A surge arrester condition monitoring system

Abstract

This invention relates to a surge arrester condition monitoring system, comprising a data acquisition module, a lightning strike assessment module, a prediction module, and a threshold adjustment module. The data acquisition module collects current and voltage waveforms from multiple lightning strikes within a single lightning event, as well as the time intervals between adjacent strikes. The lightning strike assessment module calculates the intensity ratio of single lightning strikes based on the surge arrester's baseline withstand parameters, quantifies the cumulative effect of consecutive lightning strikes using a time interval-based correction coefficient, and obtains a weighted fusion index of the comprehensive impact of lightning events on the surge arrester. The prediction module uses this index, combined with the natural aging rate, to update the surge arrester's estimated remaining lifespan. The threshold adjustment module adjusts the abnormal monitoring thresholds for operating parameters such as leakage current, resistive current, and temperature based on the updated remaining lifespan. This invention achieves quantitative assessment of the surge arrester's aging state, dynamic prediction of remaining lifespan, and intelligent adjustment of monitoring thresholds, effectively improving the foresight and accuracy of condition monitoring.

Description

Technical Field

[0001] This invention relates to the field of surge arrester technology, and more specifically to a surge arrester condition monitoring system. Background Technology

[0002] Surge arresters are critical overvoltage protection devices in power systems, used to limit lightning or switching overvoltages and protect the insulation of electrical equipment from impact damage. Their operating status directly affects the safety and stability of the power grid. Therefore, real-time status monitoring of surge arresters, timely detection of performance degradation, and early warning are important means to achieve predictive maintenance of power equipment and prevent the escalation of faults.

[0003] Currently, surge arrester condition monitoring technology has gradually evolved from the traditional manual periodic inspection mode to an online real-time monitoring mode. Existing online monitoring technologies mostly focus on the direct acquisition and measurement of electrical parameters and fixed threshold alarms. A typical technical solution is a surge arrester real-time online monitoring method disclosed in Chinese invention patent No. 201710362553.5. This technology uses thermistors placed on the surface of key components inside the surge arrester to infer the amplitude of the lightning current flowing through the device by using temperature changes. When the lightning current exceeds the device's nominal maximum discharge current, the number of lightning strikes exceeds the warning threshold, or abnormal operating voltage or excessive ambient temperature and humidity occurs, the system triggers an alarm signal, prompting maintenance personnel to intervene.

[0004] However, the existing technology focuses on the instantaneous parameters of a single lightning strike (such as whether the current of a single lightning strike exceeds the limit) or simply accumulates the number of lightning strikes when it is implemented. However, the life loss of a surge arrester is a cumulative process, which is not only related to the intensity of a single lightning strike, but also closely related to the overall characteristics of the lightning event. For example, the heat accumulation effect generated by multiple lightning strikes in a short period of time (high density), and the impact energy type represented by the waveform characteristics of the lightning current, have different damage mechanisms and degrees on the valve plate material.

[0005] In lightning strikes, the degree of damage to surge arresters is related not only to the instantaneous intensity of a single lightning strike but also to the specific circumstances of the event. For example, high-density impacts from multiple consecutive lightning strikes within a short period can easily trigger a thermal accumulation effect on the varistor material, thereby accelerating its aging process. Different lightning current waveforms carry different types of impact energy, resulting in varying degrees of damage to the varistor material. However, current technologies only focus on the instantaneous parameters of a single lightning strike or simply accumulate the number of strikes, failing to comprehensively characterize the combined impact of lightning strikes on the lifespan of surge arresters.

[0006] Existing technologies often use alarm thresholds that are fixed values ​​preset based on experience, making it difficult to adapt to the dynamic process of surge arresters aging gradually with increasing service life and accumulated lightning strikes. During long-term operation, the performance of the internal varistor of a surge arrester will continue to deteriorate after repeated lightning strikes, and its impact resistance will gradually decrease. Under these conditions, even if the intensity of the lightning strike does not exceed the standard limit, its failure risk is much higher than that of a newly commissioned surge arrester.

[0007] In summary, existing monitoring technologies for surge arresters in power systems suffer from core shortcomings, including limited monitoring dimensions, static threshold settings, and a lack of lifespan assessment. The sheer number of monitoring targets not only increases the system's operational burden but also makes it difficult to efficiently identify abnormal equipment and accurately detect early, latent damage such as micro-cracks in valve plates and accelerated aging of insulation layers. Consequently, it fails to achieve proactive prediction and adaptive intelligent monitoring of surge arrester operating conditions. Therefore, the development of a surge arrester condition monitoring system is essential. Summary of the Invention

[0008] Therefore, the purpose of this invention is to provide a surge arrester condition monitoring system. Faced with the vast number of monitoring targets in power systems, it is difficult to efficiently identify abnormal surge arresters and achieve proactive fault prediction. This results in problems such as high identification difficulty and low prediction accuracy. Furthermore, the excessive monitoring load further affects monitoring efficiency, ultimately leading to the inability to accurately identify early hidden damage and hindering adaptive intelligent monitoring. Therefore, the purpose of this invention is to provide a surge arrester condition monitoring system that effectively solves the core problems of existing monitoring methods, such as high identification difficulty and low prediction accuracy caused by the massive number of monitored targets, inefficient anomaly identification, and lack of fault prediction, thereby achieving accurate monitoring and proactive early warning of surge arresters.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is: a surge arrester condition monitoring system, comprising...

[0010] The data acquisition module is used to collect the intensity characteristics of multiple lightning strikes within a lightning strike event and the time interval between adjacent lightning strikes.

[0011] The lightning strike assessment module is used to calculate the impact index of the event on the life of the surge arrester based on the characteristics of the lightning strike intensity and the time interval.

[0012] The prediction module is used to accumulate the impact index of the surge arrester, form a comprehensive impact index, and use the comprehensive impact index to update the estimated remaining life of the surge arrester.

[0013] The threshold adjustment module is used to adjust the abnormal range thresholds for monitoring the operating parameters of the surge arrester under normal operating conditions, based on the updated estimated remaining lifespan.

[0014] Furthermore, in response to an increase in the number of lightning strikes, the number of lightning strikes is collected within a preset monitoring time window until the number of lightning strikes no longer increases within the preset monitoring time window after the increase, and this is recorded as a single lightning strike event.

[0015] Furthermore, based on the surge arrester model, the baseline withstand parameters are retrieved, and the ratio of the intensity characteristics of a single lightning strike to the baseline parameters is calculated. Based on the correction coefficient of the time interval, the cumulative effect of continuous lightning strikes is quantitatively corrected. The corrected impact indices are then weighted and fused to obtain the comprehensive impact index.

[0016] Furthermore, the lightning strike intensity characteristics include peak current and lightning energy; a single lightning strike waveform is acquired, and current characteristic parameters are extracted from the lightning current waveform, including peak lightning current, wavefront time, and half-peak time; based on the current waveform and the voltage waveform across the surge arrester, the lightning energy of a single lightning strike is calculated; the time interval is the time difference between two adjacent peak lightning currents.

[0017] Furthermore, the relative deviation between the current characteristic parameters of subsequent lightning strikes and the corresponding parameters of the first lightning strike is calculated to generate a waveform distortion index; if the waveform distortion index exceeds a preset threshold, it is determined that the surge arrester varistor has performance degradation, and an early warning signal is generated.

[0018] Furthermore, the estimated remaining lifespan calculated previously is obtained, and combined with the natural aging rate, the lifespan of the surge arrester is reduced using the lifespan reduction factor corresponding to this lightning strike event.

[0019] Furthermore, after the lightning strike event, the recovery curves of the surge arrester's leakage current and temperature are collected within a preset time. If the recovery time exceeds the standard value calculated based on the lifespan conversion factor, it is determined that there is latent damage.

[0020] Furthermore, the lifespan utilization rate of the surge arrester is calculated. If the lifespan utilization rate exceeds the preset level, the abnormal range threshold of the operating parameters is narrowed.

[0021] Furthermore, the operating parameters include the upper limit threshold of the total leakage current, the upper limit threshold of the resistive current component, and the upper limit threshold of the temperature. When the remaining lifetime is lower than the preset value or the operating parameters exceed the preset abnormal range threshold, a maintenance signal is issued.

[0022] Furthermore, the lightning intensity characteristics and time intervals between adjacent lightning strikes of all lightning events experienced by the surge arrester within a preset historical time period are collected; based on the model and specification parameters of the surge arrester, the historical lightning intensity characteristics, time interval data and surge arrester model parameters are used to train a life prediction model, and the estimated remaining life of the surge arrester is output based on the life prediction model.

[0023] The beneficial effects of the above technical solution are as follows: In response to the core pain points of existing surge arrester monitoring methods in large-scale application scenarios, such as massive monitoring data, inefficient anomaly identification, inability to achieve forward-looking fault prediction, and insufficient targeted maintenance, this invention significantly improves the efficiency, accuracy, and forward-looking nature of large-scale surge arrester status monitoring by constructing a monitoring and prediction system based on lightning strike events. It upgrades the traditional operation and maintenance mode that relies on manual labor and has a slow response to a data-driven, predictive, and efficient operation and maintenance mode.

[0024] This invention identifies lightning strike events using high-precision sensing equipment, collecting core parameters such as lightning strike intensity characteristics and time intervals between adjacent strikes. It simultaneously records current and voltage waveform data, preprocesses it, and transmits it to the backend. Furthermore, it employs a dynamic time window method to accurately define individual lightning strike events, avoiding the splitting and merging of consecutive strikes. This precisely captures the core characteristic data of lightning strike events, fully reconstructing the essential process of lightning strikes and laying a solid data foundation for subsequent analysis of the actual aging degree of surge arresters from the perspective of lightning damage. Simultaneously, it effectively focuses on surge arresters affected by lightning strikes and at risk of aging or damage, accurately locating monitoring targets and significantly reducing ineffective monitoring load. This improves the efficiency and accuracy of monitoring large quantities of surge arresters from the source, providing reliable support for targeted monitoring based on aging degree and accurate analysis of the actual impact of lightning strikes on surge arresters.

[0025] In practical implementation, this invention introduces a cumulative effect correction mechanism based on time intervals to accurately quantify and assess the actual impact of lightning strikes on the lifespan of surge arresters. Based on this, the remaining lifespan of surge arresters is dynamically updated, realizing the transformation of surge arrester failure from post-event repair to pre-event accurate prediction, and reserving sufficient time for maintenance personnel to make maintenance and replacement decisions.

[0026] This invention starts from the damage mechanism of surge arresters caused by lightning strikes. By analyzing the distortion characteristics of the current waveform during a lightning strike, it accurately captures the early performance degradation of the valve plate caused by lightning strike losses. At the same time, it monitors the recovery characteristics of the leakage current and temperature of the surge arrester after a lightning strike and compares them with the standard recovery values ​​of the matching equipment under the current state. This effectively identifies the latent damage induced by lightning strike losses that has no obvious external manifestations, and realizes the accurate identification of early fault hazards of surge arresters, thereby avoiding the risk of lightning strike losses aggravating equipment damage from the root.

[0027] To address the challenges of managing large quantities of surge arresters, this invention develops an adaptive threshold adjustment strategy based on the remaining lifespan of the arresters obtained from lightning strike loss assessment. This strategy enables differentiated and precise monitoring of surge arresters: gradient monitoring thresholds are set according to the different degrees of aging caused by lightning strike loss. The more severe the aging of the surge arrester and the greater the impact of lightning strike loss, the stricter the monitoring threshold becomes. This effectively avoids the problems of missed reports for aging equipment and false reports for new equipment under the fixed threshold mode, further improving the accuracy of surge arrester monitoring in large-scale scenarios.

[0028] In summary, this invention focuses on the impact of lightning strikes on surge arrester losses, constructing a closed-loop management and control system for surge arresters suitable for large-scale application scenarios. This provides reliable technical support for predictive maintenance of surge arresters, accurately locates monitoring targets, improves operation and maintenance efficiency, reduces operation and maintenance costs, and prevents equipment safety hazards caused by aggravated lightning strike losses from the source. It has significant engineering application value for improving the overall safe and stable operation of the power grid. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the implementation of the present invention;

[0030] Figure 2 This is a system logic block diagram of the present invention;

[0031] Figure 3 A logic diagram for early warning;

[0032] Figure 4 This is a logic diagram for assessing the impact of lightning strikes. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0034] Example 1: This example aims to provide a surge arrester status monitoring system, primarily used for precise monitoring of the operating status of a large number of surge arresters in power systems. Addressing the core issues of existing monitoring methods in scenarios involving a large number of surge arresters—namely, the massive volume of monitoring data, low efficiency in anomaly identification, difficulty in quickly locating abnormal devices, and consequently, the inability to achieve proactive fault prediction and precise maintenance—this example provides a surge arrester status monitoring system. By optimizing the monitoring logic and identification mechanism, it effectively solves the pain points of existing technologies, such as high identification difficulty, low prediction accuracy, and insufficient maintenance targeting. This system achieves efficient anomaly identification, precise status monitoring, and proactive early warning for a large number of surge arresters, providing reliable technical support for predictive maintenance of surge arresters.

[0035] like Figure 1 As shown, this embodiment provides a surge arrester status monitoring system, including a data acquisition module, a lightning strike assessment module, a life prediction module, and a threshold adjustment module. The modules interact bidirectionally via industrial Ethernet. A distributed deployment approach is adopted to meet the monitoring needs of large-scale surge arresters. The data acquisition module is deployed at the site of each surge arrester, while the lightning strike assessment module, life prediction module, and threshold adjustment module are centrally deployed in the back-end monitoring center. A unique device identifier enables full data link traceability for a single surge arrester, solving the problems of device identification and data attribution in large-scale monitoring scenarios.

[0036] In this embodiment, the acquisition module is the front-end data acquisition unit of the surge arrester status monitoring system. Its core function is to accurately identify lightning strike events and acquire relevant characteristic parameters, used to acquire the lightning intensity characteristics of multiple lightning strikes within a lightning strike event and the time interval between adjacent lightning strikes. Specific acquisition equipment includes current acquisition equipment and voltage acquisition equipment. Specifically, a high-frequency current transformer is used connected to the surge arrester's grounding wire for real-time monitoring of the lightning current waveform; a high-voltage divider or capacitively coupled sensor is connected in parallel across the surge arrester to synchronously acquire the voltage waveform of the surge arrester during the lightning strike process.

[0037] like Figure 2 As shown, in this embodiment, during the collection of a lightning strike event, in response to the increase in the number of lightning strikes, the number of lightning strikes is collected within a preset monitoring time window until the number of lightning strikes no longer increases within the preset monitoring time window after the increase in the number of lightning strikes, and it is recorded as a lightning strike event.

[0038] In practice, this embodiment monitors the signal amplitude output by the current sensor in real time. When a current peak value exceeds a preset trigger threshold, it is determined that a lightning strike has occurred. A preset monitoring time window is immediately initiated. Within the initial time window, the system continuously monitors for new lightning strike events. If a new lightning current peak value (exceeding the threshold) is detected within the window period, the end time of the window is automatically reset to the current time plus T. This process is repeated until no new lightning strike occurs within a continuous time T after the last lightning strike. All records from the first lightning strike to the last lightning strike are considered to constitute a complete lightning strike event, and a unique identifier is assigned to this event.

[0039] This embodiment records the complete current waveform and voltage waveform across the surge arrester during the current lightning strike, and extracts lightning strike intensity features from the current and voltage waveforms. These features include the peak current and lightning energy. Specifically, a single lightning strike waveform is acquired, and the peak current, wavefront time, and half-peak time are extracted from the lightning current waveform. The wavefront time is the time required for the current to rise from 10% to 90% of its peak value, and the half-peak time is the time required for the current to fall from its peak value to 50% of its peak value. Based on the current waveform and the voltage waveform across the surge arrester, the lightning energy of a single lightning strike is calculated. Specifically, this is done by numerically integrating the current and voltage waveforms to obtain the energy of the single lightning strike.

[0040] The time interval is the time difference between two adjacent lightning strike current peak values. The time difference between the current lightning strike and the previous lightning strike current peak value is calculated, and the timestamp of the current lightning strike is recorded.

[0041] After the lightning strike event ends, the acquisition module will package the following data and send it to the system's evaluation module, specifically including the event identifier and total duration, the number of lightning strikes during the event, the intensity characteristics and timestamps of each lightning strike, the time interval sequence between adjacent lightning strikes, the lightning strike density, and the data quality identifier used to verify the results.

[0042] The lightning strike assessment module is used to calculate the impact index of the event on the life of the surge arrester based on the characteristics of the lightning strike intensity and the time interval. In specific implementation, this embodiment calculates the ratio of the intensity of a single lightning strike based on the benchmark withstand parameters of the surge arrester model, uses a correction coefficient based on the time interval to quantify the cumulative effect of continuous lightning strikes, and obtains the comprehensive impact index of the lightning strike event on the surge arrester after weighted fusion.

[0043] The lightning strike assessment module receives data packets from the acquisition module, standardizes and cleans the packets to remove outliers, and then extracts features from the packets. Based on the surge arrester model, it retrieves the baseline withstand current and withstand energy. It also retrieves the peak lightning current and lightning energy from a single lightning strike waveform. The peak lightning current ratio is obtained by comparing the peak lightning current to the baseline withstand current; the lightning energy ratio is obtained by comparing the lightning energy to the withstand energy. Finally, the impact index of a single lightning strike is obtained by weighted fusion of the lightning energy ratio and the peak lightning current ratio. The specific calculation formula is as follows:

[0044] In the formula, , As weight, and These are the reference withstand current and withstand energy (reference values ​​retrieved from the preset parameter library based on the surge arrester model). The peak current of the i-th lightning strike (extracted from the waveform). The energy of the i-th lightning strike (calculated by integrating the current and voltage waveforms).

[0045] like Figure 4 As shown, for consecutive lightning strikes, the impact of the next lightning strike is amplified by the residual effect of the previous lightning strike. In this embodiment, a correction coefficient based on the time interval between the previous and next lightning strikes is introduced to correct the calculated impact index of the next lightning strike. The specific calculation formula is as follows:

[0046]

[0047]

[0048] In the formula, This is the cumulative effect intensity coefficient. The time decay constant, It is an exponentially decaying function. Let be the time difference between the i-th lightning strike and the (i-1)-th lightning strike. This is a correction factor; when When the frequency is very low (due to dense lightning strikes), this value is close to 1, which is the correction factor. As it grows larger, it reflects a cumulative effect; when When it is very large, this value is close to 0, and the correction factor is... ≈1 indicates no cumulative effect.

[0049] The prediction module is used to accumulate the impact index of the surge arrester to form a comprehensive impact index. The comprehensive impact index is used to update the estimated remaining life of the surge arrester. Its purpose is to transform the impact of discrete, one-off lightning strike events into a continuous and dynamic prediction of the overall remaining service life of the surge arrester.

[0050] This embodiment obtains the impact index of each lightning strike through iteration, and finally weights and fuses the obtained impact indices to obtain the comprehensive impact index of the lightning strike event. The specific formula is as follows:

[0051]

[0052] In the formula, The weight of the i-th lightning strike, where n is the number of lightning strikes associated with this event.

[0053] The estimated remaining lifespan calculated previously is obtained, and combined with the natural aging rate, the lifespan of the surge arrester is reduced using the lifespan reduction factor corresponding to this lightning strike event. In specific implementation, the lifespan prediction module receives the comprehensive impact index of this lightning strike event from the lightning strike assessment module, and retrieves the rated service life, current remaining lifespan L, and natural aging rate R from the database based on the surge arrester model.

[0054]

[0055] In the formula, and These represent the remaining lifespan before and after the lightning strike, respectively. Natural wear and tear from the last update to this update. The lifespan conversion factor is based on the impact index of this event. The mapping relationship is determined by a preset mapping table; this embodiment uses a surge arrester model as an example to illustrate the specific mapping relationship table;

[0056] , The value is 0.999. , The value is 0.99. , The value is 0.95. , The value is 0.9.

[0057] In addition, during implementation, machine learning algorithms can be used to collect the lightning intensity characteristics and time intervals between adjacent lightning strikes experienced by the surge arrester within a preset historical time period; based on the model and specifications of the surge arrester, a life prediction model can be trained based on the historical lightning intensity characteristics, time interval data and the model parameters of the surge arrester, and the estimated remaining life of the surge arrester can be output based on the life prediction model.

[0058] The threshold adjustment module is used to adjust the abnormal range threshold of the monitoring parameters of the surge arrester under normal operating conditions according to the updated estimated remaining lifespan. This enables differentiated monitoring based on the equipment's lifespan status, solving the problems of fixed thresholds in existing technologies that cannot adapt to the equipment aging process, and facilitating the rapid identification and accurate maintenance of abnormal equipment.

[0059] like Figure 3 As shown, by calculating the lifespan utilization rate of the surge arrester, if the lifespan utilization rate exceeds a preset level, the abnormal range threshold of the operating parameters is narrowed, achieving differentiated management with stricter monitoring for equipment that is more severely aged. In specific implementation, the operating parameters include the upper limit threshold of the total leakage current, the upper limit threshold of the resistive current component, and the upper limit threshold of the temperature. When the remaining lifespan is lower than the preset value or the operating parameters exceed the preset abnormal range threshold, a maintenance signal is issued.

[0060] The threshold adjustment module sends the adjusted monitoring threshold to the field acquisition module. The acquisition module monitors the real-time operating parameters of the surge arrester according to the new threshold. Once the operating parameters are detected to exceed the adjusted abnormal range threshold, or the expected remaining life of the surge arrester is lower than the preset value, a graded early warning signal is immediately triggered.

[0061] The threshold adjustment module centrally displays the lifespan utilization, monitoring threshold status, and abnormal signals of all surge arresters, forming a large-scale surge arrester status dashboard in the backend monitoring center. Maintenance personnel can quickly filter out abnormal devices with warning or maintenance requirements through this dashboard, significantly improving the efficiency of anomaly identification in scenarios with a large number of surge arresters and solving the problems of massive data volume and difficult identification in existing technologies.

[0062] This embodiment quantifies the comprehensive impact index of lightning strikes and dynamically updates the estimated remaining lifespan of surge arresters by combining it with the natural aging rate. This not only accurately characterizes the current aging state of the equipment but also predicts its remaining service life, upgrading traditional post-event alarms to pre-event predictions. This provides maintenance personnel with ample time for maintenance and replacement decisions, solving the problem of existing technologies' inability to achieve proactive fault prediction. It abandons fixed monitoring thresholds and adopts an adaptive threshold adjustment strategy based on lifespan utilization. Different monitoring standards are set for surge arresters with different aging levels; the more severely aged the equipment, the stricter the monitoring, effectively avoiding missed or false alarms caused by fixed thresholds. Simultaneously, the system's warning and maintenance signals are accompanied by a unique equipment identifier and specific abnormal data, allowing maintenance personnel to directly conduct targeted on-site inspections and maintenance. This enables precise predictive maintenance of surge arresters, solving the problems of lack of targeted maintenance and low efficiency in existing technologies. Taking full account of the cumulative effect of continuous lightning strikes, this technology accurately characterizes the impact of lightning strike intensity and sequence on the loss of surge arresters by introducing a time interval correction coefficient. This changes the one-sidedness of existing technologies that only focus on single lightning strike parameters or simply accumulate the number of lightning strikes, and realizes a quantitative assessment of the impact of lightning events on the lifespan of surge arresters. At the same time, the lifespan prediction module combines lightning strike loss and natural aging loss to construct a full life cycle life assessment model, which greatly improves the scientificity and accuracy of surge arrester lifespan prediction.

[0063] Example 2

[0064] Performance degradation of the valve element (such as microcracks, zinc oxide grain aging, and poor interfacial contact) directly leads to changes in the surge arrester's volt-ampere characteristics, reflected in the lightning current waveform as distortions in core characteristic parameters such as peak current, wavefront time, and half-peak time. This embodiment selects the peak current, wavefront time, and half-peak time as the core characteristic parameters for waveform distortion analysis. These parameters directly characterize the valve element's current-carrying capacity and transient response characteristics, and parameter distortion is strongly correlated with valve element performance degradation.

[0065] While calculating the comprehensive impact index of lightning strike events, the lightning strike assessment module adds a sub-module for identifying the performance degradation of arrester valves. By analyzing the relative deviation of the current characteristic parameters between subsequent lightning strikes and the first lightning strike within a lightning strike event, a waveform distortion index is generated to accurately identify the early performance degradation of arrester valves. If the waveform distortion index exceeds a preset threshold, it is directly determined that the arrester valve has performance degradation, and an early warning signal for valve degradation is immediately generated. This provides a direct basis for identifying early hidden dangers in large-scale arrester scenarios and makes up for the deficiency of existing technologies that cannot identify early hidden degradation of valves.

[0066] In the specific identification process, this embodiment generates a waveform distortion index by calculating the relative deviation between the current characteristic parameters of subsequent lightning strikes and the corresponding parameters of the first lightning strike. The peak current directly reflects the current-carrying capacity of the arrester and is most sensitive to degradation; the half-peak time reflects the transient recovery characteristics of the arrester and is less sensitive; the wavefront time reflects the rapid response characteristics of the arrester and is relatively less sensitive. This embodiment assigns different weight coefficients to each characteristic parameter based on their sensitivity, and generates the waveform distortion index for the nth lightning strike by weighted fusion of the relative deviations of each parameter. If the waveform distortion index exceeds a preset threshold, it is determined that the arrester arrester valve has performance degradation, and a warning signal is generated.

[0067] This embodiment uses the current characteristic parameters of the first lightning strike in this lightning event as the benchmark parameters. The reason is that when the first lightning strike occurs, the surge arrester varistor is not affected by the heat accumulation and impact fatigue in this lightning event. Its current waveform can truly reflect the current actual performance state of the varistor. Using this as a benchmark can effectively avoid the interference of the cumulative effect of continuous lightning strikes on the identification of degradation.

[0068] Example 3

[0069] After a lightning strike event, a preset monitoring window is established to collect the recovery curves of the surge arrester's leakage current and temperature. The time required for the leakage current and temperature to recover to steady-state values ​​is calculated as the actual recovery time. Based on the impact index of this lightning strike event, a preset standard recovery time is queried. If the actual recovery time exceeds the standard recovery time, it is determined that the surge arrester has hidden damage, and a hidden damage warning signal is generated.

[0070] In this embodiment, after a lightning strike, the recovery curves of the surge arrester's leakage current and temperature are collected within a preset time. If the recovery time exceeds the standard value calculated based on the lifespan conversion factor, latent damage is determined to exist. Specifically, the average leakage current and temperature of the surge arrester during a stable period before the lightning strike are used as the steady-state reference value, and the temperature reference value is calibrated in conjunction with the ambient temperature. When the leakage current and temperature recover to the preset deviation range and remain stable for a period of time, recovery is considered complete, and the corresponding time is the actual recovery time of each parameter; the longer of the two times is taken as the overall actual recovery time of the surge arrester. If the recovery to the steady-state range is not achieved within the preset monitoring window, the actual recovery time is directly determined to exceed the window duration.

[0071] This embodiment uses the recovery curve characteristics of the surge arrester's leakage current and temperature after a lightning strike, combined with the dual-benchmark judgment criteria of lightning strike impact index and life conversion factor, to accurately identify early hidden damages without obvious external manifestations, such as micro-cracks in the valve plate and internal aging of the insulation layer.

Claims

1. A surge arrester condition monitoring system, characterized in that: include The data acquisition module is used to collect the intensity characteristics of multiple lightning strikes within a lightning strike event and the time interval between adjacent lightning strikes. The lightning strike assessment module is used to calculate the impact index of the event on the life of the surge arrester based on the characteristics of the lightning strike intensity and the time interval. The prediction module is used to accumulate the impact index of the surge arrester, form a comprehensive impact index, and use the comprehensive impact index to update the estimated remaining life of the surge arrester. The threshold adjustment module is used to adjust the abnormal range thresholds for monitoring the operating parameters of the surge arrester under normal operating conditions, based on the updated estimated remaining lifespan.

2. The surge arrester condition monitoring system according to claim 1, characterized in that: In response to an increase in the number of lightning strikes, the number of lightning strikes is collected within a preset monitoring time window until the number of lightning strikes no longer increases within the preset monitoring time window after the increase, and this is recorded as a single lightning strike event.

3. The surge arrester condition monitoring system according to claim 1, characterized in that: Based on the surge arrester model, the baseline withstand parameters are retrieved, and the ratio of the intensity characteristics of a single lightning strike to the baseline parameters is calculated. Based on the correction coefficient of the time interval, the cumulative effect of continuous lightning strikes is quantitatively corrected. The corrected impact indices are weighted and fused to obtain the comprehensive impact index.

4. The surge arrester condition monitoring system according to claim 1, characterized in that: The lightning strike intensity characteristics include peak current and lightning energy; a single lightning strike waveform is acquired, and current characteristic parameters are extracted from the lightning current waveform, including peak lightning current, wavefront time, and half-peak time. Based on the current waveform and the voltage waveform across the surge arrester, the lightning energy of a single lightning strike is calculated; the time interval is the time difference between two adjacent lightning current peak values.

5. The surge arrester condition monitoring system according to claim 4, characterized in that: The relative deviation between the current characteristic parameters of subsequent lightning strikes and the corresponding parameters of the first lightning strike is calculated to generate a waveform distortion index. If the waveform distortion index exceeds a preset threshold, it is determined that the surge arrester varistor has performance degradation, and an early warning signal is generated.

6. The surge arrester condition monitoring system according to claim 1, characterized in that: The estimated remaining lifespan calculated previously is obtained, and combined with the natural aging rate, the lifespan of the surge arrester is reduced using the lifespan reduction factor corresponding to this lightning strike event.

7. The surge arrester condition monitoring system according to claim 1, characterized in that: After a lightning strike, the recovery curves of the surge arrester's leakage current and temperature are collected within a preset time. If the recovery time exceeds the standard value calculated based on the lifespan conversion factor, it is determined that there is latent damage.

8. The surge arrester condition monitoring system according to claim 1, characterized in that: Calculate the lifespan utilization rate of the surge arrester. If the lifespan utilization rate exceeds the preset level, then reduce the threshold of abnormal range of operating parameters.

9. The surge arrester condition monitoring system according to claim 8, characterized in that: The operating parameters include the upper limit threshold of the total leakage current, the upper limit threshold of the resistive current component, and the upper limit threshold of the temperature. When the remaining lifetime is lower than the preset value or the operating parameters exceed the preset abnormal range threshold, a maintenance signal is issued.

10. The surge arrester condition monitoring system according to claim 1, characterized in that: Collect the lightning intensity characteristics and time intervals between all lightning strikes experienced by the surge arrester within a preset historical time period; based on the surge arrester's model and specifications, and using the historical lightning intensity characteristics, time interval data, and surge arrester model parameters, train a life prediction model, and output the surge arrester's estimated remaining life based on the life prediction model.

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