CN110880062B - Method for determining state maintenance time of power distribution equipment - Google Patents

Method for determining state maintenance time of power distribution equipment Download PDF

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CN110880062B
CN110880062B CN201911049328.1A CN201911049328A CN110880062B CN 110880062 B CN110880062 B CN 110880062B CN 201911049328 A CN201911049328 A CN 201911049328A CN 110880062 B CN110880062 B CN 110880062B
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欧阳健娜
高立克
俞小勇
周扬珺
李珊
梁朔
陈绍南
黄伟翔
秦丽文
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Abstract

The invention provides a method for determining state maintenance time of power distribution equipment, belongs to the technical field of power engineering, and solves the problems that conventional equipment maintenance of a power distribution network is mainly regular maintenance and after-repair, the utilization efficiency of maintenance resources is low, and fault prevention cannot be performed according to different equipment differences. The method comprises the steps of carrying out health state grade division according to equipment comprehensive deduction values, applying an entropy weight method to weight equipment state evaluation indexes, generating a health state cloud picture of equipment to be evaluated through a cloud model, and calculating membership degrees between health state clouds of the equipment to be evaluated and health state grade clouds; and finally, inputting the membership vector, the times of failure, the operation records and other data into a long-term and short-term memory network for training, so as to realize the prediction of the next failure occurrence time of the equipment to be evaluated and reasonably plan a maintenance plan. And the reasonable planning of the operation equipment maintenance plan is realized according to the predicted fault occurrence time, the maintenance resources are effectively and reasonably utilized, and the equipment fault risk is reduced.

Description

Method for determining state maintenance time of power distribution equipment
Technical Field
The invention belongs to the technical field of electric power engineering, and particularly relates to a method for determining state maintenance time of power distribution equipment.
Background
The traditional equipment maintenance of distribution network is mostly regular maintenance and overhaul afterwards, overhauls the low and can't carry out the fault prevention to all kinds of equipment differences of resource utilization efficiency. Therefore, the state maintenance is carried out on the distribution equipment by utilizing the historical operating data and the test data of the distribution equipment, the maintenance blindness is avoided, the maintenance resources can be fully utilized, and the maintenance efficiency is improved.
Disclosure of Invention
The invention provides a method for determining the state maintenance time of power distribution equipment, which can solve the problems that the conventional equipment maintenance of a power distribution network is mostly regular maintenance and after-repair, the utilization efficiency of maintenance resources is low, and the fault prevention can not be performed according to the difference of various equipment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for determining the state maintenance time of power distribution equipment is characterized by comprising the following steps: the method comprises the following steps of 1, classifying equipment according to the health degree: according to the comprehensive deduction value of the equipment, the health degree g of the z-th equipment with the same class and voltage class is countedz(Z is 1,2, …, Z), g according to health degreezThe device health status is ranked. Step 2, an entropy weight method determines index weight and solves membership and membership vector by using a cloud model: and after weighting the equipment state evaluation index by using an entropy weight method, generating a health state cloud picture of the equipment to be evaluated through a cloud model, and calculating the membership degree and the membership degree vector between the health state cloud of the equipment to be evaluated and each health state grade cloud. Step 3, training a power distribution equipment fault occurrence time prediction model based on the long-term and short-term memory network: firstly, the operation record indexes of the sample equipment are normalized to form a matrix BzThen the membership vector delta of the sample devicezNumber of failed times lzMatrix BzAs input samples, the LSTM neural network is input for training. Step 4, predicting the next fault occurrence time of the equipment to be evaluated, and determining the maintenance time: according to a membership vector delta ' ═ delta ' of the equipment to be evaluated '1,δ′2,δ′3,δ′4]Inputting a matrix consisting of the failed times l 'and the running record index normalization matrix B' into a trained LSTM neural network to calculate to obtain a predicted value T 'of the next failure occurrence time of the equipment to be evaluated'tAccording to the formula Tz＝Tplan+Top+Ts＜T′tDetermining a time to overhaul TzWherein, TplanFor planning maintenance time, TopTime required for inspection, TsA safety margin time.
Further, in step 1, according to the health degree gzThe equipment is divided into four grades of normal, attention, abnormal and serious, which are respectively usedf1、f2、f3、f4And (4) showing.
Further, by the formula
Figure GDA0003640843490000011
Cloud number feature calculating 4 health status levels, afIs the minimum value of the health status grade interval, bfMaximum value of the health status grade interval, ExfIs an expected value of a device health level f, EnfEntropy, H, of the device health level fefSuper entropy, H, for a device health level fefTake 0.01, where f is 1,2,3,4, corresponding to four state levels f1、f2、f3、f4。
Further, the entropy weight method in step 2 includes the following steps, and firstly, an equipment state evaluation index matrix is constructed: an index matrix composed of m secondary indexes, which are n evaluation objects, is as follows, X ═ Xij)n×m＝(X1,X2,...Xi,...,Xm),x＝{x1,x2,...,xi,...,xm}，
Wherein, X is an index matrix constructed by n multiplied by m index values; xiThe evaluation index is the ith index column vector in the index matrix, namely the vector formed by the ith evaluation indexes of the n evaluation objects; x is a radical of a fluorine atomijThe j index value is the ith evaluation object; x is index set, xjIs the jth index in the index set; m is the index number; n is the number of evaluation objects; secondly, the equipment state evaluation index normalization processing: the positive indicators and the negative indicators are respectively normalized as follows,
Figure GDA0003640843490000021
Figure GDA0003640843490000022
a normalized device status evaluation index matrix is obtained, as follows,
Figure GDA0003640843490000023
thirdly, calculating the entropy value of each equipment state evaluation index: the formula for calculating the entropy of the jth evaluation index is as follows,
Figure GDA0003640843490000024
ejentropy for the jth evaluation index, k being 1/ln (n), pijIs the ratio of the score of the ith sample device on the jth index relative to the scores of all the objects to be evaluated on the index,
Figure GDA0003640843490000025
fourthly, calculating the entropy weight of each equipment state evaluation index: the entropy weight calculation formula of the jth evaluation index is as follows,
Figure GDA0003640843490000026
wjthe entropy weight of the jth evaluation index.
Further, the cloud model generating cloud picture in step 2 mainly comprises the following steps of firstly, calculating a reverse cloud generator: expectation of j-th secondary index ExjEntropy EnjHyper entropy HejThe formula of (c) is as follows,
Figure GDA0003640843490000027
Figure GDA0003640843490000028
wherein P is the index sample number, xiThe index value is a secondary index value; the digital characteristic parameters of the cloud model of the level of the target layer are obtained by combining the digital characteristics of the cloud model of the related indexes of each level, the calculation formula is shown as follows,
Figure GDA0003640843490000031
second, a forward cloud generator is calculated: is characterized by a number ofC(Ex,En,He) The positive cloud generator randomly generates N cloud droplets (x)i,yi) The method comprises the following specific steps:
a, with EnTo expectation, HeGenerating a normal distribution random number E 'as a standard deviation'n；
b, with ExL E 'to expect |'nI is standard deviation to generate normal distribution random number xi；
c, by E'n、xiAs a variable, into the formula yi＝exp(-(xi-Ex)2/2(E'n)2) Producing cloud droplets (x)i,yi)；
And d, repeating the steps a to c until N cloud droplets are generated, and drawing a cloud model graph according to the N cloud droplets.
Furthermore, K cloud drops are arranged at the intersection point of the cloud picture of the sample device and the f-th level cloud picture, the mean value of the membership degree values of the K cloud drops is taken as the membership degree of the state value of the device, as shown in the formula,
Figure GDA0003640843490000032
wherein, f is 1,2,3,4, the membership vector of the z-th sample device
Figure GDA0003640843490000033
Further, the LSTM training algorithm in step 3 is a back propagation algorithm, and includes three steps, first, calculating an output value of the LSTM memory module forward in combination with the weight matrix, second, calculating an error term of each memory module backward, and third, calculating a gradient of each weight matrix used in the first step according to the corresponding residual term, and updating the weight matrix.
Further, after the sample data of each sample device is input into the LSTM neural network for training, the model training is finished; or the model training is finished when the training error is set to be less than 1 e-6.
Further, the output value of the LSTM memory module, namely the true time of next fault occurrence of the sample equipment, is calculated in the forward directionValue TtIs as follows, ft＝σ(Wfxxt+WfhΤt-1+bf)，it＝σ(Wixxt+WihTt-1+bi)，
Figure GDA0003640843490000034
ot＝σ(Woxxt+WohTt-1+bo)，Tt＝ot·tanh(ct) (ii) a σ is sigmoid function, xt＝[δz,lz,Bz]Is the input matrix at time t, Wfx、Wix、WoxRepresentation and current input xtWeight matrix of multiplications, Wfh、Wih、Woh、WchIndicating the output value T at the time T-1t-1A multiplied weight matrix. bf、bi、bc、boRespectively as the offset terms of the forgetting gate, the input gate, the state unit and the output gate, ft、it、otActivation functions of forgetting gate, input gate, output gate, respectively, ct、
Figure GDA0003640843490000035
Is a vector of state units and instantaneous states; t istThe current output of the LSTM is the real value of the next failure occurrence time of the sample device.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, historical operating data and test data of the power distribution equipment are processed through an entropy weight method and a cloud model, input into an LSTM neural network for training, processed according to corresponding index data of the equipment to be evaluated and input into the LSTM neural network to predict next fault occurrence time of the equipment to be evaluated and determine maintenance time, reasonable planning of an in-operation equipment maintenance plan is achieved, maintenance blindness is avoided, maintenance resources can be fully utilized, maintenance efficiency is improved, and equipment fault risks are reduced.
Drawings
FIG. 1 is a flow chart of a method for determining a condition maintenance time of a power distribution device in accordance with the present invention;
FIG. 2 is a state evaluation index system for a distribution transformer in accordance with the present invention;
FIG. 3 is a diagram of a distribution line condition evaluation index system according to the present invention;
FIG. 4 illustrates an LSTM memory module structure according to the present invention;
FIG. 5 is an expanded view of the timing of the LSTM network of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following detailed description is made with reference to the accompanying drawings and specific examples, and specific embodiments of the present invention are described in detail as follows:
the invention provides a method for determining the state maintenance time of power distribution equipment, the flow of the method is shown in figure 1, the state maintenance of the power distribution equipment in question is actually graded into health states according to the comprehensive deduction value of the equipment; after an entropy weight method is applied to weight the equipment state evaluation index, a health state cloud picture of the equipment to be evaluated is generated through a cloud model, and then the membership degree between the health state cloud of the equipment to be evaluated and each health state grade cloud is calculated; and finally, inputting the membership vector, the times of failure, the operation records and other data into a long-term and short-term memory network for training, so as to realize the prediction of the next failure occurrence time of the equipment to be evaluated. The method mainly comprises the following steps:
step 1: and carrying out health state grade division according to the equipment comprehensive deduction value.
According to the comprehensive deduction value of the equipment, the health degree g of the z-th equipment with the same class and voltage class is countedz(Z: 1,2, …, Z), g according to health degreezThe health grade of the equipment is divided into four grades of normal, attention, abnormity and severity, and f is used for the four grades1、f2、f3、f4Expressed that the corresponding value ranges are [80,100, respectively]、[60,80)、[40,60)、[0,40)。
Step 2: and after the entropy weight method is used for weighting the equipment state evaluation index, generating a health state cloud picture of the equipment to be evaluated through a cloud model, and calculating the membership degree between the health state cloud of the equipment to be evaluated and each health state grade cloud.
Normalizing each index of the sample equipment according to the formulas (4) and (5), inputting the normalized numerical value and the corresponding weight into a reverse cloud generator, calculating the cloud digital characteristics according to the formulas (10) to (13), and generating a cloud picture by the forward cloud generator.
According to the range of the device state value [0,1]Corresponding to the level of health of the apparatus, f1、f2、f3、f4The four state value ranges are respectively [0.8,1 ]]0.6,0.8), 0.4,0.6, 0.4). Calculating the cloud digital characteristics of 4 health state grades by the formula (1), and generating a corresponding cloud picture according to the step (2-2), afIs the minimum value of the health status grade interval, bfMaximum value of the health status grade interval, ExfExpected value of the device health status level f, EnfEntropy, H, of the device health level fefHyper-entropy of the device health level f, according to EnfSize of (A), determined by empirical and repetitive tests, HefTake 0.01. Wherein, f is 1,2,3,4, which corresponds to four state levels f1、f2、f3、f4。
Figure GDA0003640843490000051
The health status classification grade of the equipment obtained by calculation and the corresponding cloud model digital characteristics are shown in table 1.
Table 1 cloud model-based digital feature device status rating scheme
Figure GDA0003640843490000052
(1) The entropy weight method comprises the following calculation steps:
(1-1) constructing an equipment state evaluation index matrix:
an index matrix composed of m secondary indexes of n evaluation objects is shown in formula (2).
X＝(xij)n×m＝(X1,X2,...Xi,...,Xm) (2)
x＝{x1,x2,...,xi,...,xm} (3)
Wherein, X is an index matrix constructed by n multiplied by m index values; xiThe evaluation index is the ith index column vector in the index matrix, namely the vector formed by the ith evaluation indexes of the n evaluation objects; x is the number ofijThe jth index value of the ith evaluation object; x is index set, xjIs the jth index in the index set; m is the index number; n is the number of evaluation targets.
(1-2) device status evaluation index normalization processing:
considering that the equipment state evaluation index system comprises both positive indexes and negative indexes (the higher the positive index value is, the better the negative index value is), respectively performing normalization processing on the positive indexes and the negative indexes, as shown in formula (4) and formula (5):
Figure GDA0003640843490000061
Figure GDA0003640843490000062
and (4) after the normalization processing is carried out on the positive indexes and the negative indexes, obtaining a normalized index matrix shown as the formula (6).
Figure GDA0003640843490000063
And (1-3) calculating entropy values of the equipment state evaluation indexes. The formula for calculating the entropy of the jth evaluation index is shown in (7):
Figure GDA0003640843490000064
wherein k is 1/ln (n), pijIs the ratio of the score of the ith sample device on the jth index to the scores of all the objects to be evaluated on the index, as shown in formula (8).
Figure GDA0003640843490000065
And (1-4) calculating the entropy weight of each equipment state evaluation index. The entropy weight calculation formula of the j-th evaluation index is shown in (9).
Figure GDA0003640843490000066
(2) The cloud model comprises the following main steps: normalizing each index of the sample equipment according to the formulas (4) and (5), inputting the normalized numerical value and the corresponding weight into a reverse cloud generator, calculating the cloud numerical characteristics, and generating a cloud picture through the forward cloud generator
(2-1) calculating a reverse cloud generator
Expectation of j-th level index ExjEntropy EnjEntropy of HejThe calculation formula of (a) is as follows:
Figure GDA0003640843490000067
Figure GDA0003640843490000068
Figure GDA0003640843490000069
Figure GDA00036408434900000610
wherein P is the index sample number, xiThe index value is a second-level index value.
Can calculate each two according to entropy weight methodLevel index weight wjAnd combining the digital characteristics of the cloud model of the relevant indexes at each level to obtain the digital characteristic parameters of the cloud model of the level of the target layer. The calculation formula is as follows:
Figure GDA0003640843490000071
Figure GDA0003640843490000072
Figure GDA0003640843490000073
(2-2) calculating a forward cloud Generator
Characterised by the number C (E)x,En,He) The positive cloud generator randomly generates N cloud droplets (x)i,yi). The method comprises the following specific steps:
(2-2-1) with EnTo expectation, HeGenerating a normal distribution random number E 'as a standard deviation'n；
(2-2-2) with ExL E 'to expect |'nI is standard deviation to generate normal distribution random number xi；
(2-2-3) with E'n、xiAs a variable, into the formula yi＝exp(-(xi-Ex)2/2(E'n)2) Producing cloud droplets (x)i,yi)；
(2-2-4) repeating the steps (2-2-1) to (2-2-3) until N cloud drops are generated, and drawing a cloud model map according to the N cloud drops.
(2-2-5) if K cloud drops exist at the intersection point of the cloud picture of the sample equipment and the f-th level cloud picture, taking the mean value of the membership values of the K cloud drops as the membership of the equipment state value, as shown in the formula
Figure GDA0003640843490000074
Wherein, f is 1,2,3,4, the membership vector of the z-th sample device
Figure GDA0003640843490000075
And step 3: the power distribution equipment fault occurrence time prediction model training based on the long-term and short-term memory network,
normalizing the operation record index (the index of the 'operation record B' class in the equipment state evaluation index system, as shown in figures 2 and 3) of the sample equipment according to formulas (4) to (5) to form a matrix BzThen the membership degree vector of the sample device
Figure GDA0003640843490000076
Number of failed times lzRunning record index normalization matrix BzAs input samples, the LSTM neural network is input for training (the topology is shown in FIG. 4).
The training algorithm of the LSTM neural network is a back propagation algorithm and is divided into the following three steps, and when the sample data of each sample device is input into the LSTM neural network for training, the model training is finished; or when the training error is set to be less than 1e-6, the model training is finished, the training error is the total training error of the sample set which is trained each time, the training error is not the training error of single sample data, and 1e-6 is a value obtained by experience and repeatability tests.
(3-1) calculating the output value of the LSTM memory module in the forward direction;
ft＝σ(Wfxxt+WfhΤt-1+bf) (18)
it＝σ(Wixxt+WihTt-1+bi) (19)
Figure GDA0003640843490000081
Figure GDA0003640843490000082
ot＝σ(Woxxt+WohTt-1+bo) (22)
Tt＝ot·tanh(ct) (23)
wherein sigma is sigmoid function, xtIs the input matrix at time t, formed by membership vectors
Figure GDA0003640843490000083
Number of failed times lzRunning record index normalization matrix BzComposition i.e. xt＝[δz,lz,Bz]；Wfx、Wix、Wox、WcxRepresentation and current input xtWeight matrix of multiplications, Wfh、Wih、Woh、WchIndicating the output value T at the time T-1t-1A multiplied weight matrix. bf、bi、bc、boRespectively as the offset terms of the forgetting gate, the input gate, the state unit and the output gate, ft、it、otActivation functions of forgetting gate, input gate, output gate, respectively, ct、
Figure GDA0003640843490000088
Is a vector of state units and instantaneous states; t istThe current output of the LSTM is the true value of the next time the sample device fails.
(3-2) calculating the error term delta of each memory module reversely, wherein the error term of the LSTM propagates along two directions. The error term is propagated reversely along the time, namely the error term at the T-1 moment is calculated, and the output value of the LSTM at the T moment is TtDefining the error term at time t as
Figure GDA0003640843490000084
Where E is a loss function, then
Figure GDA0003640843490000085
From the formula of the full derivative
Figure GDA0003640843490000086
About
Figure GDA0003640843490000089
δf,t、δi,t、δo,tThe relationship of the four items is that,
Figure GDA0003640843490000087
δf,t、δi,t、δo,tdefined as follows, the symbol "·" denotes multiplication by elements:
δo,t＝δt·tanh(ct)·ot·(1-ot) (24)
δf,t＝δt·ot·(1-tanh(ct)2)·ct-1·ft·(1-ft) (25)
Figure GDA0003640843490000091
Figure GDA0003640843490000092
the substitution of each item is solved by using a partial derivative formula to solve the following problems:
Figure GDA0003640843490000093
equation (28) is the equation for the error to propagate back to the previous time instant, and thus the equation for the error term to propagate back to any k time instant can be derived:
Figure GDA0003640843490000094
defining the current layer as the l-th layer, the error term of the l-1 layer is the inverse of the weighted input of the error function to the l-1 layer neurons, i.e.
Figure GDA0003640843490000095
Input of LSTM
Figure GDA0003640843490000096
fl-1Representing the activation function of the l-1 layer. Equation (30) is obtained using the fully-derived equation, which is a calculation to pass the error to the previous layer.
Figure GDA0003640843490000097
(3-3) calculating the gradient of each weight matrix in (3-1) according to the corresponding residual error term, and updating the weight matrix, wherein the formula is as follows:
Figure GDA0003640843490000098
Figure GDA0003640843490000099
Figure GDA00036408434900000910
Figure GDA00036408434900000911
Figure GDA00036408434900000912
Figure GDA00036408434900000913
Figure GDA00036408434900000914
Figure GDA0003640843490000101
Figure GDA0003640843490000102
Figure GDA0003640843490000103
Figure GDA0003640843490000104
Figure GDA0003640843490000105
and 4, step 4: and predicting the next fault occurrence time of the equipment to be evaluated, and determining the overhaul time.
Generating a health state cloud picture of the equipment to be evaluated according to the cloud model in the step 2, and calculating a membership degree vector delta ' ═ delta ' of the equipment to be evaluated according to the formula (17) '1,δ′2,δ′3,δ′4](ii) a Normalizing the operation record index (shown in fig. 2 and 3) of the equipment to be evaluated according to formulas (4) to (5) to form a matrix B ', and then, converting the membership degree vector delta ' of the equipment to be evaluated into [ delta '1,δ′2,δ′3,δ′4]The failed times l ' and the running record index normalization matrix B ' form a matrix x 't＝[δ',l',B']Substitution type (18) - (23) are calculated to obtain a predicted value T 'of the next failure occurrence time of the equipment to be evaluated'tThe maintenance time T is calculated according to the formula (43)z，
Tz＝Tplan+Top+Ts＜T′t (43)
Wherein, TplanFor planning maintenance time, TopThe time required for maintenance and the safety margin time T for ensuring the reliability of the power supplys。
According to the method, the historical operation data and the test data of the power distribution equipment are utilized to predict the next fault occurrence time of the equipment to be evaluated and determine the maintenance time, so that the reasonable planning of the maintenance plan of the in-operation equipment is realized, the maintenance blindness is avoided, the maintenance resources can be fully utilized, the maintenance efficiency is improved, and the equipment fault risk is reduced.
The above description of embodiments of the invention with reference to the drawings is not intended to be limiting, and those skilled in the art can make many variations without departing from the spirit of the invention and the scope of the appended claims, which fall within the scope of the invention.

Claims (3)

1. A method for determining the state maintenance time of power distribution equipment is characterized by comprising the following steps: the method comprises the following steps of,
step 1, the health degree is divided into equipment grades: according to the comprehensive deduction value of the equipment, the health degree g of the z-th equipment with the same class and voltage class is countedz(Z is 1,2, …, Z), g according to health degreezGrading the health state of the equipment;
according to the degree of health gzThe health state of the equipment is divided into four grades of normal, attention, abnormal and serious, and f is used for the health state of the equipment1、f2、f3、f4Represents;
is composed of
Figure FDA0003640843480000011
Cloud number feature calculating 4 health status levels, afIs the minimum value of the health status grade interval, bfMaximum value of the health status grade interval, ExfIs an expected value of a device health level f, EnfEntropy, H, of the device health level fefAs the state of health of the apparatus, etcSuper entropy of stage f, HefTake 0.01, where f is 1,2,3,4, corresponding to four state levels f1、f2、f3、f4；
Step 2, determining the equipment state evaluation index weight by an entropy weight method, and solving the membership by a cloud model: after weighting the equipment state evaluation index by applying an entropy weight method, generating a health state cloud picture of the equipment to be evaluated through a cloud model, and then calculating the membership between the health state cloud of the equipment to be evaluated and each health state grade cloud to obtain a membership vector;
the entropy weight method is carried out as follows,
firstly, constructing an equipment state evaluation index matrix: an index matrix composed of m secondary indexes, which are n evaluation objects, is as follows, X ═ Xij)n×m＝(X1,X2,...Xi,...,Xm)，x＝{x1,x2,...,xi,...,xm}，
Wherein, X is an index matrix constructed by n multiplied by m index values; xiThe evaluation index is the ith index column vector in the index matrix, namely the vector formed by the ith evaluation indexes of the n evaluation objects; x is the number ofijThe j index value is the ith evaluation object; x is index set, xjIs the jth index in the index set; m is the index number; n is the number of evaluation objects;
secondly, the equipment state evaluation index normalization processing: the positive indicators and the negative indicators are normalized respectively, as follows,
Figure FDA0003640843480000012
Figure FDA0003640843480000013
a normalized device status evaluation index matrix is obtained, as follows,
Figure FDA0003640843480000014
thirdly, calculating the entropy value of each equipment state evaluation index: the calculation formula is as follows,
Figure FDA0003640843480000015
ejentropy for the jth evaluation index, k being 1/ln (n), pijIs the ratio of the score of the ith sample device on the jth index relative to the scores of all the objects to be evaluated on the index,
Figure FDA0003640843480000021
fourthly, calculating the entropy weight of each equipment state evaluation index: the calculation formula is as follows,
Figure FDA0003640843480000022
wjentropy weights for the jth evaluation index;
the cloud model generates the cloud picture mainly by the following steps,
first, calculate the inverse cloud generator: expectation of j-th level index ExjEntropy EnjEntropy of HejThe formula of (c) is as follows,
Figure FDA0003640843480000023
wherein S is2Is the variance, P is the number of index samples, xiThe index value is a secondary index value; the digital characteristic parameters of the cloud model of the level of the target layer are obtained by combining the digital characteristics of the cloud model of the related indexes of each level, the calculation formula is shown as follows,
Figure FDA0003640843480000024
Figure FDA0003640843480000025
second, calculate a forward cloud generator: characterised by the number C (E)x,En,He) The positive cloud generator randomly generates N cloud droplets (x)i,yi) The method comprises the following specific steps:
a, with EnTo expectation, HeGenerating a normal distribution random number E 'as a standard deviation'n；
b, with ExL E 'to expect |'nI is standard deviation to generate normal distribution random number xi；
c, and E'n、xiAs variables, into the formula yi＝exp(-(xi-Ex)2/2(E'n)2) Producing cloud droplets (x)i,yi)；
d, repeating the steps a to c until N cloud drops are generated, and drawing a cloud model graph according to the N cloud drops;
k cloud drops are arranged at the intersection point of the cloud picture of the sample device and the f-th level cloud picture, the mean value of the membership degree values of the K cloud drops is taken as the membership degree of the device state value, as shown in the formula,
Figure FDA0003640843480000026
wherein, f is 1,2,3,4, the membership vector of the z-th sample device
Figure FDA0003640843480000027
Step 3, training a power distribution equipment fault occurrence time prediction model based on the long-term and short-term memory network: firstly, normalizing the equipment operation record indexes in the sample equipment state evaluation indexes to form a matrix BzThen the membership vector delta of the sample devicezFailure times lzMatrix BzInputting the LSTM neural network as an input sample for training;
the training algorithm of the LSTM neural network is a back propagation algorithm and comprises three steps, namely, firstly, the output value of an LSTM memory module is calculated in a forward direction by combining a weight matrix, secondly, the error item of each memory module is calculated in a reverse direction, and thirdly, the gradient of each weight matrix used in the first step is calculated according to the corresponding residual error item, and the weight matrix is updated;
step 4, predicting the next fault occurrence time of the equipment to be evaluated, and determining the maintenance time: according to a membership degree vector delta ' ═ delta ' of the equipment to be evaluated '1,δ'2,δ'3,δ'4]Inputting a matrix consisting of the failed times l 'and the equipment operation record index normalization matrix B' into the trained LSTM neural network to calculate to obtain a predicted value T 'of the next failure occurrence time of the equipment to be evaluated'tAccording to the formula Tz＝Tplan+Top+Ts＜T'tDetermining a time to overhaul TzWherein, TplanFor planning maintenance time, TopTime required for inspection, TsA safety margin time.
2. The method of determining electrical distribution equipment condition repair time of claim 1, wherein: after the sample data of each sample device is input into the LSTM neural network for training, the model training is finished; or finishing the model training when the training error is set to be less than 1 e-6.
3. The method of determining electrical distribution equipment condition repair time of claim 1, wherein: calculating the output value of the LSTM memory module in the forward direction, namely the true value T of the next fault occurrence time of the sample equipmenttIs as follows, ft＝σ(Wfxxt+WfhΤt-1+bf)，it＝σ(Wixxt+WihTt-1+bi)，
Figure FDA0003640843480000031
Figure FDA0003640843480000032
ot＝σ(Woxxt+WohTt-1+bo)，Tt＝ot·tanh(ct) (ii) a σ is sigmoid function, xt＝[δz,lz,Bz]Is the input matrix at time t, Wfx、Wix、Wox、WcxRepresentation and current input xtA multiplied weight matrix, Wfh、Wih、Woh、WchIndicating the output value T at the time T-1t-1Multiplied weight matrix, bf、bi、bc、boRespectively as the offset terms of the forgetting gate, the input gate, the state unit and the output gate, ft、it、otActivation functions of forgetting gate, input gate, output gate, respectively, ct、
Figure FDA0003640843480000033
Is a vector of state units and instantaneous states; t is a unit oftThe current output of the LSTM neural network is the real value of the next fault occurrence time of the sample device.
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