JPH08194069A - Transmission wire damage-by-snow predicting method and apparatus - Google Patents

Abstract

PURPOSE: To provide a transmission wire damage-by-snow predicting apparatus which is hardly affected by the differences of temperatures by computing a plurality of quantities of snow precipitation and setting a presumed quantity of snow precipitation by calculating the weighted average figured out by weighting a probability density function to these quantities of snow precipitation. CONSTITUTION: Meteorological data received by a data reception apparatus 1 is forwarded to a statistically processing circuit 2 and a neural net computing circuit 9. In the circuit 2 n errors are set for the temperature T in the meteorological data. In the circuit 2, probability density is calculated for each error and the temperature to which the error is added is supplied and classified by three systems in a temperature determining circuit 3. The conditions for snow precipitation are considered in each system and the quantities of snow precipitation is calculated in each snow precipitation quantity calculating circuit 5 (a circuit for which a snow precipitation quantity presuming calculation equation is employed.). In a snow precipitation quantity integrating circuit 8, the output obtained by the three systems are weighted and accumulated and when n snow precipitation quantities are accumulated completely, the presumption quantity of snow precipitation can be obtained.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a transmission line snow damage prediction method and apparatus for predicting snow damage based on estimation of snow accretion on a transmission line, and particularly to a transmission line snow damage prediction method that is less susceptible to temperature error. And the device.

[0002]

2. Description of the Related Art Power transmission lines are exposed to a harsh natural environment and are at risk of various natural disasters. In particular, during winter snowfall, the load on the power transmission line may become excessive due to snow accumulating on the power transmission line, resulting in a wire breakage accident or a steel tower collapse accident. Therefore, it is extremely important to quantitatively estimate the snow accretion amount of the transmission line during snowfall from the viewpoint of accident prevention.

In order to estimate the snow accretion amount based on meteorological data such as temperature, wind speed, and precipitation amount, the following various snow accretion amount estimation formulas have been proposed.

1) Admirat's formula P. Admirat et al., "Measurement of snow-accumulation models based on actual cases in Japan and France; September 1988, Fourth International Conference on Atmospheric Ice Accumulation of Structures" 2) Erbic Formula M. Ervik et al., "Extensive measurement model for icing transmission lines applied to laboratory and field observation; September 1988, 4th International Conference on atmospheric icing of structures" 3) McConnen Formula Makkonen's formula “Estimation of wet snow growth on structures; science and technology in cold regions” 4) Sakamoto's formula According to Central Research Institute of Electric Power Industry, Sakamoto.

On the other hand, a neural network for predicting snow damage has been proposed. That is, using the meteorological data in the case of snow damage in the past, if the meteorological data is input to the neural network, the neural network is trained to output the event of “accident” or “no accident”. By inputting weather forecast data to this neural network, it is possible to predict by classifying into either "accident" or "no accident".

Further, a system has been devised in which a neural network judges "there is an accident" and issues an alarm when the estimated snow accretion amount calculated by the snow accretion amount estimation formula exceeds a predetermined value. There is.

[0007]

By the way, it has been estimated by wind tunnel experiments and the like that the snow accretion rate on a power transmission line becomes maximum at a certain specific temperature. However, among the above-mentioned formulas of the estimated snow accretion amount, in the Admirato's formula and McConnen's formula, the snow accretion amount does not depend on temperature at all. In the Erbic equation, the amount of snow accretion increases linearly with the temperature. On the other hand, in Sakamoto's equation, the change in the amount of snowfall with respect to the change in temperature well reflects the results of wind tunnel experiments.

However, Sakamoto's formula tends to cause a large difference in the amount of snow accretion even if the temperature slightly changes.
In general, the weather forecast data used to predict snow damage has some error with respect to the true weather data.
Therefore, if the Sakamoto equation is used, the calculation result will change significantly due to the error, and the prediction accuracy will deteriorate.

SUMMARY OF THE INVENTION It is an object of the present invention to solve the above problems and provide a transmission line snow damage prediction method and device which are less susceptible to temperature errors.

[0010]

In order to achieve the above object, the method of the present invention is a snow accretion amount estimation formula for estimating the amount of snow accretion on a power transmission line from weather forecast data such as temperature, wind speed, and precipitation. In the transmission line snow damage prediction method for predicting the presence or absence of snow damage based on this estimated snow accretion amount, the probability density function of error is set in advance for the temperature of the above weather forecast data, and the temperature of the given weather forecast data is set. By substituting a plurality of temperatures with arbitrary errors into the above snowfall amount estimation calculation formula, we calculate a plurality of snowfall amounts, and obtain a weighted average by weighting these snowfall amounts with a probability density function. This is the estimated snow accretion amount.

Further, the apparatus of the present invention has a snow accretion amount estimation calculation circuit for estimating an amount of snow accretion on the power transmission line from weather forecast data such as temperature, wind speed, precipitation amount, etc., and based on this estimated snow accretion amount. In a transmission line snow damage prediction device that predicts the presence or absence of snow damage, a probability density function of an error is set in advance for the temperature of the weather forecast data, and a plurality of temperatures obtained by adding an arbitrary error to the temperature of the given weather forecast data are set. A statistical processing device that calculates a plurality of snow accretion amounts by substituting each in the snow accretion amount estimation calculation formula, and a cumulative circuit that obtains a weighted average by weighting these snow accretion amounts with a probability density function to obtain the estimated snow accretion amount And are provided.

The probability density function may be set such that the error in the temperature of the weather forecast data follows a normal distribution.

A weighted average may be obtained by normalizing the total number of errors to which the probability density function is added, weighting each snow accretion amount with the probability density function, and accumulating.

The total number of errors to which the above probability density function is added may be at least 10, and positive and negative errors may be provided evenly.

The temperature used for the snowfall estimation calculation is 0 to 2
℃, 0 ℃ or less, it is determined whether it is 2 ℃ or more, if the air temperature is 0 ~ 2 ℃, calculate the snow accretion amount as it is, the air temperature is 0
If the temperature is below ℃, it is determined whether snow adheres to the transmission line, and then the snow accretion amount is calculated. If the temperature is at 2 ° C. or higher, the snow accretion amount is calculated. Good.

Whether or not the snow adheres to the power transmission line is determined by determining whether the snow has already adhered to the power transmission line or the wind speed of 3 m /
If the water content is 50% or more, it is determined that there is snowfall, and if it is less than 50%, it is determined that there is snowfall. It may be free from snowfall.

It is also possible to predict the presence or absence of snow damage by a neural network based on the meteorological data, and determine whether or not to issue an alarm based on the result of this prediction and the prediction of the presence or absence of snow damage based on the estimated snow accretion amount.

[0018]

As described above, the temperature of the weather forecast data used to predict snow damage has a certain degree of error with respect to the true temperature. It is considered that the error of the temperature of the weather forecast data with respect to the true temperature follows some error distribution. Therefore, the relationship between the magnitude of the error and the probability that the error of that magnitude appears can be represented by a probability density function. On the contrary, when the temperature of the weather forecast data is given, the probability that the true temperature exists with an error of a certain magnitude follows the probability density function.

In the method and configuration of the present invention, the error probability density function is set in advance for the temperature of the weather forecast data. When multiple snow accretion amounts are calculated by substituting a plurality of temperatures with arbitrary errors added to the temperature of the given weather forecast data into the snow accretion amount estimation calculation formula, each snow accretion amount is the true snow accretion amount. The probability of being a quantity follows a probability density function. The weighted average of these snow accretion amounts weighted with a probability density function is the expected value of the true snow accretion amount, and by using this expected value as the estimated snow accretion amount, the error of the temperature is directly estimated. The change given to the calculation result can be mitigated. In other words, it is possible to predict snow damage that is unlikely to be affected by temperature error.

It is considered that the temperature error of the weather forecast data follows a normal distribution. Therefore, a probability density function representing a normal distribution is used as the probability density function to be set. According to the normal distribution, the probability ψ with the error x is the standard deviation s and the average value m.
When,

[0021]

[Equation 1]

It is represented by

The calculation of the estimated snow accretion amount using the normal distribution is as follows.

First, arbitrary n errors x ₁ , x ₂ , ...
, Xn are set, and the probability densities ψ ₁ , ψ are calculated by the equation (1).
₂ , ..., Calculate ψn. The standard deviation s and the average value m are
It has been calculated in advance.

Next, temperature T + x ₁ , T + x ₂ , ..., T obtained by adding an error to the temperature T ₀ of the weather forecast data.
+ Xn is calculated, and these temperatures are substituted into the snow amount estimation calculation formula to calculate the snow amount w ₁ , w ₂ , ..., Wn. As shown in the equation (2), the snow accretion amount W is calculated by weighting these snow accretion amounts with a probability density function and averaging them.

[0026]

[Equation 2]

The total number of errors to which the probability density function is added is standardized. That is, the sum of the probabilities of errors to be added is set to 1. If each snow accretion amount is weighted with a probability density function and accumulated, a weighted average is obtained when all errors are accumulated.

If the total number of errors that add the probability density function is too small, the reliability is concerned. On the other hand, if there are too many, the amount of calculation will increase and it will be troublesome. At least 1 total error
If the number is 0, the required reliability can be obtained. Moreover, it is considered that the distribution of errors is positive and negative and has symmetry. Therefore, the positive error and the negative error are evenly provided.

In general, the amount of snow accreted on a power transmission line is 0 to 0.
It is the maximum at 2 ° C., and the snow accretion amount estimation calculation formula is satisfied as it is under this temperature condition. When the temperature is 0 ° C. or lower or 2 ° C. or higher, there are added conditions that it is difficult for snow to accrue or the snow that accompanies snow falls. Therefore, the temperature is 0 to 2 ℃, below 0 ℃,
It is advisable to estimate the snow accretion amount according to the temperature conditions separately when the temperature is 2 ° C or higher. First, it is determined whether the temperature used for the snow accretion amount estimation calculation is 0 to 2 ° C, 0 ° C or lower, and 2 ° C or higher. If the temperature is 0 to 2 ° C, the snow accretion amount may be calculated as it is. If the temperature is 0 ° C. or lower, the snow accretion amount is calculated after determining whether snow adheres to the power transmission line. Further, if the temperature is 2 ° C. or higher, it is determined whether or not there is snow after calculating the amount of snow accretion.

When the temperature is 0 ° C. or lower, the amount of snowfall is large if snowfall has already occurred (according to the snowfall amount estimation calculation), but it can be determined that the snowfall amount is small if snowfall has not occurred. Further, it can be judged that the snow accretion amount is large when the wind speed is low, but the snow accretion amount is small when the wind speed is high. Therefore, it is determined that the snow has already adhered to the power transmission line, or if the wind speed is 3 m / s or less, it adheres, and otherwise the snow accretion amount is calculated.

When the temperature is 2 ° C. or higher, the presence or absence of snowfall can be determined by the water content in the snow. Therefore, if the water content of snow is 50% or more, there is snowfall, and if it is less than 50%, there is no snowfall. If snowfall has occurred, the previously determined snowfall amount will be deleted, and if snowfall has not occurred, the snowfall amount will be saved.

As described above, the presence or absence of snow damage can be predicted by a neural network based on the meteorological data.
The reliability of the warning is further improved by combining the prediction result and the prediction of the presence or absence of snow damage based on the estimated snow accretion amount to determine whether or not to issue the warning.

[0033]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

The transmission line snow damage prediction apparatus of the present invention has a snow accretion amount estimation calculation circuit for estimating the amount of snow accretion on the transmission line from weather forecast data such as temperature, wind speed, and precipitation amount. Whether or not there is snow damage can be predicted based on In addition, a probability density function of an error is set in advance for the temperature of the weather forecast data, and a plurality of temperatures obtained by adding an arbitrary error to the temperature of the given weather forecast data are respectively substituted into the snow accretion amount estimation calculation formula. A statistical processing device that calculates a plurality of snow accretion amounts and a cumulative circuit that obtains a weighted average of these snow accretion amounts by weighting a probability density function to obtain an estimated snow accretion amount are provided.

As an example, the snow damage warning system for a power transmission line shown in FIG. 1 includes a data receiving device 1 for receiving weather data including a current weather condition and a weather forecast from the outside, and a statistic for calculating the formula (1). The processing circuit 2, the temperature determination circuit 3 that determines whether the temperature of the meteorological data is 0 to 2 ° C., 2 ° C. or higher, and 0 ° C. or lower, and 0 to 2 of the temperature determination circuit 3.
Driven by an output of ℃, a snow accretion amount calculation circuit 5a that calculates the amount of snow accretion at that time, and a water content calculation circuit that is driven by an output of the temperature determination circuit 3 of 2 ° C. or higher and calculates a water content at that time 6 and snow accretion amount calculation circuit 5 for calculating the amount of snow accretion
b, a snowfall determination circuit 7 for determining snowfall based on the amount of snow accretion and the water content, and a dry snow adhesion determination circuit 4 for determining snow adhesion at that time, driven by an output of 0 ° C. or less of the temperature determination circuit And a snow accretion amount calculation circuit 5c for calculating the snow accretion amount, a snow accumulative amount accumulation circuit 8 for calculating the equation (2), a neural network calculation circuit 9 for calculating a neural network using weather data as an input, An alarm determination device 10 for determining whether to issue an alarm according to the output of the snow accumulation circuit 8 and the output of the neural network is provided.

In the statistical processing circuit 2, a probability density function of an error that follows a normal distribution is set in advance for the temperature of the weather forecast data, and an arbitrary n number of errors x ₁ , x ₂ , ..., Xn are set, According to the equation (1), the probability density ψ ₁ , ψ ₂ , ...
ψn is calculated, and an error is added to the temperature T ₀ of the meteorological forecast data, and the temperature T + x ₁ , T + x ₂ , ..., T
It is designed to calculate + xn. The temperature determination circuit 3
Temperatures T + x ₁ , T + x ₂ , ..., T + x with error added
It functions to sort n into three circuits.

In this embodiment, snow accretion amount calculation circuits 5a, 5
b and 5c are circuits using Sakamoto's equation as the snow accretion amount estimation equation, and the equation (3) is

[0038]

(Equation 3)

It is shown by.

Here, w: snow accretion amount (g / cm) T; air temperature (° C.) V; wind speed (m / s) Pnt; effective precipitation intensity during time t.

Next, the formula for calculating the water content is obtained by experiment, and the formula (4) is

[0042]

[Equation 4]

Here, LWC; water content D; snow accretion outer diameter (cm) D ₀ ; electric wire outer diameter (cm) H; humidity (%) I; load current (A) R; electric resistance (Ω) α ₀ Initial water content θ; angle between electric wire and wind direction ω; snowflake falling velocity (cm) γ; 0.04T ² .

In the snow accretion amount calculation circuit 5a, the temperature is 0
When the temperature is up to 2 ° C, the snow accretion amount is calculated by the equation (3). In the water content calculation circuit 6, the snow accretion amount calculation circuit 5b, and the snowfall determination circuit 7, the water content is obtained by the equation (4) when the air temperature is 2 ° C. or higher. Assuming there was no snow, the water content was 50
If it is less than%, the amount of snow accretion is calculated by the equation (3) with the temperature of 2 ° C. In the dry snow adhesion determination circuit 4 and the snow accretion amount calculation circuit 5c, when the temperature is 0 ° C. or lower, it is determined whether the electric wire is already snowed or the wind speed is 3 m / s or lower, and In either case, the temperature is set to 0 ° C. and the snow accretion amount is calculated by the equation (3). If not, the snow accretion amount is set to zero. Cumulative snow accumulation circuit 8
Is designed to weight the outputs obtained by the circuits of the above three systems and accumulate them.

If the estimated snow accretion amount calculation value from the snow accretion amount accumulation circuit 8 is equal to or more than a set value and the neural network calculation circuit 9 judges that "there is an accident", the alarm determination device 10
It is supposed to give an alarm.

Next, the operation of the embodiment will be described.

The operation will be described according to the flow of data.
First, the meteorological data such as temperature, wind speed, and precipitation received by the data receiving device 1 is passed to the statistical processing circuit 2 and the neural network calculation circuit 9. In the statistical processing circuit 2, there are n errors x ₁ , x ₂ , ..., Xn for the temperature T in the meteorological data.
Set. The number n is preferably 10 or more. It is appropriate that the errors x ₁ , x ₂ , ..., Xn have positive and negative errors evenly. Next, the statistical processing circuit 2 calculates the probability densities ψ ₁ , ψ ₂ , ..., ψn according to equation (1) for each error. Then, the temperatures T + x ₁ , T + x ₂ , ..., T + xn to which errors have been added are sorted by the temperature determination circuit 3 into three circuits and supplied. In each system, considering the conditions of snow accretion, the snow accretion amount calculation circuit 5a,
At 5b and 5c, the snow accretion amounts w ₁ , w ₂ , ..., Wn are calculated. In the snow accretion amount accumulation circuit 8, the outputs obtained by the circuits of three systems are weighted and accumulated, and when the n snow accretion amounts are accumulated, the estimated snow accretion amount W is obtained.

On the other hand, in the neural network calculation circuit 9,
Whether there is a risk of damage is determined by a neural network that has been learned from the meteorological data when past snow damage accidents occurred. The alarm determination device 10 issues an alarm when the estimated snow accretion amount calculation value from the snow accretion amount accumulation circuit 8 is equal to or greater than a certain set value and the neural network 9 determines that there is an accident.

Although the temperature of the weather forecast data is statistically processed in this embodiment, it may be applied to forecast weather data (wind speed, precipitation) other than the temperature.

[0050]

The present invention exhibits the following excellent effects.

(1) Even when using a formula such as Sakamoto's formula in which the amount of snowfall changes greatly due to a slight change in temperature, it is possible to predict snow damage with high accuracy, that is, less susceptible to temperature error. Becomes

[Brief description of drawings]

FIG. 1 is a block diagram of a snow damage warning system showing an embodiment of the present invention.

[Explanation of symbols]

 2 statistical processing circuit 3 temperature determination circuit 4 dry snow adhesion determination circuit 5a, 5b, 5c snow accretion amount calculation circuit 6 moisture content calculation circuit 7 snowfall determination circuit 8 snow accretion amount accumulation circuit 9 neural network calculation circuit

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Yoshio Ichi 5-1, 1-1 Hidaka-cho, Hitachi-shi, Ibaraki Hitachi Cable Co., Ltd.

Claims (8)

[Claims] 1. A snowfall amount estimation calculation formula for estimating the snowfall amount on a power transmission line from weather forecast data such as temperature, wind speed, precipitation amount, etc. is used to predict the presence or absence of snow damage based on this estimated snowfall amount. In the electric wire snow damage prediction method, a probability density function of an error is set in advance for the temperature of the weather forecast data, and a plurality of temperatures obtained by adding an arbitrary error to the temperature of the given weather forecast data are respectively set to the snow accretion amount. A method for predicting snow damage on a transmission line, which comprises calculating a plurality of snow accretion amounts by substituting them into an estimation calculation formula, and obtaining a weighted average of these snow accretion amounts by weighting a probability density function as the estimated snow accretion amount. 2. A snow accretion amount estimation calculation circuit for estimating an amount of snow accretion on a transmission line from meteorological forecast data such as temperature, wind speed, precipitation amount, etc., and predicting presence or absence of snow damage based on the estimated snow accretion amount. In the transmission line snow damage prediction device, a probability density function of an error is set in advance for the temperature of the weather forecast data, and a plurality of temperatures obtained by adding an arbitrary error to the temperature of the given weather forecast data are used to estimate the snow accretion amount, respectively. A statistical processing device that calculates a plurality of snow accretion amounts by substituting in a calculation formula and a cumulative circuit that obtains a weighted average of these snow accretion amounts by weighting a probability density function to obtain an estimated snow accretion amount are provided. A power transmission line snow damage prediction device. 3. The transmission line snow damage prediction method and apparatus according to claim 1, wherein the probability density function is set so that the error of the temperature of the weather forecast data follows a normal distribution. 4. A weighted average is obtained by normalizing the total number of errors to which the probability density function is added and weighting each probability of snowfall with a probability density function to obtain a weighted average. The transmission line snow damage prediction method and device according to any one of the above. 5. The transmission line snow damage according to claim 1, wherein the total number of errors to which the probability density function is added is at least 10 and positive and negative errors are evenly provided. Prediction method and device. 6. The temperature used in the snow accretion amount estimation calculation is 0 to
It is judged whether it is 2 ℃, 0 ℃ or lower, or 2 ℃ or higher. If the air temperature is 0 to 2 ℃, the amount of snow accretion is calculated as it is. The power transmission line according to claim 1 or 2, wherein the snow accretion amount is calculated after determining whether or not there is snowfall after the snow accretion amount is calculated if the temperature is 2 ° C or higher. Snow damage prediction method and device. 7. The determination as to whether or not the snow adheres to the power transmission line is whether the snow has already adhered to the power transmission line or the wind speed is 3 m.
If the water content is 50% or more, it is determined that there is snowfall, and if the water content is less than 50%, If there is no snowfall, the method and apparatus for predicting snow damage in a power transmission line according to any one of claims 1 to 5, wherein there is no snowfall. 8. A method for predicting the presence or absence of snow damage by a neural network based on the meteorological data, and determining whether or not to issue an alarm based on the prediction result and the presence or absence of snow damage based on the estimated snow accretion amount. The method and apparatus for predicting snow damage in a power transmission line according to any one of claims 1 to 6.