CN113361906A

CN113361906A - Transformer state maintenance decision system and decision method based on grey correlation analysis

Info

Publication number: CN113361906A
Application number: CN202110619276.8A
Authority: CN
Inventors: 杨奕飞; 左文杰; 何祖军; 齐亮; 袁伟; 苏贞; 叶树霞
Original assignee: Jiangsu University of Science and Technology
Current assignee: Jiangsu University of Science and Technology
Priority date: 2021-06-03
Filing date: 2021-06-03
Publication date: 2021-09-07

Abstract

The invention relates to the technical field of power maintenance, and discloses a transformer state maintenance decision system and a decision method based on grey correlation analysis. The system comprises a transformer, a monitoring parameter acquisition module, a fault diagnosis module and a grey correlation analysis maintenance decision module; the transformer is connected with the monitoring parameter acquisition module; the fault diagnosis module is connected with the monitoring parameter acquisition module; the grey correlation analysis maintenance decision module is connected with the fault diagnosis module; the monitoring parameter acquisition module is used for acquiring parameters of monitoring points distributed by the transformer; the fault diagnosis module is used for diagnosing faults of all parts of the transformer according to analysis of characteristic indexes of the transformer monitoring parameters; and the grey correlation analysis maintenance decision module is used for forming a grey correlation analysis decision system and guiding the final determination of the suboptimal maintenance strategy of the transformer.

Description

Transformer state maintenance decision system and decision method based on grey correlation analysis

Technical Field

The invention relates to the technical field of power maintenance research, in particular to a transformer state maintenance decision system and a decision method based on grey correlation analysis.

Background

Along with the continuous service of the novel ships which adopt advanced information technology and new technology in large scale in China, the complexity and technical requirements of the equipment are greatly improved, and the maintenance requirements in the later stage of fault diagnosis are also greatly improved. The marine transformer is used as a junction device of a shipboard power system, and the operation reliability of the marine transformer is closely related to the safety and stability of the power system. As shown in fig. 1, with regard to the maintenance of the transformer, China always performs post maintenance, planned maintenance or state maintenance on the transformer according to the fault mode characteristics of the power equipment, and plays a very important role in the safe operation of the whole power system. However, according to the regular maintenance strategy adopted after the transformer finds a fault at present, the transformer needing to be repaired (which means that equipment without the potential hazard detected in the overhaul period) cannot be repaired, so that an accident is caused; the transformer which does not need to be overhauled (the transformer which is in a repair period and has a good health state) is still overhauled according to the period, and finally, the waste of manpower and materials is caused.

In the prior art CN201891593U a maintenance decision system based on fault diagnosis, although it is proposed to divide vibration data of a reciprocating compressor monitored in real time to obtain a fault diagnosis result and a corresponding maintenance decision, so as to improve the fault maintenance efficiency of the reciprocating compressor and optimize the maintenance resource allocation of enterprises. However, this method only remains to solve and optimize the problem found, and cannot perform early warning analysis on the non-problem of the equipment.

Due to the increase of marine power equipment and the limited marine space, the requirements of people reduction, efficiency improvement and power supply reliability are further improved, and the maintenance requirements of marine transformers cannot be completely met gradually by periodical maintenance in the later period of fault diagnosis. The state maintenance is a technology for arranging maintenance according to the result provided by fault diagnosis by monitoring the state of the transformer, and has strong pertinence, economy and reasonableness. With the widespread acceptance of stateful maintenance techniques, the replacement of periodic maintenance by stateful maintenance techniques has become a necessity. Therefore, it is a problem to be solved in the art to provide a state maintenance decision method to achieve a technical transition from the "to repair and repair-necessary" policy of the transformer to "repair and repair-necessary" policy.

Disclosure of Invention

In order to solve the defects in the prior art, the invention provides a transformer state maintenance decision system based on grey correlation analysis and a decision method thereof, which are used for analyzing transformer parameters monitored in real time to obtain corresponding fault consequences and suboptimal maintenance strategies, improving the transformer fault maintenance efficiency and optimizing the configuration of maintenance resources.

The invention is realized by the following technical scheme: a transformer state maintenance decision-making system based on grey correlation analysis comprises a transformer, a monitoring parameter acquisition module, a fault diagnosis module and a grey correlation analysis maintenance decision-making module; the transformer is connected with the monitoring parameter acquisition module; the fault diagnosis module is connected with the monitoring parameter acquisition module; the grey correlation analysis maintenance decision module is connected with the fault diagnosis module; the monitoring parameter acquisition module is used for acquiring parameters of monitoring points distributed by the transformer; the fault diagnosis module is used for diagnosing faults of all parts of the transformer according to analysis of characteristic indexes of the transformer monitoring parameters; and the grey correlation analysis maintenance decision module is used for forming a grey correlation analysis decision system and guiding the final determination of the suboptimal maintenance strategy of the transformer.

Further, the basic method of the decision system of the grey correlation maintenance decision module is as follows:

let S be { S ═ S_ij|a_i∈A,b_jE.B is the local potential set,

the optimal effect vector is obtained; if it is

Corresponding situation

Then

For ideal optimal effect vectors, corresponding

Called the ideal optimal situation;

let S be { S ═ S_ij|a_i∈A,b_jE.g. B) as local potential set, local potential s_ijThe corresponding effect vector is u_ij＝(u_ij ⁽¹⁾,u_ij ⁽²⁾,...,u_ij ^(s))；i＝1,2,...,n；j＝1,2,...,m；

When the k target effect value is larger, the better the k target effect value is, the

When the k target effect value approaches a proper value u₀When it is good, get

When the k-target effect value is as small as possible,

then

An ideal optimal effect vector is obtained;

let S be { S ═ S_ij|a_i∈A,b_jE.g. B) as local potential set, local potential s_ijThe corresponding effect vector is

i＝1,2,...,n；j＝1,2,...,m。

For an optimal effect vector, ε_ij(

i

1, 2.. times.n;

j

1, 2.. times.m) is u_ijAnd

absolute correlation of gray value of if s_ijSatisfy for any i ∈ { i ≠ 1,2₁And any j ∈ { j ≠ 1,2₁Constantly have

Then u is_ijIn order to be a sub-optimal effect vector,

is in a suboptimal situation;

let a sequence of actions X_i(x_i(1),x_i(2),...,x_i(n)); marking line (x)_i(1)-x_i(1),x_i(2)-x_i(1),...,x_i(n)-x_i(1) Is X)_i-x_i(1) Let us order

Then

When X is present_iFor growing sequence, s_i≥0；

When X is present_iIn order to attenuate the sequence, s_i≤0；

When X is present_iIn the oscillating sequence, s_iThe sign is indefinite;

let sequence X₀And X_iSame length, s₀，s_iAs shown in the above definition, then call

Wherein

Is X₀And X_iAbsolute correlation of gray.

Further, the decision method comprises the following specific steps:

step one, determining an event set A ═ a₁,a₂,...,a_nAnd a countermeasure set B ═ B₁,b₂,...,b_nAnd constructing a local potential set S ═ S_ij＝(a_i,b_j)|a_i∈A,b_j∈B}；

Step two, determining a

decision target

1, 2.. multidot.s;

step three, solving different situations s_ij(

i

1, 2.. multidot.n;

j

1, 2.. multidot.m) the effect value at the target k

Step four, solving the situation effect sequence u under the k target^(k)Mean image of (1), still note

Step five, writing the situation s according to the result of the previous step_ijEffect vector u of_ij＝(u_ij ⁽¹⁾,u_ij ⁽²⁾,...,u_ij ^(s))；i＝1,2,...,n；j＝1,2,....,m；

Step six, solving the ideal optimal effect vector

Step seven, calculating u_ijAnd

degree of gray correlation ε_ij(

i

1, 2.. multidot.n;

j

1, 2.. multidot.m); by

Obtaining a suboptimal effect vector

And sub-optimal situation

Further, the first step specifically includes: determining the event set and the strategy set; recording the state maintenance of the transformer as event a₁Then event set a ═ a₁Recording the instant power failure overhaul scheme as the countermeasure b₁Priority is given to the strategy b₂Taking a timely targeted maintenance scheme as a countermeasure b₃Suspending the maintenance, enhancing the monitoring and monitoring scheme as a countermeasure b₄Delayed maintenance as countermeasure b₅(ii) a Then the countermeasure set B ═ B₁,b₂,b₃,b₄,b₅Then there is a situation set S ═ S_ij＝(a_i,b_j)|a_i∈A,b_j∈B}＝{s₁₁,s₁₂,s₁₃,s₁₄,s₁₅}。

Further, the second step specifically includes: determination of the decision target; through comprehensive analysis, on the basis of considering both safety and economy, the following indexes are selected as decision targets: the technical level required for maintenance is a target 1, the risk of maintenance measures is a target 2, the influence of maintenance measures on production is a target 3, the harm of maintenance measures on safety is a target 4, the comprehensive cost of maintenance is a target 5, and the effect of maintenance measures is a target 6.

Further, the determining of the local effect sequence under the target specifically includes: the sequence of the local effect on the level of skill required for maintenance is u⁽¹⁾＝{u₁₁ ⁽¹⁾,u₁₂ ⁽¹⁾,u₁₃ ⁽¹⁾,u₁₄ ⁽¹⁾,u₁₅ ⁽¹⁾}; the sequence of the local effect on the repair risk is u⁽²⁾＝{u₁₁ ⁽²⁾,u₁₂ ⁽²⁾,u₁₃ ⁽²⁾,u₁₄ ⁽²⁾,u₁₅ ⁽²⁾}; the sequence of the situational effects on the effect of maintenance on production is u⁽³⁾＝{u₁₁ ⁽³⁾,u₁₂ ⁽³⁾,u₁₃ ⁽³⁾,u₁₄ ⁽³⁾,u₁₅ ⁽³⁾}; the sequence of the local effect on the hazard of a fault to safety is u⁽⁴⁾＝{u₁₁ ⁽⁴⁾,u₁₂ ⁽⁴⁾,u₁₃ ⁽⁴⁾,u₁₄ ⁽⁴⁾,u₁₅ ⁽⁴⁾}; the sequence of the local effect on the maintenance complex cost is u⁽⁵⁾＝{u₁₁ ⁽⁵⁾,u₁₂ ⁽⁵⁾,u₁₃ ⁽⁵⁾,u₁₄ ⁽⁵⁾,u₁₅ ⁽⁵⁾}; the sequence of the local effect on the maintenance effect is u⁽⁶⁾＝{u₁₁ ⁽⁶⁾,u₁₂ ⁽⁶⁾,u₁₃ ⁽⁶⁾,u₁₄ ⁽⁶⁾,u₁₅ ⁽⁶⁾}。

Compared with the prior art, the invention has the following beneficial effects: the invention absorbs the advantages of a maintenance analysis method taking a grey correlation analysis decision method as a center, and simultaneously fully considers the principles of economy and reliability and is a maintenance mode combined with some advanced equipment diagnosis technologies at present. The suboptimal maintenance strategy determined by the decision system and the decision method can detect and diagnose potential faults as early as possible within the control range of the transformer and carry out reasonable and timely maintenance, thereby avoiding the occurrence of safety accidents and reducing the production loss; for equipment which can safely operate after fault diagnosis and analysis, unnecessary maintenance cost can be correspondingly reduced, and therefore optimal configuration of maintenance resources is achieved.

Drawings

FIG. 1 is a classification principle of the transformer maintenance strategy of the present invention.

Fig. 2 is a schematic structural diagram of a transformer state maintenance decision system.

Fig. 3 is a flowchart of a transformer maintenance decision method based on gray correlation analysis.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Moreover, the technical solutions in the embodiments of the present invention may be combined with each other, but it is necessary to be able to be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of the present invention.

Referring to the accompanying drawings 2-3, the invention provides a transformer state maintenance decision system based on grey correlation analysis, which comprises: the system comprises a transformer 1, a monitoring parameter acquisition module 2, a fault diagnosis module 3 and a grey correlation analysis maintenance decision module 4; the transformer 1 is connected with the monitoring parameter acquisition module 2, the fault diagnosis module 3 is connected with the monitoring parameter acquisition module 2, and the grey correlation analysis maintenance decision module 4 is connected with the fault diagnosis module 3.

The monitoring parameter acquisition module is used for acquiring parameters of monitoring points distributed by the transformer. The main parameters collected include temperature and humidity value, current and voltage value, vibration monitoring value and the like.

The fault diagnosis module is used for diagnosing the faults of all the parts according to the analysis of the characteristic indexes of all the monitoring parameters of the transformer, classifying and dividing each type of fault and fault parts to form a fault diagnosis report.

The grey correlation analysis maintenance decision module is used for forming a decision system, the decision system can determine the technical level required by maintenance, the maintenance risk, the influence of maintenance on production, the safety hazard of diagnosed faults, the comprehensive maintenance cost and the maintenance effect according to the fault diagnosis report, and the formation of the decision system can guide the final determination of suboptimal maintenance strategies of the transformer.

Fig. 3 shows an implementation of a transformer state maintenance decision method based on gray correlation analysis, which specifically includes the following steps:

step one, determining an event set and a strategy set after fault diagnosis. Recording the state maintenance of the transformer as event a₁Then event set a ═ a₁Recording the instant power failure overhaul scheme as the countermeasure b₁The scheme is preferably arranged as countermeasure b₂Taking a timely targeted maintenance scheme as a countermeasure b₃Suspending the maintenance, enhancing the monitoring and monitoring scheme as a countermeasure b₄Delayed maintenance as countermeasure b₅. Then the countermeasure set B ═ B₁,b₂,b₃,b₄,b₅Then there is a situation set S ═ S_ij＝(a_i,b_j)|a_i∈A,b_j∈B}＝{s₁₁,s₁₂,s₁₃,s₁₄,s₁₅}。

And step two, selecting the following indexes as decision targets on the basis of considering safety and economy through comprehensive analysis:

the technical level required for maintenance is a target 1, the maintenance risk is a target 2, the influence of maintenance on production is a target 3, the safety hazard of diagnosed faults is a target 4, the comprehensive cost of maintenance is a target 5, and the maintenance effect is a target 6.

And step three, determining the situation effect sequence of the target.

The sequence of the local effects on the level of skill required for maintenance is u⁽¹⁾＝{u₁₁ ⁽¹⁾,u₁₂ ⁽¹⁾,u₁₃ ⁽¹⁾,u₁₄ ⁽¹⁾,u₁₅ ⁽¹⁾H (high, medium, high);

the sequence of the local effect on the maintenance risk is u⁽²⁾＝{u₁₁ ⁽²⁾,u₁₂ ⁽²⁾,u₁₃ ⁽²⁾,u₁₄ ⁽²⁾,u₁₅ ⁽²⁾(high, medium, generally, medium, low);

the sequence of the local effects on the influence of maintenance on production is u⁽³⁾＝{u₁₁ ⁽³⁾,u₁₂ ⁽³⁾,u₁₃ ⁽³⁾,u₁₄ ⁽³⁾,u₁₅ ⁽³⁾(large, medium, generally small, very small);

the sequence of the local effects on the hazard of a fault to safety is u⁽⁴⁾＝{u₁₁ ⁽⁴⁾,u₁₂ ⁽⁴⁾,u₁₃ ⁽⁴⁾,u₁₄ ⁽⁴⁾,u₁₅ ⁽⁴⁾(small, generally, medium, large, very small);

the sequence of the local effect on the maintenance complex costs is u⁽⁵⁾＝{u₁₁ ⁽⁵⁾,u₁₂ ⁽⁵⁾,u₁₃ ⁽⁵⁾,u₁₄ ⁽⁵⁾,u₁₅ ⁽⁵⁾-large, medium, small, very small;

the sequence of local effect about the maintenance effect is u⁽⁶⁾＝{u₁₁ ⁽⁶⁾,u₁₂ ⁽⁶⁾,u₁₃ ⁽⁶⁾,u₁₄ ⁽⁶⁾,u₁₅ ⁽⁶⁾Good, bad). Since the situation effect sequences are expressed by language variables, they are only required to be quantitative targets.

Step four, solving the situation effect sequence u under the k target^(k)Mean image of

Step five, obtaining the situation s from the result of the step four_ijEffect vector u of_ij＝(u_ij ⁽¹⁾,u_ij ⁽²⁾,...,u_ij ⁽⁶⁾)；i＝1；j＝1,2,...,5。

Step six, solving the ideal optimal effect vector

If the skill level goal required for repair is as small as possible, then

If the maintenance measure risk objective is as small as possible, then

If the target of the influence of the maintenance measures on the production is as small as possible, the smaller the target is

If the smaller the maintenance measure is to the safety target, the better it is

If the maintenance cost objective is as small as possible, then

If the effect target of the maintenance measure is larger and better, then

There is a notional optimal effect vector

Step seven, calculating u_ijAnd

degree of gray correlation ε_ij(i＝1；j＝1,2,...,5)；

And step eight, making a decision. Comparing the results of step seven to obtain a maximum value of the gray correlation, i.e.

And obtaining the suboptimal situation s_pq(p 1,

q

1, 2.., 5). And determining suboptimal countermeasures in the 5 maintenance schemes in the step one according to the suboptimal situation correspondence.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims

1. A transformer state maintenance decision-making system based on grey correlation analysis is characterized in that: the system comprises a transformer, a monitoring parameter acquisition module, a fault diagnosis module and a grey correlation analysis maintenance decision module; the transformer is connected with the monitoring parameter acquisition module; the fault diagnosis module is connected with the monitoring parameter acquisition module; the grey correlation analysis maintenance decision module is connected with the fault diagnosis module;

the monitoring parameter acquisition module is used for acquiring parameters of monitoring points distributed by the transformer;

the fault diagnosis module is used for diagnosing faults of all parts of the transformer according to analysis of characteristic indexes of the transformer monitoring parameters;

and the grey correlation analysis maintenance decision module is used for forming a grey correlation analysis decision system and guiding the final determination of the suboptimal maintenance strategy of the transformer.

2. The grey correlation analysis-based transformer state maintenance decision system according to claim 1, wherein: the basic method of the decision system of the grey correlation maintenance decision module is as follows:

let S be { S ═ S_ij|a_i∈A,b_jE.B is the local potential set,

the optimal effect vector is obtained; if it is

Corresponding situation

Then

For ideal optimal effect vectors, corresponding

Called the ideal optimal situation;

When the k-target effect value is as small as possible,

then

To the ideal optimum effectA fruit vector quantity;

let S be { S ═ S_ij|a_i∈A,b_jE.g. B) as local potential set, local potential s_ijThe corresponding effect vector is u_ij＝(u_ij ⁽¹⁾,u_ij ⁽²⁾,...,u_ij ^(s))；i＝1,2,...,n；j＝1,2,...,m。

For an optimal effect vector, ε_ij(i 1, 2.. times.n; j 1, 2.. times.m) is u_ijAnd

Then u is_ijIn order to be a sub-optimal effect vector,

is in a suboptimal situation;

let a sequence of actions X_i(x_i(1),x_i(2),...,x_i(n)); marking line (x)_i(1)-x_i(1),x_i(2)-x_i(1),...,x_i(n)-x_i(1) Is X)_i-x_i(1) Let s_i＝∫₁ ⁿ(X_i-x_i(1) Dt, then

When X is present_iFor growing sequence, s_i≥0；

When X is present_iIn order to attenuate the sequence, s_i≤0；

When X is present_iIn the oscillating sequence, s_iThe sign is indefinite;

Wherein

Is X₀And X_iAbsolute correlation of gray.

3. The transformer state maintenance decision method based on the grey correlation analysis and applied to the claims 1 or 2 is characterized in that: the decision method comprises the following specific steps:

Step two, determining a decision target 1, 2.. multidot.s;

step three, solving different situations s_ij(i 1, 2.. multidot.n; j 1, 2.. multidot.m) the effect value at the target k

Step six, solving the ideal optimal effect(Vector)

Step seven, calculating u_ijAnd

degree of gray correlation ε_ij(i 1, 2.. multidot.n; j 1, 2.. multidot.m); by

Obtaining a suboptimal effect vector

And sub-optimal situation

4. The grey correlation analysis-based transformer state maintenance decision method according to claim 3, characterized in that: the first step specifically comprises: determining the event set and the strategy set; recording the state maintenance of the transformer as event a₁Then event set a ═ a₁Recording the instant power failure overhaul scheme as the countermeasure b₁Priority is given to the strategy b₂Taking a timely targeted maintenance scheme as a countermeasure b₃Suspending the maintenance, enhancing the monitoring and monitoring scheme as a countermeasure b₄Delayed maintenance as countermeasure b₅(ii) a Then the countermeasure set B ═ B₁,b₂,b₃,b₄,b₅Then there is a situation set S ═ S_ij＝(a_i,b_j)|a_i∈A,b_j∈B}＝{s₁₁,s₁₂,s₁₃,s₁₄,s₁₅}。

5. The grey correlation analysis-based transformer state maintenance decision method according to claim 3, characterized in that: the second step specifically comprises: determination of the decision target; through comprehensive analysis, on the basis of considering both safety and economy, the following indexes are selected as decision targets: the technical level required for maintenance is a target 1, the risk of maintenance measures is a target 2, the influence of maintenance measures on production is a target 3, the harm of maintenance measures on safety is a target 4, the comprehensive cost of maintenance is a target 5, and the effect of maintenance measures is a target 6.

6. The grey correlation analysis-based transformer state maintenance decision method according to claim 3, characterized in that: the determining of the situation effect sequence under the target specifically comprises the following steps: the sequence of the local effect on the level of skill required for maintenance is u⁽¹⁾＝{u₁₁ ⁽¹⁾,u₁₂ ⁽¹⁾,u₁₃ ⁽¹⁾,u₁₄ ⁽¹⁾,u₁₅ ⁽¹⁾}; the sequence of the local effect on the repair risk is u⁽²⁾＝{u₁₁ ⁽²⁾,u₁₂ ⁽²⁾,u₁₃ ⁽²⁾,u₁₄ ⁽²⁾,u₁₅ ⁽²⁾}; the sequence of the situational effects on the effect of maintenance on production is u⁽³⁾＝{u₁₁ ⁽³⁾,u₁₂ ⁽³⁾,u₁₃ ⁽³⁾,u₁₄ ⁽³⁾,u₁₅ ⁽³⁾}; the sequence of the local effect on the hazard of a fault to safety is u⁽⁴⁾＝{u₁₁ ⁽⁴⁾,u₁₂ ⁽⁴⁾,u₁₃ ⁽⁴⁾,u₁₄ ⁽⁴⁾,u₁₅ ⁽⁴⁾}; the sequence of the local effect on the maintenance complex cost is u⁽⁵⁾＝{u₁₁ ⁽⁵⁾,u₁₂ ⁽⁵⁾,u₁₃ ⁽⁵⁾,u₁₄ ⁽⁵⁾,u₁₅ ⁽⁵⁾}; the sequence of the local effect on the maintenance effect is u⁽⁶⁾＝{u₁₁ ⁽⁶⁾,u₁₂ ⁽⁶⁾,u₁₃ ⁽⁶⁾,u₁₄ ⁽⁶⁾,u₁₅ ⁽⁶⁾}。