CN107515339A - A kind of Risk Identification Method and system based on DC current distribution situation - Google Patents
A kind of Risk Identification Method and system based on DC current distribution situation Download PDFInfo
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- CN107515339A CN107515339A CN201710735977.1A CN201710735977A CN107515339A CN 107515339 A CN107515339 A CN 107515339A CN 201710735977 A CN201710735977 A CN 201710735977A CN 107515339 A CN107515339 A CN 107515339A
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- magnetic bias
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
The invention discloses a kind of Risk Identification Method and system based on DC current distribution situation, for solve at present still without it is a kind of can efficiently, rapidly the running situation to transformer station and risk carry out the technical problem of analysis knowledge method for distinguishing according to transforming plant DC bias current Monitoring Data.The present invention includes：The Historical Monitoring data of DC magnetic bias current are obtained, and extract the characteristic value of DC magnetic bias current in the Historical Monitoring data；The confidential interval of DC magnetic bias current and corresponding confidence interval values in the Historical Monitoring data are asked for according to the characteristic value of the DC magnetic bias current；The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, obtains the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed.
Description
Technical field
The present invention relates to electrical equipment monitoring technology data processing field, more particularly to a kind of DC current that is based on to be distributed feelings
The Risk Identification Method and system of condition.
Background technology
During the debugging of DC transmission system or monopole failure, DC transmission system is greatly to return to the method for operation to straight
Stream earthing pole flows into the earth current that may be up to thousands of peaces.The continuous action of so big earth current can be in power system
The high-tension electricity transformer of ground connection operation causes serious influence, or even damage high-tension electricity transformer.Earth current can pass through
Inflow transformer winding at transformer neutral ground point, transformer core saturation, noise increase, vibration aggravation harmonic is caused to increase
Greatly, not only the operation to transformer body and power network produces security threat, while can also produce noise pollution to the environment of surrounding,
The life of severe jamming resident in the neighbourhood.In order to study influence of the earth current to transformer, domestic scholars propose a variety of
Monitoring method, such as Hall sensor monitoring method, photoelectric type transformer DC magnetic bias current monitoring method, example show this
A little methods achieve preferable application effect, that is, can be good at monitoring due to earth current and caused by transformer is produced
The data of the DC magnetic bias current of considerable influence.
However, on the basis of the monitoring to DC magnetic bias current is further improved, accurate rational data how are chosen
It is the key for controlling DC magnetic bias current to influence that model, which carries out analysis on DC magnetic bias current,.Domestic power network is more paid attention to straight always
The harm of magnetic bias is flowed, and there are substantial amounts of transformer dc current monitoring data.But how from huge transformer dc electric current
Useful direct current flow data are extracted in Monitoring Data, and according to these useful direct current flow data to substation operation
Situation is analyzed the protection for then taking corresponding measure to carry out to transformer station and stills need further to study.Do not have still at present
It is a kind of can efficiently, rapidly the running situation to transformer station and risk be carried out according to transforming plant DC bias current Monitoring Data
Method for distinguishing is known in analysis.
The content of the invention
The embodiments of the invention provide a kind of Risk Identification Method and system based on DC current distribution situation, solve
At present still without it is a kind of can it is efficient according to transforming plant DC bias current Monitoring Data, rapidly to the running situation of transformer station
And risk carries out the technical problem that method for distinguishing is known in analysis.
A kind of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention, including：
The Historical Monitoring data of DC magnetic bias current are obtained, and extract DC magnetic bias current in the Historical Monitoring data
Characteristic value, the maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, median, 20 percent
Five-digit number and 70 percent five-digit number；
The confidence of DC magnetic bias current in the Historical Monitoring data is asked for according to the characteristic value of the DC magnetic bias current
Section and corresponding confidence interval values；
The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, obtains institute
State the running situation of transformer station corresponding to the Monitoring Data value of DC magnetic bias current to be analyzed.
Preferably, the characteristic value according to the DC magnetic bias current asks for D.C. magnetic biasing in the Historical Monitoring data
The confidential interval of electric current and corresponding confidence interval values specifically include：
Determine the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data；
The confidence interval values of DC magnetic bias current are asked for according to the confidential interval Pr and DC magnetic bias current minimum value
I1；
Putting for DC magnetic bias current is asked for according to the minimum value of DC magnetic bias current, 20 percent five-digit number and standard deviation
Believe interval value I2；
Putting for DC magnetic bias current is asked for according to the maximum of DC magnetic bias current, 70 percent five-digit number and standard deviation
Believe interval value I3；
The confidence interval values of DC magnetic bias current are asked for according to the confidential interval Pr and DC magnetic bias current maximum
I4；
Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
Preferably, the Monitoring Data value of the DC magnetic bias current being analysed to is judged with the confidence interval values
Compare, the running situation for obtaining transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed specifically includes：
The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if described
The Monitoring Data value of DC magnetic bias current to be analyzed is less than confidence interval values I1 or more than confidence interval values I4, then described to treat
The running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current of analysis is that monitoring unit failure or generation be not right
Claim failure.
Preferably, the Monitoring Data value of the DC magnetic bias current being analysed to is judged with the confidence interval values
Compare, the running situation for obtaining transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed is specifically also wrapped
Include：
The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if described
The Monitoring Data value of DC magnetic bias current to be analyzed is between confidence interval values I1 and confidence interval values I2 or positioned at putting
Between believing interval value I3 and confidence interval values I4, then corresponding to the Monitoring Data value that calculates the DC magnetic bias current to be analyzed
The distance between transformer station and direct current grounding pole position；
If the distance between transformer station and direct current grounding pole position are less than preset distance, the running situation of transformer station is attached most importance to
Point is aggrieved, and otherwise, the running situation of transformer station is monitoring unit failure.
Preferably, the Monitoring Data value of the DC magnetic bias current being analysed to is judged with the confidence interval values
Compare, the running situation for obtaining transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed is specifically also wrapped
Include：
The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if described
The Monitoring Data value of DC magnetic bias current to be analyzed is between confidence interval values I2 and confidence interval values I3, then described in judgement
Whether transformer station corresponding to the Monitoring Data value of DC magnetic bias current to be analyzed has installed blocking device；
If blocking device has been installed by transformer station, the running situation of transformer station is good for DC magnetic bias current isolation effect, no
Then, the running situation of transformer station is small to be influenceed by DC magnetic bias current.
A kind of risk recognition system based on DC current distribution situation provided in an embodiment of the present invention, including：
Extraction module, for obtaining the Historical Monitoring data of DC magnetic bias current, and extract the Historical Monitoring data
The characteristic value of middle DC magnetic bias current, the maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, in
Digit, 20 percent five-digit number and 70 percent five-digit number；
Module is asked for, it is inclined for asking for direct current in the Historical Monitoring data according to the characteristic value of the DC magnetic bias current
The confidential interval of magnetoelectricity stream and corresponding confidence interval values；
Judge module, the Monitoring Data value of the DC magnetic bias current for being analysed to are sentenced with the confidence interval values
It is disconnected to compare, obtain the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed.
Preferably, the module of asking for specifically includes：
Determination sub-module, for determining the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data；
First asks for submodule, inclined for asking for direct current according to the confidential interval Pr and DC magnetic bias current minimum value
The confidence interval values I1 of magnetoelectricity stream；
Second asks for submodule, for the minimum value according to DC magnetic bias current, 20 percent five-digit number and standard deviation
Ask for the confidence interval values I2 of DC magnetic bias current；
3rd asks for submodule, for the maximum according to DC magnetic bias current, 70 percent five-digit number and standard deviation
Ask for the confidence interval values I3 of DC magnetic bias current；
4th asks for submodule, inclined for asking for direct current according to the confidential interval Pr and DC magnetic bias current maximum
The confidence interval values I4 of magnetoelectricity stream；
Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
Preferably, the judge module specifically includes：
First judging submodule, for the Monitoring Data value of DC magnetic bias current and the confidence interval values being analysed to
Judgement comparison is carried out, if the Monitoring Data value of the DC magnetic bias current to be analyzed is less than confidence interval values I1 or more than putting
Believe interval value I4, then the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed is monitoring
Unbalanced fault occurs for cell failure.
Preferably, the judge module specifically also includes：
Second judging submodule, for the Monitoring Data value of DC magnetic bias current and the confidence interval values being analysed to
Judgement comparison is carried out, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I1 and confidential interval
Between value I2 or between confidence interval values I3 and confidence interval values I4, then the DC magnetic bias current to be analyzed is calculated
Monitoring Data value corresponding to the distance between transformer station and direct current grounding pole position；
If the distance between transformer station and direct current grounding pole position are less than preset distance, the running situation of transformer station is attached most importance to
Point is aggrieved, and otherwise, the running situation of transformer station is monitoring unit failure.
Preferably, the judge module specifically also includes：
3rd judging submodule, for the Monitoring Data value of DC magnetic bias current and the confidence interval values being analysed to
Judgement comparison is carried out, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I2 and confidential interval
Between value I3, then judge whether transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed has installed blocking
Device；
If blocking device has been installed by transformer station, the running situation of transformer station is good for DC magnetic bias current isolation effect, no
Then, the running situation of transformer station is small to be influenceed by DC magnetic bias current.
As can be seen from the above technical solutions, the embodiment of the present invention has advantages below：
By obtaining Historical Monitoring data of the grid company to the DC magnetic bias current of transformer station, and extract the history
It can be used for the DC magnetic bias current for the confidential interval and corresponding confidence interval values for asking for DC magnetic bias current in Monitoring Data
Characteristic value, the confidence for the regularity of distribution that DC magnetic bias current can be presented then is sought out according to the characteristic value of DC magnetic bias current
Section and corresponding confidence interval values.Due to striked confidence interval values can as the distribution node of DC magnetic bias current,
The scope of DC magnetic bias current is divided into multiple sections, and size and the power transformation of DC magnetic bias current value are known by engineering experience
The running situation stood is closely bound up, therefore the Monitoring Data value for the DC magnetic bias current being analysed to is entered with the confidence interval values
Row judgement is compared, and knows the specific area divided by confidence interval values residing for the Monitoring Data value of DC magnetic bias current to be analyzed
Between, you can the running situation of transformer station corresponding to acquisition.By introducing the history by DC magnetic bias current in the embodiment of the present invention
The estimation interval (i.e. confidential interval) for the population parameter that monitoring big data is constructed, by the regularity of distribution of DC magnetic bias current and directly
The running situation of transformer station has carried out hook-up corresponding to stream bias current, by judging DC magnetic bias current value to be analyzed
Relation between corresponding confidence interval values can quickly judge transformer station corresponding to the DC magnetic bias current value to be analyzed
Running situation, for early warning, exclude transformer station operation risk lay a good foundation, solve does not have still one kind can basis at present
Efficiently, rapidly the running situation to transformer station and risk analyze the side of identification to transforming plant DC bias current Monitoring Data
The technical problem of method.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, without having to pay creative labor, may be used also
To obtain other accompanying drawings according to these accompanying drawings.
Fig. 1 is an a kind of reality of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
Apply the schematic flow sheet of example.
Fig. 2 is a kind of specifically should for Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
With the schematic flow sheet of embodiment.
Fig. 3 is that a kind of structure of the risk recognition system based on DC current distribution situation provided in an embodiment of the present invention is shown
It is intended to.
Embodiment
The embodiments of the invention provide a kind of Risk Identification Method and system based on DC current distribution situation, for solving
Certainly at present still without it is a kind of can, rapidly operation feelings to transformer station efficient according to transforming plant DC bias current Monitoring Data
Condition and risk carry out the technical problem that method for distinguishing is known in analysis.
To enable goal of the invention, feature, the advantage of the present invention more obvious and understandable, below in conjunction with the present invention
Accompanying drawing in embodiment, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that disclosed below
Embodiment be only part of the embodiment of the present invention, and not all embodiment.Based on the embodiment in the present invention, this area
All other embodiment that those of ordinary skill is obtained under the premise of creative work is not made, belongs to protection of the present invention
Scope.
Embodiment one：
Referring to Fig. 1, it is a kind of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
One embodiment schematic flow sheet.
A kind of one embodiment of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
Including：
101st, the Historical Monitoring data of DC magnetic bias current are obtained, and extract D.C. magnetic biasing in the Historical Monitoring data
The characteristic value of electric current, maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, median, percent
20 five-digit numbers and 70 percent five-digit number.
Because grid company more payes attention to the harm of D.C. magnetic biasing always, and store substantial amounts of transformer dc electric current prison
Data are surveyed, therefore the Historical Monitoring data of substantial amounts of DC magnetic bias current can be got in database, it is final in order to ensure
The reliability of analysis result, more sufficient sample size should be chosen, such as chooses the monitoring of DC magnetic bias current nearly ten years
Data.The extraction of the characteristic value of DC magnetic bias current is to draw transformer station's neutral point direct current bias current in monitoring range
The regularity of distribution and carry out, that is, extract the maximum Imax of DC magnetic bias current, minimum value Imin, standard deviation std,
Median M, 20 percent five-digit number M25% and 70 percent five-digit number M75%.Wherein, standard deviation std is one kind
Metric data (i.e. the Historical Monitoring data of DC magnetic bias current) divides the standard of spread of distribution, deviates to weigh data value
The degree of arithmetic mean of instantaneous value.Standard deviation is smaller, and these value deviation averages are fewer, and vice versa.The size of standard deviation
It can be weighed by the multiplying power relation of standard deviation and average value.
102nd, DC magnetic bias current in the Historical Monitoring data is asked for according to the characteristic value of the DC magnetic bias current
Confidential interval and corresponding confidence interval values.
The confidential interval of DC magnetic bias current refers to the estimation interval of the population parameter constructed by sample statistic,
Such as all power transformations in be configured to by every month in 10 years to the Monitoring Data of the DC magnetic bias current of each transformer station 10 years
One estimation interval of the Monitoring Data for all DC magnetic bias currents stood.In statistics, the confidence area of a probability sample
Between be interval estimation to some population parameter of this sample.What confidential interval showed is that the actual value of this parameter has necessarily
Probability falls the degree around measurement result.What confidential interval provided is the credibility for the measured value for being measured parameter, i.e.,
Above required " probability ".Therefore, the confidential interval of DC magnetic bias current and correspondingly in Historical Monitoring data is obtained to obtain
Confidence interval values can obtain the regularity of distribution of DC magnetic bias current and DC magnetic bias current is distributed in the general of some section
Rate, the regularity of distribution and DC magnetic bias current of the DC magnetic bias current obtained by substantial amounts of sample data are distributed in some area
Between probability can with reference to judge corresponding to transformer station running situation.I.e. according to the monitor value of DC magnetic bias current in confidential interval
Location, judge the running situation of current transformer station.
103rd, the Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, obtains
Obtain the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed.
The regularity of distribution and engineering experience presented by DC magnetic bias current in big data know, the operation of transformer station
Situation directly affects the numerical value of DC magnetic bias current, therefore can be by judging the Monitoring Data of DC magnetic bias current to be analyzed
(compare the Monitoring Data value and boundary of DC magnetic bias current in value location in the confidential interval obtained based on big data
Magnitude relationship between the confidence interval values of confidential interval), it is counter to push away the monitoring number for obtaining the DC magnetic bias current to be analyzed
According to the running situation of transformer station corresponding to value.
By obtaining Historical Monitoring data of the grid company to the DC magnetic bias current of transformer station in the embodiment of the present invention, and
Extract in the Historical Monitoring data available for the confidential interval and corresponding confidence interval values for asking for DC magnetic bias current
The characteristic value of DC magnetic bias current, DC magnetic bias current can be presented by then being sought out according to the characteristic value of DC magnetic bias current
The confidential interval of the regularity of distribution and corresponding confidence interval values.Because striked confidence interval values can be used as D.C. magnetic biasing electricity
The distribution node of stream, the scope of DC magnetic bias current is divided into multiple sections, and DC magnetic bias current is known by engineering experience
The size of value and the running situation of transformer station are closely bound up, therefore the Monitoring Data value for the DC magnetic bias current being analysed to and institute
State confidence interval values and carry out judgement comparison, know residing for the Monitoring Data value of DC magnetic bias current to be analyzed by confidential interval
It is worth the specific section of division, you can the running situation of transformer station corresponding to acquisition.By introducing by direct current in the embodiment of the present invention
The estimation interval (i.e. confidential interval) for the population parameter that the Historical Monitoring big data of bias current is constructed, by DC magnetic bias current
The running situation of regularity of distribution transformer station corresponding with DC magnetic bias current carried out hook-up, it is to be analyzed by judging
Relation between DC magnetic bias current value and corresponding confidence interval values can quickly judge the DC magnetic bias current to be analyzed
The running situation of transformer station corresponding to value, the operation risk for early warning, exclusion transformer station are laid a good foundation, solve and do not have still at present
Have it is a kind of can be according to transforming plant DC bias current Monitoring Data efficiently, rapidly the running situation and risk of transformer station are entered
The technical problem of method for distinguishing is known in row analysis.
Embodiment two：
Referring to Fig. 2, it is a kind of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
Concrete application embodiment schematic flow sheet.The Monitoring Data value of DC magnetic bias current to be analyzed is represented in figure with K.
A kind of another implementation of Risk Identification Method based on DC current distribution situation provided in an embodiment of the present invention
Example includes：
201st, the Historical Monitoring data of DC magnetic bias current are obtained, and extract D.C. magnetic biasing in the Historical Monitoring data
The characteristic value of electric current, maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, median, percent
20 five-digit numbers and 70 percent five-digit number.
202nd, the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data is determined.
Confidential interval Pr can be tried to achieve by formula one, and formula one is specially：
Pr (c1 ＜=μ ＜=c2)=1- α； (1)
Wherein, Pr is confidential interval, and α is significance (taking 0.05), and 100%* (1- α) refers to confidence level.
203rd, the confidence area of DC magnetic bias current is asked for according to the confidential interval Pr and DC magnetic bias current minimum value
Between value I1, putting for DC magnetic bias current is asked for according to the minimum value of DC magnetic bias current, 20 percent five-digit number and standard deviation
Believe interval value I2, DC magnetic bias current is asked for according to the maximum of DC magnetic bias current, 70 percent five-digit number and standard deviation
Confidence interval values I3, the confidence of DC magnetic bias current is asked for according to the confidential interval Pr and DC magnetic bias current maximum
Interval value I4.Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
Wherein, confidence interval values I1 is tried to achieve by formula two, and the formula two is specially：
I1=Imin*Pr (2)
The I2 is tried to achieve by formula three, and the formula three is specially：
In formula, m is the Historical Monitoring data total amount of the DC magnetic bias current in m month, and Imin, M25% and σ std are
The data statistics description value in i-th of month.
The I3 is tried to achieve by formula four, and the formula four is specially：
In formula, m is the Historical Monitoring data total amount of the DC magnetic bias current in m month, and Imax, M75% and σ std are
The data statistics description value in i-th of month.
The I4 is tried to achieve by formula five, and the formula five is specially：
I4=Imax*Pr (5)
It can be determined to divide five section models in confidential interval by tetra- confidence interval values of confidence interval values I1, I2, I3, I4
Enclose, wherein, confidence interval values ask for according to predominantly according to the regularity of distribution of the monitoring big data of DC magnetic bias current and
Expertise in engineering practice is determined, can be according to the regularity of distribution of DC magnetic bias current well by actual motion
The interval range that DC magnetic bias current value is likely to be at has carried out clearly defining differentiation, and according to DC magnetic bias current value institute
The interval range at place, the running situation of the corresponding transformer station of DC magnetic bias current value is subjected to hook-up, so as to obtain
The running situation of current transformer substation.
204th, the Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if
The Monitoring Data value of the DC magnetic bias current to be analyzed is less than confidence interval values I1 or more than confidence interval values I4, then institute
The running situation for stating transformer station corresponding to the Monitoring Data value of DC magnetic bias current to be analyzed is monitoring unit failure or generation
Unbalanced fault.
It is understood that, it is necessary to monitoring number according to the DC magnetic bias current being analysed in comparison procedure is judged
Title, geographical position and the time of monitoring of transformer station corresponding with the Monitoring Data value are subjected to lookup record according to value, and
After the running situation of transformer station corresponding to acquisition, the transformer station is labeled as monitoring unit failure transformer station, i.e. E classes power transformation
Stand.
205th, the Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if
The Monitoring Data value of the DC magnetic bias current to be analyzed is between confidence interval values I1 and confidence interval values I2 or position
Between confidence interval values I3 and confidence interval values I4, then the Monitoring Data value pair of the DC magnetic bias current to be analyzed is calculated
The distance between the transformer station answered and direct current grounding pole position；If the distance between transformer station and direct current grounding pole position are less than pre-
Distance is put, then the running situation of transformer station is that emphasis is aggrieved, and otherwise, the running situation of transformer station is monitoring unit failure.
Wherein, preset distance is the experience distance value in engineering practice, and can choose influences distance 200km.When transformer station
It is the aggrieved early warning class transformer station of emphasis, i.e. W classes transformer station by the transformer station mark when running situation is that emphasis is aggrieved.
206th, the Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, if
The Monitoring Data value of the DC magnetic bias current to be analyzed then judges between confidence interval values I2 and confidence interval values I3
Whether transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed has installed blocking device；If transformer station is
Blocking device is installed, then the running situation of transformer station is that DC magnetic bias current isolation effect is good, otherwise, the running situation of transformer station
It is small to be influenceed by DC magnetic bias current.
When the running situation of transformer station is good for DC magnetic bias current isolation effect, the transformer station can be filled labeled as blocking
Put effect Hao Lei transformer stations, i.e. U classes transformer station.When the running situation of transformer station is is influenceed small by DC magnetic bias current, will become
Power station is influenceed compared with group transformer station, i.e. B classes transformer station labeled as D.C. magnetic biasing.
It should be noted that the execution sequence of step 204~206 is in no particular order, can be according to actual conditions progress order
Preferentially selection, can also individually perform, the embodiment of the present invention only does the detailed description of one of which order, on other
Feasible execution sequence will not be repeated here.
When the Monitoring Data value to all DC magnetic bias currents to be analyzed is judged and has marked corresponding power transformation
After the classification stood, all kinds of transformer station's bursts can be stored, the extension appropriate to U classes, B classes transformer station is to DC magnetic bias current
The sampling time is monitored, is advantageous to optimize the storage organization of database.Device inspection is carried out to E classes transformer station, fixed a breakdown.To W
The emphasis monitoring of class transformer station, and further take safeguard procedures.
By obtaining Historical Monitoring data of the grid company to the DC magnetic bias current of transformer station in the embodiment of the present invention, and
Extract in the Historical Monitoring data available for the confidential interval and corresponding confidence interval values for asking for DC magnetic bias current
The characteristic value of DC magnetic bias current, DC magnetic bias current can be presented by then being sought out according to the characteristic value of DC magnetic bias current
The confidential interval of the regularity of distribution and corresponding confidence interval values I1, I2, I3 and I4.Because striked confidence interval values can be made
For the distribution node of DC magnetic bias current, the scope of DC magnetic bias current is divided into multiple sections, and known by engineering experience
The size of DC magnetic bias current value and the running situation of transformer station are closely bound up, therefore the prison for the DC magnetic bias current being analysed to
Survey data value and carry out judgement comparison with the confidence interval values, know residing for the Monitoring Data value of DC magnetic bias current to be analyzed
By confidence interval values divide specific section, you can the running situation of transformer station corresponding to acquisition.Lead in the embodiment of the present invention
The estimation interval (i.e. confidential interval) for introducing the population parameter constructed by the Historical Monitoring big data of DC magnetic bias current is crossed, will
The running situation of the regularity of distribution of DC magnetic bias current transformer station corresponding with DC magnetic bias current has carried out hook-up, passes through
Judge that the relation between DC magnetic bias current value to be analyzed and corresponding confidence interval values can quickly judge that this is to be analyzed
Simultaneously transformer station is marked point according to the running situation of transformer station for the running situation of transformer station corresponding to DC magnetic bias current value
Class, the operation risk for quick early warning, exclusion transformer station are laid a good foundation, and solve does not have still one kind can be according to power transformation at present
Efficiently, rapidly the running situation to transformer station and risk carry out analysis knowledge method for distinguishing to DC magnetic bias current Monitoring Data of standing
Technical problem.
Embodiment three：
Referring to Fig. 3, it is a kind of risk recognition system based on DC current distribution situation provided in an embodiment of the present invention
Structural representation.
A kind of risk recognition system based on DC current distribution situation provided in an embodiment of the present invention includes：
Extraction module 301, for obtaining the Historical Monitoring data of DC magnetic bias current, and extract the Historical Monitoring number
According to the characteristic value of middle DC magnetic bias current, the maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation,
Median, 20 percent five-digit number and 70 percent five-digit number；
Module 302 is asked for, it is straight in the Historical Monitoring data for being asked for according to the characteristic value of the DC magnetic bias current
Flow the confidential interval of bias current and corresponding confidence interval values；The module 302 of asking for specifically includes：
Determination sub-module 3021, for determining the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data；
First asks for submodule 3022, for being asked for directly according to the confidential interval Pr and DC magnetic bias current minimum value
Flow the confidence interval values I1 of bias current；
Second asks for submodule 3023, for the minimum value according to DC magnetic bias current, 20 percent five-digit number and mark
Quasi- difference asks for the confidence interval values I2 of DC magnetic bias current；
3rd asks for submodule 3024, for the maximum according to DC magnetic bias current, 70 percent five-digit number and mark
Quasi- difference asks for the confidence interval values I3 of DC magnetic bias current；
4th asks for submodule 3025, for being asked for directly according to the confidential interval Pr and DC magnetic bias current maximum
Flow the confidence interval values I4 of bias current；
Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
Judge module 303, the Monitoring Data value of the DC magnetic bias current for being analysed to are entered with the confidence interval values
Row judgement is compared, and obtains the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed.Institute
Judge module 303 is stated to specifically include：
First judging submodule 3031, for the Monitoring Data value of DC magnetic bias current and the confidence area being analysed to
Between value carry out judgement comparison, if the Monitoring Data value of the DC magnetic bias current to be analyzed is less than confidence interval values I1 or big
In confidence interval values I4, then the running situation of transformer station is corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed
Unbalanced fault occurs for monitoring unit failure.
Second judging submodule 3032, for the Monitoring Data value of DC magnetic bias current and the confidence area being analysed to
Between value carry out judgement comparison, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I1 and confidence
Between interval value I2 or between confidence interval values I3 and confidence interval values I4, then the D.C. magnetic biasing to be analyzed is calculated
The distance between transformer station and direct current grounding pole position corresponding to the Monitoring Data value of electric current；
If the distance between transformer station and direct current grounding pole position are less than preset distance, the running situation of transformer station is attached most importance to
Point is aggrieved, and otherwise, the running situation of transformer station is monitoring unit failure.
3rd judging submodule 3033, for the Monitoring Data value of DC magnetic bias current and the confidence area being analysed to
Between value carry out judgement comparison, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I2 and confidence
Between interval value I3, then judge whether transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed has installed
Blocking device；
If blocking device has been installed by transformer station, the running situation of transformer station is good for DC magnetic bias current isolation effect, no
Then, the running situation of transformer station is small to be influenceed by DC magnetic bias current.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, the corresponding process in preceding method embodiment is may be referred to, will not be repeated here.
In several embodiments provided herein, it should be understood that disclosed system, apparatus and method can be with
Realize by another way.For example, device embodiment described above is only schematical, for example, the unit
Division, only a kind of division of logic function, can there is other dividing mode, such as multiple units or component when actually realizing
Another system can be combined or be desirably integrated into, or some features can be ignored, or do not perform.It is another, it is shown or
The mutual coupling discussed or direct-coupling or communication connection can be the indirect couplings by some interfaces, device or unit
Close or communicate to connect, can be electrical, mechanical or other forms.
The unit illustrated as separating component can be or may not be physically separate, show as unit
The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple
On NE.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, can also
That unit is individually physically present, can also two or more units it is integrated in a unit.Above-mentioned integrated list
Member can both be realized in the form of hardware, can also be realized in the form of SFU software functional unit.
If the integrated unit is realized in the form of SFU software functional unit and is used as independent production marketing or use
When, it can be stored in a computer read/write memory medium.Based on such understanding, technical scheme is substantially
The part to be contributed in other words to prior art or all or part of the technical scheme can be in the form of software products
Embody, the computer software product is stored in a storage medium, including some instructions are causing a computer
Equipment (can be personal computer, server, or network equipment etc.) performs the complete of each embodiment methods described of the present invention
Portion or part steps.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can store journey
The medium of sequence code.
Described above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to before
Embodiment is stated the present invention is described in detail, it will be understood by those within the art that：It still can be to preceding
State the technical scheme described in each embodiment to modify, or equivalent substitution is carried out to which part technical characteristic；And these
Modification is replaced, and the essence of appropriate technical solution is departed from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (10)
- A kind of 1. Risk Identification Method based on DC current distribution situation, it is characterised in that including：The Historical Monitoring data of DC magnetic bias current are obtained, and extract the spy of DC magnetic bias current in the Historical Monitoring data Value indicative, the maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, median, 25 percent Number and 70 percent five-digit number；The confidential interval of DC magnetic bias current in the Historical Monitoring data is asked for according to the characteristic value of the DC magnetic bias current And corresponding confidence interval values；The Monitoring Data value for the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, is treated described in acquisition The running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current of analysis.
- 2. the Risk Identification Method according to claim 1 based on DC current distribution situation, it is characterised in that described The confidential interval of DC magnetic bias current and correspondingly is asked in the Historical Monitoring data according to the characteristic value of the DC magnetic bias current Confidence interval values specifically include：Determine the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data；The confidence interval values I1 of DC magnetic bias current is asked for according to the confidential interval Pr and DC magnetic bias current minimum value；The confidence area of DC magnetic bias current is asked for according to the minimum value of DC magnetic bias current, 20 percent five-digit number and standard deviation Between value I2；The confidence area of DC magnetic bias current is asked for according to the maximum of DC magnetic bias current, 70 percent five-digit number and standard deviation Between value I3；The confidence interval values I4 of DC magnetic bias current is asked for according to the confidential interval Pr and DC magnetic bias current maximum；Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
- 3. the Risk Identification Method according to claim 2 based on DC current distribution situation, it is characterised in that described to incite somebody to action The Monitoring Data value of DC magnetic bias current to be analyzed carries out judgement comparison with the confidence interval values, obtains described to be analyzed The running situation of transformer station specifically includes corresponding to the Monitoring Data value of DC magnetic bias current：The Monitoring Data value of the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, is treated point if described The Monitoring Data value of the DC magnetic bias current of analysis is less than confidence interval values I1 or more than confidence interval values I4, then described to be analyzed DC magnetic bias current Monitoring Data value corresponding to transformer station running situation for monitoring unit failure or occur it is asymmetric therefore Barrier.
- 4. the Risk Identification Method according to claim 2 based on DC current distribution situation, it is characterised in that described to incite somebody to action The Monitoring Data value of DC magnetic bias current to be analyzed carries out judgement comparison with the confidence interval values, obtains described to be analyzed The running situation of transformer station specifically also includes corresponding to the Monitoring Data value of DC magnetic bias current：The Monitoring Data value of the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, is treated point if described The Monitoring Data value of the DC magnetic bias current of analysis is between confidence interval values I1 and confidence interval values I2 or positioned at confidence area Between between value I3 and confidence interval values I4, then calculate power transformation corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed Stand the distance between direct current grounding pole position；If the distance between transformer station and direct current grounding pole position are less than preset distance, the running situation of transformer station attach most importance to by Evil, otherwise, the running situation of transformer station is monitoring unit failure.
- 5. the Risk Identification Method according to claim 2 based on DC current distribution situation, it is characterised in that described to incite somebody to action The Monitoring Data value of DC magnetic bias current to be analyzed carries out judgement comparison with the confidence interval values, obtains described to be analyzed The running situation of transformer station specifically also includes corresponding to the Monitoring Data value of DC magnetic bias current：The Monitoring Data value of the DC magnetic bias current being analysed to carries out judgement comparison with the confidence interval values, is treated point if described The Monitoring Data value of the DC magnetic bias current of analysis between confidence interval values I2 and confidence interval values I3, then judge described in treat point Whether transformer station corresponding to the Monitoring Data value of the DC magnetic bias current of analysis has installed blocking device；If blocking device has been installed by transformer station, the running situation of transformer station is good for DC magnetic bias current isolation effect, otherwise, becomes The running situation in power station is small to be influenceed by DC magnetic bias current.
- A kind of 6. risk recognition system based on DC current distribution situation, it is characterised in that including：Extraction module, for obtaining the Historical Monitoring data of DC magnetic bias current, and extract straight in the Historical Monitoring data Flow the characteristic value of bias current, the maximum of the characteristic value including DC magnetic bias current, minimum value, standard deviation, median, 20 percent five-digit number and 70 percent five-digit number；Module is asked for, for asking for D.C. magnetic biasing electricity in the Historical Monitoring data according to the characteristic value of the DC magnetic bias current The confidential interval of stream and corresponding confidence interval values；Judge module, the Monitoring Data value of the DC magnetic bias current for being analysed to carry out judgement ratio with the confidence interval values Compared with the running situation of transformer station corresponding to the Monitoring Data value of the acquisition DC magnetic bias current to be analyzed.
- 7. the risk recognition system according to claim 6 based on DC current distribution situation, it is characterised in that described to ask Modulus block specifically includes：Determination sub-module, for determining the confidential interval Pr of DC magnetic bias current in the Historical Monitoring data；First asks for submodule, for asking for D.C. magnetic biasing electricity according to the confidential interval Pr and DC magnetic bias current minimum value The confidence interval values I1 of stream；Second asks for submodule, is asked for for the minimum value according to DC magnetic bias current, 20 percent five-digit number and standard deviation The confidence interval values I2 of DC magnetic bias current；3rd asks for submodule, is asked for for the maximum according to DC magnetic bias current, 70 percent five-digit number and standard deviation The confidence interval values I3 of DC magnetic bias current；4th asks for submodule, for asking for D.C. magnetic biasing electricity according to the confidential interval Pr and DC magnetic bias current maximum The confidence interval values I4 of stream；Wherein, I1 ＜ I2 ＜ I3 ＜ I4.
- 8. the risk recognition system according to claim 7 based on DC current distribution situation, it is characterised in that described to sentence Disconnected module specifically includes：First judging submodule, the Monitoring Data value of the DC magnetic bias current for being analysed to are carried out with the confidence interval values Judgement is compared, if the Monitoring Data value of the DC magnetic bias current to be analyzed is less than confidence interval values I1 or more than confidence area Between value I4, then the running situation of transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed is monitoring unit Unbalanced fault occurs for failure.
- 9. the risk recognition system according to claim 7 based on DC current distribution situation, it is characterised in that described to sentence Disconnected module specifically also includes：Second judging submodule, the Monitoring Data value of the DC magnetic bias current for being analysed to are carried out with the confidence interval values Judgement is compared, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I1 and confidence interval values I2 Between or between confidence interval values I3 and confidence interval values I4, then calculate the prison of the DC magnetic bias current to be analyzed Survey the distance between transformer station and direct current grounding pole position corresponding to data value；If the distance between transformer station and direct current grounding pole position are less than preset distance, the running situation of transformer station attach most importance to by Evil, otherwise, the running situation of transformer station is monitoring unit failure.
- 10. the risk recognition system according to claim 7 based on DC current distribution situation, it is characterised in that described Judge module specifically also includes：3rd judging submodule, the Monitoring Data value of the DC magnetic bias current for being analysed to are carried out with the confidence interval values Judgement is compared, if the Monitoring Data value of the DC magnetic bias current to be analyzed is located at confidence interval values I2 and confidence interval values I3 Between, then judge whether transformer station corresponding to the Monitoring Data value of the DC magnetic bias current to be analyzed has installed blocking dress Put；If blocking device has been installed by transformer station, the running situation of transformer station is good for DC magnetic bias current isolation effect, otherwise, becomes The running situation in power station is small to be influenceed by DC magnetic bias current.
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