CN113408816A - Power grid disaster situation evaluation method based on deep neural network - Google Patents

Power grid disaster situation evaluation method based on deep neural network Download PDF

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CN113408816A
CN113408816A CN202110763124.5A CN202110763124A CN113408816A CN 113408816 A CN113408816 A CN 113408816A CN 202110763124 A CN202110763124 A CN 202110763124A CN 113408816 A CN113408816 A CN 113408816A
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林爽
王裴培
唐钰翔
孙怡长
秦萃丽
毛健
余志强
邹勇
吴歧
李沛
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Abstract

The invention discloses a power grid disaster situation evaluation method based on a deep neural network, which comprises the steps of obtaining historical power grid disaster situation scene data for statistical analysis; preprocessing the historical power grid disaster situation scene data, and establishing a rating system by using disaster situation evaluation indexes; constructing a neural network model, and classifying the disaster according to the severity of the disaster to be used as a model for outputting; after the preprocessed sample data is loaded to the neural network model, selecting training parameters to train and learn the neural network model; and acquiring a new power grid scene in real time, inputting the new power grid scene to the trained neural network model, acquiring the disaster situation evaluation grade in real time, and solving the problem that the specific optimization calculation of the existing scene-based disaster situation evaluation optimization model is difficult to solve.

Description

Power grid disaster situation evaluation method based on deep neural network
Technical Field
The invention relates to the technical field of power grid safety, in particular to a power grid disaster situation evaluation method based on a deep neural network.
Background
In recent years, the operation environment of the power grid is increasingly severe and complex, strong convection disasters such as rain, snow, ice, storm, tide, typhoon, storm, thunder and lightning and the like, extreme weather (which refers to rare meteorological events in the history, small occurrence probability and large social influence) such as haze, strong wind, sand storm, mountain fire and the like, power utilization services and the influence of dangerous hidden dangers generated by power grid facility faults on the power grid are increasingly large. Hidden dangers of the power grid can not be known in time and remedial measures can not be taken, so that immeasurable loss can be caused in the operation of the power grid.
At present, a researcher carries out power grid disaster evaluation by establishing a disaster model, in the aspect of major disaster model research, scholars at home and abroad apply 3S and computer technology and combine various mathematical methods to form a large number of qualitative and quantitative disaster evaluation models, and along with the rapid development of the technology, a natural disaster evaluation model library gradually becomes an effective tool for disaster prevention and reduction, so that a scientific basis is provided for comprehensively mastering and analyzing disaster loss and for emergency management. However, the calculation effect of the existing disaster assessment optimization model based on the scene depends on the size of the number of scenes, and in order to fully represent the characteristics of random variables, the number of scenes directly obtained by a scene generation method is usually huge, so that the specific optimization calculation is difficult to solve, and the method becomes one of the problems to be solved in the field.
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 invention is provided in view of the problems of the existing disaster situation evaluation optimization model based on scenes.
Therefore, the technical problem solved by the invention is as follows: the problem that the specific optimization calculation of the existing scene-based disaster assessment optimization model faces difficulty in solving is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a power grid disaster situation assessment method based on a deep neural network comprises the steps of obtaining historical power grid disaster situation scene data and conducting statistical analysis; preprocessing the historical power grid disaster situation scene data, and establishing a rating system by using disaster situation evaluation indexes; constructing a neural network model, and classifying the disaster according to the severity of the disaster to be used as a model for outputting; after the preprocessed sample data is loaded to the neural network model, selecting training parameters to train and learn the neural network model; and acquiring a new power grid scene in real time, inputting the new power grid scene to the neural network model which completes training, and acquiring a disaster situation evaluation grade in real time.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: preprocessing the historical power grid disaster situation scene data comprises collecting disaster situation data, rainstorm data, fault data and power grid distribution data; pre-processing the collected data: data cleaning: empty value cleaning, format content cleaning, logic error cleaning and non-demand data cleaning; data transformation: carrying out feature construction, data grading and data quantization on data; data integration: carrying out data statistics on the data after data transformation, and merging the data into a unified data storage; and detecting and removing samples which are possibly abnormal in the data samples by adopting an outlier sample detection strategy based on clustering.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: the method for establishing a rating system by utilizing the disaster evaluation indexes comprises the following steps of,
Figure BDA0003149735790000021
wherein Q isvRepresenting the evaluation integrity of the v index types, n and m representing the iteration times, and ZijWeight coefficient, M, representing the ith evaluation index in the jth disaster evaluation indexijA value representing the ith evaluation index in the jth disaster evaluation index, theta represents an adjustment coefficient, ZjAnd the weight coefficient represents the jth disaster evaluation index.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: the step of constructing a neural network model and classifying the disaster according to the severity thereof as model output comprises the step of when Z is more than 106，0.87＜ZijWhen the number is less than 0.99, the disaster is very serious; when 104≤Z≤106，0.64≤ZijWhen the content is less than or equal to 0.87, indicating serious disasters; when 103＜Z＜104，0.42＜ZijIf the number is less than 0.64, indicating medium disaster; when 102≤Z≤103，0.21≤ZijWhen the content is less than or equal to 0.42, indicating a light disaster; when Z is less than 102，0＜ZijIf < 0.21, it indicates a disaster.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: the training process of the neural network model comprises the following steps of obtaining sample data and preprocessing the sample data, wherein the sample data is classified according to disaster severity; inputting the sample data into a neural network model to obtain the output of the neural network model; calculating the adjustment data of the neural network model according to the output of the neural network model and each pre-specified connection weight and input/output threshold; sending the adjustment data to the parameter server; receiving superposed data obtained by superposing the adjustment data of each training device according to a preset superposition mode and fed back by the parameter server; and updating the network parameters of the neural network model based on the superposition data, and finishing the model training.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: acquiring the new grid scenario in real time further comprises using a scenario reduction method, comprising the steps of,
defining an n-dimensional random data process
Figure BDA0003149735790000031
Through a limited number of scenarios
Figure BDA0003149735790000032
i 1, S and its probability pi，
Figure BDA0003149735790000033
To approximate; defining a set of scenes by Q
Figure BDA0003149735790000034
And corresponding probability value qj，j＝1，……，
Figure BDA0003149735790000035
Another n-dimensional random variable process of representation
Figure BDA0003149735790000036
A probability measure of (c).
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: defining a set of scenes by Q
Figure BDA0003149735790000037
And corresponding probability value qj，j＝1，……，
Figure BDA0003149735790000038
Another said n-dimensional random variable process of representation
Figure BDA0003149735790000039
The probability measure of (a) is in particular,
Figure BDA00031497357900000310
wherein the content of the first and second substances,
Figure BDA00031497357900000311
t＝1，……T，cTis the probability distance of the scene in the entire epoch 1, … …, T.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: the scene-reduction further comprises the steps of,
expressing the probability measure after xi reduction by Q, i.e. by scene set xijforj ∈ {1, … …, S } \ J, J represents a deleted scene set;
for fixation
Figure BDA00031497357900000312
Scene-based collections
Figure BDA00031497357900000313
Q of the representation has the smallest D for the original probability distribution Pk-distance, expressed as:
Figure BDA00031497357900000314
scene xi preserved after reductionj，
Figure BDA0003149735790000041
Probability of (q)jExpressed as:
Figure BDA0003149735790000042
wherein J (j): { I ∈ I: j ═ j (i) },
Figure BDA0003149735790000043
the probability value representing the reserved scene is equal to the original probability value of itself plus all the values c with itTThe probability value of the deleted scene under the smallest measure is measured.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: the optimal reduction problem of finding the optimal scene set J with a fixed number # J of deleted scenes can be expressed as,
Figure BDA0003149735790000044
where S' ═ S- # J > 0 indicates the number of scenes remaining after downscaling.
The invention discloses a preferable scheme of a power grid disaster situation evaluation method based on a deep neural network, wherein the method comprises the following steps: and developing a fast heuristic algorithm by using an objective function structure to solve the optimal reduction problem, wherein the fast heuristic algorithm comprises the following steps of,
calculating distances between pairs of scenes
Figure BDA0003149735790000045
Computing
Figure BDA0003149735790000046
Selecting
Figure BDA0003149735790000047
Set up J[1]：＝{1，...，S}\{u1}；
And (3) calculating:
Figure BDA0003149735790000048
Figure BDA0003149735790000049
selecting
Figure BDA00031497357900000410
Set up J[i]：＝J[i-1]\{ui}J：＝J[S-s]；
Wherein, cT(ξk，ξu) Representing a scene
Figure BDA00031497357900000411
The distance between them.
The invention has the beneficial effects that: the grid disaster evaluation method based on the deep neural network provided by the invention maintains the important characteristics of the random variable scene tree model while reducing the number of scenes as much as possible on the premise of ensuring the feasibility of solving, can effectively meet the requirements through a scene reduction technology for measuring and controlling the approximation degree of a random process by a certain probability, and solves the problem that the specific optimization calculation of the existing disaster evaluation optimization model based on the scenes is difficult to solve.
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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 of a method for evaluating grid disaster based on a deep neural network according to the present invention;
fig. 2 is a detailed diagram of the disaster situation in the area within 10 months.
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
The calculation effect of the existing disaster evaluation optimization model based on the scene depends on the size of the number of scenes, and in order to fully represent the characteristics of random variables, the number of scenes directly obtained by a scene generation method is usually huge, so that the specific optimization calculation faces the solving difficulty.
Therefore, referring to fig. 1 and fig. 2, the present invention provides a grid disaster situation evaluation method based on a deep neural network, including:
s1: acquiring historical power grid disaster situation scene data for statistical analysis;
it should be noted that: the power grid disaster situation scene data comprises disaster situation data, rainstorm data, fault data and power grid distribution data.
S2: preprocessing disaster situation scene data, and establishing a rating system by using disaster situation evaluation indexes;
specifically, the method for preprocessing by using the acquired historical power grid disaster situation scene data and establishing a rating system comprises the following steps:
collecting disaster situation data, rainstorm data, fault data and power grid distribution data;
pre-processing the collected data:
data cleaning: empty value cleaning, format content cleaning, logic error cleaning and non-demand data cleaning;
data transformation: carrying out feature construction, data grading and data quantization on data;
data integration: carrying out data statistics on the data after data transformation, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the data samples by adopting an outlier sample detection strategy based on clustering.
Further, establishing a rating system by using the disaster evaluation index comprises:
Figure BDA0003149735790000071
wherein Q isvRepresenting the evaluation integrity of the v index types, n and m representing the iteration times, and ZijWeight coefficient, M, representing the ith evaluation index in the jth disaster evaluation indexijA value representing the ith evaluation index in the jth disaster evaluation index, theta represents an adjustment coefficient, ZjAnd the weight coefficient represents the jth disaster evaluation index.
Wherein, the following table 1 shows the disaster grade and the single index grading standard:
table 1: disaster grade and single index grading standard table.
Figure BDA0003149735790000072
S3: constructing a neural network model, and classifying the disaster according to the severity of the disaster to be used as a model for outputting;
further, constructing a neural network model, and classifying the disaster according to the severity thereof as model output comprises:
when Z > 106，0.87＜ZijWhen the number is less than 0.99, the disaster is very serious;
when 104≤Z≤106，0.64≤ZijWhen the content is less than or equal to 0.87, indicating serious disasters;
when 103＜Z＜104，0.42＜ZijIf the number is less than 0.64, indicating medium disaster;
when 102≤Z≤103，0.21≤ZijWhen the content is less than or equal to 0.42, indicating a light disaster;
when Z is less than 102，0＜ZijIf < 0.21, it indicates a disaster.
S4: after the preprocessed sample data is loaded to the neural network model, selecting training parameters to train and learn the neural network model;
specifically, the process of training the neural network model comprises the following steps:
acquiring sample data, and preprocessing the sample data, wherein the sample data is classified according to disaster severity;
inputting the sample data into a neural network model to obtain the output of the neural network model;
calculating the adjustment data of the neural network model according to the output of the neural network model and each pre-specified connection weight and input/output threshold;
sending the adjustment data to a parameter server;
receiving superposed data obtained by superposing the adjustment data of each training device according to a preset superposition mode and fed back by a parameter server;
and updating network parameters of the neural network model based on the superposition data, and finishing model training.
S5: and acquiring a new power grid scene in real time, inputting the new power grid scene to the trained neural network model, and acquiring the disaster evaluation grade in real time.
The method for acquiring the new power grid scene in real time further comprises a scene reduction method, and specifically comprises the following steps:
assuming an n-dimensional random data process
Figure BDA0003149735790000081
Through a limited number of scenarios
Figure BDA0003149735790000082
i 1, S and their probabilities pi，
Figure BDA0003149735790000083
To approximate the estimate.
The scene reduction algorithm determines a subset of scenes and assigns new probabilities to the modified scenes. The corresponding reduced probability distribution Q is closest to the original probability distribution P in terms of the distance between a probability P and Q.
This probability distance balances the scene probability and the distance between scene values. Kantorovich probability distance DkAs a commonly used probability measure, representing a discrete probability distribution with multiple scenes is equivalent to a linear transportation problem.
Defining a set of scenes by Q
Figure BDA0003149735790000084
And corresponding probability value qj，j＝1，……，
Figure BDA0003149735790000085
Another n-dimensional random variable process of representation
Figure BDA0003149735790000086
The probability distance is defined as:
Figure BDA0003149735790000087
wherein the content of the first and second substances,
Figure BDA0003149735790000088
t＝1，……T，cTis the probability distance of the scene in the entire epoch 1, … …, T.
Where the ξ -reduced probability measure is denoted by Q, i.e.From scene set xijforj ∈ {1, … …, S } \ J, J represents the deleted set of scenes. For fixation
Figure BDA0003149735790000091
Scene-based collections
Figure BDA0003149735790000092
Q of the representation has the smallest D for the original probability distribution Pk-distance, expressed as:
Figure BDA0003149735790000093
scene xi preserved after reductionj，
Figure BDA0003149735790000094
Probability of (q)jExpressed as:
Figure BDA0003149735790000095
wherein J (j): { I ∈ I: j ═ j (i) },
Figure BDA0003149735790000096
it represents the optimal probability reassignment principle, i.e. the probability value of the reserved scene is equal to the original probability value of itself plus all the c's with itTThe probability value of the deleted scene under the smallest measure is measured.
The optimal reduction problem of finding the optimal scene set J with a fixed number # J of deleted scenes can be expressed as:
Figure BDA0003149735790000097
wherein, S- # J > 0 represents the number of scenes retained after reduction, and the description in the formula is a scene coverage problem, which is an NP-hard problem, and it is difficult to find an effective solving algorithm in a general sense, the invention utilizes an objective function structure to develop a fast heuristic algorithm, as follows:
assuming that # J is 1, i.e. only one scene is deleted, the foregoing problem can be described as:
Figure BDA0003149735790000098
if the minimum value is reached in l ∈ { 1.,..,. S }, the deleted scene is ξlObtaining a reduced probability measure Q by using a probability redistribution principle; if it is not
Figure BDA0003149735790000099
With corresponding scene probability of qj＝qj+plFor all
Figure BDA00031497357900000910
This optimal deletion of a scene may be repeated iteratively over and over again until the number of predetermined deleted scenes reaches the target of S-S'.
The algorithm flow is as follows:
calculating distances between pairs of scenes
Figure BDA00031497357900000911
Computing
Figure BDA0003149735790000101
Selecting
Figure BDA0003149735790000102
Set up J[1]：＝{1，...，S}\{u1}
And (3) calculating:
Figure BDA0003149735790000103
Figure BDA0003149735790000104
selecting
Figure BDA0003149735790000105
Set up J[i]：＝J[i-1]\{ui}J：＝J[S-s]；
J：＝J[S-s]Is the set obtained after deleting the scene, in the formula: c. CT(ξk，ξu) Representing a scene
Figure BDA0003149735790000106
The distance between them.
A home appliance network company (Guizhou power network Zunyi office) is selected, the existing evaluation method and the invention are adopted to carry out disaster evaluation for 10 months, as shown in figure 2, the disaster detail condition of the area within 10 months is shown in the following table 2, and the invention is a comparison table of the effect of disaster evaluation by adopting the invention and the prior art:
table 2: disaster evaluation effect comparison table
Evaluation time (min) Accuracy of evaluation (100%) Number of appearance of failure to evaluate
Prior Art 6.001 79.44 4
The invention 6.71 92.18 0
As shown in the table 2, in the disaster evaluation within 10 months, the phenomenon that the evaluation cannot be performed does not occur, and the evaluation accuracy is far higher than that of the prior art.
Specifically, when the model of the invention is used for disaster assessment, the absolute difference of each index is calculated by using the optimization model of the invention to obtain:
Δ01＝(0.562，0.244，0.137，0.092)，Δ02＝(0.372，0.245，0.241，0.159)
Δ03＝(0.517，0.272，0.309，0.350)，Δ04＝(0.327，0.205，0.053，0.020)
Δ05＝(0.356，0.306，0.046，0.061)，Δ06＝(0.453，0.316，0.258，0.148)
Δ07＝(0.393，0.269，0.133，0.021)，Δ08＝(0.493，0.250，0.362，0.281)
Δ09＝(0.534，0.140，0.345，0.169)，Δ10＝(0.267，0.123，0.173，0.040)
then, the following formula is obtained according to the optimization formula provided by the invention:
ξ01＝(0.640，0.804，0.879，0.916)，ξ02＝(0.729，0.803，0.806，0.863)
ξ03＝(0.659，0.786，0.764，0.741)，ξ04＝(0.754，0.829，0.949，0.980)
ξ05＝(0.737，0.766，0.956，0.943)，ξ06＝(0.688，0.759，0.795，0.871)
ξ 07＝(0.718，0.788，0.883，0.979)，ξ08＝(0.670，0.799，0.734，0.781)
ξ09＝(0.652，0.877，0.743，0.855)，ξ010＝(0.789，0.890，0.853，0.962)。
it should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
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 (10)

1. A power grid disaster situation assessment method based on a deep neural network is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring historical power grid disaster situation scene data for statistical analysis;
preprocessing the historical power grid disaster situation scene data, and establishing a rating system by using disaster situation evaluation indexes;
constructing a neural network model, and classifying the disaster according to the severity of the disaster to be used as a model for outputting;
after the preprocessed sample data is loaded to the neural network model, selecting training parameters to train and learn the neural network model;
and acquiring a new power grid scene in real time, inputting the new power grid scene to the neural network model which completes training, and acquiring a disaster situation evaluation grade in real time.
2. The grid disaster situation assessment method based on the deep neural network as claimed in claim 1, wherein: the preprocessing of the historical power grid disaster situation scene data comprises,
collecting disaster situation data, rainstorm data, fault data and power grid distribution data;
pre-processing the collected data:
data cleaning: empty value cleaning, format content cleaning, logic error cleaning and non-demand data cleaning;
data transformation: carrying out feature construction, data grading and data quantization on data;
data integration: carrying out data statistics on the data after data transformation, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the data samples by adopting an outlier sample detection strategy based on clustering.
3. The grid disaster situation assessment method based on the deep neural network as claimed in claim 1 or 2, wherein: the method for establishing a rating system by utilizing the disaster evaluation indexes comprises the following steps of,
Figure FDA0003149735780000011
wherein Q isvRepresenting the evaluation integrity of the v index types, n and m representing the iteration times, and ZijWeight coefficient, M, representing the ith evaluation index in the jth disaster evaluation indexijA value representing the ith evaluation index in the jth disaster evaluation index, theta represents an adjustment coefficient, ZjAnd the weight coefficient represents the jth disaster evaluation index.
4. The grid disaster situation assessment method based on the deep neural network as claimed in claim 3, wherein: the construction of the neural network model and the classification of the disaster according to the severity thereof as the output of the model comprises,
when Z is>106，0.87<Zij<0.99, indicating a serious disaster;
when 104≤Z≤106，0.64≤ZijWhen the content is less than or equal to 0.87, indicating serious disasters;
when 103<Z<104，0.42<Zij<When 0.64, indicating a disaster;
when 102≤Z≤103，0.21≤ZijWhen the content is less than or equal to 0.42, indicating a light disaster;
when Z is<102，0<Zij<When 0.21, the disaster is a disaster.
5. The grid disaster situation assessment method based on the deep neural network as claimed in claim 4, wherein: the process of training the neural network model comprises the following steps,
obtaining sample data, and preprocessing the sample data, wherein the sample data is classified according to disaster severity;
inputting the sample data into a neural network model to obtain the output of the neural network model;
calculating the adjustment data of the neural network model according to the output of the neural network model and each pre-specified connection weight and input/output threshold;
sending the adjustment data to the parameter server;
receiving superposed data obtained by superposing the adjustment data of each training device according to a preset superposition mode and fed back by the parameter server;
and updating the network parameters of the neural network model based on the superposition data, and finishing the model training.
6. The grid disaster situation assessment method based on the deep neural network as claimed in claim 5, wherein: acquiring the new grid scenario in real time further comprises using a scenario reduction method, comprising the steps of,
defining an n-dimensional random data process
Figure FDA0003149735780000021
Through a limited number of scenarios
Figure FDA0003149735780000022
And its probability pi，
Figure FDA0003149735780000023
To approximate;
defining a set of scenes by Q
Figure FDA0003149735780000024
And corresponding probability value qj，j＝1，……，
Figure FDA0003149735780000025
Another n-dimensional random variable process of representation
Figure FDA0003149735780000026
A probability measure of (c).
7. The grid disaster situation assessment method based on the deep neural network as claimed in claim 6, wherein: defining a set of scenes by Q
Figure FDA0003149735780000027
And corresponding probability value qj，j＝1，……，
Figure FDA0003149735780000028
Another said n-dimensional random variable process of representation
Figure FDA0003149735780000029
The probability measure of (a) is in particular,
Figure FDA0003149735780000031
Figure FDA0003149735780000032
wherein the content of the first and second substances,
Figure FDA0003149735780000033
cTis the probability distance of the scene in the entire epoch 1, … …, T.
8. The grid disaster situation assessment method based on the deep neural network as claimed in claim 6 or 7, wherein: the scene-reduction further comprises the steps of,
expressing the probability measure after xi reduction by Q, i.e. by scene set xijforj ∈ {1, … …, S } \ J, J represents a deleted scene set;
for fixation
Figure FDA0003149735780000034
Scene-based collections
Figure FDA0003149735780000035
Q of the representation has the smallest D for the original probability distribution Pk-distance, expressed as:
Figure FDA0003149735780000036
scene xi preserved after reductionj，
Figure FDA0003149735780000037
Probability of (q)jExpressed as:
Figure FDA0003149735780000038
wherein j (j) is ∈ I: j (I),
Figure FDA0003149735780000039
the probability value representing the reserved scene is equal to the original probability value of itself plus all the values c with itTThe probability value of the deleted scene under the smallest measure is measured.
9. The grid disaster assessment method based on the deep neural network as claimed in claim 8, wherein: the optimal reduction problem of finding the optimal scene set J with a fixed number # J of deleted scenes can be expressed as,
Figure FDA00031497357800000310
where S' ═ S- # J > 0 indicates the number of scenes remaining after downscaling.
10. The grid disaster assessment method based on the deep neural network as claimed in claim 9, wherein: and developing a fast heuristic algorithm by using an objective function structure to solve the optimal reduction problem, wherein the fast heuristic algorithm comprises the following steps of,
calculating distances between pairs of scenes
Figure FDA0003149735780000041
Computing
Figure FDA0003149735780000042
Selecting
Figure FDA0003149735780000043
Set up J[1]:＝{1,...,S}\{u1}；
And (3) calculating:
Figure FDA0003149735780000044
Figure FDA0003149735780000045
selecting
Figure FDA0003149735780000046
Is provided with
Figure FDA0003149735780000047
Wherein, cT(ξk,ξu) Representing a scene
Figure FDA0003149735780000048
The distance between them.
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