CN113379326A - Power grid disaster emergency drilling management system based on deep neural network - Google Patents
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Abstract
The invention discloses a power grid disaster emergency drilling management system based on a deep neural network, which comprises a disaster variable capturing module, a disaster variable capturing module and a disaster section processing module, wherein the disaster variable capturing module is used for capturing the electric quantity change characteristics when a disaster occurs through the historical experience of a disaster section; the scene screening and analyzing module is connected with the disaster variable capturing module, acquires a rule by analyzing the change characteristics of the electrical quantity, and captures a corresponding disaster scene from a plurality of scenes by applying the rule; the disaster self-learning module is connected with the scene screening and analyzing module and is used for screening disaster scenes by adopting a deep neural network algorithm; the evaluation analysis module is connected with the disaster self-learning module and is used for quantitatively evaluating the severity of the power grid disaster by using the screened disaster scene; the emergency management module is connected with the evaluation analysis module, and is used for carrying out power grid disaster emergency drilling management according to the result of quantitative evaluation, so that the problem that the conventional power grid disaster emergency drilling management system is difficult to solve when the specific optimization calculation of the number of numerous disaster scenes is faced is solved.
Description
Technical Field
The invention relates to the technical field of power grid safety, in particular to a power grid disaster emergency drilling management system based on a deep neural network.
Background
As the electric power industry in China enters the stages of large power grids, large units, high voltage and high automation, the management complexity of a power system is increased, the difficulty of the power safety management work is also increased, and as a control department of the overall operation risk, the safety supervision department faces a plurality of problems in managing the whole-network safety production emergency management work.
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 power grid disaster emergency drilling management system.
Therefore, the technical problem solved by the invention is as follows: the problem that the existing power grid disaster emergency drilling management system is difficult to solve when specific optimization calculation of the number of numerous disaster scenes is faced is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a power grid disaster emergency drilling management system based on a deep neural network comprises a disaster variable grabbing module, a disaster section monitoring module and a disaster section monitoring module, wherein the disaster variable grabbing module is used for grabbing electric quantity change characteristics when a disaster occurs through historical experience of a disaster section; the scene screening and analyzing module is connected with the disaster variable capturing module, acquires a rule by analyzing the change characteristics of the electrical quantity, and captures a corresponding disaster scene from a plurality of scenes by applying the rule; the disaster situation self-learning module is connected with the scene screening and analyzing module and is used for screening the disaster scene by adopting a deep neural network algorithm; the evaluation analysis module is connected with the disaster self-learning module and used for quantitatively evaluating the severity of the power grid disaster by using the screened disaster scene; and the emergency management module is connected with the evaluation analysis module and is used for carrying out power grid disaster emergency drilling management according to the result of quantitative evaluation.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the electrical quantity change characteristics captured by the disaster variable capturing module comprise current, voltage and frequency.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the disaster self-learning module comprises a preprocessing module and a ranking system, wherein the preprocessing module is used for preprocessing historical data of disaster sections and establishing the ranking system by using disaster evaluation indexes; the building module is used for building a neural network model and classifying the disasters according to the severity of the disasters to be output as the model; the training module is used for loading the historical data of the disaster section processed by the preprocessing module into the neural network model, and then selecting training parameters to train and learn the historical data; and the output module is used for 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 the disaster evaluation grade in real time.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the preprocessing module is used for preprocessing the historical data of the disaster section and establishing a rating system by using the disaster evaluation indexes, and the acquiring unit is used for collecting disaster data, rainstorm data, fault data and power grid distribution data; and constructing the rating system through hazard identification by a processing unit, and outputting the rating system as a network after normalization processing.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the method for selecting training parameters to train and learn the disaster situation section after the training module loads the historical data of the disaster situation section processed by the preprocessing module to the neural network model comprises the following steps of preprocessing data; network initialization, wherein each connection weight and input/output threshold are randomly given; giving a training sample and target output, and calculating and outputting actual output values of various neurons; adjusting the connection weight between the input layer and the hidden layer and between the hidden layer and the output layer; and repeating iteration until the error between the actual output and the target output reaches the preset requirement, and finishing the training of the model.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the adjusting of the connection weight comprises setting an initial value of the weight between the input layer and the hidden layer by using a random number; converting the input vector x to (x)1,x2,…,xn)TInput to the input layer; calculating the distance between the weight vector of the hidden layer and the input vector; selecting the neuron with the minimum distance of the weight vector to update the connection weight; judging whether the connection weight meets the requirement, and if so, stopping updating; and if not, inputting the input vector to the input layer again.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: calculating the distance between the weight vector of the hidden layer and the input vector comprises,
wherein d is the distance between the weight vector of the hidden layer and the input vector, k is the number of neurons in the input layer, aiIs the weight of the ith neuron, wijIs the weight between the i neuron of the input layer and the j neuron of the hidden layer.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: updating the connection weight value includes updating the connection weight value,
Δwij＝βh(j,j*)(ai-wij)
wherein, Δ wijFor updated connection weights, h (j, j)*) Is the learning rate.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the neural network model constructed by the construction module comprises the step of calculating disaster damage data by using a grey correlation analysis method to obtain comprehensive correlation degree, wherein the larger the comprehensive correlation degree is, the more serious the disaster situation is.
As an optimal scheme of the power grid disaster emergency drilling management system based on the deep neural network, the system comprises the following steps: the emergency management module comprises an emergency plan correction improvement module, a drilling plan and training correction improvement module and an evaluation index and weight correction improvement module; the emergency plan correction improvement module tracks emergency disposal targets corresponding to index items needing to be improved, determines root causes, correction method measures and completion time of problems, and tracks implementation conditions of correction measures of insufficient items and rectification items found in exercise so as to realize closed-loop management; the drilling scheme and training correction improvement module is used for optimizing and improving the drilling scheme and the pre-performance training by utilizing emergency treatment analysis record data and drilling correction measures; the evaluation index and weight correction improvement module utilizes emergency treatment analysis recorded data and index correction measures to optimize and improve an evaluation index system and weight setting.
The invention has the beneficial effects that: the power grid disaster emergency drilling management system based on the deep neural network grasps the change characteristics of the electrical quantity when the disaster occurs through the historical experience of the disaster section, captures corresponding disaster scenes from a plurality of scenes, and then screens the scenes through the power grid disaster evaluation self-learning module based on the deep neural network and arranged in the power grid disaster evaluation self-learning module, so that the severity of the power grid disaster is quantitatively evaluated, and the problem that the conventional power grid disaster emergency drilling management system is difficult to solve when the specific optimization calculation of the number of the plurality of disaster scenes is faced is solved.
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 system block diagram of a power grid disaster emergency drilling management system based on a deep neural network provided by the invention;
FIG. 2 is a schematic diagram of the disaster situation in a 10-month area;
FIG. 3 is a schematic flow chart of an emergency drilling management system according to the present invention;
FIG. 4 is a diagram of the actual topology of the system application of the present invention;
fig. 5 is a flowchart of a method of the grid disaster assessment method based on the deep neural network provided in the system 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" are to be construed broadly and include, 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 conventional power grid disaster emergency drilling management system has the problem of difficulty in solving when the specific optimization calculation of the number of numerous disaster scenes is faced, and cannot timely and effectively judge the management of power grid disaster emergency drilling, so that the power grid disaster emergency drilling management system becomes one of the problems to be solved in the field.
Therefore, referring to fig. 1 to 5, the present invention provides a power grid disaster emergency drilling management system based on a deep neural network, including:
the disaster variable capturing module 100 is used for capturing the electrical quantity change characteristics when a disaster occurs through the historical experience of a disaster section;
the scene screening and analyzing module 200 is connected with the disaster variable capturing module 100, acquires a rule by analyzing the change characteristics of the electrical quantity, and captures a corresponding disaster scene from a plurality of scenes by applying the rule;
the disaster self-learning module 300 is connected with the scene screening and analyzing module 200 and is used for screening disaster scenes by adopting a deep neural network algorithm;
the evaluation analysis module 400 is connected with the disaster self-learning module 300 and used for quantitatively evaluating the severity of the power grid disaster by using the screened disaster scene;
and the emergency management module 500 is connected with the evaluation analysis module 400 and is used for carrying out power grid disaster emergency drilling management according to the result of quantitative evaluation.
Specifically, the electrical quantity change characteristics captured by the disaster variable capture module 100 include current, voltage, and frequency.
Further, the disaster self-learning module 300 includes:
the preprocessing module 301 is used for preprocessing the historical data of the disaster section and establishing a rating system by using the disaster evaluation indexes;
the building module 302 is used for building a neural network model and classifying the disasters according to the severity of the disasters as model output;
the training module 303 is used for selecting training parameters to train and learn the disaster situation section after loading the historical data of the disaster situation section processed by the preprocessing module 301 into the neural network model;
and the output module 304 is used for 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 preprocessing module 301 preprocesses the historical data of the disaster section, and establishes a rating system by using the disaster evaluation index, including:
collecting disaster situation data, rainstorm data, fault data and power grid distribution data through an acquisition unit;
and a rating system is constructed through hazard identification by the processing unit, and the rating system is used as the output of the network after normalization processing.
Wherein the evaluation indexes comprise the disaster area, the number of casualties and the direct economic loss.
The data is normalized using the following formula,
the formula for normalization processing is used as the data input of the model, so that the calculation of the model is simplified, and the data output of the model is accelerated.
The disaster ratings and single index grading criteria are shown in table 1 below:
table 1: disaster grade and single index grading standard
And then, performing corresponding function transformation by using the function transformation of the disaster grading index, as follows:
(1) conversion function of disaster area (hm2) and casualties (people)
(2) Conversion function of economic loss (element)
Furthermore, the method for selecting training parameters to train and learn the disaster situation section after the training module 303 loads the historical data of the disaster situation section processed by the preprocessing module 301 into the neural network model comprises the following steps:
preprocessing data;
network initialization, wherein each connection weight and input/output threshold are randomly given;
giving a training sample and target output, and calculating and outputting actual output values of various neurons;
adjusting the connection weight between the input layer and the hidden layer and between the hidden layer and the output layer;
and repeating iteration until the error between the actual output and the target output reaches the preset requirement, and finishing the training of the model.
The specific steps of adjusting the connection weights between the input layer and the hidden layer and between the hidden layer and the output layer are as follows:
(1) setting an initial value of a weight between an input layer and a hidden layer by using a random number;
the random number is, for example, an integer of 0, 1, 2 ….
(2) Converting the input vector x to (x)1,x2,…,xn)TInputting to an input layer;
(3) calculating the distance between the weight vector of the hidden layer and the input vector;
wherein d is the distance between the weight vector of the hidden layer and the input vector, k is the number of neurons in the input layer, aiIs the weight of the ith neuron, wijIs the weight between the i neuron of the input layer and the j neuron of the hidden layer.
(4) Selecting the neuron with the minimum distance of the weight vector to update the connection weight;
Δwij＝βh(j,j*)(ai-wij)
wherein, Δ wijFor updated connection weights, h (j, j)*) The learning rate is in a range of (0, 1), and σ is a constant greater than 0 and smaller than 1, and decreases as the number of updates increases.
(5) Judging whether the connection weight meets the requirement, and if so, stopping updating; otherwise, re-inputting the input vector to the input layer.
If the connection weight value is in accordance with the set value, the updating is stopped.
Further, the disaster self-learning module 300 adopting a deep neural network algorithm to screen disaster scenes further includes:
to be reducedThe probability distribution Q of (1) represents a probability measure after xi reduction, namely xi is measured by a scene set xijforj ∈ {1, … …, S } \ J, J represents a deleted scene set;
for fixation
Scene-based collections
Q of the representation has the smallest D for the original probability distribution Pk-distance, expressed as:
Dk(P,Q)＝∑pimincT(ξi,ξj)
scene xi preserved after reductionj，
Probability of (q)jExpressed as:
wherein j (j) is ∈ I: j (I),
it represents the optimal probability reallocation principle, representing the probability value of the reservation scenario equal to the original probability value itself plus all 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:
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:
if the minimum value is reached in l ∈ { 1.,..,. S }, the deleted scene is ξlThe probability measurement Q after reduction can be obtained by utilizing the probability redistribution principle; if it is not
With corresponding scene probability of qj＝qj+plFor all
ql＝pl(ii) a This optimal deletion of a scene may be repeated iteratively until the predetermined number of deleted scenes reaches the target of S-S'.
Further, the method for quantitatively evaluating the severity of the power grid disaster by the evaluation analysis module 400 includes:
calculating distances between pairs of scenes
Computing
Selecting
Set up J[1]:＝{1,...,S}\{u1}；
And (3) calculating:
selecting
Set up J[i]:＝J[i-1]\{ui}J:＝J[S-s]；
Wherein, cT(ξk,ξu) Representing a scene
The distance between them.
The neural network model constructed by the construction module 302 includes calculating disaster data by using a grey correlation analysis method to obtain a comprehensive correlation degree, and the larger the correlation degree is, the more serious the disaster is.
Further, the obtaining of the comprehensive association degree includes:
determining a reference sequence and a comparison sequence;
defining a function conversion value when the disaster loss is maximum as a reference sequence and a difference sequence;
acquiring a correlation coefficient of the comparison sequence and the reference sequence;
and obtaining the comprehensive association degree.
The grey correlation analysis method is a method for measuring the degree of correlation between factors according to the similarity or dissimilarity of development trends between the factors, namely, the "grey correlation degree". The specific calculation steps are as follows:
a reference series reflecting characteristics of the system's behavior and a comparison series affecting the system's behavior are determined. The data sequence reflecting the behavior characteristics of the system is called a reference sequence. A data sequence consisting of factors influencing the system behavior is called a comparison sequence;
and carrying out non-dimensionalization processing on the reference number sequence and the comparison number sequence. Due to the different physical meanings of the factors in the system, the data dimensions are not necessarily the same, which is inconvenient for comparison or makes it difficult to obtain correct conclusions during comparison. Therefore, when performing grey correlation analysis, data processing without dimensionless data is generally performed;
and solving a gray correlation coefficient Xi (Xi) of the reference sequence and the comparison sequence. The degree of correlation is substantially the degree of difference in geometry between curves. Therefore, the difference value between the curves can be used as the measurement scale of the correlation degree. For a reference sequence X0, there are several comparison sequences X1, X2, …, Xn, and the correlation coefficient ξ (Xi) of each comparison sequence with the reference sequence at each time (i.e., each point in the curve) can be calculated by the following formula:
where ρ is the resolution coefficient, ρ >0, usually 0.5;
the degree of association ri is calculated by the following formula:
since the correlation coefficient is the degree of correlation between the comparison series and the reference series at each time (i.e., each point in the curve), it is more than one and the information is too scattered to facilitate a global comparison. Therefore, it is necessary to integrate the correlation coefficients at each time (i.e. each point in the curve) into one value, i.e. to obtain an average value thereof, which is expressed as the number of degrees of correlation between the comparison sequence and the reference sequence;
and sorting the relevance. The degree of association between the factors is mainly described by the order of magnitude of the degree of association, not just the magnitude of the degree of association. The association degrees of m subsequences to the same mother sequence are arranged according to the magnitude order to form an association sequence, which is marked as { x }, and reflects the 'good and bad' relationship of each subsequence to the mother sequence. If r0i > r0j, then it is said that { xi } is better than { xj } for the same mother sequence { x0} and is denoted as { xi } > { xj }; if r0i Table 1 represents the characteristic value of the flag-county reference number series, comparison number series.
Additionally, the emergency management module 500 includes an emergency plan correction improvement module 501, a drilling plan and training correction improvement module 502, and an evaluation index and weight correction improvement module 503;
the emergency plan correction improvement module 501 tracks an emergency disposal target corresponding to an index item to be improved, determines a root cause, a correction method measure and completion time of a problem, and tracks implementation conditions of correction measures of insufficient items and rectification items found in a maneuver to realize closed-loop management;
the drilling scheme and training correction improvement module 502 performs optimization improvement on the drilling scheme and the pre-performance training by using emergency treatment analysis record data and drilling correction measures;
the evaluation index and weight correction improvement module 503 performs optimization improvement on an evaluation index system and weight setting by using emergency disposal analysis recorded data and index correction measures.
A home appliance network company (Guizhou power network Zunyi office) is selected, and meanwhile, the existing emergency system and the invention are adopted to evaluate the disaster emergency effect in 10 months, as shown in fig. 2, the disaster situation details in 10 months are shown in the following table 2, which is a comparison table of the effect of disaster evaluation by adopting the invention and the prior art:
table 2: disaster emergency effect comparison table
| Emergency time (min) | Emergency accuracy (100%) | Number of times of emergency occurrence | |
| Prior Art | 6.001 | 79.44 | 4 |
| The invention | 6.71 | 92.18 | 0 |
Specifically, when the model of the invention is used for disaster emergency, the system of the invention is used for calculating the absolute difference of each index 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)
ξ07＝(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)。
wherein,
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) that is executed collectively 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 methods 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. Additionally, 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. The 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 the 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 is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can 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. The utility model provides a power grid disaster emergency drilling management system based on deep neural network which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the disaster situation variable capturing module (100) is used for capturing the electric quantity change characteristics when a disaster occurs through the historical experience of a disaster situation section;
the scene screening and analyzing module (200) is connected with the disaster variable capturing module (100), obtains a rule by analyzing the change characteristics of the electrical quantity, and captures a corresponding disaster scene from a plurality of scenes by applying the rule;
the disaster self-learning module (300) is connected with the scene screening and analyzing module (200) and screens the disaster scene by adopting a deep neural network algorithm;
the evaluation analysis module (400) is connected with the disaster self-learning module (300) and is used for quantitatively evaluating the severity of the power grid disaster by using the screened disaster scene;
and the emergency management module (500) is connected with the evaluation analysis module (400) and is used for carrying out power grid disaster emergency drilling management according to the result of quantitative evaluation.
2. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 1, wherein: the electrical quantity change characteristics captured by the disaster variable capturing module (100) include current, voltage and frequency.
3. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 2, wherein: the disaster self-learning module (300) comprises,
the preprocessing module (301) is used for preprocessing the historical data of the disaster section and establishing a rating system by utilizing the disaster evaluation indexes;
the building module (302) is used for building a neural network model and classifying the disasters according to the severity of the disasters as model output;
the training module (303) is used for loading the historical data of the disaster section processed by the preprocessing module (301) into the neural network model, and then selecting training parameters to train and learn the historical data;
and the output module (304) is used for acquiring a new power grid scene in real time, inputting the new power grid scene to the trained neural network model and acquiring a disaster evaluation grade in real time.
4. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 3, wherein: the preprocessing module (301) preprocesses the historical data of the disaster section, and establishes the rating system by using the disaster evaluation index,
collecting disaster situation data, rainstorm data, fault data and power grid distribution data through an acquisition unit;
and constructing the rating system through hazard identification by a processing unit, and outputting the rating system as a network after normalization processing.
5. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 3 or 4, wherein: the method for selecting training parameters to train and learn the disaster situation section after the training module (303) loads the historical data of the disaster situation section processed by the preprocessing module (301) to the neural network model comprises the following steps,
preprocessing data;
network initialization, wherein each connection weight and input/output threshold are randomly given;
giving a training sample and target output, and calculating and outputting actual output values of various neurons;
adjusting the connection weight between the input layer and the hidden layer and between the hidden layer and the output layer;
and repeating iteration until the error between the actual output and the target output reaches the preset requirement, and finishing the training of the model.
6. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 5, wherein: the adjusting of the connection weight value includes,
setting an initial value of a weight between an input layer and a hidden layer by using a random number;
converting the input vector x to (x)1,x2,…,xn)TInput to the input layer;
calculating the distance between the weight vector of the hidden layer and the input vector;
selecting the neuron with the minimum distance of the weight vector to update the connection weight;
judging whether the connection weight meets the requirement, and if so, stopping updating; otherwise, re-inputting the input vector to the input layer.
7. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 6, wherein: calculating the distance between the weight vector of the hidden layer and the input vector comprises,
wherein d is the distance between the weight vector of the hidden layer and the input vector, k is the number of neurons in the input layer, aiIs the weight of the ith neuron, wijI neuron as input layer andweights between the j neurons of the hidden layer.
8. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 6, wherein: updating the connection weight value includes updating the connection weight value,
Δwij＝βh(j,j*)(ai-wij)
wherein, Δ wijFor updated connection weights, h (j, j)*) Is the learning rate.
9. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 3, wherein: the neural network model constructed by the construction module (302) comprises the steps of calculating disaster damage data by using a grey correlation analysis method to obtain comprehensive correlation degree, wherein the larger the comprehensive correlation degree is, the more serious the disaster is.
10. The power grid disaster emergency drilling management system based on the deep neural network as claimed in claim 9, wherein: the emergency management module (500) comprises an emergency plan correction improvement module (501), a drilling plan and training correction improvement module (502) and an evaluation index and weight correction improvement module (503);
the emergency plan correction improvement module (501) tracks emergency disposal targets corresponding to index items needing to be improved, determines root causes, correction method measures and completion time of problems, and tracks implementation conditions of correction measures of insufficient items and rectification items found in exercise so as to realize closed-loop management;
the drilling scheme and training correction improvement module (502) utilizes emergency treatment analysis record data and drilling correction measures to optimize and improve the drilling scheme and the pre-performance training;
the evaluation index and weight correction improvement module (503) performs optimization improvement on an evaluation index system and weight setting by using emergency disposal analysis record data and index correction measures.
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