JP5008733B2 - Wet state estimation method, wet state estimation device, wire deterioration estimation method, and wire deterioration estimation system - Google Patents

Description

  The present invention relates to a wetting state estimation method, a wetting state estimation device, a wire degradation estimation method, and a wire degradation estimation system that estimate a degradation state of an electric wire based on an estimated wetting time.

  An electric wire such as a power transmission line deteriorates, for example, when moisture containing salt enters the electric wire. Although there is a method of investigating the entire span in order to grasp the deterioration state of the electric wire, this method is not practical from the viewpoint of the amount of equipment and the cost. For this purpose, there are the following methods. According to this method, first, when there is a transmission line that is assumed to be most deteriorated at present, the position of the steel tower that supports the transmission line is set as a representative point of the transmission line. And the actual tensile strength is investigated by sampling the jumper line which connects between transmission lines at a representative point. In addition, the main span is investigated by eddy current flaw diagnosis.

  By the way, representative points are selected as follows. Obtain the pollution classification, sea / river vicinity, etc. from the maximum amount of salt attached to the insulator during rapid pollution. Then, representative points are selected from these. However, with such a technique, there arises a problem that it is difficult to select a location where deterioration is truly progressing. In addition, in order to grasp the deterioration state of the electric wire, it is necessary for an operator to go to the site to collect and diagnose a jumper wire.

  There is an evaluation device that eliminates these points (for example, see Patent Document 1). This evaluation apparatus obtains the wetting time, the amount of attached salt and the amount of sulfurous acid gas in the corresponding area, that is, the electric wire installation area. Thereafter, this evaluation apparatus calculates the corrosion rate by an evaluation formula generated based on data such as relative humidity, and evaluates the remaining corrosion life based on the current corrosion state and corrosion rate. The current corrosion state is examined with an ACM (Atmospheric Corrosion Monitor) corrosion sensor.

  There are also the following evaluation devices (for example, see Patent Document 2). This evaluation apparatus performs a multiple regression analysis with the corrosion rate of a metal material as an objective variable and environmental and terrain factors that affect the corrosion rate as explanatory variables. At this time, this evaluation apparatus uses, for example, a virtual wetting time weighted by relative humidity as one of explanatory variables. The virtual wetting time in this case is the product of the weighting factor and the time corresponding to the width of the relative humidity value. For example, the time when the relative humidity is 100% to 80%, the time when the relative humidity is 80% to 60%, the time when the relative humidity is 60% to 40%, and the relative humidity is 40% to The virtual wetting time is a value obtained by multiplying the time when the relative humidity is 20% to 0% and the time when the relative humidity is 20% to 0%. And this evaluation apparatus calculates | requires a corrosion rate estimation formula by the multiple regression analysis method, and estimates and calculates the corrosion rate of the metal material of a non-measurement area based on this corrosion rate estimation formula.

JP-A-2005-337838 JP 2008-224405 A

  However, each evaluation apparatus described above has the following problems. For example, an evaluation apparatus that uses a predetermined evaluation formula needs to generate an evaluation formula based on data such as relative humidity. In other words, this equipment requires observation data of relative humidity in all electric cable installation areas. Moreover, in this evaluation apparatus, the synergistic effect of the salt adhesion amount and the wetting time is not reflected. Since the amount of salt adhesion differs depending on the object to be evaluated, it is necessary to separately measure the amount of salt adhesion at each position with great expense and time.

  On the other hand, an evaluation apparatus that performs multiple regression analysis uses a range of relative humidity values to obtain a virtual wetting time. For this reason, when the metal material is an electric wire, this evaluation apparatus requires observation data such as relative humidity in a predetermined area in order to obtain a corrosion rate estimation formula. In addition, the explanatory variable of the multiple regression equation is the wetting time alone, and does not reflect the synergistic effect of the salt adhesion amount and the wetting time.

  The object of the present invention is to solve the above-mentioned problems, and to determine the deterioration of the electric wire from the factors of both the amount of deposited salt and the wetting time, even if all the observation data of relative humidity are not available. An object of the present invention is to provide an estimation method, a wetting state estimation device, a wire deterioration estimation method, and a wire deterioration estimation system.

  In order to solve the above-mentioned problem, the invention of claim 1 is a wet state estimation method for estimating an average relative humidity, which is an average temperature obtained from an organization such as the Japan Meteorological Agency, and from the average temperature at an observation point. In addition to estimating the amount of water vapor in the air in a predetermined area including the observation point, the saturated water vapor amount for this average temperature is obtained from the water vapor amount saturation curve, and from the estimated water vapor amount in the predetermined area and the obtained saturated water vapor amount, A wet state estimation method characterized by calculating an average relative humidity at an observation point.

  The invention of claim 2 is the wet state estimation method according to claim 1, wherein an approximate expression obtained from the relationship between the monthly average temperature observed at the observation point and the monthly water vapor amount is used, The amount of water vapor corresponding to the annual average temperature is estimated to be the amount of water vapor in a predetermined area.

  According to a third aspect of the present invention, in the wetting state estimation method according to the first or second aspect, the wetting time at the observation point is calculated from a predetermined first calculation formula based on the calculated average relative humidity and average temperature. It is characterized by calculating.

  The invention according to claim 4 is a wetting state estimation device that estimates the wetting time of an observation point by a wetting time due to water vapor in the air, and the wetting state estimation device is an average obtained from an organization such as the Japan Meteorological Agency. Estimate the amount of water vapor in the air in the specified area including the observation point from the average temperature at the observation point, and obtain the saturated water vapor amount for this average temperature from the water vapor saturation curve, and estimate the predetermined area. An apparatus for estimating a wet state, wherein an average relative humidity at an observation point is calculated from a water vapor amount and a determined saturated water vapor amount.

The invention of claim 5 is an electric wire deterioration estimation method for estimating the corrosion rate of an electric wire, wherein the degree of adhesion of the electric wire to the salinity is determined from a salinity arrival map of a predetermined area, which is created based on the amount of salt passage obtained by simulation. The average relative humidity is calculated based on the average relative humidity and the average temperature, and when there is no data on the average relative humidity, the average relative humidity estimated by the wet state estimation method according to any one of claims 1 to 3. From the wetting time map of the predetermined area created using, obtain the wetting time degree of the wire, and estimate the corrosion rate of the wire based on the obtained degree of salt adhesion and wetting time degree,
This is a method for estimating electric wire deterioration.

  The invention of claim 6 is an electric wire deterioration estimation system for estimating the corrosion rate of an electric wire, and creates a salinity arrival map of a predetermined area based on the amount of salt passage obtained by simulation, and also calculates an average relative humidity and an average temperature. Based on the above, a wetting time map of a predetermined area is created, and when there is no data on average relative humidity, the average relative humidity estimated by the wetting state estimation method according to any one of claims 1 to 3 is used. Based on the first device and the salinity attainment map of the predetermined area, obtain the salinity degree of the electric wire and the wetting time degree of the electric wire from the wetting time map of the predetermined area. And a second device for estimating the corrosion rate of the electric wire.

  According to the first and fourth aspects of the invention, the average relative humidity can be obtained even at an observation point where there is no data on the average relative humidity.

  According to the invention of claim 2, since the water vapor amount in the predetermined area is obtained from the relationship between the monthly average temperature and the monthly water vapor amount, it is not necessary to obtain the water vapor amount at each observation point having no data on the average relative humidity. Can be.

  According to the invention of claim 3, the wetting time can be obtained even at an observation point where there is no data of average relative humidity.

  According to the inventions of claim 5 and claim 6, since the corrosion rate of the electric wire is estimated based on the degree of salt adhesion and the degree of wetting time, when estimating the corrosion rate of the electric wire, the amount of salt adhesion and the wetting time are estimated. And can reflect the synergistic effects of both factors.

It is a block diagram which shows the electric wire degradation estimation system by Embodiment 1. It is a figure which shows an example of salt content attainment map data. It is a figure which shows an example of wetting time map data. It is a figure explaining adoption of wetting time. It is a figure which shows the conversion table used for wetting time calculation. It is a figure which shows the conversion table used for wetting time calculation. It is a figure which shows the area comparison in the Sanin side. It is a figure which shows the regional comparison in the Sanyo side. It is a figure which shows the regional comparison in a mountain part and a coastal part. It is a figure explaining the correlation of average temperature and relative humidity. It is a figure which shows a water vapor amount saturation curve. It is a figure which shows the relationship between average temperature and the amount of water vapor in the air. It is a flowchart which shows an example of a wetting time calculation process. It is a figure which shows the approximate expression of average temperature and the amount of water vapor | steam. It is a figure which shows the calculation result of an annual average relative humidity. It is a figure which shows an example of salt adhesion related data. It is a figure which shows an example of the wetting time related data. It is a figure which shows an example of coefficient data. It is a flowchart which shows an example of an electric wire deterioration estimation process. It is a figure explaining the dispersion | variation in an estimated value. It is a figure which shows the relationship between an altitude and a wetting time.

  Next, embodiments of the present invention will be described in detail with reference to the drawings.

(Embodiment 1)
A wire deterioration estimation system according to Embodiment 1 of the present invention is shown in FIG. The wire deterioration estimation system in FIG. 1 estimates wire deterioration, and includes a terminal 10 operated by a person in charge and a server 20 connected to a communication network NW such as a LAN (Local Area Network). I have.

  The server 20 creates salinity attainment map data from data provided by various external organizations such as the Japan Meteorological Agency. In the salinity attainment map data, data representing the state of attainment of salinity in each district is recorded. Each area is formed by dividing, for example, the Chugoku region into a mesh shape as a predetermined area. An example of this salinity attainment map data is shown in FIG. In this salinity attainment map data, the amount of salt (kg) passing through each area is recorded in the column of salinity passage amount. Then, a salinity arrival map is obtained by recording the salt passage amount of the same value on the map with color classification or pattern classification.

  The server 20 obtains the salt passage amount by simulation. Such simulation is performed by software. In this software, in order to perform analysis in consideration of wind direction distribution etc., it is necessary to obtain wind condition data in the analysis target area from observation results, but this data can be used as AMeDAS data of the Japan Meteorological Agency, Observation data is unnecessary.

  The server 20 creates wetting time map data from data provided by various organizations. In the wetting time map data, data representing the wetting time of the year in each district obtained by subdividing the map target region into a mesh shape is recorded. An example of this wetting time map data is shown in FIG. The wetting time map data records the wetting time (h) in each district. Then, a wetting time map is obtained by recording the wetting time of the same value on the map with color coding or pattern coding.

Wetting time map data is created based on the wetting time obtained from data of multiple AMeDAS stations. For example, as shown in FIG. 4, when obtaining the wetting time at point P, when there is no AMeDAS observatory at point P, the K5 observatory that is the closest to the AMeDAS observatory from K1 to K5 And the wetting time at this station is adopted as the wetting time at point P. Thus, the wet time map data uses data from a plurality of AMeDAS stations. The wetting time is a time when the relative humidity is 80% or more at a temperature higher than 0 ° C. That is, the wetting time represents the time for which the electric wire or the like is wet with water vapor in the air. The server 20 obtains the wetting time from the following predetermined calculation formula.
Wetting time = 8766 x P (RH) x P (T)
here,
P (RH): Probability coefficient based on annual average relative humidity (% RH) P (T): Probability coefficient based on annual average temperature (° C). This wetting time calculation formula uses a calculation method according to the “Air Exposure Test Handbook [II] Metal Edition” (Japan Weathering Test Center, January 2007). The coefficient of the wetting time calculation formula, the annual average relative humidity and the annual average temperature are based on the meteorological statistical data of each AMeDAS station published on the website of the Japan Meteorological Agency. In other words, the annual average temperature used was a value compiled for 5 to 30 years at each observation point, and the annual average relative humidity was used for a point with observation data.

  The server 20 calculates the probability coefficient P (RH) representing the possibility of the annual average relative humidity of each value such as “20 (% RH)” in the wetting time calculation formula, as described in the “Atmospheric Exposure Test Handbook”. It is obtained by the conversion by [II] Metal Edition, that is, the conversion using the conversion table shown in FIG. Further, the server 20 converts the probability coefficient P (T) representing the possibility of the annual average temperature of each value such as “−13 ° C.” into the conversion by the “air exposure test handbook [II] metal edition”. It is obtained by conversion using the conversion table shown in FIG.

  By the way, when there is no observation data of the annual average relative humidity at a point where the AMeDAS observatory is located (hereinafter referred to as “observation point”), the server 20 calculates the annual average relative humidity as follows. This calculation is based on the following analysis by the present inventors. The present inventor compared the regional characteristics of the monthly average temperature and average relative humidity at each weather station and each observation station. As a result, for example, as shown in FIGS. 7 and 8, the Sanin side and the Sanyo side show the same tendency for each region, but as shown in FIG. 9, the mountainous part and the coastal part have different characteristics. The relationship shown in FIGS. 7 to 9 is between the line L1 and the line L2, as shown in FIG. 10, and the correlation coefficient is 0.375. Therefore, the relative humidity in each place is determined from the relationship between the average temperature and the relative humidity. Cannot be assumed due to poor correlation.

  However, the present inventor calculates the amount of water vapor contained in the air from the average temperature and average relative humidity for each weather station and each observation station, and the general water vapor saturation curve shown in FIG. We made a comparison. As a result, as shown in FIG. 12, the relationship between the average temperature and the amount of water vapor in the air is between line L11 and line L12. In other words, this relationship has little regional variation as seen in the relationship shown in FIG. 10, and the correlation between the average temperature and the amount of water vapor in the air (correlation coefficient: 0.968) is good. found.

  Based on such analysis results, the server 20 calculates the saturated water vapor amount with respect to the annual average temperature and the water vapor amount with respect to the annual average temperature at each AMeDAS observatory for the observation points for which there is no observation data of the annual average relative humidity, Calculate the annual average relative humidity. Then, the wetting time of each district is calculated from the annual average temperature and the calculated annual average relative humidity by a wetting time calculation formula. That is, the server 20 performs the wetting time calculation process shown in FIG. When starting the wetting time calculation process, the server 20 estimates the amount of water vapor in the air (step S1).

In step S1, for example, in the Chugoku region, the server 20 calculates the approximate expression y = 3.3823e ^0.0641x shown in FIG. 14 from the average value of 80 average annual temperatures at each AMeDAS station.
Thus, the amount of water vapor in the air is estimated. This approximate expression is obtained by applying, for example, the least square method to each weather station such as the AMeDAS station, the monthly average temperature observed at each station, and the monthly water vapor amount. And
x = 13.925
As an approximation,
y = 3.3823e ^{0.0641 × 13.925}
= 8.257 (g / m ³ )
To get the value In addition, 13.925 ° C. is an average value of the annual average temperature of 80 AMeDAS in the Chugoku region. This value is constant in each region as the standard water vapor volume in the Chugoku region. This makes it unnecessary to process a large number of temperature and water vapor data to calculate a standard water vapor amount.

  When step S1 ends, the server 20 obtains a saturated water vapor amount with respect to the annual average temperature of each AMeDAS station from a general water vapor amount saturation curve (FIG. 11) (step S2). Thereafter, the server 20 obtains the relative humidity from the water vapor amount obtained in step S1 and the saturated water vapor amount obtained in step S2, and sets this as the annual average relative humidity (step S3). An example of the calculation result of the annual average relative humidity for verifying the accuracy of the calculation result is shown in FIG. FIG. 15 summarizes the calculation results of the annual average relative humidity (estimated value) at the observation point where the observation data of the annual average relative humidity is present. The accuracy of the estimated annual average relative humidity is a high accuracy of about 10% as a result of comparison with the observed value.

  When step S3 ends, the server 20 obtains the probability coefficient P (RH) and the probability coefficient P (T) from the annual average relative humidity and the annual average temperature obtained in step S3 (step S4). Thereafter, the server 20 calculates the wetting time by the wetting time calculating formula using the obtained probability coefficient (step S5), and ends the wetting time calculating process.

  In this way, even when there is no observation data of annual average relative humidity at the observation point, the wetting time at this observation point can be calculated.

  The terminal 10 includes an input unit 11, a reading unit 12, a storage unit 13, a processing unit 14, a display unit 15, an output unit 16, and a communication unit 17. The input unit 11 is a device such as a keyboard operated by a person in charge. When various instructions and data related to the deterioration estimation of the electric wire are input, the input unit 11 sends them to the processing unit 14. The reading unit 12 is a device that reads data from a recording medium, and sends the read data to the processing unit 14. The display unit 15 is a display device such as an LCD (Liquid Crystal Display) that displays data related to the estimation of electric wire deterioration. That is, the display unit 15 displays instructions and data input to the input unit 11, data processed by the processing unit 14, and the like. The output unit 16 outputs the data processed by the processing unit 14 by printing out. The communication unit 17 transmits / receives data to / from an external communication network NW network, for example, a LAN (Local Area Network) under the control of the processing unit 12.

  The memory | storage part 13 of the terminal 10 is a memory | storage device which memorize | stores the data relevant to electric wire deterioration estimation. Moreover, the memory | storage part 13 has memorize | stored in advance the program required for the deterioration estimation of an electric wire.

  The data stored in the storage unit 13 of the terminal 10 includes salt adhesion related data. The salt adhesion related data is data used for estimating the deterioration of the electric wire. An example of the salt adhesion related data is shown in FIG. In this data related to salt adhesion, the average wind speed of the sea breeze near the steel tower holding the wire is recorded in the sea wind average wind speed column and the frequency of sea breeze is recorded in the sea wind frequency column for each line of the wire. Yes. These data are obtained from data recorded in the past, and observation data is unnecessary.

  In the salt adhesion related data, the salt arrival rate representing the state of salt arrival for each electric wire is recorded. The salinity attainment rate is data obtained from the salinity attainment map data of the server 20. For example, the amount of salt passage at sea is obtained from the salt arrival map data, and the amount of salt passage in the area where each electric wire is installed is obtained in the same manner. Then, the salinity attainment rate is obtained based on the two salinity passage amounts.

  In the salt adhesion related data, a salt shielding rate representing the degree of salt shielding by the transmission line is recorded, and a salt concentration degree (EXP) representing the degree of concentration of the salt reaching the transmission line is recorded. These data are obtained from the average values of data recorded in the past, and observation data is unnecessary. The distance from each electric wire to the coast is recorded in the data related to salt adhesion. This data is obtained from map data etc., and observation data is unnecessary. Furthermore, in the salt adhesion related data, the cross-sectional area of the electric wire used for each line is recorded. This data is provided by the wire manufacturer and no actual measurement data is required.

  The data stored in the storage unit 13 includes wetting time related data. The wetting time related data is data used for estimating the deterioration of the electric wire. An example of this wetting time related data is shown in FIG. In this wetting time-related data, the annual wetting time rate representing the percentage of the wetting time per year is recorded for each line of the electric wire. The annual wetting time rate is data obtained from the wetting time map data of the server 20. For example, the wetting time in the area where each electric wire is installed is obtained. Then, an annual wetting time rate is obtained from the ratio of the wetting time to the year.

  In the wet time-related data, the annual sunshine hour rate representing the state of annual sunshine is recorded. This data is obtained from the Japan Meteorological Agency, and observation data is unnecessary.

The data stored in the storage unit 13 includes coefficient data. The coefficient data is an atmospheric corrosion coefficient α and a salinity strength reduction coefficient β, which are coefficients used when estimating the deterioration of the electric wire. The coefficient data is read from the reading unit 12 or the communication unit 17 under the control of the processing unit 14 and stored in the storage unit 13. An example of the coefficient data is shown in FIG. The coefficients α and β are recorded for each wire size. The coefficients α and β are obtained as follows. The same value “1” is input to the coefficients α and β as initial values. The residuals of the predicted value obtained from the degree of deterioration of the electric wire and the actual corrosion rate (decrease in tensile strength per year from the initial value of 110%) are obtained, and the coefficients α and β that minimize the residual sum of squares are obtained. At this time, for example, the wire size is
120 mm ² ≦ 160 mm ² ≦ 330 mm ² ≦ 410 mm ²
In such a case, the thinner the wire diameter forming the electric wire, the more susceptible to the influence of salt, the lower the strength. For this reason, as a condition for determining the coefficient β,
β ₁₂₀ ≧ β ₁₆₀ ≧ β ₃₃₀ ≧ β ₄₁₀
Set. Under this condition, β ₁₂₀ represents the value of β when the wire size is 120 mm ² . Others are the same. Note that since the coefficient α is a coefficient including other uncertain elements, conditions such as limiting the magnitude of the value are not set. The coefficients α and β are obtained from average values of data recorded in the past, and observation data is unnecessary.

  The processing unit 14 executes a program stored in the storage unit 13. The program executed by the processing unit 14 includes a data update process. When the salinity attainment map data and the wetting time map data of the server 20 are changed, or when the data provided by each engine is changed, the processing unit 14 stores the salt adhesion related data, the wetting time related data, and the coefficient data. Update. In addition, also when the data read from the reading part 12 are changed, the salt adhesion related data etc. of the memory | storage part 13 are updated similarly.

  The program executed by the processing unit 14 includes a wire deterioration estimation process. When the processing unit 14 receives an instruction to start processing from the input unit 11, the processing unit 14 starts the wire deterioration estimation processing illustrated in FIG. 19. When starting this process, the processing unit 14 selects the first line from the installed lines (step S21).

Thereafter, the processing unit 14 calculates the degree of wire salt adhesion for the selected line (step S22). In step S22, the processing unit 14 refers to the salt adhesion related data and the coefficient data, and calculates the degree of wire salt adhesion by the following formula.
Wire salinity adhesion = (β × Sea wind average wind speed near the tower × Sea wind frequency × Salt reach
X salt shielding rate x salt concentration) / (distance from the coast)
When the wire salinity adhesion degree is calculated in step S22, the processing unit 14 calculates the wire wetting time degree (step S23). In step S23, the processing unit 14 refers to the wetting time related data and calculates the wetting time degree by the following equation.
Wetting time degree = Annual wetting time rate x (1-Annual sunshine time rate)
Thereafter, the processing unit 14 uses the wire salinity adhesion degree calculated in step S22 and the wetting time degree calculated in step S23, and refers to the coefficient data to estimate the corrosion rate from the following corrosion rate equation. Is calculated (step S24).
Corrosion rate estimated value = (α + β x salt adhesion degree) x wetting time degree When the corrosion rate of the wire is estimated by this corrosion rate equation, the amount of salt adhesion and the wetting time are multiplied. The synergistic effect of both factors with wetting time is reflected.

  Thereafter, the processing unit 14 examines variation in the calculated estimated value (step S25). In step S25, for example, as illustrated in FIG. 20, the processing unit 14 checks the variation of the estimated value based on the deviation 3σ with respect to the average value. Thereafter, the processing unit 14 determines whether or not there is an unselected line among the installed lines (step S26). If there is an unselected line in step S26, the processing unit 14 selects the next line (step S27), and returns the process to step S22. Further, if there is no unselected track in step S26, the processing unit 14 selects a dangerous point where the corrosion of the electric wire is advanced by the estimated corrosion rate obtained in steps S24 and S25 (step S28). The wire deterioration estimation process ends.

  Next, the operation of the wire deterioration estimation system according to this embodiment will be described. The server 20 periodically checks whether there is a change in data provided by various external organizations. If there is a change in the data, the server 20 updates the salinity arrival map data and the wetting time map data. At this time, the server 20 updates the wetting time map data by performing a wetting time calculation process for an observation point having no observation data.

  Similarly, the terminal 10 periodically checks whether there is a change in the salinity arrival map data and the wetting time map data of the server 20. If there is a change in the data, the salt update related data, the wetting time related data, and the coefficient data are updated by the data update process.

  By the way, when the person in charge operates the terminal 10 and inputs an instruction for estimating the deterioration of the electric wire, the terminal 10 refers to the salt adhesion related data, the wetting time related data, and the coefficient data to estimate the electric wire deterioration. Process. By this process, the terminal 10 selects a dangerous point where the corrosion of the electric wire has progressed. Thereafter, the terminal 10 displays the selection result and prints out the selection result according to an instruction from the person in charge.

  Thus, according to this embodiment, the annual average relative humidity can be estimated by the wetting time calculation process even at an observation point where there is no observation data of the annual average relative humidity, thereby obtaining the wetting time. . Thereby, it is possible to generate a highly accurate annual average relative humidity data and create a wetting time map to predict the wire corrosion rate with higher accuracy. Further, according to this embodiment, since the corrosion rate of the electric wire is estimated based on a value obtained by multiplying the salt adhesion amount and the wetting time, a synergistic effect due to both factors of the salt adhesion amount and the wetting time is reflected. can do. In addition, according to this embodiment, since the salinity arrival map data and the wetting time map data are used, it is not necessary to perform actual measurement of the amount of salt adhesion and the wetting time, and it is possible to estimate the location of the wire corrosion risk at low cost. . Further, according to this embodiment, since the corrosion rate can be predicted, on-site diagnosis is unnecessary, and the cost can be reduced. Furthermore, according to this embodiment, since the corrosion rate can be predicted, it is possible to estimate the replacement time of the wires, and to contribute to the cost reduction effect by leveling the renewal equipment amount.

(Embodiment 2)
In the first embodiment, the wetting time map data is created based on the wetting time obtained from data such as a plurality of AMeDAS stations. In this embodiment, the altitude is taken into account when the wetting time is adopted. That is, as shown in FIG. 21, the higher the altitude, the longer the wetting time. Therefore, in this embodiment, as shown in FIG. 4, when the elevation of the P point is higher than that of the adopted K5 station, the wetting time of the K5 station is calculated using the relationship of FIG. Correct it.

  As a result, even in an area where there is no observation data of the annual average relative humidity, more accurate annual average relative humidity data is generated, so that the wire corrosion rate can be predicted with high accuracy.

  The present invention is not limited to the estimation of the corrosion rate of electric wires, but can be used for estimating the corrosion rate of overhead wire fittings, steel tower coatings, and the like.

10 terminal (second device)
DESCRIPTION OF SYMBOLS 11 Input part 12 Reading part 13 Memory | storage part 14 Processing part 15 Display part 16 Output part 17 Communication part 20 Server (1st apparatus)

Claims (6)

A wet state estimation method for estimating average relative humidity,
The average temperature obtained from an organization such as the Japan Meteorological Agency, and the amount of water vapor in the air in a given area including the observation point is estimated from the average temperature at the observation point. From
Calculate the average relative humidity of the observation point from the estimated water vapor amount in the predetermined area and the obtained saturated water vapor amount.
A method for estimating a wet state. Using the approximate expression obtained from the relationship between the monthly average temperature observed at the observation point and the monthly water vapor amount, the water vapor amount corresponding to the annual average temperature in the predetermined region is estimated, and the water vapor amount in the predetermined region To
The wet state estimation method according to claim 1. Calculate the wetting time at the observation point from the predetermined first calculation formula based on the calculated average relative humidity and the average temperature.
The wetting state estimation method according to claim 1 or 2. A wetting state estimation device for estimating a wetting time at an observation point, which is a wetting time due to water vapor in the air,
This wet state estimation device is an average temperature obtained from an organization such as the Japan Meteorological Agency, and estimates the amount of water vapor in the air in a predetermined area including the observation point from the average temperature of the observation point, and saturates the average temperature. Obtain the water vapor amount from the water vapor amount saturation curve, and calculate the average relative humidity at the observation point from the estimated water vapor amount in the predetermined area and the obtained saturated water vapor amount.
Wetting state estimation device characterized by the above. A wire deterioration estimation method for estimating the corrosion rate of a wire,
From the salinity arrival map of the predetermined area created based on the salt passage amount obtained in the simulation, the degree of adhesion of the salinity of the electric wire is obtained, and it is created based on the average relative humidity and the average temperature, and the average relative humidity When there is no data, the wetting time degree of the electric wire is obtained from the wetting time map of the predetermined area created using the average relative humidity estimated by the wetting state estimation method according to any one of claims 1 to 3.
Estimate the corrosion rate of the wire based on the obtained salt adhesion and wetting time,
An electric wire deterioration estimation method characterized by the above. A wire deterioration estimation system that estimates the corrosion rate of a wire,
Based on the amount of salt passing through the simulation, create a salinity arrival map for the specified area, create a wetting time map for the specified area based on the average relative humidity and the average temperature, and provide data on the average relative humidity When there is no first device using the average relative humidity estimated by the wet state estimation method according to any one of claims 1 to 3,
Obtain the degree of salt adhesion of the wire from the salt reach map in the specified area, and determine the wire wetting time from the wetting time map of the specified area, and calculate the corrosion rate of the wire based on the obtained degree of salt adhesion and wetting time. A second device to estimate;
An electric wire deterioration estimation system comprising:

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