CN106779324A - Distribution transformer state deterioration process based on Markov chain model describes method - Google Patents
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Abstract
The present invention relates to the deterioration rule assessment technology of distribution main equipment, and in particular to the distribution transformer state deterioration process based on Markov chain model describes method, comprises the following steps：Step 1, collection, arrangement homotype equipment deterioration historical record；Step 2, the state space for determining description degradation；The foundation of step 3, statistical sample；The calculating of step 4, state transition probability.Method is capable of the state parameter of exact expression distribution transformer degradation, support of the state parameter to the faulty mechanism of confidence level that degradation is expressed, it is furthermore possible to be defined with the status parameter values being observed that the brand-new state and functional fault state of system, therefore distribution transformer state deterioration process describes objective, reasonable, accurate.
Description
Technical field
The present invention relates to the deterioration rule assessment technology field of distribution main equipment, more particularly to based on Markov chain model
Distribution transformer state deterioration process method is described.
Background technology
Used as the nucleus equipment of power system, the operation conditions of transformer directly affects the electricity consumption peace of whole electricity consumption side user
Entirely with stably.Transformer deterioration process is estimated, power operation personnel can be helped to need to understand the strong of transformer in time
Health state, to be overhauled to it and to be safeguarded.Distribution transformer includes multiple parts, and each part has different status informations,
There are different states at different time points, studies the metastatic rule of transformer different conditions in deterioration process to transformer life
Assessment and O＆M have great importance.At present, Transformer State Assessment mainly uses Fuzzy Evaluation Method, Bayes networks
Method etc., and the life-span of transformer is predicted using Weibull distribution, in today that detection means becomes increasingly abundant, Weibull point
Cloth is being difficult to accurately describe the deterioration process of transformer.
The content of the invention
It is an object of the invention to provide a kind of side of the state deterioration process that can objectively and accurately describe power transformer
Method, descriptive model is reasonable, visual result, accurate, reliability.
To achieve the above object, the technical solution adopted by the present invention is：Distribution transformer based on Markov chain model
State deterioration process describes method, comprises the following steps：
Step 1, collection, arrangement homotype equipment deterioration historical record；
Step 2, the state space for determining description degradation；
Step 3, set up statistical sample；
Step 4, calculating state transition probability.
In the above-mentioned distribution transformer state deterioration process based on Markov chain model describes method, step 1
Realization includes：
1. firsthand information is collected；Trouble shooting, the maintenance record of field apparatus technology machine account record are collected, and large and small is repaiied
Summary on technology；
2. state change course description；It is individual by observation of individual equipment, the change course of state parameter is described as one
The orderly state parameter of group, the observation interval of two adjacent state parameters should be equal.
In the above-mentioned distribution transformer state deterioration process based on Markov chain model describes method, step 2
Realization includes, between the corresponding state parameter of brand-new and functional fault, equipment is divided into some adjacent states, provides each
The corresponding state parameter scope of state；State demarcation is carried out by following principle：
3. brand-new state is corresponded to, state parameter change is slow, and the corresponding parameter area of state obtains larger；
4. the observation result according to state parameter, slow parameter interval, the corresponding parameter of state are changed in state parameter
Scope obtains larger；Conversely, then obtaining smaller；
5. near the decision-making value of experience failure, the corresponding parameter area of state obtains smaller；
6. the corresponding parameter area of functional fault state needs, according to failure mechanism, to determine with reference to associated specifications.
In the above-mentioned distribution transformer state deterioration process based on Markov chain model describes method, step 3
Realization includes：
7. individual state change course will be observed and will be described as discrete state transfer track；
8. there is the total degree N of a certain state i in each individuality in statistical samplei, state L-1 is counted on from state 0；
9. each individual after there is a certain state i in statistical sample, next observation space state remains as the times N of iii, i
State L-1 is counted on from state 0；
10. each individual after there is a certain state i in statistical sample, next observation space state is the times N of jij, i is from shape
State 0 counts on state L-1, j and counts on state L from state i+1.
In the above-mentioned distribution transformer state deterioration process based on Markov chain model describes method, step 4 is counted
Calculate the formula of state transition probability：
(1), in (2) formula, piiFor certain moment from state i by a moment to state i probability, pijFor certain moment from
State i by a moment to state j probability, NiIt is the total degree N of a certain state ii, NiiUnder after a certain state i of appearance
One observation space state remains as the number of times of i, NijFor next observation space state is the number of times of j after there is a certain state i.
The beneficial effects of the invention are as follows：The side of the distribution transformer state deterioration process description based on Markov chain model
Method is capable of the state parameter of exact expression distribution transformer degradation, and the state parameter has to the confidence level that degradation is expressed
The support of failure mechanism, is furthermore possible to be defined with the status parameter values being observed that the brand-new state and functional fault of system
State, therefore distribution transformer state deterioration process describes objective, reasonable, accurate.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of one embodiment of the invention.
Specific embodiment
Embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.
The example of the embodiment is shown in the drawings, wherein same or similar label represents identical or class from start to finish
As element or the element with same or like function.Below with reference to Description of Drawings embodiment be it is exemplary, only
For explaining the present invention, and it is not construed as limiting the claims.
Following disclosure provides many different embodiments or example is used for realizing different structure of the invention.For letter
Change disclosure of the invention, hereinafter the part and setting to specific examples are described.They are only merely illustrative, and purpose is not
It is to limit the present invention.Additionally, the present invention can in different examples repeat reference numerals and/or letter.This repetition be for
Simplify and clearly purpose, the relation between discussed various embodiments and/or setting itself is not indicated.Additionally, this hair
It is bright there is provided various specific techniques and the example of material, but those of ordinary skill in the art can be appreciated that other techniques
The use of applicability and/or other materials.In addition, fisrt feature described below second feature it " on " structure can be with
Be formed as the embodiment of directly contact including the first and second features, it is also possible to be formed in first and second including other feature
Embodiment between feature, such first and second feature may not be directly contact.
, it is necessary to explanation in description of the invention, unless otherwise prescribed and limit, term " connected " " connection " should do extensively
Reason and good sense solution, can be joined directly together for example, it may be mechanically connecting or electrical connection, or two connections of element internal,
Can also be indirectly connected to by intermediary, for those of ordinary skill in the related art, can managed as the case may be
Solve the concrete meaning of above-mentioned term.
The technical scheme that the present embodiment is used is as follows：Distribution transformer state deterioration process based on Markov chain model
Description method, comprises the following steps：
Step 1, collection, arrangement homotype equipment deterioration historical record；
Step 2, the state space for determining description degradation；
Step 3, set up statistical sample；
Step 4, calculating state transition probability.
Further, the realization of step 1 includes：
1. firsthand information is collected；Trouble shooting, the maintenance record of field apparatus technology machine account record are collected, and large and small is repaiied
Summary on technology；
2. state change course description；It is individual by observation of individual equipment, the change course of state parameter is described as one
The orderly state parameter of group, the observation interval of two adjacent state parameters should be equal.
Further, the realization of step 2 includes, between the corresponding state parameter of brand-new and functional fault, equipment is divided
It is some adjacent states, provides the corresponding state parameter scope of each state；State demarcation is carried out by following principle：
3. brand-new state is corresponded to, state parameter change is slow, and the corresponding parameter area of state obtains larger；
4. the observation result according to state parameter, slow parameter interval, the corresponding parameter of state are changed in state parameter
Scope obtains larger；Conversely, then obtaining smaller；
5. near the decision-making value of experience failure, the corresponding parameter area of state obtains smaller；
6. the corresponding parameter area of functional fault state needs, according to failure mechanism, to determine with reference to associated specifications.
Further, the realization of step 3 includes：
7. individual state change course will be observed and will be described as discrete state transfer track；
8. there is the total degree N of a certain state i in each individuality in statistical samplei, state L-1 is counted on from state 0；
9. each individual after there is a certain state i in statistical sample, next observation space state remains as the times N of iii, i
State L-1 is counted on from state 0；
10. each individual after there is a certain state i in statistical sample, next observation space state is the times N of jij, i is from shape
State 0 counts on state L-1, j and counts on state L from state i+1.
Further, step 4 calculates the formula of state transition probability：
(1), in (2) formula, piiFor certain moment from state i by a moment to state i probability, pijFor certain moment from
State i by a moment to state j probability, NiIt is the total degree N of a certain state ii, NiiUnder after a certain state i of appearance
One observation space state remains as the number of times of i, NijFor next observation space state is the number of times of j after there is a certain state i.
During specific implementation, one, the mathematical modeling based on Markov Chain method
Markov model portrays the mathematical description of deterioration process：
Define 1 and assume random sequence { Xn, n ∈ N } and to arbitrary state i0,i1,…,in,in+1∈ I, state space I=
{i0,i1,…,in..., time parameter collection N={ 1,2 ..., N ... } has P={ X0=i0,…,Xn=in＞ 0, and
P{Xn+1=in+1|X0=i0,…,Xn=in}=P { Xn+1=in+1|Xn=in}
Then claim the random process { Xn, n ∈ N } and it is Markov Chain (Markov chain).
Markov chain represents that a conditional probability for random sequence is only relevant with nearest system mode, and is with previous
System state is unrelated, and sometimes, Markov is also referred to as markov property.It is worth noting that defined herein Markov chain is one-dimensional
Situation, can be generalized to the situation of multidimensional.
Define 2 and set random process { Xn, n ∈ N } and it is a Markov Chain, then a step transition probability：P{Xn+1=j | Xn=
I }=pij(n)Referred to as the n moment from state i by a moment to state j probability.Note P={ pij(n)},i,j
∈ I are random process { Xn, n ∈ N } a step transition probability matrix.
Knowable to the definition of Markov chain, the key for constructing Markov chain is how to define its state set.Utilize
The definition of Markov chain, can obtain：
If { Xn, n ∈ N } and be homogeneous Markov chains, then
This shows that the finite dimension Joint Distribution of homogeneous Markov chains can use initial distribution probability and a step transition probability matrix
Be given.
2nd, the method and step of the distribution transformer state deterioration process description based on Markov chain model is as follows, such as Fig. 1
It is shown：
Step S101：Collect, arrange homotype equipment deterioration historical record；
Step S102：It is determined that the state space of description degradation；
Step S103：Statistical sample is set up；
Step S104：The calculating of state transition probability；
In below in conjunction with specific example implement process to such scheme in each step be described in detail.
The collection of the step S101, arrangement homotype equipment deterioration historical record.Groundwork includes：
(1) firsthand information is collected.Collect field apparatus technology machine account (trouble shooting, maintenance record) and large and small repair technology
Summarize.
(2) state change course description.It is individual by observation of individual equipment, the change course of state parameter is described as one
The orderly state parameter of group, the observation interval of two adjacent state parameters should be equal.
For example, an equipment experiences 5 observation intervals, the observation result of state parameter is followed successively by A, B, C, D, E.
The individual state change course is described as：
(A,B,C,D,E)t
In formula, subscript t is spaced for observation.
The determination of the step S102 describes the state space of degradation.In the corresponding state ginseng of brand-new and functional fault
Between number, equipment is divided into some adjacent states, provides the corresponding state parameter scope of each state.
State demarcation can contemplate to be carried out by following principle：
(3) the brand-new state of correspondence, usual state parameter change is slow, and the corresponding parameter area of state can obtain big by one
A bit.
(4) the observation result according to state parameter, slow parameter interval, the corresponding parameter of state are changed in state parameter
Scope can obtain larger.Conversely, smaller.
(5) near the decision-making value of experience failure, the corresponding parameter area of state can obtain smaller.
(6) the corresponding parameter area of functional fault state needs, according to failure mechanism, to determine with reference to associated specifications.
According to mentioned above principle state demarcation is carried out, state demarcation the results are shown in Table 1.
The distribution transformer winding insulation deterioration state of table 1 correspondence
The statistical sample of the step S103 is set up includes following work：
(7) individual state change course will be observed and will be described as discrete state transfer track.
(8) each individual total degree N for a certain state i occur in statistical samplei.State L-1 is counted on from state 0.
(9) each individual after there is a certain state i in statistical sample, next observation space state remains as the times N of iii,
I counts on state L-1 from state 0.
(10) each individual after there is a certain state i in statistical sample, next observation space state is the times N of jij, i from
State 0 counts on state L-1, j and counts on state L from state i+1.
Statistical sample is established, sample is provided by table 2 to the form of table 8.
The constant number of times statistics of the state of table 2
| Number of state indexes | 0 | 1 | 2 | 3 | 4 | 5 |
| Total occurrence number N of each statei | 500 | 178 | 76 | 40 | 21 | 6 |
| The times N that each state keeps on an observation intervalii | 106 | 39 | 17 | 9 | 5 | 2 |
The state of table 30 is transferred to the number of times statistics of other states
| Number of state indexes | 1 | 2 | 3 | 4 | 5 | 6 |
| Total occurrence number N of each state0j | 138 | 59 | 39 | 29 | 20 | 2 |
The state of table 41 is transferred to the number of times statistics of other states
| Number of state indexes | 2 | 3 | 4 | 5 | 6 |
| Total occurrence number N of each state1j | 48 | 21 | 14 | 10 | 7 |
The state of table 52 is transferred to the number of times statistics of other states
| Number of state indexes | 3 | 4 | 5 | 6 |
| Total occurrence number N of each state2j | 22 | 12 | 6 | 2 |
The state of table 63 is transferred to the number of times statistics of other states
| Number of state indexes | 4 | 5 | 6 |
| Total occurrence number N of each state3j | 12 | 6 | 3 |
The state of table 74 is transferred to the number of times statistics of other states
| Number of state indexes | 5 | 6 |
| Total occurrence number N of each state4j | 7 | 3 |
The state of table 85 is transferred to the number of times statistics of other states
| Number of state indexes | 6 |
| Total occurrence number N of each state5j | 2 |
The calculating of the state transition probability of the step S104：
Wherein, piiFor certain moment from state i by a moment to state i probability, pijFor certain moment passes through from state i
Spend a moment to the probability of state j, NiIt is the total degree N of a certain state ii, NiiFor between next observation after a certain state i of appearance
The number of times of i, N are remained as every stateijFor next observation space state is the number of times of j after there is a certain state i.Shape obtained by calculating
State transfer matrix is as follows：
It should be appreciated that the part that this specification is not elaborated belongs to prior art.
Although the specific embodiment of the invention above in association with Description of Drawings, those of ordinary skill in the art should
Understand, these are merely illustrative of, and various deformation or modification can be made to these implementation methods, without departing from original of the invention
Reason and essence.The scope of the present invention is only limited by the claims that follow.
Claims (5)
1. the distribution transformer state deterioration process based on Markov chain model describes method, it is characterised in that including following
Step：
Step 1, collection, arrangement homotype equipment deterioration historical record；
Step 2, the state space for determining description degradation；
Step 3, set up statistical sample；
Step 4, calculating state transition probability.
2. the distribution transformer state deterioration process based on Markov chain model as claimed in claim 1 describes method, its
It is characterised by, the realization of step 1 includes：
1. firsthand information is collected；Trouble shooting, the maintenance record of field apparatus technology machine account record are collected, and large and small repaiies technology
Summarize；
2. state change course description；Individual by observation of individual equipment, the change course of state parameter is described as into one group has
The state parameter of sequence, the observation interval of two adjacent state parameters should be equal.
3. the distribution transformer state deterioration process based on Markov chain model as claimed in claim 2 describes method, its
It is characterised by, the realization of step 2 includes, between the corresponding state parameter of brand-new and functional fault, equipment is divided into some
Adjacent states, provide the corresponding state parameter scope of each state；State demarcation is carried out by following principle：
3. brand-new state is corresponded to, state parameter change is slow, and the corresponding parameter area of state obtains larger；
4. the observation result according to state parameter, slow parameter interval, the corresponding parameter area of state are changed in state parameter
Obtain larger；Conversely, then obtaining smaller；
5. near the decision-making value of experience failure, the corresponding parameter area of state obtains smaller；
6. the corresponding parameter area of functional fault state needs, according to failure mechanism, to determine with reference to associated specifications.
4. the distribution transformer state deterioration process based on Markov chain model as claimed in claim 3 describes method, its
It is characterised by, the realization of step 3 includes：
7. individual state change course will be observed and will be described as discrete state transfer track；
8. there is the total degree N of a certain state i in each individuality in statistical samplei, state L-1 is counted on from state 0；
9. each individual after there is a certain state i in statistical sample, next observation space state remains as the times N of iii, i is from shape
State 0 counts on state L-1；
10. each individual after there is a certain state i in statistical sample, next observation space state is the times N of jij, i is from state 0
Count on state L-1, j and count on state L from state i+1.
5. the distribution transformer state deterioration process based on Markov chain model as claimed in claim 4 describes method, its
It is characterised by, step 4 calculates the formula of state transition probability：
(1), in (2) formula, piiFor certain moment from state i by a moment to state i probability, pijIt is certain moment from state i
By the probability at a moment to state j, NiIt is the total degree N of a certain state ii, NiiIt is next observation after a certain state i of appearance
Space state remains as the number of times of i, NijFor next observation space state is the number of times of j after there is a certain state i.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111814901A (en) * | 2020-07-21 | 2020-10-23 | 西北工业大学 | Physician operation manipulation simulation method based on data mining and state learning |
| CN113610249A (en) * | 2021-08-13 | 2021-11-05 | 中国石油大学(华东) | Method for maintaining fully-electrically-controlled underground safety valve according to conditions |
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| CN103323707A (en) * | 2013-06-05 | 2013-09-25 | 清华大学 | Transformer failure rate predicating method based on half Markoff process |
| CN103413586A (en) * | 2013-07-26 | 2013-11-27 | 国核电站运行服务技术有限公司 | Method for maintaining system with multiple components in nuclear power plant |
| CN104036131A (en) * | 2014-06-06 | 2014-09-10 | 清华大学 | Transformer aging fault rate estimation method |
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| CN103048573A (en) * | 2012-12-31 | 2013-04-17 | 重庆市电力公司 | Method and device for electric power system operation risk assessment |
| CN103323707A (en) * | 2013-06-05 | 2013-09-25 | 清华大学 | Transformer failure rate predicating method based on half Markoff process |
| CN103413586A (en) * | 2013-07-26 | 2013-11-27 | 国核电站运行服务技术有限公司 | Method for maintaining system with multiple components in nuclear power plant |
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| CN111814901A (en) * | 2020-07-21 | 2020-10-23 | 西北工业大学 | Physician operation manipulation simulation method based on data mining and state learning |
| CN113610249A (en) * | 2021-08-13 | 2021-11-05 | 中国石油大学(华东) | Method for maintaining fully-electrically-controlled underground safety valve according to conditions |
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