CN113919763A - Power grid disaster analysis method and device based on fuzzy evaluation matrix - Google Patents

Power grid disaster analysis method and device based on fuzzy evaluation matrix Download PDF

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Publication number
CN113919763A
CN113919763A CN202111514153.4A CN202111514153A CN113919763A CN 113919763 A CN113919763 A CN 113919763A CN 202111514153 A CN202111514153 A CN 202111514153A CN 113919763 A CN113919763 A CN 113919763A
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fuzzy
power grid
evaluation
disaster
fuzzy evaluation
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蔡木良
李琼
周求宽
刘娟
徐在德
熊健豪
黄扬琪
刘卓睿
李坤峰
陈亚奇
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Hangkong University
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Hangkong University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
    • G06N7/023—Learning or tuning the parameters of a fuzzy system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention discloses a power grid disaster analysis method and device based on a fuzzy evaluation matrix, wherein the method comprises the following steps: evaluating the acquired historical power grid disaster data; dividing the power grid disaster evaluation result into 5 different grades, forming a fuzzy evaluation set, and obtaining a fuzzy evaluation matrix based on the established membership function corresponding to the fuzzy evaluation set; attribute reduction is carried out on the fuzzy evaluation matrix based on the information entropy, so that the reduced fuzzy evaluation matrix is obtained; acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to each index in the real-time power grid disaster data to form an objective weight set; and carrying out fuzzy synthesis on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula so as to obtain a power grid disaster analysis result. The attribute reduction method based on the information entropy reduces the attributes of the disaster conditions, calculates the importance of the attributes from the perspective of information, and avoids redundancy of a large number of attributes.

Description

Power grid disaster analysis method and device based on fuzzy evaluation matrix
Technical Field
The invention belongs to the technical field of power grid disaster analysis, and particularly relates to a power grid disaster analysis method and device based on a fuzzy evaluation matrix.
Background
In recent years, with the continuous progress of national economic development and the continuous improvement of living standard, the power system serving as the national support industry faces a serious challenge due to the influence of natural disasters, once a power grid disaster occurs, a large amount of power supply load loss and damage to power grid equipment and even traffic facilities are generated, and the social economy and life are seriously influenced.
Under the condition, the analysis, early warning and defense of the power grid to external disasters are important tasks, so that a corresponding analysis index system is established, and the long-term and important practical significance is realized in analyzing the influence degree of natural disasters on the power grid.
Disclosure of Invention
The invention provides a power grid disaster analysis method and device based on a fuzzy evaluation matrix, which are used for solving the technical problem of how to analyze the influence degree of natural disasters on a power grid.
In a first aspect, the invention provides a power grid disaster analysis method based on a fuzzy evaluation matrix, which comprises the following steps: evaluating the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result; dividing the power grid disaster evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the fuzzy evaluation set
Figure 923724DEST_PATH_IMAGE001
={
Figure 919362DEST_PATH_IMAGE002
Normal,
Figure 384978DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 440659DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 726147DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 791054DEST_PATH_IMAGE006
and (2) red early warning }, obtaining a fuzzy evaluation matrix based on the established membership function corresponding to the fuzzy evaluation set, wherein the expression of the fuzzy evaluation matrix is as follows:
Figure 111177DEST_PATH_IMAGE007
in the formula (I), wherein,
Figure 63391DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 101754DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 439195DEST_PATH_IMAGE010
、
Figure 348245DEST_PATH_IMAGE011
、
Figure 745728DEST_PATH_IMAGE012
、
Figure 5808DEST_PATH_IMAGE013
、
Figure 146940DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set; attribute reduction is carried out on the fuzzy evaluation matrix based on the information entropy, so that the reduced fuzzy evaluation matrix is obtained; acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to all indexes in the real-time power grid disaster data to form an objective weight set
Figure 176075DEST_PATH_IMAGE015
Wherein the expression of the objective weight is:
Figure 478881DEST_PATH_IMAGE016
in the formula (I), wherein,
Figure 491836DEST_PATH_IMAGE017
is as follows
Figure 905500DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 54722DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 531358DEST_PATH_IMAGE020
the number of the indexes is,
Figure 766030DEST_PATH_IMAGE021
the number of samples;
Figure 983385DEST_PATH_IMAGE022
in the formula (I), wherein,
Figure 987113DEST_PATH_IMAGE023
is as follows
Figure 631721DEST_PATH_IMAGE018
Under the individual index
Figure 353689DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 374735DEST_PATH_IMAGE025
in the formula (I), wherein,
Figure 232969DEST_PATH_IMAGE026
to reduce to
Figure 314058DEST_PATH_IMAGE024
A first sample of
Figure 257743DEST_PATH_IMAGE018
The numerical value of each index; fuzzy synthesis is carried out on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula, so that the power grid disaster analysis result is obtained
Figure 816900DEST_PATH_IMAGE027
Wherein the grid disaster analysis result is calculated
Figure 795221DEST_PATH_IMAGE027
The expression of (a) is:
Figure 50140DEST_PATH_IMAGE028
in the formula (I), wherein,
Figure 746701DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 843970DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 676797DEST_PATH_IMAGE030
is as follows
Figure 99688DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
In a second aspect, the present invention provides a power grid disaster analysis device based on a fuzzy evaluation matrix, including: the evaluation module is configured to evaluate the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result; an output module, configured to divide the power grid disaster evaluation result into 5 different grades, and form a fuzzy evaluation set, where the fuzzy evaluation set is used for evaluating the power grid disaster
Figure 752386DEST_PATH_IMAGE001
={
Figure 918925DEST_PATH_IMAGE002
Normal,
Figure 606258DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 200051DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 340045DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 44696DEST_PATH_IMAGE006
red warning }, baseObtaining a fuzzy evaluation matrix according to the established membership function corresponding to the fuzzy evaluation set, wherein the expression of the fuzzy evaluation matrix is as follows:
Figure 586536DEST_PATH_IMAGE007
in the formula (I), wherein,
Figure 354159DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 247029DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 489791DEST_PATH_IMAGE010
、
Figure 417296DEST_PATH_IMAGE011
、
Figure 87312DEST_PATH_IMAGE012
、
Figure 201898DEST_PATH_IMAGE013
、
Figure 513931DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set; the reduction module is configured to perform attribute reduction on the fuzzy evaluation matrix based on information entropy so as to obtain a reduced fuzzy evaluation matrix; the evaluation module is configured to acquire real-time power grid disaster data associated with the reduced fuzzy evaluation matrix, and assign objective weights to all indexes in the real-time power grid disaster data respectively to form an objective weight set
Figure 358259DEST_PATH_IMAGE015
Wherein the expression of the objective weight is:
Figure 464755DEST_PATH_IMAGE016
in the formula (I), wherein,
Figure 66638DEST_PATH_IMAGE017
is as follows
Figure 728168DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 364686DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 642084DEST_PATH_IMAGE020
the number of the indexes is,
Figure 996841DEST_PATH_IMAGE021
the number of samples;
Figure 385098DEST_PATH_IMAGE022
in the formula (I), wherein,
Figure 407280DEST_PATH_IMAGE023
is as follows
Figure 917896DEST_PATH_IMAGE018
Under the individual index
Figure 494371DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 282723DEST_PATH_IMAGE025
in the formula (I), wherein,
Figure 893833DEST_PATH_IMAGE026
to reduce to
Figure 44191DEST_PATH_IMAGE024
A first sample of
Figure 107962DEST_PATH_IMAGE018
The numerical value of each index; a synthesis module configured to synthesize according to the fuzzy healdThe objective weight set and the reduced fuzzy evaluation matrix are subjected to fuzzy synthesis by the combined evaluation formula, so that the power grid disaster analysis result is obtained
Figure 838021DEST_PATH_IMAGE027
Wherein the grid disaster analysis result is calculated
Figure 303637DEST_PATH_IMAGE027
The expression of (a) is:
Figure 93739DEST_PATH_IMAGE028
in the formula (I), wherein,
Figure 379227DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 178555DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 501608DEST_PATH_IMAGE030
is as follows
Figure 197031DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
In a third aspect, an electronic device is provided, comprising: the system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the fuzzy evaluation matrix-based power grid disaster analyzing method according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, the computer program comprising program instructions, which when executed by a computer, cause the computer to perform the steps of the grid disaster analysis method based on the fuzzy evaluation matrix according to any of the embodiments of the present invention.
According to the power grid disaster analysis method and device based on the fuzzy evaluation matrix, an evaluation index system of the influence degree of disasters on a power grid is established from power grid loss evaluation through meteorological data, geography data, the power grid, equipment data and social data collected by all channels, so that the accuracy and the objectivity of disaster evaluation are improved, the attribute reduction method based on the information entropy is used for reducing the attributes of disaster conditions, the attribute importance is calculated from the information perspective, the redundancy of a large number of attributes is avoided, the system calculation complexity is reduced, and the generalization capability is improved.
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 are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a power grid disaster analysis method based on a fuzzy evaluation matrix according to an embodiment of the present invention;
fig. 2 is a block diagram of a power grid disaster analysis device based on a fuzzy evaluation matrix according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1, which shows a flowchart of a grid disaster analysis method based on a fuzzy evaluation matrix according to the present application.
As shown in fig. 1, the power grid disaster analysis method based on the fuzzy evaluation matrix specifically includes the following steps:
and S101, evaluating the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result.
In this embodiment, the preset grid disaster assessment index system includes a branch overload degree, a bus voltage out-of-limit degree, a line load loss proportion and an important load loss proportion, where an expression for calculating the branch overload degree is:
Figure 969815DEST_PATH_IMAGE032
，
in the formula (I), the compound is shown in the specification,
Figure 41677DEST_PATH_IMAGE034
in order not to overload the number of branches,
Figure 216306DEST_PATH_IMAGE035
the total number of the branches is,
Figure 348210DEST_PATH_IMAGE036
are respectively the first
Figure 342711DEST_PATH_IMAGE037
Weight factor of difference in importance of branch and
Figure 483842DEST_PATH_IMAGE038
the weighting factors of the difference in the importance of the branch bars,
Figure 247399DEST_PATH_IMAGE039
are respectively the first
Figure 815783DEST_PATH_IMAGE037
Strip current value and
Figure 297580DEST_PATH_IMAGE038
the value of the strip branch current is,
Figure 976823DEST_PATH_IMAGE040
are respectively the first
Figure 860466DEST_PATH_IMAGE037
Upper limit of strip branch current and
Figure 602681DEST_PATH_IMAGE038
upper limit of strip branch current;
the expression for calculating the out-of-limit degree of the bus voltage is as follows:
Figure 837354DEST_PATH_IMAGE042
，
in the formula (I), the compound is shown in the specification,
Figure 54708DEST_PATH_IMAGE043
are respectively the first
Figure 58436DEST_PATH_IMAGE037
Strip branch voltage amplitude and
Figure 703044DEST_PATH_IMAGE038
the amplitude of the strip branch voltage is,
Figure 425013DEST_PATH_IMAGE044
is the bus voltage limit;
the expression for calculating the line load loss ratio is as follows:
Figure 180479DEST_PATH_IMAGE045
，
Figure 38714DEST_PATH_IMAGE046
，
in the formula (I), the compound is shown in the specification,
Figure 588644DEST_PATH_IMAGE047
、
Figure 797908DEST_PATH_IMAGE048
respectively lost load directly caused by a disaster and lost load indirectly caused by the disaster,
Figure 357065DEST_PATH_IMAGE049
in order to lose the load,
Figure 335386DEST_PATH_IMAGE050
for the maximum load in the area to be considered,
Figure 587376DEST_PATH_IMAGE051
the influence coefficients of disaster relief caused by power failure in disaster areas and the influence coefficients of disaster relief caused by power failure in non-disaster areas are respectively obtained;
the expression for calculating the important load loss ratio is as follows:
Figure 9568DEST_PATH_IMAGE052
，
in the formula (I), the compound is shown in the specification,
Figure 106837DEST_PATH_IMAGE053
in order to be the total number of important loads,
Figure 205243DEST_PATH_IMAGE054
in order to be able to lose a significant amount of load,
Figure 362555DEST_PATH_IMAGE055
、
Figure 343149DEST_PATH_IMAGE056
are respectively the first
Figure 509688DEST_PATH_IMAGE057
Power of an important load and
Figure 197021DEST_PATH_IMAGE058
the power of one of the important loads is,
Figure 790814DEST_PATH_IMAGE059
、
Figure 196387DEST_PATH_IMAGE060
are respectively the first
Figure 635459DEST_PATH_IMAGE057
Weight of the important load and
Figure 442878DEST_PATH_IMAGE058
the weight of the one of the important loads,
Figure 944922DEST_PATH_IMAGE061
the value of the line belonging to the disaster area is taken,
Figure 837792DEST_PATH_IMAGE062
the value of the line which does not belong to the disaster area is taken.
According to the method, an evaluation index system of the influence degree of the disaster on the power grid is established from the power grid loss evaluation through the meteorological data, the geographic data, the power grid data, the equipment data and the social data collected by all channels, so that the accuracy and the objectivity of the disaster evaluation are improved.
Step S102, dividing the power grid disaster evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the fuzzy evaluation set
Figure 80554DEST_PATH_IMAGE001
={
Figure 742480DEST_PATH_IMAGE002
Normal,
Figure 678075DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 792661DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 839115DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 355547DEST_PATH_IMAGE006
red warning }, based onAnd obtaining a fuzzy evaluation matrix by the established membership function corresponding to the fuzzy evaluation set.
In the embodiment, the establishment of the membership function corresponding to the fuzzy evaluation set is specifically that discrete attribute data can be directly processed by a rough set; for continuous attribute data, it is necessary to use trapezoidal membership for intuitive fuzzification, assuming continuous attribute
Figure 196464DEST_PATH_IMAGE063
Value range of
Figure 63926DEST_PATH_IMAGE064
(meaning the value of the conditional attribute is within the range), and
Figure 648491DEST_PATH_IMAGE065
(are all in
Figure 19429DEST_PATH_IMAGE063
In a range of values) together
Figure 296827DEST_PATH_IMAGE066
The parameters will be continuous attributes
Figure 654514DEST_PATH_IMAGE063
Attribute value division of
Figure 777191DEST_PATH_IMAGE067
Intervals (interval width may be unequal), will
Figure 2636DEST_PATH_IMAGE063
Is divided into
Figure 450935DEST_PATH_IMAGE068
An intuition fuzzy set
Figure 558568DEST_PATH_IMAGE069
,
Figure 484936DEST_PATH_IMAGE070
. To be continuousThe attribute, namely the voltage in the power grid data is taken as an example, the value range can be divided into 5 fuzzy sets which are respectively 'normal', 'blue early warning', 'yellow early warning', 'orange early warning' and 'red early warning' after being set. Taking yellow warning as an example, the trapezoidal membership function of the yellow warning is given as
Figure 830467DEST_PATH_IMAGE071
，
In the formula (I), the compound is shown in the specification,
Figure 184088DEST_PATH_IMAGE072
for a moderate degree of membership in voltage,
Figure 247859DEST_PATH_IMAGE073
is a value of the voltage to be applied,
Figure 243496DEST_PATH_IMAGE074
the lower limit value of the range of the voltage corresponding fuzzy evaluation set 'blue early warning',
Figure 443534DEST_PATH_IMAGE075
the lower limit value of the range of the voltage corresponding fuzzy evaluation set 'yellow early warning',
Figure 968056DEST_PATH_IMAGE076
the lower limit value of the range of the voltage corresponding to the fuzzy evaluation set orange early warning is obtained,
Figure 253544DEST_PATH_IMAGE077
and the lower limit value of the range of the voltage corresponding to the fuzzy evaluation set 'red early warning'.
Set the selected voltage class as
Figure 790223DEST_PATH_IMAGE078
By way of example, according to voltage fluctuations
Figure 844767DEST_PATH_IMAGE079
Is limited by the moderate voltage
Figure 805769DEST_PATH_IMAGE080
kV，
Figure 844132DEST_PATH_IMAGE081
kV is determined according to the statistical rule that the proportion of membership degree 1 accounts for 80%
Figure 650414DEST_PATH_IMAGE082
、
Figure 90623DEST_PATH_IMAGE083
And obtaining the membership degree of the voltage value to each ambiguity.
The process of obtaining the fuzzy evaluation matrix comprises the following steps: the disaster assessment is divided into five states of 'normal', 'blue early warning', 'yellow early warning', 'orange early warning' and 'red early warning', and the disaster assessment state can be used as an assessment item
Figure 956948DEST_PATH_IMAGE084
Evaluation was performed. The degree of membership of the disaster condition attribute state comment set under each evaluation item is
Figure 217028DEST_PATH_IMAGE085
Then, the membership degree set of all disaster condition attributes can be obtained
Figure 92580DEST_PATH_IMAGE086
. The evaluation matrix of each evaluation item is composed of the membership degree of each disaster condition attribute, for example, so as to
Figure 121716DEST_PATH_IMAGE087
For example, the evaluation matrix is (1):
Figure 690101DEST_PATH_IMAGE007
（1）
in the formula (I), the compound is shown in the specification,
Figure 171897DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 585561DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 749431DEST_PATH_IMAGE010
、
Figure 223138DEST_PATH_IMAGE011
、
Figure 457810DEST_PATH_IMAGE012
、
Figure 409586DEST_PATH_IMAGE013
、
Figure 678893DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set;
the method of the embodiment converts qualitative evaluation into quantitative evaluation, and uses fuzzy mathematics to make an overall evaluation on objects or objects which are limited by various factors.
And S103, performing attribute reduction on the fuzzy evaluation matrix based on the information entropy to obtain the reduced fuzzy evaluation matrix.
In the embodiment, the attribute reduction algorithm based on the information entropy can reduce the search space in the reduction process, and the minimum attribute reduction is obtained in most cases. The overall idea of the algorithm is as follows: the attributes are removed in order, starting from the corpus. If the information entropy of the decision attribute is increased due to the absence of the attribute, the attribute is beneficial to the judgment of the decision attribute and is a core attribute. Every time the importance of all attributes is calculated, if the importance of the attribute is 0 for the current condition set, the attribute can be deleted. And then selecting the most important attribute to be added into the current condition set, and carrying out the next round of circulation. Until the condition information entropy of the current condition set is equal to the condition information entropy of the original data. The current set can replace the original set, and the algorithm is finished.
The specific algorithm steps are as follows:
inputting: decision information system
Figure 323501DEST_PATH_IMAGE088
And U is the domain of discourse,
Figure 45469DEST_PATH_IMAGE089
wherein C is a disaster condition attribute, D is a decision attribute, V is a value range of the attribute A, and F is an information function of UxA → V;
and (3) outputting: decision information system
Figure 800936DEST_PATH_IMAGE088
A reduction of (a);
step 1: calculating information entropy H (A);
step 2: b is initialized, and B = the front beam;
step 3: solving the core attribute core (A) of the decision information system, if any a belongs to A
Figure 393591DEST_PATH_IMAGE090
Then, then
Figure 209100DEST_PATH_IMAGE091
B obtained after traversal is core (A);
step 4: judging whether H (A) is equal to H (B), if not, turning to Step 5; if the two are equal, B is the reduction, B is output, and the algorithm is ended;
step 5: for any a ∈ A-B, the importance of a relative to B is calculated according to the formula sig (a, B; A) = H (B ∈ { a }) -H (B), such that
Figure 418365DEST_PATH_IMAGE092
Turning to Step 4.
Step S104, acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to each index in the real-time power grid disaster data to form an objective weight set
Figure 977522DEST_PATH_IMAGE015
。
In this embodiment, the expression of the objective weight is:
Figure 690263DEST_PATH_IMAGE016
，
in the formula (I), the compound is shown in the specification,
Figure 679603DEST_PATH_IMAGE017
is as follows
Figure 110585DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 473433DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 571839DEST_PATH_IMAGE020
the number of the indexes is,
Figure 463572DEST_PATH_IMAGE021
the number of samples;
Figure 647428DEST_PATH_IMAGE022
，
in the formula (I), the compound is shown in the specification,
Figure 548388DEST_PATH_IMAGE023
is as follows
Figure 235722DEST_PATH_IMAGE018
Under the individual index
Figure 563935DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 969508DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 674159DEST_PATH_IMAGE026
to reduce to
Figure 215999DEST_PATH_IMAGE024
A first sample of
Figure 715113DEST_PATH_IMAGE018
Numerical value of each index.
In the method, after multi-dimensional data acquisition is performed, attribute reduction is performed, and a key disaster-causing factor is extracted, which is particularly important in multi-dimensional data acquisition and can avoid dimension disasters. Meanwhile, the method can avoid the phenomenon that some key hidden characteristic indexes are easy to ignore in the traditional method for selecting the indexes according to experts. In addition, the weight is determined by combining an entropy weight method and expert weight, and the variable weight model can better integrate each index quantity compared with a normal weight model, so that the adaptability of the algorithm is improved.
Step S105, carrying out fuzzy synthesis on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula so as to obtain a power grid disaster analysis result
Figure 610913DEST_PATH_IMAGE027
。
In this embodiment, the fuzzy comprehensive evaluation formula is:
Figure 853675DEST_PATH_IMAGE093
，
in the formula (I), the compound is shown in the specification,
Figure 515601DEST_PATH_IMAGE094
in order to be an objective set of weights,
Figure 185616DEST_PATH_IMAGE095
in order to reduce the fuzzy evaluation matrix after the reduction,
Figure 300203DEST_PATH_IMAGE096
is a generalized fuzzy operator. Wherein, theThe generalized fuzzy operator is specifically a weighted average fuzzy operator, and the expression for calculating the weighted average fuzzy operator is as follows:
Figure 346656DEST_PATH_IMAGE097
，
in the formula (I), the compound is shown in the specification,
Figure 863088DEST_PATH_IMAGE098
in order to be a weighted average type of blurring operator,
Figure 704005DEST_PATH_IMAGE099
is as follows
Figure 571467DEST_PATH_IMAGE018
The weight of each index is calculated according to the weight of each index,
Figure 156032DEST_PATH_IMAGE100
is as follows
Figure 792550DEST_PATH_IMAGE018
The pair of the individual indexes
Figure 804368DEST_PATH_IMAGE101
The degree of membership of each evaluation state,
Figure 627968DEST_PATH_IMAGE020
is the number of indexes.
Calculating the power grid disaster analysis result
Figure 281803DEST_PATH_IMAGE027
The expression of (a) is:
Figure 510178DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 958477DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 534952DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 461319DEST_PATH_IMAGE030
is as follows
Figure 806850DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
According to
Figure 426050DEST_PATH_IMAGE027
The results are classified into "normal", "blue warning", "yellow warning", "orange warning", and "red warning" by value. The specific corresponding indexes are as follows:
Figure 489821DEST_PATH_IMAGE102
。
in the method of the embodiment, the weighted average fuzzy operator is adopted to not only take care of the influence of all evaluation indexes on disaster registration, but also contain all information of a single factor, so that the method is more suitable for actual conditions.
In summary, according to the method, through meteorological data, geography data, power grids, equipment data and social data collected by channels, an evaluation index system of the influence degree of disasters on the power grids is established from power grid loss evaluation, the accuracy and objectivity of disaster evaluation are improved, the attribute reduction method based on the information entropy is used for reducing the attributes of disaster conditions, the attribute importance degree is calculated from the perspective of information, redundancy of a large number of attributes is avoided, the system calculation complexity is reduced, and the generalization capability is improved.
Please refer to fig. 2, which shows a block diagram of a power grid disaster analysis apparatus based on a fuzzy evaluation matrix according to the present application.
As shown in fig. 2, the grid disaster analysis apparatus 200 includes an evaluation module 210, an output module 220, a reduction module 230, an assignment module 240, and a synthesis module 250.
The evaluation module 210 is configured to evaluate the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system, so as to obtain a power grid disaster evaluation result;
an output module 220 configured to divide the power grid disaster evaluation result into 5 different grades and form a fuzzy evaluation set, where the fuzzy evaluation set is a set of fuzzy evaluations
Figure 219880DEST_PATH_IMAGE001
={
Figure 154338DEST_PATH_IMAGE002
Normal,
Figure 210018DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 495506DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 29256DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 83799DEST_PATH_IMAGE006
and (2) red early warning }, obtaining a fuzzy evaluation matrix based on the established membership function corresponding to the fuzzy evaluation set, wherein the expression of the fuzzy evaluation matrix is as follows:
Figure 36013DEST_PATH_IMAGE007
，
in the formula (I), the compound is shown in the specification,
Figure 808797DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 880658DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 55288DEST_PATH_IMAGE010
、
Figure 187192DEST_PATH_IMAGE011
、
Figure 447272DEST_PATH_IMAGE012
、
Figure 322824DEST_PATH_IMAGE013
、
Figure 351960DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set;
a reduction module 230 configured to perform attribute reduction on the fuzzy evaluation matrix based on information entropy, so as to obtain a reduced fuzzy evaluation matrix;
the assignment module 240 is configured to acquire real-time power grid disaster data associated with the reduced fuzzy evaluation matrix, and assign objective weights to indexes in the real-time power grid disaster data, so as to form an objective weight set
Figure 389186DEST_PATH_IMAGE015
Wherein the expression of the objective weight is:
Figure 402141DEST_PATH_IMAGE016
，
in the formula (I), the compound is shown in the specification,
Figure 815805DEST_PATH_IMAGE017
is as follows
Figure 965027DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 438733DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 676335DEST_PATH_IMAGE020
the number of the indexes is,
Figure 893690DEST_PATH_IMAGE021
the number of samples;
Figure 897418DEST_PATH_IMAGE103
，
in the formula (I), the compound is shown in the specification,
Figure 542026DEST_PATH_IMAGE023
is as follows
Figure 263994DEST_PATH_IMAGE018
Under the individual index
Figure 19461DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 877695DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 958784DEST_PATH_IMAGE026
to reduce to
Figure 902469DEST_PATH_IMAGE024
A first sample of
Figure 461626DEST_PATH_IMAGE018
The numerical value of each index;
a synthesis module 250 configured to perform fuzzy synthesis on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula so as to obtain a power grid disaster analysis result
Figure 174367DEST_PATH_IMAGE027
Wherein the grid disaster analysis result is calculated
Figure 160778DEST_PATH_IMAGE027
The expression of (a) is:
Figure 857338DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 957537DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 790364DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 947676DEST_PATH_IMAGE030
is as follows
Figure 865953DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
It should be understood that the modules depicted in fig. 2 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 2, and are not described again here.
In other embodiments, the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions may execute the power grid disaster analysis method based on the fuzzy evaluation matrix in any of the above method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
evaluating the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result;
dividing the power grid disaster evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the fuzzy evaluation set
Figure 766913DEST_PATH_IMAGE001
={
Figure 719826DEST_PATH_IMAGE002
Normal,
Figure 48039DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 453612DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 158263DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 700103DEST_PATH_IMAGE006
red early warning }, and a fuzzy evaluation matrix is obtained based on the established membership function corresponding to the fuzzy evaluation set;
attribute reduction is carried out on the fuzzy evaluation matrix based on the information entropy, so that the reduced fuzzy evaluation matrix is obtained;
acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to all indexes in the real-time power grid disaster data to form an objective weight set
Figure 464797DEST_PATH_IMAGE015
；
Fuzzy synthesis is carried out on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula, so that the power grid disaster analysis result is obtained
Figure 92087DEST_PATH_IMAGE027
。
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the grid disaster analysis device based on the fuzzy evaluation matrix, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located from the processor, and the remote memory may be connected to the grid disaster analysis device based on the fuzzy evaluation matrix 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.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the nonvolatile software programs, instructions and modules stored in the memory 320, that is, the method for analyzing the power grid disaster based on the fuzzy evaluation matrix of the above method embodiment is implemented. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the grid disaster analysis device based on the fuzzy evaluation matrix. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to a power grid disaster analysis device based on a fuzzy evaluation matrix, and is used for a client, and the electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
evaluating the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result;
dividing the power grid disaster evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the fuzzy evaluation set
Figure 69270DEST_PATH_IMAGE001
={
Figure 999705DEST_PATH_IMAGE002
Normal,
Figure 669721DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 49886DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 830760DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 347192DEST_PATH_IMAGE006
red early warning }, and a fuzzy evaluation matrix is obtained based on the established membership function corresponding to the fuzzy evaluation set;
attribute reduction is carried out on the fuzzy evaluation matrix based on the information entropy, so that the reduced fuzzy evaluation matrix is obtained;
acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to all indexes in the real-time power grid disaster data to form an objective weight set
Figure 453689DEST_PATH_IMAGE015
；
Fuzzy synthesis is carried out on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula, so that the power grid disaster analysis result is obtained
Figure 55571DEST_PATH_IMAGE027
。
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (9)

1. A power grid disaster analysis method based on a fuzzy evaluation matrix is characterized by comprising the following steps:
evaluating the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result;
dividing the power grid disaster evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the fuzzy evaluation set
Figure 501949DEST_PATH_IMAGE001
={
Figure 215828DEST_PATH_IMAGE002
Normal,
Figure 792302DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 984249DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 598289DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 483068DEST_PATH_IMAGE006
and (2) red early warning }, obtaining a fuzzy evaluation matrix based on the established membership function corresponding to the fuzzy evaluation set, wherein the expression of the fuzzy evaluation matrix is as follows:
Figure 546839DEST_PATH_IMAGE007
，
in the formula (I), the compound is shown in the specification,
Figure 542477DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 8094DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 798195DEST_PATH_IMAGE010
、
Figure 349262DEST_PATH_IMAGE011
、
Figure 883012DEST_PATH_IMAGE012
、
Figure 203134DEST_PATH_IMAGE013
、
Figure 429716DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set;
attribute reduction is carried out on the fuzzy evaluation matrix based on the information entropy, so that the reduced fuzzy evaluation matrix is obtained;
acquiring real-time power grid disaster data associated with the fuzzy evaluation matrix after reduction, and respectively giving objective weights to all indexes in the real-time power grid disaster data to form an objective weight set
Figure 482728DEST_PATH_IMAGE015
Wherein the expression of the objective weight is:
Figure 554589DEST_PATH_IMAGE016
，
in the formula (I), the compound is shown in the specification,
Figure 729219DEST_PATH_IMAGE017
is as follows
Figure 861123DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 386782DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 262334DEST_PATH_IMAGE020
the number of the indexes is,
Figure 291470DEST_PATH_IMAGE021
the number of samples;
Figure 859855DEST_PATH_IMAGE022
，
in the formula (I), the compound is shown in the specification,
Figure 607231DEST_PATH_IMAGE023
is as follows
Figure 286474DEST_PATH_IMAGE018
Under the individual index
Figure 170116DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 909402DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 147004DEST_PATH_IMAGE026
to reduce to
Figure 98779DEST_PATH_IMAGE024
A first sample of
Figure 368087DEST_PATH_IMAGE018
The numerical value of each index;
fuzzy synthesis is carried out on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula, so that the power grid disaster analysis result is obtained
Figure 12695DEST_PATH_IMAGE027
Wherein the grid disaster analysis result is calculated
Figure 734663DEST_PATH_IMAGE027
The expression of (a) is:
Figure 755709DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 613943DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 695032DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 904296DEST_PATH_IMAGE030
is as follows
Figure 729033DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
2. The power grid disaster analysis method based on the fuzzy evaluation matrix as claimed in claim 1, wherein the preset power grid disaster evaluation index system comprises branch overload degree, bus voltage out-of-limit degree, line load loss proportion and important load loss proportion, wherein an expression for calculating the branch overload degree is as follows:
Figure 972932DEST_PATH_IMAGE032
，
in the formula (I), the compound is shown in the specification,
Figure 696693DEST_PATH_IMAGE034
in order not to overload the number of branches,
Figure 393254DEST_PATH_IMAGE035
the total number of the branches is,
Figure 756102DEST_PATH_IMAGE036
are respectively the first
Figure 588929DEST_PATH_IMAGE037
Weight factor of difference in importance of branch and
Figure 746241DEST_PATH_IMAGE038
the weighting factors of the difference in the importance of the branch bars,
Figure 930097DEST_PATH_IMAGE039
are respectively the first
Figure 362216DEST_PATH_IMAGE037
Strip current value and
Figure 783970DEST_PATH_IMAGE038
the value of the strip branch current is,
Figure 377762DEST_PATH_IMAGE040
are respectively the first
Figure 783336DEST_PATH_IMAGE037
Upper limit of strip branch current and
Figure 487987DEST_PATH_IMAGE038
upper limit of strip branch current;
the expression for calculating the out-of-limit degree of the bus voltage is as follows:
Figure 295406DEST_PATH_IMAGE042
，
in the formula (I), the compound is shown in the specification,
Figure 328608DEST_PATH_IMAGE043
are respectively the first
Figure 955899DEST_PATH_IMAGE037
Strip branch voltage amplitude and
Figure 464240DEST_PATH_IMAGE038
the amplitude of the strip branch voltage is,
Figure 126166DEST_PATH_IMAGE044
is the bus voltage limit;
the expression for calculating the line load loss ratio is as follows:
Figure 61761DEST_PATH_IMAGE045
，
Figure 176347DEST_PATH_IMAGE046
，
in the formula (I), the compound is shown in the specification,
Figure 222801DEST_PATH_IMAGE047
、
Figure 4812DEST_PATH_IMAGE048
respectively lost load directly caused by a disaster and lost load indirectly caused by the disaster,
Figure 111308DEST_PATH_IMAGE049
in order to lose the load,
Figure 713191DEST_PATH_IMAGE050
for the maximum load in the area to be considered,
Figure 563335DEST_PATH_IMAGE051
the influence coefficients of disaster relief caused by power failure in disaster areas and the influence coefficients of disaster relief caused by power failure in non-disaster areas are respectively obtained;
the expression for calculating the important load loss ratio is as follows:
Figure 456643DEST_PATH_IMAGE052
，
in the formula (I), the compound is shown in the specification,
Figure 468461DEST_PATH_IMAGE053
in order to be the total number of important loads,
Figure 557640DEST_PATH_IMAGE054
in order to be able to lose a significant amount of load,
Figure 211475DEST_PATH_IMAGE055
、
Figure 702500DEST_PATH_IMAGE056
are respectively the first
Figure 150798DEST_PATH_IMAGE057
Power of an important load and
Figure 992853DEST_PATH_IMAGE058
the power of one of the important loads is,
Figure 450379DEST_PATH_IMAGE059
、
Figure 795909DEST_PATH_IMAGE060
are respectively the first
Figure 680689DEST_PATH_IMAGE057
Weight of the important load and
Figure 478880DEST_PATH_IMAGE058
the weight of the one of the important loads,
Figure 743027DEST_PATH_IMAGE061
the value of the line belonging to the disaster area is taken,
Figure 943064DEST_PATH_IMAGE062
the value of the line which does not belong to the disaster area is taken.
3. The method as claimed in claim 1, wherein the membership function is expressed as:
Figure 733166DEST_PATH_IMAGE063
，
in the formula (I), the compound is shown in the specification,
Figure 284233DEST_PATH_IMAGE064
for a moderate degree of membership in voltage,
Figure 817982DEST_PATH_IMAGE065
is a value of the voltage to be applied,
Figure 138105DEST_PATH_IMAGE066
the lower limit value of the range of the voltage corresponding fuzzy evaluation set 'blue early warning',
Figure 364687DEST_PATH_IMAGE067
the lower limit value of the range of the voltage corresponding fuzzy evaluation set 'yellow early warning',
Figure 403050DEST_PATH_IMAGE068
the lower limit value of the range of the voltage corresponding to the fuzzy evaluation set orange early warning is obtained,
Figure 474912DEST_PATH_IMAGE069
and the lower limit value of the range of the voltage corresponding to the fuzzy evaluation set 'red early warning'.
4. The method for analyzing grid disaster based on the fuzzy evaluation matrix as claimed in claim 1, wherein the specific step of attribute reduction of the fuzzy evaluation matrix comprises:
inputting: decision information system
Figure 915120DEST_PATH_IMAGE070
And U is the domain of discourse,
Figure 315533DEST_PATH_IMAGE071
wherein C is a disaster condition attribute, D is a decision attribute, V is a value range of the attribute A, and F is an information function of UxA → V;
and (3) outputting: decision information system
Figure 575613DEST_PATH_IMAGE070
A reduction of (a);
step 1: calculating information entropy H (A);
step 2: b is initialized, and B = the front beam;
step 3: solving the core attribute core (A) of the decision information system, if any a belongs to A
Figure 451165DEST_PATH_IMAGE072
Then, then
Figure 745881DEST_PATH_IMAGE073
B obtained after traversal is core (A);
step 4: judging whether H (A) is equal to H (B), if not, turning to Step 5; if the two are equal, B is the reduction, B is output, and the algorithm is ended;
step 5: for any a ∈ A-B, the importance of a relative to B is calculated according to the formula sig (a, B; A) = H (B ∈ { a }) -H (B), such that
Figure 48686DEST_PATH_IMAGE074
Turning to Step 4.
5. The method for analyzing grid disaster based on fuzzy evaluation matrix as claimed in claim 1, wherein the fuzzy comprehensive evaluation formula is:
Figure 61641DEST_PATH_IMAGE075
，
in the formula (I), the compound is shown in the specification,
Figure 272043DEST_PATH_IMAGE076
in order to be an objective set of weights,
Figure 483581DEST_PATH_IMAGE077
in order to reduce the fuzzy evaluation matrix after the reduction,
Figure 222867DEST_PATH_IMAGE078
is a generalized fuzzy operator.
6. The grid disaster analysis method based on the fuzzy evaluation matrix as claimed in claim 5, wherein the generalized fuzzy operator is specifically a weighted average fuzzy operator, and an expression for calculating the weighted average fuzzy operator is as follows:
Figure 460469DEST_PATH_IMAGE079
，
in the formula (I), the compound is shown in the specification,
Figure 412245DEST_PATH_IMAGE080
in order to be a weighted average type of blurring operator,
Figure 212710DEST_PATH_IMAGE081
is as follows
Figure 857318DEST_PATH_IMAGE018
The weight of each index is calculated according to the weight of each index,
Figure 579287DEST_PATH_IMAGE082
is as follows
Figure 334753DEST_PATH_IMAGE018
The pair of the individual indexes
Figure 458567DEST_PATH_IMAGE083
The degree of membership of each evaluation state,
Figure 539655DEST_PATH_IMAGE020
is the number of indexes.
7. The utility model provides a power grid disaster analysis device based on fuzzy evaluation matrix which characterized in that includes:
the evaluation module is configured to evaluate the acquired historical power grid disaster data according to a preset power grid disaster evaluation index system to obtain a power grid disaster evaluation result;
an output module, configured to divide the power grid disaster evaluation result into 5 different grades, and form a fuzzy evaluation set, where the fuzzy evaluation set is used for evaluating the power grid disaster
Figure 748920DEST_PATH_IMAGE001
={
Figure 42498DEST_PATH_IMAGE002
Normal,
Figure 20818DEST_PATH_IMAGE003
The early warning of the blue color is carried out,
Figure 287456DEST_PATH_IMAGE004
the early warning of the yellow color is carried out,
Figure 718438DEST_PATH_IMAGE005
an orange early warning is given to the user,
Figure 81286DEST_PATH_IMAGE006
and (2) red early warning }, obtaining a fuzzy evaluation matrix based on the established membership function corresponding to the fuzzy evaluation set, wherein the expression of the fuzzy evaluation matrix is as follows:
Figure 914113DEST_PATH_IMAGE007
，
in the formula (I), the compound is shown in the specification,
Figure 337004DEST_PATH_IMAGE008
to be an evaluation itemU 1 The evaluation matrix of (a) is obtained,
Figure 255281DEST_PATH_IMAGE009
to evaluate the itemsU 1 To (1) aiThe set of membership degrees of the individual indices,
Figure 156241DEST_PATH_IMAGE010
、
Figure 843574DEST_PATH_IMAGE011
、
Figure 702946DEST_PATH_IMAGE012
、
Figure 842940DEST_PATH_IMAGE013
、
Figure 813170DEST_PATH_IMAGE014
respectively is the membership value of each corresponding fuzzy evaluation set;
the reduction module is configured to perform attribute reduction on the fuzzy evaluation matrix based on information entropy so as to obtain a reduced fuzzy evaluation matrix;
the evaluation module is configured to acquire real-time power grid disaster data associated with the reduced fuzzy evaluation matrix, and assign objective weights to all indexes in the real-time power grid disaster data respectively to form an objective weight set
Figure 355010DEST_PATH_IMAGE015
Wherein the expression of the objective weight is:
Figure 122634DEST_PATH_IMAGE016
，
in the formula (I), the compound is shown in the specification,
Figure 749924DEST_PATH_IMAGE017
is as follows
Figure 727107DEST_PATH_IMAGE018
The objective weight of each index is determined,
Figure 389033DEST_PATH_IMAGE019
for the index j the entropy value is given,
Figure 324628DEST_PATH_IMAGE020
the number of the indexes is,
Figure 439214DEST_PATH_IMAGE021
the number of samples;
Figure 220088DEST_PATH_IMAGE022
，
in the formula (I), the compound is shown in the specification,
Figure 2100DEST_PATH_IMAGE023
is as follows
Figure 843017DEST_PATH_IMAGE018
Under the individual index
Figure 710479DEST_PATH_IMAGE024
The proportion of each sample in the index;
Figure 295044DEST_PATH_IMAGE025
，
in the formula (I), the compound is shown in the specification,
Figure 665982DEST_PATH_IMAGE026
to reduce to
Figure 943380DEST_PATH_IMAGE024
A first sample of
Figure 766979DEST_PATH_IMAGE018
The numerical value of each index;
the synthesis module is configured to carry out fuzzy synthesis on the objective weight set and the reduced fuzzy evaluation matrix according to a fuzzy comprehensive evaluation formula so as to analyze the power grid disasterResults
Figure 158165DEST_PATH_IMAGE027
Wherein the grid disaster analysis result is calculated
Figure 649189DEST_PATH_IMAGE027
The expression of (a) is:
Figure 831909DEST_PATH_IMAGE028
，
in the formula (I), the compound is shown in the specification,
Figure 408384DEST_PATH_IMAGE029
analyzing results for grid disasters
Figure 334751DEST_PATH_IMAGE027
The weight function of the following is used,
Figure 680282DEST_PATH_IMAGE030
is as follows
Figure 299482DEST_PATH_IMAGE018
The weights of the individual indices are combined with the ambiguity of the evaluation matrix.
8. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 6.
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