Insulator abnormity diagnosis method and terminal

Abstract

The invention discloses an insulator abnormity diagnosis method and a terminal, and the method comprises the steps: obtaining the real-time humidity of an insulator application environment, and obtaining the thermogram characteristics of an insulator when the real-time humidity meets a preset humidity; detecting thermogram characteristics, regarding the insulator with the thermogram characteristics presenting a preset hue as an abnormal insulator, judging the temperature difference between the abnormal insulator and a normal insulator, and regarding the abnormal insulator as a zero-value insulator when the temperature difference meets a preset threshold value; and the normal insulator is an insulator which is applied to the same environment as the abnormal insulator, is located at the same position in the insulator string and has a normal working state. According to the invention, the sensitivity of detection and determination of low-value and zero-value insulators can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of insulator detection, in particular to an insulator abnormality diagnosis method and terminal. BACKGROUND

[0002] In the existing operation and maintenance of high-altitude insulators in substations, the state of the insulators is mainly confirmed by artificial cleaning, zero measurement and point hanging inspection after power off. Although this method can directly obtain insulator resistance data, it needs to be operated in a high-altitude environment, which has a high risk of induced electric shock and falling from a high place, and occupies a large number of operation and maintenance resources, and the operation efficiency is low. With the expansion of the power grid scale and the continuous increase of the number of insulators, it is difficult to meet the demand of high-frequency state evaluation by relying on power-off maintenance.

[0003] In order to improve the detection efficiency, an infrared thermal imager is gradually used to detect the temperature of the live insulator, but the traditional infrared detection often takes temperature difference as the main criterion, which is easily affected by environmental temperature, light, wind speed and temperature measurement angle, etc., so that the judgment accuracy is limited. SUMMARY

[0004] The technical problem to be solved by the present application is to provide an insulator abnormality diagnosis method and terminal, which can improve the sensitivity of low-value and zero-value insulator detection and judgment.

[0005] In order to solve the above technical problems, the technical scheme adopted by the present application is: An insulator abnormality diagnosis method, comprising the steps of: obtaining real-time humidity of an insulator application environment, and obtaining thermal image features of the insulator when the real-time humidity meets a preset humidity; detecting the thermal image features, regarding the insulator with a preset color tone as an abnormal insulator, judging the temperature difference between the abnormal insulator and a normal insulator, and regarding the abnormal insulator as a zero-value insulator when the temperature difference meets a preset threshold; the normal insulator is an insulator with the same application environment as the abnormal insulator, the same position in the insulator string and normal working state.

[0006] In order to solve the above technical problems, another technical scheme adopted by the present application is: An insulator abnormality diagnosis terminal, comprising a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the following steps when executing the computer program: obtaining real-time humidity of an insulator application environment, and obtaining thermal image features of the insulator when the real-time humidity meets a preset humidity; The thermal image features are detected, and insulators whose thermal image features show a preset color tone are regarded as abnormal insulators. The temperature difference between the abnormal insulator and the normal insulator is determined. When the temperature difference meets a preset threshold, the abnormal insulator is regarded as a zero-value insulator. The normal insulator is an insulator that is used in the same environment as the abnormal insulator, is located in the same position in the insulator string, and is in normal working condition.

[0007] The beneficial effects of this invention are as follows: It provides an insulator anomaly diagnosis method and terminal. By acquiring thermal images of the insulator under specified humidity conditions, and using the color tone of the thermal image as a preliminary criterion, combined with a comparison with the temperature difference of insulators at the same position in the same string, stable identification of zero-value insulators is achieved. The principle is based on the fact that a decrease in insulation resistance alters the voltage distribution and leakage current on the insulator surface, causing the thermal image to appear noticeably darker or exhibit a unique color tone. Using insulators at the same position as a reference eliminates interference from light, wind speed, and overall temperature fluctuations, thus obtaining a reliable judgment. The above steps can be completed under energized conditions, without requiring power outages for zero-value measurements, significantly reducing the risks of high-altitude operations and improving inspection efficiency. Simultaneously, color tone determination can compensate for the missed detection problems caused by traditional reliance solely on temperature differences, improving the sensitivity for detecting and judging low-value and zero-value insulators. Attached Figure Description

[0008] Figure 1 This is a flowchart of an insulator anomaly diagnosis method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an insulator abnormality diagnosis terminal according to an embodiment of the present invention; Label Explanation: 1. An insulator anomaly diagnosis terminal; 2. A memory; 3. A processor. Detailed Implementation

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] Before detailing the embodiments of this application, some related concepts will first be explained: Zero-value insulators refer to insulators whose individual insulation resistance has dropped significantly, approaching zero or failing to provide effective insulation. Typical characteristics include a large imbalance in distributed voltage and a significant increase in leakage current, causing the insulator to no longer participate in voltage sharing. In thermal imaging, zero-value insulators often appear as an overall dark color or exhibit temperatures significantly lower than normal. This is because their insulation resistance is extremely low, and the imbalance in their internal electric field structure results in a thermal distribution completely different from that of normal insulators.

[0011] Low-value insulators refer to insulators whose individual insulation resistance has decreased significantly, but has not yet dropped to zero, and still retains some residual insulation capacity. Their insulation degradation exhibits a gradual characteristic, typically progressing from a slight decrease in insulation resistance to significant heat generation, and then to a peak heat generation.

[0012] Surface flashover risk refers to the possibility of surface discharge or flashover caused by the combined effects of creepage distance, electric field strength, and the degree of contamination moisture along the insulator surface. This risk is closely related to factors such as the amount of contamination on the insulator surface, air humidity, electric field distortion, and the degree of corona discharge. When contamination accumulates on the shed surface, the local electric field strengthens, and in high humidity environments, the contamination becomes wet, significantly reducing the effective creepage path and making the insulator more susceptible to corona discharge.

[0013] To at least solve the above problems, please refer to Figure 1 This invention provides a method for diagnosing insulator anomalies, comprising the following steps: The real-time humidity of the insulator's application environment is obtained, and when the real-time humidity meets the preset humidity, the thermal image features of the insulator are obtained. The thermal image features are detected, and insulators whose thermal image features show a preset color tone are regarded as abnormal insulators. The temperature difference between the abnormal insulator and the normal insulator is determined. When the temperature difference meets a preset threshold, the abnormal insulator is regarded as a zero-value insulator. The normal insulator is an insulator that is used in the same environment as the abnormal insulator, is located in the same position in the insulator string, and is in normal working condition.

[0014] As described above, the beneficial effects of this invention are as follows: by acquiring thermal images of insulators under specified humidity conditions, and using the color tone of the thermal image as a preliminary criterion, combined with a comparison with the temperature difference of insulators at the same position in the same string, stable identification of zero-value insulators is achieved. The principle is based on the fact that as insulation resistance decreases, the voltage distribution and leakage current on the insulator surface change, causing the thermal image to appear noticeably darker or with a unique color tone. Using insulators at the same position as a reference eliminates interference from light, wind speed, and overall temperature fluctuations, thus obtaining a reliable judgment. The above steps can be completed under energized conditions, without requiring power outages for zero-value measurement, significantly reducing the risk of high-altitude operations and improving inspection efficiency. Simultaneously, color tone determination can compensate for the missed detection problems caused by traditional reliance solely on temperature differences, improving sensitivity to low-value and zero-value insulators.

[0015] Specifically, the preset color tone is a dark color tone that appears on a thermal image, derived from experience.

[0016] In some implementations, the steps also include: The thermal image features are detected, and insulators whose thermal image features exhibit preset characteristics are considered low-value insulators. The thermal image features of the low-value insulator are continuously monitored. When the thermal image features of the low-value insulator lose the preset features, the low-value insulator is regarded as a zero-value insulator.

[0017] As described above, by continuously tracking the thermal image characteristics of low-value insulators, the system uses the characteristic trajectory of their temperature change from heating to peak and then to the disappearance of heating to determine whether they have further deteriorated into zero-value insulators. Analysis reveals that the principle is as follows: due to the irreversibility of insulation degradation, low-value insulators enter a thermal imaging blind zone after their insulation continues to decline. The heating characteristics suddenly disappear, and the overall image becomes dark. At this point, a single detection could easily be mistaken for normal, but trend analysis can identify this critical turning point. This mechanism can significantly improve the detection rate of zero-value insulators, avoid missed detections due to the disappearance of heating, and shift the insulator status from static judgment to dynamic analysis, thus identifying potential serious defects in advance and providing sufficient basis for maintenance. In actual operation and maintenance, monitoring a string of insulators for three consecutive months revealed that the iron cap first heated, then reached a peak, and subsequently the heating completely disappeared, leaving the overall image dark. Based on this, the system determined that the insulation had further deteriorated. Finally, a power outage and zero-value measurement confirmed that it had entered the zero-value range, effectively avoiding potential safety hazards.

[0018] In some embodiments, insulators whose thermal image features exhibit preset characteristics are considered low-value insulators, and the method further includes the following steps: The thermal image features are divided into iron cap area features and umbrella skirt area features based on the insulator structure features. When the iron cap area feature is the heat generation center in the thermal image features, the corresponding thermal image features are considered to meet the preset features.

[0019] As described above, by dividing the thermal image according to the structure of the insulator's iron cap area and skirt area, and using whether the iron cap area becomes the heat center as the criterion, an effective distinction can be made between electrical heating and pollution-related heating. The principle is that when insulation performance deteriorates, it mainly manifests as voltage concentration and localized electric field enhancement in the iron cap area, while skirt heating is mostly caused by surface contamination and has no direct relationship with internal insulation resistance. Structured analysis ensures a clearer physical orientation in the diagnostic process, avoiding misinterpreting skirt hotspots as insulation degradation and improving accuracy. This method is applicable to insulators of different models and batches, standardizing and stabilizing the criteria, and facilitating the unified application of UAV and manual inspection.

[0020] In some implementations, the preset humidity range is a relative humidity of 70%-80%.

[0021] Based on industry experience, insulators with temperatures more than 1K below normal levels exhibit a dark thermal image, indicating they are zero-value insulators. As the insulation resistance of individual porcelain insulators decreases further, the distributed voltage across the insulator also changes accordingly. Zero-value insulators, due to their unique infrared thermal image characteristics, are easily identified as such during infrared detection. However, the existence of a blind zone causes some zero-value insulators to be missed during infrared detection. To address this blind zone issue, through multiple experiments, the optimal detection humidity range was clearly set at 70% to 80% relative humidity, making the characteristics of zero-value insulators more prominent in thermal imaging detection. The principle is that the voltage distribution of the insulator string redistributes as humidity increases. When the humidity enters this range, the surface leakage current significantly increases, amplifying the color and temperature differences in the thermal image of zero-value insulators, thus compensating for the detection blind zone under low humidity conditions and improving the detection rate. Conducting inspections within the optimal humidity window improves the stability and repeatability of diagnostics, supporting the management of both batch drone inspections and manual retesting. In practical applications, year-long monitoring revealed that most zero-value insulators are difficult to distinguish when humidity is below 60%, but exhibit a significantly darker spectrum and temperature variations when humidity reaches approximately 75%, ultimately enabling the early identification of multiple potential hazards and fully demonstrating the engineering value of humidity settings.

[0022] In some implementations, the steps also include: Visual inspection is performed on the surface of the insulators. Based on the results of the visual inspection, insulators that meet the preset pollution level are selected, and the corona discharge state of the insulators that meet the preset pollution level is obtained. The surface flashover risk value of the corresponding insulator is evaluated based on the corona discharge state, and insulators whose surface flashover risk value exceeds the preset threshold are shut down.

[0023] As described above, by establishing a continuous correlation between visual detection of insulator surface contamination, ultraviolet corona detection, and surface flashover risk assessment, potential electrical risks caused by contamination can be identified before thermal anomalies occur. The principle is that surface contamination causes a decrease in the creepage distance of the insulator skirts and local electric field distortion. Corona discharge often occurs before temperature rise, therefore, ultraviolet characteristics can serve as an early risk signal. Combining this with surface flashover risk values ​​quantifies the anomaly, shifting maintenance decisions from experience-based to data-driven. This technology can effectively distinguish between high-risk insulators that truly require power outages and lightly contaminated targets that only need tracking, avoiding unnecessary power outages and reducing maintenance costs.

[0024] Please refer to Figure 2An insulator anomaly diagnosis terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3. When the processor 3 executes the computer program, it implements the steps of an insulator anomaly diagnosis method.

[0025] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for diagnosing insulator anomalies employs live-line inspection using a drone. First, the ambient humidity is collected in real-time. Once the relative humidity is detected to be within the optimal range of 70% to 80%, infrared thermography is initiated. This humidity range makes the voltage distribution changes of zero-value insulators more pronounced, resulting in a more prominent overall dark or sub-dark tone (preset tone) in the thermal image. The drone captures infrared thermal images of each string of insulators along a preset trajectory. The system intelligently identifies the tone characteristics of the thermal images, marking targets where the entire insulator is dark or exhibits an abnormal tone as abnormal insulators. Subsequently, the system automatically retrieves the thermal image temperature of a normal insulator at the same location in the same string as a comparison, calculating whether the temperature difference exceeds a preset threshold. If the condition is met, the insulator is determined to be a zero-value insulator.

[0026] During the analysis of all thermal images, the system further divides the thermal characteristics of each insulator into iron cap area and skirt area features according to structured rules, paying particular attention to whether the iron cap area is the heat center, in order to distinguish between electrical heating and localized temperature rise in the skirt caused by surface contamination. When some insulators show high brightness or temperature fluctuations in the iron cap area, the system identifies them as low-value insulators and automatically includes them in the tracking management, subsequently continuously acquiring their thermal image features during weekly fixed inspections. When it is found that the heat peak disappears and the overall temperature is lower than that of adjacent normal insulators and shows an overall darkening trend, it is judged that the insulation performance has further deteriorated and transformed into a zero-value insulator. Meanwhile, the system uses visible light to identify all insulators with obvious contamination deposits on their skirts, and further triggers the ultraviolet detection module to obtain their corona discharge status. When obvious corona spots are detected near the skirt, the system calculates the surface flashover risk value of the insulator and directly includes targets with risk values ​​exceeding a preset threshold into the high-risk list requiring power outage. If contamination exists but there are no corona characteristics, it is included in the light contamination tracking file, and the development of contamination is continuously observed through subsequent inspections. After several months of practical application, this comprehensive method successfully identified multiple zero-value insulators that were originally in the infrared blind zone under high humidity conditions, and detected the insulation degradation inflection point of low-value insulators in advance through trend tracking. At the same time, structural analysis avoids misjudgments caused by skirt hotspots, and ultraviolet detection is used to promptly lock in high-risk contamination flashover hazards. This achieves comprehensive, accurate, and predictable diagnosis of electrical defects, contamination status, and surface flashover risks of insulators under live inspection conditions, greatly improving inspection efficiency and safety.

[0027] Specifically, in a further extension of this embodiment, in order to enable the insulator anomaly diagnosis method to run through the entire process of inspection, tracking and handling, a closed-loop operation and maintenance strategy consisting of three parts—daily inspection, tracking diagnosis and anomaly handling—was established.

[0028] The system first incorporates all routine inspections into the live-line inspection system. By using drones to conduct visible light and infrared thermal imaging inspections simultaneously on cloudy days or after sunset but before it gets dark (when humidity is between 70% and 80%), more stable temperature and spectral characteristics can be obtained. With the high-frequency inspection capability of drones, the number of live-line inspection batches is significantly increased. Through the linkage and cooperation between various detection methods, an equivalent replacement for traditional power outage zero-point testing and hanging point inspection is achieved, thus completing the systematic optimization of the routine inspection strategy.

[0029] In terms of periodic inspections, the system formulates differentiated inspection plans according to different voltage levels. For example, 1000kV insulators need to be manually inspected with infrared and ultraviolet imaging before and after the peak summer season, and infrared inspection is carried out again in the middle of the peak summer season. The 500kV and 220kV insulators underwent infrared and ultraviolet testing in their respective batches according to the same pattern.

[0030] If a substation experiences no moderate or heavy rain for two consecutive months, the system will automatically trigger manual infrared and ultraviolet imaging detection tasks to address electric field changes caused by contamination buildup in a long-term dry environment. When abnormalities are detected in the thermal image, visible light, or ultraviolet imaging, the system will initiate a follow-up diagnostic process based on the type of anomaly. For insulators with severe surface contamination, drone infrared, manual infrared, and visible light re-tests will be completed within one week, and ultraviolet imaging will be conducted in a high-humidity environment within one month to observe corona discharge. If corona discharge is significant and the risk of surface flashover is assessed as high, the system will immediately initiate an anomaly handling process, involving power outage operations to clean the contaminants. For insulators with low risk, the system will perform monthly drone infrared and visible light inspections, supplemented by manual infrared and ultraviolet detection, until the thermal image characteristics return to normal before being removed from the diagnostic process.

[0031] If a low-value insulator is identified during the detection, the system will complete the retesting by drone and manual infrared within one week. After confirming the low value, it will be included in the abnormal log. The system will conduct dual temperature measurement by drone and manual infrared on a monthly basis and record the trend. The insulation resistance will be retested or the insulator will be replaced during the next power outage.

[0032] If an insulator is identified as having a zero-value insulator, the system immediately arranges for both drone and manual retesting and confirmation. Upon confirmation, a power outage is immediately requested to complete the insulation resistance verification and replacement, thereby ensuring that the zero-value insulator does not operate under energized conditions for an extended period. Through this strategy extension, this embodiment integrates diagnostic algorithms, detection methods, trend analysis, and on-site strategies into a unified management system. This enables automated triggering, closed-loop management, and proactive risk control throughout the entire process of insulator maintenance, from daily inspections to defect tracking and final disposal.

[0033] Please refer to Figure 2 Embodiment two of the present invention is as follows: An insulator anomaly diagnosis terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3. When the processor 3 executes the computer program, it implements the steps of an insulator anomaly diagnosis method.

[0034] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An insulator abnormality diagnosis method characterized by comprising: The method comprises the steps of: acquiring real-time humidity of an application environment of an insulator, and acquiring a thermal image feature of the insulator when the real-time humidity meets a preset humidity; detecting the thermal image feature, regarding an insulator with the thermal image feature presenting a preset color tone as an abnormal insulator, judging a temperature difference between the abnormal insulator and a normal insulator, and regarding the abnormal insulator as a zero-value insulator when the temperature difference meets a preset threshold; the normal insulator is an insulator that is applied in the same environment as the abnormal insulator, is located at the same position in an insulator string, and has a normal working state.

2. The abnormality diagnosis method of a bushing according to claim 1, characterized by The method further comprises the steps of: detecting the thermal image feature, regarding an insulator with the thermal image feature presenting a preset feature as a low-value insulator; continuously detecting the thermal image feature of the low-value insulator, and regarding the low-value insulator as a zero-value insulator when the thermal image feature of the low-value insulator loses the preset feature.

3. The abnormality diagnosis method of an insulator according to claim 2, characterized by Regarding an insulator with the thermal image feature presenting a preset feature as a low-value insulator, the method further comprises the steps of: dividing the thermal image feature into a cap region feature and a shed region feature based on an insulator structure feature, and regarding the corresponding thermal image feature as meeting the preset feature when the cap region feature is a heat center in the thermal image feature.

4. The abnormality diagnosis method of a bushing according to claim 1, characterized by The preset humidity is in a range of 70% to 80% in relative humidity.

5. The abnormality diagnosis method of an insulator according to claim 1, characterized by The method further comprises the steps of: performing visual detection on a surface of the insulator, screening out insulators meeting a preset pollution degree according to a result of the visual detection, acquiring a corona discharge state of the insulators meeting the preset pollution degree, evaluating a risk value of a surface flashover of the corresponding insulator according to the corona discharge state, and performing shutdown processing on the insulator whose risk value of the surface flashover exceeds a preset threshold.

6. An insulator abnormality diagnosis terminal comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized by, The processor implements the following steps when executing the computer program: acquiring real-time humidity of an application environment of an insulator, and acquiring a thermal image feature of the insulator when the real-time humidity meets a preset humidity; detecting the thermal image feature, regarding an insulator with the thermal image feature presenting a preset color tone as an abnormal insulator, judging a temperature difference between the abnormal insulator and a normal insulator, and regarding the abnormal insulator as a zero-value insulator when the temperature difference meets a preset threshold; the normal insulator is an insulator that is applied in the same environment as the abnormal insulator, is located at the same position in an insulator string, and has a normal working state.

7. The abnormality diagnosis terminal for an insulator according to claim 6, characterized by The method further comprises the steps of: detecting the thermal image feature, regarding an insulator with the thermal image feature presenting a preset feature as a low-value insulator; continuously detecting the thermal image feature of the low-value insulator, and regarding the low-value insulator as a zero-value insulator when the thermal image feature of the low-value insulator loses the preset feature.

8. The abnormality diagnosis terminal for an insulator according to claim 7, characterized by Regarding an insulator with the thermal image feature presenting a preset feature as a low-value insulator, the method further comprises the steps of: dividing the thermal image feature into a cap region feature and a shed region feature based on an insulator structure feature, and regarding the corresponding thermal image feature as meeting the preset feature when the cap region feature is a heat center in the thermal image feature.

9. The abnormality diagnosis terminal for an insulator according to claim 6, characterized by The preset humidity is in a range of 70% to 80% in relative humidity.

10. The abnormality diagnosis terminal for an insulator according to claim 6, characterized by The method further comprises the steps of: The surface of the insulator is visually detected, insulators meeting a preset pollution degree are screened according to a result of the visual detection, and a corona discharge state of the insulators meeting the preset pollution degree is obtained, a risk value of surface flashover of the corresponding insulator is evaluated according to the corona discharge state, and the insulator whose risk value of surface flashover exceeds a preset threshold is subjected to shutdown treatment.

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