CN117894133A - Icing alarm method and system for power transmission line - Google Patents
Icing alarm method and system for power transmission line Download PDFInfo
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Abstract
The invention relates to a method and a system for warning icing of a power transmission line, comprising the following steps: setting a data acquisition module along the transmission line at fixed intervals, wherein the data acquisition module is used for acquiring current image data and environment data; acquiring image data in a data acquisition module, preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data, and fusing the enhanced image data with initial image data to obtain final enhanced image data; according to the environmental data and the final enhanced image data, calculating to obtain an icing type and an icing thickness, and obtaining an icing degree evaluation value based on the icing thickness; an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and alarm signals are output. According to the invention, the influence of the icing type, the icing thickness and the icing degree evaluation value on the power transmission line is comprehensively considered, the condition of the power transmission line is obtained more truly, and the accuracy of icing alarm of the power transmission line is improved.
Description
Technical Field
The invention relates to the technical field of monitoring and alarming of power transmission lines, in particular to a method and a system for alarming icing of a power transmission line.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, partial south areas suffer from rare extreme ice and snow weather, because the power transmission line is generally located in high-altitude space, and a plurality of power transmission lines are located in remote mountain areas, traffic is inconvenient, workers are difficult to know the condition of the power transmission line in time, in addition, the situation that the power transmission line is subjected to extreme ice and snow weather and humidity increase and temperature dip can cause thick ice layers on the power transmission line, the frozen power transmission line has the fatal problems of overweight, increased windward area, cold shrinkage and the like, and the power transmission line is easy to jump and twist to a certain extent, so that the power transmission line is broken and provided with a reverse tower. The traditional method is labor-intensive and labor-intensive in a manual inspection mode, so that labor intensity is increased, the power transmission line is often erected at high altitude, and accurate judgment is difficult to be made by staff on the ground.
With the continuous rise of neural network model technology, a person skilled in the art gradually introduces a neural network into the art, in the prior art, a method, a system, a storage medium and equipment for predicting line icing faults are disclosed in patent application number CN116050599a, and an icing dataset is collected, and the icing dataset comprises historical meteorological data, current meteorological data, historical icing data, equipment information, overhaul information and fault information; preprocessing the icing data set; constructing a meteorological icing model based on historical meteorological data and historical icing data; inputting current meteorological data into a meteorological icing model, and predicting an icing area; based on the ice coating area and the equipment information, obtaining an ice coating line through calculation; analyzing the icing data set, calculating the characteristics of the line icing fault, and constructing a line icing fault model; and acquiring the characteristics of the icing line, and inputting the characteristics into the line icing fault model to obtain the predicted fault condition of the icing line. In the scheme, the characteristic of the line icing fault is obtained by analyzing the icing data set, so that the current characteristic of the icing line is used for judging whether the current line can be faulty or not. A plurality of network models are arranged, but the influence of the icing type and the icing thickness on the power transmission line is not considered, so that the icing real condition of the power transmission line cannot be accurately obtained by the existing prediction method.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for warning icing of a power transmission line, which are used for considering the icing type and the icing thickness by integrating current environmental data and image data, and improving the accuracy of monitoring and warning icing of the power transmission line.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for warning icing on a power transmission line, including:
Setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
acquiring image data in a data acquisition module, preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data, and fusing the enhanced image data with the image data to obtain final enhanced image data;
According to the environmental data acquired by the data acquisition module and the final enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
According to a further technical scheme, the image data is preprocessed in a nonlinear mapping mode, and the method specifically comprises the following steps:
Performing convolution operation processing on the image data;
Normalizing the convolution result by using group normalization;
processing the normalized convolution result by using an activation function to obtain initial image data;
Inputting the initial image data into a pre-trained depth convolution generation countermeasure network, outputting enhanced image data, and fusing the enhanced image data with the initial image data to obtain final enhanced image data.
According to a further technical scheme, the environmental data comprise the current temperature, humidity, wind speed and wind direction.
According to a further technical scheme, the freezing type comprises rime, mixed rime and soft rime.
According to a further technical scheme, the icing type is obtained according to the environmental data acquired by the data acquisition module, and the specific method comprises the following steps:
When the temperature is 0-5 ℃ and the humidity is 100% and the wind speed is more than 15m/s, the icing type is obtained as rime; when the temperature is between 7 ℃ below zero and 0 ℃ and the humidity is between 80% and 100%, and the wind speed is less than 15m/s, the icing type soft rime is obtained; when the environmental data is continuously changed under the formation condition of the rime and soft rime, the freezing type is obtained as mixed rime.
According to a further technical scheme, the icing degree obtained by calculation is as follows:
Wherein,/> is an icing degree evaluation value,/> is an average thickness of the enhanced image data after the transmission line is frozen, and d is the diameter of the transmission line; and the thickness maximum value in a plurality of position points after the transmission line is frozen in the enhanced image data is obtained by carrying out the process of/> , and the M is the quantity of the enhanced image data.
According to a further technical scheme, the construction of the icing alarm model is a neural network prediction model, and the construction method specifically comprises the following steps:
Dividing training data and verification data according to the environment data and the icing degree, wherein the training data is used for network training, and the verification data is used for testing the fitting performance of a network;
Setting parameters and determining the number of nodes; the number of hidden layer nodes of the neural network prediction model is n, an optimal neural network structure is obtained, and a calculation formula of the hidden layer nodes is as follows: Wherein a is an input layer variable, b is an output layer variable, m is an integer and 0< m <10;
Inputting training data into an initial neural network prediction model to obtain a trained prediction model; and verifying the trained prediction model through verification data, so as to obtain a final neural network prediction model, namely an icing alarm model.
In a second aspect, the present invention provides a power line icing alarm system comprising:
A data acquisition module configured to: setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
a data processing module configured to: acquiring image data in a data acquisition module, and preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data;
a computing module configured to: according to the environmental data acquired by the data acquisition module and the enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
an alarm module configured to: an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
Compared with the prior art, the invention has the beneficial effects that:
1. the influence of the icing type, the icing thickness and the icing degree evaluation value on the power transmission line is comprehensively considered, the condition of the power transmission line can be obtained more truly, and the accuracy of icing alarm of the power transmission line is improved;
2. According to the application, the acquisition module is utilized to acquire environmental data and image data, and the icing type and the icing thickness are obtained according to the temperature, humidity, wind speed and wind direction conditions. The advantages are more obvious when working in a relatively harsh environment than in the prior art. Meanwhile, the application adopts a neural network prediction model structure and utilizes the weight threshold value to improve the icing prediction precision of the power transmission line to a certain extent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a method for warning of icing on a transmission line according to the present invention;
Detailed Description
The invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, embodiments of the invention and features of embodiments may be combined with one another without conflict.
Example 1
As shown in fig. 1, the embodiment provides a method for warning icing on a power transmission line, which includes the following steps:
s1: setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
S2: acquiring image data in a data acquisition module, preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data, and fusing the enhanced image data with initial image data to obtain final enhanced image data;
s3: according to the environmental data acquired by the data acquisition module and the final enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
s4: an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
In step S1, environmental data collected by the data collection module is set at the monitoring point including, but not limited to, current temperature, humidity, wind speed, and wind direction.
In step S2, preprocessing the initial image data in a nonlinear mapping mode; the method comprises the following specific steps:
S21: performing convolution operation processing on the image data;
S22: normalizing the convolution result by using group normalization;
S23: processing the normalized convolution result by using an activation function to obtain initial image data;
S24: the initial image data is input into a pre-trained deep convolution generation countermeasure network, and the enhanced image data is output.
Inputting the image data into a pre-trained deep convolution generating countermeasure network DCGAN, and outputting initial image data; wherein the training step of the pre-trained deep convolution generation countermeasure network DCGAN includes:
Constructing a deep convolution generation countermeasure network DCGAN; the deep convolution generating countermeasure network DCGAN generates a countermeasure network DCGAN for the deep convolution having a U-Net structure;
The training set is constructed, the training set is utilized to train the deep convolution generating countermeasure network DCGAN, in the training process, a network generating image is generated, the generating network inputs the generating image into the judging network, meanwhile, the image with normal illumination is also input into the judging network, the judging network judges the true or false of the image, and when the loss function reaches the minimum value, the trained deep convolution generating countermeasure network is obtained.
Furthermore, the enhanced image data and the initial image data are fused, so that the final enhanced image data can highlight main information, noise is suppressed, and the purpose of further enhancing the image is achieved.
In the step S3, freezing types comprise rime, mixed rime and soft rime; the method comprises the following steps of:
When the temperature is 0-5 ℃ and the humidity is 100% and the wind speed is more than 15m/s, the icing type is obtained as rime; when the temperature is between 7 ℃ below zero and 0 ℃ and the humidity is between 80% and 100%, and the wind speed is less than 15m/s, the icing type soft rime is obtained; when the environmental data is continuously changed under the formation condition of the rime and soft rime, the freezing type is obtained as mixed rime.
The icing degree is calculated as follows:
Wherein,/> is an icing degree evaluation value,/> is an average thickness of the enhanced image data after the transmission line is frozen, and d is the diameter of the transmission line; and the thickness maximum value in a plurality of position points after the transmission line is frozen in the enhanced image data is obtained by carrying out the process of/> , and the M is the quantity of the enhanced image data.
The icing thickness can be obtained from the temperature, humidity, wind speed and wind direction conditions in the environmental data, specifically:
h=kx (-/>）×(1+/> ×v), where h is the icing thickness; k is the current environmental humidity; the current ambient temperature is,/> , and the icing starting temperature is,/> ; v is wind speed; and/> is the wind direction correspondence factor provided by the relevant standard or experience.
In the present embodiment, the icing type and the icing thickness are obtained according to the temperature, humidity, wind speed and wind direction conditions. The advantages are more obvious when working in a relatively harsh environment than in the prior art.
In step S4, an icing alarm model is constructed as a neural network prediction model, and the specific steps are as follows:
Dividing training data and verification data according to the environment data and the icing degree, wherein the training data is used for network training, and the verification data is used for testing the fitting performance of a network; dividing training data and verification data, wherein 90% of the data sets are used as training data for network training, and 10% of the data sets are used as verification data for testing the fitting performance of a network; then, data normalization processing: the input and output data are normalized by using a minimum and maximum normalization method, the calculation formula is shown as formula (1), the data are normalized by using formula (1), and the data are calculated to be the values of the [ -1,1] interval.
（1）
Wherein x is an initial value before data normalization, y is a calculated value after data normalization, and x max and x min are maximum and minimum values obtained by original data respectively;
Setting parameters and determining the number of nodes; the number of hidden layer nodes of the neural network prediction model is n, an optimal neural network structure is obtained, and a calculation formula of the hidden layer nodes is as follows: Wherein a is an input layer variable, b is an output layer variable, m is an integer, and 0< m <10.
Inputting training data into an initial neural network prediction model to obtain a trained prediction model; the trained prediction model is verified through verification data, so that a final neural network prediction model, namely an icing alarm model, is obtained, the influence of icing type, icing thickness and icing degree evaluation values on the power transmission line is comprehensively considered in the embodiment, the condition of the power transmission line can be obtained more truly, and the accuracy of icing alarm of the power transmission line is improved.
Example two
The embodiment provides a power transmission line icing alarm system, which specifically comprises the following modules:
A data acquisition module configured to: setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
a data processing module configured to: acquiring image data in a data acquisition module, and preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data;
a computing module configured to: according to the environmental data acquired by the data acquisition module and the enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
an alarm module configured to: an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. A method for warning of icing on a power transmission line, comprising:
Setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
Acquiring image data in a data acquisition module, preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data, and fusing the enhanced image data with initial image data to obtain final enhanced image data;
According to the environmental data acquired by the data acquisition module and the final enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
2. The method for warning icing on a power transmission line according to claim 1, wherein the preprocessing of the image data by means of nonlinear mapping comprises the following specific steps:
Performing convolution operation processing on the image data;
Normalizing the convolution result by using group normalization;
processing the normalized convolution result by using an activation function to obtain initial image data;
Inputting the initial image data into a pre-trained depth convolution generation countermeasure network, outputting enhanced image data, and fusing the enhanced image data with the initial image data to obtain final enhanced image data.
3. A method of warning of icing on a power transmission line as claimed in claim 1 wherein said environmental data comprises current temperature, humidity, wind speed and wind direction.
4. A method of warning of icing on a transmission line as claimed in claim 1, characterized in that the icing type comprises rime, mixed rime and soft rime.
5. The method for warning icing on a power transmission line according to claim 1, wherein the icing type is obtained according to the environmental data collected by the data collection module, and the method comprises the following steps:
When the temperature is 0-5 ℃ and the humidity is 100% and the wind speed is more than 15m/s, the icing type is obtained as rime; when the temperature is between 7 ℃ below zero and 0 ℃ and the humidity is between 80% and 100%, and the wind speed is less than 15m/s, the icing type soft rime is obtained; when the environmental data is continuously changed under the formation condition of the rime and soft rime, the freezing type is obtained as mixed rime.
6. The method for warning of icing on a power transmission line according to claim 1, wherein the calculated icing level is:
Wherein,/> is an icing degree evaluation value,/> is an average thickness of the enhanced image data after the transmission line is frozen, and d is the diameter of the transmission line; and the thickness maximum value in a plurality of position points after the transmission line is frozen in the enhanced image data is obtained by carrying out the process of/> , and the M is the quantity of the enhanced image data.
7. The method for warning icing on a power transmission line according to claim 1, wherein the construction of the icing warning model is a neural network prediction model, and comprises the following specific steps:
Dividing training data and verification data according to the environment data and the icing degree, wherein the training data is used for network training, and the verification data is used for testing the fitting performance of a network;
Setting parameters and determining the number of nodes; the number of hidden layer nodes of the neural network prediction model is n, an optimal neural network structure is obtained, and a calculation formula of the hidden layer nodes is as follows: Wherein a is an input layer variable, b is an output layer variable, m is an integer and 0< m <10;
Inputting training data into an initial neural network prediction model to obtain a trained prediction model; and verifying the trained prediction model through verification data, so as to obtain a final neural network prediction model, namely an icing alarm model.
8. A power line icing alarm system comprising:
A data acquisition module configured to: setting a monitoring point along the transmission line at fixed distance intervals, and setting a data acquisition module at the monitoring point for acquiring current image data and environment data;
a data processing module configured to: acquiring image data in a data acquisition module, and preprocessing the image data in a nonlinear mapping mode to obtain enhanced image data; fusing the enhanced image data with the image data to obtain final enhanced image data;
A computing module configured to: according to the environmental data acquired by the data acquisition module and the final enhanced image data preprocessed by the data processing module, the icing type and the icing thickness are calculated, and an icing degree evaluation value is obtained based on the icing thickness;
an alarm module configured to: an icing alarm model is built, icing type, icing thickness and icing degree evaluation values are input, and an alarm signal is output.
9. A computer readable storage medium having stored thereon a program, which when executed by a processor, implements the steps of a power line icing alarm method according to any of claims 1-7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor performs the steps of a method for warning of icing on a power line according to any of claims 1-7 when said program is executed.
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| CN202410290356.7A CN117894133B (en) | 2024-03-14 | Icing alarm method and system for power transmission line |
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