CN113792923A - Visual scheduling window method based on power failure sensitivity prediction model - Google Patents
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Abstract
The invention discloses a visual scheduling window method based on a power failure sensitivity prediction model, which comprises the steps of constructing the power failure sensitivity prediction model based on a random forest algorithm; acquiring schedulable resources of the current distribution network in power failure by using the power failure sensitivity prediction model, and uploading the schedulable resources to the distribution network system; the distribution network system generates a structure view in a self-adaptive manner according to a network topological structure, and correspondingly displays a power failure area in the structure view through an associated icon; and the power grid maintenance personnel can reasonably schedule the schedulable resources according to the displayed associated icons. According to the invention, random sampling training calculation is carried out on the power failure sensitive source through a random forest algorithm to obtain a better average parameter, so that resource scheduling is reasonably analyzed according to the better average parameter, and the positions in the association diagram are fused by generating a visual topological structure diagram, so that maintenance personnel can be more accurately helped to make a resource scheduling plan.
Description
Technical Field
The invention relates to the technical field of power failure visual scheduling, in particular to a visual scheduling window method based on a power failure sensitivity prediction model.
Background
Along with social development, requirements of people on power demand and power supply reliability are increasingly improved, meanwhile, the structure of a power grid is increasingly complex, internal management in the power industry is continuously and deeply promoted, and particularly, higher requirements are provided for scheduling plan arrangement.
The current distribution network system lacks effective means and a unified software platform, the dispatching plan is simply dependent on manual examination and low in offline communication efficiency, multi-department and cross-professional work coordination is very difficult, the efficiency needs to be improved urgently, and in addition, the system is limited by professional barriers, data of all parties are difficult to be communicated, each word is difficult to say, the caliber is disordered, and great difficulty is brought to statistical analysis; when some power failure sensitive sources are faced, hands and feet are overwhelmed, and resources cannot be reasonably scheduled, which causes immeasurable loss to a distribution network system.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a visual scheduling window method based on a power failure sensitivity prediction model, which can solve the problem that the reasonable scheduling of power grid resources is influenced because the current power failure sensitive source cannot be accurately judged.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of constructing a power failure sensitivity prediction model based on a random forest algorithm; acquiring schedulable resources of the current distribution network in power failure by using the power failure sensitivity prediction model, and uploading the schedulable resources to the distribution network system; the distribution network system generates a structure view in a self-adaptive manner according to a network topological structure, and correspondingly displays a power failure area in the structure view through an associated icon; and the power grid maintenance personnel can reasonably schedule the schedulable resources according to the displayed associated icons.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the power failure sensitivity prediction model is constructed by acquiring power failure data and power supply influence factor data of the power system; cleaning, characteristic marking and screening the data, and comprehensively analyzing the characteristics of the power failure sensitive nodes to obtain power failure sensitive characteristic marking information; and substituting the power failure sensitivity characteristic mark information into the random forest algorithm for test training, and outputting to obtain the power failure sensitivity prediction model.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the random forest algorithm comprises the steps of carrying out random data sampling on the collected power failure data and the collected power supply influence factor data; constructing different data training sets according to different sampling data to carry out training tests; generating different power failure sensitivity prediction models based on the training test, and outputting different prediction results through calculation of the different models; and summarizing all the different prediction results, carrying out average evaluation, and outputting a final prediction result.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein f (x) is an average function of the aggregated blackout sensitivity prediction models, fm(x) For summaryAnd M is a constant and M is the number of samples in the power failure sensitivity prediction model function.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the current distribution network power failure schedulable resources comprise branch line data, power failure information, equipment defects, coal-to-electricity line conditions, power failure maintenance plan information of power system management, power grid scheduling plan information and work order information of each power consumption unit.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the uploading to the distribution network system comprises the steps of carrying out normalization processing on the acquired schedulable resources of the current distribution network power failure, and forming a table corresponding to rows and columns for storage; and connecting the form with the distribution network system through a communication port, and uploading the form content.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the distribution network system generates a structural view in a self-adaptive manner according to a network topological structure; importing the power failure sensitivity prediction model to perform power failure area marking association to form associated icon display; and performing operation processing on the node icons through the fusion judgment strategy, and outputting to obtain the node icons needing to be associated.
As an optimal scheme of the visual scheduling window method based on the power outage sensitivity prediction model, the method comprises the following steps: the fusion judgment strategy comprises the following steps of,
wherein f ism: model requiring fusion, round: rounding off, and m is a constant.
The invention has the beneficial effects that: according to the invention, random sampling training calculation is carried out on the power failure sensitive source through a random forest algorithm to obtain a better average parameter, so that resource scheduling is reasonably analyzed according to the better average parameter, and the positions in the association diagram are fused by generating a visual topological structure diagram, so that maintenance personnel can be more accurately helped to make a resource scheduling plan.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a flowchart illustrating a visual dispatch window method based on a blackout sensitivity prediction model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an accuracy comparison test curve of a visual scheduling window method based on a power outage sensitivity prediction model according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a visual scheduling window method based on a blackout sensitivity prediction model, including:
s1: and constructing a power failure sensitivity prediction model based on a random forest algorithm. It should be noted that, the building of the blackout sensitivity prediction model includes:
acquiring power failure data and power supply influence factor data of a power system;
cleaning, marking and screening the data, and comprehensively analyzing the characteristics of the power failure sensitive nodes to obtain power failure sensitive characteristic marking information;
and substituting the power failure sensitivity characteristic mark information into a random forest algorithm for test training, and outputting to obtain a power failure sensitivity prediction model.
Specifically, the random forest algorithm includes:
carrying out random data sampling on the collected power failure data and power supply influence factor data;
constructing different data training sets according to different sampling data to carry out training tests;
generating different power failure sensitivity prediction models based on training tests, and outputting different prediction results through calculation of different models;
and summarizing all different prediction results, carrying out average evaluation, and outputting a final prediction result.
Further, the method also comprises the following steps:
wherein f (x) is an average function of the aggregated outage sensitivity prediction models, fm(x) For the summarized blackout sensitivity prediction model function, M is a constant and M is the number of samples.
S2: and acquiring the schedulable resources of the current distribution network in power failure by using the power failure sensitivity prediction model, and uploading the schedulable resources to the distribution network system. The steps to be explained are as follows:
the current distribution network power failure schedulable resources comprise branch line data, power failure information, equipment defects, coal-to-electricity line conditions, power failure maintenance plan information of power system management, power grid scheduling plan information and work order information of each power consumption unit.
Further, upload to distribution network system includes:
normalizing the acquired schedulable resources of the current distribution network in power failure to form a table corresponding to rows and columns for storage;
and connecting the network distribution system through the communication port, and uploading the table content.
S3: and the distribution network system generates a structure view in a self-adaptive manner according to the network topological structure, and correspondingly displays the power failure area in the structure view through the associated icon. Among them, it is also to be noted that:
the distribution network system generates a structural view in a self-adaptive manner according to the network topological structure;
importing a power failure sensitivity prediction model to perform power failure area marking association to form associated icon display;
and performing operation processing on the node icons through the fusion judgment strategy, and outputting to obtain the node icons needing to be associated.
Specifically, the fusion judgment strategy includes:
wherein f ism: model requiring fusion, round: rounding off, and m is a constant.
S4: and the power grid maintenance personnel can reasonably schedule the schedulable resources according to the displayed associated icons.
Preferably, the method carries out random sampling training calculation on the power failure sensitive source through a random forest algorithm to obtain a better average parameter, carries out reasonable analysis on resource scheduling according to the better average parameter, and combines positions in the association diagram through generating a visual topological structure diagram to more accurately help maintenance personnel to make a resource scheduling plan.
Example 2
In order to better verify and explain the technical effects adopted in the method, the embodiment selects the traditional power failure prediction manual scheduling method to perform a comparative test with the method of the invention so as to verify the real effect of the method of the invention; in order to verify that the method has higher prediction precision and more comprehensive prediction range compared with the traditional method, the traditional method and the method are adopted in the embodiment to respectively test and compare different power failure sensitive source data in a certain area.
And (3) testing environment: (1) the traditional manual power failure prediction scheduling method reads data source extraction attribute features and utilizes a prediction model to output feature prediction for visual analysis;
(2) the two methods adopt Python to write a program and MATLB to run a simulation output data curve;
(3) the method of the invention utilizes a plurality of models to predict the power failure sensitive source in the area, combines the output results of the fusion models to calculate the average value to obtain the final prediction result,
referring to fig. 2, a solid line is a curve output by the method of the present invention, a dotted line is a curve output by a conventional method, and according to the schematic diagram of fig. 2, it can be seen intuitively that the method of the present invention has a very high accuracy in the comprehensive detection for various data compared to the conventional method, and the prediction accuracy of the dotted line is decreased linearly when predicting a large amount of data, and the amplitude is large, which illustrates the error of the conventional method.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A visual scheduling window method based on a power failure sensitivity prediction model is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
establishing a power failure sensitivity prediction model based on a random forest algorithm;
acquiring schedulable resources of the current distribution network in power failure by using the power failure sensitivity prediction model, and uploading the schedulable resources to the distribution network system;
the distribution network system generates a structure view in a self-adaptive manner according to a network topological structure, and correspondingly displays a power failure area in the structure view through an associated icon;
and the power grid maintenance personnel can reasonably schedule the schedulable resources according to the displayed associated icons.
2. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 1, wherein: constructing the outage sensitivity prediction model may include,
acquiring power failure data and power supply influence factor data of a power system;
cleaning, characteristic marking and screening the data, and comprehensively analyzing the characteristics of the power failure sensitive nodes to obtain power failure sensitive characteristic marking information;
and substituting the power failure sensitivity characteristic mark information into the random forest algorithm for test training, and outputting to obtain the power failure sensitivity prediction model.
3. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 2, wherein: the random forest algorithm comprises the steps of,
carrying out random data sampling on the collected power failure data and the power supply influence factor data;
constructing different data training sets according to different sampling data to carry out training tests;
generating different power failure sensitivity prediction models based on the training test, and outputting different prediction results through calculation of the different models;
and summarizing all the different prediction results, carrying out average evaluation, and outputting a final prediction result.
4. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 2 or 3, wherein: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein f (x) is the aggregated outage sensitivity predictionMean function of the model, fm(x) For the summarized blackout sensitivity prediction model function, M is a constant and M is the number of samples.
5. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 4, wherein: the current distribution network power failure schedulable resources comprise branch line data, power failure information, equipment defects, coal-to-electricity line conditions, power failure maintenance plan information of power system management, power grid scheduling plan information and work order information of each power consumption unit.
6. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 5, wherein: the uploading to the distribution network system comprises that,
normalizing the acquired schedulable resources of the current distribution network in power failure to form a table corresponding to rows and columns for storage;
and connecting the form with the distribution network system through a communication port, and uploading the form content.
7. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 6, wherein: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the distribution network system generates a structural view in a self-adaptive manner according to a network topological structure;
importing the power failure sensitivity prediction model to perform power failure area marking association to form associated icon display;
and performing operation processing on the node icons through the fusion judgment strategy, and outputting to obtain the node icons needing to be associated.
8. The visual dispatch window method based on blackout sensitivity prediction model as claimed in claim 7, wherein: the fusion judgment strategy comprises the following steps of,
wherein f ism: model requiring fusion, round: rounding off, and m is a constant.
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