Method and system for evaluating residual service life of transformer based on insulation deterioration evolution

Abstract

The application discloses a transformer residual service life evaluation method and system based on insulation deterioration evolution, relates to the technical field of power transformer state and life evaluation, and comprises the following steps: acquiring state data in the operation process of a transformer and determining a temperature distribution state; determining corresponding hot spot positions at different operation time according to the temperature distribution state, performing correlation analysis, and obtaining hot spot space migration characteristics; dividing the internal insulation area of the transformer into a plurality of deterioration evaluation areas based on the hot spot space migration characteristics, and determining the insulation deterioration state of each area according to the corresponding historical temperature effect of each area; calculating the residual service life according to the insulation deterioration state, and taking the minimum value of the life as the residual service life evaluation result of the whole transformer; and the application solves the problem that the prior art cannot reflect the insulation differentiation deterioration process of different space areas caused by hot spot migration, thereby causing the problem of insufficient life evaluation accuracy.

Description

Technical Field

[0001] This invention relates to the field of power transformer condition and life assessment technology, and more specifically, to a method and system for assessing the remaining service life of transformers based on insulation degradation evolution. Background Technology

[0002] Power transformers are crucial equipment in power systems, and their operational reliability directly impacts the safe and stable operation of the power grid. As transformers age, their insulation systems gradually deteriorate, and declining insulation performance is a significant cause of transformer failures. Therefore, analyzing the insulation degradation process and assessing the remaining service life of transformers is of great importance for implementing condition-based maintenance and improving operational reliability.

[0003] Currently, methods for assessing the remaining service life of transformers mainly include those based on thermal aging models, those based on insulation condition parameters, and data-driven methods. Among these, methods based on thermal aging models are the most widely used. These methods typically calculate cumulative life loss based on the winding hot spot temperature during transformer operation, combined with the insulation thermal aging characteristics, and further predict the remaining service life of the transformer. Existing methods usually use the winding hot spot temperature as an important basis for insulation aging assessment and assume that the location of the maximum hot spot remains unchanged.

[0004] However, in actual operation, the internal temperature distribution of a transformer is affected by factors such as load changes, ambient temperature changes, and cooling conditions. This causes variations in temperature rise across different areas of the windings, potentially leading to spatial migration of the hotspot location depending on operating conditions. Because existing lifespan assessment methods typically use fixed hotspot locations or single hotspot temperatures for insulation aging calculations, they fail to consider the migration characteristics of hotspot areas due to changes in operating conditions. This results in an inaccurate reflection of the differentiated degradation process of insulation materials at different spatial locations, leading to biases in the remaining service life assessment results. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for assessing the remaining service life of transformers based on insulation degradation evolution. This solves the problem that existing methods for assessing the remaining service life of transformers use fixed hotspot locations or single hotspot temperatures for life calculation, which fails to reflect the differential insulation degradation process in different spatial regions caused by hotspot migration, resulting in insufficient accuracy in life assessment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: Firstly, this application provides a method for assessing the remaining service life of a transformer based on insulation degradation evolution. This method includes: acquiring state data during transformer operation and determining the temperature distribution state inside the transformer as it changes over time; determining hotspot locations corresponding to different operating moments based on the temperature distribution state, and performing correlation analysis on hotspot locations at consecutive moments to obtain hotspot spatial migration characteristics; dividing the insulation region inside the transformer into multiple degradation assessment regions based on the hotspot spatial migration characteristics, and determining the insulation degradation state of each region according to its corresponding historical temperature effect; calculating the remaining service life of each region based on its insulation degradation state, and using the lowest remaining service life value as the overall remaining service life assessment result of the transformer.

[0007] In one embodiment, acquiring state data during transformer operation and determining the temperature distribution state inside the transformer as it changes over time includes: acquiring multi-source state data during the operation of the transformer to be evaluated and establishing a transformer state data sequence corresponding to the operating time; determining the heat source distribution state corresponding to different operating times based on the transformer state data sequence; determining the internal temperature transfer state of the transformer based on the heat source distribution state and the equipment heat dissipation state; and obtaining the internal space temperature distribution state of the transformer based on the temperature transfer state.

[0008] In one embodiment, determining the heat source distribution state corresponding to different operating times based on the transformer state data sequence includes: analyzing the changes of different loss sources inside the transformer with operating time based on the load state information in the state data sequence, wherein the loss sources include winding losses, core losses, and additional losses of structural components; mapping various loss sources to corresponding spatial regions based on the internal structural parameters of the transformer to obtain the heat generation state of each region with operating time; and correlating the heat generation of each spatial region at each operating time in a time sequence to form a dynamic heat source distribution state that includes spatial location, heat source intensity, and its changing trend.

[0009] In one embodiment, determining the internal temperature transfer state of the transformer based on the heat source distribution state and the equipment heat dissipation state includes: extracting heat input information of each heat-generating area based on the heat source distribution state, and combining the transformer's internal structural parameters and heat dissipation state information to determine the heat transfer channels and heat exchange relationships between different spatial areas; performing spatial conduction analysis on the heat input of each heat-generating area according to the heat transfer channels and heat exchange relationships to obtain temperature transfer correlation information between different spatial areas; and performing correlation analysis on the changes in heat source input and heat dissipation conditions at each time point during continuous operation to determine the temperature change process of each spatial area with operating time, forming a dynamic temperature transfer state that includes time change characteristics and spatial correlation characteristics.

[0010] In one embodiment, determining the hotspot locations corresponding to different operating times based on the temperature distribution status includes: establishing spatial temperature distribution data containing spatial coordinates, operating time, and temperature values ​​based on the dynamic temperature distribution status, and dividing the internal space of the transformer into regions to obtain each temperature analysis region; extracting the temperature status of each temperature analysis region at each operating time, and determining the region with the highest temperature value and meeting the preset temperature rise condition as a hotspot candidate region; performing correlation analysis on the temperature changes of adjacent times during continuous operation to determine whether the hotspot candidate region continuously meets the highest temperature region determination condition within the preset continuous time node; if so, determining the region as the hotspot location for the corresponding operating time; and establishing a hotspot location sequence according to the chronological order to obtain hotspot location information characterizing the change of the highest temperature region inside the transformer with operating time.

[0011] In one embodiment, correlation analysis is performed on hotspot locations at consecutive times to obtain hotspot spatial migration characteristics. This includes: associating hotspot locations at different operating times with their corresponding hotspot temperature values ​​and operating times to establish a hotspot state sequence; based on the hotspot state sequence, spatial matching is performed on hotspot locations at adjacent operating times to determine the spatial position change state of the hotspot during continuous operation, and the hotspot temperatures at adjacent times are compared to determine the heat intensity change state; the duration of hotspot action in different spatial regions is statistically analyzed, and combined with the hotspot temperature and temperature change amount corresponding to each region, the thermal effect change process experienced by each spatial region during hotspot migration is determined; the spatial position change state, heat intensity change state, and thermal effect change process of each region are fused to construct a hotspot spatial migration characteristic that includes spatial position change characteristics, heat intensity change characteristics, and time accumulation characteristics.

[0012] In one embodiment, based on hotspot spatial migration characteristics, the internal insulation area of ​​the transformer is divided into multiple degradation assessment areas, including: acquiring thermal effect data of each insulation space location during hotspot migration based on the hotspot spatial migration characteristics; statistically analyzing the cumulative thermal effect information of each insulation space location based on the thermal effect data, and comparing the differences in thermal effect characteristics between different insulation space locations; and classifying insulation space locations with the same thermal effect characteristics into the same degradation assessment area based on the differences in thermal effect characteristics.

[0013] In one embodiment, the insulation degradation state of each region is determined based on the historical temperature effects corresponding to each region. This includes: acquiring historical temperature effects information of each degradation assessment region during continuous operation, and calculating the duration of each region's effects at different temperature levels to determine the cumulative thermal effects of each region; correcting the cumulative thermal effects based on the heat resistance characteristics of the insulation material corresponding to each degradation assessment region to obtain the degree of regional thermal aging influence; and determining the insulation degradation state corresponding to each degradation assessment region based on the degree of regional thermal aging influence to form regional insulation degradation state distribution information.

[0014] In one embodiment, the remaining service life of each insulation region is calculated based on its insulation degradation state, and the lowest value among these is taken as the overall remaining service life assessment result of the transformer. This includes: establishing an insulation degradation state change sequence for each degradation assessment region based on regional insulation degradation state distribution information, and determining the corresponding insulation degradation development rate for each region; determining the degradation evolution stage of each region based on the changing trend of the degradation development rate over continuous operation time, and performing nonlinear correction on the insulation degradation development rate to obtain the corrected regional degradation development rate; determining the remaining service life of each insulation region based on the current degree of thermal aging impact, the preset insulation failure threshold, and the corrected regional degradation development rate; comparing the remaining service lives of each insulation region, and taking the lowest value among them as the overall remaining service life assessment result of the transformer.

[0015] Secondly, this application provides a transformer remaining service life assessment system based on insulation degradation evolution. The system includes: a temperature state determination module, used to acquire state data during transformer operation and determine the temperature distribution state inside the transformer as operating time changes; a hotspot migration analysis module, used to determine the hotspot locations corresponding to different operating times based on the temperature distribution state, and to perform correlation analysis on the hotspot locations at consecutive times to obtain hotspot spatial migration characteristics; an insulation degradation assessment module, used to divide the internal insulation area of ​​the transformer into multiple degradation assessment areas based on the hotspot spatial migration characteristics, and to determine the insulation degradation state of each area based on the historical temperature effects corresponding to each area; and a service life assessment module, used to calculate the remaining service life of each area based on its insulation degradation state, and to take the lowest service life value as the overall remaining service life assessment result of the transformer.

[0016] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: By introducing hotspot spatial migration characteristics, the assessment of the transformer's internal insulation area is transformed from a holistic evaluation to a differentiated evaluation based on the thermal history of different spatial areas. This approach fully considers the non-uniform thermal aging effects of hotspot location changes on different insulation areas during operation. Simultaneously, by calculating the remaining service life based on the insulation degradation state of each degradation assessment area and using the area with the lowest service life as the basis for overall service life assessment, weak insulation areas that limit the safe operation of the transformer can be identified. This avoids the assessment bias caused by traditional methods that rely solely on overall average temperature or fixed hotspot locations for service life prediction, thereby improving the accuracy, reliability, and adaptability to changes in actual operating conditions in transformer remaining service life assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the transformer remaining service life assessment method based on insulation degradation evolution provided in this application embodiment.

[0019] Figure 2 A schematic diagram of the transformer remaining service life assessment system based on insulation degradation evolution provided in this application embodiment.

[0020] Figure 3 This is a schematic diagram of the dynamic temperature distribution inside a transformer provided in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the distribution of insulation regional degradation states provided in an embodiment of this application.

[0022] Figure 5 The curve showing the comparison between the remaining service life prediction results obtained by the method provided in this application embodiment and the traditional fixed hot spot temperature life assessment method is shown. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Reference Figure 1 As shown in the diagram, the transformer remaining service life assessment method based on insulation degradation evolution provided by this invention includes the following steps: S1, acquire the status data during the operation of the transformer, and determine the temperature distribution state inside the transformer as the operating time changes based on the status data.

[0025] In this embodiment, acquiring state data during transformer operation and determining the temperature distribution state inside the transformer as it changes over time based on the state data includes: Acquire multi-source status data during the operation of the transformer to be evaluated, including load status data, environmental status data, and equipment operating status data; The load status data includes transformer operating current, load rate, and load change information; the environmental status data includes ambient temperature, ambient humidity, and heat dissipation conditions; and the equipment operating status data includes oil temperature data, cooling device operating status, and transformer structural parameters.

[0026] Based on the obtained multi-source state data, a transformer state data sequence corresponding to the operating time is established; The heat source distribution state at different operating times is determined based on the transformer state data sequence. The internal temperature transfer state of the transformer is determined based on the heat source distribution state and the equipment heat dissipation state. The internal temperature distribution of the transformer is obtained based on the temperature transfer state. Based on the obtained temperature transfer state, the temperature state corresponding to each spatial location inside the transformer at different operating times is determined, forming a dynamic temperature distribution state that includes spatial location information and time change information. The temperature distribution state is used to characterize the temperature change process of different locations inside the transformer as the operating time changes.

[0027] Further, determining the heat source distribution state at different operating times based on the transformer state data sequence includes: After acquiring the state data sequence during transformer operation, the operating conditions of the transformer at different operating times are first determined based on the load state information in the state data sequence. Specifically, the acquired transformer operating current, operating voltage, and load rate data are processed for time synchronization, so that different types of operating data correspond to the same time node, forming a state data sequence indexed by operating time. Based on the load state change information in the state data sequence, the changes in transformer internal losses with operating conditions are analyzed. Since the heat inside the transformer mainly comes from the heat generated by electrical energy loss, the loss change state at different operating times is determined according to the load state. This loss change state includes changes in winding losses caused by changes in winding current, changes in core losses caused by changes in magnetic flux, and changes in additional structural losses caused by leakage magnetic field. By distinguishing different loss sources, basic information on the generation of heat inside the transformer at different operating times is obtained. A correspondence between loss sources and spatial locations is established based on the transformer's internal structural parameters. These parameters include the winding spatial location, core arrangement location, oil duct distribution location, and the location of structural components affecting heat generation and transfer. Based on the generation areas corresponding to different loss types, the loss variation states are mapped to corresponding spatial areas within the transformer, thus assigning each loss source to a specific heat-generating area. For example, winding losses are mapped to the winding conductor area, core losses to the core area, and structural component-related losses to the area where the structural component is located, thereby obtaining the heat generation state of different spatial areas at the current operating moment. After obtaining the heat generation status corresponding to each spatial region, a correlation analysis is performed on the changes in heat generation of each spatial region at different operating times based on the continuity of time, to determine the relationship between the heat source intensity of each spatial region and the operating time. Specifically, the heat generation corresponding to each spatial region at each operating time is arranged in chronological order to form a heat source information sequence corresponding to the spatial region and the time change, so as to characterize the dynamic change process of heat source intensity at different locations inside the transformer as the operating status changes; Based on the heat source information sequence, the dynamic heat source distribution state inside the transformer is further determined. This dynamic heat source distribution state includes the location, intensity, and trend of heat sources at different time points, describing how the location of heat generation inside the transformer changes with operating conditions. Because the load level, cooling status, and environmental conditions constantly change during transformer operation, the heat generation capacity of different areas also changes accordingly. Therefore, the dynamic heat source distribution state can reflect the spatiotemporal variation characteristics of heat sources inside the transformer.

[0028] Furthermore, determining the internal temperature transfer state of the transformer based on the heat source distribution state and the equipment heat dissipation state includes: Based on the heat source distribution, the location and corresponding heat source intensity of each heating area inside the transformer are extracted at different operating times to determine the heat input information of each heating area under the corresponding operating conditions. Based on the heat input information corresponding to the heat-generating area, the structural parameter information and heat dissipation status information of the transformer for heat transfer are obtained. The structural parameter information includes the winding arrangement, insulation layer distribution, oil channel spatial position and tank structure information. The heat dissipation status information includes the transformer oil circulation status, cooling device operation status and environmental heat exchange conditions. Based on the structural parameter information and heat dissipation status information, determine the heat transfer channels between different spatial regions and the heat exchange relationship between each spatial region; Specifically, the spatial connection relationship between each heat-generating area and the heat-receiving area inside the transformer is determined based on the structural parameter information, and the heat transfer path and heat transfer intensity in the solid medium conduction, insulating oil flow heat transfer and external cooling heat dissipation process are determined in combination with the heat dissipation state information, thereby obtaining the heat transfer channel and heat exchange relationship between different spatial areas.

[0029] Based on the heat transfer channels and heat exchange relationships, the heat input information is analyzed according to the spatial location relationship to determine the conduction, convection and heat dissipation processes of heat between different areas inside the transformer, and to establish the temperature influence relationship between the heat-generating area and the surrounding heated area in order to obtain temperature transfer correlation information between different spatial areas. Based on the temperature transfer correlation information, correlation analysis is performed on the changes in heat source input and heat dissipation conditions at different time points during continuous operation to determine the temperature change process of each spatial region with operating time, forming a temperature transfer state that includes time change characteristics and spatial correlation characteristics. Based on the temperature transfer status, the heat input, heat transfer, and heat loss processes of each space region inside the transformer at different operating times are comprehensively correlated to obtain temperature transfer information that characterizes the dynamic temperature change process inside the transformer, and the temperature transfer information is used as the basic data for determining the dynamic temperature distribution state inside the transformer.

[0030] like Figure 3 The diagram shows the temperature distribution inside a transformer during a specific continuous operating time. The horizontal axis represents the internal space of the transformer, and the vertical axis represents the operating time. The shades of color in different areas indicate the temperature at the corresponding locations. Figure 3 It can be seen that as operating time increases, the high-temperature region inside the transformer does not remain fixed in the same spatial location, but rather changes dynamically under the influence of load variations and changes in heat dissipation conditions, forming a temperature distribution state that evolves over time. By establishing this dynamic temperature distribution state, a data foundation can be provided for subsequent hotspot location identification and hotspot spatial migration analysis.

[0031] It should be noted that by acquiring multi-source state data during transformer operation and combining it with load changes, equipment structural parameters, and heat dissipation conditions, spatiotemporal correlation analysis is performed on the generation, transfer, and temperature evolution of internal heat sources in the transformer. This achieves a shift from traditional fixed hotspot temperature analysis to dynamic heat source distribution and dynamic temperature field evolution analysis. This approach not only considers the impact of load fluctuations, cooling state changes, and environmental factors on the internal temperature distribution during transformer operation, but also accurately reflects the changes in heat source intensity and temperature state over time in different spatial regions. This improves the accuracy of hotspot location identification within the transformer, providing more realistic and continuous temperature data for subsequent analysis of hotspot spatial migration processes and insulation degradation evolution patterns. It avoids the insulation aging assessment bias caused by existing technologies based on single hotspot temperatures or fixed temperature models, thus improving the accuracy and reliability of transformer remaining service life assessment.

[0032] S2, determine the hot spot locations corresponding to different operating times based on the temperature distribution state, and perform correlation analysis on the hot spot locations under continuous operating times to obtain the spatial migration characteristics of hot spots inside the transformer.

[0033] In this embodiment, determining the hotspot locations corresponding to different operating times based on the temperature distribution status includes: Based on the obtained dynamic temperature distribution, temperature information corresponding to different spatial locations inside the transformer at each time node is extracted in the order of operation time. Based on the correspondence between the spatial location and the temperature information, spatial temperature distribution data containing spatial coordinate information, operation time information and temperature value information is established. Based on the spatial temperature distribution data, according to the internal structural arrangement of the transformer and the distribution of insulation areas, the internal space of the transformer is divided into regions. Spatial locations with the same structural features or temperature change features are assigned to the same temperature analysis region, and the correlation between each temperature analysis region and the corresponding spatial location is established. Based on the correlation between the temperature analysis area and the spatial location, the temperature state of each temperature analysis area at different operating times is extracted, and the temperature change of each area is determined according to the temperature difference between each temperature analysis area. Based on the comparison of the temperature state of each temperature analysis area at the same operating time, the temperature analysis area with the highest temperature value and that meets the preset temperature rise condition is determined as the hot spot candidate area. The preset temperature rise condition is determined based on the allowable temperature rise range under the rated operating conditions of the transformer, and is used to determine whether the temperature analysis area meets the hot spot identification requirements. Specifically, the temperature value of the temperature analysis area at each operating time is compared with the preset temperature rise threshold. When the temperature value corresponding to a certain temperature analysis area is greater than or equal to the preset temperature rise threshold, the temperature analysis area is determined as a hot spot candidate area.

[0034] Based on the hotspot candidate region, a correlation analysis is performed on the temperature changes corresponding to adjacent operating times during continuous operation to determine whether the hotspot candidate region continuously exhibits the highest temperature region, and the hotspot location corresponding to each operating time is determined based on the correlation analysis results. The correlation analysis of temperature changes corresponding to adjacent running times during continuous operation refers to comparing the temperature values ​​and spatial locations of each hotspot candidate region at adjacent sampling times according to the running time sequence, and determining whether the temperature value of the same hotspot candidate region is still higher than the temperature values ​​of other temperature analysis regions at adjacent time nodes. When the hotspot candidate region meets the highest temperature region determination condition within the preset continuous time nodes, the hotspot candidate region is determined as the hotspot location of the corresponding running time.

[0035] Based on the hotspot locations corresponding to different operating times, a hotspot location sequence is established in chronological order to obtain hotspot location information that characterizes the change of the highest temperature region inside the transformer with operating time. The hotspot location information is used for subsequent analysis of hotspot spatial migration characteristics.

[0036] Furthermore, correlation analysis was performed on the hotspot locations during continuous operation to obtain the spatial migration characteristics of hotspots inside the transformer, including: Based on the hotspot location information corresponding to different running times, the hotspot spatial location corresponding to each time node is extracted in chronological order of running time, and the hotspot temperature value and hotspot duration information corresponding to the hotspot location are obtained. The hotspot spatial location, hotspot temperature value and running time under the same running time are associated to establish a hotspot state sequence containing spatial location information, temperature intensity information and time information. Based on the hotspot state sequence, the hotspot spatial location information corresponding to adjacent running times is matched, the hotspot locations corresponding to adjacent time nodes are spatially associated, and the hotspot migration distance is determined based on the spatial coordinate difference between the hotspot spatial locations corresponding to adjacent time nodes, so as to obtain the hotspot spatial location change status during continuous operation. Based on the hotspot spatial location change status, the hotspot temperature intensity information corresponding to adjacent operating times is compared to determine the hotspot temperature change between adjacent time nodes, and the heat intensity change status during hotspot migration is judged based on the hotspot temperature change. Specifically, when the hotspot temperature change corresponding to adjacent operating times is positive, the hotspot migration process is determined to be a state of increased heat intensity; when the hotspot temperature change is negative, the hotspot migration process is determined to be a state of decreased heat intensity; and when the hotspot temperature change is within a preset temperature change range, the hotspot migration process is determined to be a state of stable heat intensity. The preset temperature change range is determined based on the temperature measurement error range under normal transformer operation and the sampling accuracy of the temperature monitoring device. The condition that the hot spot temperature change between adjacent operating times is less than or equal to the temperature measurement error range is used as the criterion for determining the stable state of thermal intensity.

[0037] Based on the changes in the spatial location of the hotspot and the changes in the thermal intensity, the occurrence time of the hotspot in the same spatial area during continuous operation is statistically analyzed to determine the duration of the hotspot's effect in different spatial areas. Furthermore, based on the correspondence between the changes in the hotspot's location, temperature, and duration of effect in different spatial areas during the hotspot migration process, the process of change in the hotspot's thermal effect on different spatial areas is determined. Specifically, based on the correspondence between the hotspot location changes, hotspot temperature changes, and duration of action in different spatial regions during hotspot migration, the spatial regions traversed by the hotspot are associated according to the operating time sequence. Each spatial region is treated as an independent thermal object, and the hotspot temperature value, temperature change, and hotspot dwell time corresponding to that spatial region during the hotspot's passage are obtained. The hotspot temperature value characterizes the intensity of thermal action received by the spatial region, the hotspot dwell time characterizes the duration of thermal action received by the spatial region, and the temperature change characterizes the trend of thermal action intensity over time. Furthermore, the thermal action intensity, duration, and trend corresponding to the same spatial region are combined to determine the thermal action start time, intensity change, and cumulative process of different spatial regions during hotspot migration, thereby obtaining a thermal action change process characterizing the dynamic influence of the hotspot on each spatial region.

[0038] Based on the changes in the thermal effects of hot spots on different spatial regions, the spatial location, temperature intensity, and duration of hot spots are correlated in chronological order according to the operating time to construct a three-dimensional hot spot spatial migration feature that includes spatial location change characteristics, thermal intensity change characteristics, and time accumulation characteristics. The hot spot spatial migration feature is used to characterize the spatial migration process of the hot spot area inside the transformer as the operating state changes, as well as the dynamic change process of thermal intensity during the migration process.

[0039] It should be noted that by combining the dynamic temperature distribution state inside the transformer, the hotspot locations at different operating times are identified. Furthermore, multi-dimensional information such as changes in hotspot spatial location, temperature intensity, and duration of action is integrated to construct a three-dimensional hotspot spatial migration feature encompassing "location-intensity-time." Compared to existing technologies that only determine fixed hotspots based on the highest temperature region or only analyze the spatial movement trajectory of hotspots, this method can more comprehensively describe the spatial migration and thermal evolution of hotspot areas during transformer operation caused by changes in load and heat dissipation conditions. Simultaneously, by analyzing the thermal intensity, duration, and trends experienced by different spatial regions during hotspot migration, the non-uniform thermal aging process experienced by different insulation regions can be accurately reflected. This avoids biases in insulation degradation assessment caused by neglecting changes in hotspot intensity and the cumulative effect over time. This provides a more reliable data foundation for subsequently establishing insulation degradation evolution considering the impact of hotspot spatial migration and improving the accuracy of transformer remaining service life assessment.

[0040] S3. Based on the hotspot spatial migration characteristics, the internal insulation area of ​​the transformer is divided into multiple deterioration assessment areas, and the insulation deterioration status of each area is determined according to the historical temperature action process corresponding to each deterioration assessment area.

[0041] In this embodiment, based on the hotspot spatial migration characteristics, the internal insulation region of the transformer is divided into multiple degradation assessment regions, including: Based on the hotspot spatial migration characteristics, the spatial location traversed by the hotspot during continuous operation, the hotspot temperature intensity at the corresponding operating time, and the duration of the hotspot's effect at each spatial location are obtained. Based on the correspondence between the hotspot spatial location and the insulation area spatial location, the thermal effect data corresponding to each insulation spatial location during the hotspot migration process are determined. The step of determining the thermal effect data corresponding to each insulation space location based on the correspondence between the hotspot spatial location and the insulation area spatial location refers to matching the hotspot spatial coordinates at each moment during continuous operation with the pre-established spatial coordinates of the insulation area inside the transformer. When the hotspot location falls within the range of a certain insulation space area or the spatial distance between it and a certain insulation space area is less than a preset spatial association range, the hotspot temperature value, hotspot temperature change, and hotspot duration of action at that moment are assigned to the corresponding insulation space area, forming thermal effect data to characterize the thermal impact on the insulation space area during the hotspot migration process. The thermal effect data includes hotspot action temperature information, thermal effect duration information, and hotspot action frequency information. The hotspot action temperature information is used to indicate the temperature intensity when the hotspot exerts a thermal effect on the insulation space area. The thermal effect duration information is used to indicate the length of time the insulation space area is continuously affected by the hotspot. The hotspot action frequency information is used to indicate the number of times the insulation space area is passed by or affected by the hotspot during continuous operation.

[0042] Based on the thermal effect data corresponding to each insulation space location, the cumulative thermal effect information corresponding to each insulation space location is determined by statistically analyzing the hot spot temperature intensity, duration of action, and number of passes at different insulation space locations. The cumulative thermal effect information is used to characterize the cumulative degree of thermal influence of the hot spot on the corresponding insulation space location during continuous operation. Based on the accumulated thermal effect information, the degree of hot spot influence on different insulation space locations is compared. According to the differences between the thermal effect intensity, thermal effect duration and the number of accumulated thermal effects corresponding to each space location, the differences in thermal effect characteristics between different insulation space locations inside the transformer are determined. The comparison of the degree of influence of hot spots on different insulation spatial locations refers to extracting the corresponding thermal effect intensity, thermal effect duration, and number of hot spot effects for each insulation spatial location based on the cumulative thermal effect information, and comparing these parameters one by one for different spatial locations according to the same evaluation dimension. Specifically, the hot spot effect temperature value corresponding to each insulation spatial location is used as the basis for comparing thermal effect intensity, the cumulative duration of hot spot influence is used as the basis for comparing thermal effect duration, and the cumulative number of times hot spots pass through or act on the spatial location is used as the basis for comparing thermal effect frequency. By comparing the differences in the parameters between different spatial locations, the relative relationship of the degree of influence of hot spots on each insulation spatial location is determined. When the thermal effect intensity, duration, or number of effects corresponding to a certain spatial location is higher than that of other spatial locations, it is determined that the spatial location has a higher degree of thermal effect, and the difference in thermal effect characteristics between different insulation spatial locations is determined based on the comprehensive differences of multiple comparison parameters.

[0043] Based on the differences in thermal effects, the internal insulation space of the transformer is divided according to the thermal effects. Insulation space locations with the same thermal effects are assigned to the same degradation assessment area. The degradation assessment area is used to characterize insulation areas that have undergone similar thermal effects during hot spot migration. Based on the degradation assessment area division results, the insulation structure type, spatial location and cumulative thermal effect information corresponding to each degradation assessment area are obtained, and the correlation between each degradation assessment area and the corresponding insulation condition evaluation object is established, so that different degradation assessment areas correspond to different thermal effect histories and insulation degradation evolution processes. Based on the insulation condition evaluation objects corresponding to each degradation assessment area, a regional insulation degradation assessment unit is established, which includes regional spatial location, hot spot intensity and time accumulation characteristics, so as to determine the corresponding insulation degradation state according to the different thermal effects on different degradation assessment areas.

[0044] It should be noted that by combining the spatial migration characteristics of hotspots, the traditional method of dividing insulation regions based on fixed structural locations is transformed into a dynamic method based on actual thermal history. Based on information such as the intensity, duration, and number of thermal effects corresponding to different insulation spatial locations during hotspot migration, cumulative thermal effect information characterizing the differences in heating in each region is established. This results in multiple degradation assessment regions with different thermal aging characteristics, enabling the assessment of transformer internal insulation status to fully consider the spatial non-uniform thermal effects caused by hotspot movement. Compared to existing methods that use single hotspot temperatures or the entire insulation region for life assessment, this scheme can accurately reflect the differentiated temperature effects experienced by different insulation regions, improving the accuracy of insulation degradation status identification and providing a more regionalized assessment basis that conforms to actual operating conditions for subsequent remaining service life prediction.

[0045] Furthermore, the insulation degradation state of each degradation assessment area is determined based on the historical temperature action process corresponding to that area, including: Based on the regional insulation degradation assessment unit corresponding to each degradation assessment area, the historical temperature effect information of each degradation assessment area during continuous operation is obtained. The historical temperature effect information includes the regional temperature value, temperature change and duration of the corresponding temperature state at each operating moment. The historical temperature effect information is used to characterize the temperature change process and thermal effect duration process experienced by the corresponding deterioration assessment area during hotspot migration.

[0046] Based on the historical temperature effect information, a historical temperature effect sequence corresponding to each degradation assessment area is established according to the operating time sequence, and the temperature state of the corresponding area at each operating time is obtained according to the historical temperature effect sequence; the temperature state of the area is compared with the allowable operating temperature range of the corresponding insulation structure; when the temperature of the area exceeds the allowable operating temperature range, it is determined that the area corresponding to the operating time is in a state of thermal aging influence, and the duration of action corresponding to different temperature levels is counted. Based on the temperature level, duration of action, and number of occurrences corresponding to the thermal aging influence state, the cumulative degree of thermal effect on each deterioration assessment area is calculated. The area temperature value represents the intensity of thermal effect per unit time, the duration of action represents the cumulative time of thermal effect, and the number of hotspot effects represents the situation in which the area is repeatedly passed through or repeatedly affected by hotspots during the hotspot migration process. The specific formula for calculating the cumulative thermal effect is as follows:

[0047] In the formula, The cumulative thermal effect degree corresponds to the i-th degradation assessment region, which is used to characterize the cumulative degree of hot spot thermal effect on this region throughout the entire operation process. This represents the total number of temperature samples taken during continuous operation. Let be the temperature value of the i-th degradation assessment region at the t-th operating time. Here, represents the reference temperature of the insulation material corresponding to the i-th degradation assessment region, used to indicate the reference temperature when this insulation region is not significantly affected by thermal aging. This represents the reference heat resistance temperature of the insulation material corresponding to the i-th degradation assessment region. This is a temperature-weighted coefficient used to characterize the nonlinear effect of temperature rise on the degree of thermal insulation. Let be the duration of the hotspot effect on the i-th degradation assessment region during the t-th operating time interval. This represents the number of times the i-th deterioration assessment region is affected by hotspots during the t-th time period.

[0048] Based on the cumulative thermal effect, the thermal withstand capability of each region is determined according to the insulation structure type and heat resistance characteristics of the insulation material corresponding to each degradation assessment region, and the cumulative thermal effect is corrected. Specifically, a material heat resistance correction coefficient is determined based on the reference temperature, allowable heat resistance temperature, and reference temperature of the insulation material corresponding to each degradation assessment region. The material heat resistance correction coefficient is then correlated with the cumulative thermal effect of the corresponding region to obtain the degree of regional thermal aging influence considering the differences in the heat resistance of the insulation material. This allows regions subjected to the same hot spot but with different insulation heat resistance capabilities to obtain different insulation degradation evaluation results. The specific formula for calculating the heat resistance correction factor of the material is as follows:

[0049] In the formula, This is a material heat resistance correction factor, used to reflect the differences in the ability of different insulating materials or insulating structures to withstand the same thermal effects. The reference insulation temperature threshold is a reference temperature used to uniformly evaluate the degree of thermal aging effects in different areas. It can be determined based on the rated insulation operating temperature in the transformer design standard.

[0050] The specific calculation formula for the degree of thermal aging impact in the aforementioned area is as follows:

[0051] In the formula, The degree of regional thermal aging influence is used to characterize the actual equivalent thermal aging influence of each deterioration assessment area after considering different insulation material types, insulation structure heat resistance and regional thermal effects. It corrects the cumulative thermal effect by adjusting the material heat resistance characteristics so that different insulation areas can obtain deterioration evaluation results that match their own heat resistance under the same thermal effects.

[0052] Based on the degree of influence of regional thermal aging, the insulation degradation state corresponding to each degradation assessment area is determined, and a correspondence between the regional location and the degree of influence of regional thermal aging is established according to the spatial relationship of the degradation assessment areas, forming regional insulation degradation state distribution information that includes the differences in the degree of degradation of different spatial areas; wherein, the regional insulation degradation state distribution information is used to represent the differentiated insulation degradation evolution state generated after different insulation areas are affected by different thermal effects during the hot spot migration process.

[0053] Specifically, determining the insulation degradation state of each degradation assessment area based on the degree of regional thermal aging influence means comparing the degree of regional thermal aging influence of each degradation assessment area with a pre-set insulation degradation judgment range, mapping the degree of regional thermal aging influence to the corresponding insulation degradation level, and thus determining the current insulation degradation state of each area. Specifically, based on the thermal aging law of insulation materials during long-term operation, the degree of regional thermal aging influence is divided into multiple continuous degradation intervals. When the degree of regional thermal aging influence is in a low interval, the corresponding area is determined to be in a slightly degraded state; when it is in a medium interval, the corresponding area is determined to be in a moderately degraded state; and when it reaches a high interval, the corresponding area is determined to be in a severely degraded state. The judgment results are then correlated based on the insulation structure type, cumulative thermal effect, and degree of thermal aging influence of each degradation assessment area to form a regional insulation degradation state characterizing the degree of insulation performance degradation in different spatial areas.

[0054] The insulation degradation judgment range is determined based on the type of insulation material, the initial state of insulation, and the design and operating conditions of the transformer. The setting method is to divide the degree of thermal aging influence of regional insulation corresponding to different degradation assessment areas into multiple level intervals in order from small to large. Each level interval corresponds to an insulation degradation state, so as to realize the determination of discrete insulation degradation state based on the continuously changing degree of thermal aging influence.

[0055] like Figure 4 The diagram shown illustrates the distribution of insulation regionalization degradation states based on hotspot migration characteristics. The diagram divides the transformer's internal insulation into multiple degradation assessment regions, and determines the degradation state level of each region based on the cumulative thermal effect and material heat resistance correction results. Figure 4 It can be seen that due to the differences in the number of times, temperature and duration of hot spot exposure in different areas, each insulation area exhibits a different degree of degradation. The degradation level of the area where the hot spot has been present for a long time is significantly higher than that of other areas, indicating that this method can reflect the spatially differentiated insulation aging process caused by hot spot migration.

[0056] It should be noted that by combining the historical temperature effects on different degradation assessment areas during the hotspot migration process, a cumulative thermal effect evaluation method is established that considers temperature intensity, duration of action, and number of hotspot repetitions. Furthermore, this method is modified by incorporating the influence of different insulation structure types and material heat resistance characteristics on the degree of thermal effect, thereby achieving differentiated assessment of the insulation degradation state in different spatial regions within the transformer. Compared to existing methods based on single hotspot temperature or overall insulation state, this scheme fully considers the non-uniform thermal effect process caused by hotspot migration, accurately reflects the degradation differences resulting from different insulation regions experiencing different thermal histories, and avoids assessment biases caused by neglecting localized long-term heating, repeated heating, and differences in material heat resistance. Simultaneously, it forms regional insulation degradation state distribution information that includes the correspondence between spatial location and degradation degree, providing a more reliable data foundation for improving the accuracy of transformer remaining service life prediction.

[0057] S4. Determine the remaining service life of each insulation region based on the insulation degradation status of the region, and take the service life value corresponding to the insulation region with the lowest remaining service life as the evaluation result of the remaining service life of the transformer.

[0058] In this embodiment, the remaining service life of each insulation region is determined based on the insulation degradation state of the region, and the service life value corresponding to the insulation region with the lowest remaining service life is used as the transformer's remaining service life assessment result, including: Based on the regional insulation degradation state distribution information, the degree of regional thermal aging influence, spatial location information and corresponding historical operating time information of each degradation assessment area are obtained, and the insulation degradation state change sequence corresponding to each degradation assessment area is established according to the operating time sequence. The insulation degradation state change sequence is used to characterize the degradation process of insulation performance of each insulation region over time under the influence of differentiated thermal effects caused by hot spot migration. Based on the insulation degradation state change sequence, the change in the degree of regional thermal aging influence corresponding to adjacent operating time nodes is extracted, and the insulation degradation development rate corresponding to each degradation assessment area is determined according to the relationship between the change in the degree of regional thermal aging influence and the corresponding time interval. The insulation degradation rate is used to represent the degree of insulation performance degradation of the corresponding insulation area within a unit operating time, and is specifically determined based on the ratio of the difference in the degree of thermal aging influence of adjacent time nodes to the time interval.

[0059] Based on the insulation degradation development rate, the trend of degradation development rate at different time points during continuous operation is analyzed to determine the current stage of insulation degradation evolution in each degradation assessment area. Specifically, based on whether the rate of insulation degradation increases with operating time, the insulation degradation evolution stage is divided into a slow degradation stage, a stable development stage, and an accelerated degradation stage. When the change in the rate of degradation between adjacent time points is less than a preset rate change threshold, the corresponding region is determined to be in the slow degradation stage. When the change in the rate of degradation is equal to the preset rate change threshold, the corresponding region is determined to be in the stable development stage. When the change in the rate of degradation is greater than the preset rate change threshold, the corresponding region is determined to have entered the accelerated degradation stage.

[0060] The preset rate change threshold is determined based on the sampling accuracy of the insulation degradation status monitoring data and the normal fluctuation range of the degradation development rate during the historical stable operation phase. Specifically, it can be set as the maximum fluctuation value of the degradation development rate change in adjacent time phases under normal operation, which is used to distinguish between normal measurement fluctuations and actual accelerated degradation changes.

[0061] Based on the aforementioned degradation evolution stages, the insulation degradation development rate corresponding to each degradation assessment area is dynamically corrected to obtain the regional degradation development rate considering the nonlinear degradation characteristics of insulation. Specifically, a rate correction coefficient is determined based on the current degradation evolution stage of each degradation assessment area. This rate correction coefficient is quantified according to the degree of change in the insulation degradation development rate within different degradation stages. Specifically, firstly, the insulation degradation development rate sequence of each degradation assessment area during historical operation is obtained, and the ratio between the current degradation development rate and the average degradation development rate during historical stable stages is calculated. This ratio is used as the basis for determining the rate correction coefficient. When the ratio is less than or equal to 1, it indicates that the insulation degradation process in the corresponding area has not shown significant acceleration, and the rate correction coefficient is set to 1. When the ratio is greater than 1, it indicates that the insulation degradation development rate in the corresponding area is higher than the historical average level. The basic degradation development rate is increased based on this ratio to reflect the nonlinear change process of insulation aging acceleration caused by the continuous action of hot spots. Finally, the corrected regional degradation development rate is obtained by multiplying the rate correction coefficient by the basic degradation development rate.

[0062] Based on the corrected regional degradation development rate, and according to the difference between the current degree of regional thermal aging impact and the insulation failure threshold of each degradation assessment area, the remaining operating time required for each degradation assessment area to reach the insulation failure state is determined, and the remaining service life corresponding to each insulation area is obtained; wherein, the remaining service life is determined jointly based on the current degree of regional thermal aging impact, the insulation failure threshold and the corrected regional degradation development rate, specifically expressed as follows;

[0063] In the formula, Let i be the remaining useful life corresponding to the i-th degradation assessment area. This is the insulation failure threshold, used to represent the maximum permissible degree of degradation when the corresponding insulation area reaches a state where it can no longer operate. This represents the degree of thermal aging impact corresponding to the i-th degradation assessment area. Let be the corrected degradation rate corresponding to the i-th degradation assessment region.

[0064] Based on the remaining service life corresponding to each degradation assessment area, the service life prediction results corresponding to different spatial locations are compared to determine the insulation area with the lowest remaining service life, and the remaining service life corresponding to this insulation area is used as the overall remaining service life assessment result of the transformer; wherein, the overall remaining service life of the transformer is used to characterize the shortest remaining time that limits the safe operation of the transformer under the condition that hot spot migration causes different nonlinear degradation evolution in different insulation areas inside.

[0065] like Figure 5 The figure shows a comparison curve of the remaining service life prediction results obtained using this method and the traditional fixed hotspot temperature life assessment method. Figure 5 It is known that traditional methods, by assuming fixed hotspot locations, cannot reflect the differences in insulation aging in different regions during hotspot migration, and their prediction results deviate from the actual insulation degradation process. In contrast, this method, by considering the regional thermal effects caused by hotspot migration and the nonlinear evolution of insulation degradation, makes the predicted lifetime curve closer to the actual degradation trend, thus improving the accuracy of the remaining service life assessment results.

[0066] It should be noted that by combining regional insulation degradation state evolution analysis with nonlinear degradation rate correction method, not only are the spatial degradation differences formed after different insulation regions are subjected to differentiated thermal effects during hot spot migration considered, but the degradation evolution stages of different regions are further identified by utilizing the change law of insulation degradation state with operating time. The life prediction process is dynamically corrected according to the change of degradation development rate, so that the remaining service life assessment is transformed from the traditional static prediction method based on fixed thermal aging rate to a dynamic prediction method that considers the changes in actual operating conditions. This can more accurately reflect the impact of accelerated degradation in the later stage of insulation aging on life, and improve the accuracy and reliability of transformer remaining service life assessment results.

[0067] Reference Figure 2 As shown in the diagram, the transformer remaining service life assessment system based on insulation degradation evolution provided by this invention includes a temperature condition determination module, a hotspot migration analysis module, an insulation degradation assessment module, and a service life assessment module. The modules are interconnected. The temperature status determination module is used to acquire status data during transformer operation and determine the temperature distribution inside the transformer as it changes over time. The hotspot migration analysis module is used to determine the hotspot locations corresponding to different operating times based on the temperature distribution status, and to perform correlation analysis on the hotspot locations at consecutive times to obtain the hotspot spatial migration characteristics. The insulation degradation assessment module is used to divide the internal insulation area of ​​the transformer into multiple degradation assessment areas based on the hot spot spatial migration characteristics, and to determine the insulation degradation state of each area according to the historical temperature effect corresponding to each area. The life assessment module is used to calculate the remaining service life of each transformer based on its insulation degradation state, and uses the lowest life value as the overall remaining service life assessment result of the transformer.

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0069] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0071] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the remaining service life of a transformer based on the evolution of insulation degradation, characterized in that, include: Acquire the status data of the transformer during operation and determine the temperature distribution inside the transformer as it changes over time; Based on the temperature distribution, the hotspot locations corresponding to different operating times are determined, and correlation analysis is performed on the hotspot locations at consecutive times to obtain the hotspot spatial migration characteristics. Based on the hotspot spatial migration characteristics, the internal insulation area of ​​the transformer is divided into multiple deterioration assessment areas, and the insulation deterioration state of each area is determined according to the historical temperature effect corresponding to each area. The remaining service life of each transformer is calculated based on its insulation degradation state, and the lowest service life value is taken as the overall remaining service life assessment result of the transformer.

2. The method according to claim 1, characterized in that, The acquisition of state data during transformer operation and determination of the internal temperature distribution of the transformer over time include: Acquire multi-source state data of the transformer under evaluation during its operation and establish a transformer state data sequence corresponding to the operating time; Determine the heat source distribution status at different operating times based on the transformer status data sequence; The internal temperature transfer state of the transformer is determined based on the heat source distribution and equipment heat dissipation status. The temperature distribution within the transformer's internal space is obtained based on the temperature transfer state.

3. The method according to claim 2, characterized in that, The step of determining the heat source distribution state at different operating times based on the transformer state data sequence includes: Based on the load status information in the state data sequence, the changes of different loss sources inside the transformer with operating time are analyzed. The loss sources include winding loss, core loss and additional loss of structural components. Based on the internal structural parameters of the transformer, various loss sources are mapped to corresponding spatial regions to obtain the heat generation status of each region over time. By correlating the heat generation of each spatial region at each operating time according to the time sequence, a dynamic heat source distribution state is formed, which includes spatial location, heat source intensity and its changing trend.

4. The method according to claim 2, characterized in that, The determination of the internal temperature transfer state of the transformer based on the heat source distribution state and equipment heat dissipation state includes: Based on the heat source distribution status, heat input information of each heat-generating area is extracted, and combined with the internal structural parameters of the transformer and heat dissipation status information, the heat transfer channels and heat exchange relationships between different spatial areas are determined. Based on the heat transfer channels and heat exchange relationships, spatial conduction analysis is performed on the heat input of each heating area to obtain temperature transfer correlation information between different spatial areas. Correlation analysis is performed on the changes in heat source input and heat dissipation conditions at each time point during continuous operation to determine the temperature change process of each spatial region over time, forming a dynamic temperature transfer state that includes both temporal variation characteristics and spatial correlation characteristics.

5. The method according to claim 1, characterized in that, The step of determining the hotspot locations corresponding to different operating times based on temperature distribution includes: Based on the dynamic temperature distribution status, spatial temperature distribution data containing spatial coordinates, operating time, and temperature values ​​is established, and the internal space of the transformer is divided into regions to obtain each temperature analysis region. Extract the temperature status of each temperature analysis region at each running time, and determine the region with the highest temperature value and that meets the preset temperature rise condition as the hot spot candidate region; A correlation analysis is performed on the temperature changes at adjacent times during continuous operation to determine whether the hotspot candidate region continuously meets the highest temperature region determination condition within a preset continuous time node. If so, the region is determined as the hotspot location at the corresponding operation time. By establishing a hotspot location sequence in chronological order, information on the hotspot location that characterizes the change of the highest temperature region inside the transformer over time can be obtained.

6. The method according to claim 1, characterized in that, The correlation analysis of hotspot locations at consecutive time points to obtain hotspot spatial migration characteristics includes: By associating the hotspot locations at different operating times with their corresponding hotspot temperature values ​​and operating times, a hotspot status sequence is established; Based on the hotspot state sequence, spatial matching is performed on the hotspot locations at adjacent operating times to determine the spatial position change status of the hotspots during continuous operation, and the hotspot temperatures at adjacent times are compared to determine the thermal intensity change status. The duration of hotspot activity in different spatial regions was statistically analyzed, and the thermal effects experienced by each spatial region during hotspot migration were determined by combining the hotspot temperature and temperature change in each region. By integrating the spatial location change state, thermal intensity change state, and thermal effect change process of each region, a hotspot spatial migration feature is constructed, which includes spatial location change characteristics, thermal intensity change characteristics, and time accumulation characteristics.

7. The method according to claim 1, characterized in that, Based on the hotspot spatial migration characteristics, the internal insulation region of the transformer is divided into multiple degradation assessment regions, including: Based on the hotspot spatial migration characteristics, data on the thermal effects experienced by each insulating spatial location during the hotspot migration process are obtained. Based on the thermal effect data, the cumulative thermal effect information of each insulation space location is statistically analyzed, and the differences in thermal effect characteristics between different insulation space locations are compared. Based on the differences in thermal characteristics, insulation spatial locations with the same thermal characteristics are assigned to the same degradation assessment area.

8. The method according to claim 7, characterized in that, The determination of the insulation degradation state of each region based on its historical temperature effect includes: Obtain historical temperature impact information for each degradation assessment area during continuous operation, and calculate the duration of continuous impact at different temperature levels for each area, and calculate the cumulative thermal impact degree for each area. Based on the heat resistance characteristics of the insulation material corresponding to each deterioration assessment area, the cumulative thermal effect degree is corrected to obtain the degree of regional thermal aging influence. The insulation degradation status of each degradation assessment area is determined based on the degree of influence of regional thermal aging, thus forming information on the distribution of regional insulation degradation status.

9. The method according to claim 1, characterized in that, The calculation of the remaining service life based on the insulation degradation state, and the use of the lowest remaining service life value as the overall remaining service life assessment result of the transformer, includes: Based on the regional insulation degradation status distribution information, an insulation degradation status change sequence for each degradation assessment region is established, and the corresponding insulation degradation development rate for each region is determined. Based on the changing trend of the degradation development rate during continuous operation, the degradation evolution stage of each region is determined, and the insulation degradation development rate is nonlinearly corrected to obtain the corrected regional degradation development rate. The remaining service life of each insulation region is determined based on the current degree of thermal aging impact, the preset insulation failure threshold, and the corrected regional degradation rate. Compare the remaining service life of each insulation region, and take the lowest value as the overall remaining service life assessment result of the transformer.

10. A system for assessing the remaining service life of a transformer based on insulation degradation evolution as described in any one of claims 1-9, characterized in that, include: The temperature status determination module is used to acquire status data during transformer operation and determine the temperature distribution inside the transformer as the operating time changes. The hotspot migration analysis module is used to determine the hotspot locations corresponding to different operating times based on the temperature distribution status, and to perform correlation analysis on the hotspot locations at consecutive times to obtain the hotspot spatial migration characteristics. The insulation degradation assessment module is used to divide the internal insulation area of ​​the transformer into multiple degradation assessment areas based on the hot spot spatial migration characteristics, and to determine the insulation degradation state of each area according to the historical temperature effect corresponding to each area. The life assessment module is used to calculate the remaining service life of each transformer based on its insulation degradation state, and to take the lowest life value as the overall remaining service life assessment result of the transformer.

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