CN116244964B - Power distribution network storm disaster power failure prediction method based on numerical simulation and SVD model - Google Patents
Power distribution network storm disaster power failure prediction method based on numerical simulation and SVD model Download PDFInfo
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Abstract
The invention relates to a power failure prediction method for a power distribution network heavy wind disaster based on numerical simulation and SVD model, which comprises the following steps: matching body parameters, meteorological parameters, running state parameters and outage probability and duration data after the occurrence of a power failure fault under the condition of a strong wind disaster, and establishing a power distribution network historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B; based on the sample library, an SVD method is adopted to construct an SVD model of influence of a strong wind disaster on power failure faults of the power distribution network; adopting a regional numerical mode, and carrying out numerical simulation by combining an initial boundary field provided by global weather forecast data to obtain a weather parameter forecast value of a target region; and forecasting the power failure probability and the power failure time under the strong wind disaster of the power distribution network based on the SVD model and the meteorological parameter forecasting value. The invention realizes the refined large wind disaster forecast based on numerical simulation and statistical method, and is suitable for disaster prevention and reduction of various complicated distribution networks.
Description
Technical Field
The invention belongs to the technical field of power grid disaster prevention, and particularly relates to a power failure prediction method for a power distribution network disaster damage based on numerical simulation and SVD (singular value decomposition) model.
Background
The power grid is an important basic industry related to national and civil works, but due to the self structure and operation characteristics, the smooth operation of related hardware facilities can be obviously influenced by surrounding meteorological elements. In the global warming background, disastrous weather climate events frequently occur, and natural disasters such as strong wind, storm, thunder and lightning cause the normal operation environment of hardware facilities such as a power distribution network to be severely challenged. The probability of occurrence of the wind disaster is higher, the influence range is wider, the damage is larger, faults such as broken lines, inverted towers and windage flashover of power equipment can be directly caused, sundries such as branches, cloth strips and balloons can be wound on the wires, and indirect damage is caused to a power transmission line. If the relevant early warning can be issued in time, preparation is made in advance, and enough time is striven for the rush repair and protection power supply work of the lines in the region affected by the strong wind, so that the method is important for ensuring the safe and reliable power supply of the regional power grid. Therefore, the method for forecasting the power failure of the power distribution network in the case of the storm disaster is provided.
In the meteorological field, along with the continuous development of a numerical weather forecast mode, the accuracy of forecasting each meteorological element in a weather scale is greatly improved, and a kilometer-level strong wind forecast product is correspondingly formed. However, as the topography and topography of the distribution network are complex and various, micro topography and micro meteorological features are commonly present, if strong wind early warning is required to be carried out on each distribution network in an area, the horizontal resolution of the current numerical forecasting product is still difficult to meet the requirement. Meanwhile, each power distribution network also lacks an effective strong wind monitoring means, and an effective statistical prediction model is difficult to construct based on a single power distribution network. Therefore, a refined weather forecast and disaster early warning technology facing the power distribution network is to be researched. If the method can combine the coarse resolution meteorological elements with the historical fault data of the distribution network in the area based on the numerical forecasting product and the distribution network fault data set, the time-space connection of the coarse resolution meteorological elements and the historical fault data of the distribution network in the area is established, and the method has important significance in realizing the fine and large wind disaster forecasting and early warning of the distribution network.
Disclosure of Invention
Aiming at the defects of the existing method, the power failure prediction method for the large wind disaster of the power distribution network based on the numerical simulation and SVD model realizes the fine large wind disaster prediction based on the numerical simulation and statistical method, and is suitable for disaster prevention and reduction of the power distribution network with complex conditions.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a power failure prediction method for a power distribution network storm disaster based on numerical simulation and SVD model comprises the following steps:
collecting a historical meteorological parameter data set, acquiring body parameters and running state parameters of a power distribution network, and establishing the running state data set of the power distribution network;
matching body parameters, meteorological parameters, running state parameters and outage probability and duration data after the occurrence of a power failure fault under the condition of a strong wind disaster, and establishing a power distribution network historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B;
based on a historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B, an SVD (singular value decomposition) model of influence of strong wind disasters on power distribution network power failure faults is built by adopting an SVD method;
adopting a regional numerical mode, and carrying out numerical simulation by combining an initial boundary field provided by global weather forecast data to obtain a weather parameter forecast value of a target region;
and forecasting the outage probability and the outage duration of the power distribution network under the strong wind disaster based on the SVD model of the influence of the strong wind disaster on the power distribution network outage fault and the weather parameter forecast value of the target area.
Preferably, the body parameters include tower information and line information of the power distribution network.
Preferably, the tower information comprises tower type, number, longitude, latitude, altitude, manufacturer, installation position, geological environment information, horizontal span, vertical span, strain tower rotation, insulator string model number, string number and insulator sheet number angle of the tower.
Preferably, the line information includes line number, voltage class, line number, line name, start-stop location, line specification, line type, number of loops, power transmission length, design wind speed, design ice thickness, number of splits and gap of splits.
Preferably, the meteorological parameters include wind speed and wind direction.
Preferably, the SVD model construction steps of the influence of the heavy wind disaster on the power failure fault of the power distribution network are as follows:
s1, inputting a historical meteorological parameter sample library A of a power distribution network and a strong wind fault sample library B of the power distribution network:
A＝(A 11 ,A 12 ,…,A 21 ,…,A ij ,…,A NM ),
B＝(B 11 ,B 12 ,…,B 21 ,…,B ik ,…,B NS )，
wherein A is ij Is the meteorological parameter of the jth lattice point in the ith time sample, B ik Inputting parameters for a fault sample of a kth power distribution network in an ith time sample, namely a power distribution network body parameter, an operation state parameter, a power failure probability and a power failure time length when a strong wind disaster fault sample occurs;
s2, performing singular value decomposition on covariance matrixes of the historical meteorological parameter sample library A of the power distribution network and the strong wind fault sample library B of the power distribution network to obtain a time coefficient X of a main mode of the historical meteorological parameter sample library A of the power distribution network:
X＝(X 1 ,…,X i ,…,X N )，
wherein X is N A coefficient corresponding to an nth time sample representing a main mode of the meteorological parameter;
and further obtaining a correlation coefficient C of the main space modes of X and B, namely, the influence weight on each power distribution network:
C＝(C 1 ,…,C i ,…,C S )，
wherein C is S Representing a correlation coefficient corresponding to the S-th power distribution network;
s3, constructing a prediction model by using the C:
wherein x is i For the meteorological parameter of the ith time sample,is the average value of meteorological parameters, y i The method is a predicted value of power failure probability or power failure duration of the power distribution network under the disaster of strong wind.
Preferably, the method also comprises the step of dividing the wind disaster into a plurality of grades by combining the power failure probability and the power failure duration under the large wind disaster of the power distribution network, and carrying out wind disaster early warning.
A power distribution network storm disaster power failure prediction system based on numerical simulation and SVD model comprises: the power distribution network disaster damage prediction method based on numerical simulation and SVD model comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes any one of the above methods when executing the computer program.
A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for predicting a power outage of a power distribution network based on numerical simulation and SVD model according to any one of the above.
The invention has the positive beneficial effects that:
1. the invention discloses a power failure prediction method for a power distribution network with a large wind disaster based on numerical simulation and SVD (scalable vector graphics) models, which adopts a history meteorological parameter data set and a power distribution network running state data set which are easy to acquire, establishes a statistical relationship model of meteorological elements and each power distribution network in space and time through the SVD method, namely establishes an SVD model of influence of the large wind disaster on the power distribution network with the power failure, distributes the influence of the coarse resolution meteorological elements to each power distribution network in a target area according to weights, and fully utilizes the meteorological information of surrounding environment when the power distribution network fails; then, integrating to obtain a weather parameter forecast value of kilometer level by utilizing an area numerical mode and matching with an initial boundary field provided by global weather forecast data; finally, combining the weather parameter forecast value of the target area with the SVD model, a microclimate forecast product for a single power distribution network can be obtained, so that the forecast of the power failure probability and the power failure time of the refined wind disaster of each power distribution network in the target area is realized, the wind disaster can be classified based on the power failure probability and the power failure time under the wind disaster, and the warning of the wind disaster of the power distribution network is realized according to the forecast result. The invention realizes the refined large wind disaster forecast based on numerical simulation and statistical method, and is suitable for disaster prevention and reduction of various complicated distribution networks.
Drawings
FIG. 1 is a flow chart of a method for forecasting power failure of a power distribution network in a storm disaster based on numerical simulation and SVD model.
Detailed Description
The invention will be further illustrated with reference to a few specific examples.
Example 1
A power failure prediction method for a power distribution network heavy wind disaster based on numerical simulation and SVD model, see FIG. 1, comprises the following steps:
collecting a historical meteorological parameter data set, acquiring body parameters and running state parameters of a power distribution network, and establishing the running state data set of the power distribution network;
matching body parameters, meteorological parameters, running state parameters and outage probability and duration data after the occurrence of a power failure fault under the condition of a strong wind disaster, and establishing a power distribution network historical meteorological parameter sample library A (comprising meteorological parameters) and a power distribution network strong wind fault sample library B (comprising body parameters, running state parameters and outage probability and duration data after the occurrence of the fault);
based on a historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B, analyzing the influence of strong wind on each power distribution network in a target area by adopting an SVD method, and constructing an SVD model of the influence of strong wind disaster on power failure faults of the power distribution network;
adopting a regional numerical mode, combining an initial boundary field provided by global weather forecast data, and performing numerical simulation to obtain a weather parameter forecast value of a target region, thus obtaining a lattice-like strong wind forecast product;
forecasting the power failure probability and the power failure time under the strong wind disaster of the power distribution network based on the SVD model and the latticed strong wind forecasting products of the strong wind disaster on the power failure fault of the power distribution network;
and combining the power failure probability and the power failure duration of the power distribution network under the severe wind disaster, classifying the wind disaster into a plurality of grades, and carrying out wind disaster early warning.
Further, the body parameters include tower information and line information of the power distribution network.
Further, the tower information comprises tower type, number, longitude, latitude, altitude, manufacturer, installation position, geological environment information, horizontal span, vertical span, strain tower rotation, insulator string model number, string number and insulator sheet number angle of the tower.
Further, the line information includes line number, voltage class, line number, line name, start-stop location, line specification, line type, number of loops, power transmission length, design wind speed, design ice thickness, number of splits, and split gap.
Further, the meteorological parameters include wind speed magnitude and wind direction.
Further, the SVD model construction steps of the influence of the heavy wind disaster on the power failure fault of the power distribution network are as follows:
s1, inputting a historical meteorological parameter sample library A of a power distribution network and a strong wind fault sample library B of the power distribution network:
A＝(A 11 ,A 12 ,…,A 21 ,…,A ij ,…,A NM ),
B＝(B 11 ,B 12 ,…,B 21 ,…,B ik ,…,B NS )，
wherein A is ij Is the meteorological parameter of the jth lattice point in the ith time sample, B ik Inputting parameters for a fault sample of a kth power distribution network in an ith time sample, namely a power distribution network body parameter, an operation state parameter, a power failure probability and a power failure time length when a strong wind disaster fault sample occurs;
s2, performing singular value decomposition on covariance matrixes of the historical meteorological parameter sample library A of the power distribution network and the strong wind fault sample library B of the power distribution network to obtain a time coefficient X of a main mode of the historical meteorological parameter sample library A of the power distribution network:
X＝(X 1 ,…,X i ,…,X N )，
wherein X is N A coefficient corresponding to an nth time sample representing a main mode of the meteorological parameter;
and further obtaining a correlation coefficient C of the main space modes of X and B, namely, the influence weight on each power distribution network:
C＝(C 1 ,…,C i ,…,C S )，
wherein C is S Representing a correlation coefficient corresponding to the S-th power distribution network;
s3, constructing a prediction model by using the C:
wherein x is i For the meteorological parameter of the ith time sample,is the average value of meteorological parameters, y i The method is a predicted value of power failure probability or power failure duration under the condition of large wind disaster of the power distribution network.
A power distribution network storm disaster power failure prediction system based on numerical simulation and SVD model comprises: the power distribution network disaster damage prediction method based on numerical simulation and SVD model comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes any one of the above methods when executing the computer program.
The processor and the memory may be connected by a bus or other means.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
It should be noted that, the power failure prediction system of the power distribution network based on numerical simulation and SVD model in this embodiment may include a service processing module, an edge database, a server version information register, and a data synchronization module, where the processor implements the power failure prediction method of the power distribution network based on numerical simulation and SVD model when executing a computer program.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for predicting a power outage of a power distribution network based on numerical simulation and SVD model according to any one of the above.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
Finally, it is noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and that other modifications and equivalents thereof by those skilled in the art should be included in the scope of the claims of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. A power failure prediction method for a power distribution network heavy wind disaster based on numerical simulation and SVD model is characterized by comprising the following steps:
collecting a historical meteorological parameter data set, acquiring body parameters and running state parameters of a power distribution network, and establishing the running state data set of the power distribution network;
matching body parameters, meteorological parameters, running state parameters and outage probability and duration data after the occurrence of a power failure fault under the condition of a strong wind disaster, and establishing a power distribution network historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B;
based on a historical meteorological parameter sample library A and a power distribution network strong wind fault sample library B, an SVD (singular value decomposition) model of influence of strong wind disasters on power distribution network power failure faults is built by adopting an SVD method;
adopting a regional numerical mode, and carrying out numerical simulation by combining an initial boundary field provided by global weather forecast data to obtain a weather parameter forecast value of a target region;
forecasting the power failure probability and the power failure time under the strong wind disaster of the power distribution network based on the SVD model of the influence of the strong wind disaster on the power failure fault of the power distribution network and the weather parameter forecasting value of the target area;
the SVD model construction method for the influence of the large wind disaster on the power failure fault of the power distribution network comprises the following steps:
s1, inputting a historical meteorological parameter sample library A of a power distribution network and a strong wind fault sample library B of the power distribution network:
A＝(A 11 ,A 12 ,…,A 21 ,…,A ij ,…,A NM ),
B＝(B 11 ,B 12 ,…,B 21 ,…,B ik ,…,B NS )，
wherein A is ij Is the meteorological parameter of the jth lattice point in the ith time sample, B ik Inputting parameters for a fault sample of a kth power distribution network in an ith time sample, namely a power distribution network body parameter, an operation state parameter, a power failure probability and a power failure time length when a strong wind disaster fault sample occurs;
s2, performing singular value decomposition on covariance matrixes of the historical meteorological parameter sample library A of the power distribution network and the strong wind fault sample library B of the power distribution network to obtain a time coefficient X of a main mode of the historical meteorological parameter sample library A of the power distribution network:
X＝(X 1 ,…,X i ,…,X N )，
wherein X is N A coefficient corresponding to an nth time sample representing a main mode of the meteorological parameter;
and further obtaining a correlation coefficient C of the main space modes of X and B, namely, the influence weight on each power distribution network:
C＝(C 1 ,…,C i ,…,C S )，
wherein C is S Representing a correlation coefficient corresponding to the S-th power distribution network;
s3, constructing a prediction model by using the C:
wherein x is i For the meteorological parameter of the ith time sample,is the average value of meteorological parameters, y i The method is a predicted value of power failure probability or power failure duration of the power distribution network under the disaster of strong wind.
2. The method for predicting the power failure of the power distribution network in the storm disaster based on numerical simulation and SVD model as set forth in claim 1, wherein the body parameters comprise tower information and line information of the power distribution network.
3. The method for predicting the power failure of the power distribution network on the basis of numerical simulation and SVD (support vector machine) models according to claim 2, wherein the tower information comprises tower type, number, longitude, latitude, altitude, manufacturer, installation position, geological environment information, horizontal span, vertical span, strain tower rotation, and insulator string type, string number and insulator sheet number angle of the tower.
4. The method for predicting the power failure of the power distribution network in the storm disaster based on the numerical simulation and the SVD model according to claim 2, wherein the line information comprises a line serial number, a voltage grade, a line number, a line name, a start-stop place, a line specification, a line type, a loop number, a power transmission length, a design wind speed, a design ice thickness, a splitting number and a splitting gap.
5. The method for forecasting the power failure of the large wind disaster of the power distribution network based on numerical simulation and SVD model according to claim 1, wherein the meteorological parameters comprise wind speed and wind direction.
6. The method for predicting power failure of a large wind disaster of a power distribution network based on numerical simulation and SVD model according to any one of claims 1-5, further comprising classifying the wind disaster into a plurality of grades by combining the power failure probability and the power failure duration under the large wind disaster of the power distribution network, and carrying out wind disaster early warning.
7. The utility model provides a distribution network strong wind disaster power failure prediction system based on numerical simulation and SVD model which characterized in that includes: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for predicting power failure of a power distribution network based on numerical simulation and SVD model according to any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the method for predicting a power outage of a power distribution network based on numerical simulation and SVD model according to any one of claims 1 to 6.
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| 奇异值分析法对四川电网电压静稳极限的分析预测;吴国梁, 刘俊勇, 陈谦;四川电力技术(第01期);第1-4、17页 * |
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