# Power transmission line ice coating real-time distribution module calculation method

## A distribution model and transmission line technology, applied in computing, electrical digital data processing, special data processing applications, etc., can solve the problems that the model cannot achieve accuracy, the real-time meteorological consideration is insufficient, and the transmission line icing information cannot be controlled in real time. Achieve the effect of improving security, reliability, and reliable technical support

Active Publication Date: 2016-07-27

ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1

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## AI-Extracted Technical Summary

### Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a calculation method for the real-time distribution model of icing on power transmission lines, so as to solve the problem that the icing trend model of power transmission lines in the prior art does not fully consid...

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View more## Abstract

The invention discloses a power transmission line ice coating real-time distribution module calculation method, which comprises the steps of screening real-time meteorological data; recognizing the real-time meteorological data; building a real-time ice coating model; calculating the real-time ice coating thickness of each station; performing ice coating climate condition zoning; building an ice coating trend calculation model; determining a model recurrence period; correcting the ice coating thickness; building an ice coating real-time distribution model; and deducing a final ice coating real-time ice thickness distribution diagram by zone real-time ice thickness distribution features and ice coating real-time ice thickness. The method solves the technical problems that the power transmission line ice coating trend model in the prior art does not sufficiently consider the real-time weather, so that the model cannot reach the precision required for simulating the actual ice coating condition; the ice coating information of a power transmission line cannot be mastered in real time; and the guidance on the power transmission line anti-icing and disaster reduction work cannot be realized, and the like.

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## Examples

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### Example Embodiment

[0033] A calculation method for the real-time distribution model of icing on transmission lines, which includes:

[0034] Step 1. Screening of real-time weather data: Collect real-time weather observation data from meteorological stations in each province in the study area by hour, process and summarize them, and obtain weather data related to icing, including minimum temperature, average temperature, maximum temperature, precipitation Volume, average relative humidity and wind speed;

[0035] Step 2. Real-time meteorological data recognition: by analyzing meteorological conditions, find out all icing processes, and effectively identify the entire process from the beginning, growth, maintenance, interruption to the end of icing;

[0036] Step 3. Establish a real-time icing model: According to the capture coefficient of supercooled droplets and the liquid water content of the droplets, combined with the rime Kathleen.F.Jones model, a real-time icing model that considers both rain and rime is established;

[0037] The expression of the real-time icing model in step 3 is:

[0038] D=D 1 +D2··································(1)

[0039] D 1 = 1 πρ i X j = 1 n ( 0.1 Pρ w ) 2 + ( 0.36 W V ) 2 ... ( 2 )

[0040] D 2 = E πρ i X j = 1 n 0.36 W 1 V ... ( 3 )

[0041] In the formula: D is the total ice coating thickness of one ice coating process; D 1 Ice thickness caused by rime;

[0042] D 2 Is the ice thickness caused by rime; P is the precipitation intensity; V is the horizontal wind speed perpendicular to the wire;

[0043] ρ i And ρ w Are the density of ice and water respectively; n is the duration of the icing process; W is the liquid water content in the air caused by rainfall; E is the capture coefficient; W 1 It is the liquid water content caused by supercooled mist.

[0044] Step 4. Calculate the real-time icing thickness of each site: use the real-time icing model of step 3 and its application rules to convert the real-time meteorological data of each site into the real-time icing thickness of each site;

[0045] Step 5. Zoning of icing climatic conditions: Draw spatial distribution maps of the two meteorological indicators of continuous icing growth days and maximum ice thickness in GIS and do overlay analysis. According to the cold air path, terrain trend and elevation distribution characteristics, Carry out icing climate condition division;

[0046] Step 6. Establish an icing trend calculation model: Based on the existing icing trend calculation model, combined with the icing climatic conditions in the area, the icing measurement and survey data in each area are used for modeling through statistical software. Combine the corresponding icing trend calculation model;

[0047] The method for establishing the ice-coating trend calculation model described in step 6 is:

[0048] Step 6.1. Filter out the icing and elevation data of each zone as the basic data for modeling;

[0049] Step 6.2. Perform regression analysis on the basic data through SPSS professional data statistics software, and initially fit the trend model with the best correlation between ice thickness and elevation in each zone;

[0050] Step 6.3. Calculate the ice thickness at the junction of adjacent partitions through the preliminarily fitted trend model of each partition, and analyze the difference of the ice thickness in the junction area. If the ice thickness is the same, then the adjacent partitions The ice-covering trend calculation model is used as the ice-covering trend calculation model; if the thickness of the ice is different, it means that the division of adjacent areas is unreasonable. It is necessary to adjust the adjacent areas and use the SPSS software to fit the new trend model to calculate the boundary area Repeat the above zoning and modeling steps until the ice thickness of the junction area is the same.

[0051] Step 7. Determine the model return period: first use the icing trend calculation model of each return period in each zone to calculate the ice thickness of all meteorological stations in the region during each return period; then compare the real-time ice thickness of each site with the corresponding The icing thickness of each recurrence period of each site is compared to determine the upper limit of the real-time recurrence period of each site; then the recurrence period of each site is counted, and the recurrence period with the largest proportion in the district is determined as the recurrence of the district. Current period; Finally, choose the same icing trend calculation model as the zone return period to calculate the initial real-time ice thickness of each zone;

[0052] Step 8. Correction of icing thickness: Correct the initial real-time ice thickness of the micro-topography area according to the icing change coefficient to obtain the real-time ice thickness of each subregion;

[0053] Step 9. Establish a real-time icing distribution model: Combine the real-time icing model and the icing trend calculation model into a dynamic model to obtain a real-time icing distribution model;

[0054] Step 10. Regional real-time ice thickness distribution characteristics: Use the kriging model to interpolate the real-time ice thickness of each site. Through the ordinary kriging method, first determine the site data characteristics, then construct the variogram, and finally perform kriging Interpolate to obtain the distribution characteristics of the real-time ice thickness of the entire subregion;

[0055] Step 11. Deduction of real-time ice thickness of icing: Integrate the distribution characteristics of real-time icing thickness of the area and the real-time distribution model of icing to obtain the final real-time ice thickness distribution map of icing.

[0056] The technical scheme of the present invention will be further elaborated below in conjunction with examples:

[0057] Step 1. Screening of real-time weather data: According to the knowledge of meteorology, there are three layers of ice crystal layer, heating layer and cold air layer in the atmosphere from top to bottom. The formation of ice is closely related to ice crystal layer, heating layer and cold air layer. In winter, the snowflakes in the ice crystal layer melt into water droplets when they fall to the warm layer. The water droplets quickly cool after entering the cold air layer and become supercooled water droplets. When they come into contact with objects below 0°C on the ground (such as wires, iron towers, etc.), based on The thermodynamic equilibrium mechanism freezes to form ice, so the formation of ice is first determined by meteorological conditions. Taking Guangxi, Guangdong, Guizhou, and Yunnan (hereinafter referred to as the four provinces) as the research area, collect real-time meteorological observation data (by hour) from meteorological stations in each province, process and summarize them, and screen out the weather with higher correlation with icing Data, including minimum temperature, average temperature, maximum temperature, precipitation, average relative humidity, wind speed, etc.;

[0058] Step 2. Real-time meteorological data recognition: The correct calculation of the maximum ice thickness in the icing process must be based on the correct recognition of the icing process. All the icing processes are found through comprehensive analysis of meteorological conditions, and the real-time meteorological data is used to distinguish the various stages of the icing process. , To effectively identify the entire process from the beginning, growth, maintenance, interruption to the end of icing. This study has summarized the meteorological conditions at each stage of the icing process.

[0059] ①The main meteorological conditions for the beginning or increase of icing are:

[0060] Condition 1: The lowest temperature is lower than 0℃;

[0061] Condition 2: The average temperature is lower than 1℃;

[0062] Condition 3: The maximum temperature is lower than 2℃;

[0063] Condition 4: The precipitation is greater than zero or the average relative humidity is greater than 90%.

[0064] ②The main meteorological conditions maintained by icing include:

[0065] Condition 1: The lowest temperature is lower than 0℃;

[0066] Condition 2: The average temperature is lower than 1℃;

[0067] Condition 3: The maximum temperature is lower than 2℃;

[0068] Condition 4: The average relative humidity is greater than 85%.

[0069] ③The main meteorological conditions for icing interruption are:

[0070] Condition 1: The highest temperature is higher than 2℃;

[0071] Condition 2: The average temperature is higher than 1℃;

[0072] Condition 3: The lowest temperature is higher than 0℃;

[0073] Condition 4: The average relative humidity is less than 80%.

[0074] If the ice coating is interrupted for more than 3 hours, it is judged that the ice coating process is over.

[0075] Real-time meteorological data must meet all the meteorological conditions corresponding to a certain stage in order to form the corresponding stage of the icing process.

[0076] Step 3. Establish a real-time icing model: by summarizing the research results of icing theory, a more mature and complete theoretical model basis for rime icing is derived

[0077] Among them, the theoretical formula for the change of ice weight when the rime is uniformly iced on a cylindrical wire is:

[0078] d M d t = β E ( Φ + 2 D ) W V s i n θ ... ( 12 )

[0079] Where:

[0080] β——freezing coefficient;

[0081] E——Capture factor;

[0082] D——Icing thickness;

[0083] Ф——wire diameter;

[0084] W-liquid water content in the air;

[0085] V——wind speed;

[0086] θ——The angle between the wind direction and the wire.

[0087] Among them, the freezing coefficient β represents the different mechanisms of dry and wet growth of icing. When β=1, it is dry growth, such as rime icing; β<1 is wet growth, such as rime icing, which can be solved by solving the ice-coated surface heat balance equation Obtained; and the calculation of the capture coefficient E is more complicated. It is theoretically equal to the ratio of the cross-sectional area of the airflow to the wire after being disturbed by the wire and the cross-sectional area of the wire, which is related to the diameter of the wire, the coefficient of air viscosity, the size of droplets in the air and the collision wire The speed is related.

[0088] Using formula (12), the theoretical formula for ice thickness change during uniform icing is further derived:

[0089] d D d t = 1 π ρ β E W V s i n θ ... ( 13 )

[0090] Where:

[0091] ρ——Icing density, which is related to the ambient temperature, the size of the supercooled droplet colliding with the wire and its speed;

[0092] The remaining symbols are consistent with those in formula (12).

[0093] The theoretical formulas (12) and (13) involve many variables, especially the freezing coefficient β is not easy to obtain, and it is difficult to apply in practice. The Kathleen.F.Jones model is derived from a scientifically simplified rime icing theoretical model, which has been widely used and has good compatibility.

[0094] The Kathleen.F.Jones model assumes that the icing mass flux of the rime icing process is treated as the vector synthesis of the supercooled raindrops falling vertically and colliding with the wire and the raindrops colliding with the wire in the horizontal direction under the action of the wind. At the same time, it is assumed that the wire is too cold. The catching coefficient of raindrops is 1, and the formula for calculating ice thickness related to meteorological factors such as precipitation, air water content and wind speed is:

[0095] D 1 = 1 πρ i X j = 1 n ( 0.1 Pρ w ) 2 + ( 0.36 W V ) 2 ... ( 14 )

[0096] Where:

[0097] P——precipitation intensity;

[0098] W-liquid water content in the air;

[0099] V—— is the horizontal wind speed perpendicular to the wire;

[0100] ρ i , Ρ w ——The density of ice and water respectively.

[0101] Among them, the liquid water content W is obtained by the precipitation intensity P, using Best's empirical formula (W = 0.067P 0.846 ).

[0102] The analysis model formula (14) found that the liquid water content W in the model is completely calculated by the precipitation intensity P, indicating that if there is no certain precipitation intensity, there will be no icing, that is to say, the prerequisite for the icing calculation of the model is Obvious precipitation is necessary, indicating that the Kathleen.F.Jones model is only a rime icing model, and does not consider the icing caused by supercooled fog through the precipitated liquid water content. According to the principle of atmospheric thermodynamics, the density of moist air is lower than that of dry air at the same temperature. As the altitude increases, the ambient temperature decreases. The decrease in the saturated water vapor pressure of the atmosphere reduces the amount of water vapor that can be contained in the air. In the near-saturated moist air carried, water vapor must condense and precipitate, forming suspended droplets in the air to form frontal fog or terrain fog. Therefore, by calculating the difference between the water vapor content in the ground-saturated humid air and the actual water vapor content after the air mass rises to a certain height, the amount of liquid water precipitated at this height, that is, the liquid water content in the fog can be calculated. Derived from the atmospheric thermodynamic formula, this part of the liquid water can be calculated by the following formula:

[0103] W = a 0 - E s R v T ... ( 15 )

[0104] Where:

[0105] a 0 —— is the water vapor content in the nearly saturated moist air on the ground (g/m 3 );

[0106] E s ——The actual saturated vapor pressure at a certain height;

[0107] T——The thermodynamic temperature (unit K);

[0108] R v ——The specific gas constant of water vapor (461.51J/kg.K).

[0109] Adding the liquid water content calculated by the above formula to the model, the total liquid water content in the air at a certain height includes two parts (W=W 1 +W 2 ),W 1 Is the liquid water content calculated from rainfall, W 2 The liquid water content brought by the supercooled mist. Also consider that by W 1 With W 2 At this time, the original simple rime icing model was improved into a more complete new model including rime and mixed rime.

[0110] In the case of rime icing, due to the large size of the supercooled raindrops in the atmosphere (diameter greater than 200μm), the probability of them colliding with the wire with the airflow is very high. The theoretical analysis can conclude that the wire capture rate of the supercooled raindrops is close to 1. In the case of rime and ice, because the diameter of the supercooled droplets is small (usually about 20μm), it is easy to bypass the wire with the airflow, which greatly reduces the probability of collision on the wire. The plan needs to add the wire to pass The capture coefficient E for the capture rate of cold droplets.

[0111] E = 1 / ( 1 + C υ V d ) ... ( 16 )

[0112] Where:

[0113] υ——is the kinematic viscosity of air;

[0114] V-is the ambient wind speed;

[0115] d——is the median volume diameter of the droplet;

[0116] C—— is an empirical constant (=1.64), and the capture rate calculated based on the experimental data is between 0.7-0.9.

[0117] This scheme considers the influence of air viscosity, ambient wind speed and droplet size on the capture rate, but does not consider the relationship between the capture rate and the diameter of the wire, and theoretically the capture rate is inversely proportional to the diameter of the wire. Through theoretical analysis, we propose a more reasonable capture rate parameterization scheme:

[0118] E = 1 / ( 1 + C υ Vd 2 ) ... ( 17 )

[0119] Where:

[0120] D—— is the actual diameter of the wire (including ice coating);

[0121] C—— is an empirical constant; other parameters are the same as above.

[0122] The invention considers the influence of the wire size on the capture rate, that is, the larger the wire diameter, the lower the capture rate, which is more complete in theory. If you take a typical droplet size (d=20μm) and ambient wind speed (V=4m/s), for different wire diameters (including icing), the comparison of the capture coefficients calculated by the two schemes is as follows:

[0123] Comparison of the results of different capture coefficient parameterization schemes

[0124] Wire diameter (mm)

[0125] According to the above-mentioned analysis of the parameterization scheme of the capture coefficient of the supercooled droplets and the liquid water content of the droplets, combined with the theory of the ice thickness change when the rain and fog song is evenly iced, the rime icing mechanism can be derived, and combined with the rain rime KJ model, the simultaneous establishment The complete real-time icing model considering rain and rime is as follows,

[0126] D=D 1 +D2··············································· ··(18)

[0127] D 1 = 1 πρ i X j = 1 n ( 0.1 Pρ w ) 2 + ( 0.36 W V ) 2 ... ( 19 )

[0128] D 2 = E πρ i X j = 1 n 0.36 W 1 V ... ( 20 )

[0129] Where:

[0130] D—— is the total ice coating thickness of one ice coating process;

[0131] D 1 ——Ice thickness caused by rime;

[0132] D 2 ——Ice thickness caused by rime;

[0133] P——precipitation intensity;

[0134] V—— is the horizontal wind speed perpendicular to the wire;

[0135] ρ i And ρ w ——The density of ice and water respectively;

[0136] n——the duration of the icing process;

[0137] W——the liquid water content in the air caused by rainfall, using the empirical formula (W=0.67P 0.846 ) Calculation;

[0138] E—— is the capture factor, calculated by formula (17);

[0139] W 1 ——The liquid water content caused by the supercooled fog is calculated by formula (15).

[0140] Step 4. Calculate the real-time icing thickness of each site: The real-time icing model application needs to be based on model application rules. A reliable and complete model application rule is a key factor to ensure the accuracy of real-time icing thickness.

[0141] Rule 1: If the real-time weather data meets the conditions for the beginning or growth of ice coating, the real-time ice coating model is used to calculate the real-time ice thickness; if not, the ice coating thickness is zero.

[0142] Rule 2: Assuming that icing has started in the previous time, if the real-time weather data of this time still meets the icing growth conditions, the icing thickness of this time should be the overlap of two icing thicknesses, and if it continues to meet the third time, the overlap will continue. .

[0143] Rule 3: Assuming that icing has started the previous time, if the current real-time weather data meets the icing maintenance conditions, the ice thickness at this time is the previous ice thickness. If the icing maintenance conditions continue to be met for the third time, then this The ice thickness at that time is still the previous ice thickness.

[0144] Rule 4: Assuming that icing has started the previous time, if the real-time weather data this time meets the ice-coating maintenance conditions, and the third real-time weather data meets the ice-coating growth conditions, the ice coating thickness at this time is the previous and the third The superposition of the ice thickness of the second time.

[0145] Rule 5: For meteorological conditions where icing interruption occurs, if the duration is within three hours (not including three hours), it will be considered in accordance with the icing maintenance situation. If the duration is greater than or equal to three hours, the current icing will be judged The ice process is over.

[0146] Through the above analysis, the final result of this process is the real-time ice thickness of each site.

[0147] Step 5. Zoning of icing climatic conditions: Draw spatial distribution maps of the two meteorological indicators of continuous icing growth days and maximum ice thickness in GIS and do overlay analysis. According to the cold air path, terrain trend and elevation distribution characteristics, Carry out icing climate condition division;

[0148] According to the regulations of the electric power sector, the principle of dividing the same zone should be:

[0149] ① Be in the same climate zone;

[0150] ②The altitude is similar and the terrain is similar;

[0151] ③The direction of power lines is roughly the same;

[0152] ④Icing characteristic parameters are basically equal.

[0153] Studies have shown that among the many meteorological factors of the maximum ice thickness that can be achieved in a continuous icing process, the number of continuous icing growth days is the most important factor. The value is obtained through comprehensive judgment of relevant meteorological conditions. The number of consecutive icing growth days is more reasonable as a zoning index of icing climate conditions. In addition, for icing climate characteristics, the multi-year average annual maximum ice thickness is an important icing climatic feature index, which can be used as the second icing climate condition zoning index. In summary, the maximum number of continuous ice growth days and the maximum ice thickness of each station year by year were calculated from the meteorological data of each province, and the 50-year climatological average value was calculated respectively as two regional indicators of icing climate conditions. Draw spatial distribution maps of these two meteorological indicators in GIS and do overlay analysis, and take into account comprehensive factors such as cold air path, topographical trend, and elevation distribution characteristics to conduct icing climate conditions in each province.

[0154] Step 6. Establish an ice-coating trend calculation model: The present invention is mainly based on the existing ice-coating model research results in each area, using a large number of ice-coating actual measurements, survey points, and corresponding elevation data to verify the existing results. If verified If the effect is better, use the model directly, otherwise, improve the model or rebuild a new model. When remodeling, it is necessary to combine the icing climatic conditions in the study area, use the icing measurement and survey data in each area to model with statistical software, and fit the corresponding icing trend calculation model. There are differences in the thickness of the ice coating in the area, and boundary difference analysis and processing are required to finally obtain a reliable and applicable icing trend calculation model.

[0155] The present invention takes Guangdong Province, Guangxi Province, and Guizhou Province as examples: the existing icing trend calculation model is used to verify the existing icing trend calculation model, and the verification rules are as follows:

[0156] For 5mm ice thickness, if the difference between the actual measured value or the investigated value and the theoretical value of the model is within ±3mm, the theoretical value is considered accurate; if the actual measured or investigated value differs from the theoretical value of the model by more than ±3mm, the theoretical value is unreliable;

[0157] For 10mm, 15mm light and medium ice thickness, if the actual measured value or survey value differs from the model theoretical value within ±5mm, the theoretical value is considered accurate; if the actual measured value or survey value differs from the model theoretical value by more than ±5mm, the theoretical value is unreliable ;

[0158] For ice thickness of 20mm, 30mm and above, if the actual value or survey value differs from the theoretical value of the model within ±10mm, the theoretical value is considered accurate; if the actual value or survey value differs from the theoretical value of the model by more than ±10mm, the theoretical value is not reliable ;

[0159] Based on the above rules, the model is verified through representative ice-coating data and elevation data in each region. If the theoretical value of the model is verified with an accuracy of 60% and above (considering the existence of micro-topography and micro-climate areas), it is considered The verification effect of the model is good, and the model can be used directly; if the theoretical value accuracy of the model verification is less than 60%, the model is deemed not applicable and the model needs to be improved or re-modeled.

[0160] The verification results of the present invention on the ice-covering trend calculation model of each province are as follows:

[0161] (1) Guangdong Province

[0162] The current ice-covering trend calculation model uses the factors of longitude, latitude, and altitude to establish the ice-covering regression equation:

[0163] D=a 0 +a 1 J+a 2 W+a 3 H+X g p ·······················(twenty one)

[0164] Where:

[0165] D——is the thickness of icing;

[0166] J-longitude;

[0167] W—— is the latitude;

[0168] H-is the altitude;

[0169] X g ——Comprehensive geographic residual;

[0170] a 0 , A 1 , A 2 , A 3 ——Undetermined coefficient, which can be obtained by least square method.

[0171] The following regression models have been established,

[0172] Once in 30 years:

[0173] D=13.241+11.998J-2.655W+0.024H p ··················(twenty two)

[0174] Once in 50 years:

[0175] D=31.604+15.329J-3.519W+0.033H························(23)

[0176] Once in 100 years:

[0177] D=49.679+19.192J-4.497W+0.053H··························(24)

[0178] Using 300 sets of representative measured icing data, field survey icing data and corresponding elevation data in the Guangdong Province area to back-calculate and verify the above icing trend calculation model, the accuracy of the theoretical value of the model is 46%, and the accuracy is within Below 60%, the theoretical value calculated by the model is generally larger than the actual value or the survey value. Therefore, it is necessary to re-establish the icing trend calculation model for the Guangdong Province area.

[0179] (2) Guangxi Province

[0180] The icing trend calculation model currently used in Guangxi Province is mainly an empirical model.

[0181] D z =D z1 (z/z 1 ) p ··································(25)

[0182] Where:

[0183] Z——The altitude value is Z, m;

[0184] Z 1 ——The altitude value is Z 1 , M;

[0185] D z —— is the thickness of ice coating with height Z;

[0186] D z1 —— is the height Z 1 The thickness of the icing;

[0187] P-is an empirical parameter.

[0188] Guangxi Province currently uses the following icing trend calculation model,

[0189] D z =D z1 (z/z 1 ) 0.8 z/z 1 ≥1·····················(26)

[0190] D z =D z1 (z/z 1 ) 0.4 z/z 1 <1····················(27)

[0191] When the model is applied, it must be accurate and reliable for the known ice thickness.

[0192] Select 300 sets of representative measured icing data, field survey icing data, and corresponding elevation data in each subregion of Guangxi Province to back-calculate and verify the above icing trend calculation model. The accuracy of the theoretical value of the model is 40%. The accuracy rate is less than 60%, indicating that the applicability of the model in Guangxi Province is poor. This is because Guangxi Province is affected by climatic conditions and topographical conditions. There are certain differences in the icing characteristics in each zone. Only one icing is used. The trend calculation model cannot guarantee accuracy. Therefore, it is necessary to re-establish the icing trend calculation model for Guangxi Province.

[0193] (3) Guizhou Province

[0194] The current districts of Guizhou Province are divided into northern, central-eastern, southern, and western. The districts are basically consistent with the present invention. The ice-covering trend calculation model used assumes that there is a polynomial function growth relationship between ice-covering thickness and elevation.

[0195] D=D 0 +Mh+Nh 2 ·····························(28)

[0196] Where:

[0197] D——Mean value of icing thickness, mm;

[0198] D 0 ——Adjustment value of icing thickness;

[0199] h——altitude value, m;

[0200] M, N-model parameters.

[0201] Existing models are performed by fitting regression analysis of representative ice measurement or survey data and corresponding elevations in each district of Guizhou Province, and calculating model parameters by the least square method. Finally, a spatial estimation model of ice thickness for each district is established as follows:

[0202] Western region model,

[0203] 30a encounter:

[0204] D=-30.15+37.24×10 -3 ×h-3.73×10 -6 ×h 2 ··············(29)

[0205] 50a encounter:

[0206] D=-27.87+35.52×10 -3 ×h-2.55×10 -6 ×h 2 ··············(30)

[0207] One encounter in 100a:

[0208] D=-42.06+56.70×10 -3 ×h-8.86×10 -6 ×h 2 ··············(31)

[0209] Northern region model,

[0210] 30a encounter:

[0211] D=-0.56+4.07×10 -3 ×h+9.78×10 -6 ×h 2 ·················(32)

[0212] 50a encounter:

[0213] D=-0.36+4.40×10 -3 ×h+10.74×10 -6 ×h 2 ················(33)

[0214] One encounter in 100a:

[0215] D=-1.92+17.13×10 -3 ×h+2.88×10 -6 ×h 2 ···············(34)

[0216] Southern region model,

[0217] 30a encounter:

[0218] D=1.13-0.72×10 -3 ×h+5.51×10 -6 ×h 2 ·················(35)

[0219] 50a encounter:

[0220] D=1.61+0.81×10 -3 ×h+5.25×10 -6 ×h 2 ·················(36)

[0221] One encounter in 100a:

[0222] D=1.65+4.31×10 -3 ×h+4.22×10 -6 ×h 2 ·················(37)

[0223] Middle East regional model,

[0224] 30a encounter:

[0225] D=4.57+6.67×10 -3 ×h+4.95×10 -6 ×h 2 ·················(38)

[0226] 50a encounter:

[0227] D=7.09+6.17×10 -3 ×h+5.55×10 -6 ×h 2 ················(39)

[0228] One encounter in 100a:

[0229] D=9.90+7.73×10 -3 ×h+4.75×10 -6 ×h 2 ················(40)

[0230] Select 300 sets of representative measured icing data, field survey icing data and corresponding elevation data in the western, northern, southern, and central-eastern regions of Guizhou Province to back-calculate and verify the icing models of each zone, and verify each model The effect is between 55-60% and the accuracy rate is below 70%. The applicability of the models in the central-eastern part and western part is low, which shows that it is necessary to refine and adjust each district of Guizhou Province before re-modeling can be obtained. A better applicable model for calculating icing trend.

[0231] In summary, the icing trend calculation models that have been applied in Guangdong, Guangxi, and Guizhou provinces are back-calculated and verified. The verification results of Guangdong, Guangxi, and Guizhou provinces are all below 70%, indicating the existing ice The applicability of the trend calculation model is poor, and it is necessary to re-adjust the partitions and model to obtain an icing trend calculation model that can accurately reflect the basic laws of icing in each partition.

[0232] The establishment of the ice-covering trend calculation model of the present invention mainly uses a large amount of basic data and SPSS professional data statistics software to analyze the basic data and fit the corresponding ice-covering trend calculation model. The specific steps of modeling are as follows: Step, filter out the icing and elevation data of each partition as the basic data for modeling. Due to the inconsistent size and icing conditions of each partition, the amount of basic data is also different. Usually, the larger the area of the partition, the larger the amount of data. , The area with severe icing, the larger the amount of data; the second step, through the SPSS professional data statistics software, regression analysis of the basic data, preliminary fitting of the trend model of the best correlation between ice thickness and elevation; third step, Through the preliminary fitting of the trend model of each zone, the ice thickness of the adjacent zone is calculated, and the difference of the ice thickness of the junction area is analyzed. If the ice thickness is the same or similar, the covering of the adjacent zone The ice trend calculation model is available; if the difference in ice thickness is obvious, it means that the division of adjacent areas is unreasonable. It is necessary to adjust the adjacent areas and use the SPSS software to fit a new icing trend calculation model to calculate the ice coating in the junction area Thickness, repeat the above division and modeling steps until the icing level of the junction area is the same; through the method of modeling while correcting, finally obtain the corresponding icing trend calculation model of each division.

[0233] In order to ensure the reliability of the icing trend calculation model, SPSS professional data statistics software selects linear, logarithmic, reciprocal, quadratic, cubic, compound, power, S, growth, exponential, and Logistic functions to perform regression analysis on the basic data. , Select the function with the best goodness of fit and significance test effect as the icing trend calculation model. When there is a difference between the goodness of fit and the significance test effect, the best fit is the selection principle.

[0234] When there is a difference in the ice thickness at the junction of adjacent partitions during the modeling process, the differences need to be analyzed to determine whether to adjust the partitions and re-model. The analysis methods are as follows:

[0235] (1) The ice thickness at the junction of adjacent subregions is the difference between light ice area and medium ice area

[0236] First, analyze the terrain and terrain conditions at the junction. If the terrain conditions are quite different, there is no need to adjust the icing trend calculation model for each zone; if the terrain conditions are similar, the zone needs to be adjusted, and the icing trend calculation model is available. Use the ice-covering trend model of the corresponding zone in the middle-ice area to calculate the ice-cover thickness until it is estimated to fit the light ice area and cover the ice.

[0237] (2) The ice thickness at the junction of adjacent subregions is the difference between light ice area/medium ice area and heavy ice area

[0238] The ice area difference is within 10mm (10mm/15mm, 20mm difference): first analyze the terrain conditions at the junction. If the terrain conditions have drastic changes, there is no need to adjust each zone, and the ice-covering trend calculation model is available; if the terrain conditions change If it is not large, it means that the division of adjacent areas is unreasonable. It is necessary to re-zone the adjacent areas and fit a new ice-covering trend calculation model to calculate the ice thickness of the junction area. Repeat the above division and modeling steps until the junction The icing level of the area is the same.

[0239] The ice area difference is more than 10mm (10mm/15mm, 30mm and more difference): As long as the above situation occurs, it means that the division of adjacent divisions is unreasonable. It is necessary to rezone the adjacent areas and fit a new icing trend calculation model To calculate the ice thickness of the junction area, repeat the above zoning and modeling steps until the icing magnitude of the junction area is the same.

[0240] (3) The ice thickness at the junction of adjacent subregions is the difference between heavy ice area and heavy ice area

[0241] The ice area difference is within 10mm: first analyze the terrain and terrain conditions at the junction. If there is a drastic change in terrain conditions, there is no need to adjust each zone, and the icing trend calculation model is available; if the terrain conditions do not change much, the adjacent zones are indicated The division is unreasonable, neighbouring areas need to be re-zoned, and a new icing trend calculation model is fitted to calculate the ice thickness of the junction area, repeat the above division and modeling steps until the icing magnitude of the junction area is the same .

[0242] The difference in ice area is more than 10mm: As long as the above situation occurs, it means that the division of adjacent areas is unreasonable. It is necessary to adjust the adjacent areas and fit a new icing trend calculation model to calculate the ice thickness of the junction area. Repeat The above zoning and modeling steps until the icing magnitude of the junction area is the same.

[0243] Through the use of SPSS professional data statistics software, the method of modeling and correction is adopted to fit the corresponding icing trend calculation model of each zone:

[0244] Take Guangdong, Guangxi, and Yunnan as examples below:

[0245] (1) Guangdong Province

[0246] Once in 30 years:

[0247] D=1.010+0.002×h+3.237×10 -5 ×h 2 -8.601×10 -9 ×h 3 ···············(41)

[0248] Once in 50 years:

[0249] D=1.104+0.002×h+3.557×10 -5 ×h 2 -9.449×10 -9 ×h 3 ·················(42)

[0250] Once in 100 years:

[0251] D=1.163+0.002×h+3.748×10 -5 ×h 2 -9.950×10 -9 ×h 3 ·················(43)

[0252] (2) Guangxi Province

[0253] Northeast Region, (Northeast Division)

[0254] Once in 30 years:

[0255] D=2.874+0.003×h+1.972×10 -5 ×h 2 -5.486×10 -9 ×h 3 ···················(44)

[0256] Once in 50 years:

[0257] D=3.168+0.004×h+2.17×10 -5 ×h 2 -6.039×10 -9 ×h 3 ···················(45)

[0258] Once in 100 years:

[0259] D=3.334+0.004×h+2.287×10 -5 ×h 2 -6.36110 -9 ×h 3 ····················(46)

[0260] Northeast region, (central part)

[0261] Once in 30 years:

[0262] D=2.222-0.001×h+2.070×10 -5 ×h 2 -5.541×10 -9 ×h 3 ··················(47)

[0263] Once in 50 years:

[0264] D=2.446-0.001×h+2.276×10 -5 ×h 2 -6.094×10 -9 ×h 3 ····················(48)

[0265] Once in 100 years:

[0266] D=2.582-0.002×h+2.403×10 -5 ×h 2 -6.432×10 -9 ×h 3 ···················(49)

[0267] Northeast region, (Southwest Division)

[0268] Once in 30 years:

[0269] D=0.454-0.006×h+2.493×10 -5 ×h 2 -7.017×10 -9 ×h 3 ···················(50)

[0270] Once in 50 years:

[0271] D=0.500-0.006×h+2.743×10 -5 ×h 2 -7.722×10 -9 ×h 3 ··················(51)

[0272] Once in 100 years:

[0273] D=0.527-0.007×h+2.892×10 -5 ×h 2 -8.14110 -9 ×h 3 ····················(52)

[0274] Northwest area, (Northern Division)

[0275] Once in 30 years:

[0276] D=-5.154+0.018×h-1.203×10 -5 ×h 2 +4.796×10 -9 ×h 3 ···················(53)

[0277] Once in 50 years:

[0278] D=-5.655+0.02×h-1.320×10 -5 ×h 2 +5.267×10 -9 ×h 3 ······················(54)

[0279] Once in 100 years:

[0280] D=-5.985+0.021×h-1.397×10 -5 ×h 2 +5.569×10 -9 ×h 3 ·····················(55)

[0281] Northwest region, (Northwest Division)

[0282] Once in 30 years:

[0283] D=-0.779+0.002×h+3.537×10 -6 ×h 2 +2.103×10 -10 ×h 3 ···················(56)

[0284] Once in 50 years:

[0285] D=-0.865+0.002×h+3.881×10 -6 ×h 2 +2.327×10 -10 ×h 3 ·················(57)

[0286] Once in 100 years:

[0287] D=-0.906+0.002×h+4.106×10 -6 ×h 2 +2.426×10 -10 ×h 3 ···················(58)

[0288] (3) Guizhou Province

[0289] Eastern region,

[0290] Once in 30 years:

[0291] D=2.177+0.01×h-4.763×10 -6 ×h 2 +6.369×10 -9 ×h 3 ····························(59)

[0292] Once in 50 years:

[0293] D=2.591+0.009×h-3.229×10 -6 ×h 2 +5.992×10 -9 ×h 3 ·····························(60)

[0294] Once in 100 years:

[0295] D=2.740+0.011×h-4.60×10 -6 ×h 2 +7.088×10 -9 ×h 3 ·····························(61)

[0296] Northern region,

[0297] Once in 30 years:

[0298] D=0.246+0.008×h-6.072×10 -6 ×h 2 +5.060×10 -9 ×h 3 ························(62)

[0299] Once in 50 years:

[0300] D=0.219+0.008×h-6.372×10 -6 ×h 2 +5.319×10 -9 ×h 3 ························(63)

[0301] Once in 100 years:

[0302] D=0.104+0.010×h-7.706×10 -6 ×h 2 +6.106×10 -9 ×h 3 ························(64)

[0303] Southern region,

[0304] Once in 30 years:

[0305] D=1.130-0.001×h+5.510×10 -6 ×h 2 +2.993×10 -15 ×h 3 ·························(65)

[0306] Once in 50 years:

[0307] D=1.610+0.001×h+5.250×10 -6 ×h 2 +8.745×10 -16 ×h 3 ·························(66)

[0308] Once in 100 years:

[0309] D=1.650+0.004×h+4.220×10 -6 ×h 2 -4.130×10 -15 ×h 3 ·························(67)

[0310] (4) Yunnan Province

[0311] Northeast area (part of the northeast)

[0312] Once in 30 years:

[0313] D=-5.348+0.009×h+6.260×10 -6 ×h 2 -1.252×10 -9 ×h 3 …………………(68)

[0314] Once in 50 years:

[0315] D=-5.887+0.010×h+6.882×10 -6 ×h 2 -1.377×10 -9 ×h 3 …………………(69)

[0316] Once in 100 years:

[0317] D=-6.199+0.011×h+7.265×10 -6 ×h 2 -1.453×10 -9 ×h 3 …………………(70)

[0318] Northeast Region, (Eastern Division)

[0319] Once in 30 years:

[0320] D=16.992-0.029×h+1.915×10 -5 ×h 2 -2.423×10 -9 ×h 3 …………………(71)

[0321] Once in 50 years:

[0322] D=18.715-0.031×h+2.108×10 -5 ×h 2 -2.668×10 -9 ×h 3 ………………(72)

[0323] Once in 100 years:

[0324] D=19.710-0.033×h+2.221×10 -5 ×h 2 -2.810×10 -9 ×h 3 ………………(73)

[0325] Northeast region, (central part)

[0326] Once in 30 years:

[0327] D=10.604-0.018×h+9.879×10 -6 ×h 2 -5.003×10 -10 ×h 3 ………………(74)

[0328] Once in 50 years:

[0329] D=11.650-0.020×h+1.085×10 -5 ×h 2 -5.482×10 -10 ×h 3 …………………(75)

[0330] Once in 100 years:

[0331] D=12.293-0.021×h+1.145×10 -5 ×h 2 -5.796×10 -10 ×h 3 ………………(76)

[0332] Northeast Region, (Western Division)

[0333] Once in 30 years:

[0334] D=7.251-0.015×h+8.544×10 -6 ×h 2 -3.663×10 -10 ×h 3 ………………(77)

[0335] Once in 50 years:

[0336] D=7.978-0.016×h+9.402×10 -6 ×h 2 -4.037×10 -10 ×h 3 ………………(78)

[0337] Once in 100 years:

[0338] D=8.423-0.017×h+9.920×10 -6 ×h 2 -4.262×10 -10 ×h 3 ………………(79)

[0339] Northwest region,

[0340] Once in 30 years:

[0341] D=1.174+1.019×10 -6 ×h 2 +2.346×10 -10 ×h 3 ……………………(80)

[0342] Once in 50 years:

[0343] D=1.235+1.000×10 -6 ×h 2 +2.449×10 -10 ×h 3 …………………(81)

[0344] Once in 100 years:

[0345] D=1.337+1.215×10 -6 ×h 2 +2.690×10 -10 ×h 3 ……………………(82)

[0346] Central area,

[0347] Once in 30 years:

[0348] D=-3.400-0.010×h-8.967×10 -6 ×h 2 +2.773×10 -9 ×h 3 ……(83)

[0349] Once in 50 years:

[0350] D=-3.153-0.010×h-9.211×10 -6 ×h 2 +2.901×10 -9 ×h 3 ……(84)

[0351] Once in 100 years:

[0352] D=-3.808-0.012×h-1.039×10 -5 ×h2+3.231×10 -9 ×h 3 ……(85)

[0353] Step 7. Determine the model return period: Since each zone is an icing trend calculation model with different return periods, when applying the model, it is necessary to select a reasonable return period icing trend calculation model. The selection of the calculation model of the icing trend of each zone is determined according to the return period of the corresponding zone.

[0354] Specific steps are as follows:

[0355] The first step is to obtain the real-time ice thickness of each weather station in a certain subregion;

[0356] The second step is to use the icing trend calculation model for each recurrence period of the subarea to calculate the ice thickness of each recurrence period of all weather stations;

[0357] The third step is to compare the real-time icing thickness of each site with the icing thickness of each return period of the corresponding site to determine the upper limit of the real-time return period of each site; the comparison rules are as follows:

[0358] When the real-time ice thickness of the site is less than or equal to 30a of the site, the upper limit of the return period of the real-time ice thickness of the site is 30a;

[0359] When site 30a encounters ice thickness

[0360] When ice thickness at site 50a

[0361] The fourth step is to count the return period of each site and determine the return period with the largest proportion in the zone as the return period of the zone.

[0362] The fifth step is to finally select the same ice-coating trend calculation model as the zone return period to calculate the initial real-time ice thickness of each zone;

[0363] Step 8. Correction of icing thickness: Correct the initial real-time ice thickness of the micro-topography area according to the icing change coefficient to obtain the real-time ice thickness of each subregion;

[0364] The correction of the ice thickness in the micro-topography area of each subregion is to ensure the accurate real-time ice thickness of each subregion. Through the research and summary, for the general terrain (flat, open, general wind speed fluency) and common special terrain points, the icing change coefficient is shown in the following table.

[0365] Table of icing variation coefficients for different terrains

[0366] Terrain category

[0367] Step 9. Establish a real-time icing distribution model: Combine the real-time icing model and the icing trend calculation model into a dynamic model to obtain a real-time icing distribution model;

[0368] The application mechanism of the model includes the following processes: obtain site real-time weather data → site real-time ice thickness → zone zone real-time ice thickness, where site real-time ice thickness is calculated based on the real-time ice coating model, and zone zone real-time ice thickness is based on ice coating trend The calculation model is calculated. Therefore, the real-time icing distribution model can be defined as a combined model that includes the real-time icing model and the icing trend calculation model; at the same time, the icing trend calculation model changes with the real-time icing situation. Therefore, the real-time distribution model of icing is a dynamic model.

[0369] Step 10. Regional real-time ice thickness distribution characteristics: use the Kriging model to interpolate the real-time ice thickness of each site. Through the ordinary Kriging method, first study the site data characteristics, and then select the appropriate model to construct the variogram, and finally Perform kriging interpolation and evaluate accuracy.

[0370] The detailed process is as follows: real-time ice thickness data at each station calculated by the ice-cover distribution model, and regional real-time ice thickness distribution characteristics calculated by kriging interpolation. The principle is to calculate the regional data through known point data. Kriging is one of the main contents of geostatistics, and its theoretical basis mainly includes regionalized variables and variation analysis. Regionalized variable is a variable that describes a certain spatial distribution, which reflects a certain characteristic or phenomenon in a region. Variation analysis is the key to Kriging interpolation, which mainly includes calculation of semivariogram and covariance function. Its function is to take the size of statistical correlation coefficient as a function of distance. It is a quantitative and quantitative expression of similarity theorems in geography, reflecting a The spatial relationship between sampling points and adjacent sampling points. The essence of Kriging interpolation is to use the original data of regionalized variables and the structural characteristics of semivariograms to make a linear unbiased optimal estimation of the value of regionalized variables at unsampled points.

[0371] Kriging method for spatial interpolation, first of all to construct a variogram, the formula is as follows:

[0372] r ( h ) = 1 2 n X i = 1 n ( z ( x i ) - z ( x i + h ) ) 2 ... ( 86 )

[0373] The variation function is based on the spatial correlation analysis and analysis of the spatial site attribute data and location, where h is the distance between points, n is the number of pairs of sample points separated by h, and z is the attribute value of the point. After calculating the semivariance values of different distances, draw a semivariance graph, the horizontal axis represents the distance, and the vertical axis represents the semivariance. There are three parameters in the semivariogram graph: nugget (representing the semivariance when the distance is zero), sill (representing a substantially constant semivariance value), and range (representing a range of values within which the semivariance increases with distance, Beyond this range, the semi-variance value tends to be constant). Use the semivariogram to find the best theoretical variogram model that fits it (this is the key). The models that can be fitted include Gaussian model, linear model, spherical model, exponential model, and circular model. Wait.

[0374] The present invention adopts the Gaussian model to fit the variogram with the best effect through experimental analysis. The theoretical formula of the Gaussian model is as follows:

[0375] C ( h ) = C 0 + C 1 ( 1 - exp ( - h 2 a 2 ) ) , h 0 0 , h ≥ 0 ... ( 87 )

[0376] The Gaussian model obtains the model parameters by means of least squares fitting, applies the maximum likelihood program of Ross et al. to obtain the best semivariogram, and uses the fitted model to calculate three parameters. Use the fitted model to estimate the attribute value of the unknown point, the equation is:

[0377] Z 0 = X i = 1 s Z x W x ... ( 88 )

[0378] Z 0 To estimate the value, Zx is the known point value, Wx is the weight, and s is the number of known points used to estimate the unknown point. If three known points are used to estimate:

[0379]

[0380] Obtain the weight coefficient W through the variation function i Value, and finally get the point data to be interpolated. Complete the expansion of discrete data to surface data, obtain real-time ice thickness distribution feature maps obtained through real-time meteorological information, and obtain the overall characteristics of real-time ice thickness distribution in the entire subregion.

[0381] Step 11. Deduction of real-time ice thickness of icing: Integrate the distribution characteristics of real-time icing thickness of the area and the real-time distribution model of icing to obtain the final real-time ice thickness distribution map of icing.

[0382] The real-time ice thickness distribution characteristics of the area obtained through the real-time ice thickness point interpolation of the weather station are only the result of plane interpolation, and the influence of terrain factors such as elevation is not taken into account. The result calculated by the real-time icing distribution model is only determined by the altitude and cannot Reflect the real-time ice thickness distribution characteristics of the entire subregion. The present invention combines the two to obtain a more comprehensive real-time ice thickness of icing that can reflect real-time conditions. The specific combination method is:

[0383] After obtaining the real-time ice thickness distribution feature map of the entire district through the spatial interpolation of the weather station, the grid map of the district ice thickness result calculated by the real-time distribution model of ice coating is imported into the GIS, and the two raster maps are superimposed and analyzed. Perform a raster operation. The calculation relationship is as follows:

[0384] Station interpolation map grid value × coefficient 1 + icing real-time distribution model calculation grid value × coefficient 2

[0385] Among them, coefficient 1 and coefficient 2 are the weighting coefficients of the two grid values, and the sum of the two is 1. In actual operation, the values of coefficient 1 and coefficient 2 are based on the real-time icing data of the district and the icing of the similar return period. The theoretical ice thickness calculated by the trend calculation model is continuously calculated by taking different combinations of coefficient 1 and coefficient 2 until the calculated ice thickness is closest to the real-time ice thickness in the zone. Coefficient 1 and Coefficient 2. Operate each area separately according to the above steps to obtain the comprehensive result of the real-time ice thickness distribution of each area, and finally merge the result maps of each area in the GIS to obtain the final real-time ice coating result map.

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