Intelligent monitoring and analyzing method for over-capacitance state of transformer

Abstract

The invention relates to an intelligent monitoring and analyzing method for the over-capacitance state of a transformer, and belongs to the field of power equipment detection. Comprising the following steps: acquiring internal temperature and oil flow velocity of a transformer through a distributed optical fiber sensor array, and fusing to generate a temperature distribution diagram and an oil flow velocity field; comparing historical data to identify dynamic dead angles, detecting oil flow velocity abrupt change to judge turbulent flow conversion, and predicting dead angle evolution based on the incidence relation between turbulent flow and dead angles; high-temperature gathering points and monitoring blind areas are identified, the acquisition frequency and density of a sensor are adaptively adjusted, the operation state of the transformer is evaluated, key risk points are extracted, and finally a precise monitoring scheme is generated. According to the method, the dynamic monitoring precision during super-capacity operation is improved, the fault risk caused by high temperature and dead angles is reduced, and intelligent support is provided for safe operation of the transformer.

Description

TECHNICAL FIELD

[0001] The application relates to a transformer over-capacity state intelligent monitoring and analysis method, and belongs to the field of power equipment detection. BACKGROUND

[0002] As the core equipment in the power system, transformers bear the important responsibility of power transmission and distribution, and the stability of their operating state directly affects the safety and efficiency of the power grid. Especially in the over-capacity operation scenario, transformers need to withstand higher loads, resulting in a significant increase in internal temperature, and the dynamic changes in the temperature field become a key factor affecting the service life and performance of the transformer.

[0003] However, the existing monitoring methods rely on fixed-position temperature sensors when dealing with complex temperature field changes in over-capacity operation, and the data collected often cannot fully reflect the spatial distribution characteristics of the internal temperature of the transformer, especially when the load fluctuates sharply, it is difficult to capture the real-time changes of the temperature mutation area.

[0004] In addition, the existing technology lacks dynamic monitoring of the internal oil flow state of the transformer, and fails to effectively combine the correlation between oil flow speed and temperature changes, resulting in insufficient understanding of the heat transfer law inside the transformer. In over-capacity operation, the temperature field inside the transformer presents a highly complex three-dimensional distribution characteristic, and the oil flow circulation mode becomes a key technical factor affecting the temperature distribution. The transition of oil flow from a steady laminar state to a turbulent state will significantly change the heat transfer path.

[0005] However, this dynamic process of state conversion is difficult to capture in real time, especially in complex structural areas such as winding ends and core corners, where oil flow is blocked and flow dead zones are easily formed. These dead zone areas not only cause local temperature accumulation, but also repeatedly form and dissipate with the periodic changes in oil flow intensity, increasing the complexity of monitoring.

[0006] The existence of flow dead zones further exacerbates another technical difficulty, namely the rapid response of temperature changes. The formation of flow dead zones and the rapid response of temperature changes together constitute the core technical challenge in the monitoring of transformer over-capacity operation. For example, when the load of the transformer suddenly increases, the oil flow speed may change from laminar flow to turbulent flow in a short period of time, causing the local temperature at the winding end to rise rapidly.

[0007] If the monitoring system cannot timely adjust the sensor collection frequency or optimize the spatial arrangement density, it will be difficult to accurately identify the existence of these high-temperature areas and their trend. This not only may lead to misjudgment of the oil quality deterioration speed, but also will affect the evaluation of the cooling system efficiency, thereby threatening the long-term stability of the transformer.

[0008] Therefore, how to capture the dynamic characteristics of oil flow state conversion and flow dead angle in overcapacity operation in real time and adjust the monitoring strategy according to the rapid change of temperature has become a key problem to improve the intelligent monitoring and analysis capability of the transformer. SUMMARY

[0009] According to the problems described in the background, the problem to be solved by the present application is to provide a transformer overcapacity state intelligent monitoring and analysis method to solve the problems mentioned above.

[0010] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a transformer overcapacity state intelligent monitoring and analysis method, comprising the following steps:

[0011] (1) collecting the temperature and oil flow speed inside the transformer through a distributed optical fiber sensor array, generating a temperature distribution map and an oil flow speed field, fusing and analyzing the temperature distribution map and the oil flow speed field to determine the temperature change amplitude and the oil flow path change;

[0012] (2) comparing the temperature change amplitude with historical data to calculate the temperature fluctuation amplitude, combining the oil flow path change and the oil flow speed to determine the position and size of the dynamic dead angle area;

[0013] (3) detecting the oil flow speed mutation in the oil flow speed field to determine the transition of oil flow from laminar flow to turbulent flow, combining the temperature fluctuation amplitude to determine the time and area of turbulent flow transition and calculating the turbulent flow area;

[0014] (4) according to the turbulent flow area and the position and size of the dynamic dead angle area, evaluating the correlation of flow disturbance, analyzing the periodicity of the temperature distribution map and the oil flow speed field, predicting the formation period and dissipation time of the dynamic dead angle area, and generating a dynamic dead angle distribution map;

[0015] (5) identifying the high-temperature aggregation point in the temperature distribution map, extracting the high-temperature accumulation area boundary information in the dynamic dead angle distribution map, combining the position and size of the dynamic dead angle area to determine the monitoring blind area range, and calculating the temperature change amplitude of the high-temperature accumulation area;

[0016] (6) according to the temperature change amplitude of the high-temperature accumulation area, adjusting the collection frequency and spatial density of the distributed optical fiber sensor array, and generating an adjusted temperature distribution map and an oil flow speed field;

[0017] (7) according to the adjusted temperature distribution map and the oil flow speed field, evaluating the overall temperature field to determine the transformer operating state;

[0018] The transformer operating state includes the hot spot temperature value;

[0019] (8) Extracting temperature abnormal fluctuation from the transformer operating state, determining the key risk point, calculating the associated oil flow path deviation value, tracking the evolution trend of the temperature change amplitude, determining the potential high temperature area path, and generating a monitoring scheme;

[0020] The monitoring scheme includes sensor arrangement position, data acquisition frequency and key monitoring area.

[0021] Preferably, the step (1) comprises the following steps:

[0022] (1.1) Distribute a distributed optical fiber sensor array along the inner wall of the transformer oil tank and the winding gap, collect optical signal wavelength shift data, calculate the temperature value of each monitoring point, and generate an initial temperature data set containing spatial coordinates and temperature values;

[0023] (1.2) Based on the optical signal phase difference data collected by the distributed optical fiber sensor array, calculate the oil flow velocity value of the spatial coordinate position corresponding to the initial temperature data set, and generate an oil flow velocity vector field containing velocity size, direction and spatial position;

[0024] (1.3) Spatial grid division is performed on the initial temperature data set, and spatial interpolation is used to fill in the sensor coverage blind area to generate a continuous temperature distribution map, calculate the temperature difference value at adjacent time, and determine the temperature change amplitude;

[0025] (1.4) Streamline tracking is performed on the oil flow velocity vector field, the oil flow motion trajectory is recorded, the oil flow path distribution is generated, and the high temperature area boundary and the oil flow path deviation angle are determined by combining the temperature change amplitude and the oil flow path distribution.

[0026] Preferably, the step (2) comprises the following steps:

[0027] (2.1) Extracting historical operation records with load deviation within ±5% and environmental temperature difference not exceeding 3 degrees Celsius from the historical database, calculating the point-by-point difference value of the temperature change amplitude and the temperature time series data, and generating a difference sequence;

[0028] (2.2) Calculate the variance value of the difference sequence to determine the temperature fluctuation amplitude;

[0029] (2.3) Combined with the spatial distribution of the oil flow path change area, identify the low-speed area where the oil flow velocity is lower than half of the normal flow velocity, and when the temperature fluctuation amplitude in the low-speed area is higher than that in the surrounding normal flow area and the fluctuation frequency is lower than the preset frequency threshold, determine that the area is the initial position of the dynamic dead angle area;

[0030] (2.4) tracking the boundary of the eddy current region formed at the winding end or core corner by the oil flow velocity vector distribution of the initial position of the dynamic dead zone region, measuring the maximum span of the eddy current region in the horizontal direction as the transverse width, measuring the maximum extension distance in the vertical direction as the longitudinal height, calculating the velocity change rate at the junction of the normal flow region and the dead zone region, determining the accurate boundary of the dead zone region according to the position where the velocity change rate exceeds the preset change rate threshold, obtaining the center position coordinates and geometric size including width and height of the dynamic dead zone region.

[0031] Preferably, the step (3) comprises the following steps:

[0032] (3.1) calculating the difference value of adjacent time of the velocity time sequence of the oil flow velocity field, generating the velocity change rate, marking the point where the velocity change rate exceeds the preset velocity threshold as the velocity abrupt point, and calculating the Reynolds number of the velocity abrupt point;

[0033] (3.2) using a double threshold judgment method to judge the transition from laminar flow to turbulent flow, extracting the temperature fluctuation amplitude when the oil flow changes from laminar flow to turbulent flow, and determining the time and region of the turbulent flow transition;

[0034] (3.3) calculating the grid cell area within the turbulent flow region contour, and generating the turbulent flow region area.

[0035] Preferably, the step (4) comprises the following steps:

[0036] (4.1) calculating the ratio of the turbulent flow region area to the dynamic dead zone area, defining the ratio as the disturbance intensity coefficient; combining the distance between the center coordinates of the dynamic dead zone region and the geometric center of the turbulent flow region, and taking the maximum inner diameter of the transformer oil tank as the reference, calculating the spatial correlation degree = 1-(distance between two centers / oil tank maximum inner diameter);

[0037] (4.2) marking the key monitoring region based on the condition that the disturbance intensity coefficient exceeds the preset intensity coefficient threshold and the spatial correlation degree exceeds the preset correlation degree threshold;

[0038] (4.3) extracting the time sequence from the temperature data of the key monitoring region, converting to the frequency domain using the fast Fourier transform algorithm, and determining the temperature change main period and phase shift;

[0039] (4.4) predicting the formation time and dissipation time of the dynamic dead zone region according to the phase shift and oil flow velocity value, and generating a dynamic dead zone distribution map containing the space-time distribution characteristics.

[0040] Preferably, the step (4.4) comprises the following steps:

[0041] (4.4.1) Extracting the time sequence change of the turbulent flow area, calculating the area change rate, combining the center coordinates and size of the dynamic dead angle area, determining the disturbance enhancement period;

[0042] (4.4.2) Recording the moving track of the dynamic dead angle area, calculating the duration from formation to dissipation, and generating a dynamic dead angle distribution map containing the formation time, duration and dissipation path.

[0043] Preferably, the step (5) comprises the following steps:

[0044] (5.1) Scanning the temperature value of the temperature distribution map, clustering the points with temperature exceeding the preset temperature threshold, and generating high-temperature accumulation points;

[0045] (5.2) Extracting the boundary coordinate sequence of the corresponding position in the dynamic dead angle distribution map, and determining the high-temperature accumulation area boundary information;

[0046] (5.3) Calculating the area within the high-temperature accumulation area boundary information, superimposing the center position and size of the dynamic dead angle area, and determining the monitoring blind area range not covered by the sensor;

[0047] (5.4) Estimating the temperature value in the monitoring blind area range by interpolation method, calculating the difference between the maximum and minimum values of the high-temperature accumulation area, and generating the temperature variation amplitude of the high-temperature accumulation area.

[0048] Preferably, the step (6) comprises the following steps:

[0049] (6.1) If the temperature fluctuation of the high-temperature area exceeds the safety threshold, encrypt the monitoring points by a preset coefficient, reduce the spacing between the points, and increase the sampling frequency to obtain the encrypted monitoring point arrangement scheme.

[0050] (6.2) According to the encrypted monitoring point arrangement scheme, adjust the scanning parameters of the optical time domain reflectometer, preferentially scan the high-temperature area at high frequency, and obtain high-resolution temperature and oil flow velocity data.

[0051] (6.3) Constructing more accurate temperature distribution map and oil flow velocity field by Kriging interpolation on high-resolution data.

[0052] Preferably, the step (7) comprises the following steps:

[0053] (7.1) Calculating the gradient of the temperature value in the adjusted temperature distribution map, and marking the area with gradient exceeding the preset gradient threshold as the temperature change area;

[0054] (7.2) Determining the hotspot temperature value in the temperature change area, combining the flow velocity value of the adjusted oil flow velocity field, and generating the transformer operating state containing the hotspot temperature value.

[0055] Preferably, step (8) includes the following steps:

[0056] (8.1) Scan the temperature time-series curve of the transformer's operating status, calculate the variance of the temperature change rate, mark the points where the variance exceeds the preset variance threshold as abnormal fluctuation points, determine the distribution density of the abnormal fluctuation points, and generate key risk points.

[0057] (8.2) Calculate the deviation between the oil flow path and the historical path of the key risk point, and generate deviation time series data;

[0058] (8.3) Track the peak movement direction of the temperature change amplitude to predict the path of potential high-temperature regions;

[0059] (8.4) Arrange sensors according to the potential high temperature area path, set the data acquisition frequency of the key risk points, and generate a monitoring plan that includes sensor placement location, data acquisition frequency and key monitoring area.

[0060] The beneficial effects of this invention are:

[0061] This invention discloses an intelligent monitoring and analysis method for transformer overcapacity conditions. Addressing the challenges of accurately monitoring abnormal temperature fluctuations, oil flow path changes, and dynamic dead zones during transformer operation, this method integrates temperature distribution maps, oil flow velocity fields, and historical data analysis to construct dynamic dead zone distribution maps and identify the boundaries of high-temperature accumulation areas. By collecting real-time temperature and oil flow velocity data, it identifies the timing and region of turbulent transitions, assesses the correlation between flow disturbances and dead zone formation, predicts the dead zone formation cycle and dissipation time, and tracks the synchronous changes in high-temperature area diffusion and dead zone dissipation. When temperature fluctuations or sudden changes in oil flow velocity exceed thresholds, it adaptively adjusts the sensor acquisition frequency and spatial density, optimizes monitoring points, reassesses the temperature field and transformer operating status, extracts key risk points, and generates precise monitoring schemes. This significantly improves the dynamic monitoring accuracy of transformer operating status, reduces the risk of faults caused by high-temperature accumulation and dead zone areas, and provides intelligent technical support for the safe and stable operation of transformers. Attached Figure Description

[0062] Figure 1 This is a flowchart of the steps of the method of the present invention; Detailed Implementation

[0063] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0064] Example 1

[0065] like Figure 1 As shown, this invention provides an intelligent monitoring and analysis method for transformer overcapacity conditions, comprising the following steps:

[0066] (1) Collecting the temperature and oil flow velocity inside the transformer by the distributed optical fiber sensor array, generating the temperature distribution map and oil flow velocity field, fusing and analyzing the temperature distribution map and oil flow velocity field to determine the temperature variation amplitude and oil flow path change;

[0067] The step (1) comprises the following steps:

[0068] (1.1) Disposing the distributed optical fiber sensor array along the inner wall of the transformer oil tank and the winding gap, collecting the optical signal wavelength shift data, calculating the temperature value of each monitoring point, and generating the initial temperature data set containing the spatial coordinates and temperature values;

[0069] Specifically, the distributed optical fiber sensors are disposed along the vertical direction of the inner wall of the transformer oil tank at a preset interval, the optical fiber sensors monitor the temperature value according to the wavelength shift of the optical signal caused by the temperature change, the optical fibers are laid transversely on the winding gap and the surface of the core to form a grid-shaped sensing array, the end of the optical fiber is connected to the optical time domain reflectometer to collect the reflected light signal intensity and time delay data, the temperature value of each monitoring point is calculated according to the corresponding relationship between the optical signal wavelength shift and the temperature, and the initial temperature data set containing the spatial coordinates and temperature values is obtained.

[0070] Among them, the deployment of the distributed optical fiber sensor follows the principle of grid layout.

[0071] On the vertical direction of the inner wall of the transformer oil tank, the optical fiber sensors are installed at a preset vertical interval from the bottom to the top of the oil tank, and each optical fiber penetrates the entire oil channel space along the height direction of the oil tank. The optical fiber sensor adopts a fiber Bragg grating structure, when the environmental temperature changes, the grating period expands or shrinks with the temperature change, resulting in a shift in the reflected light wavelength. In the winding gap area, the optical fibers are arranged in a spiral winding manner to ensure that the temperature of each layer of the winding can be monitored. The surface of the core is laid in a parallel manner, and a plurality of optical fibers are arranged transversely along the surface of the core column and the core yoke, and a fixed interval is maintained between adjacent optical fibers to form a monitoring grid covering the main heating area of the core. The end of the optical fiber is connected to the optical time domain reflectometer through a high-temperature resistant connector, the reflectometer emits a pulse optical signal into the optical fiber, when the optical signal meets the grating, it reflects, the position of the reflection point is determined by measuring the time delay of the reflected light, and the temperature value of the position is calculated by analyzing the wavelength shift of the reflected light.

[0072] Among them, the transformer internal temperature and oil flow monitoring is realized by constructing a three-dimensional sensing network to achieve all-around data collection.

[0073] The three-dimensional sensing network is composed of a distributed optical fiber sensor array and an ultrasonic Doppler sensor, the two types of sensors cooperate with each other in space position, the optical fiber sensor is responsible for temperature field monitoring, and the ultrasonic sensor is responsible for oil flow velocity field measurement.

[0074] (1.2) Based on the phase difference data of the light signal collected by the distributed optical fiber sensor array, the oil flow velocity value corresponding to the spatial coordinate position of the initial temperature data set is calculated, and an oil flow velocity vector field containing velocity size, direction and spatial position is generated;

[0075] Specifically, an ultrasonic Doppler sensor is deployed at the spatial coordinate position of the initial temperature data set, the Doppler frequency shift amount generated when the oil flow passes through the monitoring section is measured, the instantaneous velocity value of the oil flow at each monitoring point is calculated according to the proportional relationship between the frequency shift amount and the flow velocity, and the oil flow velocity value, the flow direction angle and the corresponding spatial coordinate are matched to construct an oil flow velocity vector field containing velocity size, direction and spatial position.

[0076] Wherein, the velocity measurement principle of the ultrasonic Doppler sensor is based on the frequency change phenomenon of sound wave propagation in moving medium.

[0077] The sensor transmits ultrasonic waves with a fixed frequency into the transformer oil. When the ultrasonic waves encounter flowing oil molecules, scattering occurs, and the frequency of the scattered waves is offset relative to the transmission frequency. The offset is proportional to the oil flow velocity. The sensor receives the scattered echo and calculates the frequency difference, and calculates the instantaneous velocity of the oil flow at the monitoring point according to the Doppler shift formula. At the same time, the movement direction of the oil flow is determined by analyzing the phase information of the echo signal, and the velocity size and direction information are combined to form a velocity vector.

[0078] Wherein, the ultrasonic Doppler sensor and the optical fiber sensor maintain a corresponding relationship in spatial position.

[0079] Each ultrasonic sensor is installed near the optical fiber monitoring point to ensure the consistency of temperature and flow velocity measurement positions. The raw data collected by the sensor is transmitted to the data processing unit through industrial Ethernet, and the processing unit performs time synchronization and spatial registration on the two types of data to generate temperature field and velocity field data consistent in time and space.

[0080] (1.3) Spatial grid division is performed on the initial temperature data set, and spatial interpolation is used to fill in the blind area covered by the sensor to generate a continuous temperature distribution map, and the temperature difference value between adjacent time points is calculated to determine the temperature change amplitude;

[0081] Specifically, the initial temperature data set is divided into a monitoring area according to a spatial grid, and spatial interpolation is used to fill in the blind area covered by the sensor using the temperature values of adjacent monitoring points to generate a continuous temperature distribution map, and the temperature difference value between adjacent time points is calculated to obtain the temperature change amplitude.

[0082] Wherein, the temperature data of the blind area covered by the sensor is filled in by a spatial interpolation method.

[0083] The temperature values of adjacent monitoring points are taken as known conditions, and the temperature distribution in the blind area is estimated according to the heat conduction law and the oil flow distribution characteristics. The interpolation process considers the influence of the oil flow direction on temperature transfer, and the weight of the upstream monitoring point is higher than that of the downstream monitoring point.

[0084] (1.4) Streamline tracking is performed on the oil flow velocity vector field to record the oil flow trajectory and generate the oil flow path distribution. The high-temperature region boundary and the oil flow path deviation angle are determined by combining the temperature variation amplitude and the oil flow path distribution.

[0085] Specifically, streamline tracking is performed on the oil flow velocity vector field to record the oil flow trajectory from the bottom to the top to obtain the oil flow path distribution. The temperature variation amplitude is spatially superimposed with the oil flow path distribution to identify the high-temperature region boundary with a temperature gradient exceeding a preset threshold. The deviation angle of the current oil flow path from the standard path during steady-state operation is compared, and if the deviation angle exceeds a preset angle threshold, it is determined as a path change region. The temperature variation amplitude value of the high-temperature region and the oil flow deviation degree of the path change region are obtained.

[0086] Among them, the streamline tracking of the oil flow velocity vector field starts from the oil inlet at the bottom of the oil tank and traces the oil flow trajectory point by point along the velocity vector direction.

[0087] At each monitoring point position, the next position of the oil flow is determined according to the velocity vector of the point, and the complete streamline is formed by continuous tracking. When the oil flow passes through the winding area, the streamline bends and branches due to the blocking effect of the winding structure, and the main streamline circumvents the outside of the winding, and part of the branch flow enters the internal cooling channel of the winding. The streamline tracking process records the spatial coordinate sequence of each streamline, identifies the main path and branch path of the oil flow rising from the bottom to the top, and obtains the standard path during steady-state operation through historical data statistics, representing the oil flow distribution pattern under normal working condition of the transformer. The deviation of the current path from the standard path is quantified by calculating the perpendicular distance of each point on the streamline to the standard path, and the area with a deviation distance exceeding a preset threshold is marked as a path change region. The temperature gradient is obtained by calculating the ratio of the temperature difference between adjacent monitoring points to the spatial distance, and the gradient value reflects the degree of spatial temperature variation.

[0088] Among them, the spatial superposition of the temperature variation amplitude and the oil flow path is realized through coordinate mapping.

[0089] The temperature gradient field and the oil flow path distribution are projected into the same spatial coordinate system to identify the spatial overlap area of the two. The intersection of the high-temperature gradient area and the path change area represents the key position of heat transfer anomaly, which is often the potential area of the hot spot formation inside the transformer.

[0090] The oil flow path deviation angle is obtained by vector angle calculation, and the included angle between the direction vector of the current path and the standard path direction vector is the deviation angle, which directly reflects the change degree of the oil flow movement direction.

[0091] (2) comparing the temperature variation amplitude with historical data, calculating the temperature fluctuation amplitude, combining the oil flow path change and the oil flow speed to determine the position and size of the dynamic dead angle region;

[0092] The step (2) comprises the following steps:

[0093] (2.1) extracting historical operation records with load deviation within ±5% and environmental temperature difference of no more than 3 degrees Celsius from the historical database, calculating the point-by-point difference value of the temperature variation amplitude and the temperature time series data to generate a difference value sequence;

[0094] Specifically, the temperature time series data within the past month under the same operating load and environmental temperature conditions is extracted from the historical database, and the current temperature variation amplitude is compared with the historical temperature time series data point by point, and the difference value of the current temperature value and the historical temperature average value of each monitoring point is calculated.

[0095] Wherein, the extraction of historical temperature time series data needs to match the current operating condition parameters.

[0096] The historical operation records with load deviation within ±5% and environmental temperature difference of no more than 3 degrees Celsius are screened from the database, and the temperature data sequence of each monitoring point within the past month is extracted. The arithmetic mean value of the historical temperature sequence of each monitoring point is calculated as the reference temperature, and the difference between the current real-time temperature and the reference temperature is obtained. The temperature fluctuation amplitude is obtained by calculating the variance of the deviation value sequence, and the larger the variance value, the more intense the temperature fluctuation.

[0097] (2.2) calculating the variance value of the difference value sequence to determine the temperature fluctuation amplitude;

[0098] Specifically, the variance value is calculated as the temperature fluctuation amplitude according to the difference value sequence, and the number of temperature peak value occurrences within a unit of time is counted as the fluctuation frequency to obtain a temperature fluctuation feature set containing the fluctuation amplitude value and the fluctuation frequency.

[0099] Wherein, the fluctuation frequency is based on the temperature peak value occurrence rule.

[0100] Within a unit time window, when the temperature value exceeds the average temperature plus one standard deviation, it is recorded as a peak value, and the number of peak value occurrences is counted as the fluctuation frequency.

[0101] (2.3) According to the spatial distribution of the oil flow path change area, a low-speed area with a flow speed lower than half of the normal flow speed is identified, and when the temperature fluctuation amplitude in the low-speed area is higher than that in the surrounding normal flow area and the fluctuation frequency is lower than a preset frequency threshold, it is determined that the area is the initial position of the dynamic dead angle area;

[0102] Specifically, according to the monitoring point position with a fluctuation amplitude exceeding a preset threshold in the temperature fluctuation feature set, a low-speed area with a flow speed lower than half of the normal flow speed is identified according to the spatial distribution of the oil flow path change area, and when the temperature fluctuation amplitude in the low-speed area is higher than that in the surrounding normal flow area and the fluctuation frequency is lower than a preset frequency threshold, it is determined that the area is the initial position of the dynamic dead angle area.

[0103] Wherein, the formation of the dynamic dead angle area is closely related to the oil flow speed distribution.

[0104] When the internal oil flow of the transformer encounters a structural mutation position such as the winding end or the core corner, the main flow direction changes, and a low-speed backflow area is formed on the backflow side. The oil flow speed in the low-speed area is reduced to less than half of the normal flow speed, the heat exchange efficiency is reduced, and the local temperature is increased and the fluctuation amplitude is increased.

[0105] (2.4) According to the oil flow speed vector distribution of the initial position of the dynamic dead angle area, the boundary of the vortex area formed by the oil flow at the winding end or the core corner is tracked, the maximum span of the vortex area in the horizontal direction is measured as the transverse width, the maximum extension distance in the vertical direction is measured as the longitudinal height, the speed change rate at the junction of the normal flow area and the dead angle area is calculated, the accurate boundary of the dead angle area is determined according to the position where the speed change rate exceeds a preset change rate threshold, and the center position coordinates and geometric dimensions including width and height of the dynamic dead angle area are obtained.

[0106] Specifically, according to the oil flow speed vector distribution of the initial position of the dynamic dead angle area, the boundary of the vortex area formed by the oil flow at the winding end or the core corner is tracked, the maximum span of the vortex area in the horizontal direction is measured as the transverse width, the maximum extension distance in the vertical direction is measured as the longitudinal height, the speed change rate at the junction of the normal flow area and the dead angle area is calculated, the accurate boundary of the dead angle area is determined according to the position where the speed change rate exceeds a preset change rate threshold, and the center position coordinates and geometric dimensions including width and height of the dynamic dead angle area are obtained.

[0107] Wherein, the identification of the dynamic dead angle area of the transformer is realized by combining historical data comparison and real-time flow field monitoring.

[0108] The historical data is derived from a transformer operation management database and includes temperature records of transformers of the same type under different load conditions.

[0109] The vortex region boundary is determined by analyzing the velocity vector field.

[0110] The velocity vector difference between adjacent monitoring points is calculated, and when the velocity direction changes by more than 90 degrees, it is determined to be a vortex boundary point. All boundary points are connected to form a closed curve, and the inside of the curve is the vortex region. The transverse width of the vortex region is obtained by measuring the maximum distance of the boundary points in the horizontal direction, and the vertical height is obtained by measuring the maximum distance of the boundary points in the vertical direction.

[0111] The precise boundary of the dead angle region is determined according to the rate of change of velocity.

[0112] At the junction of the normal flow region and the dead angle region, the oil flow velocity changes sharply in a short distance, and the rate of change of velocity reaches a peak. Connecting the continuous points with a rate of change of velocity exceeding a preset threshold can obtain the precise boundary profile of the dead angle region.

[0113] (3) Detecting the sudden change of oil flow velocity in the oil flow velocity field, determining the transition of oil flow from laminar flow to turbulent flow, combining the temperature fluctuation amplitude to determine the time and area of turbulent flow transition, and calculating the area of turbulent flow region;

[0114] The step (3) comprises the following steps:

[0115] (3.1) Calculate the difference between adjacent time points of the velocity time series of the oil flow velocity field to generate the rate of change of velocity, mark the points with a rate of change of velocity exceeding a preset velocity threshold as velocity sudden change points, and calculate the Reynolds number of the velocity sudden change points;

[0116] Specifically, the oil flow velocity time series of each monitoring point in the transformer is monitored, the ratio of the velocity difference between adjacent time points to the time interval is calculated to obtain the rate of change of velocity, and when the rate of change of velocity exceeds a preset sudden change threshold, it is marked as a velocity sudden change point. The Reynolds number of the velocity sudden change point is extracted, and the Reynolds number is calculated according to the product of the oil flow velocity and the characteristic size of the oil duct divided by the kinematic viscosity. If the Reynolds number suddenly increases from below a first preset threshold to above a second preset threshold, it is determined that the monitoring point has transitioned from laminar flow to turbulent flow.

[0117] The transformer oil flow state monitoring is realized through real-time velocity acquisition and flow state determination.

[0118] During the circulation of oil flow in the transformer, the flow state dynamically transitions between laminar flow and turbulent flow under the influence of temperature gradient and structural obstruction. Accurate identification of the transition time and area is crucial for temperature field prediction.

[0119] The monitoring of the sudden change of oil flow velocity is based on the difference operation of continuous time series.

[0120] The speed sensor configured at each monitoring point records the oil flow speed value at a fixed sampling frequency to form a speed time sequence. For any monitoring point, the current speed value and the previous speed value are extracted, the difference between the two is calculated and divided by the sampling time interval to obtain the instantaneous speed change rate. When the absolute value of the speed change rate exceeds the preset mutation threshold, it indicates that the oil flow state at this position changes dramatically. The physical mechanism of speed mutation is closely related to the internal structure of the transformer. When the oil flow enters the narrow channel from the wide oil way, the flow rate increases sharply; when the oil flow encounters the winding block and needs to change direction, the speed vector changes. These mutation points are often the potential positions of flow state conversion, which need to be further determined by the Reynolds number to determine the specific flow state type.

[0121] wherein the Reynolds number as a dimensionless parameter to determine the flow state of the fluid, its calculation involves three physical quantities of oil flow speed, characteristic size and kinematic viscosity.

[0122] The characteristic size depends on the geometry of the oil way. For a rectangular oil way, the hydraulic diameter is used, which is four times the flow area divided by the wetted perimeter; for a circular oil way, the pipe diameter is directly used. The kinematic viscosity changes with the oil temperature, and the viscosity decreases with the increase of temperature. At the same speed, the Reynolds number increases, and it is easier to occur turbulent flow.

[0123] (3.2) A double threshold discrimination method is used to determine the transition from laminar flow to turbulent flow. When the oil flow changes from laminar flow to turbulent flow, the temperature fluctuation amplitude is extracted to determine the time and area of the turbulent transition;

[0124] Specifically, at the monitoring point position where the laminar flow changes to turbulent flow, the temperature fluctuation amplitude value is obtained. If the temperature fluctuation amplitude exceeds the preset fluctuation threshold, the temperature change sequence in the time window before and after the monitoring point is extracted, the time when the change rate in the temperature change sequence reaches the peak value is taken as the specific time of the turbulent transition, and the spatial coordinates corresponding to the specific time are recorded as the initial position of the turbulent flow. Starting from the initial position of the turbulent flow, the difference of the velocity vectors of adjacent monitoring points is calculated, and when the velocity vector difference exceeds the preset difference threshold, it is determined as the flow field disturbance area. The flow field disturbance area is superimposed with the area where the temperature fluctuation amplitude exceeds the preset fluctuation threshold in space, and the overlapping part is determined as the turbulent influence area.

[0125] wherein the transition from laminar flow to turbulent flow is determined by a double threshold discrimination method.

[0126] When the Reynolds number is less than the first preset threshold, the oil flow is in a stable laminar flow state, and the flow lines are parallel and orderly; when the Reynolds number is greater than the second preset threshold, the oil flow enters the fully developed turbulent flow state, and the flow lines are disordered. In the transition zone between the two thresholds, the flow state is unstable, and intermittent turbulent flow may occur.

[0127] wherein the temperature fluctuation and the turbulent transition have a corresponding relationship in time.

[0128] The strong mixing effect in the turbulent state accelerates heat transfer, leading to rapid changes in local temperature. By monitoring the time when the temperature fluctuation amplitude exceeds the preset fluctuation threshold, the occurrence time of the turbulent transition can be determined.

[0129] The determination of the specific moment of turbulent transition is realized by peak value identification of the temperature change rate.

[0130] A time window is set before and after the monitoring point, and the temperature sequence within the window is extracted. The temperature change rate at each time is calculated, and the time when the change rate reaches the peak value is found as the turbulent transition time.

[0131] (3.3) Calculate the area of the grid cell within the turbulent region contour to generate the turbulent region area.

[0132] Specifically, the boundary tracking is performed on the turbulent influence area, the included angle of the velocity vectors between adjacent points on the boundary is calculated, and the points with an included angle exceeding a preset angle threshold are connected to form a turbulent region contour. The total area of the turbulent region is obtained by accumulating the areas of all grid cells within the contour.

[0133] The identification of the flow field disturbance region is based on the spatial difference analysis of the velocity vector field.

[0134] At the grid monitoring points of the transformer oil channel, each point has a corresponding velocity vector, containing velocity magnitude and direction information. The velocity vector difference between adjacent monitoring points is calculated, and the modulus of the difference reflects the degree of non-uniformity of the flow field. When the vector difference exceeds a preset difference threshold, it indicates that the flow field in this region is disturbed. The generation mechanism of the disturbance is related to the turbulent vortex structure. There are vortices of different scales in the turbulent flow, large vortices carry the main momentum, and small vortices are responsible for energy dissipation. The rotational motion of the vortex causes the velocity direction of adjacent positions to be opposite or form a large included angle, forming a significant velocity gradient. By identifying these high gradient regions, the spatial range of turbulent influence can be determined. The flow field disturbance region often has an irregular shape and may contain multiple separate sub-regions, which need to be merged by connectivity analysis to belong to the same turbulent structure.

[0135] The spatial superposition analysis matches the flow field disturbance region with the temperature fluctuation region.

[0136] The overlapping part of the two regions has both flow field disturbance and temperature anomaly, which is the core region of turbulent influence. The non-overlapping part may be the edge region of turbulent influence or an anomaly caused by other factors, which needs to be treated differently. The superposition process is carried out in the same spatial coordinate system to ensure the accuracy of the position correspondence.

[0137] The turbulent region area is calculated by accumulating the grid cells.

[0138] The monitoring area is divided into a regular grid, each grid cell has a fixed area, the number of grids falling within the turbulent flow area contour is counted, and the total area is obtained by multiplying the cell area.

[0139] (4) According to the turbulent flow area and the position and size of the dynamic dead angle area, the correlation of flow disturbance is evaluated, the periodicity of temperature distribution and oil flow velocity field is analyzed, the formation period and dissipation time of dynamic dead angle area are predicted, and the dynamic dead angle distribution map is generated.

[0140] The step (4) comprises the following steps:

[0141] (4.1) Calculate the ratio of the turbulent flow area to the dynamic dead angle area, and define the ratio as the disturbance intensity coefficient; combine the distance between the center coordinates of the dynamic dead angle area and the geometric center of the turbulent flow area, and calculate the spatial correlation degree = 1-(two center distance / oil tank maximum inner diameter) based on the maximum inner diameter of the transformer oil tank.

[0142] Wherein, the prediction of transformer dynamic dead angle area is realized by turbulent flow disturbance correlation analysis and periodicity rule extraction.

[0143] The disturbance effect of turbulent flow area will affect the formation and evolution of dead angle area. By quantifying the spatial correlation between the two, the spatiotemporal distribution characteristics of the dead angle can be predicted.

[0144] Wherein, the calculation of area ratio needs to measure two areas at the same time.

[0145] The boundary of the turbulent flow area is determined by the velocity gradient threshold, and the boundary of the dead angle area is determined by the contour line of the oil flow velocity lower than the critical value. The two areas may have partial overlap.

[0146] (4.2) Based on the condition that the disturbance intensity coefficient exceeds the preset intensity coefficient threshold and the spatial correlation degree exceeds the preset correlation degree threshold, mark the key monitoring area;

[0147] Wherein, the evaluation of disturbance correlation is based on the comprehensive analysis of geometric characteristics and spatial position.

[0148] The area of the turbulent flow region reflects the intensity and range of flow field disturbance, and the larger the area, the more extensive the disturbance effect. The area of the dynamic dead angle region represents the severity of oil flow stagnation, and the larger the area, the more serious the flow blockage. The ratio of the areas of the two regions as a disturbance intensity coefficient quantitatively describes the promoting effect of turbulence on the formation of dead angles. When the disturbance intensity coefficient is greater than 1, it indicates that the turbulent flow region is larger than the dead angle region, and the disturbance effect dominates, and the dead angle may be in the initial stage or about to dissipate; when the coefficient is less than 1, the dead angle region is relatively stable, and the disturbance effect is not enough to destroy the formed dead angle structure. The spatial correlation degree is calculated by the Euclidean distance between the geometric centers of the two regions, and the closer the distance, the stronger the interaction between turbulence and dead angle. The product of the disturbance intensity coefficient and the spatial correlation degree comprehensively reflects the overall influence degree of flow disturbance, and when the product exceeds the preset correlation threshold, the location is marked as a key monitoring area, which needs intensive sampling to capture the rapidly changing flow field characteristics.

[0149] (4.3) Extracting the time series from the temperature data of the key monitoring area, converting to the frequency domain by using the fast Fourier transform algorithm, and determining the main period and phase shift of temperature change;

[0150] Specifically, the time series are extracted from the real-time temperature data of the key monitoring area, and the Fourier transform is used to convert the temperature time series to the frequency domain, and the frequency component with the maximum amplitude in the frequency spectrum is identified as the main frequency, and the inverse of the main frequency is the main period of temperature change. At the same time, the speed fluctuation period is extracted from the oil flow speed time series, and the time difference between the temperature main period and the speed fluctuation period is calculated as the phase shift.

[0151] Among them, the Fourier transform of the temperature time series converts the time domain signal to the frequency domain for analysis.

[0152] In the transformed frequency spectrum, the horizontal axis represents the frequency, and the vertical axis represents the amplitude of the corresponding frequency component. The frequency component with the maximum amplitude is the main frequency, representing the dominant periodic component of temperature change. The inverse of the main frequency gives the main period of temperature change, with units of seconds or minutes, reflecting the cycle time of temperature rise and fall.

[0153] (4.4) According to the phase shift and oil flow speed value, predicting the formation time and dissipation time of the dynamic dead angle region, and generating a dynamic dead angle distribution map containing spatiotemporal distribution characteristics.

[0154] Specifically, according to the phase offset and the temperature main period, the oil flow speed value at the first appearance of the dynamic dead angle region in the history data is tracked, and when the real-time oil flow speed drops to the value, it is predicted that the dead angle will be formed, the prediction time of the dead angle formation is obtained by adding the phase offset to the time when the speed drops, and the dissipation time of the dead angle is predicted according to the average dissipation length of the history statistics. Based on the prediction time and the dissipation time of the dead angle formation, the frequency of the dead angle occurrence at each position is marked on the spatial grid, the probability of the occurrence is obtained by dividing the frequency by the total monitoring times, the probability value is mapped to the color depth according to the preset color scale, and the dynamic dead angle distribution diagram containing the space-time distribution characteristics is drawn by combining the position coordinates and size information of the dead angle region.

[0155] Wherein, the calculation of the phase offset needs to consider the periodic changes of the temperature and the oil flow speed.

[0156] The period of the oil flow speed is obtained by autocorrelation analysis on the time series of the speed, and the time delay corresponding to the first peak of the autocorrelation function is the speed fluctuation period. The time difference between the temperature main period and the speed fluctuation period is the phase offset, which represents the time lag of the temperature change behind the oil flow change.

[0157] Wherein, the prediction of the dead angle formation is based on the critical speed value in the history data.

[0158] When the real-time monitored oil flow speed drops to the critical value in the history statistics, it is predicted that the dead angle will be formed after the phase offset time. The prediction of the dissipation time is based on the average dissipation length, and the predicted dissipation time is obtained by adding the dissipation length to the formation time.

[0159] Wherein, the statistical analysis of the history data needs to collect the dead angle evolution records of the transformer under different operating conditions.

[0160] For each monitoring position, the number of times and the duration of the dead angle occurrence are counted. The oil flow speed at the first appearance of the dead angle is recorded as the formation critical speed, and the time for the speed to recover from the critical value to the normal value is recorded as the average dissipation length. Through statistical average of multiple records, the typical formation critical speed and the average dissipation length of the position are obtained. In actual prediction, when the detected oil flow speed approaches the formation critical speed, the system sends a warning signal to prompt the operator that the dead angle will be formed. According to the average dissipation length of the history statistics, the duration of the dead angle can be estimated to provide a time window for the adjustment of the cooling system. The dead angle characteristics of different positions are different, and the dead angle at the end of the winding usually lasts for a long time, and the dead angle at the corner of the core forms and dissipates quickly.

[0161] Wherein, the calculation of the probability of occurrence is based on the statistics of the long-term monitoring data.

[0162] For each grid position, the cumulative time of the dead angle appearing in the total monitoring time is counted, and the ratio of the two is the probability of the dead angle appearing at this position. The probability value is between 0 and 1, and the closer to 1, the easier it is to form a dead angle at this position.

[0163] The dynamic dead angle distribution map is displayed in the form of a heat map.

[0164] The probability value is mapped to color by a preset color scale, dark red represents a high probability area, and light yellow represents a low probability area, realizing intuitive visualization of the dead angle distribution.

[0165] The step (4.4) comprises the following steps:

[0166] (4.4.1) Extract the time sequence change of the turbulent flow area, calculate the area change rate, and determine the disturbance enhancement period combined with the center coordinates and size of the dynamic dead angle area;

[0167] Specifically, the area sequence of the turbulent flow area at consecutive time points is extracted, the area difference value of adjacent time points is calculated to obtain the area change rate, and the center coordinates, length and width value of the dynamic dead angle area are obtained. The area change rate is compared with the area of the dead angle area, when the area change rate exceeds the preset change threshold and the dead angle area increases synchronously, the period is marked as a disturbance enhancement period, and the time interval from the valley value to the peak value of the temperature is recorded.

[0168] The identification of the disturbance enhancement period is of great significance for predicting the evolution of the dead angle.

[0169] During this period, the degree of disorder of the oil flow is intensified, and the normal heat dissipation path is destroyed, so heat is more likely to accumulate in the local area.

[0170] The oil flow stagnation and the temperature peak value have a time delay relationship.

[0171] When the oil flow speed drops below the stagnation threshold, the convective heat transfer efficiency of the region decreases sharply, but the temperature does not immediately rise, but reaches a peak value after a heat accumulation process. The length of the heat accumulation time delay depends on the heat capacity of the oil and the local heat source intensity. By recording the difference between the stagnation start time and the temperature peak time, the delay effect can be quantified.

[0172] (4.4.2) Record the moving track of the dynamic dead angle area, calculate the duration from formation to dissipation, and generate a dynamic dead angle distribution map containing the formation time, duration and dissipation path.

[0173] Specifically, during the disturbance enhancement period, the area where the oil flow velocity drops below the preset stagnation threshold is monitored, the time when the oil flow stagnation starts is recorded, the time when the temperature reaches the peak after the time is tracked, and the difference between the two times is taken as the heat accumulation delay. The timing relationship between the dead angle formation and the temperature response is determined according to the heat accumulation delay, and the moving track is depicted by continuously recording the change of the dead angle center coordinates. The complete process of the dead angle from formation to dissipation is tracked along the moving track, the time when the dead angle first appears and the time when the oil flow returns to normal are recorded, the duration is calculated, the number of dead angle occurrences and the number of temperature fluctuations in multiple periods are counted, the temperature fluctuation period is compared with the dead angle formation period, and the period mapping coefficient is obtained. According to the period mapping coefficient, the expansion distance per unit time of the high-temperature region boundary and the contraction distance per unit time of the dead angle boundary are calculated, and when the two velocity ratios are within the preset range, it is determined that the change is synchronous. The temperature time sequence, dead angle track, formation time, duration and dissipation path data are integrated and marked on the spatial grid to obtain a dynamic distribution map.

[0174] The refined monitoring of the transformer dynamic dead angle region is realized through multi-dimensional data extraction and space-time correlation analysis.

[0175] The dynamic evolution of the turbulent region and the formation and dissipation of the dead angle region are internally related, and by tracking the space-time variation characteristics of the two, a complete dead angle evolution map can be constructed.

[0176] The real-time monitoring of the turbulent region area is based on continuous image acquisition and boundary recognition.

[0177] The oil flow velocity field data obtained at each sampling time is converted into a binary image, and the pixels with a velocity exceeding the turbulent threshold are marked as 1, and the rest are marked as 0. The number of pixels with a value of 1 is multiplied by the unit pixel area to obtain the total turbulent area. The area sequence of adjacent time forms a time function, and the derivative of the function is obtained. The geometric parameters of the dead angle region are extracted by the minimum circumscribed rectangle method, and the length and width of the rectangle are the characteristic dimensions of the dead angle, and the area of the rectangle is taken as the equivalent area of the dead angle region. When the turbulent area change rate is positive and exceeds the preset change threshold, it indicates that the turbulent flow is expanding; if the dead angle area also increases at this time, it means that the turbulent flow expansion promotes the formation of the dead angle, and this period is marked as the disturbance enhancement period. In the disturbance enhancement period, the temperature monitoring system records the time required for each monitoring point to rise from the temperature valley to the peak, and this time interval reflects the heat accumulation rate in the region.

[0178] The continuous change of the dead angle center coordinates forms a moving track.

[0179] The centroid coordinates of the dead zone region are determined at each sampling time, and the centroid coordinates at consecutive time points are connected to form a trajectory line. The curvature of the trajectory reflects the complexity of the dead zone movement, and a straight trajectory indicates that the dead zone moves in a fixed direction, while a curved trajectory indicates that the dead zone is affected by multiple factors.

[0180] The complete life cycle of the dead zone from formation to dissipation includes multiple stages.

[0181] In the initial formation stage, the oil flow velocity just drops below the critical value, and the dead zone region is small and unstable; in the development stage, the dead zone region gradually expands and tends to be stable; in the dissipation stage, as the oil flow recovers or external disturbance increases, the dead zone gradually shrinks until it disappears. The duration of each stage is recorded, and the total life cycle length is accumulated.

[0182] The mapping relationship between the temperature fluctuation period and the dead zone formation period is established through statistical analysis.

[0183] In an observation period, the number of temperature fluctuations and the number of dead zone occurrences are counted. The number of temperature fluctuations is obtained by dividing the number of times the temperature curve crosses the average value by 2, and the number of dead zone occurrences is obtained by counting the number of events where the oil flow velocity drops below the critical value. The temperature fluctuation period is equal to the observation length divided by the number of fluctuations, and the dead zone formation period is equal to the observation length divided by the number of occurrences. The ratio of the two periods is the period mapping coefficient, which reflects the frequency relationship between temperature changes and dead zone formation. When the mapping coefficient is close to 1, it indicates that temperature fluctuations and dead zone formation are highly synchronized; a coefficient greater than 1 indicates that one dead zone formation corresponds to multiple temperature fluctuations; a coefficient less than 1 indicates that multiple dead zone formations cause one significant temperature fluctuation. Through long-term monitoring, a mapping coefficient database can be established to predict the formation frequency of dead zones based on temperature monitoring results.

[0184] The expansion speed of the high-temperature region boundary is calculated by continuously monitoring the boundary position changes.

[0185] The boundary of the region where the temperature exceeds the high-temperature threshold is identified at each time point, and the displacement distance of the boundary points at adjacent time points is divided by the time interval to obtain the expansion speed. The contraction speed of the dead zone boundary is calculated in a similar manner, and when the ratio of the two speeds is within the range of 0.8 to 1.2, it is determined to be synchronous change.

[0186] The dynamic distribution map is realized by superimposing multiple layers of information on a spatial grid.

[0187] The bottom layer shows the physical structure of the transformer, the middle layer labels the formation position and movement trajectory of the dead zone, and the top layer uses color coding to represent the duration and dissipation path.

[0188] (5) identifying high-temperature aggregation points in the temperature distribution map, extracting high-temperature accumulation area boundary information in the dynamic dead angle distribution map, combining the position and size of the dynamic dead angle area to determine the monitoring blind area range, and calculating the temperature variation amplitude of the high-temperature accumulation area;

[0189] The step (5) comprises the following steps:

[0190] (5.1) scanning the temperature value of the temperature distribution map, clustering points with temperature exceeding a preset temperature threshold, and generating high-temperature aggregation points;

[0191] Specifically, the temperature values of the monitoring points in the temperature distribution map are scanned, the K-means clustering method is used to classify the adjacent points with temperature exceeding a preset high-temperature threshold into the same aggregation area, and the geometric center of each aggregation area is calculated as a high-temperature aggregation point.

[0192] The identification of the high-temperature aggregation area is realized through temperature field clustering analysis and dead angle distribution information fusion.

[0193] The high-temperature area inside the transformer often overlaps with the dead angle area in space. By identifying these overlapping areas, the weak link of the monitoring system can be found.

[0194] The K-means clustering method divides the temperature field into different temperature level areas.

[0195] The number of clusters is set to a preset value during algorithm initialization, which is usually 3 to 5 classes according to the temperature gradient distribution. Each monitoring point is assigned to the nearest cluster center according to its temperature value, and the cluster center is updated by calculating the temperature average value of all points in the class. After multiple iterations until the cluster center no longer changes, the cluster with temperature exceeding the high-temperature threshold is identified as a high-temperature aggregation area. The geometric center of each high-temperature aggregation area is obtained by calculating the arithmetic average of the coordinates of all monitoring points in the area.

[0196] (5.2) extracting the boundary coordinate sequence of the corresponding position in the dynamic dead angle distribution map to determine the high-temperature accumulation area boundary information;

[0197] Specifically, the dead angle probability value of the corresponding position is read from the dynamic dead angle distribution map generated as described above. If the probability value exceeds a preset probability threshold, the boundary coordinate sequence of the area is extracted as the high-temperature accumulation area boundary information.

[0198] The probability value in the dynamic dead angle distribution map represents the possibility of forming a dead angle at each position.

[0199] When the dead angle probability corresponding to the position of the high-temperature aggregation point exceeds the threshold, it means that the area has both high-temperature risk and is prone to oil flow dead angle, which belongs to the key attention area.

[0200] (5.3) Calculate the area within the high-temperature accumulation area boundary information, superimpose the center position and size of the dynamic dead angle area, and determine the monitoring blind area range that is not covered by the sensor;

[0201] Specifically, according to the high-temperature accumulation area boundary information, the area inside the boundary is calculated, the center position of the dynamic dead angle area is compared with the boundary in space, if the dead angle center falls inside the boundary, the length and width data of the dead angle are superimposed on the boundary range, and the area that is not covered by the sensor but has high temperature risk is identified, which is the monitoring blind area range.

[0202] Among them, the identification of the monitoring blind area is based on spatial superposition analysis.

[0203] The rectangular boundary of the dead angle area is determined by the center coordinates, length and width parameters, and the high-temperature accumulation area boundary is represented by a polygon formed by a series of coordinate points. When the center of the dead angle rectangle falls inside the polygon, the two areas overlap in space, and the overlapping part is the potential monitoring blind area.

[0204] (5.4) Estimate the temperature value in the monitoring blind area range by using interpolation method, calculate the difference between the maximum and minimum values of the temperature of the high-temperature accumulation area, and generate the temperature variation amplitude of the high-temperature accumulation area.

[0205] Specifically, virtual monitoring points are arranged at the edge of the monitoring blind area range according to a predetermined interval, the temperature of the virtual monitoring point is estimated by bilinear interpolation method according to the temperature values of the surrounding actual monitoring points, the maximum and minimum values of the temperature of all actual monitoring points and virtual monitoring points in the high-temperature accumulation area are obtained, and the difference between the two values is the temperature variation amplitude of the high-temperature accumulation area.

[0206] Among them, the temperature of the virtual monitoring point is estimated by bilinear interpolation.

[0207] For any virtual point in the monitoring blind area, find the nearest four actual monitoring points around it, which form a rectangular grid. The temperature value of the virtual point is calculated by twice linear interpolation: first, linearly interpolate two pairs of monitoring points in the horizontal direction to get two intermediate values; then linearly interpolate the two intermediate values in the vertical direction to get the final estimated temperature. The interpolation weight is determined inversely proportional to the distance from the virtual point to each monitoring point.

[0208] Among them, the temperature variation amplitude is obtained by statistical extreme value of all points in the area.

[0209] The actual monitoring points provide real temperature values, and the virtual monitoring points provide estimated temperature values, which together constitute a complete temperature field description, and the difference between the maximum and minimum values quantifies the temperature non-uniformity of the area.

[0210] (6) Adjust the acquisition frequency and spatial density of the distributed optical fiber sensor array according to the temperature variation amplitude of the high-temperature accumulation area, and generate an adjusted temperature distribution map and oil flow velocity field;

[0211] The step (6) comprises the following steps:

[0212] (6.1) If the temperature fluctuation of the high-temperature area exceeds the safety threshold, the monitoring points are encrypted by a preset coefficient, the spacing between the points is reduced, and the sampling frequency is increased, to obtain an encrypted monitoring point arrangement scheme.

[0213] Specifically, if the temperature variation amplitude of the high-temperature accumulation area is higher than a preset safety threshold, the monitoring points are increased in the area boundary according to a preset encryption coefficient, the original monitoring point spacing is reduced to the original spacing multiplied by a preset reduction coefficient, the optical fiber sensor branches are deployed at the new positions, and the acquisition frequency of the sensors in the area is increased to the original frequency multiplied by a preset increase coefficient, to obtain an encrypted monitoring point arrangement scheme.

[0214] Among them, the dynamic adjustment mechanism of the transformer monitoring system adaptively optimizes the sensor arrangement according to the temperature risk level.

[0215] When it is detected that the temperature variation amplitude of the high-temperature accumulation area exceeds the safety threshold, the system automatically starts the monitoring point encryption program to improve the monitoring accuracy of the dangerous area.

[0216] Among them, the setting of the safety threshold is based on the thermal stability parameters of the transformer oil and the heat resistance characteristics of the insulating materials.

[0217] When the temperature variation amplitude approaches or exceeds the threshold, it indicates that there is a risk of thermal runaway in the area. The encryption of the monitoring points adopts a progressive strategy, and the preset reduction coefficient is usually set to 0.5, that is, the new monitoring point spacing is half of the original spacing. The encryption process gradually expands from the high-temperature center outward, ensuring that the area with the largest temperature gradient is covered with the densest monitoring. The increase coefficient of the acquisition frequency is dynamically adjusted according to the temperature rise rate, the faster the temperature rises, the larger the frequency increase coefficient, and the faster the temperature changes are captured in time.

[0218] Among them, the deployment of the optical fiber sensor branches takes advantage of the flexibility of the optical fiber, and the branch optical fiber is introduced from the main trunk optical fiber through an optical splitter, and is laid along a preset path to the new monitoring point position, and a temperature sensing point is formed at a specific position by grating inscription.

[0219] (6.2) According to the encrypted monitoring point arrangement scheme, adjust the scanning parameters of the optical time domain reflectometer, preferentially scan the high-temperature area at high frequency, and obtain high-resolution temperature and oil flow velocity data.

[0220] Specifically, according to the encrypted monitoring point arrangement scheme, the scanning parameters of the optical time domain reflectometer are reconfigured, the high-temperature accumulation area is set as a priority collection area and the scanning frequency thereof is increased, and the other areas remain the original collection frequency. The temperature data and oil flow velocity data of each monitoring point are collected through the reconfigured scanning parameters to obtain a high-resolution data set.

[0221] The scanning parameter configuration of the optical time domain reflectometer adopts a partition management manner.

[0222] The high-temperature accumulation area is marked as a first-level priority area, and the scanning interval is shortened to one-third of that of the normal area. The transition area adjacent to the high-temperature area is a second-level priority area, and the scanning interval is one-half of that of the normal area. The remaining areas remain the original scanning frequency. Through this differentiated scanning strategy, the monitoring accuracy and system resource consumption are balanced.

[0223] (6.3) The high-resolution data is processed through Kriging interpolation to construct a more accurate temperature distribution map and oil flow velocity field.

[0224] Specifically, the high-resolution data set is processed through Kriging interpolation, the values between points are estimated according to the values of adjacent monitoring points and spatial correlation, the data gaps between monitoring points are filled to construct an adjusted temperature distribution map, and the velocity vector field is recalculated according to the oil flow velocity data of the encrypted area to obtain an adjusted oil flow velocity field.

[0225] The Kriging interpolation processing is based on the principle of spatial correlation.

[0226] The method assumes that the closer the points are in space, the stronger the correlation of their attribute values. For a position that needs to be estimated, Kriging interpolation first calculates the spatial distance between the point and the surrounding known monitoring points, and then determines the weight coefficients of the monitoring points according to the variogram. The variogram describes the statistical law of the change of temperature values with spatial distance and is obtained by fitting the measured data. In the interpolation process, the monitoring points with a short distance are given a larger weight, and the points with a long distance are given a smaller weight. The estimated temperature value is obtained by weighted averaging.

[0227] The adjusted temperature distribution map and oil flow velocity field are reconstructed with high precision through data fusion technology.

[0228] The data of the new monitoring points and the data of the original monitoring points are aligned in time and complementary in space, and together constitute a complete description of the temperature field and flow field, providing accurate data support for the evaluation of the transformer operating state.

[0229] (7) According to the adjusted temperature distribution map and oil flow velocity field, the overall temperature field is evaluated, and the transformer operating state is determined.

[0230] The transformer operating state includes the hot spot temperature value.

[0231] The step (7) comprises the following steps:

[0232] (7.1) Calculate the gradient of the temperature value in the adjusted temperature distribution map, and mark the area where the gradient exceeds the preset gradient threshold as the area with rapid temperature change;

[0233] Specifically, according to the temperature value of each monitoring point in the adjusted temperature distribution map, the temperature gradient is calculated by calculating the ratio of the temperature difference value and the distance of adjacent monitoring points, and the area where the gradient exceeds the preset gradient threshold is identified as the area with rapid temperature change.

[0234] The comprehensive evaluation of the transformer operating state is realized by temperature field reconstruction and multi-parameter fusion determination.

[0235] The adjusted high-precision temperature distribution map and the oil flow velocity field provide a comprehensive data basis for the operating state evaluation, and through gradient analysis, margin calculation and stability determination, a complete state evaluation system is formed.

[0236] The calculation of the temperature gradient is based on the difference operation of the discrete monitoring points.

[0237] For the grid-arranged monitoring points, four adjacent points around the target point are selected, and the temperature difference values in the horizontal and vertical directions are calculated respectively, and then divided by the corresponding spatial distance to obtain the gradient components in the two directions. The vector sum of the two gradient components is the temperature gradient value of the point. The gradient threshold is pre-set according to the heat dissipation capacity and oil flow circulation characteristics of the transformer, and is usually 1.5 times of the maximum gradient in normal operation. When the temperature gradient of a certain area continuously exceeds the threshold, it indicates that there is abnormal heat accumulation in the area, which needs to be paid attention to.

[0238] (7.2) Determine the hotspot temperature value in the area with rapid temperature change, and generate the transformer operating state containing the hotspot temperature value in combination with the flow velocity value of the adjusted oil flow velocity field.

[0239] Specifically, the highest temperature point in the region is marked as a potential hot spot, the flow velocity value of the corresponding position is extracted from the adjusted oil flow velocity field, and if the flow velocity is lower than the preset flow threshold, the hot spot position is confirmed, and the temperature value of the hot spot position is recorded as the hot spot temperature value. The temperature margin coefficient is obtained by dividing the transformer rated temperature limit value by the hot spot temperature value, the average value and the standard deviation of the temperature of all monitoring points are calculated according to the adjusted temperature distribution map, the temperature margin coefficient is compared with the preset safety coefficient, if the temperature margin coefficient is less than the safety coefficient and the standard deviation exceeds the preset deviation threshold, it is determined that the running state is high risk level, otherwise it is determined as normal running level. Based on the running state level, the temperature change rate is calculated by dividing the difference between the current hot spot temperature value and the previous hot spot temperature value by the time interval, if the temperature change rate is positive and the running state is high risk level, it is determined that the transformer is in an unstable state, the hot spot temperature value, the running state level and the stability determination result are integrated to determine the transformer running state containing the hot spot temperature value.

[0240] Among them, the confirmation of the hot spot position adopts double criteria of temperature and flow velocity.

[0241] The highest temperature point may be a temporary heat accumulation, and only when the oil flow velocity at this position is also lower than the flow threshold can it be confirmed as a real hot spot. The flow threshold is usually set to 30% of the normal flow rate, and a value lower than this indicates that the oil flow circulation is blocked.

[0242] Among them, the temperature margin coefficient reflects the closeness of the current temperature to the safety limit. The transformer rated temperature limit value is determined according to the insulation level, and the A-class insulation is 105 degrees Celsius and the B-class insulation is 130 degrees Celsius. The closer the coefficient obtained by dividing the hot spot temperature by the rated temperature limit value to 1, the smaller the temperature margin and the higher the risk of operation. The safety coefficient is usually set to 0.85, and when the temperature margin coefficient exceeds this value, cooling measures need to be taken.

[0243] Among them, the qualitative determination is realized by analyzing the time evolution characteristics of the hot spot temperature.

[0244] The calculation of the temperature change rate requires continuous monitoring data, which is obtained by dividing the temperature difference between the current time and the previous sampling time by the time interval. A positive change rate indicates a temperature rise, and a negative value indicates a temperature drop. When the change rate is continuously positive and the value is large, it means that the hot spot temperature is in a rapid rising stage, and the transformer is unstable. Combined with the running state level, if it is also in the high risk level, it is determined as an emergency state and immediate intervention is needed.

[0245] The final determination of the transformer running state integrates the information of the hot spot temperature value, the running level and the stability of the three dimensions, forms a comprehensive state description, and provides a comprehensive reference basis for operation and maintenance decision-making.

[0246] (8) Extracting temperature abnormal fluctuation from the transformer operating state, determining the key risk point, calculating the associated oil flow path deviation value, tracking the evolution trend of temperature change amplitude, determining the potential high temperature area path, and generating a monitoring scheme;

[0247] The monitoring scheme includes sensor arrangement position, data acquisition frequency and key monitoring area.

[0248] The step (8) comprises the following steps:

[0249] (8.1) Scan the temperature time series curve of the transformer operating state, calculate the variance of the temperature change rate, mark the points whose variance exceeds the preset variance threshold as abnormal fluctuation points, determine the distribution density of the abnormal fluctuation points, and generate the key risk points;

[0250] Specifically, the temperature time series curve is scanned from the transformer operating state data, the variance of the temperature change rate at adjacent time is calculated, and when the variance exceeds the preset fluctuation threshold, it is marked as an abnormal fluctuation point. The number of times the abnormal fluctuation point appears in a unit area is counted as the distribution density, and the area whose density exceeds the preset density threshold is determined as the key risk point, and the oil flow path coordinate sequence corresponding to the key risk point position is extracted.

[0251] Among them, the transformer intelligent monitoring scheme is based on the deep mining and risk prediction of operating state data.

[0252] By identifying the temperature abnormal fluctuation mode, tracking the evolution of the oil flow path deviation, and predicting the development trend of the potential high temperature area, the dynamic optimization configuration of the monitoring resources is realized.

[0253] Among them, the identification of temperature abnormal fluctuation starts from the statistical characteristics of time series data.

[0254] For each monitoring point, the temperature time series is calculated in a fixed time window sliding window. The change rate is defined as the temperature difference between adjacent sampling time divided by the time interval, which reflects the instantaneous change speed of temperature. The variance of the change rate sequence is calculated, and the variance value quantifies the instability of temperature change. When the temperature changes relatively smoothly, the variance is small; when an abnormality occurs, the temperature is high and low, and the variance increases significantly. The fluctuation threshold is determined according to the variance distribution of historical normal operation data, and the 95th percentile is usually taken as the threshold. The time points exceeding the threshold are marked as abnormal fluctuation points, and the distribution of these points on the time axis reflects the frequency and duration of abnormal events.

[0255] Among them, the grid method is used for the statistics of spatial density.

[0256] The monitoring area is divided into a regular grid, and the number of abnormal fluctuation points in each grid is counted to obtain a density value by dividing the grid area. The density threshold is determined according to the structural characteristics of the transformer and historical fault data, and the grid higher than the threshold is identified as a key risk point.

[0257] (8.2) Calculate the deviation value of the oil flow path of the key risk point from the historical path to generate deviation time series data;

[0258] Specifically, the oil flow path coordinate sequence is compared with the oil flow path during historical normal operation point by point, the distance from each coordinate point to the nearest point on the historical path is calculated, and the sum of all distance values is added to obtain the path deviation value. According to the change of the path deviation value with time, the deviation time series data is constructed, the growth rate of the deviation value is calculated, and if the growth rate is positive, it is determined that the deviation is expanding, and the spatial region with continuously expanding deviation is recorded.

[0259] Wherein, the calculation of the oil flow path deviation needs to establish a reference benchmark.

[0260] The historical normal operation oil flow path is obtained by statistical average of long-term monitoring data, representing the standard cycle mode of oil flow. The comparison between the current path and the historical path uses the point-to-line distance measurement. For each coordinate point on the path, the shortest distance to the historical path is calculated. The sum of all distance values is the path deviation value, and the larger the value, the more serious the deviation of the current oil flow from the normal mode.

[0261] Wherein, the determination of the deviation trend is realized by time series analysis.

[0262] The deviation values at consecutive time points are constructed into a time series, and the difference between adjacent time points is calculated. A positive value indicates that the deviation increases, and a negative value indicates that the deviation decreases. When the difference value of consecutive time points is positive, it is determined that the deviation is expanding.

[0263] (8.3) Track the moving direction of the peak value of the temperature change amplitude to predict the path of the potential high temperature region;

[0264] Specifically, in the spatial region with continuously expanding deviation, the position change of the temperature peak value at adjacent time points is tracked, the moving speed and direction of the temperature peak value are calculated according to the position change, and the position where the future temperature peak value may reach is predicted along the moving direction. The region where the temperature may exceed the preset high temperature threshold on the predicted path is marked as a potential high temperature region, and the center coordinates and boundary range of the potential high temperature region are determined.

[0265] Wherein, the tracking of the temperature peak value is based on the dynamic monitoring of the spatial position.

[0266] At each sampling time, the highest temperature point in the temperature field is identified, and its spatial coordinates are recorded. The sequence of coordinates at consecutive times depicts the moving trajectory of the temperature peak. The moving speed is calculated by dividing the position change between adjacent times by the time interval, and the moving direction is determined by the change of the position vector.

[0267] (8.4) According to the potential high-temperature area path, arrange sensors, set the data collection frequency of the key risk points, and generate a monitoring scheme containing sensor arrangement positions, data collection frequencies, and key monitoring areas.

[0268] Specifically, according to the center coordinates of the potential high-temperature area, arrange core sensors, arrange auxiliary sensors at a preset interval within the boundary range, set the data collection frequency of the key risk points to the normal frequency multiplied by a preset boost coefficient, and maintain the normal collection frequency for other areas. Integrate the sensor position coordinates, collection frequency values, and potential high-temperature area range to obtain the final monitoring scheme.

[0269] Wherein, the prediction of the potential high-temperature area considers the physical law of temperature propagation.

[0270] The propagation of heat in transformer oil is affected by both convection and conduction mechanisms. When the oil flow speed is fast, convection dominates, and the temperature peak moves quickly along the oil flow direction; when the oil flow is slow, conduction is enhanced, and the temperature spreads uniformly in all directions. According to the current oil flow velocity field and temperature gradient field, the possible position of the temperature peak at future times can be predicted. The temperature at each position on the predicted path is estimated by the heat conduction equation, and when the estimated temperature exceeds the high-temperature threshold, the position is marked as a potential high-temperature area. The high-temperature threshold is usually set to 80% of the heat resistance limit of the insulating material, leaving a safety margin. The boundary of the potential high-temperature area is determined by the isotherm, and the center coordinates are the arithmetic average of all point coordinates within the area.

[0271] Wherein, the sensor arrangement adopts a hierarchical strategy.

[0272] Core sensors are deployed in the center of the potential high-temperature area and undertake the main monitoring task; auxiliary sensors are evenly distributed within the boundary range at a preset interval to provide supplementary information. The determination of the interval considers the spatial correlation of the temperature field, usually taking half of the temperature correlation length, ensuring that the monitoring range of adjacent sensors has appropriate overlap.

[0273] Wherein, the integration of the monitoring scheme needs to balance the monitoring accuracy and system resources.

[0274] The collection frequency of the key risk points is adjusted by the boost coefficient, and the boost coefficient is determined according to the risk level, and the coefficient of the high-risk area can reach 3 to 5 times. The final scheme contains the precise coordinates of each sensor, the assigned collection frequency, and the responsible monitoring area range.

Claims

1. A transformer overcapacity state intelligent monitoring analysis method, characterized in that, The method comprises the following steps: (1) Collecting the temperature and oil flow velocity inside the transformer through a distributed optical fiber sensor array, generating a temperature distribution map and an oil flow velocity field, fusing and analyzing the temperature distribution map and the oil flow velocity field to determine the temperature variation amplitude and the oil flow path change; (2) Comparing the temperature variation amplitude with historical data to calculate the temperature fluctuation amplitude, combining the oil flow path change and the oil flow velocity to determine the location and size of the dynamic dead angle area; (3) Detecting the oil flow velocity mutation in the oil flow velocity field to determine the transition from laminar flow to turbulent flow, combining the temperature fluctuation amplitude to determine the time and area of turbulent flow transition and calculate the turbulent flow area; (4) According to the turbulent flow area and the location and size of the dynamic dead angle area, evaluating the correlation of flow disturbance, analyzing the periodicity of the temperature distribution map and the oil flow velocity field, predicting the formation period and dissipation time of the dynamic dead angle area, and generating a dynamic dead angle distribution map; (5) Identifying the high temperature aggregation points in the temperature distribution map, extracting the high temperature accumulation area boundary information in the dynamic dead angle distribution map, combining the location and size of the dynamic dead angle area to determine the monitoring blind area range, and calculating the temperature variation amplitude of the high temperature accumulation area; (6) According to the temperature variation amplitude of the high temperature accumulation area, adjusting the collection frequency and spatial density of the distributed optical fiber sensor array, and generating an adjusted temperature distribution map and an oil flow velocity field; (7) According to the adjusted temperature distribution map and the oil flow velocity field, evaluating the overall temperature field to determine the transformer operating state; The transformer operating state includes the hot spot temperature value; (8) Extracting temperature abnormal fluctuation from the transformer operating state to determine the key risk point, calculating the associated oil flow path deviation value, tracking the evolution trend of the temperature variation amplitude, determining the potential high temperature area path, and generating a monitoring scheme; The monitoring scheme includes sensor arrangement position, data collection frequency and key monitoring area.

2. The method of claim 1, wherein the method further comprises: The step (1) comprises the following steps: (1.1) Distribute the distributed optical fiber sensor array along the inner wall of the transformer oil tank and the winding gap, collect the optical signal wavelength shift data, calculate the temperature value of each monitoring point, and generate an initial temperature data set containing spatial coordinates and temperature values; (1.2) Based on the optical signal phase difference data collected by the distributed optical fiber sensor array, calculate the oil flow velocity value of the spatial coordinate position corresponding to the initial temperature data set, and generate an oil flow velocity vector field containing velocity size, direction and spatial position; (1.3) Spatial grid division is performed on the initial temperature data set, and spatial interpolation is used to fill in the sensor coverage blind area to generate a continuous temperature distribution map, calculate the temperature difference value of adjacent time, and determine the temperature variation amplitude; (1.4) Streamline tracking is performed on the oil flow velocity vector field, the oil flow motion trajectory is recorded, the oil flow path distribution is generated, and the high temperature area boundary and the oil flow path deviation angle are determined by combining the temperature variation amplitude and the oil flow path distribution.

3. The method of claim 1, wherein the method further comprises: The step (2) comprises the following steps: (2.1) Extract historical operation records with load deviation within 5% and ambient temperature difference within 3 degrees Celsius from the historical database, calculate the point-by-point difference value of the temperature variation amplitude and the temperature time series data to generate a difference sequence; (2.2) Calculate the variance value of the difference sequence to determine the temperature fluctuation amplitude; (2.3) Combine the spatial distribution of the oil flow path change area to identify low-speed areas with oil flow speed lower than half of the normal flow speed. When the temperature fluctuation amplitude in the low-speed area is higher than that in the surrounding normal flow area and the fluctuation frequency is lower than the preset frequency threshold, determine that the area is the initial position of the dynamic dead angle area; (2.4) Track the vortex area boundary formed by the oil flow at the winding end or the core corner through the oil flow speed vector distribution of the initial position of the dynamic dead angle area, measure the maximum span of the vortex area in the horizontal direction as the transverse width, measure the maximum extension distance in the vertical direction as the longitudinal height, calculate the speed variation rate at the junction of the normal flow area and the dead angle area, determine the accurate boundary of the dead angle area according to the position where the speed variation rate exceeds the preset variation rate threshold, and obtain the center position coordinates and geometric size including width and height of the dynamic dead angle area.

4. The method of claim 1, wherein the method further comprises: The step (3) comprises the following steps: (3.1) Calculate the difference value of adjacent time points of the speed time sequence of the oil flow speed field to generate a speed variation rate, mark the points where the speed variation rate exceeds the preset speed threshold as speed mutation points, and calculate the Reynolds number of the speed mutation points; (3.2) Use a double-threshold discrimination method to determine the transition from laminar flow to turbulent flow. When the oil flow changes from laminar flow to turbulent flow, extract the temperature fluctuation amplitude to determine the time and area of the turbulent flow transition; (3.3) Calculate the grid cell area within the turbulent flow area contour to generate the turbulent flow area.

5. The method of claim 1, wherein the method further comprises: The step (4) comprises the following steps: (4.1) Calculate the ratio of the turbulent flow area to the dynamic dead angle area, define the ratio as the disturbance intensity coefficient, and calculate the spatial correlation degree = 1-(distance between the two centers / oil tank maximum inner diameter) based on the distance between the center coordinates of the dynamic dead angle area and the geometric center of the turbulent flow area, with the maximum inner diameter of the transformer oil tank as the reference; (4.2) Mark the key monitoring area based on the condition that the disturbance intensity coefficient exceeds the preset intensity coefficient threshold and the spatial correlation degree exceeds the preset correlation degree threshold; (4.3) Extract the time sequence from the temperature data of the key monitoring area, convert to the frequency domain using the fast Fourier transform algorithm, and determine the temperature change main period and phase shift; (4.4) According to the phase shift and oil flow speed value, predict the formation time and dissipation time of the dynamic dead angle area, and generate a dynamic dead angle distribution map containing space-time distribution characteristics.

6. The method of claim 5, wherein the method further comprises: The step (4.4) comprises the following steps: (4.4.1) Extract the time sequence variation of the turbulent flow area, calculate the area variation rate, and determine the disturbance enhancement period based on the center coordinates and size of the dynamic dead angle area; (4.4.2) Record the movement trajectory of the dynamic dead angle area, calculate the duration from formation to dissipation, and generate a dynamic dead angle distribution map containing the formation time, duration, and dissipation path.

7. The method of claim 1, wherein the method further comprises: The step (5) comprises the following steps: (5.1) scanning the temperature value of the temperature distribution map, clustering points with temperature exceeding a preset temperature threshold, to generate high-temperature accumulation points; (5.2) extracting the boundary coordinate sequence of the corresponding position in the dynamic dead angle distribution map, to determine the high-temperature accumulation region boundary information; (5.3) calculating the area within the high-temperature accumulation region boundary information, superimposing the center position and size of the dynamic dead angle region, to determine the monitoring blind area range not covered by the sensor; (5.4) estimating the temperature value in the monitoring blind area range by using an interpolation method, calculating the difference between the maximum value and the minimum value of the temperature of the high-temperature accumulation region, to generate the temperature variation amplitude of the high-temperature accumulation region.

8. The method of claim 1, wherein the method further comprises: The step (6) comprises the following steps: (6.1) if the temperature fluctuation of the high-temperature region exceeds a safety threshold, encrypting the monitoring points by a preset coefficient, reducing the spacing between the points, and increasing the sampling frequency, to obtain an encrypted monitoring point arrangement scheme. (6.2) adjusting the scanning parameters of the optical time domain reflectometer according to the encrypted monitoring point arrangement scheme, preferentially scanning the high-temperature region at a high frequency, to obtain high-resolution temperature and oil flow velocity data. (6.3) constructing a more accurate temperature distribution map and oil flow velocity field by Kriging interpolation on the high-resolution data.

9. The method of claim 1, wherein the method further comprises: The step (7) comprises the following steps: (7.1) calculating the gradient of the temperature value in the adjusted temperature distribution map, marking the region with a gradient exceeding a preset gradient threshold as a temperature variation intense region; (7.2) determining the hotspot temperature value in the temperature variation intense region, combining the flow velocity value of the adjusted oil flow velocity field, to generate a transformer operating state containing the hotspot temperature value.

10. The method of claim 1, wherein the method further comprises: The step (8) comprises the following steps: (8.1) scanning the temperature time sequence curve of the transformer operating state, calculating the variance of the temperature variation rate, marking the point with a variance exceeding a preset variance threshold as an abnormal fluctuation point, determining the distribution density of the abnormal fluctuation point, to generate a key risk point; (8.2) calculating the deviation value of the oil flow path of the key risk point from the historical path, to generate deviation time sequence data; (8.3) tracking the moving direction of the peak value of the temperature variation amplitude, to predict a potential high-temperature region path; (8.4) arranging a sensor according to the potential high-temperature region path, setting the data acquisition frequency of the key risk point, to generate a monitoring scheme containing the sensor arrangement position, data acquisition frequency and key monitoring region.

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