JP2008157745A - Method and program for predicting snow accretion - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To minimize damages due to snow by enhancing the accuracy of prediction of the amount of snow accretion on wire. <P>SOLUTION: For each mesh set in an object area with arbitrary dimensions, "rainfall amount", "wind velocity", "wind direction", and "ambient temperature" at arbitrary output time intervals (every one hour, for example) are predicted until arbitrary n hours (48 hours, for example) later by a numerical weather model beforehand. Moreover, by applying the "ambient temperature" at each mesh to a rate-of-snow-accretion function wherein the ambient temperature is a parameter, "rate of snow accretion" at each mesh is found beforehand. Furthermore, a snow accretion potential in each wind direction (direction of the wire) is separately found as a value proportional to a snow accretion amount, from the "rainfall amount", "wind velocity", "wind direction", and "rate of snow accretion". <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a snowfall prediction method and a snowfall prediction program. More specifically, the present invention relates to a snowfall prediction method and a snowfall prediction program suitable for predicting the amount of snowfall on an overhead line.

  When a large amount of snow adheres to an overhead line such as a power transmission line during snowfall, there is a risk of power damage such as disconnection of the overhead line or a power failure resulting from the disconnection. In this specification, the overhead line mainly refers to, for example, a power transmission line, a communication line, a railway overhead line, etc., and is used as including a linear support structure such as a messenger wire, an overhead ground wire, and a tension line. Yes. Moreover, an overhead wire support means the support body of the said overhead wire, for example, in the case of a power transmission line, it is a power transmission tower.

  In power companies, etc., in order to respond quickly to snow damage caused by snowfall on power transmission lines and to minimize damage, workers are assigned in advance in consideration of the extent of snow damage predicted for each region. The facility is being strengthened. In this work, it is required to efficiently arrange a limited number of personnel, and it is required to accurately predict the amount of snowfall on the transmission line in a small area unit.

  For example, a power transmission line snow damage warning system has been proposed that corrects the announced forecast data 24 hours ahead, determines the occurrence of snow damage by a neural network, and generates an alarm when snow damage is predicted to occur ( Patent Document 1).

  In addition, a cylinder snow model has been proposed that estimates the amount of snowfall on the assumption that the snowfall on the transmission line grows without falling into a cylinder under the snowfall weather condition (Non-Patent Document 1).

JP-A-9-243757 Tatezaki, Sakamoto et al. "Development of a snow accretion forecasting system for transmission lines" Dentsu Central Research Report, T89001, 1989

  However, the technique described in Patent Document 1 has a problem in that it takes time to learn a case because the determination is performed using a neural network. Furthermore, the weather data that is a teacher signal to the neural network is only corrected based on the three-hour ahead forecast data, and there is a problem that the estimation accuracy of the weather data is low. Furthermore, since only the evaluation for each mesh can be performed, the amount of snowfall is only estimated for each mesh of 5 km, and the amount of snowfall for each transmission line cannot be evaluated.

  Further, in the technique of Non-Patent Document 1, it is assumed that snow adheres to the transmission line evenly in a cylindrical shape, and based on the diameter of the transmission line and the state where the snow is attached to the transmission line in a cylindrical shape, Although the amount of snow is estimated, in reality, the snow does not adhere evenly, and the snow that falls off in the middle is not taken into account, so the estimation model has a problem that the prediction error is large.

  Therefore, an object of the present invention is to provide a snow accretion prediction method and a snow accretion prediction program that can easily and accurately predict the amount of snow accretion on an overhead line such as a power transmission line.

  In order to achieve this object, the snow accretion prediction method according to claim 1 divides a target area for snow accretion prediction of an overhead line into a mesh of an arbitrary size, and a numerical weather forecast model based on weather data at the time of prediction. Predicts precipitation, wind speed, wind direction, and temperature up to an arbitrary n hours later at an arbitrary output time interval for each mesh, and predicts the temperature for each mesh and the relationship between the pre-built temperature and the snowfall rate The snow accretion rate for each mesh is obtained by the snow accretion rate function indicating, and the snow accretion potential that is proportional to the snow accretion amount is obtained for each mesh based on the precipitation amount, wind speed, wind direction and snow accretion rate. .

  According to a fourth aspect of the present invention, there is provided a snow accretion prediction program that sets a target area for snow cover prediction of an overhead line and a mesh of an arbitrary size that divides the target area for map data stored in a storage device. In addition, based on the input value of the meteorological data at the time of prediction, the numerical weather forecast model predicts the value predicted at any output time interval for each mesh of precipitation, wind speed, wind direction, and temperature up to any n hours later Store in the storage device, read out the predicted temperature for each mesh, and calculate the snow coverage rate for each mesh by calculating the snow coverage rate function indicating the relationship between the temperature and the snow coverage rate stored in advance. Processing to obtain and store in a storage device, and to read out precipitation, wind speed, wind direction and snow accretion rate and calculate a snow accretion potential proportional to the snow accretion amount for each mesh according to the wind direction. It is.

  Therefore, “precipitation”, “wind speed”, “wind direction”, and “temperature” at an arbitrary output time interval (for example, every hour) by a numerical weather model for each mesh set in an arbitrary size in the target region. ”Is predicted up to an arbitrary n hours (for example, 48 hours) later, and a database is constructed, and“ mesh temperature ”for each mesh is applied to a snow accretion function using air temperature as a parameter for each mesh. The “snow coverage” is calculated in advance. Then, the snow potential is obtained for each wind direction (each traveling direction of the overhead line) as a value proportional to the amount of snowfall from the predicted values of “Precipitation”, “Wind speed”, “Wind direction” and “Snow coverage”. ing.

  The snow accretion prediction method according to claim 2 divides a target area for snow accretion prediction of an overhead line into a mesh of an arbitrary size, and after an arbitrary n hours by a numerical weather forecast model based on meteorological data at the time of prediction. Precipitation, wind speed, wind direction, and air temperature are predicted at arbitrary output time intervals for each mesh, and latitude, longitude, altitude and overhead travel direction are associated with each overhead support in advance. And the predicted temperature based on the altitude, and the corrected temperature and the pre-constructed temperature and the snow rate function indicating the relationship between the snow rate and the snow rate for each overhead support, and the wind speed, The speed component perpendicular to the overhead line is obtained based on the wind direction and the traveling direction of the overhead line, and the snow accretion potential proportional to the amount of snowfall is obtained for each overhead line based on the precipitation, the speed component perpendicular to the overhead line and the snow accretion rate. I have to.

  According to a fifth aspect of the present invention, there is provided a snow accretion prediction program that sets a target area for snow cover prediction of an overhead line and a mesh of an arbitrary size that divides the target area for map data stored in a storage device. In addition, based on the input value of the meteorological data at the time of prediction, the numerical weather forecast model predicts the value predicted at any output time interval for each mesh of precipitation, wind speed, wind direction, and temperature up to any n hours later The latitude and longitude stored in the storage device, and further stored in the storage device in advance in association with the latitude, longitude, altitude and the traveling direction of the overhead line for each overhead line support, and stored for each overhead line support And the altitude is read out, the predicted temperature is corrected and the correction value is stored in the storage device, and the correction value is read out to calculate a snow accretion rate function indicating the relationship between the temperature and the snow accretion rate stored in advance. By each Processing to obtain the snow accretion rate for each bush and store it in the storage device, processing to read the wind speed, wind direction and overhead direction of the overhead line, to obtain the velocity component perpendicular to the overhead line and to store it in the storage device, and to precipitation and overhead line The computer executes a process for calculating a snow accumulating potential proportional to the amount of snow accumulating for each overhead line based on the right-angle velocity component and the snow accumulating rate.

  Therefore, “precipitation”, “wind speed”, “wind direction”, and “temperature” at an arbitrary output time interval (for example, every hour) by a numerical weather model for each mesh set in an arbitrary size in the target region. A database is constructed by performing prediction until an arbitrary n hours (for example, 48 hours). For each overhead wire support, the position information (latitude, longitude, altitude) and the traveling direction of each overhead wire are input to construct a database. Then, by correcting the “temperature” predicted for the mesh in which the overhead wire support exists by the “elevation” of the overhead wire support, the correction value is applied to the snow accretion rate function using the temperature as a parameter. Obtain the “snow coverage” for each mesh. Furthermore, the velocity component of the wind speed perpendicular to the traveling direction of the overhead wire corresponding to each overhead wire support is obtained, and it is proportional to the amount of snowfall from "Precipitation", "Speed component perpendicular to the overhead wire" and "Snow coverage". The snow potential is obtained for each overhead line as a value.

  According to a third aspect of the present invention, in the snowfall prediction method according to the first or second aspect, the snowfall potential is further integrated over time, and the distribution of the snowfall potential of each mesh is determined according to the statistic. The snow potential distribution shown is created. Further, the invention according to claim 6 is the snow accretion prediction program according to claim 4 or 5, further integrating the snow accretion potential with time, and displaying the map data of the target area displayed on the output device, The computer executes a process of overlaying the distribution of the statistics of snow accretion potential.

  Therefore, according to the statistics obtained by time integration of the snow accretion potential calculated for each mesh at an arbitrary time interval (for example, every 12 hours), the location indicating each mesh in the target area is color-coded and displayed on the output device By doing so, the snow potential distribution is visualized.

  According to the snow accretion prediction method and the snow accretion prediction program according to the present invention, the snow accretion amount for each mesh can be accurately predicted for each wind direction. As a result, it is possible to predict in advance the region and time zone where an accident due to snowfall is likely to occur. Therefore, in the inspection work of the above-mentioned electric power company, it becomes possible to arrange workers and equipment at an appropriate place and time, and damage can be minimized. In addition, it is possible to quickly perform recovery work when damage occurs. This makes it possible to reduce damage countermeasure costs.

  According to the snow accretion prediction method according to claim 2 and the snow accretion prediction program according to claim 5, the amount of snow accretion for each overhead line can be accurately predicted. Snowfall prediction is possible. Thereby, it is possible to predict an overhead line and a time zone that are highly likely to cause an accident due to snow. Similarly, in the inspection work of the above-mentioned electric power company, it becomes possible to perform arrangement of workers and facilities at an appropriate place and time, and damage can be minimized. In addition, it is possible to quickly perform recovery work when damage occurs. This makes it possible to reduce damage countermeasure costs.

  In addition, according to the snow accretion prediction method according to claim 3 and the snow accretion prediction program according to claim 6, a mesh having a high possibility of causing snow damage is obtained by referring to the snow accretion potential distribution displayed on the monitor. Since the (area) can be visually recognized at a glance, it is possible to quickly and accurately determine the location of the worker.

  Hereinafter, the configuration of the present invention will be described in detail based on the embodiment shown in FIGS. In the present embodiment, prediction of snow accretion on a transmission line will be described as an example.

  First, the snow accretion prediction program of this embodiment will be described. The snow accretion prediction program of the present embodiment is executed by, for example, the following hardware (hereinafter referred to as the snow accretion prediction device 1). The snow accretion prediction apparatus 1 of this embodiment is demonstrated. FIG. 1 shows an example of the configuration of the snow accretion prediction device 1 of the present embodiment. A snowfall prediction device 1 according to this embodiment includes an output device 2 such as a display, an input device 3 such as a keyboard and a mouse, a central processing arithmetic device (CPU) 4 that performs arithmetic processing, data being calculated, parameters, and the like. Is stored, a hard disk 6 serving as an auxiliary storage device in which calculation results and the like are recorded, a communication interface 7 for communicating with the outside, and the like. Hereinafter, the main storage device 5 and the auxiliary storage device 6 are collectively referred to simply as a storage device. The hardware resources are electrically connected through the bus 8, for example.

  Moreover, the snowfall prediction program 9 of this embodiment is recorded on the auxiliary storage device 6, for example, and the computer functions as the snowfall prediction device 1 by the CPU 4 reading and executing the program. Data necessary for the execution is loaded into the RAM 5. Further, the snow accretion prediction device 1 includes a snow accretion potential calculation means 10 for executing the overall processing for calculating the snow accretion potential, and a snow accretion potential distribution creation means 11 for executing the overall processing for creating the snow accretion potential distribution. is there.

  The auxiliary storage device 6 includes a weather database 12 and a support database (referred to as a power transmission tower database 13 in this embodiment). The weather database 12 stores the values of “temperature”, “wind direction”, “wind speed”, and “precipitation” calculated for each mesh in association with the position information of each mesh. Furthermore, “humidity” and “amount of solar radiation” are preferably calculated and stored. Also, the transmission tower database 13 stores “latitude / longitude”, “elevation”, and “overhead travel direction” in association with a unique transmission tower number assigned to each transmission tower. Furthermore, it is preferable to store the “overhead height” together.

  The hardware configuration described above is merely an example, and the present invention is not limited to this. For example, the auxiliary storage device 6 may be provided as an external storage device and read through the network 14 such as a LAN.

  Next, the snow accretion prediction method of this embodiment is demonstrated. The snow accretion prediction method according to the present embodiment divides a target area for snow accretion prediction of a transmission line into a mesh of an arbitrary size, and after an arbitrary n hours by a numerical weather forecast model based on weather data at the time of prediction. Precipitation, wind speed, wind direction, and temperature are predicted for each mesh at an arbitrary output time interval, and the predicted temperature for each mesh and the pre-constructed relationship between temperature and snow coverage are shown. The snow accretion rate for each mesh is obtained, and the snow accretion potential proportional to the snow accretion amount is obtained for each mesh based on the precipitation amount, wind speed, wind direction and snow accretion rate.

  In the snow accretion prediction method of the present embodiment, first, a target area for which snow accretion prediction is performed is set, and the target area is divided by a mesh of an arbitrary size. The size of the mesh can be set to 5 km square, for example, but is not particularly limited.

  For the setting of the target area and the mesh, for example, the setting of the target area is performed by storing existing map data (not shown) in the auxiliary storage device 6, reading the map data into the memory 5, and setting the mesh in the map data To do. An existing method may be used for setting the mesh. For example, the setting can be performed by storing the latitude and longitude of the four corners constituting each mesh in the storage device.

  In this embodiment, the transmission tower database 13 is constructed in advance and stored in the auxiliary storage device 6. Specifically, the “position (latitude / longitude)”, “altitude”, and “traveling direction of overhead line” information is input for each power transmission tower. The “traveling direction of the overhead line” refers to the direction in which the transmission line is facing. In this embodiment, for example, 0 ° from the north to the south, 90 ° from the west to 90 °, and 180 ° from the south to the north. It is represented by a value of ˜180 °. However, the “traveling direction of the overhead line” is not limited to this, and may be expressed by, for example, 16 directions. Since the direction in which power is sent is not considered, the opposite side has the same value (for example, 90 ° from the east to the west). This associates which mesh each transmission tower is included in. Moreover, it is preferable to input "overhead line height" information. “Overhead line height” is the height (m) of the overhead line from the ground. By considering “overhead line height” in the “elevation” information, the height position of the overhead line can be set to a more accurate value. it can.

  Next, weather data is predicted using a numerical weather forecast model. In the present embodiment, prediction of “temperature”, “wind direction”, “wind speed” and “precipitation” (hereinafter also referred to as weather data) for each set mesh is performed.

  The numerical weather forecast model may be a known or new numerical weather forecast model, and is not particularly limited. In the present embodiment, as a numerical weather forecast model, for example, a central weather research and analysis system (Meteorological Application and Research System: hereinafter referred to as MARS) is used. MARS is a model for calculating regional weather from hours to days.

  An example of a weather data prediction method using MARS will be outlined with reference to the flowchart of FIG. As shown in FIG. 2, the processing by MARS is roughly divided into three blocks: a preprocessing portion (S1), a main calculation portion (S2), and a postprocessing portion (S3).

  In the pre-processing portion (S1), first, as weather data at the time of prediction, a weather forecast value of the Japan Meteorological Agency or a weather forecast value of the US Marine Meteorological Agency is acquired and set as an initial value. The forecast value that is the basis of the initial value is not particularly limited.

  Next, the data format of the acquired initial value is converted, and the air, seawater temperature, topography, and land use data are cut out and interpolated into calculation grid points. In addition, what is necessary is just to acquire the data regarding seawater temperature, for example, the seawater temperature data announced by the United States Oceanic and Atmospheric Administration. For land use data, for example, a 24-class data set of USGS (American Geological Survey) can be used.

  In this calculation part (S2), weather prediction / reproduction calculation is performed by a numerical weather model (WRF) based on this initial value. WRF (Weather and Research Forecasting) is a successor model of MM5 (The Fifth-Generation Penn State / NCAR Mesoscale Model), which is a fifth generation mesoscale model developed by the National Institute for Atmospheric Science (NCAR) and the like.

  The basic equation system of the WRF model consists of an equation of motion, a continuity equation, a thermodynamic equation, and a conserving equation for the mixing ratio of water vapor, and this part is called a dynamic model. The WRF model is similar to MM5 in that it is composed of a dynamic model and a physical model that expresses physical processes such as radiation, the ground surface, the atmospheric boundary layer, and precipitation processes, but it is more accurate than MM5. Numerical calculation schemes and physical models have been introduced.

  Note that the prediction of WRF and weather data using the same is a well-known technique, and a detailed description thereof will be omitted. Details of WRF processing are described in, for example, Skamarock, WC, JB Klemp, J. Dudhia, DO Gill, DM Barker, W. Wang, and JGPowers. (2005) A description lf the advanced research WRF version 2. NCAR / TN -468 + STR, 88 pp.

  In the weather data prediction according to the present embodiment, for example, three regions having different sizes are calculated as shown in FIG. For example, the outermost range of 4500 km × 4500 km is defined as the first region 15, and the horizontal spatial resolution is an interval of 45 km per grid. Further, the range of 2250 km × 2250 km is defined as the second region 16, and the horizontal spatial resolution is set to be an interval of 15 km per grid. Furthermore, the range of 1000 km × 1000 km is set as the third region 17, and the horizontal spatial resolution is set to 5 km per grid. A method of calculating while gradually reducing the lattice in this way is called a nesting method, and a large field result obtained in the first region 15 is obtained in the second region 16 as a boundary condition of the second region 16. As a result, the boundary condition of the third region 17 can be influenced. Furthermore, between the first region 15 and the second region 16 and between the second region 16 and the third region 17, the calculation using the bidirectional nesting method is used, so that the influence calculated in the third region 17 can be reduced. The influence calculated in the second area 16 in the second area 16 can be fed back to the calculation result in the first area 15. Since the nesting method and the bidirectional nesting method are well-known techniques, description thereof will be omitted. For details of the bidirectional nesting method, see Zhang, D.-L., H.-R. Chang, NL Seaman, TT Warner and JM Fritsch: A two-way interactive nesting procedure with variable terrain resolution. Rev., 114, 1330-1339,1986. The number of grid points is set to 150 grids in the east-west direction, 150 grids in the north-south direction, and 30 vertical layers in the first area 15, the second area 16, and the third area 17, and the uppermost layer of the model is set to 50 hPa. The same value was set from 15 to the third region 17.

  Of course, the MM5 may be used instead of WRF as a numerical weather forecast model. For details of MM5, see Dudhia, J .: A multi-layer soil temperature model for MM5. Preprints, The Sixth PSU / NCAR Mesoscale Model Users' Workshop, 22-24 July 1996, Boulder, Colorado, 49-50, 1996, etc. There is a description. Numerical weather forecast models include, for example, ANSMOS (Japan Meteorological Association, Suzuki, Utsunomiya, Mishima, Hashimoto, Nagai: Ocean wind estimation using local wind prediction model LAWEPS, Ocean Development Papers, Vol. 19, Japan Society of Civil Engineers, pp.49-52, 2003), LOCALS (CRC Solutions Inc., Ryoichi Tanikawa, 2003: Development and evaluation of wind conditions simulation model using LOCALSTM, refer to flow 22, 405-415), NHM (Japan Meteorological Agency, Japan Meteorological Agency) Forecasting Department: JMA Non-Hydrostatic Model, Numerical Forecast Division Report 49, 2003), RAMS (Colorado State University, Pielke, RA, WR Cotton, RL Walko, CJ Tremback, WA Lyons, LD Grasso, ME Nicholls, MD Moran, DA Wesley, TJ Lee, and JH Copeland .: A comprehensive meteorological modeling system RAMS. Meteor. Atmos. Phys., 49, 69-91, 1992) may be used.

  In the present embodiment, the output time interval for predicting weather data is not particularly limited. For example, the output time interval for predicting weather data is 1 hour, and values up to 48 hours later can be output. Moreover, although the amount of calculation increases, it is also possible to output every 10 minutes, for example. It is known that a certain amount of error occurs when the prediction time is long. However, the maximum time to be predicted may be selected as appropriate according to the necessity of business, and is not particularly limited.

  In the post-processing part (S3), the calculation result is visualized and uploaded to the homepage. In the present embodiment, since the calculation result (predicted value) may be recorded in the weather database 12, this process is not essential.

  Thus, the predicted values of “temperature”, “wind direction”, “wind speed” and “precipitation” for each mesh in the target area calculated by the numerical weather forecast model are recorded in the weather database 12. The calculation based on the numerical weather forecast model may be executed by other hardware different from the above-described snow accretion prediction device 1, and only the calculation result may be acquired via the network 14 and stored in the weather database 12. Of course.

  In this embodiment, when the output time interval is 1 hour, the values calculated by the numerical weather forecast model are as follows. For “temperature” and “precipitation”, statistics for one hour for each mesh are stored in the weather database 12. “Wind direction” and “wind speed” are calculated from 0 ° to 360 °, but in this embodiment, 16 directions (8 directions: north-south, north-northwest-southeast, northwest-southeast, west-northwest-east-southeast, southwest -Northeast, south-southwest-north-northeast, west-east, west-southwest-east-northeast), that is, eight values are recorded in the weather database 12 for each 45 ° range.

  Further, a value obtained by correcting the predicted “temperature” with the “elevation” value in the transmission tower database 13 is also stored in order to predict the amount of snowfall for each transmission line. In this embodiment, since the temperature in the sky can be calculated three-dimensionally based on the number of vertical layers in the numerical weather forecast model, the temperature is corrected based on the “elevation” and “overhead height” information of each transmission tower. It is carried out. The temperature correction method is not particularly limited. For example, the temperature at the corresponding point is estimated based on the temperature difference and altitude difference between the temperature value of the mesh closest to the ground in the vertical direction and the mesh immediately above it. be able to.

  In addition, the inventors of the present application conducted various studies on the snow accretion phenomenon on the overhead line, and the snow accretion phenomenon on the overhead line has three meteorological values of “precipitation”, “speed component perpendicular to the overhead line” and “temperature”. Has led to the finding that

As shown in FIG. 4, the wind direction perpendicular to the overhead line (transmission line 18) is the wind direction perpendicular to the “traveling direction of the overhead line” of each transmission tower stored in the transmission tower database 13. Therefore, the wind speed perpendicular to the overhead line is the wind speed in the direction of the wind, and can be obtained from Equation 1. In the present embodiment, it is assumed that one transmission line exists between the transmission tower and the transmission tower. Here, there are a plurality of transmission lines between actual towers, but there is no need to evaluate them individually. As an example, it is assumed that there is one transmission line in one transmission tower. It is carried out.
<Equation 1>
V _n = V × sin β = V × cos θ
here,
V _n : Wind speed perpendicular to the overhead line V: Actual wind speed at the overhead line height (absolute value)
β: Angle formed by the overhead line and the wind direction θ: Angle formed by the perpendicular direction of the overhead line and the wind direction.

  “Temperature” is a very large factor because the snow accretion rate and the snow accretion density vary depending on how the snow melts. Here, when the temperature is below freezing point, the ratio of solid particles in precipitation is high, so the snowfall rate is low. On the other hand, snowfall is not a problem because most of the rain falls when the temperature exceeds 2 ° C. From these, it can be said that the temperature of 0 to 2 ° C. is a snowfall danger temperature zone.

Therefore, in the present embodiment, the change in the snow accretion rate due to the temperature T is represented by the snow accretion rate function α (T) having a maximum value of 1 ° C. as shown in FIG. The snow accretion rate function is an example, and is not necessarily limited to the above example. For example, it may be expressed as a nonlinear function with 1 ° C. being the maximum value.

  In the snowfall prediction method of this embodiment, the snowfall rate is calculated only when the temperature T is 0 ° C. to 2 ° C. As described above, the predicted temperature is not included in the range. For, the snow accretion rate is zero.

  Furthermore, when the humidity is low, the snow accretion rate decreases. Further, when the amount of solar radiation is large, the temperature of the snow surface becomes higher than the predicted temperature. That is, it can be said that the snowfall rate is closely related to the values of “humidity” and “amount of solar radiation”.

  Therefore, for example, the snowfall rate obtained by Equation 2 may be corrected to be low when the humidity is low and to be high when the amount of solar radiation is large. With this correction, it is possible to enhance the snow accretion rate function that also considers “humidity” and “amount of solar radiation”. As for “humidity” and “amount of solar radiation”, statistics for one hour for each mesh are calculated by the above-mentioned numerical weather forecast model (for example, WRF), and are combined with the weather database 12 in the same manner as other weather data. Just remember.

  Furthermore, as a result of extensive research conducted by the inventors of the present application (see Example 1), the predicted value of the weather data by the numerical weather forecast model and the correction value thereof, and the “velocity component perpendicular to the overhead line” calculated based thereon We have newly found that it is possible to define a value proportional to the amount of snowfall (hereinafter referred to as snowfall potential) that can be obtained from the “snow coverage”.

  By obtaining the snow accretion potential for each overhead line in each mesh or for each mesh, it is possible to determine which overhead line or which mesh has a high risk of snow accretion.

The snow potential as an index of the amount of snow on the overhead line can be obtained by Equation 3.

  In this embodiment, by substituting the “precipitation amount”, “speed component perpendicular to the overhead line”, and “snow coverage” for each transmission line into Formula 3, time integration is performed, so that the snow arrival potential ( 1 value per transmission line) can be calculated. As described above, the “precipitation” uses the predicted value of the mesh where the transmission line (transmission tower) exists, and the “speed component perpendicular to the overhead line” is perpendicular to the “traveling direction of the overhead line”. A value obtained by using Equation 1 for a simple wind speed component is used. For the “snow coverage”, a value obtained by substituting a value obtained by correcting the predicted value of the “temperature” of the mesh based on the “elevation” at which the transmission line (transmission tower) exists into Equation 2 is used.

  In addition, when calculating the snow accretion potential for each mesh, the calculation is performed for each of the eight wind directions for each mesh instead of the “velocity component perpendicular to the overhead line” in Equation 3. Thus, the snow accumulating potential (8 values per mesh) for each mesh can be obtained for each mesh. Note that the predicted value of the mesh may be used for “precipitation” and “temperature”. Therefore, if the goal is to calculate only the snow accretion potential for each mesh, the above-mentioned process to correct the “temperature” by “elevation” and “overhead height” and “speed component perpendicular to the overhead line” are calculated. This processing is unnecessary.

  Thus, according to the snow accretion prediction method of this embodiment, the amount of snow accretion for each power transmission line can be predicted by obtaining the snow accretion potential for each power transmission line. Further, by obtaining the snow accretion potential for each mesh in eight directions, it is possible to predict the amount of snow accretion for each traveling direction of the transmission line in each mesh.

  Furthermore, in the snowfall prediction method of the present embodiment, a snowfall potential distribution in which the snowfall potential distribution is visualized and displayed on the output device 2 can be created.

The snow landing potential distribution is obtained by calculating the statistics for every several hours of the snow landing potential calculated every hour for each mesh on the map of the target area displayed on the output device 2 in the color corresponding to the statistics. The overlay is displayed at the corresponding position. The map creation is not particularly limited as long as a known or new creation method is used. In the present embodiment, the snow accretion potential distribution is created for each of the eight directions. An example of the snow accretion potential distribution is shown in FIG. FIG. 6 shows (a) 12 hours (total after 0 to 12 hours), (b) 24 hours (total after 12 hours to 24 hours), (c) ) Distribution of snow potential (kg · m / m ² ) after 36 hours (total after 24 hours to 36 hours), (d) 48 hours later (total after 36 hours to 48 hours) is there.

  Using this snow potential distribution as a reference, it is possible to determine at a glance which snowfall occurs in which region in which time zone, and determine the location of personnel such as when and where and how many personnel It can be used as support. Further, the same effect can be obtained by outputting the mesh positions in the text format in the order of meshes having the larger snow landing potential values in conjunction with or instead of creating the snow landing potential distribution.

  Next, processing executed by the snow accretion prediction program of this embodiment will be described. Below, the process performed when the snow accretion prediction program of this embodiment calculates the snow accretion potential for every mesh is demonstrated using the flowchart of FIG. Details of each processing are as described above.

  First, map data of an area to be predicted for snow accretion is read out, and the target area and mesh are set and stored in the storage device (S101). Next, calculation of weather data by a numerical weather forecast model (WRF) is performed (S102). In this process, “temperature”, “precipitation” and “wind direction” and “wind speed” for each of the eight directions are calculated for each mesh using the numerical weather forecast model described above, and the results are stored in the weather database 12. .

  Next, the snow accretion rate is calculated (S103). In this process, the value of “temperature” for each mesh is read from the weather database 12, the snow accumulation rate function shown in Equation 2 is calculated by the CPU 4 to obtain the “snow accumulation rate”, and the “snow accumulation rate” is calculated for each mesh. The “snow coverage” is stored in the storage device.

  Next, the snow landing potential is calculated (S104). In this process, “precipitation”, “wind speed” by 8 directions, “snow coverage”, etc. are read from the weather database 12 and the above equation 3 is changed to “velocity component perpendicular to the overhead line” by the CPU 4 in 8 directions. The calculation is performed for each different wind speed, for example, the snow accretion potential for each direction of each mesh in eight directions is obtained and stored in the storage device.

  Finally, a snow landing potential distribution is created (S105). In this process, the snow accretion potential for each mesh stored in the storage device is displayed on the output device 2 as a distribution corresponding to, for example, a total value of 12 hours (S105). As described above, according to the snow accretion prediction program of the present embodiment described above, it is possible to obtain the snow accretion potential amount for each of the eight transmission lines in each mesh.

  Further, a process executed when calculating the snow accretion potential for each power transmission line will be described with reference to the flowchart of FIG.

  First, in the same manner as in S101, a countermeasure area / mesh is set (S201). Next, the transmission tower position information is input (S202). In this process, information on “Position (Latitude / Longitude)”, “Elevation”, “Direction of overhead line”, and “Overhead height” information is input for all transmission towers in each target area. A database 13 is constructed.

  Next, the weather data is calculated by the numerical weather forecast model (S203) in the same manner as S102, and “temperature”, “precipitation” and “wind direction” and “wind speed” for each of the eight directions are calculated for each mesh. The result is stored in the weather database 12.

  Next, temperature elevation correction processing is performed (S204). Based on the position information of each power transmission tower, which mesh each power transmission tower is in is associated. Therefore, for each power transmission tower, the “temperature” is read from the weather database, and the value of the “temperature” is transmitted to the power transmission tower. Correct based on the "elevation" and "overhead height" information of the tower. The correction value is stored in the storage device.

  Further, the corrected “temperature” is read from the storage device, and the snow accumulation rate obtained by calculating the snow accumulation rate calculation (Equation 2) for each power transmission line is stored in the storage device in the same manner as in S103 (S205). .

  Next, a wind speed component perpendicular to the traveling direction of the transmission line is calculated (S206). The CPU 4 calculates the wind speed component perpendicular to the traveling direction of the power transmission line by calculating the above-described Equation 1 from the “traveling direction of the overhead line” and “wind speed” of each power transmission tower, and stores them in the storage device.

  Finally, calculation of the snow accretion potential (Formula 3) is calculated in the same manner as in S104, and the snow accretion potential is calculated for each power transmission tower, that is, for each power transmission line (S207). As described above, according to the snow accretion prediction program of the present embodiment described above, it is possible to calculate the snow accretion potential for each transmission line. Therefore, priority is given to dangerous transmission lines that are predicted to have a large amount of snow accretion. It is possible to perform patrols and the like.

  Further, the positions of all power transmission towers are displayed on the map displayed on the output device 2 with X marks, etc., and the X marks are displayed according to the magnitude of the snow landing potential value in the same manner as the above-described snow landing amount map creation processing. By displaying in different colors, it is possible to identify at a glance the power transmission lines that are highly likely to be damaged by snow. Even if the distribution is not displayed, it is possible to determine the power transmission lines that are likely to cause snow damage by outputting the power transmission tower numbers in the descending order of the snow landing potential.

  The above-described embodiment is a preferred embodiment of the present invention, but is not limited thereto, and various modifications can be made without departing from the gist of the present invention. For example, since the weather forecast data that is the base of the numerical weather forecast model is updated at regular intervals, it is preferable to predict the weather data based on the latest weather forecast data. Therefore, for example, it is preferable to recalculate with a numerical weather forecast model every several hours when weather forecast data is announced, and to perform the loop processing of S102 to S105 and S203 to S207. Thereby, it is possible to perform snowfall prediction based on the latest weather forecast data.

  Moreover, according to the snowfall prediction method and the snowfall prediction program of the present invention, the present invention is not limited to a linear form such as an overhead line, but can be applied to prediction of the amount of snowfall on a road sign or the like.

(Example 1)
In January 2005, a large amount of snow arrived at the power transmission facility due to heavy snow in the vicinity of the former Unazuki Town (current Kurobe City) in Toyama Prefecture. In this experiment, the effectiveness of the snow accretion potential was confirmed by predicting the snow accretion potential under the same weather conditions.

  In this experiment, first, the predicted values of precipitation, wind speed, wind direction, and temperature were obtained with a mesh of a minimum of 5 km up to 48 hours later using the numerical weather forecast model (MM5) described above. As a result, it was confirmed that a temperature range (0 to 2 ° C.) dangerous for snowfall lasted for a long time in the vicinity of Unazuki, and that the wind direction and the wind speed were almost the same as the observed values.

  Furthermore, the predicted value was applied to the concept (snow landing potential distribution, Equation 3) proportional to the amount of snowfall on the overhead line with precipitation, wind speed and temperature as parameters.

  FIG. 6 described above shows a distribution every 12 hours from the calculation start time regarding the amount of snow accretion potential from northwest to southeast in the same direction as the overhead line of the former Unazuki town. FIG. 6 shows that the amount of snow in Old Unazuki Town increases after 24 hours.

  FIG. 9 shows the snow potential distribution according to the traveling direction of the overhead line after 48 hours (FIG. 6D). In addition, the direction described on each snow accretion potential distribution shown in FIG. 9 indicates the direction of the overhead line. For example, “north-south” means the snow accretion potential distribution with respect to the transmission line extending from north to south. It means that. According to FIG. 9, it was confirmed that “northwest-southeast” having the same direction as the overhead line in which the abnormal snowfall occurred in the former Unazuki town had the largest value.

  In this way, the snow accretion potential in the vicinity of Unazuki in Toyama Prefecture is larger than in other regions, and the value on the transmission line extending from northwest to southeast is the highest, resulting in the actual accident site and situation. Confirmed that they match. The northern part of the Noto Peninsula, the southern part of Ishikawa Prefecture, and the border between Ishikawa and Toyama Prefecture have large snow accretion centering on “north-south”, but this is because the altitude is high unlike the former Unazuki Town. In Hakusan and Hida Mountains, where there is a lot of precipitation, the temperature is below freezing, so no snow accretion potential occurs.

  Through this experiment, it was confirmed that the value of snow landing potential obtained only from precipitation, wind speed and temperature is proportional to the actual amount of snow landing, and that snow prediction can be predicted by predicting snow landing potential. did it.

It is a figure which shows an example of the hardware constitutions of the snow accretion prediction apparatus regarding this invention. It is a flowchart which shows an example of the outline | summary of the process by the numerical weather forecast model of this embodiment. It is a figure for demonstrating the nesting method in the prediction of the weather data by a numerical weather forecast model. It is a figure for demonstrating the velocity component orthogonal to an overhead line. It is a graph which shows the snow accretion rate (alpha) (T), a horizontal axis represents air temperature and a vertical axis | shaft represents the snow accretion rate. It is a figure which shows an example of the snow potential distribution displayed on an output device, and is a snow potential distribution every 12 hours from the calculation start time from northwest to southeast in the same direction as the overhead line of the former Unazuki town. It is a flowchart which shows an example of the process which calculates the snow accretion potential for every mesh by the snow accretion prediction program of this invention. It is a flowchart which shows an example of the process which calculates the snow accretion potential for each power transmission line by the snow accretion prediction program of this invention. It is a snow potential distribution according to the traveling direction of the overhead line after 48 hours shown in FIG.

Explanation of symbols

1 Snow accretion prediction device 9 Snow accretion prediction program

Claims (6)

  The target area for snowfall prediction of overhead lines is divided into meshes of arbitrary size, and the precipitation, wind speed, wind direction, and temperature up to any n hours later are calculated using the numerical weather forecast model based on the weather data at the time of prediction. Prediction at each output time interval for each mesh, and the predicted snow temperature for each mesh based on the predicted temperature for each mesh and a snow coverage rate function indicating the relationship between the pre-constructed temperature and snow coverage. And a snow accumulating potential proportional to the amount of snow accumulating based on the precipitation amount, the wind speed, the wind direction and the snow accumulating rate is obtained for each mesh according to the wind direction.   The target area for snowfall prediction of overhead lines is divided into meshes of arbitrary size, and the precipitation, wind speed, wind direction, and temperature up to any n hours later are calculated using the numerical weather forecast model based on the weather data at the time of prediction. Predicted at an arbitrary output time interval for each mesh, and previously associated latitude, longitude, altitude and travel direction of the overhead line with each overhead line support, and predicted based on the latitude, longitude and altitude The temperature is corrected, and the snow arrival rate for each overhead wire support is calculated by the snow arrival rate function indicating the relationship between the corrected air temperature and the pre-established temperature and the snow arrival rate, and the wind speed, the wind direction, and the wind speed A speed component perpendicular to the overhead line is obtained based on the traveling direction of the overhead line, and a snow accretion potential proportional to the amount of snowfall is calculated for each overhead line based on the precipitation, the speed component perpendicular to the overhead line, and the snow accretion rate. Asking Snow accretion prediction wherein the.   3. The snow accumulating potential distribution according to claim 1, wherein the snow accumulating potential is time-integrated and a snow accumulating potential distribution indicating a distribution of the snow accumulating potential of each mesh is generated according to a statistic. Snowfall prediction method.   Based on the input value of the meteorological data at the time of prediction, the target area for performing snowfall prediction of the overhead line on the map data stored in the storage device and a mesh of any size that divides the target area are set Values predicted at arbitrary output time intervals for each mesh of precipitation, wind speed, wind direction, and temperature up to an arbitrary n hours after the numerical weather forecast model are stored in a storage device in advance, and each predicted A process of reading the temperature for each mesh, calculating a snow coverage rate function indicating a relationship between the temperature stored in advance and the snow coverage rate, and calculating the snow coverage rate for each mesh and storing it in a storage device; A process of reading the precipitation amount, the wind speed, the wind direction, and the snow accretion rate and calculating a snow accretion potential proportional to the snow accretion amount for each mesh for each wind direction. Snow accumulation prediction program to be.   Based on the input value of the meteorological data at the time of prediction, the target area for performing snowfall prediction of the overhead line on the map data stored in the storage device and a mesh of any size that divides the target area are set Predicted in the storage device are the values predicted at any output time interval for each mesh of precipitation, wind speed, wind direction and temperature up to any n hours after the numerical weather forecast model. The latitude, longitude, altitude, and overhead traveling direction of the object are associated with each other and stored in advance in the storage device, and the latitude, longitude, and altitude stored for each overhead line support are read and predicted. A process of correcting the temperature and storing the correction value in a storage device, and reading out the correction value and calculating a snow accretion rate function indicating a relationship between the air temperature and the snow accretion rate stored in advance, for each mesh. A process of obtaining a snow rate and storing it in a storage device; a process of reading out the wind speed, the wind direction and the traveling direction of the overhead line to obtain a velocity component perpendicular to the overhead line and storing it in the storage device; and the precipitation amount and the overhead line A computer program for causing a computer to execute a process of calculating, for each overhead line, a snow accumulating potential proportional to the amount of snow accumulating based on a speed component perpendicular to the snow accumulating rate and the snow accumulating rate.   Further, the present invention is characterized in that the snow accumulation potential is time-integrated and a computer is caused to execute a process of overlaying the distribution of the snow arrival potential statistics on the map data of the target area displayed on the output device. The snow accumulating prediction program according to claim 4 or 5.