CN106126875A - A kind of Transformer condition evaluation theoretical based on Situation Awareness - Google Patents

Abstract

The invention discloses a kind of Transformer condition evaluation theoretical based on Situation Awareness, comprise the following steps: first to comprehensive transformer body status monitoring amount and operation of power networks scene monitoring variable, establish 3 sub-goals: 1) transformator major insulation situation；2) whether transformator generates heat exception；3) transformator mechanical deformation situation；Next each sub-goal decision-making is carried out Situation Awareness demand analysis the most one by one, determine each monitoring variable needing perception, i.e. perception amount, and use uncertainty theory to be analyzed, understand each perception amount and transformator major insulation affected size, it is achieved the prediction of the Developing Trend following to major insulation；The method is compared with the conventional method, many information such as transformator electricity, heat, change can not only be detected, more demand based on operations staff these multi-source informations can be carried out specific integration, in order to each side truth understanding transformator of operations staff's more simple, intuitive.

Description

A kind of Transformer condition evaluation theoretical based on Situation Awareness

Technical field

The present invention relates to technical field of electric power detection, comment particularly to a kind of transformer state theoretical based on Situation Awareness Estimate method.

Background technology

The state estimation of power equipment and repair based on condition of component are operations of power networks and safeguard one of important goal of seek assiduously, when Before, the secondary monitoring technology of power equipment is quickly improved and is applied, but, laid particular emphasis on according to one-sided monitoring variable in the past State estimation, seldom considers physical interconnection and the restriction relation of many monitoring variables, and it is accurate that this has become raising evaluating status of electric power The bottleneck of exactness.Introduce electrical secondary system and estimate that many monitoring informations are merged by theory, and then improve evaluating status of electric power Accuracy, this becomes the approach of breakthrough bottleneck.Transformator is applied as the core power equipment of grid nodes, its electrical secondary system Degree is high, body monitoring variable (as oil chromatography, oil temperature, failure wave-recording, core current, office put) is comprehensive, in conjunction with transformator Operation information (such as load, ambient temperature, the operation time limit etc.), is carried out as introduced the Situation Awareness method estimated based on electrical secondary system State estimation, the both many monitoring variables of integrated treatment transformer body, include again operation of power networks scene affects information, the most fully examines The time and space characteristic considering monitoring information carries out information fusion, and this will improve the technology of Transformer State Assessment from method aspect Level, it is ensured that management and running and the economy of repair based on condition of component and motility, improves the power supply reliability of modern power network simultaneously.

Transformer state is estimated based on substantial amounts of electrical secondary system information, such as oil chromatography, oil temperature, voltage, electric current, fault record The data message such as is put in ripple, core current, office.But in the face of the data of these magnanimity, operations staff is often more difficult to acquisition than in the past to be had Information, its state estimation made the most also is single aspect, can not effecting reaction transformator current state comprehensively. So it is easily caused operations staff and the judgement of mistake occurs, thus produce the decision-making of mistake.These substantial amounts of electrical secondary system data And the gap between the useful information needed for operations staff is referred to as telecoms gap.As can be seen here, a state effective, reliable is commented Estimate the problem that the key of system solves telecoms gap exactly.

In view of this, Situation Awareness is just introduced Transformer State Assessment by the present invention such that it is able to overcome existing assessment hands The defect of section, meets.

Summary of the invention

In view of this, it is an object of the invention to provide a kind of Transformer condition evaluation theoretical based on Situation Awareness. The method compared with the conventional method, can not only detect many information such as transformator electricity, heat, change, more can be based on running people These multi-source informations are carried out specific integration by the demand of member, in order to each side understanding transformator of operations staff's more simple, intuitive Face truth.

It is an object of the invention to be achieved through the following technical solutions:

Said method comprising the steps of:

Step 1: comprehensive transformer body status monitoring amount and operation of power networks scene monitoring variable, establishes 3 sub-goals: 1) change Depressor major insulation situation；2) whether transformator generates heat exception；3) transformator mechanical deformation situation；

Step 2: sub-goal 1 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, I.e. perception amount, and use uncertainty theory to be analyzed, understand each perception amount size that affects on transformator major insulation, real The prediction of now following to the major insulation Developing Trend；

Step 3: sub-goal 2 decision-making is carried out Situation Awareness demand analysis one by one, determines perception amount, and employing does not knows reason Opinion is analyzed, and understanding each perception amount to what transformator entirety was generated heat affects size, it is achieved to following transformer temperature The prediction of change；

Step 4: sub-goal 3 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, And use uncertainty theory to be analyzed, understand the size of each perception amount reflection deformation situation, it is achieved to transformator machinery Deformation reaches to bear the prediction of the time of the limit.

Further, in step 2, perception amount includes voltage, the magnitude of current, insulation resistance, iron core grounding current waveform, oil Chromatographic parameter feature, micro-water content feature, record ripple fault message, material unaccounted for streaming current, external impact size of current and continue Time, external impact voltage swing and persistent period；

Further, in step 3, described perception amount includes temperature of oil in transformer, Transformer Winding temperature, transformer case temperature Degree, ambient temperature, voltage, the magnitude of current, bushing temperature, end shield current waveform.

Further, in described step 4, described perception amount include iron core grounding current waveform, vibration amplitude, vibration frequency, Noise, external impact number of times, external impact size of current and persistent period, external impact voltage swing and persistent period.

Further, the specifically comprising the following steps that of described step 2

Step 1.1: determine the evidence model of transformer device state.Determine transformator major insulation state identification framework H= {H₁, H₂..., H_M, wherein H₁, H₂..., H_MFor separate state (bothI ≠ j) fuzzy set, then Determine detection means X={X according to actual needs₁, X₂..., X_NNumber N；

Step 1.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0 ≤(X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree, the biggest on the impact of state estimation conclusion, Importance degree (X_i) ' the biggest；Otherwise, (X_i) ' the least；

Step 1.3: by (X_i) ' normalization.Choosing the maximum in importance degree as reference value, making its value is 1, other Value compares therewith and obtains corresponding normalization importance degree, if (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process Obtain: (X_i)=(X_i)′/(X_j) '={ (X₁), (X₂) ..., (X_N), obtain normalized importance degree vector (X)=1,1,1, 1,1,1,1,1,1}.

Step 1.4: determine degree of membership, according to detection means X_i(i=1,2 ..., N) measurement data, it is carried out normalizing Change, determine its fuzzy set H in state identification framework H the most again_j(j=1,2 ..., M) degree of membership, and degree of membership Meet=1, specify that data and historical data obtain the normalization quantized data of measurement data, in [0,1] according to corresponding code Interval division is in each fuzzy set (good, normally, suspicious, reliability decrease, dangerous)；

Step 1.5: determine confidence level CF (X_i), confidence level CF (X_i) the accurate journey of measurement data of reflection detection means Xi Degree, the namely description to the degree of faith of data acquired.Environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor The biggest, CF (X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement The suspection of the accuracy of data is the least,

Step 1.6: calculating basic probability assignment:

$m_{i,f}=\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,\mathrm{...},M\\ 1-\underset{j=1}{\overset{M}{\Σ}}{m}_{i,f}& i=1,2,\mathrm{...},N;j=M+1\end{array};$

Step 1.7: build matrix M=[m_{I, j}]；

Step 1.8: composite calulation, according to D-S compositional rule formula, it is G that synthesis obtains result；

Step 1.9: determine state: by the main value m obtaining judging in synthesis result G_j, if above taking fixed threshold value, then Can be regarded as major insulation state in shape, and closer in normal condition.

Further, M takes 5, i.e. it is that H={ is good that H takes fixed five kind state, normally, suspicious, and reliability decrease is dangerous }；

Especially, the concrete steps of step 3 are basically identical with the concrete steps of step 2, and the perception amount simply chosen is different, The perception amount that step 2 is chosen includes temperature of oil in transformer, Transformer Winding temperature, transformer case temperature, ambient temperature, voltage Amount, the magnitude of current, bushing temperature, end shield current waveform.

Especially, the concrete steps of step 4 are basically identical with the concrete steps of step 3, and the perception amount simply chosen is different, The perception amount perception amount that step 4 is chosen includes iron core grounding current waveform, vibration amplitude, vibration frequency, noise, external impact time Number, external impact size of current and persistent period, external impact voltage swing and persistent period.

The invention has the beneficial effects as follows:

A kind of based on Situation Awareness the Transformer condition evaluation that the present invention provides, the method and existing method phase Ratio, can not only detect many information such as transformator electricity, heat, change, more can demand based on operations staff by these multi-sources Information carries out specific integration, in order to each side truth understanding transformator of operations staff's more simple, intuitive, comprehensively examines Consider the various characteristic quantities of equipment, established scientific and effective unitary analysis model, improve the accuracy of condition evaluation results And reliability, provide strong support and guidance for O&M science decision, and can greatly strengthen the work of operations staff Efficiency and decision-making capability, reduce the error in judgement of operations staff, such that it is able to reduce operating cost, maintenance cost in a large number, extends The operation time of transformator.

Other advantages, target and the feature of the present invention will be illustrated to a certain extent in the following description, and And to a certain extent, will be apparent to those skilled in the art based on to investigating hereafter, or can To be instructed from the practice of the present invention.The target of the present invention and other advantages can be realized by description below and Obtain.

Accompanying drawing explanation

In order to make the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing the present invention made into The detailed description of one step, wherein:

Fig. 1 is the analysis and assessment schematic flow sheet of the present invention.

Detailed description of the invention

Hereinafter with reference to accompanying drawing, the preferred embodiments of the present invention are described in detail.Should be appreciated that preferred embodiment Only for the explanation present invention rather than in order to limit the scope of the invention.

As it is shown in figure 1, a kind of Transformer condition evaluation theoretical based on Situation Awareness of the present invention, method include with Lower step:

Step 1: comprehensive transformer body status monitoring amount and operation of power networks scene monitoring variable, establishes 3 sub-goals: 1) change Depressor major insulation situation；2) whether transformator generates heat exception；3) transformator mechanical deformation situation；

Step 2: sub-goal 1 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, I.e. perception amount, and use uncertainty theory to be analyzed, understand each perception amount size that affects on transformator major insulation, real The prediction of now following to the major insulation Developing Trend；In step 2, perception amount includes voltage, the magnitude of current, insulation resistance, ferrum Core earth current waveform, oil chromatography parameter attribute, micro-water content feature, record ripple fault message, material unaccounted for streaming current, outside punching Hit size of current and persistent period, external impact voltage swing and persistent period, in practice, can be according to concrete measurement Require therefrom to carry out selecting corresponding perception amount.

Step 3: sub-goal 2 decision-making is carried out Situation Awareness demand analysis one by one, determines perception amount, and employing does not knows reason Opinion is analyzed, and understanding each perception amount to what transformator entirety was generated heat affects size, it is achieved to following transformer temperature The prediction of change；Described perception amount includes temperature of oil in transformer, Transformer Winding temperature, transformer case temperature, ambient temperature, electricity Pressure amount, the magnitude of current, bushing temperature, end shield current waveform.In practice, therefrom can select according to concrete measurement requirement Select corresponding perception amount.

Step 4: sub-goal 3 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, And use uncertainty theory to be analyzed, understand the size of each perception amount reflection deformation situation, it is achieved to transformator machinery Deformation reaches to bear the prediction of the time of the limit.Described perception amount includes iron core grounding current waveform, vibration amplitude, vibrations frequency Rate, noise, external impact number of times, external impact size of current and persistent period, external impact voltage swing and persistent period.Real Trample in operation, can therefrom carry out according to concrete measurement requirement selecting corresponding perception amount.

The present invention uses D-S evidence theory the multidimensional information amount of the sensing layer of each sub-goal to be merged, thus Situation Awareness realizes from sensing layer to the conversion understanding layer.After D-S evidence theory uses prior probability partition function method to obtain The evidence tested is interval, and the credibility of evidence interval quantization proposition and likelihood probability, it can be more preferable to compare traditional theory of probability The non-intellectual of assurance problem and uncertainty, thus be used widely in Multi-information acquisition.

When having two and above evidence to same subset or is not empty subset allocation degree of belief to two common factors, just Create the process problem of the combination of belief function, i.e. information.D-S evidence theory provides the merging rule formula of following D-S, claims This problem is processed for compositional rule.

$m\left(C\right)=\left\{\begin{array}{cc}0,& C=\mathrm{\φ}\\ k\underset{A\∩B=C}{\Σ}{m}_1\left(A\right)m_1\left(B\right),& C\≠\mathrm{\φ}\end{array}\right.;$

In formula, k is referred to as specification coefficient, and:

M (C) is also denoted asK can describe the collision peculiarity of evident information, whenTime, the letter of evidence A The belief function appointing function and evidence B imparts degree of belief to two incompatible propositions respectively, now k → ∞'s, i.e. evidence A Belief function and trusting of evidence B there occurs conflict on this problem with the evidence corresponding to Eucalyptus.Knot to multiple evidences CloseCan promote from the situation that two evidences combine and obtain.

To be embodied as illustrating to the present invention by multiple embodiments below.

Embodiment one (major insulation)

The method according to the invention measures the major insulation situation of certain transformator, test site: Tongren district Guizhou Province city 110KV becomes In power station.

Evaluation and test step is as follows:

Step 1.1: determine transformator major insulation state identification framework H={H₁, H₂..., H_M, in this example, M takes 5, i.e. passes through H is defined as H={ well by 5 scales, normally, suspicious, and reliability decrease is dangerous }, wherein H₁, H₂..., H_MFor separate State (i.e.I ≠ j) fuzzy set, determine detection means X={X further according to being actually needed₁, X₂..., X_NNumber N, in the present embodiment, the monitoring variable chosen for the sensing layer of sub-goal 1 includes voltage, the magnitude of current, insulated electro Resistance, iron core grounding current waveform, oil chromatography parameter attribute, micro-water content feature, material unaccounted for streaming current, external impact electric current, External impact voltage totally 9 kinds；

Step 1.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0 ≤(X_i) '≤1, (X_iShape is estimated the size of conclusion disturbance degree by) ' reflection detection means Xi.It is the biggest on the impact of state estimation conclusion, Importance degree (X_i) ' the biggest；Otherwise, (X_i) ' the least.Here set 9 kinds of monitoring variables and there is identical importance degree, then importance degree vector (X_i) '={ 1,1,1,1,1,1,1,1,1}.

Step 1.3: choosing the maximum in importance degree as reference value, making its value is 1, other values compare therewith and obtain phase The regular importance degree answered.If (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/ (X_j) '={ (X₁), (X₂) ..., (X_N), obtain normalized importance degree vector (X)=(1,1,1,1,1,1,1,1,1}.

Step 1.4: determine degree of membership: according to detection means X_i(i=1,2 ..., N) measurement data, it is carried out normalizing Change, determine its fuzzy set H in state identification framework H the most again_j(j=1,2 ..., M) degree of membership, and degree of membership Meet=1.Specifying that data and historical data obtain the normalization quantized data of measurement data according to corresponding code is=[1 1 0.723 0.582 0.903 0.927 0.962 0.913 0.831 0.814], it is 5 fuzzy sets in [0,1] interval division (good, normally, suspicious, reliability decrease, dangerous), result of calculation is as shown in the table:

The degree of membership of one 9 kinds of monitoring variables of table

Step 1.5: determine confidence level CF (X_i): confidence level CF (X_i) the accurate journey of measurement data of reflection detection means Xi Degree, the namely description to the degree of faith of data acquired, detection environment is the most severe, measuring instrument more inaccuracy or anthropic factor The biggest, CF (X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement The suspection of the accuracy of data is the least, in this example, and confidence level CF (X_i) all take 0.9.

Step 1.6: calculate basic probability assignment.

$m_{i,f}=\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,\mathrm{...},M\\ 1-\underset{j=1}{\overset{M}{\Σ}}{m}_{i,f}& i=1,2,\mathrm{...},N;j=M+1\end{array};$

Step 1.7: build matrix M=[m_{I, j}], obtain

$M=\left[\begin{array}{cccccc}0.9& 0& 0& 0& 0& 0.1\\ 0.9& 0& 0& 0& 0& 0.1\\ 0.198& 0.702& 0& 0& 0& 0.1\\ 0.099& 0.279& 0.495& 0.027& 0& 0.1\\ 0.108& 0.792& 0& 0& 0& 0.1\\ 0.693& 0.207& 0& 0& 0& 0.1\\ 0.801& 0.099& 0& 0& 0& 0.1\\ 0.558& 0.189& 0.153& 0& 0& 0.1\\ 0.486& 0.297& 0.117& 0& 0& 0.1\end{array}\right];$

Step 1.8: according to D-S compositional rule formula, it is G=[0.5324 0.274 0.0664 00 that synthesis obtains result 0.027]；

Step 1.9: determine state: by the main value m obtaining judging in synthesis result G_j=0.5324, taking threshold value is 0.5, then Can be regarded as major insulation state in shape, and closer in normal condition.

Embodiment two (heating)

The method according to the invention measures the heat condition of certain transformator, test site: the 110KV power transformation of Tongren district Guizhou Province city In standing.

Evaluation and test step is as follows:

Step 2.1: determine transformator febrile state framework of identification H={H₁, H₂..., H_M, in this example, M takes 5, i.e. H= { good, normally, suspicious, reliability decrease, dangerous } wherein H₁, H₂..., H_MFor separate state (both I ≠ j) fuzzy set, determine detection means X={X further according to being actually needed₁, X₂..., X_NNumber N, for sub-goal 2 The monitoring variable chosen of sensing layer include temperature of oil in transformer, transformer case temperature, ambient temperature, transformer voltage amount, transformation The device magnitude of current, bushing temperature and end shield current waveform totally 7 kinds.

Step 2.2: determine the importance degree of each detection limit Xi.(X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0 ≤(X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree.It is the biggest on the impact of state estimation conclusion, Importance degree (X_i) ' the biggest；Otherwise, (X_i) ' the least.Here set 7 kinds of monitoring variables and there is identical importance degree, then importance degree vector (X_i) '={ 1,1,1,1,1,1,1}

Step 2.3: choosing the maximum in importance degree as reference value, making its value is 1, other values compare therewith and obtain phase The regular importance degree answered, if (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/ (X_j) '={ (X₁), (X₂) ..., (X_N)}.Obtain normalized importance degree vector (X)={ 1,1,1,1,1,1,1}；

Step 2.4: determine degree of membership, according to detection means X_i(i=1,2 ..., N) measurement data, it is carried out normalizing Change, determine its fuzzy set H in state identification framework H the most again_j(j=1,2 ..., M) degree of membership, and degree of membership Meet=1.According to corresponding code specify data and historical data obtain the normalization quantized data of measurement data for= [0.723 0.803 0.701 11 0.521 0.414], are that 5 fuzzy sets (well, normally, can in [0,1] interval division Doubt, reliability decrease, dangerous), result of calculation is as shown in the table:

The degree of membership of 28 kinds of monitoring variables of table

Step 2.5: determine confidence level CF (X_i): confidence level CF (X_i) the accurate journey of measurement data of reflection detection means Xi Degree, the namely description to the degree of faith of data acquired.Environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor The biggest, CF (X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement The suspection of the accuracy of data is the least, in this example, and confidence level CF (X_i) all take 0.9.

Step 2.6: calculate basic probability assignment.

$m_{i,f}=\left\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,\mathrm{...},M\\ 1-\underset{j=1}{\overset{M}{\Σ}}{m}_{i,f}& i=1,2,\mathrm{...},N;j=M+1\end{array}\right.$

Step 2.7: build matrix M=[m_{I, j}], obtain

$M=\left[\begin{array}{cccccc}0.2332& 0.6668& 0& 0& 0& 0.1\\ 0.4627& 0.4373& 0& 0& 0& 0.1\\ 0.3303& 0.5697& 0& 0& 0& 0.1\\ 0.9& 0& 0& 0& 0& 0.1\\ 0.9& 0& 0& 0& 0& 0.1\\ 0& 0.4994& 0.4006& 0& 0& 0.1\\ 0& 0.0141& 0.8859& 0& 0& 0.1\end{array}\right]$

Step 2.8: according to D-S compositional rule formula, it is G=[0.8064 0.1886 0.0048 0 that synthesis obtains result 0 0.0001]；

Step 2.9: determine state: by the main value m obtaining judging in synthesis result G_j=0.8064, taking threshold value is 0.5, then Can be regarded as major insulation state in shape.

Embodiment three (mechanical deformation)

The method according to the invention measures the mechanical deformation situation of certain transformator, test site: Tongren district Guizhou Province city 110KV In transformer station.

Evaluation and test step is as follows:

Step 3.1: determine transformator mechanical deformation state identification framework H={H₁, H₂..., H_M, in this example, M takes 5, i.e. H ={ good, normally, suspicious, reliability decrease, dangerous } wherein H₁, H₂..., H_MFor separate state (bothI ≠ j) fuzzy set, determine detection means X={X further according to being actually needed₁, X₂..., X_NNumber N, the monitoring variable that the sensing layer of sub-goal 3 is chosen include iron core grounding current waveform, vibration amplitude, vibration frequency, noise, External impact number of times, external impact size of current and persistent period, external impact voltage swing and the persistent period totally 7 kinds.

Step 3.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0 ≤(X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree.It is the biggest on the impact of state estimation conclusion, Importance degree (X_i) ' the biggest；Otherwise, (X_i) ' the least.Here set 7 kinds of monitoring variables and there is identical importance degree, then importance degree vector (X_i) '={ 1,1,1,1,1,1,1}

Step 3.3: choosing the maximum in importance degree as reference value, making its value is 1, other values compare therewith and obtain phase The regular importance degree answered: set (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/ (X_j) '={ (X₁), (X₂) ..., (X_N)}.Obtain normalized importance degree vector (X)={ 1,1,1,1,1,1,1}.

Step 3.4: determine degree of membership, according to detection means X_i(i=1,2 ..., N) measurement data, it is carried out normalizing Change, determine its fuzzy set H in state identification framework H the most again_j(j=1,2 ..., M) degree of membership, and degree of membership Meet=1.According to corresponding code specify data and historical data obtain the normalization quantized data of measurement data for= [0.582 0.942 0.574 0.763 0.618 0.831 0.814], are that 5 fuzzy sets are (good in [0,1] interval division Good, normally, suspicious, reliability decrease, dangerous), result of calculation is as shown in the table:

The degree of membership of 37 kinds of monitoring variables of table

Step 3.5: determine confidence level CF (X_i).Confidence level CF (X_i) the accurate journey of measurement data of reflection detection means Xi Degree, the namely description to the degree of faith of data acquired.Environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor The biggest, CF (X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement The suspection of the accuracy of data is the least, in this example, and confidence level CF (X_i) all take 0.9.

Step 3.6: calculate basic probability assignment.

$m_{i,f}=\left\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,\mathrm{...},M\\ 1-\underset{j=1}{\overset{M}{\Σ}}{m}_{i,f}& i=1,2,\mathrm{...},N;j=M+1\end{array}\right.$

Step 3.7: build matrix M=[m_{I, j}], obtain

$M=\left[\begin{array}{cccccc}0.099& 0.279& 0.495& 0.027& 0& 0.1\\ 0.9& 0& 0& 0& 0& 0.1\\ 0& 0& 0.9& 0& 0& 0.1\\ 0.8572& 0.0428& 0& 0& 0& 0.1\\ 0& 0.2175& 0.6825& 0& 0& 0.1\\ 0.558& 0.189& 0.153& 0& 0& 0.1\\ 0.486& 0.297& 0.117& 0& 0& 0.1\end{array}\right]$

Step 3.8: according to D-S compositional rule formula, it is G=[0.4205 0.5602 0.0191 0 that synthesis obtains result 0.0002]；

Step 3.9: determine state.By the main value m obtaining judging in synthesis result G_j=0.5602, taking threshold value is 0.5, then Can be regarded as major insulation state and be in normal condition.

Finally illustrating, above example is only in order to illustrate technical scheme and unrestricted, although with reference to relatively The present invention has been described in detail by good embodiment, it will be understood by those within the art that, can be to the skill of the present invention Art scheme is modified or equivalent, and without deviating from objective and the scope of the technical program, it all should be contained in the present invention Right in the middle of.

Claims (10)

1. a Transformer condition evaluation based on Situation Awareness theory, it is characterised in that: described method includes following step Rapid:

Step 1: comprehensive transformer body status monitoring amount and operation of power networks scene monitoring variable, establishes 3 sub-goals: 1) transformator Major insulation situation；2) whether transformator generates heat exception；3) transformator mechanical deformation situation；

Step 2: sub-goal 1 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, i.e. feels The amount of knowing, and use uncertainty theory to be analyzed, understanding each perception amount affects size to transformator major insulation, it is achieved right The prediction of the Developing Trend that major insulation is following；

Step 3: sub-goal 2 decision-making is carried out Situation Awareness demand analysis one by one, determines perception amount, and use uncertainty theory Being analyzed, understanding each perception amount to what transformator entirety was generated heat affects size, it is achieved the change to following transformer temperature Prediction；

Step 4: sub-goal 3 decision-making is carried out Situation Awareness demand analysis one by one, determines each monitoring variable needing perception, and adopts It is analyzed with uncertainty theory, understands the size of each perception amount reflection deformation situation, it is achieved to transformator mechanical deformation Reach to bear the prediction of the time of the limit.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, its feature exists In:, in step 2, perception amount includes that voltage, the magnitude of current, insulation resistance, iron core grounding current waveform, oil chromatography parameter are special Levy, micro-water content feature, record ripple fault message, material unaccounted for streaming current, external impact size of current and persistent period, outside punching Hit voltage swing and persistent period.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, it is characterised in that: In step 3, described perception amount includes temperature of oil in transformer, Transformer Winding temperature, transformer case temperature, ambient temperature, electricity Pressure amount, the magnitude of current, bushing temperature, end shield current waveform.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, it is characterised in that: In described step 4, described perception amount includes iron core grounding current waveform, vibration amplitude, vibration frequency, noise, external impact time Number, external impact size of current and persistent period, external impact voltage swing and persistent period.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, it is characterised in that: Specifically comprising the following steps that of described step 2

Step 1.1: determine the evidence model of transformer device state: determine transformator major insulation state identification framework H={H₁, H₂..., H_M, wherein H₁, H₂..., H_MFor separate state (bothI ≠ j) fuzzy set, then root Border is it needs to be determined that detection means X={X factually₁, X₂..., X_NNumber N；

Step 1.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0≤ (X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree, the biggest on the impact of state estimation conclusion, weight Spend (X_i) ' the biggest；Otherwise, (X_i) ' the least；

Step 1.3: by (X_i) ' normalization: choosing the maximum in importance degree as reference value, making its value is 1, and other values are therewith Relatively obtain corresponding normalization importance degree, if (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/(X_j) '={ (X₁), (X₂) ..., (X_N), obtain normalized importance degree vector (X)=1,1,1,1,1,1, 1,1,1}；

Step 1.4: determine degree of membership: according to detection means X_i(i=1,2 ..., N) measurement data, it is normalized, so After determine its fuzzy set H in state identification framework H again_j(j=1,2 ..., M) degree of membership, and degree of membership meet= 1, specify that data and historical data obtain the normalization quantized data of measurement data according to corresponding code, draw in [0,1] interval It is divided in each fuzzy set；

Step 1.5: determine confidence level CF (X_i): confidence level CF (X_i) order of accuarcy of measurement data of reflection detection means Xi, also Be exactly the description of the degree of faith to data acquired, environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor the biggest, CF(X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement data The suspection of accuracy is the least；

Step 1.6: according to following formula calculating basic probability assignment:

$m_{i,f}=\left\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,...,M\\ 1-\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{m}_{i,f}& i=1,2,...,N;j=M+1\end{array}\right.;$

Step 1.7: build matrix M=[m_{I, j}]；

Step 1.8: composite calulation, according to D-S compositional rule formula, it is G that synthesis obtains result；

Step 1.9: determine state: by the main value m obtaining judging in synthesis result G_j, if above taking fixed threshold value, then it is appreciated that In shape for major insulation state and closer in normal condition.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 5, it is characterised in that: M takes 5, i.e. it is that H={ is good that H takes fixed five kind state, normally, suspicious, and reliability decrease is dangerous }.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, it is characterised in that: Specifically comprising the following steps that of described step 3

Step 2.1: determine transformator febrile state framework of identification H={H₁, H₂..., H_M, wherein H₁, H₂..., H_MFor the most solely Vertical state was (bothI ≠ j) fuzzy set, determine detection means X={X further according to being actually needed₁, X₂..., X_NNumber N；

Step 2.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0≤ (X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree, the biggest on the impact of state estimation conclusion, weight Spend (X_i) ' the biggest；Otherwise, (X_i) ' the least；

Step 2.3: by (X_i) ' normalization: choosing the maximum in importance degree as reference value, making its value is 1, and other values are therewith Relatively obtain corresponding normalization importance degree, if (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/(X_j) '={ (X₁), (X₂) ..., (X_N), obtain normalized importance degree vector (X)=1,1,1,1,1,1, 1,1,1}；

Step 2.4: determine degree of membership: according to detection means X_i(i=1,2 ..., N) measurement data, it is normalized, so After determine its fuzzy set H in state identification framework H again_j(j=1,2 ..., M) degree of membership, and degree of membership meet= 1, specify that data and historical data obtain the normalization quantized data of measurement data according to corresponding code, draw in [0,1] interval It is divided in each fuzzy set；

Step 2.5: determine confidence level CF (X_i): confidence level CF (X_i) order of accuarcy of measurement data of reflection detection means Xi, also It it is exactly the description of the degree of faith to data acquired.Environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor the biggest, CF(X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement data The suspection of accuracy is the least；

Step 2.6: according to following formula calculating basic probability assignment:

$m_{i,f}=\left\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,...,M\\ 1-\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{m}_{i,f}& i=1,2,...,N;j=M+1\end{array}\right.;$

Step 2.7: build matrix M=[m_{I, j}]；

Step 2.8: composite calulation, according to D-S compositional rule formula, it is G that synthesis obtains result；

Step 2.9: determine state: by the main value m obtaining judging in synthesis result G_j, if above taking fixed threshold value, then it is appreciated that In shape for major insulation state and closer in normal condition.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 7, it is characterised in that: M takes 5, the most i.e. it is that H={ is good that H takes fixed five kind state, normally, suspicious, and reliability decrease is dangerous }.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 1, it is characterised in that: Specifically comprising the following steps that of described step 4

Step 3.1: determine transformator mechanical deformation state identification framework H={H₁, H₂..., H_M, wherein H₁, H₂..., H_MFor phase The most independent state was (bothI ≠ j) fuzzy set, determine detection means X={X further according to being actually needed₁, X₂..., X_NNumber N；

Step 3.2: determine the importance degree of each detection limit Xi: (X) '={ (X₁) ', (X₂) ' ..., (X_N) ' }, wherein 0≤ (X_i) '≤1, (X_i) ' reflection detection means X_iShape is estimated the size of conclusion disturbance degree, the biggest on the impact of state estimation conclusion, weight Spend (X_i) ' the biggest；Otherwise, (X_i) ' the least；

Step 3.3: by (X_i) ' normalization: choosing the maximum in importance degree as reference value, making its value is 1, and other values are therewith Relatively obtain corresponding normalization importance degree, if (X_j) '=max{ (X₁) ', (X₂) ' ..., (X_N) ' }, normalization process obtains: (X_i)=(X_i)′/(X_j) '={ (X₁), (X₂) ..., (X_N), obtain normalized importance degree vector (X)=1,1,1,1,1,1, 1,1,1}；

Step 3.4: determine degree of membership: according to detection means X_i(i=1,2 ..., N) measurement data, it is normalized, so After determine its fuzzy set H in state identification framework H again_j(j=1,2 ..., M) degree of membership, and degree of membership meet= 1, specify that data and historical data obtain the normalization quantized data of measurement data according to corresponding code, draw in [0,1] interval It is divided in each fuzzy set；

Step 3.5: determine confidence level CF (X_i): confidence level CF (X_i) order of accuarcy of measurement data of reflection detection means Xi, also It it is exactly the description of the degree of faith to data acquired.Environment is the most severe in detection, measuring instrument more inaccuracy or anthropic factor the biggest, CF(X_i) the least, i.e. the biggest to the suspection of the accuracy of measurement data；Otherwise, CF (X_i) the biggest, i.e. to measurement data The suspection of accuracy is the least；

Step 3.6: according to following formula calculating basic probability assignment:

$m_{i,f}=\left\{\begin{array}{cc}\mathrm{\λ}\left(X_i\right){\mathrm{\μ}}_{u_i}\left(X_i\right)CF\left(X_i\right),& i=1,2,...,M\\ 1-\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{m}_{i,f}& i=1,2,...,N;j=M+1\end{array}\right.;$

Step 3.7: build matrix M=[m_{I, j}]；

Step 3.8: composite calulation, according to D-S compositional rule formula, it is G that synthesis obtains result；

Step 3.9: determine state: by the main value m obtaining judging in synthesis result G_j, if above taking fixed threshold value, then it is appreciated that In shape for major insulation state and closer in normal condition.

A kind of Transformer condition evaluation theoretical based on Situation Awareness the most according to claim 9, its feature exists In: M takes 5, the most i.e. it is that H={ is good that H takes fixed five kind state, normally, suspicious, and reliability decrease is dangerous }.