Method and system for checking potential safety hazards of power transformation equipment

Abstract

The invention discloses a method and a system for checking potential safety hazards of power transformation equipment, particularly relates to the technical field of intelligent operation and maintenance of power equipment, and is used for solving the problem of hidden operation risks caused by mismatching of equipment aging characteristics and generalization strategies in the prior art. The method comprises the following steps: acquiring multi-source operation state data of power transformation equipment, constructing an asynchronous data set, and dynamically identifying a spatial electric field distortion gradient and a contact resistance fluctuation quantity as abnormal parameters; differentiated weight distribution is carried out on the abnormal parameters based on equipment structure features, and risk levels of different regions are quantified; analyzing the time sequence relevance between the contact resistance and the electric field response, and judging the breakdown risk caused by insulation degradation; a maintenance period optimization scheme and electric field compensation parameters are generated in combination with risk levels, an aging model is dynamically updated based on execution feedback, early warning and accurate intervention of hidden dangers are realized through dynamic adaptation of equipment states and control strategies, and the problem of control mismatch caused by neglecting of material degradation characteristics in a traditional method is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of power equipment, and more specifically, to a method and system for detecting potential safety hazards of substation equipment. Background Art

[0002] In the intelligent transformation of the power system, the core of the digital upgrade of old transformers, switchgears and other equipment is to achieve equipment energy efficiency improvement and operation and maintenance optimization through the coordination of intelligent control strategies (such as dynamic reactive power compensation, load scheduling optimization, etc.) and data monitoring platforms; however, during long-term operation, the material properties of old equipment will undergo irreversible degradation. For example, the hysteresis loss of the transformer core shows a non-linear offset, and the contact resistance of the switchgear contact continues to increase due to oxidation, resulting in a significant difference between the actual electromagnetic response of the equipment and the standard equipment model.

[0003] There is a mismatch problem between the general intelligent control strategy in the prior art and the personalized electromagnetic characteristics of old equipment. That is, due to the material property degradation caused by long-term service (such as the distortion of the core hysteresis loop and the contact resistance volatility exceeding 50% of the design value), the dynamic response of old equipment under the new strategy will cause hidden risks. For example, the mismatch between the control instruction and the true state of the equipment may cause local overheating or loss accumulation, and the existing data analysis methods are difficult to identify such abnormal trends in a timely manner due to the lack of integration of equipment personalized parameters, resulting in unforeseen operation risks for old equipment after intelligent transformation. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for detecting potential safety hazards of substation equipment to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for detecting potential safety hazards of substation equipment, comprising the following steps:

[0007] S1. Obtain the operation status data and aging-related parameters of the target substation equipment, where the operation status data includes electrical parameters and temperature parameters;

[0008] S2. Based on a preset aging rule, perform dynamic deviation analysis on the electrical parameters to identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection component as the target abnormal parameters;

[0009] S3. According to the structural characteristics of the substation equipment, assign weights to the target abnormal parameters to determine the differential impact degree of different target abnormal parameters on equipment aging;

[0010] S4. Analyze the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components;

[0011] S5. Generate the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters based on the differential influence degree and the insulation breakdown risk;

[0012] S6. Execute the control instructions corresponding to the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution.

[0013] In a preferred embodiment, obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters, including:

[0014] Obtain the real-time monitored electrical parameters from the distributed sensors of the target substation equipment. The electrical parameters include voltage fluctuation data, current harmonic components, and partial discharge intensity;

[0015] Synchronously collect the temperature parameters through an infrared thermal imager and a temperature sensor. The temperature parameters include the surface temperature gradient of the electrical connection components and the temperature rise rate of the insulating material;

[0016] Extract the aging-related parameters from the equipment historical operation and maintenance database. The aging-related parameters include the oxidation rate of the electrical connection components and the dielectric constant attenuation record of the insulating material;

[0017] Align the electrical parameters, temperature parameters, and aging-related parameters according to the time stamp to construct a multi-source asynchronous data set.

[0018] In a preferred embodiment, perform dynamic deviation analysis on the electrical parameters based on the preset aging rules, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as the target abnormal parameters, including:

[0019] Compare the voltage fluctuation data, current harmonic components, and partial discharge intensity with the dynamic thresholds according to the electrical parameter aging thresholds in the preset aging rules, and screen out the electrical parameters exceeding the thresholds;

[0020] Based on the time distribution characteristics of the electrical parameters exceeding the thresholds, screen out the clusters of electrical parameters exceeding the thresholds that repeatedly appear within consecutive time windows, calculate the electric field intensity difference between adjacent time windows of the clusters of electrical parameters exceeding the thresholds, and generate the spatial electric field distortion gradient;

[0021] Extract the historical reference value of the contact resistance of the electrical connection components, and calculate the deviation percentage between the current contact resistance and the corrected reference value as the contact resistance fluctuation;

[0022] Mark the spatial electric field distortion gradient and the contact resistance fluctuation as target abnormal parameters, and store the target abnormal parameters in association with the temperature parameters in the multi-source asynchronous data set.

[0023] In a preferred embodiment, weight distribution is performed on the target abnormal parameters according to the structural characteristics of the power transformation equipment to determine the differential influence degree of different target abnormal parameters on equipment aging, including:

[0024] The structural characteristics of the power transformation equipment include the spatial distribution of electrical connection components and the heat dissipation path characteristics;

[0025] According to the spatial distribution of the electrical connection components of the power transformation equipment, calculate the spatial density of the target abnormal parameters in the area where the electrical connection components are dense, and generate a spatial density weight coefficient;

[0026] According to the heat dissipation path characteristics of the power transformation equipment, calculate the thermal resistance influence weight coefficient of the target abnormal parameters in the area where the thermal resistance coefficient of the heat dissipation path is greater than the preset thermal resistance threshold;

[0027] Perform weighted accumulation on the spatial density weight coefficient and the thermal resistance influence weight coefficient according to a preset ratio to generate a comprehensive weight value of the target abnormal parameters;

[0028] According to the comprehensive weight value and the preset aging level comparison table, determine the differential influence degree of the target abnormal parameters on equipment aging.

[0029] In a preferred embodiment, analyze the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components, including:

[0030] Extract the time-series data of the contact resistance fluctuation and the spatial electric field distortion gradient, and generate a change trend curve of the contact resistance fluctuation and a change curve of the spatial electric field distortion gradient according to time windows;

[0031] Calculate the difference between the absolute value of the slope of the contact resistance fluctuation change trend curve and the absolute value of the slope of the spatial electric field distortion gradient change curve as the trend deviation degree;

[0032] Analyze the lag time of the spatial electric field distortion gradient change curve relative to the contact resistance fluctuation change curve. The lag time is the peak time difference between the two curves;

[0033] When the trend deviation degree is greater than the preset deviation threshold and the lag time is less than the preset lag threshold, it is determined that the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient do not match, and there is a risk of insulation breakdown caused by the deterioration of the electrical connection components.

[0034] In a preferred embodiment, based on the differential impact degree and the risk of insulation breakdown, the maintenance cycle adjustment amount of the electrical connection component and the electric field balance compensation parameter are generated, including:

[0035] According to the aging risk level corresponding to the differential impact degree, match the maintenance cycle adjustment amount of the electrical connection component from the preset maintenance cycle comparison table;

[0036] According to the insulation breakdown risk determination result and the spatial electric field distortion gradient value, select the corresponding electric field balance compensation parameter from the electric field compensation parameter library;

[0037] Associate the maintenance cycle adjustment amount of the electrical connection component with the electric field balance compensation parameter according to the equipment number to generate a control instruction set, and the control instruction set includes an execution time window and a priority mark.

[0038] In a preferred embodiment, the maintenance cycle adjustment amount of the electrical connection component is the time percentage of shortening the maintenance interval; the maintenance cycle shortening ratio of the preset maintenance cycle comparison table is positively correlated with the aging risk level; the electric field balance compensation parameter includes the adjacent shielding layer grounding resistance adjustment value and the grading ring spacing correction amount; the priority mark is set based on the number of trend mismatches in the insulation breakdown risk determination result.

[0039] In a preferred embodiment, execute the control instructions corresponding to the maintenance cycle adjustment amount of the electrical connection component and the electric field balance compensation parameter, and update the aging correlation parameter according to the temperature parameter after execution, including:

[0040] According to the execution time window and the priority mark in the control instruction set, dynamically adjust the execution order of the control instructions corresponding to the electric field balance compensation parameter to generate an execution sequence with self-adaptive priority;

[0041] Execute the maintenance work order corresponding to the maintenance cycle adjustment amount of the electrical connection component and the equipment control instruction corresponding to the electric field balance compensation parameter in the order of the execution sequence;

[0042] Collect the surface temperature gradient of the electrical connection component and the insulation material temperature rise rate after execution to generate the temperature parameter after execution;

[0043] Fuse and update the temperature parameter after execution and the historical aging correlation parameter according to the weighted average algorithm to generate the updated aging correlation parameter.

[0044] On the other hand, the present invention provides a substation equipment safety hazard detection system, including:

[0045] Data acquisition module: Obtain the operation state data and aging correlation parameters of the target substation equipment, and the operation state data includes electrical parameters and temperature parameters;

[0046] Abnormality recognition module: Based on preset aging rules, perform dynamic deviation analysis on electrical parameters, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as target abnormal parameters;

[0047] Weight allocation module: Allocate weights to the target abnormal parameters according to the structural characteristics of the substation equipment, and determine the differential influence degree of different target abnormal parameters on equipment aging;

[0048] Risk determination module: Analyze the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components;

[0049] Strategy generation module: Based on the differential influence degree and the insulation breakdown risk, generate the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters;

[0050] Closed-loop feedback module: Execute the control instructions corresponding to the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. By integrating multi-dimensional operation status data and equipment aging characteristics, a closed-loop management mechanism for hidden danger identification and risk prevention and control is constructed. Based on the actual operation characteristics of the equipment, abnormal parameters are dynamically identified, and the spatial electric field distortion gradient and the contact resistance fluctuation are used as the core monitoring indicators, which can accurately capture the early signals of insulation material deterioration and electrical connection component aging. By combining the equipment structure characteristics for weight allocation, the harm degree of abnormalities at different positions can be dynamically evaluated, effectively distinguishing key risk points from general parameter fluctuations, so as to accurately locate hidden dangers under complex working conditions; At the same time, a time-series correlation analysis mechanism of contact resistance and electric field response is introduced. By monitoring the matching of the trend changes of the two, the chain reaction caused by the decline of material performance can be predicted in advance, significantly improving the predictability of hidden faults;

[0053] 2. By establishing a dynamic adaptation mechanism between the control strategy and the equipment status, a complete closed-loop from risk identification to optimal control is formed. The maintenance strategy generated based on the differential risk assessment results not only considers the urgency of local defects of the equipment, but also takes into account the stability requirements of the overall operation condition, realizing the accurate allocation of maintenance resources. The feedback of the temperature parameters after execution and the real-time update of the aging model can dynamically track the material degradation trajectory, avoiding the disconnection problem between the traditional regular maintenance mode and the real state of the equipment. It not only effectively inhibits the spread of safety hazards such as local overheating and insulation breakdown, but also prolongs the service life of the equipment and reduces the operation and maintenance cost by continuously optimizing the equipment operation status. Description of the Drawings

[0054] Figure 1 This is a flowchart of a method for detecting potential safety hazards in substation equipment according to the present invention;

[0055] Figure 2 This is a schematic structural diagram of a system for detecting potential safety hazards in substation equipment according to the present invention. Specific embodiments

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] Embodiment 1: Figure 1 A method for detecting potential safety hazards in substation equipment according to the present invention is given, which includes the following steps:

[0058] S1. Obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters;

[0059] S2. Conduct dynamic deviation analysis on the electrical parameters based on a preset aging rule, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as target abnormal parameters;

[0060] S3. Assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment to determine the different degrees of differential impact of the target abnormal parameters on equipment aging;

[0061] S4. Analyze the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by deterioration of the electrical connection components;

[0062] S5. Generate an adjustment amount for the maintenance cycle of the electrical connection components and an electric field balance compensation parameter based on the differential impact degree and the insulation breakdown risk;

[0063] S6. Execute the control instructions corresponding to the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameter, and update the aging-related parameters according to the temperature parameters after execution.

[0064] S1. Obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters, including:

[0065] In step S1, the operation status data and aging-related parameters of the target substation equipment are obtained. Specifically, it includes: electrically parameter of the target substation equipment are collected in real time through distributed sensors, and the distributed sensors include voltage sensors, current sensors and partial discharge detectors. For example, the voltage sensor is a capacitive voltage divider sensor installed at the incoming end of the high-voltage bus to monitor the bus voltage fluctuation data, and the voltage fluctuation data includes the instantaneous voltage fluctuation amplitude and the frequency offset. The current sensor is a Rogowski coil sleeved on the main circuit wire to collect current harmonic components, such as the third harmonic and the fifth harmonic components. The partial discharge detector is a high-frequency current transformer deployed on the grounding wire of the insulating bushing to record the partial discharge intensity, such as the amplitude and repetition frequency of the partial discharge pulse.

[0066] The surface temperature gradient distribution image is obtained by scanning the surface of the electrical connection components with an infrared thermal imager. For example, when the electrical connection component is a circuit breaker contact, the infrared thermal imager scans along the contact sliding track to capture the temperature gradient distribution of the contact surface; when the electrical connection component is a busbar connection, the infrared thermal imager scans the temperature distribution of the connection bolt area from a top-down perspective. The temperature sensor is a fiber Bragg grating sensor arranged in the hot spot area of the insulating material, such as the surface of the insulating paper tube at the root of the bushing or the insulating spacer at the end of the winding, to monitor the temperature rise rate. For example, when the insulating material is epoxy resin, the temperature sensor monitors the hot spot temperature rise rate in its curing area; when the insulating material is oil-impregnated cardboard, the temperature sensor monitors the temperature rise rate at its interlayer interface.

[0067] The aging-related parameters are extracted from the equipment historical operation and maintenance database. The aging-related parameters include the oxidation rate record of the electrical connection components and the dielectric constant attenuation record of the insulating material. For example, the oxidation rate record of the electrical connection components is obtained by measuring the surface oxidation layer thickness with an X-ray diffractometer during regular maintenance, and the oxidation rate is calculated as the difference between the thicknesses measured in two adjacent times divided by the time interval. The dielectric constant attenuation record of the insulating material is converted from the test results of the tangent of the dielectric loss angle in regular insulation tests. For example, the dielectric constant attenuation rate is determined by the product of the annual growth rate of the tangent of the dielectric loss angle and the initial dielectric constant of the material.

[0068] The electrically parameters and temperature parameters collected in real time are aligned with the extracted aging-related parameters according to the unified timestamp. After the timestamp alignment, a multi-source asynchronous data set is constructed, and each record in the data set contains a timestamp mark and the corresponding parameter value. For example, a data record is "Timestamp: 2023-10-01 12:00:00.000, Voltage fluctuation amplitude: 1.5%, Third harmonic component: 3.2%, Partial discharge amplitude: 20 pC, Contact surface temperature gradient: 2.5 °C / cm, Epoxy resin temperature rise rate: 0.3 °C / min, Oxidation rate: 0.02 mm / year, Dielectric constant attenuation rate: 0.6%".

[0069] The deployment locations of the distributed sensors are set according to the structural characteristics of the target substation equipment. For example, capacitive voltage dividers are installed at the incoming and outgoing ends of the high-voltage busbars to compare the voltage fluctuation differences between the incoming and outgoing lines; Rogowski coils are sleeved on the main circuit conductors and branch circuit conductors to monitor the harmonic distribution differences between the main circuit and the branch circuits; high-frequency current transformers are deployed on the ground wires of the insulating bushings, the shielding layer ground wires, and the equipment housing ground wires to distinguish the partial discharge signals of different grounding paths. The scanning paths of the infrared thermal imagers are set according to the spatial distribution of the electrical connection components. For example, for the busbar connections inside the switchgear, the infrared thermal imager scans along the horizontal track; for the bushing connections of the transformer, the infrared thermal imager scans along the vertical track. The arrangement positions of the fiber Bragg grating temperature sensors are set according to the aging-sensitive areas of the insulating materials. For example, in oil-immersed transformers, the sensors are arranged at the junction of the insulating paper tube and the oil gap at the end of the winding; in dry-type transformers, the sensors are arranged in the areas prone to cracks in the epoxy resin castings.

[0070] The storage structure of the equipment historical operation and maintenance database is classified according to the equipment type. For example, the oxidation rate records of transformer-type equipment include the oxidation data of the bushing contacts and tap changer contacts; the oxidation rate records of switchgear-type equipment include the oxidation data of the busbar connection bolts and disconnector contacts. The dielectric constant attenuation records are classified according to the type of insulating material. For example, the dielectric constant attenuation records of oil-impregnated paperboard include the test results at different oil temperatures, and the dielectric constant attenuation records of epoxy resin include the test results in different humidity environments.

[0071] During the construction of the multi-source asynchronous data set, the interpolation compensation method is selected according to the data type. For example, linear interpolation is used for temperature gradient data, and nearest neighbor interpolation is used for partial discharge pulse data. Timestamp synchronization is achieved through a GPS timing module, and the clock signal error of the GPS timing module is less than 1 millisecond. The storage format of the data set is a structured table, and the table fields include equipment number, timestamp, parameter type, parameter value, and data source. For example, a record is "Equipment number: GIS-001, Timestamp: 2023-10-01 12:00:00.123, Parameter type: Voltage fluctuation amplitude, Parameter value: 1.2%, Data source: Capacitive voltage divider sensor".

[0072] S2. Conduct dynamic deviation analysis on electrical parameters based on preset aging rules, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as target abnormal parameters, including:

[0073] In step S2, according to the electrical parameter aging thresholds in the preset aging rules, dynamic threshold comparison is performed on the voltage fluctuation data, current harmonic components, and partial discharge intensity to screen out the electrical parameters exceeding the thresholds. The setting basis of the electrical parameter aging thresholds includes the material degradation records in the device historical operation and maintenance data and the power industry standards. For example, the voltage fluctuation threshold is determined by multiplying the average voltage fluctuation amplitude within the first three months after the device is put into operation by the safety factor of 1.5. The current harmonic component threshold is set according to the allowable value of the harmonic current injected by users into the power grid stipulated in the national standard. The partial discharge intensity threshold is comprehensively determined based on the device insulation level (for example, set to 20 pC for 35 kV devices and 50 pC for 110 kV devices) and the operation years (increasing by 5% annually). When the voltage fluctuation data monitored in real time exceeds the electrical parameter aging threshold, it is marked as an electrical parameter exceeding the threshold.

[0074] Based on the time distribution characteristics of the electrical parameters exceeding the thresholds, the clusters of electrical parameters exceeding the thresholds that repeatedly appear within the continuous time window are screened. The length of the time window is dynamically adjusted according to the historical load fluctuation period of the device, and the historical load fluctuation period is statistically obtained by analyzing the time intervals of the load peaks in the past year. For example, if the load fluctuation period is 1 hour (i.e., a load peak appears every hour), the time window is set to 10 minutes; if the load fluctuation period is 4 hours, the time window is set to 30 minutes. The determination condition for the cluster of electrical parameters exceeding the thresholds is that the electrical parameters exceeding the thresholds appear at the same monitoring point within three consecutive time windows. For example, if the voltage fluctuation at the bus inlet end exceeds the threshold within three consecutive 10-minute windows, it is marked as a cluster of electrical parameters exceeding the thresholds. Calculate the electric field intensity difference of the clusters of electrical parameters exceeding the thresholds within adjacent time windows to generate the spatial electric field distortion gradient. For example, the electric field intensity at monitoring point A is 100 kV / m in the first window and 110 kV / m in the second window, and the difference is 10 kV / m, which is used as the spatial electric field distortion gradient at this point.

[0075] Extract the historical reference value of the contact resistance of the electrical connection component. The historical reference value is the contact resistance value measured by a micro-ohmmeter at the initial stage of the device operation or during the most recent maintenance. Calculate the deviation percentage of the current contact resistance from the corrected reference value as the contact resistance fluctuation amount. The corrected reference value is dynamically compensated according to the temperature parameter in the multi-source asynchronous data set, and the compensation rule is set based on the results of the material temperature rise test. For example, the temperature rise coefficient of the copper electrical connection component is determined to be 0.004 / °C through laboratory tests, that is, when the temperature rises by 1°C, the contact resistance increases by 0.4%. When the surface temperature gradient of the electrical connection component in the temperature parameter exceeds 2°C / cm, the reference value is increased by the corresponding proportion according to the temperature rise coefficient. For example, the historical reference value is 50 μΩ, and the current surface temperature gradient is 3°C / cm, then the corrected reference value is 50×(1 + 0.004×3) = 50.6 μΩ. If the current contact resistance is 55 μΩ, the deviation percentage is (55 - 50.6) / 50.6 = 8.70%.

[0076] Mark the spatial electric field distortion gradient and the contact resistance fluctuation as target abnormal parameters, and store the target abnormal parameters and the temperature parameters in the multi-source asynchronous data set in an associated manner. The associated storage is implemented through a relational database, and the database table structure includes fields "monitoring point number", "electric field distortion gradient", "contact resistance fluctuation", "surface temperature gradient", and "temperature rise rate". For example, the data record of monitoring point A01 is "monitoring point number: A01, electric field distortion gradient: 10 kV / m, contact resistance fluctuation: 8.70%, surface temperature gradient: 3 °C / cm, temperature rise rate: 0.5 °C / min". When storing in an associated manner, a combined index is established between the monitoring point number and the parameter type, supporting the quick retrieval of associated data for a specific monitoring point or parameter type through SQL query statements.

[0077] According to the surface temperature gradient of the electrical connection component collected in real time, dynamically compensate the historical reference value. The compensation rule is that for every 1 °C / cm increase in the temperature gradient, the reference value floats up by 0.5% (for example, when the temperature gradient is 3 °C / cm, the reference value floats up by 1.5%). In the multi-source data alignment process, for data with different sampling rates (such as a partial discharge detector at 1 kHz and a temperature sensor at 1 Hz), the linear interpolation method is used to interpolate the low-frequency data onto the high-frequency time stamps to ensure data synchronization accuracy. The mapping relationship between the monitoring point number and the device number is realized through an associated query in the database to ensure a one-to-one correspondence.

[0078] It should be noted that based on the surface temperature gradient value of the electrical connection component, look up the table according to the gradient interval to determine the compensation ratio (for example, the gradient of 2 - 4 °C / cm corresponds to a compensation ratio of 1.0%). The compensated reference value = historical reference value × (1 + compensation ratio). Supplementary statistical basis for the electrical parameter aging threshold in the preset aging rule: The safety factor of 1.5 times the voltage fluctuation threshold is statistically determined based on the maximum allowable fluctuation value within 100 days after the initial operation of the device, and the annual increase of 5% in the partial discharge intensity threshold is set based on the results of the device insulation material aging test.

[0079] S3. Assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment to determine the differential impact degree of different target abnormal parameters on equipment aging, including:

[0080] In step S3, assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment to determine the differential impact degree of different target abnormal parameters on equipment aging. Specifically, it includes: According to the spatial distribution of the electrical connection components of the substation equipment, calculate the spatial density of the target abnormal parameters in the dense area of the electrical connection components, and generate a spatial density weight coefficient. The definition of the dense area of the electrical connection components is based on the equipment installation specifications and industry standards (for example, it is stipulated that the minimum distance between the busbar connections of the switchgear is 25 cm). If the actual distance between adjacent electrical connection components is less than 30 cm, it is determined as a dense area.

[0081] The calculation method of the spatial density weight coefficient is the number of target abnormal parameters in the dense area divided by the total area of the dense area. The total area is obtained by measuring with a laser rangefinder or parsing CAD drawings. For example, if the area of a dense area is 0.5 square meters and 3 target abnormal parameters are detected, the spatial density is 6 per square meter, and the spatial density weight coefficient is set to the spatial density multiplied by the coefficient 0.1. The setting basis of the coefficient 0.1 is the statistical result of the historical fault data of the equipment (the failure probability increases by 10% per abnormal point per square meter).

[0082] According to the heat dissipation path characteristics of the substation equipment, calculate the thermal resistance influence weight coefficient of the target abnormal parameters in the area where the thermal resistance coefficient of the heat dissipation path is greater than the preset thermal resistance threshold. The calculation method of the thermal resistance coefficient of the heat dissipation path is the length of the heat dissipation path (obtained by measuring with a tape measure or from the design drawings) divided by the thermal conductivity of the heat dissipation material (for example, the thermal conductivity of an aluminum heat sink is 237 W / (m·K)). If the calculation result is greater than the preset thermal resistance threshold (for example, the air-cooled path threshold is 0.01 K / W, set according to the maximum allowable value in the equipment heat dissipation design manual), it is determined as an area with poor heat dissipation. The calculation method of the thermal resistance influence weight coefficient is the number of target abnormal parameters in the area with poor heat dissipation multiplied by the proportion of the thermal resistance coefficient exceeding the threshold. For example, if the thermal resistance coefficient of a heat dissipation path is 0.015 K / W, the proportion exceeding the threshold is (0.015 - 0.01) / 0.01 = 0.5, and there are 2 target abnormal parameters in this area, then the thermal resistance influence weight coefficient is 2×0.5 = 1.0.

[0083] Weight and accumulate the spatial density weight coefficient and the thermal resistance influence weight coefficient according to a preset ratio to generate the comprehensive weight value of the target abnormal parameter. The setting basis of the preset ratio is the statistical result of the contribution frequencies of spatial density and thermal resistance influence in the historical fault cases of the equipment (for example, among 100 fault cases, 60 are mainly due to spatial density and 40 are mainly due to poor heat dissipation). Therefore, the proportion of the spatial density weight is 60%, and the proportion of the thermal resistance influence weight is 40%. For example, if the spatial density weight coefficient of a target abnormal parameter is 0.6 and the thermal resistance influence weight coefficient is 1.0, then the comprehensive weight value is 0.6×0.6 + 1.0×0.4 = 0.76.

[0084] According to the comprehensive weight value and the preset aging level comparison table, determine the differential influence degree of the target abnormal parameter on the equipment aging. The preset aging level comparison table is formulated based on the aging maintenance records within five years after the equipment is put into operation. For example, the comprehensive weight value of 0 - 0.5 corresponds to "low risk" (no immediate maintenance), 0.5 - 1.0 corresponds to "medium risk" (maintenance is recommended within three months), and greater than 1.0 corresponds to "high risk" (immediate shutdown for maintenance is required). For example, when the comprehensive weight value is 0.76, it is determined as "medium risk", the differential influence degree is marked as level two, and a maintenance work order is triggered for generation.

[0085] The preset aging level comparison table and the maintenance cycle comparison table of S5 share the same risk level classification standard: a comprehensive weight value of 0 - 0.5 corresponds to low risk, 0.5 - 1.0 corresponds to medium risk, and >1.0 corresponds to high risk, ensuring consistent risk judgment logic. In the calculation of the spatial density weight coefficient, the area of the dense area is determined by the average of the CAD drawing analysis and the measured value of the laser rangefinder, reducing measurement errors.

[0086] S4. Analyze the change trend of the contact resistance fluctuation amount and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components, including:

[0087] In step S4, the time series data of the contact resistance fluctuation amount and the spatial electric field distortion gradient are extracted. The timestamps of the time series data are synchronized by the GPS clock module, and the synchronization error is less than 1 second. The change trend curves of the contact resistance fluctuation amount and the spatial electric field distortion gradient are generated by dividing them into unified time windows. The length of the time window is dynamically adjusted according to the historical load fluctuation period of the device. The historical load fluctuation period is obtained by analyzing the time interval between the load peaks in the past year. For example, if the load fluctuation period is 1 hour (i.e., a load peak appears every hour), the time window is set to 10 minutes; if the load fluctuation period is 4 hours, the time window is set to 30 minutes. The generation method of the change trend curve of the contact resistance fluctuation amount is as follows: within each time window, calculate the arithmetic average of the contact resistance fluctuation amount as the ordinate value of the window, and the start time of the time window as the abscissa value, and connect all window values to form a curve. The generation method of the change trend curve of the spatial electric field distortion gradient is the same.

[0088] Calculate the difference between the absolute value of the slope of the change trend curve of the contact resistance fluctuation amount and the absolute value of the slope of the change trend curve of the spatial electric field distortion gradient as the trend deviation degree. The calculation method of the absolute value of the slope is: within the time window, divide the difference between the first and last two values of the contact resistance fluctuation amount or the spatial electric field distortion gradient by the length of the time window. For example, within a certain time window, the contact resistance fluctuation amount increases from 1.5% to 2.5%, and the time window is 10 minutes, then the slope is (2.5 - 1.5) / 10 = 0.1% / min, and the absolute value is 0.1; if the slope of the spatial electric field distortion gradient is 0.05 kV / (m·min), and the absolute value is 0.05, then the trend deviation degree is 0.1 - 0.05 = 0.05. The preset deviation threshold is set according to the corresponding relationship between the trend deviation degree and the occurrence of faults in the historical fault data of the device. For example, by analyzing 100 historical fault cases, it is found that when the trend deviation degree is greater than 0.2, the probability of fault occurrence exceeds 80%, so the deviation threshold is set to 0.2.

[0089] Among them, the setting basis of the deviation threshold of 0.2 is as follows: Based on the statistical analysis of 100 cases of failures in the device historical failure case library, when the trend deviation degree > 0.2, the failure probability exceeds 80%. The basis of the hysteresis threshold of 3 minutes is the minimum time record of the electric field distortion lagging behind the contact resistance fluctuation under normal operating conditions in the device operation log. Refinement of the peak detection method: The sliding window size is 3 data points (the current point and the adjacent points before and after), excluding the pseudo-peaks caused by noise interference.

[0090] Analyze the hysteresis time of the spatial electric field distortion gradient change curve relative to the contact resistance fluctuation amount change curve. The hysteresis time is the peak time difference between the two curves. The peak time is determined by the sliding window comparison method, specifically: Compare point by point in time order on the curve. If the value of a certain point is greater than the values of all adjacent points within its previous time window and the next time window, it is determined as a peak point, and the time stamp of this point is recorded. For example, the contact resistance fluctuation amount curve reaches a peak of 2.8% at 12:00:00, and the spatial electric field distortion gradient curve reaches a peak of 15 kV / m at 12:05:30, then the hysteresis time is 5 minutes and 30 seconds. If there are multiple peaks in the two curves, the time difference of the first peak is taken. The preset hysteresis threshold is set according to the empirical value of the electric field propagation delay under normal operating conditions in the device maintenance record. For example, under normal operating conditions, the time when the electric field distortion lags behind the contact resistance fluctuation should be greater than 5 minutes. If the measured hysteresis time is less than 3 minutes, it is determined as abnormal. Therefore, the hysteresis threshold is set to 3 minutes.

[0091] When the trend deviation degree is greater than the preset deviation threshold and the hysteresis time is less than the preset hysteresis threshold, it is determined that the hysteresis response of the contact resistance fluctuation amount change trend and the spatial electric field distortion gradient is mismatched. For example, the trend deviation degree is 0.25 (greater than 0.2) and the hysteresis time is 2 minutes and 50 seconds (less than 3 minutes), then it is determined as a trend mismatch. The determination result is associated and stored with the target abnormal parameter. The stored fields include "monitoring point number", "trend deviation degree", "hysteresis time", "determination result", for example, "monitoring point number: A01, trend deviation degree: 0.25, hysteresis time: 170 seconds, determination result: mismatch".

[0092] S5. Generate the adjustment amount of the maintenance cycle of the electrical connection component and the electric field balance compensation parameter based on the differential influence degree and the insulation breakdown risk, including:

[0093] In step S5, according to the aging risk level corresponding to the differential impact degree, the maintenance cycle adjustment amount of the electrical connection components is matched from the preset maintenance cycle comparison table. The preset maintenance cycle comparison table is constructed based on the statistics of the maintenance records within five years after the equipment is put into operation. The statistical method is to calculate the deviation ratio of the actual maintenance cycle to the original cycle under different risk levels. For example, among 100 devices with a low risk level (comprehensive weight value 0 - 0.5), the actual maintenance cycle is shortened by 10% on average; among 50 devices with a medium risk level (0.5 - 1.0), it is shortened by 30% on average; among 20 devices with a high risk level (>1.0), it is shortened by 50% on average. The calculation method of the maintenance cycle adjustment amount is the original maintenance cycle multiplied by (1 - shortening ratio). For example, if the original cycle is 12 months and the high risk level shortens by 50%, the adjusted cycle is 6 months. The triggering time of the maintenance work order is set through the calendar function of the equipment management system.

[0094] According to the insulation breakdown risk determination result and the spatial electric field distortion gradient value, the corresponding electric field balance compensation parameters are selected from the electric field compensation parameter library. The electric field compensation parameter library is constructed based on the laboratory simulation test data and the electromagnetic field optimization scheme provided by the equipment manufacturer. The determination rule for the adjustment value of the adjacent shielding layer grounding resistance is: when the spatial electric field distortion gradient value is in the range of 10 - 20 kV / m, the grounding resistance is reduced by 5% (for example, the original resistance of 1 Ω is adjusted to 0.95 Ω); when the gradient value is in the range of 20 - 30 kV / m, the grounding resistance is reduced by 10% (adjusted to 0.9 Ω). The determination rule for the correction amount of the grading ring spacing is: for every 5 kV / m increase in the gradient value, the spacing is reduced by 10 cm. For example, when the gradient is 15 kV / m, the spacing is corrected from the standard value of 70 cm to 50 cm. The parameter adjustment instruction is sent to the intelligent adjustment terminal through the Modbus protocol, and the instruction format is an ASCII string.

[0095] The maintenance cycle adjustment amount of the electrical connection components and the electric field balance compensation parameters are associated by equipment number to generate a control instruction set. The equipment number is the same as the monitoring point number in S1. For example, the monitoring point number A01 corresponds to the equipment number T2023001. The generation method of the control instruction set is: convert the maintenance cycle adjustment amount into the triggering time of the maintenance work order. For example, if the maintenance cycle is adjusted to 6 months, the work order triggering time is set to the current time plus 6 months; convert the electric field balance compensation parameters into equipment control instructions. For example, the grounding resistance adjustment instruction is "the grounding resistance of shielding layer 1 is adjusted to 0.5 Ω", and the grading ring spacing correction instruction is "the grading ring spacing is adjusted to 50 cm". The control instruction set includes an execution time window and a priority mark. The execution time window is set according to the low load period of the equipment. For example, from 1:00 - 3:00 am every day; the priority mark is set based on the number of trend mismatches in the insulation breakdown risk determination result. For example, when the number of trend mismatches ≤ 3 times, it is marked as "ordinary priority", and when > 3 times, it is marked as "urgent priority".

[0096] The mapping relationship between the device number and the monitoring point number is realized through the device topology relationship database. The database table structure includes fields such as "monitoring point number", "device number", and "device location" to ensure data traceability. The conflict handling rule for multiple instructions of the same device in the control instruction set is: execute in descending order of priority, and in ascending order of instruction generation time for the same priority. For example, the grounding resistance adjustment instruction (priority: urgent) of device T2023001 is executed prior to the equalizing ring spacing adjustment instruction (priority: normal).

[0097] S6. Execute the control instructions corresponding to the maintenance cycle adjustment amount of the electrical connection components and the electric field balance compensation parameters, and update the aging correlation parameters according to the temperature parameters after execution, including:

[0098] In step S6, according to the execution time window and priority mark in the control instruction set, dynamically adjust the execution order of the control instructions corresponding to the electric field balance compensation parameters to generate an execution sequence with adaptive priority. The generation rule of the execution sequence is: instructions with a priority mark of "urgent priority" are executed prior to those with "normal priority", and within the same priority, they are arranged in ascending order of device number. For example, the instruction of device number T2023001 (priority: urgent) is executed before that of T2023002 (priority: normal). The execution time window is executed according to the time period set in the control instruction set. For example, if it is set to 1:00 - 3:00 am every day, all instructions are executed in sequence within this time period.

[0099] Execute the maintenance work order corresponding to the maintenance cycle adjustment amount of the electrical connection components and the device control instructions corresponding to the electric field balance compensation parameters in the order of the execution sequence. The execution method of the maintenance work order is to send a work order trigger instruction to the device management system. For example, if the work order trigger time is June 1, 2024, the system automatically generates a maintenance task and assigns it to the corresponding team. The device control instructions corresponding to the electric field balance compensation parameters are sent to the on-site execution terminal through the Modbus protocol. After the instruction execution is completed, the execution terminal returns a status code to confirm the successful operation.

[0100] Collect the surface temperature gradient of the electrical connection components and the temperature rise rate of the insulating material after execution through temperature sensors to generate the temperature parameters after execution. The deployment location of the temperature sensors is the same as the monitoring points defined in S1. For example, the surface temperature gradient of the electrical connection components is collected by a fiber Bragg grating sensor deployed on the contact surface of the contact, with a sampling rate of 1 time per second; the temperature rise rate of the insulating material is collected by an infrared thermal imager deployed at the root of the bushing, with a scanning interval of 1 time per minute. The storage format of the temperature parameters after execution is the same as that of the multi-source asynchronous data set in S1. For example, "monitoring point number: A01, surface temperature gradient: 2.8℃ / cm, temperature rise rate: 0.3℃ / min, timestamp: 2024-06-01 03:00:00".

[0101] The post - execution temperature parameter and the historical aging - related parameter are fused and updated according to the weighted - average algorithm to generate an updated aging - related parameter. In the weighted - average algorithm, the weight of the post - execution temperature parameter is the proportion of the acquisition time in the total statistical period. For example, if the statistical period is 7 days and the acquisition time of the post - execution temperature parameter is 3 days, the weight is 3 / 7; the weight of the historical aging - related parameter is 4 / 7, and the update formula is: updated parameter = post - execution parameter×(3 / 7)+historical parameter×(4 / 7). For example, if the oxidation rate of the electrical connection component in the historical aging - related parameter is 0.02 mm / month and the post - execution parameter is 0.015 mm / month, the updated oxidation rate is 0.015×3 / 7 + 0.02×4 / 7≈0.018 mm / month. The updated parameter overwrites the original record in the device historical operation and maintenance database, forming a closed - loop feedback.

[0102] When executing the dynamic adjustment rule of the execution sequence, if the real - time load data exceeds 80% of the rated capacity of the device, the instructions with non - emergency priority are delayed until the next time window for execution. The statistical period in the weighted - average algorithm is strictly consistent with the trend mismatch determination period of S4 (both are 7 days) to ensure the unity of data timeliness. The coverage rule of the updated aging - related parameter is: when the proportion of the acquisition time of the post - execution temperature parameter exceeds 50%, the historical parameter is completely replaced; otherwise, the historical parameter version is retained and the version number is marked to support data rollback.

[0103] Embodiment 2: Figure 2 The structural schematic diagram of a power transformation equipment safety hazard detection system according to the present invention is given. A power transformation equipment safety hazard detection system includes:

[0104] Data acquisition module: Obtain the operation status data and aging - related parameters of the target power transformation equipment. The operation status data includes electrical parameters and temperature parameters;

[0105] Abnormality recognition module: Based on the preset aging rules, perform dynamic deviation analysis on the electrical parameters, and identify the spatial electric - field distortion gradient and the contact - resistance fluctuation of the electrical connection component as the target abnormal parameters;

[0106] Weight distribution module: According to the structural characteristics of the power transformation equipment, allocate weights to the target abnormal parameters to determine the different degrees of differential impact of the target abnormal parameters on the equipment aging;

[0107] Risk determination module: Analyze the change trend of the contact - resistance fluctuation and the lag response of the spatial electric - field distortion gradient. When the trends of the two are mismatched, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection component;

[0108] Strategy generation module: Based on the differential impact degree and the insulation breakdown risk, generate the adjustment amount of the maintenance period of the electrical connection component and the electric - field balance compensation parameter;

[0109] Closed-loop feedback module: Execute the control instructions corresponding to the maintenance cycle adjustment amount of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution.

[0110] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0111] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0112] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0113] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0114] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules can be in electrical, mechanical, or other forms.

[0115] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, in each embodiment of this application, the functional modules can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0117] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0118] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0119] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting potential safety hazards of substation equipment, characterized in that, It includes the following steps: S1. Obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters; S2. Conduct dynamic deviation analysis on the electrical parameters based on the preset aging rules, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as the target abnormal parameters; S3. Assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment, and determine the different degrees of differential impact of the target abnormal parameters on equipment aging; S4. Analyze the change trend of the contact resistance fluctuation and the lag response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components; S5. Generate the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters based on the differential impact degree and the insulation breakdown risk; S6. Execute the control instructions corresponding to the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution.

2. The method for detecting potential safety hazards of a power transformation device according to claim 1, wherein, Obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters, including: Obtain the real-time monitored electrical parameters from the distributed sensors of the target substation equipment. The electrical parameters include voltage fluctuation data, current harmonic components, and partial discharge intensity; Synchronously collect temperature parameters through an infrared thermal imager and a temperature sensor. The temperature parameters include the surface temperature gradient of the electrical connection components and the temperature rise rate of the insulating material; Extract the aging-related parameters from the equipment historical operation and maintenance database. The aging-related parameters include the oxidation rate of the electrical connection components and the dielectric constant attenuation record of the insulating material; Align the electrical parameters, temperature parameters, and aging-related parameters according to the time stamp, and construct a multi-source asynchronous data set.

3. A method for detecting potential safety hazards of a power transformation device according to claim 1, characterized in that, Conduct dynamic deviation analysis on the electrical parameters based on the preset aging rules, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as the target abnormal parameters, including: Compare the voltage fluctuation data, current harmonic components, and partial discharge intensity with the dynamic thresholds according to the electrical parameter aging thresholds in the preset aging rules, and screen out the electrical parameters exceeding the thresholds; Based on the time distribution characteristics of the electrical parameters exceeding the thresholds, screen out the clusters of electrical parameters exceeding the thresholds that repeatedly appear within the continuous time window, calculate the electric field intensity difference between adjacent time windows of the clusters of electrical parameters exceeding the thresholds, and generate the spatial electric field distortion gradient; Extract the historical reference value of the contact resistance of the electrical connection components, calculate the deviation percentage between the current contact resistance and the corrected reference value, and use it as the contact resistance fluctuation; Mark the spatial electric field distortion gradient and the contact resistance fluctuation as the target abnormal parameters, and store the target abnormal parameters in association with the temperature parameters in the multi-source asynchronous data set.

4. The method for detecting potential safety hazards of a power transformation device according to claim 1, wherein Assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment, and determine the different degrees of differential impact of the target abnormal parameters on equipment aging, including: The structural characteristics of the substation equipment include the spatial distribution of the electrical connection components and the heat dissipation path characteristics; According to the spatial distribution of the electrical connection components of the substation equipment, calculate the spatial density of the target abnormal parameters in the dense area of the electrical connection components, and generate the spatial density weight coefficient; According to the heat dissipation path characteristics of the power transformation equipment, calculate the thermal resistance influence weight coefficient of the target abnormal parameter in the area where the thermal resistance coefficient of the heat dissipation path is greater than the preset thermal resistance threshold; Perform weighted accumulation of the spatial density weight coefficient and the thermal resistance influence weight coefficient according to a preset ratio to generate the comprehensive weight value of the target abnormal parameter; Determine the differential influence degree of the target abnormal parameter on equipment aging according to the comprehensive weight value and the preset aging level comparison table.

5. A method for detecting potential safety hazards of a power transformation device according to claim 1, characterized in that, Analyze the change trend of the contact resistance fluctuation amount and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, it is determined that there is a risk of insulation breakdown caused by deterioration of the electrical connection components, including: Extract the time-series data of the contact resistance fluctuation amount and the spatial electric field distortion gradient, and generate the change trend curve of the contact resistance fluctuation amount and the change curve of the spatial electric field distortion gradient according to time windows; Calculate the difference between the absolute value of the slope of the contact resistance fluctuation amount change trend curve and the absolute value of the slope of the spatial electric field distortion gradient change curve as the trend deviation degree; Analyze the lag time of the spatial electric field distortion gradient change curve relative to the contact resistance fluctuation amount change curve. The lag time is the peak time difference between the two curves; When the trend deviation degree is greater than the preset deviation threshold and the lag time is less than the preset lag threshold, it is determined that the change trend of the contact resistance fluctuation amount and the hysteretic response of the spatial electric field distortion gradient do not match, and there is a risk of insulation breakdown caused by deterioration of the electrical connection components.

6. The safety hazard inspection method for a power transformation device according to claim 1, wherein Based on the differential influence degree and the insulation breakdown risk, generate the maintenance cycle adjustment amount of the electrical connection components and the electric field balance compensation parameters, including: Match the maintenance cycle adjustment amount of the electrical connection components from the preset maintenance cycle comparison table according to the aging risk level corresponding to the differential influence degree; Select the corresponding electric field balance compensation parameters from the electric field compensation parameter library according to the insulation breakdown risk determination result and the spatial electric field distortion gradient value; Associate the maintenance cycle adjustment amount of the electrical connection components with the electric field balance compensation parameters according to the equipment number to generate a control instruction set, and the control instruction set includes the execution time window and the priority mark.

7. A method for troubleshooting potential safety hazards of power transformation equipment according to claim 6, characterized in that, The maintenance cycle adjustment amount of the electrical connection components is the time percentage for shortening the maintenance interval; the maintenance cycle shortening ratio of the preset maintenance cycle comparison table is positively correlated with the aging risk level; the electric field balance compensation parameters include the adjacent shielding layer grounding resistance adjustment value and the grading ring spacing correction amount; the priority mark is set based on the number of trend mismatches in the insulation breakdown risk determination result.

8. A method for detecting potential safety hazards of a power transformation equipment according to claim 1, characterized in that, Execute the control instructions corresponding to the maintenance cycle adjustment amount of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution, including: Dynamically adjust the execution order of the control instructions corresponding to the electric field balance compensation parameters according to the execution time window and the priority mark in the control instruction set to generate an execution sequence with self-adaptive priority; Execute the maintenance work order corresponding to the maintenance cycle adjustment amount of the electrical connection components and the equipment control instructions corresponding to the electric field balance compensation parameters in the order of the execution sequence; Collect the surface temperature gradient of the electrical connection components and the insulation material temperature rise rate after execution to generate the temperature parameters after execution; Fuse and update the temperature parameters after execution and the historical aging-related parameters according to the weighted average algorithm to generate the updated aging-related parameters.

9. A power transformation equipment safety hazard investigation system for implementing the power transformation equipment safety hazard investigation method according to any one of claims 1-8, characterized in that, Including: Data acquisition module: Obtain the operation status data and aging-related parameters of the target substation equipment. The operation status data includes electrical parameters and temperature parameters; Abnormality identification module: Based on the preset aging rules, perform dynamic deviation analysis on the electrical parameters, and identify the spatial electric field distortion gradient and the contact resistance fluctuation of the electrical connection components as the target abnormal parameters; Weight assignment module: Assign weights to the target abnormal parameters according to the structural characteristics of the substation equipment, and determine the differential impact degree of different target abnormal parameters on equipment aging; Risk determination module: Analyze the change trend of the contact resistance fluctuation and the hysteretic response of the spatial electric field distortion gradient. When the trends of the two do not match, determine that there is a risk of insulation breakdown caused by the deterioration of the electrical connection components; Strategy generation module: Based on the differential impact degree and the insulation breakdown risk, generate the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters; Closed-loop feedback module: Execute the control instructions corresponding to the adjustment amount of the maintenance cycle of the electrical connection components and the electric field balance compensation parameters, and update the aging-related parameters according to the temperature parameters after execution.

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