaired or replaced.

[0036] This embodiment provides an adaptive collaborative protection method for arc angle, the specific implementation of which is as follows: In step S1, non-invasive state sensing is performed on multiple arcing angles installed within the preset power supply area to acquire multi-source sensor data related to the arcing angle's movement. Various types of sensors are deployed on the tower where the arcing angle is located. Environmental sensors such as temperature and humidity sensors are installed to monitor changes in the environment around the arcing angle, or sensors for detecting electric fields are deployed to detect electric field disturbances generated during lightning discharge. These sensors acquire data non-contactly, avoiding impact on the device's structure and electrical performance. The acquired data can include multiple physical quantities such as voltage, current, temperature, and humidity. This data is considered multi-source sensor data related to the arcing angle's movement. The sensor group is fixed to the outside of the tower crossarm by brackets or clamps, with the installation angle at 30-45° to the line connecting the arcing angle electrodes. High-frequency electromagnetic sensor signal amplitude ≥5V triggers acquisition, the acoustic sensor sound pressure threshold is set to 0.1Pa, and the optical sensor spectral response range is limited to 200-400nm.

[0037] In step S2, based on multi-source sensor data, information fusion processing is used to determine whether an action event has occurred at the arc angle. If so, an action event record containing the arc angle's identification, action time, and estimated action parameters is generated. The multi-source sensor data acquired in step S1 is transmitted to the edge processing unit. This unit can perform preliminary analysis on the received data, using the PTPv2 protocol to achieve sensor clock synchronization with a synchronization accuracy of ≤±10μs. The preset time window is fixed at 50ms, and feature extraction uses 4-level wavelet transform decomposition with a Fourier transform frequency resolution set to 1kHz. Anomaly detection is performed on measurement data from a single sensor by setting a fixed threshold. When the data from one or more sensors exceeds the preset threshold, it is preliminarily determined that an action event may have occurred. At this time, the system records the identification identifier for the arc angle and the time point of the event. Simultaneously, based on the peak value or duration of a single sensor signal, certain parameters of the action event can be roughly estimated using a simple lookup table or linear mapping method. For example, the intensity level of the discharge can be estimated. The lightning current amplitude is estimated using the linear mapping formula: I = 2 × U, where I is the lightning current amplitude and U is the high-frequency electromagnetic sensor signal amplitude. The arc energy is estimated using the formula: E = 0.8 × S × T, where E is the arc energy, S is the optical sensor signal strength, and T is the signal duration. Corresponding action event records are then generated.

[0038] In step S3, action event records are uploaded to the data processing platform to establish and update digital archives corresponding to each arc angle. These digital archives contain a set of historical action event records for that arc angle and a health status assessment value calculated based on that set. The action event records generated in step S2 are uploaded to the data processing platform via a communication network. The data processing platform creates an independent digital archive for each arc angle and stores the received action event records in chronological order within this archive, forming a set of historical action event records. The health status assessment value can be simply accumulated based on the number of actions in the historical action event record set; the more actions, the lower the health status assessment value. This assessment value is updated in real time as new action event records are added. The data processing platform automatically accesses the digital archives of each arc angle every 24 hours and triggers a remaining lifespan index update within 10 minutes after each lightning activity ends.

[0039] Weighting rules for weighted cumulative model: 1. Time weighting: Events within the last 3 months have a weight of 0.8, events within 3-12 months have a weight of 0.5, and events within 1 year or more have a weight of 0.2; 2. Intensity weighting: Lightning current amplitude ≥15kA has a weight of 1.5, 10-15kA has a weight of 1.0, and <10kA has a weight of 0.5; Arc energy ≥10kJ has a weight of 1.4, 5-10kJ has a weight of 0.9, and <5kJ has a weight of 0.4. Health status assessment value = Σ (single event lightning current weight × arc energy weight × time weight).

[0040] In step S4, in response to lightning activity warning information for a preset power supply area, and combining the digital files of multiple arcing angles stored in the data processing platform, a collaborative protection strategy for optimizing lightning current discharge paths is generated through a collaborative protection decision model. When the data processing platform receives lightning activity warning information for a specific power supply area, it triggers the collaborative protection decision model. This model can prioritize arcing angles with fewer historical actions or higher health status assessment values ​​as lightning current discharge paths based on preset simple rules. The model queries the digital files of all arcing angles in the area to obtain their current health status assessment values ​​and generates a basic collaborative protection strategy according to the above rules. This strategy indicates which arcing angles should be prioritized for guiding lightning current flow during lightning activity. The quantitative optimization objective is to extend the average remaining lifetime of the arcing angle cluster in the area by ≥20%, while reducing the line tripping rate by ≤15%. Supplementary selection rules: When health status assessment values ​​are the same, arcing angles closer to the lightning warning center area are prioritized. If the distance is the same, arcing angles with lower maintenance costs are prioritized.

[0041] In step S5, the collaborative protection strategy is converted into control commands and sent to the corresponding controllable protection devices within the preset power supply area to adjust relevant electrical parameters, thereby guiding the lightning current to preferentially flow towards the arc angle with a higher health status assessment value during lightning activity. The collaborative protection strategy generated in step S4 is received by the strategy execution module. This module parses the strategy content into control commands that can be recognized and executed by the field controllable protection devices, such as a general switching signal or a voltage regulation signal. These control commands are sent via a communication network to protection devices with remote control functions installed within the preset power supply area, such as a remotely controllable switch or a gap device capable of adjusting the breakdown voltage.

[0042] Controllable parallel gap: Based on the difference in health status, the breakdown voltage of the target arc angle is reduced by 8-12kV.

[0043] Controllable surge arresters: The pre-activation parameter is that the charging voltage reaches 80% of the rated voltage, activating in advance. By receiving and executing these instructions, the controllable protection device can change its own electrical characteristics, thereby guiding the lightning current to preferentially pass through the arc angle with the higher health status assessment value when lightning activity occurs.

[0044] In step S6, based on the digital archives of each arc angle, its remaining lifespan index is calculated, and a maintenance warning is generated when the remaining lifespan index falls below a preset threshold. The data processing platform accesses the digital archives of each arc angle after a preset period or after a specific event. Based on the total number of historical action events recorded in the archives, an empirical formula or lookup table method is used to estimate the remaining lifespan index of the arc angle. For example, the remaining lifespan index decreases by a fixed value for each action. The ablation model of the arc angle electrode material uses the formula: Δm = k × E, where Δm is the electrode mass loss, k is the material ablation coefficient, and E is the historical accumulated arc energy. Typical material coefficient: copper electrode. tungsten alloy electrode Percentage of lifespan already consumed = ,in The maximum allowable mass loss of the electrode is set to 5% of the initial mass. When the calculated remaining lifespan index falls below a preset threshold, the system automatically generates a maintenance warning message, which is sent to maintenance personnel via SMS, email, or system notification, indicating that the arc angle may need to be inspected or replaced. Remaining lifespan index = 1 - percentage of consumed lifespan; the preset warning threshold is set to 30%. When the remaining lifespan index is ≤30%, a tiered warning is automatically generated.

[0045] This application addresses the shortcomings of existing lightning protection systems for transmission lines, such as insufficient arc angle status perception, lack of coordinated protection strategies, and slow response in operation and maintenance, through a closed-loop management approach that non-intrusively integrates sensing, information fusion, digital archiving, collaborative decision-making, and predictive maintenance. This method achieves precise monitoring and quantitative assessment of the arc angle's operational status, proactively optimizes lightning current discharge paths based on lightning warnings, improves the overall reliability of regional lightning protection, and enables predictive maintenance based on actual losses. This transforms lightning protection for transmission lines from a passive response mechanism to proactive, intelligent management.

[0046] In some of the embodiments described above in this application, a non-invasive state perception method is proposed to acquire multi-source sensor data related to the arc angle action. In this process, how to deploy sensors in a non-invasive manner and ensure the reliable acquisition of multi-source data to avoid interference with the device body and the impact of environmental interference becomes a key issue.

[0047] In this regard, this application further proposes that in step S1, the non-invasive state perception is achieved by a sensor group deployed on the tower where the arc angle is located. The sensor group includes at least two of the following: a high-frequency electromagnetic sensor for capturing characteristic electromagnetic signals when the arc angle breaks down, an acoustic sensor for monitoring the shock wave signal generated by the arc, and an optical sensor for identifying characteristic spectral signals of the arc.

[0048] Non-intrusive state sensing refers to monitoring and collecting data on the operating status of a boom angle without directly contacting or altering its physical structure and electrical performance. This non-intrusive state sensing is achieved through a sensor array deployed on the tower where the boom angle is located. This means the sensors are installed on the tower structure, not on the boom angle itself. For example, the sensor array can be fixed near the tower crossarm, tower body, or grounding down conductor using brackets or clamps, ensuring a safe distance from the boom angle while effectively receiving signals generated during boom angle movement. Alternatively, the sensor array can be integrated into the tower's auxiliary equipment housing, connecting to external systems via a wireless communication module for remote data transmission and power supply, further minimizing modifications to the tower itself.

[0049] The sensor array includes at least a high-frequency electromagnetic sensor for capturing characteristic electromagnetic signals during arc-angle breakdown. A high-frequency electromagnetic sensor is a device capable of detecting changes in high-frequency electromagnetic fields. When a breakdown discharge occurs at the arc-angle, transient, high-frequency electromagnetic radiation signals are generated. These signals have specific spectral characteristics and time-domain waveforms, serving as direct evidence of the arc-angle action. Wideband Rogowski coils or magnetic ring sensors can be used to capture high-frequency electromagnetic signals by inducing changes in the magnetic field generated by the discharge current. These sensors exhibit good linearity and wideband response characteristics. Alternatively, radio frequency sensors such as dipole antennas or microstrip antennas can be used to directly receive the electromagnetic waves generated during the discharge process. By demodulating and analyzing the received signals, characteristic information of the breakdown event can be extracted.

[0050] The sensor array also includes acoustic sensors for monitoring the shock wave signals generated by the electric arc. An acoustic sensor is a device that converts sound wave signals into electrical signals. When an arc breaks down to form an electric arc, the high-temperature, high-pressure gas within the arc channel expands rapidly, generating shock waves—acoustic signals with specific frequencies and energy distributions. Monitoring these shock wave signals can serve as a basis for determining the generation and duration of the arc. Piezoelectric microphones or microelectromechanical systems (MEMS) microphones can be used; these sensors are sensitive to changes in sound pressure in the air and can capture the transient sound wave signals generated by the arc discharge. Ultrasonic sensors can also be used to further improve the accuracy of identifying discharge events and enhance their anti-interference capabilities by detecting sound wave signals within the ultrasonic frequency range generated by the arc discharge.

[0051] The sensor array also includes optical sensors for identifying characteristic spectral signals of the electric arc. An optical sensor is a device capable of detecting light signals. During an electric arc discharge, high-temperature plasma radiates light within a specific wavelength range, forming light signals with unique spectral characteristics. Identifying these spectral signals can provide information such as the arc's temperature and composition, helping to assess the arc's intensity and duration. Photodiodes or photomultiplier tubes, combined with narrow-band filters, can be used to selectively detect changes in the light intensity of characteristic spectral lines of the arc. Alternatively, a small spectrometer or multispectral imaging sensor can be used to analyze the entire visible or ultraviolet spectrum of the arc radiation, obtaining more detailed spectral information, thereby more accurately identifying arc events and assessing their parameters.

[0052] Deploying a sensor array on the tower where the arcing angle is located, rather than directly contacting the arcing angle itself, enables non-invasive sensing of the arcing angle's movement. This avoids physical and electrical interference to the arcing angle itself, ensuring that its inherent reliability remains unaffected. Simultaneously, this sensor array includes at least two sensors based on different principles, such as any combination of two of high-frequency electromagnetic sensors, acoustic sensors, and optical sensors. Each sensor captures characteristic signals of the arcing angle's movement event from different physical dimensions: the high-frequency electromagnetic sensor captures the electromagnetic radiation at the moment of breakdown, the acoustic sensor monitors the shock wave generated by the arc, and the optical sensor identifies the spectral characteristics of the arc. These multi-source data, after subsequent information fusion processing, can be cross-verified, overcoming the limitations of single sensors being susceptible to environmental noise, signal attenuation, or false positives and false negatives. When environmental noise interferes with the acoustic sensor, the high-frequency electromagnetic sensor and the optical sensor can still provide effective information for judgment. By acquiring signals from multiple dimensions and using multiple principles, the accuracy, robustness, and reliability of the perception of arc angle action events have been improved. This provides high-quality raw data support for subsequent action event recording, digital archive establishment, and collaborative protection decision-making, effectively solving the problems of the lack of traditional arc angle state perception capabilities and the insufficient reliability of existing monitoring schemes.

[0053] In some of the embodiments described above in this application, a non-intrusive state perception method using a sensor array is proposed to obtain multi-source sensor data. However, during its implementation, due to environmental interference or the limitations of the single sensor principle, the judgment of action events may be inaccurate, affecting the reliability of subsequent protection decisions.

[0054] In response, this application further proposes a specific method for determining whether an action event has occurred at the arc angle in step S2 through information fusion processing. This method includes: performing time synchronization and feature extraction on multi-source sensing data from the sensor group; matching and analyzing the extracted features with a pre-stored typical action feature library of arc angles; and determining that an action event has occurred at the arc angle when the matching and analysis results from different sensors all indicate the occurrence of a discharge event within a preset time window and the event location information points to the same arc angle.

[0055] Time synchronization of multi-source sensor data from the aforementioned sensor group aims to ensure precise alignment of data from different sensors on the time axis, eliminating time misalignment caused by transmission delays or internal clock deviations, thereby guaranteeing the accuracy of subsequent information fusion processing. This can be achieved by using a GPS timing module to provide a unified timestamp for each sensor group, ensuring clock synchronization across all data acquisition devices; alternatively, Network Time Protocol (NTP) or Precision Time Protocol (PTP) can be used to synchronize sensor nodes within a local area network, combined with a local high-precision crystal oscillator for time preservation. Feature extraction identifies and separates key information related to the arc angle action event from the raw multi-source sensor data, while suppressing noise and irrelevant signals, providing refined and effective input for subsequent matching analysis. Wavelet transform can be used to decompose the signal at multiple scales, extracting features such as energy, peak value, and duration at different frequency bands to capture the transient characteristics of the discharge event; Fourier transform can be used to analyze the signal's spectral characteristics, extracting features such as dominant frequency, bandwidth, and harmonic content; or statistical methods can be used to calculate the signal's root mean square value, peak factor, kurtosis, and other time-domain features.

[0056] The extracted features are matched and analyzed against a pre-stored database of typical arc angle action features. This pre-stored database contains characteristic patterns of known arc angle discharge events, serving as a benchmark for determining the nature of the current event. It stores feature vectors or patterns of verified typical actions such as arc angle breakdown, arc formation, and extinction, obtained from laboratory simulated discharges, historical field data, or simulation models. This feature database can be constructed by conducting simulated lightning discharge experiments on different types and health states of arc angles in a controlled laboratory environment, collecting data using sensor arrays, and extracting and labeling their typical action features. Alternatively, it can be based on historical operational data, combined with expert experience and data mining techniques, to extract common features from confirmed arc angle action events and summarize them into a feature database. The matching analysis aims to compare the real-time extracted sensor data features with typical patterns in the feature database to assess the similarity between the current event and known action patterns, thereby determining whether an arc angle action event has occurred. Machine learning classifiers such as support vector machines and neural networks are used to classify and judge real-time features by training them to learn patterns in the feature library. Alternatively, dynamic time warping algorithms or correlation analysis methods can be used to calculate the similarity or distance between real-time feature sequences and template sequences in the feature library. When the similarity exceeds a preset threshold, the match is considered successful.

[0057] When matching analysis results from different sensors all indicate a discharge event within a preset time window, and the event location information points to the same arc angle, then an action event is determined to have occurred at that arc angle. The preset time window defines a time range that limits the occurrence of discharge events detected by different sensors to within this time range to be considered part of the same arc angle action event, thus avoiding misclassification of unrelated transient signals as coordinated events. The length of this time window can be empirically set based on the physical characteristics of arc angle discharge, for example, from tens to hundreds of milliseconds; alternatively, it can be determined by statistical analysis of a large amount of historical discharge event data to identify a reasonable distribution range of time differences between different sensor signals, and the preset time window can be dynamically adjusted or optimized accordingly. The requirement that all signals indicate a discharge event emphasizes the reliability requirement for judging arc angle action events, meaning that matching analysis results from multiple sensors based on different principles, such as high-frequency electromagnetic sensors, acoustic sensors, and optical sensors, must independently confirm the occurrence of a discharge event, thereby effectively eliminating false alarms or interference that may exist from a single sensor. This can be achieved through logical AND operations, meaning this condition is met only when the matching analysis results of the high-frequency electromagnetic sensor, acoustic sensor, and optical sensor, or at least two of their respective sensors, all output a signal indicating that a discharge event has occurred. Alternatively, a weighted voting mechanism can be used, where a sufficient number or specific combination of sensors indicates a discharge event, and the sum of their confidence levels reaches a preset threshold, then this condition is considered met. The event location information pointing to the same arc angle ensures that the determined action event indeed occurred at the target arc angle, avoiding misjudging discharge phenomena from nearby equipment or lines as actions at that arc angle, thus guaranteeing accurate attribution of action event records. This can be achieved by pre-binding or logically mapping each sensor group to a specific arc angle during sensor deployment. When that sensor group detects an event, its location information naturally points to the corresponding arc angle. Alternatively, multiple sensors can be used to perform time-difference positioning or signal strength attenuation analysis of the discharge signal to accurately estimate the location of the discharge source and compare it with the geographical coordinates of the arc angle to confirm the event location.

[0058] This application improves the accuracy and reliability of arc angle action event judgment by introducing a multi-source information fusion processing mechanism. Time synchronization and feature extraction are performed on multi-source sensor data from the sensor group, ensuring precise alignment of data from different sensors on the time axis and extracting key information from the raw data, effectively suppressing noise interference. The extracted features are matched and analyzed with a pre-stored typical arc angle action feature library, using known typical action features as a benchmark to enhance the objectivity and consistency of the judgment. Only when the matching analysis results from different sensors all indicate a discharge event within a preset time window and the event location information points to the same arc angle is an action event finally determined to have occurred at that arc angle. This multi-confirmation mechanism effectively eliminates false alarms or environmental interference that may exist from a single sensor, avoiding subsequent protection decision errors caused by misjudgment. Through this rigorous judgment logic, the authenticity and accuracy of action event records are ensured, providing a reliable data foundation for the establishment of arc angle digital archives, health status assessment, and the formulation of collaborative protection strategies, thereby improving the overall effectiveness and reliability of the entire arc angle adaptive collaborative protection method.

[0059] In some of the solutions mentioned above in this application, it is proposed to determine whether an action event has occurred at the arc angle and generate an action event record through information fusion processing. However, in this process, the action event record lacks specific quantitative parameters, such as lightning current amplitude and arc energy, which limits the accuracy of subsequent health status assessment and collaborative protection decision-making.

[0060] In this regard, this application further proposes that, in step S2, the estimated action parameters include at least the lightning current amplitude parameter estimated based on the signal amplitude of the high-frequency electromagnetic sensor and the arc energy parameter estimated based on the signal strength and duration of the optical sensor.

[0061] The lightning current amplitude parameter refers to the peak value of the lightning current discharged through the arc angle when lightning strikes. High-frequency electromagnetic sensors can capture the transient electromagnetic field changes generated when lightning current passes through, and the signal amplitude has a direct correlation with the peak value of the lightning current. One implementation method is to use a high-frequency electromagnetic sensor based on the Rogowski coil principle. By measuring the rate of change of the magnetic field generated by the lightning current and integrating the data, the instantaneous waveform of the lightning current can be directly obtained, and its peak value can be extracted as the lightning current amplitude parameter. Another implementation method is to use a high-frequency electromagnetic sensor based on the Hall effect principle. By measuring the magnetic field strength generated by the lightning current and converting it into a voltage signal, the amplitude of which is proportional to the amplitude of the lightning current, the lightning current amplitude parameter can be estimated. This parameter is a key indicator for quantifying the intensity of a lightning strike event and is crucial for assessing the impact intensity borne by the arc angle and for subsequent health status assessments.

[0062] Arc energy parameters refer to the total energy released by the arc formed in the electrode gap during arcing. Arc energy is a major factor leading to electrode ablation and wear at the arcing angle. Optical sensors can detect visible or ultraviolet light generated during arc discharge; the signal intensity reflects the arc's brightness or temperature, while the duration reflects the arc's duration. Arc energy is typically related to the product of arc power and duration. One approach is to use photodiodes or phototransistors as optical sensors, measuring the intensity of the light signal emitted by the arc and recording its duration. By establishing a pre-defined mapping between arc light intensity and arc power, combined with the duration, the arc energy parameters can be estimated. Another approach is to use a spectrometer as the optical sensor, analyzing the spectrum emitted by the arc to obtain spectral intensity information in specific wavelength bands. This information more accurately reflects the arc's temperature and plasma density. Combined with the arc duration, the arc energy parameters can be calculated using a physical model. This parameter is the direct basis for assessing the wear and tear of the arc angle electrode material, and plays a decisive role in predicting the remaining life of the arc angle and formulating preventive maintenance strategies.

[0063] Incorporating lightning current amplitude and arc energy parameters into the action event record resolves the issue of missing parameters in the record. This makes the description of arc angle action events more comprehensive and quantitative, providing more accurate foundational data for establishing and updating the arc angle digital archive in subsequent step S3. The lightning current amplitude parameter accurately reflects the intensity of the lightning strike, helping to assess the instantaneous impact experienced by the arc angle, while the arc energy parameter precisely characterizes the degree of ablation of the arc angle electrode, providing crucial evidence for assessing its cumulative damage. The introduction of these quantitative parameters improves the accuracy of health status assessment values ​​calculated in the digital archive, enabling the collaborative protection decision model in step S4 to generate more optimized and refined collaborative protection strategies for lightning current discharge paths based on more reliable health status information. This achieves refined management and proactive intelligent control of transmission line lightning protection.

[0064] In some of the embodiments described above in this application, a health status assessment value is proposed to evaluate the health status of the arc angle. In its implementation, if all historical action events are treated equally without considering the recentity and intensity differences of the events, the assessment results may be inaccurate and fail to truly reflect the current status of the arc angle.

[0065] In this regard, this application further proposes that in step S3, the health status assessment value is calculated by a preset weighted cumulative model based on the estimated action parameters in the historical action event record set corresponding to the arc angle. In the weighted cumulative model, the recently occurring action events and the parameters representing higher intensity in the estimated action parameters are given greater weight.

[0066] The health status assessment value aims to quantify the degree of performance degradation of the arc-prone angle due to lightning current impact and arc erosion during long-term operation. This assessment value is a key component of the arc-prone angle's digital archive, providing data support for subsequent collaborative protection decisions and predictive maintenance. In one implementation, the weighted accumulation model can be a time-decay-based function model. For example, the estimated action parameters for each historical action event, such as lightning current amplitude and arc energy, are multiplied by a weighting coefficient inversely proportional to the time interval between events. All weighted parameters are then summed to obtain the assessment value. The shorter the time interval, the larger the weighting coefficient. In another implementation, the weighted accumulation model can be a model based on event intensity grading, dividing the estimated action parameters into different intensity levels and pre-setting different weights for each level. For example, lightning current amplitude can be divided into low, medium, and high levels, each assigned a different weight, and then the weighted intensity of each event is summed.

[0067] Furthermore, assigning greater weight to recent events ensures that the health status assessment reflects the current actual condition of the arc angle in a timely manner, preventing the cumulative effect of earlier events from masking recent deterioration trends. A sliding time window can be set, with high weighting applied only to events within the window, or an exponential decay function can be used, ensuring that the more recent the event, the greater its impact on the current health status assessment. Simultaneously, assigning greater weight to parameters representing higher intensity in the estimated action parameters is based on the physical fact that high-intensity lightning current impacts and arc energy cause more significant damage to the arc angle electrode material. For the lightning current amplitude parameter in the estimated action parameters, a threshold can be set; when the lightning current amplitude exceeds this threshold, it is given higher weight. A similar hierarchical weighting strategy can also be used for the arc energy parameter. This weighting method can more accurately reflect the cumulative damage suffered by the arc angle.

[0068] When calculating the health status assessment value of arc angles, instead of simply treating all historical action events equally, a weighted cumulative model is introduced, with particular attention paid to recent action events and estimated action parameters representing higher intensity. This weighting mechanism allows the health status assessment value to more dynamically and accurately reflect the current actual deterioration degree and potential risks of the arc angles. The higher weight of recent events ensures the timeliness of the assessment, enabling it to promptly capture the latest changes in arc angle performance, while the higher weight of high-intensity parameters allows the assessment to fully consider severe events that cause substantial damage to the arc angles, avoiding underestimation of their cumulative effects. Therefore, the obtained health status assessment value is more instructive, providing more reliable and refined input for subsequent collaborative protection decision-making models. This allows for more precise guidance of lightning current flow to those arc angles with truly better health status during lightning activity, optimizing the overall utilization efficiency of lightning protection resources and effectively extending the overall service life of the arc angle cluster.

[0069] In some of the embodiments described above in this application, a collaborative protection decision model is proposed to generate a collaborative protection strategy. However, the logic is not specific in its implementation, which may lead to inaccurate strategy generation or failure to effectively optimize the lightning current discharge path.

[0070] In response, this application further proposes that in step S4, the logic of the collaborative protection decision model generating the collaborative protection strategy includes: determining the line sections that may be affected and multiple arc angles based on the lightning activity early warning information; retrieving the current health status assessment value of the potentially affected arc angles; and generating a strategy instruction based on a preset optimization objective to instruct the lightning current to be preferentially guided through the arc angle with the better health status assessment value during lightning activity.

[0071] Upon receiving a lightning activity warning for the preset power supply area, the collaborative protection decision model first determines the potentially affected line sections and multiple arcing angles based on the warning information. The lightning activity warning information can originate from lightning warning data issued by meteorological departments, including information such as the probability of lightning occurrence, intensity, expected impact range, and time; or it can be based on real-time monitoring data from lightning location systems deployed along the transmission lines. Determining potentially affected line sections can be achieved by overlaying the lightning-affected area indicated in the warning information with the geographical information of the transmission lines to identify geographically overlapping or adjacent line sections; or by analyzing the power grid topology and combining it with the geographical location information of the lightning warning to infer specific line nodes and connected line sections that may be affected by lightning strikes. Once the potentially affected line sections are determined, the system identifies all arcing angles installed on these line sections and uses them as the decision objects for this collaborative protection strategy.

[0072] The collaborative protection decision-making model retrieves the current health status assessment value of the potentially affected arc angles. This health status assessment value is a key indicator stored in the digital archive corresponding to each arc angle in the data processing platform, reflecting the accumulated damage level and remaining lifespan potential of the arc angle in historical action events. The retrieval operation can be performed by sending a query request to the data processing platform to obtain the latest health status assessment value corresponding to a specific arc angle's identity; or the data processing platform can proactively push the health status assessment values ​​of the affected arc angles to the collaborative protection decision-making model.

[0073] Based on this, the collaborative protection decision model generates strategy instructions based on preset optimization objectives to prioritize guiding lightning current through arcing angles with better health status assessment values ​​during lightning activity. The preset optimization objectives typically aim to maximize the overall lifespan of the arcing angle group within the entire power supply area or minimize the risk of line tripping due to arcing angle degradation. Generating strategy instructions can be achieved by sorting the health status assessment values ​​of all affected arcing angles and then determining a priority list or activation order for current discharge; more complex optimization algorithms, such as multi-objective optimization methods, can be used to comprehensively consider factors such as the health status of the arcing angle, geographical location, and line importance, calculating specific adjustment parameters for each controllable protection device to achieve intelligent lightning current allocation. The strategy instructions can instruct the breakdown voltage of the controllable protection device corresponding to the arcing angle with a higher health status assessment value to be adjusted to a lower level, enabling it to operate preferentially during lightning overvoltage, thereby protecting the arcing angle with poor health status and extending its lifespan.

[0074] This application addresses the issue of ambiguous strategy generation by concretizing the logic of the collaborative protection decision-making model, thereby ensuring that the collaborative protection strategy can effectively optimize the lightning current discharge path. The step of identifying potentially affected line sections and multiple arcing angles based on lightning activity early warning information ensures the strategy's targeting, focusing only on areas and equipment actually likely to be affected by lightning. This avoids resource waste and inefficiency caused by an overly broad decision-making scope. Retrieving the current health status assessment values ​​of potentially affected arcing angles provides a decision-making basis based on real-time equipment status, making strategy generation no longer dependent on random or hypothetical data, but rooted in the actual health level of the arcing angles. Based on preset optimization targets, a strategy instruction is generated to instruct the lightning current to preferentially flow through arcing angles with better health status assessment values ​​during lightning activity. This feature directly achieves the optimization target. By explicitly instructing the lightning current to flow to arcing angles with better health status, proactive optimization of the discharge path is ensured, improving the overall coordination and reliability of the protection mechanism. This refined decision-making process enables the intelligent use of arcing angles in good health to prioritize the discharge of lightning current during lightning activity, effectively reducing the burden on arcing angles in poor health. This extends the average service life of the entire arcing angle group, reduces operation and maintenance costs, and improves the lightning protection capability and operational reliability of transmission lines.

[0075] In some of the embodiments described above in this application, a controllable protection device is proposed to adjust electrical parameters to guide lightning current. However, the specific type of device and adjustment method are not clearly defined, which may lead to inaccurate implementation or insufficient efficiency.

[0076] In this regard, this application further proposes that in step S5, the controllable protection device includes a controllable parallel gap or a controllable surge arrester installed near the arc angle, and the adjustment of relevant electrical parameters includes adjusting the breakdown voltage of the controllable parallel gap or pre-activating the controllable surge arrester.

[0077] The controllable protection device refers to a power protection device that can actively change its electrical characteristics or operating state according to external commands or preset logic. Its core function is to provide an adjustable lightning current discharge path in transmission line lightning protection. Besides the controllable parallel gap and controllable surge arrester mentioned in this application, it can also be a discharge gap with a controllable triggering mechanism, or a current limiting device integrated with an intelligent control unit, etc. The controllable protection device is installed near the arcing angle. This deployment method ensures that it can closely cooperate with the arcing angle and directly act on the lightning current discharge path. For example, it can be directly installed on the support structure of the arcing angle, or installed near the insulator string electrically connected to the arcing angle, to achieve effective intervention in the lightning current path.

[0078] The controllable parallel gap is a discharge gap with an adjustable breakdown voltage. When a lightning overvoltage occurs, it can rapidly conduct at a preset voltage level according to a control command, providing a discharge path for the lightning current. Its controllability is reflected in the ability to change its breakdown characteristics through external signals, by mechanically adjusting the electrode spacing, applying an auxiliary trigger voltage, or changing the dielectric properties. The controllable surge arrester is a surge protection device that can actively operate or change its protection characteristics under specific conditions according to control commands. Unlike the passive response of traditional surge arresters, the controllable surge arrester can, before a lightning strike or during a specific lightning activity, pre-activate its protection characteristics to better adapt to the current lightning environment and system requirements. Controllability is achieved by controlling its internal switching elements or changing the characteristics of its nonlinear resistance.

[0079] When adjusting relevant electrical parameters, if a controllable parallel gap is used, adjusting the breakdown voltage of the controllable parallel gap refers to changing the initial discharge voltage value of the controllable parallel gap through control means. This can be achieved by mechanically adjusting the electrode spacing or by applying an auxiliary trigger pulse through an electronic control unit, thereby precisely controlling when it conducts. If a controllable surge arrester is used, pre-activating the controllable surge arrester means placing it in a ready or more easily operable state in advance after a lightning activity warning is issued. This can be done by pre-charging its internal trigger circuit or placing its internal switching elements in a critical conduction state to shorten its response time and ensure rapid and reliable operation during a lightning strike.

[0080] This application clarifies the specific types of controllable protection devices and the adjustment methods of their electrical parameters, thereby resolving ambiguities in implementation and optimizing the accuracy and efficiency of lightning current guidance. By limiting the controllable protection device to controllable parallel gaps or controllable surge arresters installed near the arcing angle, it ensures a close correlation between the device and the arcing angle location, facilitating direct intervention in the lightning current path and avoiding response delays or ineffective operations due to improper location. When using controllable parallel gaps, by adjusting their breakdown voltage, the system can precisely control the gap's conduction timing according to a collaborative protection strategy, thereby prioritizing the guidance of lightning current to arcing angles with better health assessment values. When using controllable surge arresters, through pre-activation operations, they can be put into a ready state in advance during lightning activity, shortening their response time and ensuring that lightning current can be discharged quickly and reliably through a preset optimized path, reducing protection delays. This precise control and rapid response mechanism makes the selection of lightning current discharge paths more proactive and optimized, effectively preventing some arcing angles from deteriorating rapidly due to repeated actions, and improving the overall utilization efficiency and cluster lifespan of lightning protection resources.

[0081] In some of the solutions mentioned above in this application, a remaining lifetime index is proposed to generate maintenance early warning information. However, in the process of implementing this approach, the calculation of the remaining lifetime index lacks specific model support, resulting in inaccurate assessments and an inability to conduct scientific maintenance based on actual cumulative electrolytic erosion damage. This may lead to safety hazards due to untimely maintenance or waste of resources due to over-maintenance.

[0082] In this regard, this application further proposes a step for calculating the remaining lifetime index of the arc angle, including: obtaining its historical cumulative arc energy parameters from the digital archive of the arc angle; calculating the proportion of lifetime already consumed based on the historical cumulative arc energy parameters according to a pre-established ablation model of the arc angle electrode material; and determining the remaining lifetime index based on the proportion of lifetime already consumed.

[0083] Obtaining the historical cumulative arc energy parameter from the digital archive of the arc angle refers to the fact that the digital archive of the arc angle is a structured dataset stored in the data processing platform, containing all historical action event records of the arc angle since its installation. These records are accumulated from the action event records generated in step S2, and each record contains estimated action parameters, such as estimated arc energy parameters. The historical cumulative arc energy parameter refers to the total energy value obtained by summing the estimated arc energy parameters from all historical action event records in the digital archive. It objectively reflects the cumulative thermal damage suffered by the arc angle electrode during each discharge process. This parameter can be obtained through the data interface or query service provided by the data processing platform. It can be retrieved and summed from the database using SQL query statements, or the cumulative energy data of a specific arc angle can be obtained through API calls.

[0084] Based on this, using a pre-established arc angle electrode material ablation model, the proportion of lifetime already consumed is calculated according to the historical accumulated arc energy parameters. The arc angle electrode material ablation model is a mathematical or empirical model used to describe the wear law of arc angle electrode materials under the action of an electric arc. This model can be established based on the physical properties of the electrode material and the physical process of arc discharge. It can be a physical model based on the principle of energy conservation, converting the input accumulated arc energy into material mass loss or volume loss, and then mapping it to the proportion of lifetime consumed. Alternatively, the model can be an empirical formula obtained by fitting a large amount of experimental data, such as a polynomial or exponential function, which correlates the accumulated arc energy with the degree of electrode ablation. Furthermore, machine learning-based methods can be used to train the model using historical operating data and actual ablation conditions to predict lifetime consumption. Calculating the proportion of lifetime already consumed involves using the obtained historical accumulated arc energy parameters as input to the model, and the model outputs a value between 0 and 1, representing the percentage of lifetime already consumed at that arc angle.

[0085] Furthermore, the remaining lifespan index is determined based on the proportion of lifespan already consumed. The proportion of lifespan consumed is the portion of the arc-shaped angle's total design lifespan that has already been worn down. The remaining lifespan index refers to the amount of lifespan the arc-shaped angle is expected to continue operating safely in its current state. Determining the remaining lifespan index typically involves subtracting the proportion of lifespan consumed from the total lifespan. For example, if the proportion of lifespan consumed is 0.3 (30%), then the remaining lifespan index is 0.7 (70%). This index can also be further converted into a more intuitive form, such as the expected number of lightning strikes it can withstand, or the expected number of years it can operate safely. This conversion can be achieved through a preset mapping table or function, for example, mapping a 70% remaining lifespan proportion to being able to withstand approximately X standard lightning strikes or being expected to operate for another Y years.

[0086] This application enables accurate assessment and predictive maintenance of the remaining lifespan of arcing angles. By obtaining historical accumulated arc energy parameters from the arcing angle's digital archive, it fully utilizes objective data accumulated during each operation, providing a reliable data foundation for lifespan assessment. Based on this, and using a pre-established ablation model of the arcing angle's electrode material, the abstract arc energy is transformed into quantifiable electrode material loss, allowing for the scientific calculation of the proportion of lifespan already consumed. This model-based approach overcomes the shortcomings of traditional maintenance methods, such as lack of specific model support and inaccurate assessments, resulting in a more accurate assessment of the actual cumulative electro-erosion damage of arcing angles. Determining the remaining lifespan index based on the proportion of lifespan already consumed provides maintenance personnel with an intuitive and clear basis for maintenance decisions, enabling timely detection and early warning of arcing angles nearing the end of their lifespan, avoiding safety hazards caused by untimely maintenance. Simultaneously, it avoids over-maintaining arcing angles still in good condition, improving the overall economy and reliability of transmission line lightning protection. Combined with collaborative protection strategies, the use of arcing angles can be allocated more rationally, extending the lifespan of the overall protection system.

[0087] In some of the solutions mentioned above in this application, maintenance early warning information is generated for predictive maintenance. However, in the implementation process, there is a lack of a confirmation and processing feedback mechanism for the maintenance early warning information, which may lead to the early warning information being ignored or the processing results not being recorded, thereby affecting the accuracy of digital archives and the timeliness of maintenance.

[0088] In response, this application further proposes a closed-loop management mechanism for maintenance early warning information. Specifically, the method also includes step S7: receiving confirmation and processing feedback on maintenance early warning information, and updating the processing result to the digital file of the corresponding arc angle.

[0089] The system's function of receiving confirmation and processing feedback for maintenance alerts aims to ensure that it can obtain the responses of maintenance personnel to these alerts, understand whether they have been received and processed, and track the progress or results of the processing. This addresses the issue of alerts potentially being ignored or not processed in a timely manner. For example, by providing an interactive form on the user interface, maintenance personnel can confirm the alert and fill in information such as processing progress, processing method, and estimated completion time. Alternatively, by integrating alert information into the existing maintenance management system, a work order is automatically created in the system after an alert is sent. Maintenance personnel record the processing procedure and results in the work order, and the system automatically captures these status updates as feedback.

[0090] The processing results are then updated in the corresponding digital archive of the bidding angle, ensuring that the digital archive dynamically and in real-time reflects its latest maintenance history and status changes, enhancing the completeness, accuracy, and reliability of the archive. This provides a more precise basis for subsequent health assessments, life predictions, and collaborative protection decisions. After receiving confirmation and processing feedback on maintenance alerts, the data processing platform can parse the feedback content and add or modify information such as processing time, personnel, measures, and results to the historical action event record set or relevant fields of the health status assessment value in the digital archive of the bidding angle in the form of structured data. Maintenance personnel confirm the processing progress, method, and estimated completion time through an interactive form. After the processing results are parsed by the data processing platform, they are added to the corresponding digital archive of the bidding angle in the form of structured data, including information such as processing time, personnel, measures, and results, forming a complete closed-loop management system. Another approach is to send the processing results to the data processing platform via an application programming interface or message queue mechanism. The data processing platform locates the corresponding digital file based on the identity identifier and performs a database update operation, such as adding a maintenance record field to the digital file to record detailed information for each maintenance.

[0091] By introducing a confirmation and processing feedback mechanism for maintenance early warning information and updating the results in the digital archive of the arc angle, this application achieves closed-loop management of predictive maintenance for arc angles. This ensures that maintenance early warning information can be responded to and processed in a timely manner, avoiding the risk of early warnings being ignored or delayed in processing. Simultaneously, updating the processing results to the digital archive in real time allows the digital archive to dynamically reflect the latest maintenance history and status changes of the arc angle, greatly improving the completeness, accuracy, and reliability of the digital archive. This provides a more accurate and real-time basis for subsequent arc angle health assessments, remaining life predictions, and optimization of collaborative protection strategies, thereby further improving the intelligence level and operation and maintenance efficiency of transmission line lightning protection.

[0092] In traditional lightning protection for transmission lines, arc-prone angle detectors, as passive response devices, suffer from limitations such as insufficient state awareness, lack of coordination in protection strategies, and slow response in operation and maintenance. Maintenance personnel cannot obtain crucial data on arc-prone angle action, leading to a lack of basis for lightning strike analysis and difficulties in optimizing lightning protection strategies. Arc-prone angle detectors respond independently within a region, failing to coordinate and optimize lightning current discharge paths based on differences in health status, resulting in low utilization of protection resources. Furthermore, maintenance relies on periodic inspections or replacement after a fault, lacking accurate assessment based on actual damage, potentially leaving safety risks or leading to resource waste.

[0093] This application proposes an adaptive cooperative protection system for arc angles, used to implement an adaptive cooperative protection method for arc angles. The system includes a sensing module deployed on multiple towers to perform non-intrusive state sensing of the arc angles on the corresponding towers, acquiring multi-source sensor data related to the arc angle's movement. The sensing module monitors the arc angle's operating status non-contactly using external sensors, avoiding impact on the device's structure and electrical performance, thus directly providing a basic information source to address the problem of missing state sensing. Without altering the arc angle itself, the sensing module collects multi-source data such as environmental parameters and electric field disturbances, ensuring reliable acquisition of movement-related characteristics.

[0094] The edge processing unit communicates with the sensing module to perform information fusion processing to determine whether a motion event has occurred at the arc angle. If so, it generates a motion event record containing the arc angle's identifier, motion time, and estimated motion parameters. The edge processing unit performs fusion analysis on the received multi-source sensor data, improving event judgment accuracy through threshold detection and data complementarity verification, overcoming the limitations of single sensors being susceptible to environmental interference. Furthermore, this unit performs preliminary screening of abnormal data, generating records only when multi-source data collaboratively indicate a motion event, effectively reducing the false positive rate.

[0095] The data communication module is used to upload action event records to the data processing platform. This module ensures timely data transmission through wired or wireless communication networks, providing a continuous data stream for subsequent processing and ensuring the timeliness and integrity of the sensed information.

[0096] The data processing platform communicates with the data communication module to receive and store action event records, and to establish and maintain digital archives of lightning arc angles. The digital archives contain a set of historical action event records for that arc angle and a health status assessment value calculated based on that set. The data processing platform also runs a collaborative protection decision model to generate collaborative protection strategies and maintenance early warning information for optimizing lightning current discharge paths. When a lightning activity early warning information is received, the collaborative protection decision model combines the digital archives of multiple arc angles, generates a collaborative protection strategy based on the health status assessment value, and simultaneously calculates the health status assessment value and remaining lifetime index based on the historical action event record set, achieving predictive maintenance. The health status assessment value is dynamically updated through a cumulative action count and parameter decay model, while the remaining lifetime index is quantitatively assessed based on the cumulative effect of action energy.

[0097] The strategy execution module communicates with the data processing platform and the controllable protection device to receive the coordinated protection strategy and generate corresponding control commands to drive the controllable protection device. The strategy execution module parses the coordinated protection strategy into executable commands for the controllable protection device, adjusts relevant electrical parameters, and thus guides the lightning current to preferentially flow towards the arc angle with a higher health status assessment value during lightning activity. By adjusting the breakdown voltage of the controllable parallel gap, the lightning current discharge path is optimized, solving the problem of isolated protection strategies.

[0098] This application enables precise monitoring and quantitative evaluation of the working status of lightning arc angle, and can proactively optimize the lightning current discharge path based on lightning warnings, thereby improving the overall reliability and resource utilization efficiency of regional lightning protection. It also enables predictive maintenance based on actual losses, thus transforming the lightning protection of transmission lines from a passive response to proactive intelligent management and control.

[0099] The following example will provide a more detailed explanation of the above technical solution: A specific power supply area A in the power transmission network has multiple transmission lines, each equipped with multiple lightning protection devices. This method was adopted to improve the lightning protection capability and operation and maintenance efficiency of this area.

[0100] Sensor arrays were deployed on the towers containing each arcing angle within Area A to achieve non-invasive state sensing of the arcing angle. On tower A, high-frequency electromagnetic sensors, acoustic sensors, and optical sensors were installed for the arcing angle. The high-frequency electromagnetic sensors capture the characteristic electromagnetic signals generated when the arcing angle breaks down, the acoustic sensors monitor the shock wave signals generated during arc generation, and the optical sensors identify the characteristic spectral signals of the arc. These sensors continuously collect multi-source sensing data related to the arcing angle's movement.

[0101] When lightning activity occurs in area A, the arc-shaped angle may activate due to a lightning strike. At this time, the sensor array on tower A will immediately capture the corresponding signal. After receiving multi-source sensor data from these sensors, the edge processing unit performs time synchronization and feature extraction processing. It extracts waveform features of high-frequency electromagnetic signals, frequency and amplitude features of acoustic signals, and intensity and duration features of optical signals. These extracted features are then matched against a pre-stored library of typical arc-shaped angle motion features. If, within a preset time window, the matching analysis results from the high-frequency electromagnetic sensor, acoustic sensor, and optical sensor all indicate a discharge event, and the event location information clearly points to the arc-shaped angle, the edge processing unit determines that an activation event has occurred. Unlike traditional arc-shaped angles that cannot provide any motion data, this method can determine whether an arc-shaped angle has activated in real time and with high accuracy. After determining that an activation event has occurred, the edge processing unit generates an activation event record, which includes the arc-shaped angle's identifier, the precise activation time, and estimated activation parameters. These estimated action parameters include lightning current amplitude parameters estimated based on the signal amplitude of a high-frequency electromagnetic sensor, and arc energy parameters estimated based on the signal strength and duration of an optical sensor.

[0102] The generated action event records are then uploaded to the data processing platform. The platform receives and stores these records, and establishes and continuously updates a digital profile for each call corner within Area A. The digital profile of each call corner not only contains a complete set of its historical action event records but also calculates a health status assessment value based on this set. This health status assessment value is calculated using a pre-defined weighted cumulative model based on estimated action parameters from the historical action event record set. In this model, recently occurring action events and parameters representing higher intensity in the estimated action parameters are given greater weight to more accurately reflect the current degree of degradation of the call corner. This health status assessment based on real action data and a weighted model overcomes the limitations of traditional operation and maintenance models that lack accurate assessment and rely on periodic inspections or replacement after a failure.

[0103] When the data processing platform receives a lightning activity warning for area A, it immediately initiates a collaborative protection decision-making process. The platform combines its stored digital archives of multiple lightning angles, specifically retrieving the current health status assessment values ​​of lightning angles one, two, and three on potentially affected line sections. Based on preset optimization objectives, the collaborative protection decision model generates a collaborative protection strategy to optimize the lightning current discharge path. If the health status assessment value of lightning angle two is better than that of lightning angles one and three, the model generates an instruction to prioritize guiding the lightning current through lightning angle two during lightning activity. This collaborative protection strategy overcomes the limitations of traditional independent responses and random action selection for lightning angles, achieving proactive optimization of the discharge path based on the device's health status.

[0104] The data processing platform converts this collaborative protection strategy into control commands and sends them to the corresponding controllable protection devices in area A via the data communication module. If a controllable parallel gap is installed near arcing angle two, the control command will adjust the breakdown voltage of that controllable parallel gap to be slightly lower than that of other arcing angles. If a controllable surge arrester is installed, the controllable surge arrester corresponding to arcing angle two will be pre-activated. By adjusting these relevant electrical parameters, during lightning activity, the lightning current will be guided to preferentially flow to arcing angle two, which has a better health status assessment value, thereby effectively protecting other arcing angles with poorer health status and extending the service life of the overall protection device.

[0105] The data processing platform also continuously calculates the remaining lifespan index of each arcing angle based on its digital archive. For arcing angle one, the platform obtains the historical cumulative arc energy parameters from its digital archive. Based on a pre-established ablation model of the arcing angle's electrode material, it calculates the proportion of lifespan already consumed according to these historical cumulative arc energy parameters. Once the remaining lifespan index of arcing angle one falls below a preset threshold, the platform immediately generates a maintenance warning. This predictive maintenance mechanism replaces the traditional passive and delayed maintenance mode, enabling timely detection and handling of potential equipment hazards, avoiding safety risks caused by untimely replacement, and also preventing resource waste due to over-maintenance.

[0106] Once a maintenance alert is generated, the operations and maintenance personnel will receive the information and inspect or replace the first angle bracket. After processing, the personnel will upload the confirmation and processing feedback to the data processing platform, which will then update the processing result in the digital file of the first angle bracket, forming a closed-loop management system.

[0107] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for adaptive collaborative protection of arc angle, characterized in that, Includes the following steps: S1: Perform non-intrusive state sensing on multiple arc angles installed within the preset power supply area to acquire multi-source sensor data related to the arc angle action; S2: Based on the multi-source sensor data, through information fusion processing, determine whether an action event has occurred at the arc angle, and when the determination is yes, generate an action event record containing the identity of the arc angle, the action time and the estimated action parameters; S3: Upload the action event records to the data processing platform to establish and update the digital archives corresponding to each attack arc angle. The digital archives include a set of historical action event records for that attack arc angle and a health status assessment value calculated based on that set. S4: In response to the lightning activity warning information for the preset power supply area, and in combination with the digital files of multiple arc angles stored in the data processing platform, a collaborative protection strategy for optimizing the lightning current discharge path is generated through the collaborative protection decision model. S5: Convert the collaborative protection strategy into a control command and send it to the corresponding controllable protection device in the preset power supply area to adjust the relevant electrical parameters, thereby guiding the lightning current to flow preferentially to the arc angle with a better health status assessment value during lightning activity; S6: Based on the digital files of each arc angle, calculate its remaining life index, and generate maintenance warning information when the remaining life index is lower than a preset threshold.

2. The arc angle adaptive cooperative protection method according to claim 1, characterized in that, In step S1, the non-invasive state perception is achieved by a sensor group deployed on the tower where the arc angle is located. The sensor group includes at least two of the following: a high-frequency electromagnetic sensor for capturing characteristic electromagnetic signals when the arc angle breaks down, an acoustic sensor for monitoring the shock wave signal generated by the arc, and an optical sensor for identifying characteristic spectral signals of the arc.

3. The arc angle adaptive cooperative protection method according to claim 2, characterized in that, In step S2, determining whether an action event has occurred at the arc angle through information fusion processing includes: Time synchronization and feature extraction are performed on multi-source sensing data from the sensor group; The extracted features are matched and analyzed with a pre-stored database of typical action features of the arc angle. When the matching analysis results from different sensors all indicate that a discharge event has occurred within a preset time window and the event location information points to the same arc angle, it is determined that an action event has occurred at that arc angle.

4. The arc angle adaptive cooperative protection method according to claim 3, characterized in that, In step S2, the estimated action parameters include at least the lightning current amplitude parameter estimated based on the signal amplitude of the high-frequency electromagnetic sensor and the arc energy parameter estimated based on the signal strength and duration of the optical sensor.

5. The arc angle adaptive cooperative protection method according to claim 1, characterized in that, In step S3, the health status assessment value is calculated by a preset weighted cumulative model based on the estimated action parameters in the historical action event record set corresponding to the arc angle. In the weighted cumulative model, the recently occurring action events and the parameters representing higher intensity in the estimated action parameters are given greater weight.

6. The arc angle adaptive cooperative protection method according to claim 1, characterized in that, In step S4, the logic of the collaborative protection decision model generating the collaborative protection strategy includes: Based on the aforementioned lightning activity warning information, the potentially affected line sections and multiple arc angles are determined; Retrieve the current health status assessment value of the potentially affected arc angle; Based on preset optimization objectives, strategy instructions are generated to indicate that during lightning activity, lightning current should be preferentially guided through the arc angle with the better health status assessment value.

7. The arc angle adaptive cooperative protection method according to claim 1 or 6, characterized in that, In step S5, the controllable protection device includes a controllable parallel gap or a controllable surge arrester installed near the arc angle, and adjusting the relevant electrical parameters includes adjusting the breakdown voltage of the controllable parallel gap or pre-activating the controllable surge arrester.

8. The adaptive cooperative protection method for arc angle according to claim 1, characterized in that, In step S6, calculating its remaining lifespan index includes: Obtain the historical cumulative arc energy parameters from the digital archive of the arc angle; Based on the pre-established arc angle electrode material ablation model, the proportion of lifetime already consumed is calculated according to the historical cumulative arc energy parameters. The remaining lifespan index is determined based on the proportion of lifespan already consumed.

9. The adaptive cooperative protection method for arc angle according to claim 1, characterized in that, The method further includes: S7: Receive confirmation and processing feedback for the maintenance warning information, and update the processing result to the digital file of the corresponding arc angle.

10. An adaptive cooperative protection system for arc angle, used to implement the method according to any one of claims 1 to 9, characterized in that, include: The sensing module is deployed on multiple towers to perform non-intrusive state sensing of the arc angle on the corresponding towers and acquire multi-source sensor data; An edge processing unit, communicatively connected to the sensing module, is used to perform the information fusion processing to determine the arc angle action event and generate an action event record; The data communication module is used to record and upload the action events. The data processing platform is connected to the data communication module to receive and store the action event records, establish and maintain the arc angle digital archive, and run the collaborative protection decision model to generate collaborative protection strategies and maintain early warning information. The strategy execution module is communicatively connected to the data processing platform and the controllable protection device, and is used to receive the collaborative protection strategy and generate corresponding control commands to drive the controllable protection device to operate.

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