CN112070272A - Method and device for predicting icing thickness of power transmission line - Google Patents
Method and device for predicting icing thickness of power transmission line Download PDFInfo
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Abstract
The invention relates to a method and a device for predicting icing thickness of a power transmission line, comprising the following steps of: acquiring a meteorological data predicted value of the power transmission line at a future moment; inputting the future time to an autoregressive integral moving average model to obtain an initial value of the ice coating thickness output by the autoregressive integral moving average model; inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model, and obtaining an icing thickness error value output by the support vector regression model; determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the icing thickness error value; according to the technical scheme, the accuracy of the prediction of the icing thickness of the power transmission line is improved by combining the prediction models.
Description
Technical Field
The invention relates to the technical field of intelligent operation and maintenance of power systems, in particular to a method and a device for predicting icing thickness of a power transmission line.
Background
With the development of economy and the progress of society, the requirements of people on the stability and the safety of an electric power system are increasingly strong, the safety and the reliability of electric power facilities are guaranteed, and related accurate prediction and maintenance are one of the key problems which need to be solved urgently in intelligent operation and maintenance of the electric power system. Scholars at home and abroad establish a plurality of icing prediction models for the icing problem of the power transmission line, and from the principle perspective of the models, the icing prediction models can be roughly divided into a mechanism model, a traditional statistical model and an intelligent calculation model; the mechanism model focuses on the ice coating generation process, modeling analysis is carried out on the ice coating wire from the perspective of an internal mechanism by utilizing relevant theories of hydrodynamics, thermodynamics and meteorology, and the fact that the ice coating process is complex and changeable is considered, and the model has large deviation when the ice coating thickness is predicted under the same meteorology, so that the model is difficult to popularize in practical application; the traditional statistical model does not consider the actual icing process and physical mechanism, but predicts by carrying out statistical analysis on the past data; the traditional statistical model is only simple analysis and processing of data, and the generalization performance is not strong; the intelligent calculation model adopts an artificial intelligence algorithm to search for a way, but in fact, many intelligent calculation models ignore the effect of time accumulation and do not well dig out characteristic quantities contained in historical data, so that the accuracy of the prediction of the icing thickness of the power transmission line is influenced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method and a device for predicting the icing thickness of a power transmission line.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a method for predicting the icing thickness of a power transmission line, which has the improvement that the method comprises the following steps:
acquiring a meteorological data predicted value of the power transmission line at a future moment;
inputting the future time to an autoregressive integral moving average model to obtain an initial value of the ice coating thickness output by the autoregressive integral moving average model;
inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model, and obtaining an icing thickness error value output by the support vector regression model;
and determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the error value of the icing thickness.
Preferably, the training process of the autoregressive integral moving average model includes:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and taking each historical moment in the first historical period in the first training data set as input data of an autoregressive integral sliding average model, taking the icing thickness of each power transmission line at each historical moment in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, training the autoregressive integral sliding average model, and obtaining the trained autoregressive integral sliding average model.
Preferably, the training process of the support vector regression model includes:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of a support vector regression model, taking icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
Further, the determining process of the icing thickness error of the power transmission line at each historical moment in the second historical period includes:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
Preferably, the meteorological data includes: temperature, humidity and wind speed.
Preferably, the determining a final predicted value of the icing thickness of the power transmission line at a future time according to the initial value of the icing thickness and the icing thickness error value includes:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
The invention provides a device for predicting the icing thickness of a power transmission line, which is improved by comprising the following steps:
the acquisition module is used for acquiring a meteorological data predicted value of the power transmission line at a future moment;
the first prediction module is used for inputting the future time to the autoregressive integral moving average model and acquiring an initial value of the icing thickness output by the autoregressive integral moving average model;
the second prediction module is used for inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model and obtaining the icing thickness error value output by the support vector regression model;
and the determining module is used for determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the icing thickness error value.
Preferably, the training process of the autoregressive integral moving average model includes:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and taking each historical moment in the first historical period in the first training data set as input data of an autoregressive integral sliding average model, taking the icing thickness of each power transmission line at each historical moment in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, training the autoregressive integral sliding average model, and obtaining the trained autoregressive integral sliding average model.
Preferably, the training process of the support vector regression model includes:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of a support vector regression model, taking icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
Further, the determining process of the icing thickness error of the power transmission line at each historical moment in the second historical period includes:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
Preferably, the meteorological data includes: temperature, humidity and wind speed.
Preferably, the determining module is specifically configured to:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
Compared with the closest prior art, the invention has the following beneficial effects:
according to the technical scheme provided by the invention, a meteorological data predicted value of the power transmission line at the future moment is obtained; inputting the future time to an autoregressive integral moving average model to obtain an initial value of the ice coating thickness output by the autoregressive integral moving average model; inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model, and obtaining an icing thickness error value output by the support vector regression model; determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the icing thickness error value; fitting the initial value of the icing thickness by predicting the error value of the icing thickness, so that the accuracy of predicting the icing thickness of the power transmission line is improved; meanwhile, the icing thickness prediction of the power transmission line can cope with the power grid emergency, so that the rapid response and processing are realized, the safe operation of the power grid is guaranteed, and meanwhile, the method has important significance for the disaster prevention and early warning of the power grid and is beneficial to popularization in practical application; the historical icing data acquired in the model training process fully considers the time accumulation effect, the characteristic quantity contained in the historical icing data is well excavated, and the method has important significance for improving the accuracy of the power transmission line icing thickness prediction.
Drawings
FIG. 1 is a flow chart of a method of predicting icing thickness of a power transmission line;
FIG. 2 is a model structure diagram of an icing thickness prediction method of a power transmission line in a preferred embodiment of the present invention;
fig. 3 is a structural diagram of an icing thickness prediction apparatus of a power transmission line.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Because the existing ice coating thickness prediction technology of the power transmission line is based on a mechanism model or a statistical model, the construction and prediction precision of the existing ice coating thickness prediction technology cannot meet the requirements of practical application easily, and the related intelligent calculation model neglects the effect of time accumulation; the invention provides a method for predicting the icing thickness of a power transmission line based on the icing thickness prediction of the power transmission line in the intelligent operation and maintenance of a power system, as shown in figure 1, the method comprises the following steps:
103, inputting the meteorological data predicted value of the power transmission line at the future moment into a support vector regression model, and obtaining an icing thickness error value output by the support vector regression model;
and 104, determining a final predicted value of the icing thickness of the power transmission line at the future moment according to the initial value of the icing thickness and the icing thickness error value.
Preferably, the training process of the autoregressive integral moving average model includes:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and training the autoregressive integral sliding average model by taking each historical moment in the first historical period in the first training data set as input data of the autoregressive integral sliding average model and taking the icing thickness of each power transmission line in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, so as to obtain the trained autoregressive integral sliding average model.
Preferably, the training process of the support vector regression model includes:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of the support vector regression model, taking the icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
Further, the process for determining the icing thickness error of the power transmission line at each historical moment in the second historical period comprises the following steps:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
In the best embodiment of the invention, the power transmission line is divided into a plurality of observation windows by using a sliding window method, and the ice coating thickness of the power transmission line at each historical moment in a first historical period, the ice coating thickness of the power transmission line at each historical moment in a second historical period and corresponding meteorological data are respectively obtained;
through the initial value Q of the icing thickness of the power transmission line at each historical moment in the second historical periodiAnd the icing thickness P of the power transmission line at each historical moment in the second historical periodiObtaining the icing thickness error of the power transmission line at each historical moment in the second historical time period according to the following formulai：i＝Qi-Pi；
And constructing a training data set required for training different models according to the sample data acquired in the embodiment.
Preferably, the meteorological data comprises: temperature, humidity and wind speed.
Preferably, the step of determining a final predicted value of the icing thickness of the power transmission line at a future time according to the initial value of the icing thickness and the icing thickness error value includes:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
In the best embodiment of the invention, under the same terrain conditions, microclimate data such as air humidity, temperature, wind speed and the like are also main reasons for influencing the icing of the power transmission line; the collected n-dimensional meteorological data is used as input, ice coating thickness errors of the power transmission lines at all historical moments in a second historical period carrying enough non-characteristics are used as output, and a nonlinear mapping phi is formed: phi (feature)1,feature2,…,featuren) And utilizing a support vector regression model to extract the nonlinearity in the residual error time sequence.
The method comprises the steps of performing fixed-order modeling for autoregressive integrated moving average (ARIMA for short) through an icing time sequence curve, predicting the linear part of the icing thickness of the power transmission line by using a rolling prediction method, removing the nonlinear error between a predicted value and a true value in a regression fitting sliding process by using a trained support vector regression (SVR for short), and using the obtained error value for correcting the linear predicted value of the ARIMA; therefore, the predicted value pred of the ice coating thickness of the final transmission linefinalCan be expressed as a linear prediction value predlinearPlus a non-linear prediction error prednon-linear：predfinal＝predlinear+prednon-linear。
Based on the technical scheme provided by the invention, the best embodiment of the invention also provides a model diagram of the method for predicting the icing thickness of the power transmission line, which is shown in fig. 2.
In the training of the model, in order to expand training data and fuse related features, the thought of a sliding window is utilized to carry out feature observation on local data for multiple times, the length of an observation window selected in the embodiment is 20, the moving step length is 1, historical icing data is intercepted in a sliding mode with a fixed step length to serve as the input of an ARIMA model, and historical features and future features are divided according to the position of a current window, wherein the selection of ARIMA model parameters follows a classical Box-Jenkins method and comprises model identification, parameter estimation and model inspection; secondly, selecting microclimate data characteristics 6 hours after the window as input, taking the difference between a true value of historical icing data and a predicted value of an ARIMA model as output, adding the difference into a training sample of the SVR, and finishing the accumulation of a historical data nonlinear error;
error e ═ f ({ x) in fig. 221,x22,…,x26)-ARIMA({x1,x2,…,x20H), wherein x is the timing sequence of the icing thickness of the transmission line, f (-) is a nonlinear error operator to be fitted, and ARIMA (-) is an ARIMA model function.
Continuously moving the window so as to accumulate input and output data of the non-linear model SVR, and globally searching for an optimal SVR parameter by adopting a CuckooSearch (CS for short); wherein, the optimization function is selected as a prediction mean square error (MSE for short), and a (C, gamma) pair which enables the MSE to be minimum in a parameter range is found by utilizing grid search and is used as a super parameter of the SVR; the reference search ranges for C and γ of this example are [0.1,1000] and [0.01,100], respectively; when a Levy flight program is constructed, acquiring the random step length s of the cuckoo by using a Mantegna method:
wherein, the normal distribution v-N (0,1), the normal distribution u-N (0, sigma)2)，
β∈[1,3]。
In the test of the model, the future time of the power transmission line and the microclimate data which need to be predicted are converted into a digital type which is easy to process by the model by adopting a conventional data preprocessing method, normalization processing is carried out, then a linear predicted value and a nonlinear predicted value are obtained by the trained ARIMA model and the trained CSSVR model respectively, and the sum of the linear predicted value and the nonlinear predicted value is the final predicted value of the model.
The invention provides a device for predicting the icing thickness of a power transmission line, which comprises the following components as shown in figure 3:
the acquisition module is used for acquiring a meteorological data predicted value of the power transmission line at a future moment;
the first prediction module is used for inputting the future time to the autoregressive integral moving average model and acquiring an initial value of the icing thickness output by the autoregressive integral moving average model;
the second prediction module is used for inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model and obtaining the icing thickness error value output by the support vector regression model;
and the determining module is used for determining the final predicted value of the icing thickness of the power transmission line at the future moment according to the initial value of the icing thickness and the icing thickness error value.
Preferably, the training process of the autoregressive integral moving average model includes:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and training the autoregressive integral sliding average model by taking each historical moment in the first historical period in the first training data set as input data of the autoregressive integral sliding average model and taking the icing thickness of each power transmission line in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, so as to obtain the trained autoregressive integral sliding average model.
Preferably, the training process of the support vector regression model includes:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of the support vector regression model, taking the icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
Further, the process for determining the icing thickness error of the power transmission line at each historical moment in the second historical period comprises the following steps:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
Preferably, the meteorological data comprises: temperature, humidity and wind speed.
Preferably, the determining module is specifically configured to:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. A method for predicting icing thickness of a power transmission line, the method comprising:
acquiring a meteorological data predicted value of the power transmission line at a future moment;
inputting the future time to an autoregressive integral moving average model to obtain an initial value of the ice coating thickness output by the autoregressive integral moving average model;
inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model, and obtaining an icing thickness error value output by the support vector regression model;
and determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the error value of the icing thickness.
2. The method of claim 1, wherein the training process of the autoregressive integrated moving average model comprises:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and taking each historical moment in the first historical period in the first training data set as input data of an autoregressive integral sliding average model, taking the icing thickness of each power transmission line at each historical moment in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, training the autoregressive integral sliding average model, and obtaining the trained autoregressive integral sliding average model.
3. The method of claim 1, wherein the training process for the support vector regression model comprises:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of a support vector regression model, taking icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
4. The method of claim 3, wherein the determining of the icing thickness error of the power transmission line at each historical time in the second historical period comprises:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
5. The method of claim 1, wherein the meteorological data comprises: temperature, humidity and wind speed.
6. The method of claim 1, wherein determining a final predicted value of ice coating thickness of the transmission line at a future time based on the initial value of ice coating thickness and the error value of ice coating thickness comprises:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
7. An apparatus for predicting an icing thickness of a power transmission line, the apparatus comprising:
the acquisition module is used for acquiring a meteorological data predicted value of the power transmission line at a future moment;
the first prediction module is used for inputting the future time to the autoregressive integral moving average model and acquiring an initial value of the icing thickness output by the autoregressive integral moving average model;
the second prediction module is used for inputting the meteorological data predicted value of the power transmission line at the future moment into the support vector regression model and obtaining the icing thickness error value output by the support vector regression model;
and the determining module is used for determining a final predicted value of the icing thickness of the power transmission line at a future moment according to the initial value of the icing thickness and the icing thickness error value.
8. The apparatus of claim 7, wherein the training process of the autoregressive integrated moving average model comprises:
constructing a first training data set by using the icing thickness of the power transmission line at each historical moment in a first historical time period;
and taking each historical moment in the first historical period in the first training data set as input data of an autoregressive integral sliding average model, taking the icing thickness of each power transmission line at each historical moment in the first historical period in the first training data set as output data of the autoregressive integral sliding average model, training the autoregressive integral sliding average model, and obtaining the trained autoregressive integral sliding average model.
9. The apparatus of claim 7, wherein the training process of the support vector regression model comprises:
constructing a second training data set by using the ice coating thickness error of the power transmission line at each historical moment in a second historical period and meteorological data corresponding to the power transmission line at each historical moment in the second historical period;
and taking meteorological data corresponding to the power transmission lines at the historical moments in the second historical period in the second training data set as input data of a support vector regression model, taking icing thickness errors of the power transmission lines at the historical moments in the second historical period as output data of the support vector regression model, optimizing the support vector regression model by adopting a Cuckoo search algorithm, training the support vector regression model, and obtaining the trained support vector regression model.
10. The apparatus of claim 9, wherein the determining of the ice thickness error of the transmission line at each historical time in the second historical period comprises:
acquiring the icing thickness of the power transmission line at each historical moment in a second historical time period;
inputting each historical moment in the second historical period to the trained autoregressive integral sliding average model to obtain an initial value of the icing thickness of the power transmission line at each historical moment in the second historical period output by the trained autoregressive integral sliding average model;
and taking the difference value between the initial value of the icing thickness of the power transmission line at each historical moment in the second historical period and the icing thickness of the power transmission line at each historical moment in the second historical period as the icing thickness error of the power transmission line at each historical moment in the second historical period.
11. The apparatus of claim 7, wherein the meteorological data comprises: temperature, humidity and wind speed.
12. The apparatus of claim 7, wherein the determination module is specifically configured to:
determining the final predicted value H of the icing thickness of the power transmission line at the future moment k according to the following formulak：
Hk＝Yk+Gk
Wherein, YkFor an initial value of the icing thickness, G, of the transmission line at a future time kkAnd the icing thickness error value of the power transmission line at the future moment k is obtained.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128888A (en) * | 2021-04-26 | 2021-07-16 | 国网湖北省电力有限公司宜昌供电公司 | Power transmission line icing prediction method based on icing characteristic variable box grading card |
| CN113298874A (en) * | 2021-07-26 | 2021-08-24 | 广东工业大学 | Power transmission line safety distance risk assessment method and device based on unmanned aerial vehicle inspection |
| CN113554473A (en) * | 2021-08-11 | 2021-10-26 | 上海明略人工智能(集团)有限公司 | Information search amount prediction method and device, electronic equipment and readable storage medium |
| CN117932914A (en) * | 2024-01-16 | 2024-04-26 | 重庆大学 | Power transmission line conductor icing prediction method based on icing thickness dynamic matching |
| CN117932914B (en) * | 2024-01-16 | 2024-06-28 | 重庆大学 | Power transmission line conductor icing prediction method based on icing thickness dynamic matching |
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2020
- 2020-08-11 CN CN202010799590.4A patent/CN112070272A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128888A (en) * | 2021-04-26 | 2021-07-16 | 国网湖北省电力有限公司宜昌供电公司 | Power transmission line icing prediction method based on icing characteristic variable box grading card |
| CN113298874A (en) * | 2021-07-26 | 2021-08-24 | 广东工业大学 | Power transmission line safety distance risk assessment method and device based on unmanned aerial vehicle inspection |
| CN113554473A (en) * | 2021-08-11 | 2021-10-26 | 上海明略人工智能(集团)有限公司 | Information search amount prediction method and device, electronic equipment and readable storage medium |
| CN117932914A (en) * | 2024-01-16 | 2024-04-26 | 重庆大学 | Power transmission line conductor icing prediction method based on icing thickness dynamic matching |
| CN117932914B (en) * | 2024-01-16 | 2024-06-28 | 重庆大学 | Power transmission line conductor icing prediction method based on icing thickness dynamic matching |
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