A Big Data Analysis System for Monitoring Power Supply Gap

Abstract

This invention relates to the field of intelligent power system monitoring technology, and more particularly to a big data analysis system for monitoring power supply gaps. The system includes a corridor environment modeling module, a heat dissipation capacity assessment module, a dynamic capacity calculation module, a line bottleneck analysis module, and a power supply risk early warning module. It acquires geographic information system data of transmission lines, and simultaneously collects regional ambient temperature and electrical data. The ambient temperature and electrical data are mapped to the geographic coordinates of each segment of the line, forming thermal environment slices of the transmission corridor. The system analyzes the impact of terrain characteristics on local temperature in each segment of the line, calculates the temperature correction value caused by terrain, and obtains the actual ambient temperature. A heat balance equation is established, and the actual ambient temperature is substituted into the heat balance equation to solve for the maximum safe current value. This invention solves the problems of inaccurate transmission capacity assessment and blind spots in power supply gap monitoring through terrain and climate correction technology.

Description

Technical Field

[0001] This invention relates to the field of intelligent power system monitoring technology, and in particular to a big data analysis system for monitoring power supply gaps. Background Technology

[0002] Traditional transmission line capacity assessment methods neglect the impact of topography on local temperature, leading to significant biases in thermal environment assessment. Special terrain features such as valleys and basins can create a heat retention effect of 5-8°C, while slopes facing different directions can produce solar temperature differences of 4-6°C. These factors are completely ignored in traditional assessments, causing errors in line capacity calculations. Existing technologies generally employ static capacity calculation methods, assessing line current-carrying capacity based solely on seasonal or fixed temperature parameters. This fails to adapt to real-time dynamic changes in weather and solar radiation, leading to overestimation of line safety capacity under extreme weather conditions and overly conservative limitations on transmission capacity under favorable conditions. Traditional power grid dispatching systems primarily focus on electrical parameters and power flow distribution, lacking precise mechanisms for identifying thermal limiting factors. They cannot promptly detect transmission capacity reductions caused by changes in ambient temperature, and are particularly lacking in effective early warning and response measures in high-temperature, special terrain areas.

[0003] In summary, existing technologies suffer from several problems that urgently need to be addressed, including insufficient understanding of the coupled effects of terrain and temperature, static capacity assessment, and a lack of risk warning mechanisms based on thermal limitations. Summary of the Invention

[0004] Therefore, it is necessary to provide a big data analysis system for monitoring power supply gaps to solve at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, a big data analysis system for monitoring power supply gaps includes the following modules:

[0006] The corridor thermal environment modeling module is used to acquire geographic information system data of transmission lines, and at the same time collect regional ambient temperature data and electrical data. The ambient temperature data and electrical data are mapped to the geographic coordinates of each segment of the line to form a thermal environment slice of the transmission corridor.

[0007] The terrain-temperature coupling assessment module is used to analyze the impact of terrain characteristics on local temperature in each section of the line, calculate the temperature correction value caused by terrain, and obtain the actual ambient temperature of each section of the line under the influence of terrain.

[0008] The dynamic capacity calculation module is used to establish a thermal balance equation with the maximum allowable operating temperature of the conductor as the target, and substitute the actual ambient temperature into the thermal balance equation to solve the maximum safe current value under the condition that the conductor does not overheat.

[0009] The line bottleneck analysis module is used to identify the bottleneck section with the smallest heat capacity and its geographical location based on the maximum safe current value, and to calculate the maximum power transmission capacity of the entire line.

[0010] The power supply risk early warning module is used to obtain the planned transmission power of the lines in the power grid dispatch plan, compare it with the maximum power transmission capacity, and calculate the power supply gap value and generate a gap early warning signal of the corresponding level when the planned transmission power exceeds the maximum power transmission capacity.

[0011] This invention achieves accurate spatiotemporal modeling of the transmission line environment through high-precision geographic information acquisition and multi-source environmental data fusion. It solves the problems of coarse and low-resolution environmental data in traditional systems, significantly improves the accuracy of thermal environment characterization of transmission corridors, and realizes a technological leap from discrete point monitoring to continuous corridor thermal environment slicing.

[0012] Breaking through the limitations of traditional models that ignore the influence of terrain, this study achieves accurate assessment of local microclimate under complex terrain conditions by quantitatively analyzing the heat retention effect and differentiated solar radiation effect of special terrain. It improves the accuracy of temperature assessment by 3-5℃, effectively solves the technical problem of large temperature prediction deviation in special terrain areas, and provides a more realistic temperature input for heat capacity assessment.

[0013] It realizes the technological transformation from static capacity calculation to dynamic capacity assessment. By using the IEEE 738 standard thermal balance model and real-time environmental data, it accurately calculates the maximum safe current value under different environmental conditions, improving the accuracy of transmission line current carrying capacity assessment by 15%-30%, reducing the resource waste caused by excessive safety margin in traditional methods, and setting safety thresholds and early warning mechanisms for extreme temperature conditions.

[0014] By distinguishing between persistent and time-dependent bottlenecks and analyzing their causes, the system provides precise location and classification criteria for bottleneck management. Differentiated mitigation solutions are generated for different types of bottlenecks, avoiding the waste of resources in traditional "standard" transformation methods. This improves the overall utilization efficiency of transmission channels by 20%-35% and provides data support for power grid planning and transformation.

[0015] It has achieved accurate prediction and multi-dimensional assessment of power supply gaps, established a three-level early warning mechanism based on the size and duration of the gap, brought risk response forward, improved the foresight and initiative of power grid dispatch, provided targeted response measures for different levels of risks, constructed a closed-loop management system from early warning to response, and realized the transformation of management mode from passive emergency response to proactive prevention and control.

[0016] Therefore, this method constructs a coupled assessment model of thermal environment slices and terrain temperature in transmission corridors to accurately calculate the correction value of local temperature by terrain; dynamically calculates the maximum safe current value based on the heat balance equation and real-time environmental data; at the same time, it identifies the bottleneck section of line thermal capacity and its causes, calculates the supply gap value in combination with the scheduling plan, and generates graded early warning signals, thereby realizing accurate dynamic assessment and risk early warning of transmission line capacity, effectively ensuring the safe and stable operation of the power grid. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of a big data analysis system for monitoring power supply gaps;

[0018] Figure 2 This is a schematic diagram of the power supply gap monitoring big data analysis system in this invention;

[0019] Figure 3 This is a diagram of the power supply equipment in this invention.

[0020] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a big data analysis system for monitoring power supply gaps, comprising the following modules:

[0025] S1: Corridor thermal environment modeling module, used to acquire geographic information system data of transmission lines, and at the same time collect regional ambient temperature data and electrical data, and map the ambient temperature data and electrical data to the geographic coordinates of each segment of the line to form a thermal environment slice of the transmission corridor.

[0026] In this embodiment of the invention, the latitude and longitude coordinates of the transmission line towers are collected using a Global Positioning System (GPS), with an accuracy controlled within ±1 meter. The WGS84 coordinate system is used to obtain tower elevation data at a resolution of 30 meters × 30 meters and topographic data at a scale of 1:50,000. The line is divided into segments of 100 meters each, and cubic spline interpolation is used to determine the geographic coordinates of each segment. Simultaneously, environmental data such as air temperature, surface temperature, and solar radiation intensity are collected through a distributed sensor network at sampling frequencies of 15 minutes / time, 30 minutes / time, and 10 minutes / time, respectively. Electrical data such as current and voltage are also collected. A multi-dimensional data slice matrix M, containing location information, meteorological parameters, and electrical parameters, is constructed using a combination of spatial and temporal interpolation methods, forming a spatiotemporally coordinated thermal environment slice of the transmission corridor.

[0027] S2: Terrain-temperature coupling assessment module, used to analyze the impact of terrain characteristics on local temperature in each section of the line, calculate the temperature correction value caused by terrain, and obtain the actual ambient temperature of each section of the line under the influence of terrain.

[0028] In this embodiment of the invention, the planar curvature and profile curvature of a 100m × 100m grid cell are calculated. A valley is identified when the planar curvature is <-0.2 and the surrounding elevation difference is >50 meters. Basins, slopes, and ridges are identified using a similar method. The heat retention effect of valleys and basins is analyzed, and the closure index is calculated. The correction value for heat retention temperature is determined based on the degree of sealing. The temperature ranges from +5°C to +8°C in highly enclosed areas, from +2°C to +5°C in moderately enclosed areas, and from +1°C to +2°C in poorly enclosed areas. The differential solar radiation effect is analyzed, and the angle between the sunlight and the normal vector of the hillside is calculated. Determine the solar radiation temperature correction value based on the included angle. The temperature correction ranges from +4°C to +6°C in areas of strong sunshine, +2°C to +4°C in areas of moderate sunshine, and -1°C to +1°C in areas of weak sunshine. The total correction value for topographic temperature is obtained by combining both effects. Calculate the actual ambient temperature .

[0029] S3: Dynamic capacity calculation module, used to establish a thermal balance equation with the maximum allowable operating temperature of the conductor as the target, substitute the actual ambient temperature into the thermal balance equation, and solve in reverse the maximum safe current value under the condition that the conductor does not overheat.

[0030] In this embodiment of the invention, the actual ambient temperature of each segment is obtained, and the maximum allowable operating temperature Tmax is set according to the conductor type: 70℃ for ACSR conductors, 150℃ for TACSR conductors, and 210℃ for ZTACIR conductors. The thermal balance equation is established using the IEEE 738 standard thermal balance model, incorporating the Joule heating of the conductors. and solar radiation heat Input items, and convection cooling and radiative heat dissipation The loss term is determined by setting the conductor temperature Tc = Tmax, substituting the actual ambient temperature Ta into the equation, and using the Newton-Raphson iteration method to solve for the maximum safe current value Imax. When Ta > Tmax - 3℃, the value of Imax is reduced proportionally; when Ta ≥ Tmax, Imax is directly set to 0 and a red warning signal is generated, which is then pushed to the power grid dispatch and control center through a dedicated communication channel.

[0031] S4: Line bottleneck analysis module, used to identify the bottleneck section with the smallest heat capacity and its geographical location based on the maximum safe current value, and calculate the maximum power transmission capacity of the entire line.

[0032] In this embodiment of the invention, the maximum safe current value Imax of each segment is obtained to construct the current distribution vector IV, and the minimum value Imin and its corresponding index position p are found to identify the heat capacity bottleneck segment. The contribution rate of heat retention effect CR1 and the contribution rate of differential solar radiation effect CR2 are calculated. When CR1>60%, it is determined to be a persistent bottleneck, and when CR2>60%, it is determined to be a time-limited bottleneck. For three-phase AC transmission lines, the maximum power transmission capacity is calculated. Considering the N-1 safety constraint, we introduce a coefficient Ks = 0.85 to obtain... For persistent bottlenecks, generate solutions for conductor upgrades and tower height increases; for time-related bottlenecks, calculate the power flow transfer ratio β; for important transmission channels, construct an emergency resource allocation matrix (ERM); for recurring severe bottlenecks, generate medium- to long-term renovation solutions such as vegetation shading, conductor upgrades, and line replanning.

[0033] S5: Supply guarantee risk early warning module, used to obtain the planned transmission power of the lines in the power grid dispatch plan, compare it with the maximum power transmission capacity, and when the planned transmission power exceeds the maximum power transmission capacity, calculate the supply guarantee gap value and generate a gap early warning signal of the corresponding level;

[0034] In an embodiment of the present invention, the maximum power transfer capacity Ptrans of the reading line is determined, and a capacity time series matrix CTM is constructed to record the predicted values for the next 24 hours. It is docked with the power grid dispatching system through a data interface to obtain the planned transmission power Pplan and construct a planned power matrix PPM. The power supply guarantee gap value Pgap = Pplan - Ptrans and the relative gap value Rgap = Pgap / Ptrans × 100% are calculated, and the continuous gap duration Tgap is recorded. Classification is performed according to the gap value and duration: when 0 < Rgap < 10% and Tgap < 2 hours, a yellow warning is issued; when 10% ≤ Rgap ≤ 30% or 2 ≤ Tgap ≤ 6 hours, an orange warning is issued; when Rgap > 30% or Tgap > 6 hours, a red warning is issued. Suggestions for countermeasures are generated for different warning levels: the yellow warning adjusts the operation mode; the orange warning activates load transfer and starts peak-shaving power sources; the red warning starts all emergency resources and implements forced load restriction. The warning signal and suggestions are pushed to the power grid dispatching control center in real time to trigger the corresponding level of emergency response.

[0035] Preferably, the corridor thermal environment modeling module includes the following functions:

[0036] Obtain the geographic information system data of the transmission line, including the longitude, latitude, altitude of each tower, and terrain information along the line;

[0037] Obtain dynamic regional environmental temperature data and line electrical data, where the environmental temperature data includes air temperature, surface temperature, and sunshine intensity, and the electrical data includes current and voltage;

[0038] Map the environmental temperature data and electrical data to the geographical coordinates of each section of the line to form a thermal environment slice of the transmission corridor.

[0039] In an embodiment of the present invention, the corridor thermal environment modeling module collects the longitude and latitude coordinates of the transmission line towers through the global positioning system, with the accuracy controlled within ±1 meter, and adopts the WGS84 coordinate system. The altitude of the towers is obtained through a digital elevation model with a resolution of 30 meters × 30 meters. The terrain information along the line uses 1:50,000 scale terrain data, including the vector boundaries of terrain types such as valleys, basins, hillsides, and ridges. The line between adjacent towers is divided into segment units every 100 meters, and the cubic spline interpolation method is used to determine the geographical coordinates and altitudes of each segment unit, and a line spatial distribution model is constructed.

[0040] When acquiring ambient temperature and electrical data of the power lines, a distributed sensor network is used in conjunction with meteorological stations. Temperature data is collected by temperature sensors installed every 5 kilometers along the power line, at a frequency of 15 minutes per data point, with an accuracy of ±0.1℃. Surface temperature is acquired using infrared thermal imagers or satellite remote sensing, with a resolution of 100m × 100m and an update frequency of 30 minutes per data point. Solar radiation intensity is measured using radiometers, in W / m², at a frequency of 10 minutes per data point. Electrical data is collected through the power line monitoring system, with current accuracy of ±0.5% and voltage accuracy of ±0.2%, at a frequency of 5 minutes per data point. All data is timestamped to ensure synchronization and transmitted to the data processing center via 4G / 5G networks. After cleaning, the data is stored in a time-series database.

[0041] When mapping ambient temperature and electrical data to the geographic coordinates of the transmission line, a combination of spatial and temporal interpolation methods is used. Temperature data is interpolated using an inverse distance-weighted method, with the weighting coefficient being the inverse square of the distance. Surface temperature is directly extracted from the corresponding grid values ​​of the remote sensing data. Solar radiation intensity is calculated by combining the line direction and terrain slope aspect. Electrical data is used to calculate the actual current values ​​of each line segment based on power flow distribution patterns. Finally, a multi-dimensional data slice matrix M is constructed, with dimensions n×m, where n is the number of line segments and m is the number of parameter types. Each element M(i,j) represents the j-th parameter value of the i-th segment, forming a thermal environment slice of the transmission corridor.

[0042] Preferably, the terrain-temperature coupling assessment module includes the following functions:

[0043] Topographic characteristics and ambient temperature data of transmission line segments were extracted from thermal environment slices of the transmission corridor;

[0044] Identify key terrain types, including valleys, basins, slopes, and ridges, and determine how they affect temperature;

[0045] The effects of topographic features on ambient temperature correction were analyzed, including heat retention and differential solar radiation effects.

[0046] Calculate the actual ambient temperature values ​​of each segment under the influence of terrain.

[0047] In this embodiment of the invention, the terrain-temperature coupling assessment module extracts terrain characteristics and ambient temperature data of transmission line segments from thermal environment slices of the transmission corridor, analyzes the slice matrix M, and extracts the geographic coordinates (x, y, z) and basic ambient temperature of each segment. By querying the digital elevation model database using geographic coordinates, elevation gradient information with a resolution of 100m × 100m was obtained, and the slope value (0°-90°) and aspect angle (0°-359°) of each segment were calculated. Combining the elevation gradient and terrain boundary data, a terrain feature vector F was constructed, containing four components: elevation H, slope S, aspect A, and terrain type index T, establishing a preliminary terrain-temperature mapping relationship.

[0048] When identifying key terrain types, the terrain curvature analysis method and the slope-elevation composite discrimination method are used. The planar curvature and profile curvature of the grid cells are calculated. A valley is identified when the planar curvature is <-0.2 and the surrounding elevation difference is >50 meters; a basin is identified when the planar curvature is <-0.15 and the area is >1 square kilometer; a hillside is identified when the slope is >15° and the elevation difference within a 500-meter radius is >30 meters; and a ridge is identified when the profile curvature is >0.2 and located at a local highest point. A temperature influence characteristic matrix I is established for each terrain type. The influence coefficient for heat retention effect is 0.8 for valleys and basins, 0.7 for differentiated solar radiation effect on hillsides, and -0.3 for heat loss effect on ridges.

[0049] When analyzing the correction effect of topographic features on ambient temperature, the heat retention effect is calculated first. The closure index is then measured for valleys and basins. ,when A value >0.3 indicates strong retention, and <0.15 indicates strong retention. A value less than 0.3 indicates moderate retention. A value <0.15 indicates weak retention; based on this, the correction value for the heat retention temperature is determined. Differential solar radiation effect analysis is performed by calculating the solar azimuth angle. and elevation angle Combined with the orientation of the hillside Calculate the angle of incidence Determine the solar radiation intensity coefficient based on the incident angle. and temperature correction value The two effects are combined to form the total topographic temperature correction value. , This is the time-weighted coefficient.

[0050] When calculating the actual ambient temperature, the method of superimposing the baseline temperature and the correction value is used. To address the differences in time scales across different terrains, a 15-minute update frequency is used for hillsides, while a 30-minute update frequency is used for valleys and basins. The actual ambient temperature is iteratively calculated to reach a stable value, with the termination condition being that the difference between adjacent calculation results is <0.1℃. This forms a refined model of the line's thermal environment, providing accurate temperature input for dynamic capacity calculations.

[0051] Preferably, the analysis of the correction effect of terrain characteristics on ambient temperature includes the following analytical process:

[0052] Heat retention effect analysis assesses the confinement and accumulation of heat by concave topography such as valleys and basins, and calculates the heat retention temperature correction value under concave topographic conditions.

[0053] Differential solar radiation analysis calculates the differences in solar radiation intensity for terrains with different orientations based on the relationship between the orientation and slope of the hillside and the position of the sun, and derives solar radiation temperature correction values.

[0054] The combined topographic temperature correction value is obtained by superimposing the heat retention temperature correction value and the solar radiation temperature correction value.

[0055] In this embodiment of the invention, during the analysis of heat retention effect, the system uses a digital elevation model to identify concave terrain areas traversed by the transmission line and calculates the closure index. ,in The average elevation of the surrounding area of ​​the concave terrain. This is the bottom elevation. For width. Set the closure index threshold: >0.35 indicates high closure, 0.15 < ≤0.35 indicates a medium closure. A value ≤0.15 indicates low closure. The depth of the concave terrain should also be calculated. and area Construct a heat retention intensity assessment matrix For highly enclosed areas, the correction value for heat retention temperature is... Ensure a temperature range of +5℃ to +8℃; for areas with moderate closure, Ensure a temperature range of +2℃ to +5℃; for areas with low sealing, Ensure it operates within the range of +1℃ to +2℃. Introduce a correction factor. ,when When >1.2, Increase by 20%; when 0.8 < When ≤1.2, Unchanged; when When ≤0.8, Reduced by 15%.

[0056] During differential solar radiation analysis, the system acquires the hillside orientation angle A (0°-359°) and slope angle S (0°-90°), and calculates the solar azimuth angle based on the date and time. and elevation angle Calculate the angle between the sunlight and the slope normal vector. According to the included angle Determine the solar radiation intensity coefficient : When <45°, Corresponding to areas with strong sunlight; 45°≤ When <90°, This corresponds to a medium sunshine zone; When ≥90°, =-0.1, corresponding to the weak sunshine area. Calculate the sunshine temperature correction value: strong sunshine area. Medium sunshine area Low-sunlight area Introducing a time weighting function The value is 1 from 8:00 to 16:00, and decreases linearly to 0.2 during other time periods, resulting in... For steep slopes with a gradient greater than 30°, a slope enhancement factor is introduced. .

[0057] When two effects are superimposed, a superposition matrix is ​​established. , This is the interaction intensity coefficient. It applies when heat retention and strong solar radiation coexist. =1.2, when coexisting with weak sunlight =0.8. A dynamic time correction mechanism is introduced: during the day (8:00-16:00), Nighttime (8:00 PM - 6:00 AM), The transition period is a linear transition proportional to time. Final terrain temperature correction value. It acts on the base ambient temperature to form the actual ambient temperature of each segment.

[0058] Preferably, the heat retention effect analysis includes:

[0059] Determine whether a section of the line is located in a concave terrain area;

[0060] Calculate the degree of enclosure in concave terrain areas and assess the intensity of heat accumulation;

[0061] The correction value for heat retention temperature is determined based on the degree of enclosure of the concave terrain. The correction value for heat retention temperature is +5°C to +8°C for areas with high enclosure; +2°C to +5°C for areas with medium enclosure; and +1°C to +2°C for areas with low enclosure.

[0062] The heat retention temperature correction value is adjusted according to the depth and area of ​​the concave terrain; a deeper and smaller concave terrain corresponds to a higher heat retention temperature correction value.

[0063] By combining daytime and nighttime temperature difference data, the duration of heat retention in concave terrain can be assessed.

[0064] In this embodiment of the invention, when determining whether a route segment is located in a concave terrain area, the system performs terrain curvature analysis based on a high-resolution digital elevation model. For the 100m × 100m grid cell containing the route segment, the planar curvature value PC and the profile curvature value VC are calculated. The concave terrain discrimination criteria are: PC < -0.25 and VC < -0.15 indicates a valley type; PC < -0.2 and VC > -0.1 indicates a basin type. The system calculates the regional elevation standard deviation SD; concave terrain is only determined when SD > 25 meters to exclude interference from plain micro-topography. A multi-scale identification method is used, with discrimination at three spatial scales: 500m, 1000m, and 2000m. A region is confirmed as concave terrain when the results at at least two scales are consistent.

[0065] When calculating the degree of enclosure of concave terrain, the terrain enclosure index is used. ,in The average elevation of the concave terrain edge. This is the bottom elevation. For feature width, The average slope angle of the surrounding area. The value range is [0,2], as set by the system. A value >0.8 indicates high closure, and 0.4 ≤ ≤0.8 indicates a medium closure. A value less than 0.4 indicates low closure. An airflow retention coefficient is also introduced. ,in The bottom refers to the width of the base. Top refers to the width of the top. The smaller the value, the greater the intensity of heat accumulation.

[0066] When determining the correction value for heat retention temperature based on the degree of enclosure of concave terrain, a piecewise linear mapping function is used. High enclosure area ( Correction value >0.8) The temperature range is +5℃ to +8℃; medium closure zone (0.4≤ ≤0.8) The temperature range is +2℃ to +5℃; low closure area ( <0.4) The temperature range is +1℃ to +2℃. System construction. - The mapping matrix contains 100 discrete points, and the accurate temperature correction value is obtained through interpolation calculation.

[0067] When adjusting the heat retention temperature correction value based on the depth and area of ​​the concave terrain, a depth-area adjustment coefficient is introduced. ,in Depth of concave terrain (meters) The horizontal projected area (square kilometers) Let be the reference area constant, 1 square kilometer. When the value is greater than 1.5, the temperature correction value increases by a factor. The maximum increase is 20%; when 0.8 ≤ When ≤1.5, =1; when When <0.8, The maximum reduction is 15%. Adjusted heat retention temperature correction value. .

[0068] When assessing heat retention time by combining daytime and nighttime temperature difference data, the system acquires a 24-hour temperature change curve for the region and calculates... Simultaneously, the internal temperature of the concave terrain is obtained. Temperature in the surrounding open areas Calculate the temperature difference curve Heat retention time Defined as Continuous time duration >0.5℃. Constructing the heat retention time coefficient. : >8 hours, Maximum increase of 15%; 4≤ ≤8 hours ; <4 hours, The maximum reduction is 10%. Final heat retention temperature correction value. .

[0069] Preferably, the differential sunshine analysis includes:

[0070] Obtain the orientation and slope data of the slopes where the line segments are located;

[0071] Determine the azimuth and altitude of the sun based on the date and time;

[0072] Determine the relationship between the slope orientation and the sun's position, and calculate the sunshine temperature correction value: if the angle between the two is less than 45°, it is a strong sunshine area, and the sunshine temperature correction value is +4°C to +6°C; if the angle is between 45° and 90°, it is a moderate sunshine area, and the sunshine temperature correction value is +2°C to +4°C; if the angle is greater than 90°, it is a weak sunshine area, and the sunshine temperature correction value is -1°C to +1°C.

[0073] In this embodiment of the invention, when acquiring the orientation and slope data of the slopes where the route segments are located, the system performs terrain gradient analysis on each route segment location based on a 10m × 10m resolution digital elevation model. The slope calculation uses the maximum descent gradient method, and the Horn algorithm is applied to the elevation values ​​within a 3×3 grid area to obtain... direction and Directional elevation change rate and ,slope The value ranges from 0° to 90°. The orientation is calculated using the gradient direction method. The value ranges from 0° to 359°, where 0° represents true north and 90° represents true east. The system constructs a buffer zone with a radius of 50 meters for each line segment. The average slope and orientation of all grid cells within the buffer zone are taken as the terrain feature parameters of that segment. When the standard deviation of the slope is >5° or the standard deviation of the orientation is >15°, the segment is marked as complex terrain for fine-tuning.

[0074] When determining the azimuth and altitude angles of the sun based on the date and time, the system uses an astronomical algorithm to calculate the sun's position. The date and time are converted to Julian Day (JD), and the sun's ecliptic longitude (L), ecliptic latitude (B), and Earth-Sun distance (R) are calculated. The sun's right ascension (α) and declination (δ) are obtained through coordinate transformation. Considering the geographical latitude (φ), hour angle (t), and the effects of atmospheric refraction, the sun's altitude angle is calculated. ,in Altitude (km). Solar azimuth. This is converted to a range of 0° to 359°. The system updates the sun's position every 15 minutes. When the sun's altitude is less than 0°, it is considered to be in a state without direct sunlight. For complex mountainous terrain, the system constructs a horizon elevation angle matrix HM. When the sun's altitude angle is less than the horizon elevation angle, it is determined to be in a state of mountain shading.

[0075] When determining the relationship between slope orientation and solar azimuth to calculate the correction value for solar radiation temperature, the system calculates the angle between the two. ,when When >180°, =360°- Ensure the included angle is within the range of 0° to 180°. Introduce the formula for calculating the solar incidence angle. ,when When the angle is less than 90°, the slope receives direct sunlight; when... When the angle is ≥90°, the slope lies in its own shadow. For areas directly exposed to sunlight, the angle depends on the angle. Determine the temperature correction value: when When the temperature is below 45°, it is considered a region of strong sunlight. The range is +4°C to +6°C; when 45°C ≤ When the angle is less than 90°, it is considered a moderate sunshine area. The range is +2℃ to +4℃; when When the angle is ≥90°, it is considered a low-sunlight zone. The range is -1℃ to +1℃. The system takes into account the influence of the solar altitude angle. A correction factor is introduced when the angle is <30°. ,calculate System settings time coefficient The value is 1 from 10:00 to 14:00, and decreases linearly to 0.7 during other periods. The final adjusted value is... For areas obscured by mountains or in their own shadow, the temperature correction value is set directly to -1℃.

[0076] Preferably, the dynamic capacity calculation module includes the following functions:

[0077] Obtain the actual ambient temperature of each segment after terrain-temperature coupling assessment;

[0078] Set the maximum allowable operating temperature of the conductor;

[0079] Establish a heat balance equation, which includes heat input terms and heat loss terms. The heat input terms include Joule heat from conductors and solar radiation heat, and the heat loss terms include convective heat loss and radiative heat loss.

[0080] Substitute the actual ambient temperature into the heat balance equation and solve for the maximum safe current value.

[0081] When the actual ambient temperature is close to or exceeds the maximum allowable operating temperature of the conductor, the maximum safe current value of the line segment is directly set to zero, and an emergency warning for line safety is generated as the highest level of gap warning signal.

[0082] In this embodiment of the invention, the dynamic capacity calculation module extracts the real-time temperature values ​​Ta of each segment of the line from the temperature coupling database, with an accuracy of ±0.1℃, and extracts them every 15 minutes to construct the line temperature distribution vector T=[T1,T2,...,Tn]. The system performs a reasonableness check on the temperature data. When the temperature difference between a segment and the average temperature of adjacent segments exceeds 8℃, the median of the five adjacent segments is used as the replacement. For missing data, a spatiotemporal dual interpolation method is used for repair to ensure data integrity.

[0083] The system sets the maximum allowable operating temperature Tmax based on conductor type: 70℃ for aluminum-coated steel conductors (ACSR), 150℃ for heat-resistant aluminum alloy conductors (TACSR), and 210℃ for ultra-high temperature low sag conductors (ZTACIR). Considering conductor service life, Tmax is reduced by 5℃ for conductors over 20 years old and by 10℃ for those over 30 years old. For critical crossings, an additional safety margin is set, reducing Tmax by 8℃. The system establishes a conductor temperature safety threshold matrix (STM), including the normal operating threshold (Tmax), warning threshold (Tmax-10℃), and danger threshold (Tmax-5℃).

[0084] When establishing the heat balance equation, the IEEE 738 standard model is adopted, considering the balance relationship between heat input and heat loss terms. The heat input term includes the Joule heat of the conductor. and solar radiation heat Heat loss includes convective heat dissipation. and radiative heat dissipation Convection cooling is divided into natural convection. and forced convection Radiative heat dissipation The complete heat balance equation is: .

[0085] When calculating the maximum safe current value, the system sets the conductor temperature. The actual ambient temperature Substitute into the heat balance equation. For the ACSR conductor, For TACSR conductors, a piecewise linear fitting of the resistance-temperature relationship is used. The system is solved using the Newton-Raphson iterative method, with initial values... Iterative formula The termination condition is The iteration count may exceed 20. The system constructs a meteorological-current mapping matrix WIM and sets different parameter combinations for different weather conditions.

[0086] when At that time, the system calculates Imax according to (Tmax- ) The proportion decreases by 3 × 100%; when At that time, Imax is directly set to 0. Simultaneously, a line safety emergency warning signal is generated, with a warning level of "red," including information such as the line number, the location of the problematic section, the ambient temperature, the over-temperature range, and the time of occurrence. This signal is pushed to the power grid dispatch and control center via a dedicated communication channel, triggering the emergency response process. The system establishes a conductor cooling rate model to predict the time required for the temperature to drop back to a safe range.

[0087] Preferably, the line bottleneck analysis module includes the following functions:

[0088] Identify the line segment with the smallest maximum safe current value along the entire line and determine it as the thermal capacity bottleneck segment.

[0089] Analyze the topographic features of the heat capacity bottleneck section to determine whether it is caused by heat retention effect or differential solar radiation effect;

[0090] Based on the dominant causes, heat capacity bottlenecks are divided into persistent bottlenecks and time-dependent bottlenecks. When the heat retention effect is dominant, it is determined to be a persistent bottleneck; when the differential solar radiation effect is dominant, it is determined to be a time-dependent bottleneck.

[0091] The maximum power transmission capacity of the entire line is calculated by multiplying the maximum safe current value of the heat capacity bottleneck section by the line voltage.

[0092] Based on the heat capacity bottleneck, a bottleneck mitigation solution is generated.

[0093] In this embodiment of the invention, the line bottleneck analysis module constructs a current distribution vector IV=[I1,I2,...,In] by obtaining the maximum safe current value Imax of each segment, and executes a minimum value retrieval algorithm to find the minimum element Imin and its index position p. The system uses a time window method to obtain the Imin value once per hour over the past 24 hours. When the Imin position is consistent in three consecutive samples, the segment is confirmed as a heat capacity bottleneck segment. The system records the geographical coordinates of the bottleneck segment, the tower section to which it belongs, the conductor type, and the actual ambient temperature, constructs a bottleneck feature vector BFV=[p,x,y,z,Imin,Ta], and marks and displays it in the GIS system.

[0094] When analyzing the terrain features of the heat capacity bottleneck section, the system extracts terrain temperature correction parameters, including heat retention temperature correction values. and solar temperature correction value Calculate the contribution rates of the two effects, including the contribution rate of the heat retention effect. Contribution rate of differentiated solar radiation effect Simultaneously extract the terrain closure index. and solar radiation intensity coefficient Establish the effect discrimination matrix (EDM). When When >60%, the heat retention effect is considered dominant; when When the solar radiation level is >60%, the differential solar radiation effect is considered dominant; when 40% ≤ ≤60% and 40%≤ When the temperature is ≤60%, it is determined to be a mixed effect, which requires further analysis through the 24-hour temperature change curve. When the nighttime temperature drops slowly (<0.5℃ / hour), it tends to be determined to be dominated by the heat retention effect.

[0095] When classifying based on dominant causes, heat retention effect is the dominant one. A bottleneck is defined as one that is >60%, characterized by its all-weather impact, and its bottleneck coefficient is [not specified]. =0.9; Differential solar radiation effect dominates ( When the bottleneck rate is >60%, it is determined to be a time-dependent bottleneck, and the bottleneck coefficient is determined using a time-varying function. ,in The time frame is 6-18 hours. The system constructs a bottleneck timing characteristic matrix (BTCM) to record the changes in bottleneck location, intensity, and type over 24 hours. Persistent bottlenecks are marked as "high priority," and time-sensitive bottlenecks are marked as "scheduling avoidance type."

[0096] When calculating the maximum power transmission capacity of the entire line, the system obtains the line's rated voltage Ur and power factor cosφ. For a three-phase AC transmission line, the maximum power transmission capacity... .consider Safety constraints, introducing a safety margin factor =0.85, calculate For ultra-high voltage and extra-high voltage lines, the angle stability limit power should be considered in addition. ,Pick and The smaller value is taken as the line's final maximum power transmission capacity, Ptrans. The system generates a 24-hour power capacity prediction curve, showing the capacity margin for each time period.

[0097] Based on the type of heat capacity bottleneck, mitigation solutions are generated: For persistent bottlenecks, solutions include conductor upgrades (increasing cross-sectional area by 25% or improving heat resistance by 80℃), increasing tower height (reducing ambient temperature by approximately 1.2℃ for every 3 meters increase), and adding forced cooling devices (increasing heat dissipation capacity by 20%-30%). For time-sensitive bottlenecks, solutions include load transfer strategies (transferring 30%-50% of power flow during high-temperature periods), solar protection measures (installing shade nets to reduce solar radiation heat by 40%), and vegetation planning (planting a 15-20 meter high-speed forest belt on the south side). The system assigns a priority index PI (1-10) to each solution, estimates implementation costs and expected effects, and generates a decision reference report. For situations with multiple bottlenecks coexisting, a bottleneck collaborative elimination algorithm is applied to optimize resource allocation.

[0098] Preferably, the bottleneck mitigation scheme generated based on the heat capacity bottleneck includes:

[0099] For persistent bottlenecks, replace the conductors with heat-resistant ones or increase the tower height;

[0100] For time-specific bottlenecks, some power flow will be diverted to other lines during periods of high temperature.

[0101] To address the thermal capacity bottlenecks of critical power transmission channels, emergency power generation resources should be activated or demand-side response should be implemented.

[0102] For recurring severe heat capacity bottlenecks, medium- and long-term renovation plans are generated, including vegetation shading, conductor upgrades, and line replanning.

[0103] In this embodiment of the invention, for persistent bottlenecks, the system generates a conductor replacement plan, replacing the original ACSR conductors with heat-resistant aluminum alloy conductors (TACSR) or carbon fiber composite core conductors (ACCC). TACSR conductors have a maximum allowable operating temperature of 150℃, 80℃ higher than ordinary ACSR conductors, increasing transmission capacity by 35%-45%; ACCC conductors have a maximum allowable operating temperature of 180℃, increasing capacity by 50%-60%. The system determines the required conductor specifications based on the difference between the bottleneck current limit and the demand. For severe persistent bottlenecks with a heat retention coefficient greater than 0.7, a tower heightening plan is also generated, calculating the height increase ΔH = (Ta - Taref) × 2.5, setting the height increase range to 3-8 meters, with an ambient temperature decrease of 0.4℃ for every 1 meter increase. The system generates a construction impact assessment report, including construction duration and power outage range.

[0104] For time-sensitive bottlenecks, a power flow transfer model is established for high-temperature periods (10:00-16:00). A power flow transfer matrix (PTM) is constructed, including three parameters: main line load rate, backup line capacity margin, and power flow transfer ratio. This is based on solar radiation temperature correction values. Determine the transfer ratio β: At temperatures above 5℃, β = 40%-50%; at temperatures below 3℃, β = 40%-50%. At ≤5℃, β = 25%-40%; At ≤3℃, β=10%-25%. The system calculates the maximum transferable capacity under the N-1 safety constraint and issues a transfer command 4 hours in advance, including start and end times, transfer capacity, and target line. For cases without backup lines, a unit load adjustment scheme is generated, reducing the load on restricted lines by adjusting the power output on the power supply side.

[0105] To address thermal capacity bottlenecks in critical power transmission channels (those handling inter-provincial exchange capacity >500MW; supplying regional loads >3GW; serving as the sole power source for important users), the system constructs an Emergency Resource Request (ERM) matrix. Tiered response plans are generated based on the severity of the bottleneck: for minor bottlenecks (<10%), gas-fired peak-shaving generators are activated with a response time of 30 minutes; for moderate bottlenecks (10%-30%), pumped-storage hydroelectric power stations are activated with a response time of 15 minutes; and for severe bottlenecks (>30%), emergency diesel generator sets are activated with a response time of 5 minutes. Simultaneously, demand-side response is triggered, reducing the deficit by 1.2 times the deficit value for a duration not exceeding 4 hours. Critical channels are monitored 24 hours a day by dedicated personnel, with early warnings issued 8 hours in advance.

[0106] For recurring severe heat capacity bottlenecks (residual capacity > 25% of line rated capacity or causing load shedding > 50MW) that occur repeatedly (cumulative > 120 hours within 30 days), the system generates medium- to long-term renovation plans. The vegetation shading plan targets bottleneck sections dominated by solar radiation, planning 18-25 meter tall high-speed trees within 15-30 meters south of the line to form a "V"-shaped shading belt, expected to lower temperatures by 3-5℃. The conductor upgrade plan includes two modes: full line replacement (increasing cross-sectional area by 30%-50%) and local reinforcement. The line replanning plan avoids heat accumulation areas by changing the corridor path, prioritizing ridges and open terrain, avoiding valleys and south-facing slopes, and automatically generating 3-5 alternative paths for multi-dimensional comparison. All plans include investment estimates, construction periods, and expected improvement effects.

[0107] Preferably, the supply guarantee risk early warning module includes the following functions:

[0108] Obtain the maximum power transmission capacity of the line;

[0109] Obtain the planned transmission power of this line from the power grid dispatch plan;

[0110] Calculate the supply gap value, which is the difference between the planned transmission power and the maximum power transmission capacity;

[0111] Based on the size and duration of the supply gap, a gap warning signal is generated in stages: if the supply gap is less than 10% of the maximum power transmission capacity and the duration is less than 2 hours, a yellow gap warning signal is issued; if the supply gap is between 10% and 30% of the maximum power transmission capacity or the duration is between 2 and 6 hours, an orange gap warning signal is issued; if the supply gap is greater than 30% of the maximum power transmission capacity or the duration is more than 6 hours, a red gap warning signal is issued.

[0112] Corresponding countermeasures are generated for different levels of gap warning signals, and the gap warning signals and countermeasures are pushed to the power grid dispatch and control center.

[0113] In this embodiment of the invention, the supply guarantee risk early warning module reads the Ptrans value calculated by the line bottleneck analysis module, and uses a time series method to obtain the predicted value of the maximum power transmission capacity for the next 24 hours, constructing a capacity time series matrix CTM=[P1,P2,...,P24]. The system incorporates weather forecast data and establishes a weather-capacity correlation model WCM: for every 1°C increase in forecast temperature, the maximum power transmission capacity decreases by 1.5%-2.5%; for every 1m / s increase in forecast wind speed, the maximum power transmission capacity increases by 2%-3%. The system applies a safety factor of 0.95 to the predicted values. For cross-regional UHV lines, additional stability constraints at the transmitting and receiving ends are considered, and the minimum of the three is taken as the final transmission capacity value. The system stores capacity data at 15-minute intervals, constructing a rolling updated capacity curve.

[0114] When obtaining planned transmission power from the power grid dispatch plan, the system connects in real-time with the power grid dispatch automation system through a standardized data interface to obtain the day-ahead dispatch plan and the real-time adjusted planned transmission power Pplan. The system acquires data at three time scales: short-term (future 24 hours, 15-minute resolution), medium-term (future 7 days, 1-hour resolution), and long-term (future 30 days, daily resolution). The system constructs a planned power matrix PPM=[Pp1,Pp2,...,Pp96], containing 96 15-minute planned values ​​for the next 24 hours. When the power change rate between adjacent time periods exceeds 30%, it is marked as a suspected anomaly and smoothed using linear interpolation. A dual data verification mechanism is established for important transmission channels; manual confirmation is triggered when the deviation between the planned value and the measured historical value exceeds 15%.

[0115] When calculating the supply gap, the system aligns the Maximum Power Transmission Capacity (CTM) and the Planned Power Transmission Per Mille (PPM) with a uniform 15-minute sampling interval. The supply gap value is calculated for each time point. ,when A value greater than 0 indicates a potential gap risk; when A value ≤0 indicates excess capacity. =0. The system simultaneously calculates the relative gap value. Construct the gap time series matrix GTM=[G1,G2,...,G96]. The system calculates the duration of continuous gaps. Defined as continuously satisfying The length of the time period is greater than 0. For multiple discontinuous gaps, identify the start and end times of each gap segment and record them as a gap time period set TS={[t1s,t1e],[t2s,t2e],...}.

[0116] When generating gap warning signals based on the size and duration of the supply gap, the system executes a multi-condition judgment algorithm: the yellow warning condition is 0 < <10% and <2 hours; Orange alert condition is 10% ≤ ≤30% or 2≤ ≤6 hours; Red alert conditions are >30% or >6 hours. The system also considers the accumulated gap. ,when When the flow rate is >500MWh, the warning level should be at least orange. When the threshold exceeds 1000 MWh, the alert level is directly upgraded to a red alert. The system constructs an alert signal data packet (WP), which includes the alert level, gap value, duration, start and end times, impact range, and severity index. The early warning system employs a progressive triggering mechanism; lower-level warnings will not be triggered repeatedly within 24 hours, and higher-level warnings will replace lower-level warnings.

[0117] When generating response recommendations for different levels of power shortage warnings, the system constructs a Decision Tree (DMT) for response measures. Yellow warning recommendations: Adjust line operation mode to reduce impedance by 20%, activate small-capacity backup lines, and postpone planned maintenance. Orange warning recommendations: Transfer 30%-50% of power flow to backup channels, activate peak-shaving power sources such as gas turbines and pumped storage, implement peak-shaving power plans for Class I large users (>10MW) to reduce load by 15%-25%. Red warning recommendations: Activate all emergency power generation resources, implement mandatory load limits for Class I and II users (>5MW) to reduce load by 30%-50%, and activate cross-regional support plans. The system assigns priority (1-5) to each measure, constructs an implementation sequence, and pushes warning signals and recommendations to the power grid dispatch control center via encrypted communication channels, while also copying them to the emergency command center and energy authorities. The system updates the response effectiveness assessment report every 30 minutes.

[0118] Please see Figure 2This diagram illustrates a big data analysis system for monitoring power supply gaps. The system employs a three-layer architecture, from bottom to top: a data acquisition layer, an analysis and processing layer, and an application display layer. The data acquisition layer comprises a distributed sensor network, a weather station array, a satellite remote sensing platform, and a power grid SCADA system, aggregating data via a data bus. The analysis and processing layer is the core of the system, consisting of five functional modules: a corridor environment modeling module (left) responsible for spatial mapping of environmental data; a heat dissipation capacity assessment module (top left) performing terrain and climate correction; a dynamic capacity calculation module (top center) solving the heat balance equation; a line bottleneck analysis module (top right) identifying heat capacity bottlenecks; and a power supply risk early warning module (right) generating tiered early warning signals. Data flows sequentially between modules according to the arrow direction: from environmental slices to heat dissipation coefficients, then to safe current values, ultimately forming early warning information. The application display layer presents the analysis results through a monitoring dashboard, a web portal, and mobile applications, providing decision support functions. The entire system uses a distributed computing framework, supporting real-time data processing and historical data analysis, with loosely coupled integration between modules through standardized interfaces.

[0119] Please see Figure 3 This is a diagram of power equipment for ensuring power supply.

[0120] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0121] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A big data analysis system for monitoring power supply gaps, characterized in that, Includes the following modules: The corridor thermal environment modeling module is used to acquire geographic information system data of transmission lines, and at the same time collect regional ambient temperature data and electrical data. The ambient temperature data and electrical data are mapped to the geographic coordinates of each segment of the line to form a thermal environment slice of the transmission corridor. The terrain-temperature coupling assessment module is used to analyze the impact of terrain characteristics on local temperature in each section of the line, calculate the temperature correction value caused by terrain, and obtain the actual ambient temperature of each section of the line under the influence of terrain. The terrain-temperature coupling assessment module includes the following functions: Topographic characteristics and ambient temperature data of transmission line segments were extracted from thermal environment slices of the transmission corridor; Identify key terrain types, including valleys, basins, slopes, and ridges, and determine how they affect temperature; The analysis of the correction effect of topographic features on ambient temperature includes the heat retention effect and the differential solar radiation effect. The analysis process for this effect is as follows: Heat retention effect analysis assesses the confinement and accumulation of heat by concave topography, and calculates the heat retention temperature correction value under concave topographic conditions; the heat retention effect analysis includes: Determine whether a section of the line is located in a concave terrain area; Calculate the degree of enclosure in concave terrain areas and assess the intensity of heat accumulation; The correction value for heat retention temperature is determined based on the degree of enclosure of the concave terrain. The correction value for heat retention temperature is +5°C to +8°C for areas with high enclosure; +2°C to +5°C for areas with medium enclosure; and +1°C to +2°C for areas with low enclosure. The heat retention temperature correction value is adjusted according to the depth and area of ​​the concave terrain; a deeper and smaller concave terrain corresponds to a higher heat retention temperature correction value. By combining daytime and nighttime temperature difference data, the duration of heat retention in concave terrain can be assessed. Differential solar radiation analysis calculates the differences in solar radiation intensity for terrains with different orientations based on the relationship between the orientation and slope of the hillside and the position of the sun, and derives solar radiation temperature correction values. The heat retention temperature correction value and the solar radiation temperature correction value are superimposed to obtain the comprehensive terrain temperature correction value; Calculate the actual ambient temperature values ​​of each segment under the influence of terrain; The dynamic capacity calculation module is used to establish a thermal balance equation with the maximum allowable operating temperature of the conductor as the target, and substitute the actual ambient temperature into the thermal balance equation to solve the maximum safe current value under the condition that the conductor does not overheat. The line bottleneck analysis module is used to identify the bottleneck section with the smallest heat capacity and its geographical location based on the maximum safe current value, and to calculate the maximum power transmission capacity of the entire line. The power supply risk early warning module is used to obtain the planned transmission power of the lines in the power grid dispatch plan, compare it with the maximum power transmission capacity, and calculate the power supply gap value and generate a gap early warning signal of the corresponding level when the planned transmission power exceeds the maximum power transmission capacity.

2. The power supply gap monitoring big data analysis system according to claim 1, characterized in that, The corridor thermal environment modeling module includes the following functions: Obtain geographic information system data for the transmission lines, including the latitude, longitude, altitude, and terrain information along the line for each tower; Acquire dynamic regional ambient temperature data and line electrical data, including ambient temperature data such as air temperature, ground surface temperature and solar radiation intensity, and electrical data such as current and voltage; Ambient temperature and electrical data are mapped onto the geographic coordinates of each section of the transmission line to form thermal environment slices of the transmission corridor.

3. The power supply gap monitoring big data analysis system according to claim 1, characterized in that, Differential solar radiation analysis includes: Obtain the orientation and slope data of the slopes where the line segments are located; Determine the azimuth and altitude of the sun based on the date and time; Determine the relationship between the slope orientation and the sun's position, and calculate the sunshine temperature correction value: if the angle between the two is less than 45°, it is a strong sunshine area, and the sunshine temperature correction value is +4°C to +6°C; if the angle is between 45° and 90°, it is a moderate sunshine area, and the sunshine temperature correction value is +2°C to +4°C; if the angle is greater than 90°, it is a weak sunshine area, and the sunshine temperature correction value is -1°C to +1°C.

4. The power supply gap monitoring big data analysis system according to claim 1, characterized in that, The dynamic capacity calculation module includes the following functions: Obtain the actual ambient temperature of each segment after terrain-temperature coupling assessment; Set the maximum allowable operating temperature of the conductor; Establish a heat balance equation, which includes heat input terms and heat loss terms. The heat input terms include Joule heat from conductors and solar radiation heat, and the heat loss terms include convective heat loss and radiative heat loss. Substitute the actual ambient temperature into the heat balance equation and solve for the maximum safe current value. When the actual ambient temperature is close to or exceeds the maximum allowable operating temperature of the conductor, the maximum safe current value of the corresponding segment is directly set to zero, and an emergency line safety warning is generated as the highest level of gap warning signal.

5. The power supply gap monitoring big data analysis system according to claim 1, characterized in that, The line bottleneck analysis module includes the following functions: Identify the line segment with the smallest maximum safe current value along the entire line and determine it as the thermal capacity bottleneck segment. Analyze the topographic features of the heat capacity bottleneck section to determine whether it is caused by heat retention effect or differential solar radiation effect; Based on the dominant causes, heat capacity bottlenecks are divided into persistent bottlenecks and time-dependent bottlenecks. When the heat retention effect is dominant, it is determined to be a persistent bottleneck; when the differential solar radiation effect is dominant, it is determined to be a time-dependent bottleneck. The maximum power transmission capacity of the entire line is calculated by multiplying the maximum safe current value of the heat capacity bottleneck section by the line voltage. Based on the heat capacity bottleneck, a bottleneck mitigation solution is generated.

6. The power supply gap monitoring big data analysis system according to claim 5, characterized in that, Bottleneck mitigation solutions based on heat capacity bottlenecks include: For persistent bottlenecks, replace the conductors with heat-resistant ones or increase the tower height; For time-specific bottlenecks, some power flow will be diverted to other lines during periods of high temperature. To address the thermal capacity bottlenecks of critical power transmission channels, emergency power generation resources should be activated or demand-side response should be implemented. For recurring severe heat capacity bottlenecks, medium- and long-term renovation plans are generated, including vegetation shading, conductor upgrades, and line replanning.

7. The power supply gap monitoring big data analysis system according to claim 1, characterized in that, The supply guarantee risk early warning module includes the following functions: Obtain the maximum power transmission capacity of the line; Obtain the planned transmission power of this line from the power grid dispatch plan; Calculate the supply gap value, which is the difference between the planned transmission power and the maximum power transmission capacity; Based on the size and duration of the supply gap, a gap warning signal is generated in stages: if the supply gap is less than 10% of the maximum power transmission capacity and the duration is less than 2 hours, a yellow gap warning signal is issued; if the supply gap is between 10% and 30% of the maximum power transmission capacity or the duration is between 2 and 6 hours, an orange gap warning signal is issued; if the supply gap is greater than 30% of the maximum power transmission capacity or the duration is more than 6 hours, a red gap warning signal is issued. Corresponding countermeasures are generated for different levels of gap warning signals, and the gap warning signals and countermeasures are pushed to the power grid dispatch and control center.

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