CN110544016B - Method and equipment for evaluating influence degree of meteorological factors on fault probability of power equipment - Google Patents
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Abstract
The invention discloses a method and equipment for evaluating the influence degree of meteorological factors on the fault probability of electric power equipment. According to the method, the complexity of calculation of the influence degree of the attribute subsets of the meteorological factors in the information table is greatly reduced compared with that of a traditional method by dividing the indistinguishable relation between the meteorological factors and the attributes in the power equipment fault information table, and the method is favorable for quickly screening and identifying the key meteorological factors. The method can overcome the defects of the prior art, can be widely used for evaluating the influence degree of meteorological factors on the fault probability of the power equipment, is simple and efficient, provides meteorological suggestions for the operation and maintenance of the power equipment, and is favorable for improving the operation safety of the power equipment.
Description
Technical Field
The invention relates to reliability evaluation of power equipment, in particular to a method and equipment for evaluating the influence degree of meteorological factors on the fault probability of the power equipment.
Background
China is wide in territory, the scale of a power transmission network is large, the climate difference of the east and the west is large, typhoons and rainstorms are continuous in coastal areas in two seasons of spring and summer every year, and faults of a power system are frequent. Actual operation experience shows that power systems in many areas have the situation that the transmission lines, the transformation equipment and even the transformer substations are completely stopped or partially stopped due to severe weather conditions. Therefore, the possibility of power equipment failure is greatly increased under the severe external environment, and the safe operation of a power system is seriously influenced. And further, the failure modes of the power system possibly caused under different disaster weather conditions are greatly different, and the damage degree of the caused failure to the system is greatly different. For example, the system reclosing rate is low during a dirty flash and high during a lightning damage. How to properly account for the degree of influence of these various elements in fault diagnosis is a problem to be studied in depth.
At present, the method for calculating the influence degree of meteorological factors on the fault probability of the power equipment mainly comprises an Apriori algorithm and an information table method, but the Apriori algorithm needs a large amount of meteorological data and power equipment fault data, and if the occurrence frequency of some special meteorology is very few, the meteorological factors under the condition are often filtered by the algorithm because of too low support degree, so that the influence degree of the meteorological factors is not favorably calculated. The method for calculating the fault probability of the meteorological factor influencing equipment based on the information table mainly comprises an attribute subtraction method, an information entropy method and a mutual information method. The attribute reduction method adopts a method of reducing the meteorological factors in the meteorological factor attribute subsets one by one, and calculates the difference between the dependence degrees of the meteorological factor attribute subsets after reduction and before reduction to calculate the influence degree of the reduction meteorological factors, and the method has large calculation amount and is not beneficial to real-time application. The information entropy method utilizes the information entropy of different meteorological factor attribute subsets to calculate the influence degree of the attributes, but if the attributes in the information table are combined, the conditional entropy can be increased, and the method is not beneficial to the meteorological factor and power equipment fault information table with a large number of combined regular elements. The mutual information method takes the variable quantity of the mutual information of the meteorological factors relative to the fault probability level after one meteorological factor is added as the measurement of the importance of the attributes, but in most cases, the meteorological factor and each attribute in the fault information table of the power equipment are multivalued, the mutual information gain enables the result to tend to be the attributes with a large number of values, and the error of the result is large.
The existing method for calculating the influence degree of meteorological factors on the fault probability of the power equipment has the following problems: (1) the calculated amount is large, and the rapid screening and identification of meteorological factors are not facilitated; (2) the method for calculating the influence degree of the meteorological factors is limited by the meteorological factors and the power equipment fault information table, and the influence degree cannot be accurately calculated in the meteorological factors and the power equipment fault information table with any structure.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides the method and the equipment for evaluating the influence degree of the meteorological factors on the fault probability of the power equipment, which can efficiently and quickly screen and identify the key meteorological factors influencing the fault probability of the equipment, have simple and efficient method, provide the meteorological suggestions for the operation and maintenance of the power equipment and are beneficial to improving the operation safety of the power equipment.
The technical scheme is as follows: according to a first aspect of the present invention, there is provided a method of assessing the degree of influence of meteorological factors on the probability of failure of electrical equipment, comprising the steps of:
s10 rootEstablishing a weather factor attribute subset C ' to be judged according to the weather factors and the power equipment fault information table, and calculating a knowledge particle [ x ] formed by an indistinguishable relationship set IND (C ') of the weather factor attribute subset C ' in the weather factor and power equipment fault information table]C’Knowledge particle [ x]C’The method comprises the steps of representing equipment fault probability grades corresponding to meteorological factor subsets under different discretization values, and calculating a knowledge particle [ x ] formed by an indistinguishable relation set IND (D) of the fault probability grade D in a power equipment fault information table]DKnowledge particle [ x]DThe set of all rule elements under the same fault probability level is shown;
s20, calculating each knowledge particle [ x]DIn a lower approximate set R _ (X) in the indistinguishable relationship set IND (C'), the calculation of the lower approximate set reveals that the meteorological factor subset supports all rule elements of the same fault probability under the same discretization value;
s30, obtaining each knowledge particle [ x]DThe sum S of the number of all elements in the lower approximate set R _ (X) is calculated, and the dependency degree gamma of the fault probability grade D on the meteorological factor attribute subset C 'to be judged is calculated, wherein the dependency degree reveals the proportion of regular elements which can be formed under different values of the fault probability grade in the meteorological factor subset C';
s40, obtaining the influence degree sig (C ') of the meteorological factor attribute subset C ' according to the dependency degree gamma of the meteorological factor attribute subset C ' to be judged according to the fault probability grade D, wherein the larger the influence degree is, the larger the influence degree of the established meteorological factor attribute subset to be judged on the equipment fault probability is.
The meteorological factors and power equipment fault information table are established in the following mode: collecting and integrating case information of power equipment faults caused by meteorological factors, extracting the meteorological factors in equipment fault cases, constructing a meteorological factor parameter index set, taking the obtained meteorological factor parameter index set as a condition attribute set, carrying out discretization treatment on the condition attribute set, dividing the power equipment fault probability into three levels according to the collected power equipment fault cases, taking the three levels as decision attribute sets, and constructing a power equipment fault information table based on the decision attribute sets and the condition attribute sets.
Further, the establishing the meteorological factor and power equipment fault information table comprises the following steps:
a. constructing a meteorological factor parameter index set: taking a set of all meteorological factors influencing the running state of the electric power equipment in the electric power equipment fault case as a meteorological factor parameter index set, and taking the obtained parameter index set as a condition attribute set C ═ C1,c2,…,cM}，CmRepresenting meteorological factors, wherein M represents the number of main meteorological factors influencing the running state of the power equipment; for each attribute Cm(i ═ 1,2, …, M), discretizing, labeled 0, 1.. k, respectively;
b. dividing the fault probability of the power equipment into three levels according to the collected power equipment fault cases, and taking the three levels as a decision attribute set D (low fault probability, high fault probability and high fault probability) (D)1,D2,D3D1 as a decision attribute of the fault cases with the case ratio of less than 30%, D2 as a decision attribute of the fault cases with the case ratio of 30% -50%, and D3 as a decision attribute of the fault cases with the case ratio of more than 50%;
c. constructing a power equipment fault information table S, S ═<U,A,V,f>Wherein U ═ x1,x2,…,xiThe rule element set is defined as the rule element set, A is an attribute set and consists of a condition attribute C and a decision attribute D, and A is C and U; v is a set of attribute values Va (a belongs to A); f is an information function, represents the value of the power equipment fault probability of each rule element under different meteorological factor attribute values, and satisfies that f (x, a) belongs to A and x belongs to U for any a.
Further, the step S10 includes the following steps:
s101, establishing a meteorological factor attribute subset C '═ { C' to be judged1,c2,…,cn}: the meteorological factor attribute subset is a subset of meteorological factors in a meteorological factor and power equipment fault information table;
s102, calculating an indistinguishable relation set of the meteorological factor attribute subset C' in the meteorological factor and power equipment fault information tableKnowledge particles [ x ] formed by IND (C]C’: obtaining the possible value combination number e of all weather factor attribute subsets C' in the weather factor and power equipment fault information table, establishing e sets, wherein the element of each set in the e sets is the regular element x in the weather factor and power equipment fault information tablei(I-1, 2,3, … I) corresponding meteorological factor attribute subset C' having the same value of rule elements, e sets forming knowledge particles
S103, calculating a knowledge particle [ x ] formed by an indistinguishable relation set IND (D) of the failure probability grade D in the meteorological factor and power equipment failure information table]D: obtaining possible value numbers f of all fault probability levels D in the meteorological factors and power equipment fault information table, establishing f sets, wherein elements of each set in the f sets are regular elements x in the meteorological factors and power equipment fault information tablei(I-1, 2,3, … I) corresponding rule elements with the same value of failure probability level D, f sets forming knowledge particles
Further, the step S20 includes the following steps:
s201, calculating each knowledge grain
For knowledge grain [ x]C’Degree of membership of
S202, calculating each knowledge grain
Approximating the set under the set of non-resolved relationships IND (C')
To obtain
Knowledge granule with value 1
Namely the knowledge granule
Approximating the set under the set of non-resolved relationships IND (C')
Further, the step S30 is specifically implemented as follows:
s301, obtaining each knowledge particle [ x]DThe sum of the numbers of all elements in the lower approximation set R (X)
Wherein the operator | represents the number of elements in the solution set;
s302, calculating the dependency degree gamma of the failure probability grade D on the meteorological factor attribute subset C' to be judged to be S/X, wherein X is a rule element X in a meteorological factor and power equipment failure information tablei(I is 1,2,3, … I).
Further, in step S40, the influence degree sig (C') of the meteorological-element attribute subset C is 1- γ.
According to a second aspect of the present invention, there is provided a computer apparatus, the apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured for execution by the one or more processors, which when executed by the processors perform the steps of the first aspect of the invention.
Has the advantages that:
1. the method can be widely used for rapidly calculating the influence degree of meteorological factors on the fault probability of the power equipment, and the calculation complexity is greatly reduced compared with that of the traditional method through the partition based on the indistinguishable relation between the meteorological factors and the attributes in the fault information table of the power equipment, so that the method is favorable for rapidly screening and identifying the key meteorological factors influencing the fault probability of the equipment. The method is simple and efficient.
2. The method and the device take the knowledge particles as the basis for calculating the influence degree of the meteorological factor attribute subset, are favorable for identifying key meteorological factors from the meteorological factors and the power equipment fault information table, provide meteorological suggestions for the operation and maintenance of the power equipment, and are favorable for improving the operation safety of the power equipment.
3. The invention can effectively integrate the relevant case information of the equipment fault and the meteorological factors in the current power equipment management system (such as a PMS system, an operation and maintenance management system and the like), summarize to form a corresponding information table, effectively mine the incidence relation of the equipment fault and the meteorological factors, and provide support of a data information layer for evaluating the influence degree of the meteorological factors on the equipment fault probability.
4. Compared with the traditional method, the method is not limited by the structure of the information table, can realize the accurate calculation of the influence degree of the meteorological factor attribute subsets on the equipment fault probability under any number and combination in the meteorological factors and power equipment fault information table with any structure, has clear and convenient calculation method, and is beneficial to the rapid screening and identification of the key meteorological factors.
Drawings
FIG. 1 is a general flow diagram of a method of practicing the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
Referring to fig. 1, in one embodiment, to evaluate the effect of rainfall and humidity on the failure probability of the electrical equipment, an evaluation process is implemented as follows.
Step S10, a meteorological factor parameter index set is constructed, the obtained parameter index set is used as a condition attribute set, the condition attribute set is subjected to discretization, the failure probability of the power equipment is divided into three levels, the three levels are used as decision attribute sets, and a power equipment failure information table is constructed on the basis of the decision attribute sets and the condition attribute sets (meteorological factors, the failure probability levels of the power equipment and the failure information of the power equipment can be collected and sorted by power equipment operation and maintenance personnel from a PMS system or other operation and maintenance recording ways, and the division form of the meteorological factors can be divided by operation and maintenance personnel in different regions according to local meteorological conditions).
S101, constructing a meteorological factor parameter index set including all meteorological factors (rainfall, snowfall, humidity, air temperature, and wind speed) affecting the operation state of the power equipment in the power equipment fault case, as shown in table 1, where the obtained parameter index set is used as a condition attribute set C ═ rainfall, snowfall, humidity, air temperature, and wind speed ═ C1,c2,c3,c4,c55, the number of the meteorological factors is; for each attribute Cm(i ═ 1,2, …, M), discretization is performed, labeled 1,2,3 respectively, and if the rainfall is 1,2,3, respectively, the rainfall is small, medium, large, and can be divided according to the range of values.
S102, dividing the fault probability of the power equipment into three levels, and taking the three levels as a decision attribute set D (low fault probability, high fault probability and high fault probability) { D (high fault probability) }1,D2,D3}。
S103, constructing a power equipment fault information table S, wherein S is<U,A,V,f>Wherein U ═ x1,x2In }, failure xiThe rule element set is a rule element set, A is an attribute set and consists of a condition attribute C and a decision attribute D, and A is C and U; v is a set of attribute values Va (a belongs to A); f is an information function and represents the value of the fault probability of the power equipment of each rule element under different meteorological factor attribute values, wherein the fault cases are caused under the different meteorological factor attribute values, and D is taken from the decision attribute accounting for less than 30% of the collected cases1Taking D as decision attribute accounting for 30% -50%2Taking D when the ratio exceeds 50%3That is, for any a ∈ A and X ∈ U, f (X, a) ∈ Va is satisfied, as shown in Table 1, the information table is an information table of rainfall, snowfall, humidity, air temperature, and wind speed and a certain power equipment fault probability level, Table 2 is a discretized form of the information table, X lists rule elements in the weather factors and power equipment fault information table, each rule element corresponds to a rule and comprises a set of weather factor set { c ∈ U1,c2,c3,c4,c5And a corresponding failure probability level D.
TABLE 1 Meteorological factor and information table of failure probability grade of certain power equipment
| X | Amount of rainfall | Amount of snow falling | Humidity | Air temperature | Wind speed | Failure probability class D |
| x1 | Small | In | Is moderate | Is low in | Is low in | Low probability of failure |
| x2 | Small | In | Drying | Is low in | In | The failure probability is higher |
| x3 | Small | In | Is moderate | Is moderate | Height of | High failure probability |
| x4 | Small | Small | Moisture content | Is moderate | Is low in | Low probability of failure |
| x5 | Small | Small | Is moderate | Height of | In | The failure probability is higher |
| x6 | In | Small | Is moderate | Height of | In | High failure probability |
| x7 | In | Small | Drying | Is moderate | Is low in | Low probability of failure |
| x8 | In | In | Drying | Is low in | In | The failure probability is higher |
Table 2 algebraic form of information table
| X | c1 | c2 | c3 | c4 | c5 | D |
| x1 | 1 | 2 | 2 | 1 | 1 | D1 |
| x2 | 1 | 2 | 1 | 1 | 2 | D2 |
| x3 | 1 | 2 | 1 | 2 | 3 | D3 |
| x4 | 1 | 1 | 3 | 2 | 1 | D1 |
| x5 | 1 | 1 | 2 | 3 | 2 | D2 |
| x6 | 2 | 1 | 2 | 3 | 2 | D3 |
| x7 | 2 | 1 | 1 | 2 | 1 | D1 |
| x8 | 2 | 2 | 1 | 1 | 2 | D2 |
Step S20, establishing a meteorological factor attribute subset C ' to be judged, and calculating a knowledge particle [ x ] formed by an indistinguishable relationship set IND (C ') of the meteorological factor attribute subset C ' in a meteorological factor and power equipment fault information table]C’And calculating the fault probability level D in the meteorological factorsKnowledge particle [ x ] formed by non-distinguishing relation sets IND (D) in power equipment fault information table]D。
S201, according to the evaluation purpose, establishing a meteorological factor attribute subset C' ═ { C } to be judged1,c3}；
S202, calculating a knowledge particle [ x ] formed by the set IND (C ') of the indistinguishable relations of the meteorological factor attribute subset C' in the meteorological factor and power equipment fault information table]C’；
Knowledge particle [ x ] is not resolved from relation set]C’Set of compositions
The obtaining method comprises the following steps:
obtaining the possible value combination number of all weather factor attribute subsets C' in the weather factor and power equipment fault information table, wherein e is 5 as known from table 1, establishing 5 sets, wherein the element of each set in the 5 sets is the regular element x in the weather factor and power equipment fault information tablei(I-1, 2,3, … I) corresponding to the same value of the meteorological factor attribute subset C', i.e. in each knowledge grain [ x [ x ] ]]C’Middle rule element xiWeather factor c1And c3The values are the same, and different knowledge grains represent meteorological factors c1And c3Under different values, the corresponding equipment failure probability grades are formed by 5 sets
Knowledge particle [ x]C’Representing the corresponding equipment fault probability grades of the meteorological factor subset C' under different discretization values;
S203, calculating a knowledge particle [ x ] formed by an indistinguishable relation set IND (D) of the failure probability grade D in the meteorological factor and power equipment failure information table]D：
Solving the possible value number f of all fault probability grades D in the meteorological factors and power equipment fault information table, wherein f is 3 as shown in the table 1; 3 sets are established, and the element of each set in the 3 sets is a rule element x in a meteorological factor and power equipment fault information tablei(I ═ 1,2,3, … I) corresponding rule elements with the same value of the failure probability level D, i.e. the same knowledge item [ x [ ]]DAll the rule elements in the rule list have the same failure probability level, and 3 sets form knowledge particles
Knowledge particle [ x]DThe set of all rule elements under the same failure probability level is shown.
[x]D1＝{x1,x4,x7Get the failure probability grade as D1；
[x]D2＝{x2,x5,x8Get the failure probability grade as D2；
[x]D3＝{x3,x6Get the failure probability grade as D3。
And the knowledge particles of the meteorological factor attribute subset and the fault probability level are calculated, so that the fast calculation of the membership degree of each knowledge particle is facilitated. The method takes the knowledge particles as the basis for calculating the influence degree of the meteorological factor attribute subset, and is beneficial to identifying key meteorological factors from the meteorological factors and the power equipment fault information table.
Step S30, calculating each knowledge particle [ x]DIn the lower approximation set R _ (X) in the indistinguishable relationship set IND (C'), the calculation of the lower approximation set reveals that the meteorological factor subset supports all rule elements of the same fault probability under the same discretized value, including the steps of:
s301, calculating each knowledge grain
For knowledge grain [ x]C’Degree of membership of
The same is true in the calculation mode,
according to the above knowledge particle [ x]C’And knowledge particle [ x]DMeaning known and degree of membership
Is indicated in the meteorological factor c1And c3The fault probability class under different values of (a) belongs to [ x]C’To a degree of, e.g.
Indicating, in meteorological factors c1And c3When 1 and 3 are respectively taken, the probability of 1 being the failure probability level is 1;
indicating, in meteorological factors c1And c3When 1 and 2 are taken, respectively, the probability of failure probability rating 1 is 0.5.
S302, in S301
Knowledge granule with value 1
Namely the knowledge granule
Approximating the set under the set of non-resolved relationships IND (C')
Granule of each knowledge
Approximating the set under the set of non-resolved relationships IND (C')
And (3) solving the lower approximate set of the knowledge particles of each fault probability grade in the knowledge particles of the meteorological factor attribute subset, so that all approximate rule elements can be rapidly counted, and the method is efficient, clear and definite.
Step S40, calculating each knowledge particle [ x]DThe sum S of the number of all elements in the lower approximate set R _ (X) is calculated, and the dependency gamma of the fault probability level D on the weather factor attribute subset C 'to be judged is calculated, wherein the dependency reveals the proportion of regular elements which can be formed by the fault probability level under different values of the weather factor subset C'. Comprises the following steps:
s401, calculating each knowledge grain [ x]DThe sum S of the number of all elements in the lower approximation set R (X) is 2;
s402, setting X as a rule element X in a meteorological factor and power equipment fault information tableiAs can be seen from table 1, the number of (I ═ 1,2,3, … I) is X ═ 8, and the dependency γ of the weather factor attribute subset C' to be determined by calculating the failure probability level D is S/X ═ 2/8 ═ 0.25.
And calculating the dependency of the fault probability grade on the weather factor attribute subset to be judged, and being favorable for calculating the influence degree of the weather factor attribute subset according to the size of the dependency.
In step S50, the influence degree sig (C ') of the meteorological-element attribute subset C' is 1- γ 0.75. And the influence degree represents the influence degree of the established meteorological factor attribute subset to be judged on the equipment fault probability.
In order to compare and analyze the accuracy of the calculation results of the above method and the conventional method, a typical attribute reduction method is given below to calculate the meteorological factor attribute subset C' ═ { C ═ C1,c3Results of degree of influence:
(1) let C be { C ═ C1,c2,c3,c4,c5}，IND(C)＝{[x]C1,[x]C2,[x]C3,[x]C4,[x]C5,[x]C6,[x]C7,[x]C8}＝{{x1},{x2},{x3},{x4},{x5},{x6},{x7},{x8}}，IND(D)＝{{x1,x4,x7},{x2,x5,x8},{x3,x6}}，
D positive correlation domain POS at IND (C)C(D)＝{x1,x2,x3x4,x5,x6,x7,x8The correlation coefficient of D and C is YC(D)＝card(POSC(D))/card(U)＝8/8＝1；
(2) Cut Attribute c1Post order C ═ C2,c3,c4,c5}，IND(C”)＝{[x]C1,[x]C2,[x]C3,[x]C4,[x]C5,[x]C6}＝{{x1},{x2,x8},{x3},{x4},{x5,x6},{x7}}，IND(D)＝{{x1,x4,x7},{x2,x5,x8},{x3,x6Positive correlation field POS of D on IND (C')C”(D)＝{x1,x2,x8x3x4,x7The correlation coefficient of D and C' is YC”(D)＝card(POSC”(D))/card(U)＝6/8＝0.75；
(2) Cut Attribute c3Post command C' ″ { C }1,c2,c4,c5}，IND(C”’)＝{[x]C1,[x]C2,[x]C3,[x]C4,[x]C5,[x]C6,[x]C7,[x]C8}＝{{x1},{x2},{x3},{x4},{x5},{x6},{x7},{x8}}，IND(D)＝{{x1,x4,x7},{x2,x5,x8},{x3,x6Positive correlation domain POS of D on IND (C')C”’(D)＝{x1,x2,x3x4,x5,x6,x7,x8The correlation coefficient of D and C' is YC”’(D)＝card(POSC”’(D))/card(U)＝8/8＝1；
(3) According to the result, the attribute subset C' ═ { C ═ of meteorological factors1,c3The influence degree sig (C') of the meteorological factor attribute subset C is YC(D)-(YC(D)-YC”(D))-(YC(D)-YC”’(D))＝1-(1-0.75)-(1-1)＝0.75。
The steps and the results of the algorithm show that the calculation complexity of the traditional method is further increased when more meteorological factors are evaluated, and compared with the traditional method, the method provided by the invention is simple and efficient, and the calculation accuracy is not reduced. Compared with the traditional method, the method is not limited by the structure of the information table, can realize accurate calculation of the influence degree of the meteorological factor attribute subset in the meteorological factor and power equipment fault information table with any structure, has clear and convenient calculation method, and is beneficial to quick screening and identification of key meteorological factors.
Based on the same technical concept as the method embodiment, according to another embodiment of the present invention, there is provided a computer apparatus including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps in the method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. A method for assessing the degree of influence of meteorological factors on the probability of failure of electrical equipment, the method comprising the steps of:
s10, establishing a weather factor attribute subset C 'to be judged according to weather factors and a power equipment fault information table, calculating corresponding equipment fault probability levels of the weather factor attribute subset C' under different discretized values, and calculating an indistinguishable relation set IND (D) of the fault probability level D in the power equipment fault information table to form a knowledge particle [ x [ [ x ] x]D；
S20, calculating each knowledge particle [ x]DThe lower approximate set R _ (X) in the set of non-resolved relations IND (C');
s30, obtaining each knowledge particle [ x]DThe sum S of the number of all elements in the lower approximate set R _ (X), and the dependency degree gamma of the fault probability grade D on the meteorological factor attribute subset C' to be judged;
and S40, obtaining the influence degree sig (C ') of the meteorological factor attribute subset C ' according to the dependence gamma of the meteorological factor attribute subset C ' to be judged according to the fault probability grade D.
2. The method of claim 1, wherein the weather factors and the power equipment failure information table are established by: collecting and organizing case information of power equipment faults caused by meteorological factors, extracting the meteorological factors in the equipment fault cases, constructing a meteorological factor parameter index set, taking the obtained meteorological factor parameter index set as a condition attribute set, and performing discretization processing on the condition attribute set; and dividing the failure probability of the power equipment into three levels according to the collected power equipment failure cases, taking the three levels as a decision attribute set, and constructing a power equipment failure information table based on the decision attribute set and the condition attribute set.
3. The method of claim 2, wherein the step of establishing the weather factor and power equipment fault information table comprises the steps of:
a. constructing a meteorological factor parameter index set: taking a set of all meteorological factors influencing the running state of the electric power equipment in the electric power equipment fault case as a meteorological factor parameter index set, and taking the obtained parameter index set as a condition attribute set C ═ C1,c2,…,cM}，CmRepresenting meteorological factors, wherein M is 1,2, …, and M represents the number of main meteorological factors influencing the operating state of the power equipment; for each attribute CmDiscretizing, and respectively marking as 0, 1.. k;
b. dividing the fault probability of the power equipment into three levels according to the collected power equipment fault cases, and taking the three levels as a decision attribute set D (low fault probability, high fault probability and high fault probability) (D)1,D2,D3D1 is taken as a decision attribute of the fault cases with the case ratio of less than 30 percent, D2 is taken as a decision attribute of the fault cases with the case ratio of 30 percent to 50 percent, and D3 is taken as a decision attribute of the fault cases with the case ratio of more than 50 percent;
c. constructing a power equipment fault information table S, S ═<U,A,V,f>Wherein U ═ x1,x2,…,xiThe rule element set is defined as the rule element set, A is an attribute set and consists of a condition attribute C and a decision attribute D, and A is C and U; v is a set of attribute values Va, and a belongs to A; f is an information function, represents the value of the power equipment fault probability of each rule element under different meteorological factor attribute values, and satisfies that f (x, a) belongs to A and x belongs to U for any a.
4. The method for assessing the influence of meteorological factors on the fault probability of electric power equipment according to claim 3, wherein the step S10 comprises the following steps:
s101, establishing a meteorological factor attribute subset C '═ { C' to be judged1,c2,…,cn}，n<M, wherein the meteorological factor attribute subset is a subset of meteorological factors in the meteorological factors and power equipment fault information table;
s102, calculating the possible value combination number e of all weather factor attribute subsets C' in the weather factor and power equipment fault information table, and establishing e sets, wherein elements of each set in the e sets are regular elements x in the weather factor and power equipment fault information tableiThe corresponding meteorological factor attribute subset C' has the same value of regular elements, I is 1,2,3, … I, e sets form knowledge particles
S103, solving possible value numbers f of all fault probability levels D in the meteorological factor and power equipment fault information table, establishing f sets, wherein elements of each set in the f sets are regular elements x in the meteorological factor and power equipment fault information tableiThe f sets form knowledge particles corresponding to the rule elements with the same fault probability level D value
5. The method for assessing the influence of meteorological factors on the fault probability of electric power equipment according to claim 4, wherein the step S20 comprises the following steps:
s201, calculating each knowledge grain
For knowledge grain [ x]C’Degree of membership of
S202, obtaining
Knowledge granule with value 1
Namely the knowledge granule
Approximating the set under the set of non-resolved relationships IND (C')
6. The method for assessing the influence degree of meteorological factors on the fault probability of electric power equipment according to claim 5, wherein the step S30 is implemented by the following steps:
s301, obtaining each knowledge particle [ x]DThe sum of the numbers of all elements in the lower approximation set R (X)
Wherein the operator | represents the number of elements in the solution set;
s302, calculating the dependency degree gamma of the failure probability grade D on the meteorological factor attribute subset C' to be judged to be S/X, wherein X is a rule element X in a meteorological factor and power equipment failure information tableiThe number of (2).
7. The method according to claim 1, wherein in step S40, the meteorological factor attribute subset C 'influence degree sig (C') is 1- γ.
8. A computer device, the device comprising:
one or more processors;
a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs when executed by the processors implementing the steps of any of claims 1-7.
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