CN103400310A - Method for evaluating power distribution network electrical equipment state based on historical data trend prediction - Google Patents
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Abstract
A method for evaluating the power distribution network electrical equipment state based on historical data trend prediction comprises the following steps: at first, performing the longitudinal historical data evaluation on electric equipment state quantity data, and correcting bad data or wrong data through smoothing; then, dividing the data into two types to be processed according to the state quantity data dispersion degree; for the data with smaller dispersion degree, considering the mathematical expectation of historical data as current data quantity data; for the data with larger dispersion degree, adopting a grey prediction method to obtain the predicted value of the state quantity data of the current time node, and finally adopting a fuzzy analytical hierarchy process to evaluate the power distribution network electrical equipment state. The state quantity data of the current time node is predicted according to the development tendency of the state quantity historical data, data is guaranteed to reflect the state of the current equipment effectively, and the credibility of the data quantity data is improved, so that the accuracy of the state evaluation result of the power distribution equipment is ensured finally.
Description
Technical field
The present invention relates to a kind of method of the electric distribution network electrical equipment state being carried out to accurate evaluation, belong to technical field of data processing.
Background technology
Power distribution network is one of chief component of electric system, and its safe and reliable operation and national economy and people's lives are closely bound up.In power distribution network, the reliability of controller switching equipment is the basis that guarantees the power distribution network reliability service, therefore, improves the reliability of power distribution network and at first should start with from the reliability that improves controller switching equipment.Carrying out the work of controller switching equipment state estimation is one of effective measures that improve the controller switching equipment operational reliability, the controller switching equipment state is carried out to accurate evaluation, not only be conducive to improve overhaul of the equipments efficiency, reduce the input of manpower and materials and fund, and can shorten power off time, in the serviceable life of extension device, improve the reliability of power supply.
State estimation is that current practical working situation take equipment is as foundation, status monitoring means by the advanced person, evaluation means and the predicting means in life-span is carried out to the state of judgment device reliably, and position, the order of severity, the development trend that will occur or break down be assessed, the early stage sign of identification fault, drop to a certain degree or fault is overhauled before will occurring in equipment performance according to assessment result.Its purpose is exactly the time between overhauls(TBO) of extension device, increases the time interval between twice maintenance, thereby reduces the frequency of overhaul of the equipments, extension device serviceable life, improve the reliability of equipment, reduce the equipment power off time, ensure the power supply of resident and enterprise, and save a large amount of recondition expenses and resource.
One of key issue of electric distribution network electrical equipment state estimation is obtaining of equipment state amount data.The equipment state amount data that obtain are perfect, and accurately, the result of state estimation is just more accurate.Because electric distribution network electrical equipment is compared with the power transmission network electrical equipment, the coverage after significance level and stoppage in transit is all less, so routine test and the tour gap periods of for controller switching equipment, carrying out are longer.At present, the distribution Postural Evaluations of Electric Equipments is all to using the quantity of state data of the last time distance evaluation time to carry out as raw data.There are following two problems in this appraisal procedure: first, test and the tour gap periods of due to controller switching equipment, carrying out are longer, the last test and tour may be longer apart from the current time node, and its quantity of state data can not reflect the state that distribution net equipment is current fully; The second, this method is only used the last quantity of state data, ignored historical data effect (so-called historical data refer to last time equipment state overhauling finish after to before this state estimation during this period of time in the repeatedly data of same quantity of state of accumulation).The quantity of state data that record due to single test or operational inspection have contingency, may have experimental mistake or misregistration, thereby affect the accuracy of equipment state assessment result.
Summary of the invention
The object of the invention is to the drawback for prior art, a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction is provided, to improve the accuracy of distribution net equipment state estimation result.
Problem of the present invention realizes with following technical proposals:
At first a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction, described method carry out historical data to electrical equipment quantity of state data vertically passes judgment on, by smoothing processing correction bad data or misdata; Then according to large young pathbreaker's data of quantity of state data discrete degree, being divided into two classes processes: the data less to dispersion degree, and the mathematical expectation that will ask for according to historical data is as current state amount data; For the larger data of dispersion degree, the method for employing gray prediction obtains the predicted value of these quantity of state data of current time node, finally adopts Fuzzy AHP to assess the state of electric distribution network electrical equipment.
The electric distribution network electrical equipment state evaluating method of above-mentioned consideration historical data trend prediction, carry out according to following steps:
A. distribution electrical equipment quantity of state data are carried out to historical data and vertically pass judgment on, judge whether to exist bad or misdata, if there is the class likelihood data, the request of reporting to the police is confirmed, after confirmation, carries out smoothing processing;
B. according to the size of the quantity of state data discrete degree of controller switching equipment, determine Forecasting Methodology:
For quantity of state x
j (
j∈ M), M is the number of certain electrical equipment quantity of state, x
j1
, x
j2
, x
j3
, x
Ji X
jn
For the historical data under this quantity of state, get
, n is the historical data number.
If
(
Be
jThe deviation minimum value of individual quantity of state), adopt the method for asking for mathematical expectation to carry out prediction of result, predicted value is
If
, adopt the method for gray prediction to predict these quantity of state data of current time node;
C. by above-mentioned Forecasting Methodology, dope the data of controller switching equipment quantity of state current time node;
D. use Fuzzy AHP to carry out state estimation.
The electric distribution network electrical equipment state evaluating method of above-mentioned consideration historical data trend prediction judges whether to exist the concrete grammar of bad or misdata as follows:
If certain quantity of state x
j (
j∈ M), M is the number of transformer state amount, x
j1
, x
j2
, x
j3
, x
Ji X
jn
For the historical data of this quantity of state,
x j For this quantity of state data variation maximal value;
If
, there is bad or misdata in judgement.
The electric distribution network electrical equipment state evaluating method of above-mentioned consideration historical data trend prediction, adopt the method for gray prediction to predict that the method for these quantity of state data of current time node is as follows:
The historical data sequence x of establishing electricity equipment quantity of state
(0)=[x
(0)(1), x
(0)(2) ..., x
(0)(n)], the step of gray prediction GM (1,1) model foundation is as follows:
1. calculate historical data sequence x
(0)Accumulated generating sequence x
(1)
x
(1)=?[x
(1)(1),?x
(1)(2),…,?x
(1)(n)]
Wherein:
2. according to gray prediction GM (1,1) model formation
, construction data matrix B and data vector Y
N,
Wherein:
,
,
, Y
nWith B be known quantity, can calculate, A is undetermined parameter;
3. calculate the parameter of GM (1,1) model
With
:
By formula
, calculate parameter
With
4. set up grey forecasting model:
Previous step is tried to achieve
With
In generation, returned the original differential equation, obtains cumulative ordered series of numbers x
(1)Grey forecasting model be:
The grey forecasting model of original data series is:
；
5. the poor check of the posteriority of model accuracy:
Residual error mean value:
The historical data variance:
Historical data mean value:
The residual error variance:
The poor ratio of posteriority:
The little probability of error:
6. error analysis predicts the outcome.
The electric distribution network electrical equipment state evaluating method of above-mentioned consideration historical data trend prediction, the step that the utilization Fuzzy AHP carries out state estimation is as follows:
1. set up the evaluation factor collection
Using selected state parameter as evaluation factor, set up controller switching equipment running status evaluation factor collection, use U
j Represent U
j =(
u 1,
u 2,
u 3,
u n);
2. set up the evaluation rank collection
The running status of distribution net equipment is divided into to " well ", " normally ", " suspicious ", " extremely ", " danger " 5 kinds of situations, and namely the evaluation rank collection is: V={ is good, and is general, suspicious, abnormal, danger }=
v 1,
v 2,
v 3,
v 4,
v 5;
3. set up the evaluation factor weight sets
To each evaluation factor
u i Give corresponding weight coefficient
w i (
i=1,2,3 ..., n), the evaluation factor weight sets be W=(
w 1,
w 2...,
w n), weight coefficient must meet normalizing condition:
4. construct the fuzzy evaluation matrix
To passing judgment on object, press evaluation factor concentrated the
iIndividual factor
u i Advance to pass judgment on, corresponding opinion rating concentrates the
jIndividual element
v j Subjection degree be
r Ij , by the
iIndividual element
u i Evaluation result can use fuzzy set R
i =(
r i1
,,
r i2
,,
r i3
...,,
r In ) expression, take the degree of membership of each factor evaluation collection as row, form fuzzy evaluation matrix R;
5. fuzzy comprehensive evoluation
Evaluation result B=AR=(
b 1,,
b 2, ...,,
b n ), wherein " " is fuzzy operator, B
j Be called the one-level Result of Fuzzy Comprehensive Evaluation, expression is while carrying out comprehensive evaluation by all grades of factor in U, and evaluation object is in opinion rating the
jThe degree of membership of individual grade, according to maximum membership grade principle, determine to be evaluated the affiliated evaluation rank of object.
The present invention is according to the quantity of state data of the prediction of the development trend current time node of quantity of state historical data, guarantee that these data can reflect the state of current device effectively, simultaneously by vertical judge or smooth operation to same quantity of state historical data, improved the confidence level of quantity of state data, thereby finally guaranteed the accuracy of distribution net equipment state estimation result, its accuracy is also tested and is proved.
The accompanying drawing explanation
The invention will be further described below in conjunction with accompanying drawing.
Fig. 1 is the electric distribution network electrical equipment state evaluating method process flow diagram of considering the historical data trend prediction;
Fig. 2 is oil-filled transformer state estimation information structure diagram.
In literary composition, each symbol is: x
j For quantity of state, x
(0)For the historical data sequence, A is undetermined parameter, U
j For the evaluation factor collection, W is the evaluation factor weight sets.
Embodiment
The electric distribution network electrical equipment state evaluating method flow process of consideration historical data of the present invention trend prediction is referring to Fig. 1, and it mainly comprises three steps, the one, historical data pre-service; The 2nd, the historical data trend prediction; The 3rd, the Fuzzy Level Analytic Approach assessment.At first the present invention has guaranteed the accuracy of historical data by the pre-service of electrical equipment quantity of state historical data, then according to the size of electrical equipment quantity of state historical data dispersion degree, adopt different Forecasting Methodologies to obtain the quantity of state data of current time node, finally, predicted data is used to Fuzzy AHP, obtain assessment result.
In the operational process of electrical equipment, the state variation of the every quantity of state of electrical equipment changes according to certain rule, and namely the state index information of electrical equipment has specific variation tendency in certain period.This variation tendency can lie in the historical data of status information index.And the scoring of conventional status information index only can be given a mark to nearest data, has ignored the effect of historical data.Consider the electric distribution network electrical equipment state evaluating method of historical data trend prediction, can consider the effect of historical data, overcome the contingency defect of a secondary data, Changing Pattern according to the index historical data, the quantity of state of prediction current time node carries out state estimation, has effectively improved the accuracy of state estimation result.
The present invention illustrates the implementation process of described method take substation transformer (oil immersed type) as example, what Fig. 2 represented is that transformer carries out the needed index of state estimation, be the quantity of state of state estimation, the historical data of each index leaves lane database in, and described method is carried out according to following steps:
A. from lane database, read respectively each quantity of state historical data of substation transformer, the historical data of each quantity of state done to following processing:
If certain quantity of state x
j (
j∈ M), M is the number of transformer state amount, x
j1
, x
j2
, x
j3
, x
Ji X
jn
For the historical data of this quantity of state,
x j For this quantity of state data variation maximal value.
If
, there is bad or misdata in judgement, after reporting to the police and being confirmed, carries out smoothing processing.
B. get
,
, order
Be
jThe deviation minimum value of individual quantity of state.
If above-mentioned quantity of state is any
, namely the historical data dispersion degree of quantity of state is less, adopts the method for asking for mathematical expectation to produce and predicts the outcome, be i.e. predicted value
.
If c. above-mentioned quantity of state is a certain
, namely the historical data dispersion degree of quantity of state is larger, adopts the method generation of gray prediction to predict the outcome.The step that gray prediction GM (1,1) model is set up is as follows:
If the original data sequence x of DC Resistance of Transformer
(0),
x
(0)=[x
(0)(1),?x
(0)(2),…,?x
(0)(n)]，
The first step, calculate original data series x
(0)Accumulated generating sequence x
(1).
x
(1)=?[x
(1)(1),?x
(1)(2),…,?x
(1)(n)]，
Wherein:
,
Second step, according to gray prediction GM (1,1) model formation
, construction data matrix B and data vector Y
n.
Wherein:
?
,
Y
nWith B be known quantity, can calculate.A is undetermined parameter.
The 3rd step, the parameter of calculating GM (1,1) equation
With
.
By formula
, calculate parameter
With
.
The 4th step, set up grey forecasting model.
Previous step is tried to achieve
With
In generation, returned the original differential equation, obtains cumulative ordered series of numbers x
(1)Grey forecasting model be
The grey forecasting model of original data series is:
The 5th step, the poor check of the posteriority of model accuracy.
Residual error mean value:
,
The historical data variance:
,
Historical data mean value:
,
The residual error variance:
,
The poor ratio of posteriority:
,
The little probability of error:
,
Precision according to the poor ratio of posteriority and little probability of error assessment models.
The 6th step, error analysis predicts the outcome.
D. use Fuzzy AHP to carry out state estimation, the step of described method is as follows:
(1) set up the evaluation factor collection.Set of factors is the set that each factor of impact evaluation object forms.Using selected state parameter as evaluation factor, set up controller switching equipment running status set of factors, use U
j Represent U
j =(
u 1,
u 2,
u 3,
u n), then each index is decomposed again.
(2) set up the evaluation rank collection.Class set is the set that various total assessment result that the evaluator may make evaluation object forms, with V, represent, namely V=(
v 1,
v 2 , v n ).The running status of distribution net equipment is divided into to " well ", " normally ", " suspicious ", " extremely ", " danger " 5 kinds of situations, and namely the comment collection is: V={ is good, and is general, suspicious, abnormal, danger }=
v 1,
v 2,
v 3,
v 4,
v 5.
(3) set up weight sets.Weight is the significance level of each factors assessment target, to each factor, should give respective weights
w i (
i=1,2,3 ..., n), the set W=that each weight forms (
w 1,
w 2...,
w n) be called factorial power sets.Weight coefficient must meet normalizing condition:
(4) fuzzy evaluation matrix.To passing judgment on object by in set of factors the
iIndividual factor
u i Advance to pass judgment on, corresponding opinion rating concentrates the
jIndividual element
v j Subjection degree be
r Ij , by the
iIndividual element
u i Evaluation result can use fuzzy set R
i =(
r i1
,
r i2
,
r i3
...,
r In ) expression, R
i Be called the single factor evaluation collection.Take the degree of membership of each factor evaluation collection as the matrix R that row forms, be called the fuzzy evaluation matrix.
(5) fuzzy comprehensive evoluation.Evaluation result B=AR=(
b 1,
b 2...,
b n ), wherein " " is fuzzy operator.B
j Be called the one-level Result of Fuzzy Comprehensive Evaluation, expression is while carrying out comprehensive evaluation by all grades of factor in U, and evaluation object is in opinion rating the
jThe degree of membership of individual grade.According to maximum membership grade principle, determine to be evaluated the affiliated evaluation rank of object.
Therefore, the electric distribution network electrical equipment state evaluating method of consideration historical data trend prediction has creatively utilized the historical data of quantity of state, effectively overcome the contingency defect that adopts the last data, Changing Pattern according to the evaluation index historical data, the quantity of state of prediction current time node carries out state estimation, has effectively improved the accuracy of state estimation result.Has practical value.
Below with an instantiation, verify the accuracy of assessment result of the present invention.
10kV oil immersion-type distribution transformer in Yi Mou city is the example explanation, transformer capacity 315kVA, voltage 10/0.4 kV, model S11-M-315, wiring group Yy
N0.In form, be respectively the test of substation transformer and patrol and examine data, wherein, 1 times/year of substation transformer test figure, patrol and examine 1 time/season of data, and service data is real-time.
Table 3 10kV oil immersion-type distribution transformer service data
The above-mentioned substation transformer test figure of known 2006,2007,2008,2009, and in June, 2010 patrol and examine data, service data is real-time can obtaining at any time.In June, 2010, this transformer is carried out to state estimation.
Adopt method of the present invention, at first test figure is predicted, obtain the test figure in June, 2010.
Table 5 adopts the result of classic method state estimation
Table 6 adopts the result of the inventive method state estimation
By above-mentioned example, can be seen, when utilizing the last test findings to assess, the earthing device parts assessment result of this transformer is normal condition, but after the result of using the historical data trend prediction, the earthing device parts assessment result of this transformer is suspicious state.This is mainly because the stake resistance of earthing device increases the result that causes.For the correctness of the result, through this transformer test is measured to its grounding resistance of transmission, recording its stake resistance is 3.5W.Verified the validity of the inventive method.
Claims (5)
1. an electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction, is characterized in that, at first described method is carried out historical data to electrical equipment quantity of state data and vertically passed judgment on, by smoothing processing correction bad data or misdata; Then according to large young pathbreaker's data of quantity of state data discrete degree, being divided into two classes processes: the data less to dispersion degree, and the mathematical expectation that will ask for according to historical data is as current state amount data; For the larger data of dispersion degree, the method for employing gray prediction obtains the predicted value of these quantity of state data of current time node, finally adopts Fuzzy AHP to assess the state of electric distribution network electrical equipment.
2. a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction according to claim 1, is characterized in that, described method is carried out according to following steps:
A. distribution electrical equipment quantity of state data are carried out to historical data and vertically pass judgment on, judge whether to exist bad or misdata, if there is the class likelihood data, the request of reporting to the police is confirmed, after confirmation, carries out smoothing processing;
B. according to the size of the quantity of state data discrete degree of controller switching equipment, determine Forecasting Methodology:
For quantity of state x
j (
j∈ M), M is the number of certain electrical equipment quantity of state, x
j1
, x
j2
, x
j3
, x
Ji X
jn
For the historical data under this quantity of state, get
, n is the historical data number,
If
(
Be
jThe deviation minimum value of individual quantity of state), adopt the method for asking for mathematical expectation to carry out prediction of result, predicted value is
If
, adopt the method for gray prediction to predict these quantity of state data of current time node;
C. by above-mentioned Forecasting Methodology, dope the data of controller switching equipment quantity of state current time node;
D. use Fuzzy AHP to carry out state estimation.
3. a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction according to claim 2, is characterized in that, judges whether to exist the concrete grammar of bad or misdata as follows:
If certain quantity of state x
j (
j∈ M), M is the number of transformer state amount, x
j1
, x
j2
, x
j3
, x
Ji X
jn
For the historical data of this quantity of state,
x j For this quantity of state data variation maximal value,
If
, there is bad or misdata in judgement.
4. a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction according to claim 3, is characterized in that, adopts the method for gray prediction to predict that the method for these quantity of state data of current time node is as follows:
The historical data sequence x of establishing electricity equipment quantity of state
(0)=[x
(0)(1), x
(0)(2) ..., x
(0)(n)], the step of gray prediction GM (1,1) model foundation is as follows:
1. calculate historical data sequence x
(0)Accumulated generating sequence x
(1)
x
(1)=?[x
(1)(1),?x
(1)(2),…,?x
(1)(n)]，
Wherein:
2. according to gray prediction GM (1,1) model formation
, construction data matrix B and data vector Y
N,
Wherein:
,
,
, Y
nWith B be known quantity, can calculate, A is undetermined parameter;
3. calculate the parameter of GM (1,1) model
With
:
By formula
, calculate parameter
With
4. set up grey forecasting model:
Previous step is tried to achieve
With
In generation, returned the original differential equation, obtains cumulative ordered series of numbers x
(1)Grey forecasting model be:
The grey forecasting model of original data series is:
5. the poor check of the posteriority of model accuracy:
Residual error mean value:
,
The historical data variance:
,
Historical data mean value:
,
The residual error variance:
,
The poor ratio of posteriority:
,
The little probability of error:
,
6. error analysis predicts the outcome.
5. a kind of electric distribution network electrical equipment state evaluating method of considering the historical data trend prediction according to claim 4, is characterized in that, the step that the utilization Fuzzy AHP carries out state estimation is as follows:
1. set up the evaluation factor collection
Using selected state parameter as evaluation factor, set up controller switching equipment running status evaluation factor collection, use U
j Represent U
j =(
u 1,
u 2,
u 3 u n);
2. set up the evaluation rank collection
The running status of distribution net equipment is divided into to " well ", " normally ", " suspicious ", " extremely ", " danger " 5 kinds of situations, and namely the evaluation rank collection is: V={ is good, and is general, suspicious, abnormal, danger }=
v 1,
v 2,
v 3,
v 4,
v 5;
3. set up the evaluation factor weight sets
To each evaluation factor
u i Give corresponding weight coefficient
w i (
i=1,2,3 ...,
n), the evaluation factor weight sets be W=(
w 1,
w 2...,
w n), weight coefficient must meet normalizing condition:
4. construct the fuzzy evaluation matrix
To passing judgment on object, press evaluation factor concentrated the
iIndividual factor
u i Advance to pass judgment on, corresponding opinion rating concentrates the
jIndividual element
v j Subjection degree be
r Ij , by the
iIndividual element
u i Evaluation result can use fuzzy set R
i =(
r i1
,
r i2
,
r i3
...,
r in
) expression, take the degree of membership of each factor evaluation collection as row, form fuzzy evaluation matrix R;
5. fuzzy comprehensive evoluation
Evaluation result B=AR=(
b 1,
b 2...,
b n), wherein " " is fuzzy operator, B
j Be called the one-level Result of Fuzzy Comprehensive Evaluation, expression is while carrying out comprehensive evaluation by all grades of factor in U, and evaluation object is in opinion rating the
jThe degree of membership of individual grade, according to maximum membership grade principle, determine to be evaluated the affiliated evaluation rank of object.
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