CN111539842B - Overhead power transmission line icing prediction method based on meteorological and geographic environments - Google Patents
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Abstract
The invention belongs to the technical field of icing prediction, and particularly relates to an overhead power transmission line icing prediction method based on weather and geographic environments. The method is mainly based on meteorological data and geographic environment data, and a machine learning method is utilized to predict and early warn the icing condition of the overhead transmission line in a future period of time; and training a machine learning model according to the historical icing data of the target line, and solving the problems of unbalanced category, big data training and the like. The whole process comprises data processing, feature extraction and icing prediction model establishment. According to the method, on the basis of massive data, the icing prediction and the early warning of the overhead transmission line are realized through the data processing technology, the characteristic extraction technology and the classification model establishment, and the icing prediction in a future period is realized through combining all historical icing data of the transmission line with icing influence factors (weather, geographical environment and the like).
Description
Technical Field
The invention belongs to the technical field of icing prediction, and particularly relates to an overhead power transmission line icing prediction method based on weather and geographic environments.
Background
Icing of a power transmission line is one of the most important disasters of a power system, and the icing causes the load of the power transmission line to increase, so that accidents such as line disconnection, tower collapse, ice flash tripping, wire galloping, communication interruption, equipment damage of insulators, hardware fittings and the like are caused. The icing disaster seriously threatens the safe and stable operation of the power grid, and causes huge economic loss. In many places in China, serious icing disasters exist, and the situation of pre-judging icing is an important ring of power grid operation, so that the icing prediction on the power transmission line is very positive and significant to the power grid.
The ice coating mechanism of the power transmission line is complex, and the current research on the ice coating based on the power transmission line is based on a physical equation method, and the research is also carried out through dynamic and thermodynamic experiments. These methods also have many problems such as greater difficulty in application and lower accuracy in application. The complexity and the hypothesis based on the physical equation have a plurality of problems in application, the mechanism of ice coating is complex, the ice coating is difficult to describe through one physical equation, and the ice coating has a large problem in the aspect of universality; in addition, general physical equations rely on many empirical coefficients, which are difficult to determine; and the physical method cannot predict and judge future ice-covering conditions.
With the rise of big data and machine learning, the icing data about the power transmission line is more and more, so the invention adopts a machine learning method based on the big data to study the icing of the power transmission line, and invents an overhead power transmission line icing prediction method based on meteorological and geographic environments. The main disadvantages of the conventional physical method are as follows:
1. the physical method mainly provides a reasonable physical equation based on observation of a certain part, and when the method is applied in a large amount, the problem that the line is larger in universality is solved, the physical equation must be established for each part (each tower body or a section of line), and the applicability is very low.
2. The physical equation itself has many influencing factors and coefficient construction, but the determination of the coefficient is generally given according to experience or a plurality of observation processes, so that the determination of the coefficient is complex and difficult, and the rationality of the coefficient is not easy to be scientifically verified. The mechanism of ice coating formation is extremely complex and at different points in time, the possible coefficients may be different. The overall process of constructing the physical equation is therefore more difficult.
3. The physical method is mainly used for evaluating the current icing state based on the current situation, and the future period of time cannot be predicted and evaluated.
Disclosure of Invention
The method is mainly based on meteorological data and geographic environment data, and a machine learning method is utilized to predict and early warn the icing condition of the overhead transmission line in a future period of time; and training a machine learning model according to the historical icing data of the target line, and solving the problems of unbalanced category, big data training and the like. The whole process comprises data processing, feature extraction and icing prediction model establishment.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the method for predicting ice coating of the overhead transmission line based on meteorological and geographic environments is shown in fig. 1, and comprises the following steps:
s1, data collection: collecting meteorological data and geographical environment elevation data, wherein the meteorological data comprise temperature, humidity, rainfall, wind speed, wind direction, wind power grade and date;
s2, data processing:
the elevation data with the format of hdf is analyzed through hdf, and the missing value is marked as-1;
unifying the obtained temperature data at 40 ℃ which is higher than 40 ℃;
setting the obtained values with humidity smaller than 0 as 0 and the values with humidity larger than 100 as 100;
s3, performing derivative calculation on the collected data to extract more features, wherein the method specifically comprises the following steps:
deriving slope and slope from elevation data using a fitting surface method, for point e, using its nearest 8 data points, upper left, upper right, upper left, right, lower right, this 8 points plus center point e, can be a window (matrix) of 3*3 (there is a critical issue because this description is also used in the claims, which are not referenced to the drawing, i.e., the drawing cannot be used to interpret the claims, which must only be literally described, so it is intended to determine if this window can be described next) as shown in FIG. 2, the first column of data is, in turn, e 5 、e 2 、e 6 The data of the second column are sequentially e 1 、e、e 3 The data of the third column is e in turn 8 、e 4 、e 7 Slope and Slope direction Aspect meter at center point eThe calculation formula is as follows:
wherein ,
the cellsize is the resolution of the pixel point, if the value of saving around the point e is missing, the value is replaced by an adjacent value, and if the value is missing, the gradient and the slope direction of the point e are both 0;
the statistical calculation is carried out on the acquired meteorological data, and the statistical calculation comprises the following steps:
maximum v max ＝max(v t )，t＝t，t-1，t-2，t-3，t-4，...
Minimum value v min ＝min(v t )，t＝t，t-1，t-2，t-3，t-4，...
Mean value of
Standard deviation of
Extremum v p ＝v max -v min
wherein vt The method comprises the steps of calculating maximum, minimum, average, standard deviation and extremum of temperature according to current values acquired at current time in weather data acquired at t time points; the maximum, minimum, average and extreme value of the humidity are calculated; carrying out maximum value calculation on the wind speed; carrying out maximum calculation on the wind power level; the maximum, minimum and extreme value of the rainfall are calculated;
discretizing the obtained wind direction data, converting the wind direction data into a numerical value type, discretizing the wind direction data into classification variables, namely, converting 16 directions of north, east, north, northeast, southeast, southwest, west, northwest and northwest into integer values of 1-16;
s4, judging whether the data volume obtained in the step S3 is more than 10 ten thousand, if so, entering the step S5, otherwise, entering the step S6;
s5, judging class unbalance based on the fact that whether the positive sample and the negative sample in the overhead transmission line icing differ by 5 times or not, if the difference is 5 times, judging the class unbalance, inputting data into a bagging mode model, and otherwise, obtaining a prediction result by adopting a machine learning method; the bagging mode model is as follows: dividing an input data set to obtain N subsets, constructing a training model for each subset, namely constructing N training models, obtaining results of the N training models after each subset passes through the corresponding training model, and integrating the N model results by adopting a bagging algorithm to obtain a prediction result;
s6, judging class unbalance based on the fact that whether the positive sample and the negative sample in the overhead transmission line icing differ by 5 times or not, if the difference is 5 times, judging the class unbalance, inputting data into a weight model, and otherwise, obtaining a prediction result by adopting a machine learning method; the weight model is as follows: the weights taken during the training of the objective function are different, and the objective function is defined as
Where k is a class variable, w is a weight, L is a loss function, y k To observe a target variable in a sample, f (x k ) The method comprises the steps of outputting a result of a basic model, wherein the basic model comprises a logistic regression, a support vector machine, a decision tree and a boosting integrated learning classification method;
the calculation formula of the weight w is as follows:
where n is the number of samples, m class The class value is the class of the target variable for the number of samples corresponding to class;
and training according to the obtained weight w, and obtaining a prediction result by using a trained model.
The method has the beneficial effects that on massive data, the method realizes icing prediction and early warning of the overhead transmission line through a data processing technology, a characteristic extraction technology and a classification model establishment, and realizes icing prediction in a future period through combining all historical icing data of the transmission line with icing influence factors (weather, geographical environment and the like); based on big data, the formation mechanism of ice coating is combined, and a machine learning mode is adopted to train and predict a target area. The whole strategy is more flexible, each target area can be effectively predicted, and the method has universality and convenience in application, not only overcomes the definite point of a physical method, but also enables the icing prediction of the power transmission line to be more flexible, simple and easy, and easy to apply.
Drawings
FIG. 1 is a schematic diagram of the logic structure of the present invention;
FIG. 2 is a schematic diagram of a 3*3 data window for calculating grade and slope direction establishment based on elevation data;
FIG. 3 is a schematic diagram of a model constructed based on a bagging mode of the present invention;
FIG. 4 is a schematic diagram of the model training of the present invention;
fig. 5 is a graph showing the AUC and ROC curve index of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in FIG. 3, the present invention employs a plurality of sub-model strategies to build models, the base classifier of which selects a simple Logistic regression (Logistic) model, e.g., dividing the total dataset into 41 subsets, each of which builds a model, for a total of 41 models. When the method is applied, the data can pass through 41 models, and then 41 model results are output in a unified mode through voting.
Because of the large amount of data used, the training is performed in a batch-to-batch training mode. And introducing parameter optimization in the training process, and outputting an optimal model. The flow of training is shown in fig. 4.
After the model is constructed, the effect of the trained model is evaluated based on a test set, and the evaluation index mainly comprises an AUC and an ROC curve, wherein the AUC value of the invention is 0.9665, and the ROC curve is shown in figure 5.
Claims (1)
1. The overhead transmission line icing prediction method based on meteorological and geographic environments is characterized by comprising the following steps of:
s1, data collection: collecting meteorological data and geographical environment elevation data, wherein the meteorological data comprise temperature, humidity, rainfall, wind speed, wind direction, wind power grade and date;
s2, data processing:
the elevation data with the format of hdf is analyzed through hdf, and the missing value is marked as-1;
unifying the obtained temperature data at 40 ℃ which is higher than 40 ℃;
setting the obtained values with humidity smaller than 0 as 0 and the values with humidity larger than 100 as 100;
s3, performing derivative calculation on the collected data to extract more features, wherein the method specifically comprises the following steps:
deriving by elevation data to obtain gradient and slope direction, fitting curve method to form 3*3 window by surrounding point e with 8 adjacent data, the data of the first column are e in turn 5 、e 2 、e 6 The data of the second column are sequentially e 1 、e、e 3 The data of the third column is e in turn 8 、e 4 、e 7 The calculation formulas of the Slope and the Slope direction of the center point e are as follows:
wherein ,
wherein cellsize is the resolution of the pixel point, if the value of saving around the point e is missing, the value is replaced by the adjacent value, if the value is missing, the gradient and the gradient direction of the point e are 0;
the statistical calculation is carried out on the acquired meteorological data, and the statistical calculation comprises the following steps:
maximum v max ＝max(v t )，t＝t，t-1，t-2，t-3，t-4，...
Minimum value v min ＝min(v t )，t＝t，t-1，t-2，t-3，t-4，...
Mean value of
Standard deviation of
Extremum v p ＝v max -v min
wherein vt The method comprises the steps of obtaining weather data, wherein t is the nearest t time points, obtaining the current values of temperature and humidity according to the current time in the obtained weather data, and calculating the maximum, minimum, mean, standard deviation and extremum of the temperature; the maximum, minimum, average and extreme value of the humidity are calculated; carrying out maximum value calculation on the wind speed; carrying out maximum calculation on the wind power level; the maximum, minimum and extreme value of the rainfall are calculated;
discretizing the obtained wind direction data, converting the wind direction data into a numerical value type, discretizing the wind direction data into classification variables, namely, converting 16 directions of north, east, north, northeast, southeast, southwest, west, northwest and northwest into integer values of 1-16;
s4, judging whether the data volume obtained in the step S3 is more than 10 ten thousand, if so, entering the step S5, otherwise, entering the step S6;
s5, judging class unbalance based on the fact that whether the positive sample and the negative sample in the overhead transmission line icing differ by 5 times or not, if the difference is 5 times, judging the class unbalance, inputting data into a bagging mode model, and otherwise, obtaining a prediction result by adopting a machine learning method; the bagging mode model is as follows: dividing an input data set to obtain N subsets, constructing a training model for each subset, namely constructing N training models, obtaining results of the N training models after each subset passes through the corresponding training model, and integrating the N model results by adopting a bagging algorithm to obtain a prediction result;
s6, judging class unbalance based on the fact that whether the positive sample and the negative sample in the overhead transmission line icing differ by 5 times or not, if the difference is 5 times, judging the class unbalance, inputting data into a weight model, and otherwise, obtaining a prediction result by adopting a machine learning method; the weight model is as follows: the weights taken during the training of the objective function are different, and the objective function is defined as
Where k is a class variable, w is a weight, L is a loss function, y k To observe a target variable in a sample, f (x k ) The method comprises the steps of outputting a result of a basic model, wherein the basic model comprises a logistic regression, a support vector machine, a decision tree and a boosting integrated learning classification method;
the calculation formula of the weight w is as follows:
where n is the number of samples, m class The class value is the class of the target variable for the number of samples corresponding to class;
and training according to the obtained weight w, and obtaining a prediction result by using a trained model.
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