A method and system for predicting the thickness of ice on a power transmission line based on the phase evolution of a raindrop spectrum

Abstract

The application discloses a kind of based on raindrop spectrum phase evolution transmission line icing thickness prediction method and system, belong to transmission line early warning technical field.Its method includes obtaining meteorological data in the area where transmission line is located and identifying warm layer and cold layer;Combining precipitation intensity and radar reflectivity, the relationship between particle number concentration and particle size is estimated, and the particle spectrum distribution function is obtained;Theoretical heat required for particle from ice state heating and melting is calculated, whether particle is completely melted is judged by energy conservation;Estimate the residence time and heat exchange of particle in cold layer, determine the phase state of particle when reaching ground;Combining particle spectrum distribution function and the phase state of particle when reaching ground, the collision efficiency between particle and transmission line and icing thickness growth rate are calculated;Transmission line icing thickness is obtained based on time accumulation.The application can carry out high-precision quantitative prediction on the thickness change of transmission line icing, and is especially suitable for fine power grid disaster prevention demand under the background of glaze weather.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power transmission line early warning, and particularly relates to a power transmission line icing thickness prediction method and system based on raindrop spectrum phase state evolution. BACKGROUND

[0002] Power transmission line icing is a typical meteorological disaster in the power system, especially in high-altitude or cold regions, where glaze or wet snow-induced icing is prone to occur, which may cause serious consequences such as conductor breakage and tower collapse. At present, empirical formula, static meteorological parameter or image recognition methods are commonly used in engineering for power transmission line icing monitoring and prediction, but these methods often ignore the changes of precipitation particle spectrum and its micro-interaction process with the power transmission line, resulting in low prediction accuracy, especially in the background of glaze weather.

[0003] The key process of glaze formation is that raindrops in warm and humid air partially or completely melt after passing through the warm layer (>0°C), and the subsequent near-surface cold layer fails to freeze all the raindrops in time, which rapidly freeze to form icing after contacting the surface of ground objects. Therefore, the melting state, particle size and ground layer cooling intensity of raindrops have a significant impact on the formation and development of conductor icing.

[0004] Traditional models usually fail to distinguish the physical state of particles of different particle sizes in detail, and do not consider the difference in collision efficiency between particles and power transmission lines, and thus cannot effectively capture the micro-growth mechanism of conductors under the condition of glaze at the edge. Therefore, there is an urgent need for a new icing prediction method based on raindrop spectrum distribution, considering the evolution of particle state and the micro-physical action of conductors, to improve the accuracy and timeliness of power transmission line meteorological disaster prevention and control. SUMMARY

[0005] The purpose of the present application is to provide a power transmission line icing thickness prediction method and system based on raindrop spectrum phase state evolution, which fully considers the thermal changes of precipitation particles in the vertical path, the particle spectrum distribution characteristics and the micro-scale physical process of conductors, and can accurately predict the thickness changes of power transmission line icing, especially suitable for fine power grid disaster prevention under the background of glaze weather.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] The present application provides a power transmission line icing thickness prediction method based on raindrop spectrum phase state evolution, comprising:

[0008] Obtaining meteorological data of the area where the power transmission line is located and preprocessing;

[0009] Identifying the warm layer and the cold layer based on the obtained meteorological data;

[0010] The particle spectrum distribution function is obtained by combining the precipitation intensity and the radar reflectivity, estimating the relationship between the particle number concentration and the particle size, and estimating the particle spectrum distribution function;

[0011] Based on the height of the warm layer and the particle falling speed, the heat obtained from the environment during the particle falling process is determined, and the theoretical heat required for the particle to heat and melt from the ice state is calculated. Whether the particle is completely melted is determined by energy conservation;

[0012] According to the thickness of the cold layer, the ambient temperature and the particle falling speed, the residence time and heat exchange of the particle in the cold layer are estimated, and the heat transfer model is combined with whether the particle in the warm layer is completely melted to determine whether the particle is cooled to below 0°C, and the phase state of the particle when it reaches the ground is determined.

[0013] The collision efficiency between the particle and the power transmission line and the ice thickness growth rate per unit time are calculated by combining the particle spectrum distribution function and the phase state of the particle when it reaches the ground.

[0014] The ice thickness of the power transmission line is obtained based on the ice thickness growth rate and the time accumulation.

[0015] Preferably, the meteorological data of the area where the power transmission line is located is obtained and preprocessed, including:

[0016] The temperature profile is obtained from sounding, model prediction or microwave radiometer;

[0017] The precipitation intensity and the ice particle phase state are obtained;

[0018] The near-surface meteorological data, including wind speed, wind direction and relative humidity, are obtained;

[0019] And the terrain height information is obtained;

[0020] The obtained data is interpolated, coordinate matched and consistency checked to form a standard input format.

[0021] Preferably, the warm layer and the cold layer are identified based on the obtained meteorological data, including:

[0022] According to the temperature profile, a threshold temperature is set as the liquid and solid water boundary line;

[0023] The area with a temperature higher than is identified as the warm layer, and the area with a temperature lower than is identified as the cold layer;

[0024] The top height and the bottom height of the warm layer are obtained, and the warm layer thickness is calculated as: ;

[0025] The cold layer thickness is defined as the region from the bottom of the warm layer to the ground level The cold layer thickness is calculated as .

[0026] Preferably, the estimated relationship between the particle number concentration and the particle size, which gives the particle size distribution function, comprises:

[0027] The estimated relationship between the particle number concentration and the particle size, which gives the particle size distribution function, is given by a parametric model of gamma distribution, expressed as:

[0028] ,

[0029] wherein, N represents the particle number concentration, D represents the particle size, , , are all fitting parameters.

[0030] Preferably, the determining the heat acquired from the environment during the particle falling process based on the height of the warm layer and the particle falling velocity, and the calculating the theoretical heat required for the particle to be heated and melted from ice state, to judge whether the particle is completely melted or not by energy conservation, comprises:

[0031] The theoretical heat required for the particle to be heated and melted from ice state is estimated in the following way:

[0032] ,

[0033] wherein, Q represents the theoretical heat required for the particle to be heated and melted from ice state, Cp represents the specific heat capacity of ice, M represents the total mass of the particle, T represents the temperature of the warm layer environment, L represents the latent heat of fusion of ice, m represents the mass of the melted ice;

[0034] The heat acquired from the environment during the particle falling process is compared with the theoretical heat to judge whether the particle is completely melted or not;

[0035] If the particle is completely melted, it is a frozen rain particle; if not, it is a mixed phase particle, which forms a glaze after landing.

[0036] Preferably, the using the heat transfer model to judge whether the particle is cooled to below 0°C based on whether the particle is completely melted in the warm layer, to determine the phase state of the particle when it reaches the ground, comprises:

[0037] If the particle is completely melted in the warm layer, but is cooled to below 0°C, it is judged that the phase state of the particle is supercooled; ​

[0038] If the warm layer particles are completely melted and the particles are not cooled to 0°C, the phase state of the particles is determined to be liquid;

[0039] If the warm layer particles are not completely melted and the particles are cooled to 0°C, the phase state of the particles is determined to be semi-melted;

[0040] If the warm layer particles are not completely melted and the particles are cooled to below 0°C, the phase state of the particles is determined to be solid.

[0041] Preferably, the particle spectrum distribution function and the phase state of the particles when reaching the ground are combined to calculate the collision efficiency between the particles and the power transmission line and the ice thickness growth rate per unit time, comprising:

[0042] The collision efficiency between the particles and the power transmission line is calculated using the Langmuir model is represented as:

[0043] ,

[0044] wherein, is the relative wind speed, is the particle density, is the radius of the power transmission line, is the air dynamic viscosity, and is a fitting constant;

[0045] The ice mass growth rate per unit length of the power transmission line per unit time is calculated as:

[0046] ,

[0047] wherein, is the ice mass growth rate, is the icing mass of the power transmission line, and is the particle size integration range, is the density of the particles, is the falling speed of the particles, and is the freezing efficiency considering the phase state of the particles;

[0048] For particles of different phase states, the freezing efficiency is represented as a function of the particle size , the temperature of the power transmission line , and the phase state :

[0049] ,

[0050] wherein, , , are constants;

[0051] converting the ice mass growth rate into an ice thickness growth rate:

[0052] ,

[0053] wherein, is the ice thickness growth rate, is the ice density, is the time step, is the ice thickness growth step.

[0054] Preferably, the method further comprises:

[0055] According to the ice thickness and the wind speed, combined with the safe operation standard of the power grid, the risk level is divided into:

[0056] When the ice thickness is ≥ 10 mm, no matter the size of the wind speed, it is determined as level IV;

[0057] When the ice thickness is 5 mm ≤ the ice thickness < 10 mm, if the wind speed is ≥ 12 m / s, it is determined as level IV; if the wind speed is < 12 m / s, it is determined as level III;

[0058] When the ice thickness is 2 mm ≤ the ice thickness < 5 mm, if the wind speed is ≥ 8 m / s, it is determined as level III; if the wind speed is 5 m / s ≤ the wind speed < 8 m / s, it is determined as level II; if the wind speed is < 5 m / s, it is determined as level I;

[0059] When the ice thickness is < 2 mm, if the wind speed is ≥ 12 m / s, it is determined as level III; if the wind speed is 5 m / s ≤ the wind speed < 12 m / s, it is determined as level II; if the wind speed is < 5 m / s, it is determined as level I.

[0060] The application also provides a power transmission line ice thickness prediction system based on raindrop spectrum phase state evolution, which is used for realizing the power transmission line ice thickness prediction method based on raindrop spectrum phase state evolution.

[0061] The meteorological data acquisition and preprocessing module is used for acquiring meteorological data of a region where the power transmission line is located and performing preprocessing.

[0062] The warm layer and cold layer height identification module is used for identifying the warm layer and the cold layer based on the acquired meteorological data.

[0063] A particle phase state and spectrum distribution identification module is used to estimate the relationship between particle number concentration and particle size in combination with precipitation intensity and radar reflectivity, to obtain a particle spectrum distribution function; based on the height of the warm layer and the particle falling velocity, the heat obtained from the environment during the particle falling process is determined, and the theoretical heat required for the particle to heat and melt from the ice state is calculated, and the particle is determined to be completely melted by energy conservation; and according to the thickness of the cold layer, the ambient temperature and the particle falling velocity, the residence time and heat exchange of the particle in the cold layer are estimated, and the particle is determined to be cooled to below 0°C by using a heat transfer model in combination with whether the particle in the warm layer is completely melted, to determine the phase state of the particle when reaching the ground;

[0064] A wire collision efficiency and ice thickness growth calculation module is used to calculate the collision efficiency between the particle and the power transmission line and the ice thickness growth rate per unit time in combination with the particle spectrum distribution function and the phase state of the particle when reaching the ground, and to obtain the ice thickness of the power transmission line based on the ice thickness growth rate and time accumulation.

[0065] Preferably, the system further comprises:

[0066] An ice risk level assessment and early warning module is used to divide the risk level into:

[0067] When the ice thickness is ≥ 10 mm, regardless of the wind speed, it is determined to be level IV;

[0068] When the ice thickness is 5 mm ≤ ice thickness < 10 mm, if the wind speed is ≥ 12 m / s, it is determined to be level IV; if the wind speed is < 12 m / s, it is determined to be level III;

[0069] When the ice thickness is 2 mm ≤ ice thickness < 5 mm, if the wind speed is ≥ 8 m / s, it is determined to be level III; if the wind speed is 5 m / s ≤ wind speed < 8 m / s, it is determined to be level II; if the wind speed is < 5 m / s, it is determined to be level I;

[0070] When the ice thickness is < 2 mm, if the wind speed is ≥ 12 m / s, it is determined to be level III; if the wind speed is 5 m / s ≤ wind speed < 12 m / s, it is determined to be level II; if the wind speed is < 5 m / s, it is determined to be level I.

[0071] The present application provides a power transmission line ice thickness prediction method and system based on raindrop spectrum phase state evolution, which has the following remarkable beneficial effects:

[0072] (1) The present application combines temperature profile, warm / cold layer structure, particle phase state, particle size spectrum distribution and other meteorological physical parameters to realize dynamic linkage modeling of particle state and power transmission line ice behavior for the first time.

[0073] (2) The present application considers the whole process of particle state transformation, not only judges whether the particles are melted or not, but also considers whether they can be cooled to supercooled state in the cold layer, which is closer to the complex precipitation phase change behavior under natural conditions.

[0074] (3) Unlike the traditional empirical regression model, the present application is based on physical process inversion, realizes the prediction of icing growth process with hourly update and particle diameter integration, can quantitatively predict the thickness change of transmission line icing with high precision, and is especially suitable for the fine grid disaster prevention demand under the background of glaze weather. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 is a kind of transmission line icing thickness prediction method process schematic diagram based on raindrop spectrum phase state evolution provided by the embodiment of the present application;

[0076] Figure 2 is a kind of transmission line icing thickness prediction system structure schematic diagram based on raindrop spectrum phase state evolution provided by the embodiment of the present application;

[0077] Figure 3 is the temperature vertical profile distribution curve schematic diagram provided by the embodiment of the present application;

[0078] Figure 4 is the schematic diagram of different size particle phase state and temperature in precipitation process provided by the embodiment of the present application;

[0079] Figure 5 is the different size raindrop freezing efficiency curve schematic diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in conjunction with embodiments and drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application.

[0081] Here, it also needs to be explained that, in order to avoid the unnecessary details from obscuring the present application, only the structures and / or processing steps closely related to the scheme according to the present application are shown in the drawings, and other details not closely related to the present application are omitted.

[0082] It should be emphasized that the term "comprises / comprising" is used herein to indicate the presence of a feature, element, step or component, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0083] Here, it also needs to be explained that, if not specially stated, the term "connection" herein can not only mean direct connection, but also mean indirect connection with the presence of intermediate.

[0084] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0085] It is emphasized here that the step labels mentioned hereinafter are not a limitation on the order of the steps, but it should be understood that the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0086] Embodiment 1

[0087] This embodiment 1 provides a method for predicting the icing thickness of a power transmission line based on the phase evolution of a raindrop spectrum, as shown in the following steps: Figure 1

[0088] Step S1: meteorological data acquisition and preprocessing,

[0089] In this embodiment, various types of meteorological data are obtained, including temperature profile (obtained from sounding, model prediction or microwave radiometer, etc.), precipitation intensity and ice particle phase (from automatic station or radar / satellite inversion), wind speed, wind direction, relative humidity and other near-surface meteorological data, and terrain height information.

[0090] Preprocessing refers to interpolating, coordinate matching and consistency checking of the obtained various types of meteorological data to form a standard input format.

[0091] Step S2: identification of warm layer and cold layer height,

[0092] In this embodiment, the existence and thickness of the warm layer and the cold layer are identified based on the temperature profile data. The specific implementation process is as follows:

[0093] S21, according to the temperature profile, set the threshold temperature as the liquid and solid water boundary line;

[0094] S22, identify the region with temperature higher than as the “warm layer”, and the region with temperature lower than as the “cold layer”;

[0095] S23, the top height of the warm layer is , the bottom height is , and the thickness of the warm layer is ;

[0096] S24, the thickness of the cold layer is defined as the region from the bottom of the warm layer to the ground elevation , and the thickness of the cold layer is .

[0097] ​This step identifies the warm layer and the cold layer, which provides the key boundary conditions for determining the phase change process of the precipitation particles.

[0098] As shown in FIG. 1, a schematic diagram of a temperature vertical profile obtained by a sounding detection is given, in which the horizontal coordinate represents temperature and the vertical coordinate represents air pressure. There is a warm layer inversion above 850 hPa, and ice particles will melt when passing through the layer, while the temperature of 925-850 hPa is lower than 0°C, and the melting into water droplets will cool to form freezing rain, and part of the ice particles that have not completely melted will re-freeze, providing conditions for the formation of ground wire icing. Figure 3

[0099] Step S3: Particle phase and spectrum distribution identification,

[0100] The particle refers to a precipitation particle, including ice crystal particles, liquid water particles, semi-melted particles, etc. In this embodiment, the precipitation observation data and the microphysical model are used to inverse the particle phase and the spectrum distribution. The specific implementation process is as follows:

[0101] S31, combined with the precipitation intensity and the radar reflectivity, the particle number concentration is estimated by using a parameterized model of gamma distribution and the relationship between the particle size , to obtain a particle spectrum distribution function, which is expressed as:

[0102] ,

[0103] wherein , , are fitting parameters, which are derived from observation or empirical database.

[0104] S32, based on the height of the warm layer and the falling speed of the particles in the atmosphere, the heat obtained from the environment during the particle falling process is determined, and the theoretical heat required for the particles to heat and melt from ice state is calculated, and whether the particles are completely melted is judged by an energy conservation model.

[0105] It should be noted that when the particles fall, heat exchange will occur due to the difference in temperature from the surrounding air, and the process is proportional to the residence time of the particles in the warm layer (i.e. warm layer height / speed), based on which the heat obtained from the environment during the particle falling process can be determined.

[0106] The theoretical heat requirement is estimated by the following formula:

[0107] ,

[0108] wherein, is the theoretical heat required for the particles to heat and melt from ice state, is the specific heat capacity of ice, is the total mass of the particles, ​is the ambient temperature in the warm layer, is the freezing point temperature, is the latent heat of ice melting, is the mass of the melting ice. If the particle is completely melted, it is considered as a freezing rain particle; if only partially melted, it is a mixed phase particle, which can form a glaze after landing.

[0109] In this embodiment, whether the particle is completely melted is determined by comparing the heat obtained from the environment during the particle falling process with the theoretical heat calculated by the above formula.

[0110] It should be noted that the falling speed of the particle is obtained by radar measurement or according to the particle size calculated by an empirical model.

[0111] S33, the recooling process of the particle in the cold layer is considered. According to the thickness of the cold layer, the ambient temperature and the falling speed of the particle, the residence time and heat exchange amount of the particle in the cold layer are estimated, and whether the particle is cooled to below 0°C is determined by using the heat transfer model in combination with whether the particle in the warm layer is completely melted.

[0112] When liquid precipitation particles (such as raindrops) fall from a relatively warm upper air to a low-temperature air layer (cold layer) near the ground, they will exchange sensible heat with the surrounding cold air, causing the particle temperature to gradually decrease. The residence time of the particle in the cold layer is determined by the thickness of the cold layer and its falling speed; the longer the residence time, the more sufficient the sensible heat exchange, and the more the particle temperature decreases.

[0113] This process can be described by a single particle sensible heat exchange equation:

[0114] ,

[0115] wherein: is the mass of the particle, is the specific heat capacity of the particle, is the temperature of the particle, is the temperature of the cold layer, is the convective heat transfer coefficient between air and particle, is the surface area of the particle.

[0116] By solving the equation, the temperature evolution of the particle in the cold layer during the residence time can be obtained, and whether it is cooled to below 0°C can be determined.

[0117] If the particle is completely melted in the warm layer and not frozen in the cold layer but has been cooled to below 0°C, it becomes supercooled and will quickly freeze when encountering a solid surface such as a wire, forming a glaze;

[0118] If the particle is completely melted in the warm layer and the particle is not cooled to 0°C, the phase state of the particle is determined to be liquid;

[0119] If the particles in the warm layer are not completely melted, and the particles are cooled to 0°C, the particle phase state is determined to be semi-melted;

[0120] If the particles in the warm layer are not completely melted, and the particles are cooled to 0°C or below, the particle phase state is determined to be solid.

[0121] S34, after S32 and S33, the heat exchange of particles of different sizes during falling is calculated, and finally the size and phase state of the particles reaching the ground can be obtained, and the particle spectrum distribution function and the phase state information (liquid / semi-melted / supercooled / solid) under different particle sizes are output.

[0122] As shown in Figure 4 the particle temperature and phase state after the ice crystal particles of different sizes fall through a 300m thick warm layer with an average temperature of 1.5°C and a 500m thick cold layer with a temperature of -2°C are shown in the schematic diagram. Particles below 3.7mm are completely melted and form supercooled droplets; particles larger than 3.8mm are semi-melted and re-frozen to form ice particles. In this example, there are no particles in the semi-melted state.

[0123] Step S4: Based on the aerodynamic theory and microscale thermal model, combined with the particle phase state, the collision efficiency between particles of different sizes and the transmission line and the ice thickness growth rate per unit time are calculated. The specific implementation process is as follows:

[0124] S41, the Langmuir model is used to calculate the collision efficiency between particles and the transmission line :

[0125] ,

[0126] where is the relative wind speed, is the particle density, is the radius of the transmission line, is the air viscosity, is a fitting constant.

[0127] S42, calculate the ice mass growth rate per unit length of the transmission line per unit time:

[0128] ,

[0129] where is the ice mass growth rate, is the ice mass of the transmission line, and are the particle size integration range, is the density of the particles (different for liquid / ice), is the falling speed of the particles, is the freezing efficiency considering the particle phase state, Based on S31.

[0130] For particles of different phases, the freezing efficiency is expressed as a function of particle size , transmission line temperature and phase :

[0131] ,

[0132] where , , is a constant, which can be obtained by experiment, and the transmission line temperature can be obtained by monitoring the sensor.

[0133] Figure 5 The freezing efficiency curve of supercooled water droplets under different particle sizes and transmission line temperatures is given. The lower the transmission line temperature, the higher the freezing efficiency, and the larger the particle size, the lower the freezing efficiency.

[0134] S43, further, the ice mass growth rate per unit length of the transmission line per unit time is converted into the ice thickness growth rate:

[0135] ,

[0136] where is the ice density, is the time step, is the ice thickness growth step.

[0137] Based on the ice thickness growth rate and time accumulation, the ice thickness of the transmission line can be obtained.

[0138] Step S5: Perform ice risk level assessment and early warning,

[0139] In this embodiment, according to the ice thickness growth rate and cumulative value of the transmission line, combined with the safe operation standard of the power grid, the ice risk level is evaluated and the early warning suggestion is given.

[0140] In order to accurately evaluate the potential risk of ice disaster, according to the two key indicators of ice thickness and wind speed, the "nested rule coverage method" is used to classify the risk level. This method takes ice thickness as the dominant factor and wind speed as the secondary weighted factor, ensuring that the risk level assessment has physical rationality and actual applicability. The specific division rules are as follows:

[0141] 1. When the ice thickness ≥ 10 mm, it is considered to have reached the extremely serious risk level:

[0142] Regardless of the wind speed, it is determined as: Grade IV (serious risk);

[0143] 2. When 5 mm ≤ ice thickness < 10 mm, further refine the risk level according to the wind speed:

[0144] Wind speed ≥ 12 m / s: Level IV (severe risk);

[0145] Wind speed < 12 m / s: Level III (high risk);

[0146] 3. When 2 mm ≤ ice thickness < 5 mm:

[0147] Wind speed ≥ 8 m / s: Level III (high risk);

[0148] 5 m / s ≤ wind speed < 8 m / s: Level II (medium risk);

[0149] Wind speed < 5 m / s: Level I (low risk);

[0150] 4. When ice thickness < 2 mm:

[0151] Wind speed ≥ 12 m / s: Level III (high risk);

[0152] 5 m / s ≤ wind speed < 12 m / s: Level II (medium risk);

[0153] Wind speed < 5 m / s: Level I (low risk).

[0154] The embodiment of the application is based on the coupling modeling of atmospheric thermodynamic processes and microphysical characteristics, and realizes high-precision dynamic prediction of the ice thickness of the power transmission line through multi-parameter comprehensive analysis of the temperature profile, warm and cold layer structure, particle spectrum distribution, and phase evolution. Unlike the static estimation of traditional empirical formulas, the embodiment combines particle microphase transition process, wire collision efficiency, and heat exchange mechanism to simulate the attachment and freezing behavior of each particle size of precipitation particles on the wire under the condition of glaze. The embodiment is suitable for power transmission line icing warning under complex weather conditions, especially in high-cold and high-altitude areas where glaze weather occurs frequently, and can provide more scientific and efficient decision support for power operation and maintenance, significantly reducing the disaster risk of power facilities.

[0155] Embodiment 2

[0156] The embodiment 2 provides a power transmission line ice thickness prediction system based on raindrop spectrum phase evolution, which is used to realize the power transmission line ice thickness prediction method based on raindrop spectrum phase evolution of the above-mentioned embodiment 1, and participates Figure 2 The system comprises:

[0157] A meteorological data acquisition and preprocessing module for acquiring and preprocessing meteorological data in the area where the power transmission line is located;

[0158] a warm layer and cold layer height identification module for identifying the heights of the warm layer and the cold layer based on the obtained meteorological data;

[0159] a particle phase state and spectrum distribution identification module for estimating a particle spectrum distribution function in combination with a precipitation intensity and a radar reflectivity, determining a heat acquired from the environment during particle falling based on the heights of the warm layer and the cold layer and a particle falling velocity, and calculating a theoretical heat required for the particle to be heated from an ice state and melted, judging whether the particle is completely melted by energy conservation, and estimating a residence time and a heat exchange amount of the particle in the cold layer according to the cold layer thickness, an ambient temperature and the particle falling velocity, and judging whether the particle is cooled to below 0°C by using a heat transfer model in combination with whether the particle in the warm layer is completely melted, and determining a phase state of the particle when reaching the ground;

[0160] a conductor collision efficiency and ice thickness growth calculation module for calculating a collision efficiency between the particle and the power transmission line and an ice thickness growth rate per unit time in combination with the particle spectrum distribution function and the phase state of the particle when reaching the ground, and obtaining an ice thickness of the power transmission line based on the ice thickness growth rate and time accumulation;

[0161] an ice risk grade evaluation and early warning module for dividing a risk grade according to the ice thickness and a wind speed in combination with a power grid operation safety standard as:

[0162] when the ice thickness is ≥ 10 mm, regardless of the wind speed, it is determined as Grade IV;

[0163] when 5 mm ≤ the ice thickness < 10 mm, if the wind speed is ≥ 12 m / s, it is determined as Grade IV; if the wind speed is < 12 m / s, it is determined as Grade III;

[0164] when 2 mm ≤ the ice thickness < 5 mm, if the wind speed is ≥ 8 m / s, it is determined as Grade III; if 5 m / s ≤ the wind speed < 8 m / s, it is determined as Grade II; if the wind speed is < 5 m / s, it is determined as Grade I;

[0165] when the ice thickness is < 2 mm, if the wind speed is ≥ 12 m / s, it is determined as Grade III; if 5 m / s ≤ the wind speed < 12 m / s, it is determined as Grade II; if the wind speed is < 5 m / s, it is determined as Grade I.

[0166] It is worth pointing out that the system embodiment corresponds to the above-mentioned method embodiment, the implementation manners of the above-mentioned method embodiment are all applicable to the system embodiment, and can achieve the same or similar technical effects, so details are not repeated here.

[0167] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0168] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart and / or block diagram block or blocks.

[0169] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions means which implement the function specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart and / or block diagram block or blocks.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart and / or block diagram block or blocks.

[0171] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting the same. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A method for predicting icing thickness of transmission lines based on raindrop spectrum phase evolution, characterized in that, include: Acquire meteorological data for the area where the transmission line is located and perform preprocessing; Identify the warm and cold layers based on the acquired meteorological data; By combining precipitation intensity and radar reflectivity, the relationship between particle number concentration and particle size is estimated, resulting in the particle spectral distribution function, including: The relationship between particle number concentration and particle size is estimated using a parameterized model of the gamma distribution, yielding the particle spectral distribution function, expressed as: ， in, Indicates particle number concentration. Particle size, , , All are fitted parameters; Based on the height of the thermosphere and the particle's falling velocity, the heat gained by the particle from the environment during its fall is determined, and the theoretical heat required for the particle to heat from an ice state and melt is calculated. Energy conservation is used to determine whether the particle has completely melted, including: Estimate the theoretical heat required to heat a particle from an ice state to melt it as follows: ， in, It is the theoretical heat required to heat the particles from an ice state to melt them. It is the specific heat capacity of ice. It is the total mass of the particles. It is the ambient temperature of the thermosphere. It is the latent heat of fusion of ice. It is the mass of the melted ice. Threshold temperature; The heat absorbed by the particles from the environment during their fall is compared with the theoretical heat to determine whether the particles have completely melted. If all the particles melt, they are frozen rain particles; if they do not melt completely, they are mixed-phase particles, which form rime ice after falling to the ground. Based on the thickness of the cold layer, ambient temperature, and particle falling velocity, the residence time and heat exchange of particles in the cold layer are estimated. A heat transfer model is used in conjunction with whether the particles are completely melted in the warm layer to determine whether the particles are cooled below 0°C, thus determining the phase state of the particles upon reaching the ground. This includes: if the particles are completely melted in the warm layer but cooled below 0°C, the particle phase is considered supercooled; if the particles are completely melted in the warm layer and not cooled to 0°C, the particle phase is considered liquid; if the particles are not completely melted in the warm layer and cooled to 0°C, the particle phase is considered semi-melted; and if the particles are not completely melted in the warm layer and cooled below 0°C, the particle phase is considered solid. By combining the particle spectral distribution function and the phase state of the particles upon reaching the ground, the collision efficiency between the particles and the transmission lines and the rate of increase in icing thickness per unit time are calculated, including: The collision efficiency between particles and transmission lines was calculated using the Langmuir model. Represented as: ， in, Relative wind speed, For particle density, For the radius of the transmission line, Aerodynamic viscosity, These are the fitting constants; Calculate the rate of increase in icing mass per unit length of transmission line per unit time: ， in, For the rate of increase in icing mass, For the quality of icing on transmission lines, and It is the particle size integral range. For the density of particles, The velocity of the falling particle. To consider the freezing efficiency of particle phases; For particles in different phases, the freezing efficiency is expressed as the particle size. Transmission line temperature and phase Functions: ， in, , , It is a constant; Convert the ice mass growth rate into an ice thickness growth rate: ， in, The density of ice, It is the time step. The step size for increasing icing thickness; The icing thickness of transmission lines is obtained based on the icing thickness growth rate and time accumulation.

2. The method for predicting icing thickness of transmission lines based on raindrop spectrum phase evolution according to claim 1, characterized in that, The acquisition and preprocessing of meteorological data for the area where the transmission line is located includes: Temperature profiles are obtained from radiosonde, model forecasts, or microwave radiometers; Obtain precipitation intensity and ice particle phase; Acquire near-surface meteorological data, including wind speed, wind direction, and relative humidity; And obtain terrain height information; The acquired data is interpolated, coordinate matched, and checked for consistency to form a standard input format.

3. The method for predicting icing thickness of transmission lines based on raindrop spectrum phase evolution according to claim 2, characterized in that, The identification of warm and cold layers based on acquired meteorological data includes: Based on the aforementioned temperature profile, set the threshold temperature. As the boundary between liquid and solid water; Identifying temperatures higher than The area is a warm layer, with a temperature lower than [missing information]. The area is a cold layer; Obtain the top height of the warm layer and bottom height Calculate the thickness of the warm layer for: ; The thickness of the cold layer is defined as the distance from the bottom of the warm layer to the ground elevation. Calculate the thickness of the cold layer in the region. for: .

4. The method for predicting icing thickness of transmission lines based on raindrop spectrum phase evolution according to claim 3, characterized in that, The method further includes: Based on icing thickness and wind speed, and in accordance with power grid operation safety standards, the risk levels are classified as follows: When the ice thickness is ≥ 10 mm, regardless of the wind speed, it is classified as Level IV; When the ice thickness is 5 mm ≤ 10 mm, if the wind speed is ≥ 12 m / s, it is classified as Level IV; if the wind speed is < 12 m / s, it is classified as Level III. When the ice thickness is 2 mm ≤ 5 mm, if the wind speed is ≥ 8 m / s, it is classified as Level III; if the wind speed is 5 m / s ≤ 8 m / s, it is classified as Level II; if the wind speed is < 5 m / s, it is classified as Level I. When the ice thickness is < 2 mm, if the wind speed is ≥ 12 m / s, it is classified as Level III; if the wind speed is ≤ 5 m / s and < 12 m / s, it is classified as Level II; if the wind speed is < 5 m / s, it is classified as Level I.

5. A transmission line icing thickness prediction system based on raindrop spectrum phase evolution, characterized in that, The system for implementing the transmission line icing thickness prediction method based on raindrop spectrum phase evolution as described in any one of claims 1 to 4 includes: The meteorological data acquisition and preprocessing module is used to acquire and preprocess meteorological data in the area where the transmission line is located. A module for identifying the height of warm and cold layers is used to identify warm and cold layers based on acquired meteorological data. The particle phase state and spectral distribution identification module is used to combine precipitation intensity and radar reflectivity to estimate the relationship between particle number concentration and particle size, and obtain the particle spectral distribution function; based on the height of the thermosphere and the particle falling velocity, it determines the heat obtained by the particles from the environment during the falling process, and calculates the theoretical heat required for the particles to heat from the ice state and melt, and determines whether the particles have completely melted through energy conservation; and based on the thickness of the cold layer, the ambient temperature and the particle falling velocity, it estimates the residence time and heat exchange of the particles in the cold layer, and uses a heat transfer model combined with whether the particles have completely melted in the thermosphere to determine whether the particles have been cooled to below 0°C, and determines the phase state of the particles when they reach the ground. The module for calculating conductor collision efficiency and icing thickness growth is used to combine the particle spectrum distribution function and the phase state of the particles when they reach the ground to calculate the collision efficiency between particles and transmission lines and the icing thickness growth rate per unit time; and to obtain the icing thickness of the transmission line based on the icing thickness growth rate and time accumulation.

6. The transmission line icing thickness prediction system based on raindrop spectrum phase evolution according to claim 5, characterized in that, The system also includes: The icing risk level assessment and early warning module is used to classify risk levels based on icing thickness and wind speed, combined with power grid operation safety standards. When the ice thickness is ≥ 10 mm, regardless of the wind speed, it is classified as Level IV; When the ice thickness is 5 mm ≤ 10 mm, if the wind speed is ≥ 12 m / s, it is classified as Level IV; if the wind speed is < 12 m / s, it is classified as Level III. When the ice thickness is 2 mm ≤ 5 mm, if the wind speed is ≥ 8 m / s, it is classified as Level III; if the wind speed is 5 m / s ≤ 8 m / s, it is classified as Level II; if the wind speed is < 5 m / s, it is classified as Level I. When the ice thickness is < 2 mm, if the wind speed is ≥ 12 m / s, it is classified as Level III; if the wind speed is ≤ 5 m / s and < 12 m / s, it is classified as Level II; if the wind speed is < 5 m / s, it is classified as Level I.

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