CN107169628A - A kind of distribution network reliability evaluation method based on big data mutual information attribute reduction - Google Patents
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Abstract
The present invention relates to distribution network planning field, a kind of distribution network reliability evaluation method based on big data mutual information attribute reduction is provided, this method is from big data, the correlation between basic index is weighed using the mutual information concept in rough set, with reliability index strong correlation and separate index in screening magnanimity multiclass index, using these indexs as input, carry out evaluating reliability of distribution network with the BP neural network model based on genetic algorithm and work.The present invention breaches the limitation of traditional Monte Carlo simulation and analytic method, for electric power big data, realizes the evaluating reliability of distribution network based on big data mutual information attribute reduction.
Description
Technical field
The present invention relates to distribution network planning field, and in particular to a kind of power distribution network based on big data mutual information attribute reduction
Reliability estimation method.
Background technology
With internet, the development of database technology and the automation of production environment, the field such as finance, electric power, meteorology
Generate that magnanimity species is various and data of rapid development, referred to as big data, nowadays big data has penetrated into every field, into
For the important factor of production, and the new engine of industry transformation is promoted because its huge value turns into.To big data plus
With mining analysis, extract its main information and reasonable utilization, could realize the value of big data, distribution network reliability be one with
The technical indicator of many factors strong correlation, wherein related to distribution network reliability has temperature, wind speed, electricity sales amount, line loss per unit etc.
Many-sided data.Convectional reliability index is general to be estimated by modeling or sampled analog with multiple indexs, as load point refers to
Mark, power off time index, the economic class index of power failure etc., but analytic method has significant limitations, illiteracy when handling complicated electric power system
Time-consuming very long caused by special Carlow sampling non-stateful redundant, big data technology provides to carry out evaluating reliability of distribution network
New approaches.
The content of the invention
The invention aims to overcome above-mentioned the deficiencies in the prior art part, propose a kind of based on big data mutual information
The distribution network reliability evaluation method of attribute reduction, from big data, weighs basic using the mutual information concept in rough set
With reliability index strong correlation and separate index in correlation between index, screening magnanimity multiclass index, with these
Index is carried out evaluating reliability of distribution network as input, with the BP neural network model based on genetic algorithm and worked.The present invention is prominent
The limitation of traditional Monte Carlo simulation and analytic method has been broken, for electric power big data, has realized and is based on big data mutual information attribute
The evaluating reliability of distribution network of yojan.
The purpose of the present invention is achieved through the following technical measures.
A kind of distribution network reliability evaluation method based on big data mutual information attribute reduction, this method pair can with power distribution network
Pre-processed by the relevant index of property, include the discretization of continuous type index, between the concept parameter based on comentropy
Association relationship, carry out going dimension to obtain entropy correlated coefficient between index after operating, each index and reliability index judged accordingly
Correlation and each index correlation each other, carry out Indexes Reduction, then for obtained after yojan and reliability index
Strong correlation and separate index are fitted their non-linear relation using BP neural network, and combine the optimizing of genetic algorithm
The deficiency of feature supplements neural net method.Specifically include following steps：
Step 1：Data largely related to distribution network reliability are collected from academic, meteorological or statistics website；
Step 2：The index value relevant with distribution network reliability is sorted out in many data of comforming, that is, sorts out portion
The decision table of reliability index and index of correlation corresponding relation is characterized, final distribution network reliability height is represented including 1
Decision attribute (i.e. reliability index) and multiple conditional attributes for representing the factor related to reliability；
Step 3：Data in decision table are pre-processed：According to all values of each attribute, taking for attribute is judged
Value is continuous or discrete, for continuous type attribute, and the knowledge in mathematical statistics need to be utilized, which to calculate it, to be divided
Number, and with equidistant discrete method by continuous type attribute discretization；
Step 4：Probability when specific discrete value is got per attribute is calculated, the respective information of every attribute is then obtained
Entropy, conditional attribute for decision attribute conditional entropy, and then try to achieve between various conditional attributes and decision attribute, two-by-two condition belong to
Mutual information between property；
Step 5：Mutual information in step 4 between counted conditional attribute and decision attribute is normalized, with reference to letter
Breath entropy asks for the entropy correlated coefficient between conditional attribute and decision attribute, thus the phase between Rule of judgment attribute and decision attribute
Guan Xing, entropy correlated coefficient is smaller, then correlation is weaker, sets a suitable critical value to weigh the correlation between attribute,
Reject the conditional attribute weak with decision attribute correlation；
Step 6：It is similar with the method in step 5, calculate by step 5 reject after remaining conditional attribute two-by-two between entropy phase
Relation number, filters out the redundancy condition attribute weaker with remaining conditional attribute strong correlation, with decision attribute correlation, and deletes,
Obtain with reliability index strong correlation and separate conditional attribute collection, reach the purpose of yojan attribute；
Step 7：Construction three layers of BP neural network the property set after yojan is trained, with obtained by step 6 with can
By property index strong correlation conditional attribute as input, using decision attribute data as output, try to achieve make error of fitting minimum
Connection weight and hidden layer, the threshold value of output layer in network between each node layer, obtain optimal BP neural network model；For
Training precision is improved, optimal initial weight and threshold value can be tried to achieve with genetic algorithm.
In the above-mentioned technical solutions, the step 2 is comprised the steps of：
Step 2.1：Matching somebody with somebody for portion m × n is set up according to the collected mass data related to certain city's distribution network reliability
System adequacy evaluation decision table, wherein n represent the total number of decision attribute and conditional attribute, corresponding decision attribute and condition
Attribute constitutes one group of attribute data, and m represents the total group of number (i.e. sample number) of attribute data；
Step 2.2：The index of distribution network reliability will be directly represented or determined in decision table as decision attribute, such as：For
Electric reliability, remaining index related to reliability is as conditional attribute, such as：Month, temperature, integrated voltage qualification rate etc..
In the above-mentioned technical solutions, the step 3 is comprised the steps of：
Step 3.1：According to the value of all properties in decision table, judge attribute data be it is continuous or discrete, than
Such as：The attributes such as time, month only get fixed several integers, are discrete data, Analyzing Total Electricity Consumption, rate of load condensate, synthesis
The attributes such as rate of qualified voltage can get all numerical value in an interval, be continuous data；
Step 3.2：According to the data distribution characteristic and related objective factor of each factor, calculate continuous according to formula (1)
The number of partitions that type attribute should be divided；
K=1.87 × (m-1)2/5 (1)
In formula, m is the sample number of attribute data, and k is the number of partitions of connection attribute codomain；
Step 3.3：The siding-to-siding block length of continuous type attribute is calculated according to the counted number of partitions in step 3.2, with equidistant discrete
The codomain of continuous type attribute is divided into k interval by method, to one discrete integer value of each interval tax, and calculates continuous type category
Property discretization results, complete continuous data discretization.
In the above-mentioned technical solutions, the step 4 is comprised the steps of：
Step 4.1：The sample number that each attribute gets each discrete integer value is counted, is taken according to formula (2) computation attribute
To probability during specific discrete value；
In formula, k represents the attribute x discretization number of partitions, XiRepresent attribute x i-th of value, c (Xi) represent that attribute x takes
It is worth for XiSample number, U represents total sample i.e. domain, and c (U) represents total number of samples, p (Xi) represent attribute x values for XiIt is general
Rate；
Step 4.2：The respective comentropy of every attribute, conditional attribute are obtained for decision attribute according to formula (3), (4)
With a certain conditional attribute for the conditional entropy of another conditional attribute, it is necessary to which explanation, comentropy herein is used to measure category
Property provide information content, also illustrate that the ordering degree of sequence of attributes, conditional entropy is represented before completely known a certain attribute
Put, the information content of another attribute also how many；
In formula, H (x) represents attribute x comentropy；
In formula, p (Yj|Xi) represent in XiOn the premise of generation, YjThe probability of generation, and H (y | x) represent attribute y for x's
The conditional entropy of conditional entropy or y based on x；
Step 4.3：Using the result of calculation of step 4.2, according to formula (5) try to achieve every kind of conditional attribute and decision attribute it
Between, the mutual information of conditional attribute between any two, to represent the size that information content is had between these attributes,
I (x, y)=H (y)-H (y | x) (5)
In formula, H (y) represents attribute y comentropy, and I (x, y) represents attribute x and y mutual information, it is believed that be attribute y
The information content being had with x.
In the above-mentioned technical solutions, the step 5 is comprised the steps of：
Step 5.1：To eliminate dimension impact, using formula (6) to by the counted conditional attribute of step 4.3 and decision attribute
Mutual information be normalized, ask for entropy correlated coefficient value, accordingly the correlation between Rule of judgment attribute and decision attribute, entropy
Coefficient correlation is smaller, represents that correlation is weaker, and conditional attribute is also just smaller for the effect of evaluating reliability of distribution network；
In formula, ρxyFor attribute x and y entropy correlated coefficient, x and y degree of correlation is represented；
Step 5.2：One critical value e1 is set according to the entropy correlated coefficient result of calculation in step 5.1, when certain condition
When the entropy correlated coefficient of attribute and decision attribute is less than the critical value, it is believed that influence of the conditional attribute for distribution network reliability
Less, it is rejected from decision table.
In the above-mentioned technical solutions, the step 6 is comprised the steps of：
Step 6.1：It is similar with the method in step 5, calculate by step 5.2 reject after entropy between remaining conditional attribute
Coefficient correlation；
Step 6.2：One critical value e2 is set according to the entropy correlated coefficient result of calculation in step 6.1, when two conditions
When the entropy correlated coefficient of attribute exceedes this critical value, it is believed that the correlation of the two attributes is very strong, can represent mutually, i.e., two
Individual attribute is roughly the same for the influence of distribution network reliability, now to compare the two conditional attributes and decision attribute it
Between entropy correlated coefficient, delete the conditional attribute weaker with decision attribute correlation, reduce the redundancy of property set, obtain with
Reliability index strong correlation and separate conditional attribute collection.
In the above-mentioned technical solutions, the step 7 is comprised the steps of：
Step 7.1：Three layers of BP neural network of construction are trained to the attribute data after yojan, to be obtained by step 6.2
With the conditional attribute of reliability index strong correlation as input, final output is used as using decision attribute；Assuming that after yojan
There are p kind conditional attributes in decision table, then the node number of input layer and output layer is respectively p and 1；In m group attribute datas with
Machine selects b test sample, and remaining sample is as the training sample of neutral net, and sample includes conditional attribute and decision attribute
Data in sample are normalized by value；
Step 7.2：With computer random generate h group BP neural networks in each node layer initial connection weight and imply
Layer, the threshold value of output layer, are rewritten as binary coded form, constitute initial solution space, and solution is gone out with reference to neural computing
The fitness of data is solved in space；The larger preceding c solution data of fitness are selected as parent solution data, parent data are carried out
Intersect, mutation operation obtains filial generation solution space, convergence is judged whether according to the fitness of filial generation solution data, if it is, optimizing
Stop and export optimal initial weight and threshold value, otherwise, proceed to select, intersect, mutation operation；
Step 7.3：Counted initial weight in step 7.2 and threshold value are decoded, trained with BP neural network at normalization
Sample after reason, obtains the estimate of decision attribute and the error of actual value, judges whether the error meets the condition of convergence, if not
Meet, then adjust weight and threshold value, continue training network；If meeting, stop circulation, output makes the minimum weight of error and threshold
Value.
Compared with prior art, beneficial effects of the present invention are：
The present invention proposes a kind of distribution network reliability evaluation method based on mutual information and improved BP, for
The a large amount of a variety of data related to distribution network reliability occurred under big data background, it is general based on the mutual information on the basis of comentropy
Read and go dimension operation to obtain entropy correlated coefficient value, the index with distribution network reliability strong correlation is filtered out, with reference to BP nerve nets
Network is modeled to these indexs, and can not be true with the optimizing feature supplements neutral net initial weight and threshold value of genetic algorithm
Fixed deficiency, realizes comprehensive accurate rapid evaluation of distribution network reliability.
Brief description of the drawings
Fig. 1 is the evaluating reliability of distribution network flow chart based on big data mutual information attribute reduction；
Fig. 2 is the flow chart based on mutual information yojan distribution network reliability index of correlation.
Embodiment
In order that technological means, creation characteristic and the purpose of the present invention are apparent to, further is made to the present invention below
Illustrate.
Referring to Fig. 1,2, the embodiment of the present invention provides a kind of distribution network reliability based on big data mutual information attribute reduction
Appraisal procedure, is followed the steps below successively：
Step 1：Certain city is obtained inside from electric power enterprise largely matches somebody with somebody electricity consumption data, is obtained from the websites such as meteorological, statistics
The each side data related to city's distribution network reliability；
Step 2：The evaluating reliability of distribution network that portion 108 × 15 is sorted out from the mass data collected by step 1 is determined
Plan table, including 1 decision attribute --- power supply reliability (Y, %), and 14 conditional attributes --- the time (X1), month (X2),
Analyzing Total Electricity Consumption (X3, ten thousand kWh), electricity sales amount (X4, ten thousand kWh), 220kV and following line loss per unit (X5, %), rate of load condensate
(X6, %), peak load (X7, ten thousand kW), integrated voltage qualification rate (X8, %), monthly total precipitation (X9, mm), monthly mean temperature
(X10, DEG C), moon sunshine time (X11, h), monthly average wind speed (X12, m/s), moon Windy Days (X13, day), rain day moon number
(X14, day), have 108 groups of attribute datas；
Step 3：According to the value of all properties in decision table, judge attribute data be it is continuous or discrete, such as：
The attributes such as time, month only get fixed several integers, are discrete data, Analyzing Total Electricity Consumption, rate of load condensate, integrated voltage
The value of the attributes such as qualification rate is derived from some continuum, is continuous data；For ease of data dependence analysis below, it is necessary to
Sliding-model control is carried out to continuous data, specific processing mode is as follows：
According to the data distribution characteristic and related objective factor of each factor, calculating continuous type attribute according to formula (1) should
The number of partitions being divided；
K=1.87 × (m-1)2/5 (1)
In formula, m is total number of samples, and k is the number of partitions of connection attribute.
According to formula (1) counted number of partitions m=1.87 × (108-1)2/5=12.12, that is, select that all properties are whole
It is divided into 12 classes, the results are shown in Table 1；
Continuous type attribute x value is divided into k interval with equidistant discrete method, utilizes formula (2) to calculate continuous type category
Siding-to-siding block length l of the property in discretizationx, and one discrete integer value of each interval tax, that is, continuous data are being entered
1,2 are only got after row discretization ..., these discrete integer values of k；The each original value pair of the attribute is calculated further according to formula (3)
The discretization results answered, complete discretization, and discretization results are as shown in table 1.
In formula, max ([x]) and min ([x]) are respectively the maximum and minimum value of all values in attribute x, and k is setting
The interval number of discretization.
In formula, xiAttribute x i-th of value, X before expression discretizationiRepresent discretization after and xiCorresponding attribute x
I-th of value, [x] represents to round downwards, i.e. the maximum integer smaller than x.
The discretization results of table 1
Step 4：Using the discretization results in step 3, the sample number that each attribute gets each discrete integer value is counted,
Probability during specific discrete value is got according to formula (4) computation attribute；
In formula, k represents the attribute x discretization number of partitions, XiRepresent attribute x i-th of value, c (Xi) represent that attribute x takes
It is worth for XiSample number, U represents total sample i.e. domain, and c (U) represents total number of samples, p (Xi) represent attribute x values for XiIt is general
Rate.
The probability distribution tried to achieve more than, the respective comentropy of every attribute, bar are obtained according to formula (5), (6) respectively
Part attribute for decision attribute and a certain conditional attribute for another conditional attribute conditional entropy, it is necessary to explanation, herein
Comentropy be used for metric attribute provide information content, also illustrate that the ordering degree of sequence of attributes, conditional entropy is represented complete
On the premise of known a certain attribute, the information content of another attribute；
In formula, H (x) represents attribute x comentropy.
In formula, p (Yj|Xi) represent in XiOn the premise of generation, YjThe probability of generation, and H (y | x) represent attribute y for x's
The conditional entropy of conditional entropy or y based on x.
Using above result of calculation, tried to achieve according to formula (7) between various conditional attributes and decision attribute, conditional attribute two
Mutual information between two, to measure the size that information content is had between these attributes.
I (x, y)=H (y)-H (y | x) (7)
In formula, H (y) represents attribute y comentropy, and I (x, y) represents attribute x and y mutual information, it is believed that be attribute y
The information content being had with x.
Step 5：To eliminate dimension impact, using formula (8) to by the mutual of the counted conditional attribute of step 4 and decision attribute
Information is normalized, and asks for entropy correlated coefficient value, accordingly the correlation between Rule of judgment attribute and decision attribute, and entropy is related
Coefficient is smaller, represents that correlation is weaker, and conditional attribute is also just smaller for the effect of evaluating reliability of distribution network；Each conditional attribute
xiThe entropy correlated coefficient of (i=1,2 ..., 14) between decision attribute y is shown in Table 2；
In formula, ρxyFor attribute x and y entropy correlated coefficient, x and y degree of correlation is represented.
Entropy correlated coefficient between the conditional attribute of table 2 and decision attribute
| Conditional attribute | X1 | X2 | X3 | X4 | X5 | X6 | X7 |
| Entropy correlated coefficient | 0.2770 | 0.1488 | 0.1859 | 0.2027 | 0.1513 | 0.1578 | 0.1636 |
| Conditional attribute | X8 | X9 | X10 | X11 | X12 | X13 | X14 |
| Entropy correlated coefficient | 0.2874 | 0.1353 | 0.1112 | 0.1645 | 0.1569 | 0.0947 | 0.1652 |
One critical value e1 is set according to entropy correlated coefficient result of calculation, when the entropy phase of certain conditional attribute and decision attribute
When relation number is less than the critical value, it is believed that the conditional attribute is little for the influence of distribution network reliability, by it from decision table
Reject；Found out by table 2, maximum entropy correlated coefficient is no more than 0.3 between these conditional attributes and decision attribute, and e1 is chosen herein
For 0.15, the conditional attribute by entropy correlated coefficient no more than e1 removes, that is, leaves out month X2, monthly total precipitation X9, monthly mean temperature
X10, moon Windy Days X13.
Step 6：It is similar with the method in step 5, calculate by step 5 reject after the mutual entropy phase of remaining conditional attribute
Relation number, sets up correlation matrix, and result of calculation is as shown in table 3；
The mutual entropy correlated coefficient of the essential condition attribute of table 3
One critical value e2 is set according to the value condition of entropy correlated coefficient in correlation matrix, when the entropy of two conditional attributes
When coefficient correlation exceedes this critical value, it is believed that the correlation of the two attributes is very strong, can represent mutually, i.e., two attributes pair
It is roughly the same in the influence of distribution network reliability, now compares the entropy phase between the two conditional attributes and decision attribute
Relation number, deletes the conditional attribute weaker with decision attribute correlation, obtains and reliability index strong correlation and separate
Conditional attribute collection, reach the purpose of attribute reduction；
As can be seen from Table 3, the entropy correlated coefficient between X1 and X8, X3 and X4, X3 and X7 is selected herein more than 0.5
It is 0.5 to take critical value e2, and the entropy correlated coefficient size of this five conditional attributes and decision attribute is X8>X1>X4>X3>X7, because
This leaves out conditional attribute the time X1 and Analyzing Total Electricity Consumption X3 of relative redundancy.
Step 7：Construction three layers of BP neural network the attribute data after yojan is trained, with obtained by step 6 with
The conditional attribute of reliability index strong correlation is used as final output, it is assumed that after yojan as input using decision attribute data
There are p kind conditional attributes in decision table, then the node number of input layer and output layer is respectively p and 1；108 are had in this example
Group sample data, 8 groups are therefrom selected at random as test sample, remaining 100 groups as training sample, sample includes condition
Data in sample are normalized by attribute and decision attribute values；
The initial connection weight and hidden layer, output layer of each node layer in h group BP neural networks are generated with computer random
Threshold value, be rewritten as binary coded form, constitute initial solution space, calculated with reference to neutral net and data are solved in solution space
Fitness；The larger preceding c solution data of fitness are selected as parent solution data, parent data are intersected, make a variation behaviour
Filial generation solution space is obtained, convergence is judged whether according to the fitness of filial generation solution data, if it is, optimizing stops and exported most
Excellent initial weight and threshold value, otherwise, proceed to select, intersect, mutation operation；
The counted initial weight of previous step and threshold value are decoded and neutral net is input to, normalizing is trained with BP neural network
100 training samples after change processing, obtain decision attribute estimate and the error of actual value, judge whether the error meets receipts
Condition is held back, if it is not satisfied, then adjustment weight and threshold value, continues training network；If meeting, stop circulation, output makes error most
Small weight and threshold value, obtains optimal BP network models；
The reliability of 8 groups of test samples is estimated with the BP neural network model trained, assessment result with it is true
As shown in table 4, as can be seen from Table 4, assessed value is fairly close with actual value for the contrast of value, and maximum absolute error is 0.004,
It can be seen that, the Evaluated effect of the appraisal procedure is preferable.
Table 4 predicts the outcome
| Sequence number | Actual value | Predicted value | Absolute error |
| 1 | 99.989 | 99.990 | 0.001 |
| 2 | 99.973 | 99.973 | 0.000 |
| 3 | 99.974 | 99.975 | 0.001 |
| 4 | 99.989 | 99.985 | 0.004 |
| 5 | 99.994 | 99.992 | 0.002 |
| 6 | 99.980 | 99.981 | 0.001 |
| 7 | 99.988 | 99.987 | 0.001 |
| 8 | 99.987 | 99.987 | 0.000 |
The content not being described in detail in this specification, belongs to prior art known to those skilled in the art.
Claims (6)
1. a kind of distribution network reliability evaluation method based on big data mutual information attribute reduction, it is characterized in that this method include with
Lower step：
(1) data largely related to distribution network reliability are collected from academic, meteorological or statistics website；
(2) a decision table for characterizing reliability index and index of correlation corresponding relation is sorted out in many data of comforming, wherein wrapping
Include 1 decision attribute i.e. reliability index for representing final distribution network reliability height it is related to reliability with multiple expressions because
The conditional attribute of element；
(3) data in decision table are pre-processed：According to all values of each attribute, the value for judging attribute is continuous
Or it is discrete, for continuous type attribute, calculate its number that should be divided, and belonged to continuous type with equidistant discrete method
Property discretization；
(4) probability when specific discrete value is got per attribute is calculated, the respective comentropy of every attribute, condition category is then obtained
Property and then is tried to achieve between various conditional attributes and decision attribute, two-by-two between conditional attribute for the conditional entropy of decision attribute
Mutual information；
(5) mutual information in step (4) between counted conditional attribute and decision attribute is normalized, combining information entropy is asked
The entropy correlated coefficient between conditional attribute and decision attribute is taken, thus the correlation between Rule of judgment attribute and decision attribute,
Entropy correlated coefficient is smaller, then correlation is weaker, sets suitable critical value to weigh the correlation between attribute, rejects and decision-making
The weak conditional attribute of Attribute Correlation；
(6) calculate by step (5) rejecting after remaining conditional attribute two-by-two between entropy correlated coefficient, filter out and remaining condition belong to
Property strong correlation, the redundancy condition attribute weaker with decision attribute correlation, and delete, obtain and reliability index strong correlation and phase
Mutually independent conditional attribute collection, reaches the purpose of yojan attribute；
(7) three layers of BP neural network are constructed to be trained the property set after yojan, is referred to what is obtained by step (6) with reliability
The conditional attribute of strong correlation is marked as input, using decision attribute data as output, is tried to achieve in the network for making error of fitting minimum
Connection weight and hidden layer, the threshold value of output layer between each node layer, obtain optimal BP neural network model.
2. the distribution network reliability evaluation method according to claim 1 based on big data mutual information attribute reduction, it is special
Levy is that the concrete mode of a decision table for characterizing reliability index and index of correlation corresponding relation is sorted out in step (2) such as
Under：
Step one, the power distribution network for setting up portion m × n according to the collected mass data related to certain city's distribution network reliability can
By property evaluation decision table, wherein n represents the total number of decision attribute and conditional attribute, corresponding decision attribute and conditional attribute structure
Into one group of attribute data, m represents the total group of number i.e. sample number of attribute data；
Step 2, will directly represent or determines the index of distribution network reliability as decision attribute in decision table, remaining with it is reliable
Property related index be used as conditional attribute.
3. the distribution network reliability evaluation method according to claim 1 based on big data mutual information attribute reduction, it is special
Levy the concrete mode for being to be pre-processed to the data in decision table in step (3) as follows：
Step one, according to the value of all properties in decision table, it is continuous or discrete to judge attribute data；
Step 2, according to the data distribution characteristic and related objective factor of each factor, continuous type category is calculated according to below equation
The number of partitions that property should be divided；
K=1.87 × (m-1)2/5
In formula, m is the sample number of attribute data, and k is the number of partitions of connection attribute codomain；
Step 3, the siding-to-siding block length of continuous type attribute is calculated according to the counted number of partitions, with equidistant discrete method by continuous type attribute
Codomain be divided into k it is interval, to it is each it is interval assign a discrete integer value, and calculate the discretization knot of continuous type attribute
Really, the discretization of continuous data is completed.
4. the distribution network reliability evaluation method according to claim 1 based on big data mutual information attribute reduction, it is special
Levy be try to achieve between various conditional attributes and decision attribute in step (4), the specific side of mutual information two-by-two between conditional attribute
Formula is as follows：
Step one, the sample number that each attribute gets each discrete integer value is counted, spy is got according to below equation computation attribute
Determine probability during centrifugal pump,
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In formula, k represents the attribute x discretization number of partitions, XiRepresent attribute x i-th of value, c (Xi) represent that attribute x values are
XiSample number, U represents total sample i.e. domain, and c (U) represents total number of samples, p (Xi) represent attribute x values for XiProbability；
Step 2, the respective comentropy of every attribute, conditional attribute are obtained for decision attribute and a certain kind according to below equation
Conditional attribute for another conditional attribute conditional entropy, it is necessary to which explanation, comentropy herein is used for what metric attribute was provided
Information content, also illustrates that the ordering degree of sequence of attributes, conditional entropy is represented on the premise of completely known a certain attribute, another
The information content of attribute also how many；
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In formula, H (x) represents attribute x comentropy；
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In formula, p (Yj|Xi) represent in XiOn the premise of generation, YjThe probability of generation, and H (y | x) represent conditional entropies of the attribute y for x
Or the conditional entropy of the y based on x；
Step 3, using the result of calculation of upper step, is tried to achieve between every kind of conditional attribute and decision attribute, condition according to below equation
The mutual information of attribute between any two, to represent the size that information content is had between these attributes,
I (x, y)=H (y)-H (y | x)
In formula, H (y) represents attribute y comentropy, and I (x, y) represents attribute x and y mutual information, is the letter that attribute y and x has
Breath amount.
5. the distribution network reliability evaluation method according to claim 1 based on big data mutual information attribute reduction, it is special
It is related being normalized counted conditional attribute and the mutual information between decision attribute in step (5) and weighing attribute to levy
The concrete mode of property is as follows：
Step one, to eliminate dimension impact, the mutual information of counted conditional attribute and decision attribute is carried out using below equation
Normalization, asks for entropy correlated coefficient value, accordingly the correlation between Rule of judgment attribute and decision attribute, entropy correlated coefficient is got over
It is small, represent that correlation is weaker, conditional attribute is also just smaller for the effect of evaluating reliability of distribution network；
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In formula, ρxyFor attribute x and y entropy correlated coefficient, x and y degree of correlation is represented；
Step 2, a critical value is set according to the result of calculation of entropy correlated coefficient, when certain conditional attribute and decision attribute
When entropy correlated coefficient is less than the critical value, it is believed that the conditional attribute for distribution network reliability influence less, by it from decision-making
Rejected in table.
6. the distribution network reliability evaluation method according to claim 1 based on big data mutual information attribute reduction, it is special
Levy be obtain in step (7) optimal BP neural network model concrete mode it is as follows：
Step one, construct three layers of BP neural network to be trained the attribute data after yojan, with obtain and reliability index
The conditional attribute of strong correlation is used as final output as input using decision attribute；Assuming that having p kind bars in decision table after yojan
Part attribute, then the node number of input layer and output layer is respectively p and 1；B test specimens are randomly choosed in m group attribute datas
This, remaining sample is as the training sample of neutral net, and sample includes conditional attribute and decision attribute values, to the number in sample
According to being normalized；
Step 2, the initial connection weight of each node layer and hidden layer, output in h group BP neural networks are generated with computer random
The threshold value of layer, is rewritten as binary coded form, constitutes initial solution space, is gone out with reference to neural computing in solution space and is solved
The fitness of data；The larger preceding c solution data of fitness are selected as parent solution data, parent data are intersected, become
ETTHER-OR operation obtains filial generation solution space, and convergence is judged whether according to the fitness of filial generation solution data, if it is, optimizing stops and defeated
Go out optimal initial weight and threshold value, otherwise, proceed to select, intersect, mutation operation；
Step 3, decodes to the counted initial weight of upper step and threshold value, the sample after normalized is trained with BP neural network,
The estimate of decision attribute and the error of actual value are obtained, judges whether the error meets the condition of convergence, if it is not satisfied, then adjusting
Weight and threshold value, continue training network；If meeting, stop circulation, output makes the minimum weight and threshold value of error.
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