CN108256234A - A kind of method and system for being used to assess transformer DC magnetic bias influence - Google Patents

A kind of method and system for being used to assess transformer DC magnetic bias influence Download PDF

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Publication number
CN108256234A
CN108256234A CN201810052427.4A CN201810052427A CN108256234A CN 108256234 A CN108256234 A CN 108256234A CN 201810052427 A CN201810052427 A CN 201810052427A CN 108256234 A CN108256234 A CN 108256234A
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current
neutral point
magnetic bias
website
transformer
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CN108256234B (en
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曹楠
王冬青
张也
许勇
张辉
郭文明
陈翔宇
李广
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State Grid Corp of China SGCC
NARI Group Corp
China EPRI Electric Power Engineering Co Ltd
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State Grid Corp of China SGCC
NARI Group Corp
China EPRI Electric Power Engineering Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The present invention provides a kind of for assessing the method and system of transformer DC magnetic bias influence, probability is influenced on the magnetic bias of substation in the power grid divided in advance including calculating metro stray current；Calculate the risk of each substation's influenced by DC magnetic biasing；Risk based on magnetic bias monitoring data and each substation's influenced by DC magnetic biasing, the DC current impact factor of calculating transformer neutral point；The DC current impact factor of risk and transformer neutral point is influenced according to the magnetic bias of non-monitored website, calculates the DC current of non-monitored website neutral point；Correlation rule based on monitoring data assesses monitoring station neutral point direct current level；DC current appreciable levels based on monitoring station neutral point and non-monitored website neutral point, delimit the magnetic bias warning grade of each website, and draw a circle to approve the involved site-bound of magnetic bias influence.It is of the invention to find in time and rapid device for transferring hidden danger, improve the managerial ability that influences on magnetic bias and using responding ability.

Description

A kind of method and system for being used to assess transformer DC magnetic bias influence
Technical field
The invention belongs to alternating current-direct current series-parallel connection field of power transmission, and in particular to a kind of to be used to assess transformer DC magnetic bias influence Method and system.
Background technology
A pole of the power supply traction network generally use rail of some existing city undergrounds as power supply.It can not possibly be by subway Rail is accomplished completely insulated with the earth, therefore when subway train is started and run, will generate part stray electrical current and is leaked through rail To underground, cause the variation of rail circumferentially Potential distribution.When subway and periphery AC power station are closer to the distance, ground potential distribution Variation may cause to occur more than the direct current point of the transformer dc ability to bear upper limit in city network main transformer neutral point The problems such as measuring, causing transformer DC magnetic bias.
Rail of subway is generally buried-pipe laying, and region span is big, metro stray current by rail resistance rate, transition resistance, The influence of the factors such as locomotive traction electric current, power supply siding-to-siding block length, locomotive operation mode and subterranean railway layout, is induced by it Ground connection main transformer bias current amplitude and direction will change with the change of underground engines position, the method for operation, have very big Randomness, it is difficult to carry out accurate judgement and assessment by means such as simulation calculations.
Accordingly, it is desirable to provide a kind of technical solution is of the existing technology to solve the problems, such as.
Invention content
For the needs of the prior art, the present invention provides it is a kind of for assess the method for transformer DC magnetic bias influence and System.
A kind of method for being used to assess transformer DC magnetic bias influence, including step：Metro stray current is calculated to advance The magnetic bias of substation influences probability in the power grid of division；Magnetic bias based on each substation influence probability, each website importance with And the degree of association between each website, calculate the risk of each substation's influenced by DC magnetic biasing；Monitoring number based on monitoring station According to the risk with each substation's influenced by DC magnetic biasing, the DC current impact factor of calculating transformer neutral point；According to non- The magnetic bias of monitoring station influences the DC current impact factor of risk and transformer neutral point, and it is neutral to calculate non-monitored website The DC current of point；Correlation rule based on monitoring data assesses transformer neutral point DC current level in power grid； Based on the assessment result to transformer neutral point DC current level in power grid, the magnetic bias warning grade of each website, doubling-up delimited Determining magnetic bias influences involved site-bound.
Power grid is divided, calculate metro stray current influences probability to the magnetic bias of electric power networks Zhong Ge substations, including：
Calculate arbitrary power transformation website tiThe probability set distance P_distance of subway station stray electrical current in region：
According to the probability set distance P_distance of stray electrical current, define probability and locally peel off factor Plof(ti)：M is ground The number of iron website, S=[s1,s2,…sj…,sm] be subway station coordinate set；T=[t1,t2,…ti…,tn] it is power transformation The coordinate set of website, n are the number of substation site；
According to the probability set distance P_distance of stray electrical current, define probability and locally peel off factor Plof(ti)：
Locally peeled off factor P using probabilitylof(ti), it calculates probability and locally peels off the standard deviation e of the factorp_lof：
Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
The standard deviation e for the factor that locally peeled off based on probabilityp_lof, using Gauss error function, calculate peeling off for power transformation website Rate loop (ti)：
Rate loop (the t that peel off of power transformation websitei) value range be [0,1].
Magnetic bias based on each substation influences the association between probability, the importance of each website and each website Degree calculates the risk of each substation's influenced by DC magnetic biasing, including：
Importance D (i) calculation formula of each power transformation website in electric power networks are as follows：
In formula, d (ti,tj) represent with node tiFor starting point, node tjShortest path length for terminal；S (i) represents to appoint Pass through node t between meaning node pairiShortest path number；Without node t between B (i) expression arbitrary nodes pairiShortest path Diameter number；
The magnetic bias of each power transformation website influences the calculation formula of risk R, as follows：
Wherein, loop is the rate that peels off of power transformation website, is calculated two in electric power networks by the reciprocal of path length between two stations Degree of association C (t between associated stationi,tj)。
Correlation rule based on monitoring data assesses monitoring station neutral point direct current level, including：
J-th of factor is in fjkLevel is in i to transformer neutral point DC currentkSupport, i.e. Ai,j→BiBranch Degree of holding calculation formula is as follows：
Sup(Ai,j→Bi)=P (Ai,j∪Bi)
=(σ (Ai,j∪Bi)/|δi|) × 100%
=(σ (Ai,j∪δi)/|δi|) × 100%；
J-th of factor is in fjkLevel is to the confidence level of transformer neutral point DC current level, i.e. Ai,j→BiConfidence It is as follows to spend calculation formula：
C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
=(σ (Ai,j∪δi)/σ(Ai,j)) × 100%；
Wherein, item collection Ai,j={ when transformer neutral point DC analogue quantity is in ikWhen j-th of factor be in fjkIt is horizontal }； Item collection Bi={ transformer is in magnetic bias state }=δi；ikBelong to and divide transformer neutral point DC simulation by magnetic bias influence degree Section I={ the i of magnitude1,i2,..,im}；δi(i=1,2 ..., N) is in neutral point direct current area during magnetic bias state for transformer Between and
Choosing support under the conditions of transformer is in magnetic bias, higher than 70%, influence factor of the confidence level higher than 90% is used as strong Continuous item carries out forecast assessment to transformer neutral point DC current level, obtains transformer neutral point DC current set I1.
The risk of monitoring data and each substation's influenced by DC magnetic biasing based on monitoring station, calculating transformer are neutral The DC current impact factor of point, including：
The calculation formula of the impact factor σ of transformer neutral point DC current, as follows in electric power networks：
R (j) is that the magnetic bias of each power transformation website influences risk, and I1 is transformer neutral point DC current set, and s is arbitrary Pass through node t between node pairiShortest path number.
According to the magnetic bias of the monitoring station influence risk and the transformer neutral point DC current influence because Son calculates the DC current of non-monitored website neutral point, including：
Impact factor σ based on DC current, non-monitored website neutral point direct current ikCalculation formula, it is as follows：
ik=σ R (k) ik∈ I2k=1,2 ..., r；
I2 is the transformer neutral point DC current set of non-monitored website.
A kind of system for being used to assess transformer DC magnetic bias influence, including：Magnetic bias influences probability evaluation entity, based on Calculate metro stray current influences probability to the magnetic bias of substation in the power grid divided in advance；Risk computing module, for being based on The magnetic bias of each substation influences the degree of association between probability, the importance of each website and each website, calculates each substation by straight Flow the risk that magnetic bias influences；Impact factor computing module, for the monitoring data based on monitoring station and each substation by straight Flow the risk that magnetic bias influences, the DC current impact factor of calculating transformer neutral point；DC current calculates module, for root The DC current impact factor of risk and transformer neutral point is influenced according to the magnetic bias of non-monitored website, calculates non-monitored website The DC current of neutral point；Neutral point direct current proficiency assessment module, for the correlation rule based on monitoring data, to power grid Middle transformer neutral point DC current level is assessed；Magnetic bias warning grade delimit module, for being based on to transformation in power grid The assessment result of device neutral point direct current level, delimit the magnetic bias warning grade of each website, and draw a circle to approve involved by magnetic bias influence Site-bound.
Magnetic bias influences probability evaluation entity, including：Probability set is apart from computational submodule, for calculating arbitrary power transformation website ti The probability set distance P_distance of subway station stray electrical current in region：
Probability locally peels off because of sub-definite submodule, fixed for the probability set distance P_distance according to stray electrical current Adopted probability locally peels off factor Plof(ti)：Numbers of the m for subway station, S=[s1,s2,…sj…,sm] it is subway station coordinate Set；T=[t1,t2,…ti…,tn] be substation site coordinate set, n be substation site number；
Standard deviation computational submodule, for the factor P that locally peeled off using probabilitylof(ti), it calculates probability and locally peels off the factor Standard deviation ep_lof：
Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
The rate computational submodule that peels off of power transformation website, for the standard deviation e for the factor that locally peeled off based on probabilityp_lof, utilize Gauss error function calculates the rate loop (t that peel off of power transformation websitei)：
Rate loop (the t that peel off of the power transformation websitei) value range be [0,1].
Risk computing module, including：Importance computational submodule, for each power transformation website in electric power networks important It is as follows to spend D (i) calculation formula：
In formula, d (ti,tj) represent with node tiFor starting point, node tjShortest path length for terminal；S (i) represents to appoint Pass through node t between meaning node pairiShortest path number；Without node t between B (i) expression arbitrary nodes pairiShortest path Diameter number；
Magnetic bias influences risk computational submodule, influences the calculation formula of risk R for the magnetic bias of each power transformation website, such as Shown in lower：
Wherein, loop is the rate that peels off of power transformation website, is calculated two in electric power networks by the reciprocal of path length between two stations Degree of association C (t between associated stationi,tj)。
Monitoring station evaluation module, including：Support submodule is in f for calculating j-th of factorjkLevel is to transformation Device neutral point direct current is in ikSupport：
Sup(Ai,j→Bi)=P (Ai,j∪Bi)
=(σ (Ai,j∪Bi)/|δi|) × 100%
=(σ (Ai,j∪δi)/|δi|) × 100%；
Confidence level submodule is in f for calculating j-th of factorjkLevel is to transformer neutral point DC current level Confidence level：
C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
=(σ (Ai,j∪δi)/σ(Ai,j)) × 100%；
Wherein, item collection Ai,j={ when transformer neutral point DC analogue quantity is in ikWhen j-th of factor be in fjkIt is horizontal }； Item collection Bi={ transformer is in magnetic bias state }=δi；ikBelong to and divide transformer neutral point DC simulation by magnetic bias influence degree Section I={ the i of magnitude1,i2,..,im}；δi(i=1,2 ..., N) is in neutral point direct current area during magnetic bias state for transformer Between and
Monitoring station evaluation module, further includes：Transformer neutral point DC current set chooses submodule, becomes for choosing Support is higher than 70% under the conditions of depressor is in magnetic bias, and influence factor of the confidence level higher than 90% is as strong correlation item, to transformation Device neutral point direct current level carries out forecast assessment, obtains transformer neutral point DC current set I1.
The impact factor of transformer neutral point DC current in impact factor computing module
R (j) is that the magnetic bias of each power transformation website influences risk, and I1 is transformer neutral point DC current set, and s is arbitrary Pass through node t between node pairiShortest path number.
DC current calculates non-monitored website neutral point direct current in module
ik=σ R (k) ik∈ I2k=1,2 ..., r；
Wherein, I2 is the transformer neutral point DC current set of non-monitored website.
Compared with the latest prior art, technical solution provided by the invention has following excellent effect：
1st, the method that technical solution provided by the invention uses probability statistics, correlation rule and Multi-information acquisition is established D.C. magnetic biasing correlation model under the influence of metro stray current, realize under metro stray current environment AC network connect The on-line early warning of landlord's inversion of direct current magnetic bias and the assessment of influence；
2nd, the appraisal procedure involved by technical solution provided by the invention to power grid inside points by being configured with monitoring system Ground connection neutral point of main transformer DC current, magnetic bias warning information monitoring and statistics, with reference to subway line figure deployment scenarios and Power equipment in the case of magnetic bias, the safety of power grid normal operation and steady has been effectively ensured in subway train train operation density situation It is qualitative；
3rd, the assessment algorithm involved by technical solution provided by the invention, it is right using probability analysis and association rules method Substation transformer neutral point direct current level is assessed in region, and magnetic bias situation is grasped comprehensively to exchange for power supply department The influence of power grid large size main transformer operation characteristic, control magnetic bias coverage provide effective analysis means；Meanwhile assessment algorithm Application can also remind staff that potential variation tendency is judged and intervened to transformer DC magnetic bias in time, in time send out Now and rapid device for transferring hidden danger, the managerial ability that influences on magnetic bias is improved and using responding ability.
Description of the drawings
Fig. 1 is electric system of the present invention and electric power supply system for subway coupled relation figure；
Fig. 2 is D.C. magnetic biasing appraisal procedure estimation flow figure of the present invention.
Specific embodiment
The specific embodiment of the present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in Figure 1, electric power networks provide the energy for underground railway track traffic system, while miscellaneous by underground railway track traffic system The influence of electric current is dissipated, coupled relation therebetween is extremely complex.Although metro stray current can exchanged by Soil mediation Certain DC current is generated in system, but major part enters ground stray electrical current still through too greatly returning to traction substation.Therefore, Electric power supply system for subway and electric power network decoupling can be analyzed.
Appraisal procedure according to the present invention constructs underground railway track traffic network design first as a result, and substation is regarded as Scatterplot in underground railway track traffic network design calculates peel off rate of each substation in subway network using clustering algorithm, into And obtain the risk index of each substation's influenced by DC magnetic biasing；By magnetic bias risk index and monitoring station monitoring and statistics number According to being combined, (have straight with monitoring station using non-monitored website (not having the substation of D.C. magnetic biasing monitoring system, similarly hereinafter) Flow magnetic bias monitoring system substation, similarly hereinafter) between the degree of association, tested and assessed in advance to non-monitored website D.C. magnetic biasing level Estimate, obtain the regularity of distribution that AC transformer is influenced by magnetic bias under the influence of metro stray current, so as to draw a circle to approve subway stray electrical Flow the range influenced on AC network magnetic bias.
As shown in Fig. 2, the realization flow of appraisal procedure of the present invention is as follows：
1. according to subway line distribution map, using sub-interchange point as network node, using subway line as side, power grid is drawn It is divided into several grids, calculate metro stray current influences probability to the magnetic bias of electric power networks Zhong Ge substations；
2. there is the substation of ground connection main transformer as network node in electric power networks, using aerial/cable run as side, according to Electric power networks distribution calculates the degree of association in electric power networks between the importance of each website and arbitrary two website；
3. the degree of association meter between probability, the importance of website and arbitrary two website is influenced according to the magnetic bias of each substation The risk of Suan Ge substations influenced by DC magnetic biasing；
4. according to the monitoring data statistical conditions of monitoring station, correlation analysis, construction monitoring are carried out to its influence factor Correlation rule between statistical data and its influence factor；
5. the transformer neutral point DC current for assessing monitoring station is horizontal, the magnetic bias development trend of monitoring station is judged；
6. the risk influenced using the magnetic bias assessment result of monitoring station and its magnetic bias, calculates the transformer in network The DC current impact factor of neutral point；
7. according to the magnetic bias development trend of monitoring station and the transformer neutral point DC current impact factor of each website, Judge the magnetic bias development trend of non-monitored website；
8. each website magnetic bias warning grade delimited according to the magnetic bias tolerance of each website transformer, and according to magnetic bias early warning Situation delineation magnetic bias influences involved site-bound, predicts magnetic bias development trend.
Subway station collection is combined into S=[s1,s2,……,sm], m is subway station total number, and substation site collection is combined into T= [t1,t2,……,tn], n is substation's total number with ground connection main transformer.Electric power networks figure G=(T, E) expressions, wherein：T Gather for substation, i.e. the node set of G；E={ e1, e2... ..., elRepresent power transformation website between overhead transmission line set, i.e., The line set of G.Note monitoring station collection is combined intoAnd the transformer neutral point after monitoring station assessment is straight Galvanic electricity adfluxion is combined into I1={ i1, i2... ..., is}；Non-monitored Website Hosting is N=[t1,t2,……,tr], M ∪ N=T, and it is non- Transformer neutral point DC current collection after monitoring station assessment is combined into I2={ i1, i2... ..., ir}。
Since the factor for influencing underground railway track traffic system stray electrical current is numerous, and as electric system research subway fortune Row, track resistance and underground railway track insulation data can not obtain, it is contemplated that the regularity of subway circulation and ground resistance Long-time stability, therefore underground railway track stray electrical current influences each substation's magnetic bias outlier probabilistic algorithm can be used to obtain, and obtains Step is as follows：
1) for arbitrary power transformation website tiThe probability set distance of subway station is in region：
According to the probability set distance P_distance of stray electrical current, define probability and locally peel off factor Plof(ti)：M is ground The number of iron website, S=[s1,s2,…sj…,sm] be subway station coordinate set；T=[t1,t2,…ti…,tn] it is power transformation The coordinate set of website, n are the number of substation site.
2) according to the probability distribution density of stray electrical current, the probability factor that locally peels off is defined as follows：
3) it calculates probability locally to peel off the standard deviation of the factor, standard deviation calculation formula is as follows：
Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
4) using Gauss error function, the rate of peeling off for calculating power transformation website is：
For the rate value range that peels off of power transformation website for [0,1], the rate that peels off value is bigger, illustrates that the power transformation website is spuious by subway D.C. magnetic biasing situation is smaller caused by electric current.
By the degree of association C (t reciprocal calculated in electric power networks between two associated stations of path length between two stationsi,tj)。
Correlation rule is by calculating support and confidence level realization between monitoring and statistics data and each influence factor, method It is as follows：
4.1) transformer neutral point DC simulation magnitude is divided into several sections according to magnetic bias influence degree, be denoted as：
I={ i1,i2,..,im, wherein ik(k=1,2 ..., m) is the different sections of transformer neutral point DC analogue quantity Section；
4.2) transformer bias alarm section set is denoted as E, E={ δ1,δ2,..,δN, δi(i=1,2 ..., N) to become
Neutral point direct current section when depressor is in magnetic bias state and
4.3) sets of factors for influencing transformer neutral point DC current is denoted as F={ F1, F2,F3, F4, Fj={ fj1, fj2...,
fjn, j=1,2,3,4, it is a certain influence factor；fjk, k=1,2 ..., n are in kth section level for j-th of factor On；
4.4) item collection AI, j={ when transformer neutral point DC analogue quantity is in ikWhen j-th of factor be in fjkIt is horizontal }；
4.5) item collection Bi={ transformer is in magnetic bias state }=δi
4.6) j-th of factor is in fjkLevel is in i to transformer neutral point DC currentkSupport, i.e. AI, j→
BiSupport calculation formula it is as follows：
Sup(AI, j→Bi)=P (AI, j∪Bi)
=(σ (Ai,j∪Bi)/|δi|) × 100%
=(σ (Ai,j∪δi)/|δi|) × 100% (4.6.1)
1) j-th of factor is in fjkLevel is to the confidence level of transformer neutral point DC current level, i.e. Ai,j→BiPut Reliability calculation formula is as follows：
C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
=(σ (Ai,j∪δi)/σ(Ai,j)) × 100% (4.6.2)
By calculating, choose transformer be in support under the conditions of magnetic bias higher than 70% confidence level be higher than 90% influence because Element carries out forecast assessment to the transformer neutral point DC current level of all monitoring stations, obtains transformation as strong correlation item Device neutral point direct current set I1.
Importance D (i) calculation formula of each power transformation website in electric power networks are as follows：
Wherein：d(ti,tj)-represent with node tiFor starting point, node tjShortest path length for terminal；
Pass through node t between S (i)-expression arbitrary node pairiShortest path number；
Without node t between B (i)-expression arbitrary node pairiShortest path number；
D (i) is bigger, illustrates that the importance of the website in electric power networks is higher.
By the rate that peels off, relevance and the significance level of each power transformation website, the magnetic bias shadow of each power transformation website can be calculated Risk is rung, calculation formula is as follows：
Transformer in electric power networks is calculated in transformer neutral point DC current after being assessed as a result, by monitoring station The impact factor σ of neutral point direct current.
Non-monitored website neutral point direct current situation can be evaluated according to the impact factor of DC current.
ik=σ R (k) ik∈ I2k=1,2 ..., r (8)
Embodiment one：
By taking certain region subway and power transformation website as an example, statistics subway, the power transformation station data with ground connection main transformer are needed first, Including subway station number, subway length, subway circulation time interval, power transformation station data and each power transformation website to subway The range data stood.
Stray electrical current probability distribution can by subway length, subway train run time interval acquiring, if subway length be L, T is divided between subway train run time, then the rail vehicle number run on subway at this time is L/T, is estimated according to rail vehicle number After counting out probability distribution density of the stray electrical current in different distance at this time, each change is calculated respectively according to formula (1)-formula (6) The rate that peels off, significance level and the magnetic bias risk of power station point.
The prediction of the development trend of monitoring station transformer neutral point DC current can be by constructing neutral point direct current amount It is obtained with the correlation rule of subway circulation period, seasonal factor, climatic factor and the AC network method of operation.
By calculating, choose transformer be in support under the conditions of magnetic bias higher than 70% confidence level be higher than 90% influence because Element carries out forecast assessment to the transformer neutral point DC current level of all monitoring stations, that is, is estimating as strong correlation item Choose following subway circulation period, seasonal factor, climatic factor and the horizontal f of the AC network method of operationjkOn the basis of, assessment becomes Interval value where depressor neutral point direct current intensity, obtains transformer neutral point DC current set I1.
The impact factor of transformer neutral point DC current and non-monitored station in electric power networks are calculated by formula (7), formula (8) The transformer neutral point DC current of point is horizontal, and then according to the magnetic bias tolerance of each substation transformer, it is pre- to divide magnetic bias Alert grade, according to the coverage of the scale evaluation D.C. magnetic biasing of transformer warning grade height.
Based on same inventive concept, the present invention also provides a kind of for assessing the system of transformer DC magnetic bias influence, It is illustrated below.
The control system includes：Magnetic bias influences probability evaluation entity, for calculating metro stray current in advance dividing The magnetic bias of power grid Zhong Ge substations influences probability；Risk computing module, for influencing probability, each based on the magnetic bias of each substation The degree of association between the importance of website and each website calculates the risk of each substation's influenced by DC magnetic biasing；Influence because Sub- computing module for the monitoring data based on monitoring station and the risk of each substation's influenced by DC magnetic biasing, is calculated and is become The DC current impact factor of depressor neutral point；DC current calculates module, for influencing wind according to the magnetic bias of non-monitored website The DC current impact factor of dangerous degree and transformer neutral point calculates the DC current of non-monitored website neutral point；Neutral point DC current levels evaluation module, for the correlation rule based on monitoring data, to transformer neutral point DC current in power grid Level is assessed；Magnetic bias warning grade delimit module, for based on to transformer neutral point DC current level in power grid Assessment result, delimit the magnetic bias warning grade of each website, and draw a circle to approve the involved site-bound of magnetic bias influence.
Magnetic bias influences probability evaluation entity, including：Probability set is apart from computational submodule, for calculating arbitrary power transformation website ti The probability set distance P_distance of subway station stray electrical current in region：
Probability locally peels off because of sub-definite submodule, fixed for the probability set distance P_distance according to stray electrical current Adopted probability locally peels off factor Plof(ti)：Numbers of the m for subway station, S=[s1,s2,…sj…,sm] it is subway station coordinate Set；T=[t1,t2,…ti…,tn] be substation site coordinate set, n be substation site number；
Standard deviation computational submodule, for the factor P that locally peeled off using probabilitylof(ti), it calculates probability and locally peels off the factor Standard deviation ep_lof：
Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
The rate computational submodule that peels off of power transformation website, for the standard deviation e for the factor that locally peeled off based on the probabilityp_lof, Using Gauss error function, the rate loop (t that peel off of power transformation website are calculatedi)：
Rate loop (the t that peel off of power transformation websitei) value range be [0,1].
Risk computing module, including：Importance computational submodule, for each power transformation website in electric power networks important It is as follows to spend D (i) calculation formula：
In formula, d (ti,tj) represent with node tiFor starting point, node tjShortest path length for terminal；S (i) represents to appoint Pass through node t between meaning node pairiShortest path number；Without node t between B (i) expression arbitrary nodes pairiShortest path Diameter number；
Magnetic bias influences risk computational submodule, influences the calculation formula of risk R for the magnetic bias of each power transformation website, such as Shown in lower：
Wherein, loop is the rate that peels off of power transformation website, is calculated two in electric power networks by the reciprocal of path length between two stations Degree of association C (t between associated stationi,tj)。
Monitoring station evaluation module, including：Support submodule is in f for calculating j-th of factorjkLevel is to transformation Device neutral point direct current is in ikSupport：
Sup(Ai,j→Bi)=P (Ai,j∪Bi)
=(σ (Ai,j∪Bi)/|δi|) × 100%
=(σ (Ai,j∪δi)/|δi|) × 100%；
Confidence level submodule is in f for calculating j-th of factorjkLevel is to transformer neutral point DC current level Confidence level：
C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
=(σ (Ai,j∪δi)/σ(Ai,j)) × 100%；
Wherein, item collection Ai,j={ when transformer neutral point DC analogue quantity is in ikWhen j-th of factor be in fjkIt is horizontal }； Item collection Bi={ transformer is in magnetic bias state }=δi；ikBelong to and divide transformer neutral point DC simulation by magnetic bias influence degree Section I={ the i of magnitude1,i2,..,im}；δi(i=1,2 ..., N) is in neutral point direct current area during magnetic bias state for transformer Between and
Monitoring station evaluation module, further includes：Transformer neutral point DC current set chooses submodule, becomes for choosing Support is higher than 70% under the conditions of depressor is in magnetic bias, and influence factor of the confidence level higher than 90% is as strong correlation item, to transformation Device neutral point direct current level carries out forecast assessment, obtains transformer neutral point DC current set I1.
The impact factor of transformer neutral point DC current in impact factor computing module
R (j) is that the magnetic bias of each power transformation website influences risk, and I1 is transformer neutral point DC current set, and s is arbitrary Pass through node t between node pairiShortest path number.
DC current calculates non-monitored website neutral point direct current in module
ik=σ R (k) ik∈ I2k=1,2 ..., r；
Wherein, I2 is the transformer neutral point DC current set of non-monitored website.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program Product.Therefore, the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware can be used in the application Apply the form of example.Moreover, the computer for wherein including computer usable program code in one or more can be used in the application The computer program production that usable storage medium is implemented on (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) The form of product.
The application is with reference to the flow according to the method for the embodiment of the present application, equipment (system) and computer program product Figure and/or block diagram describe.It should be understood that it can be realized by computer program instructions every first-class in flowchart and/or the block diagram The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided The processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce A raw machine so that the instruction performed by computer or the processor of other programmable data processing devices is generated for real The device of function specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices.
Finally it should be noted that：The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent Pipe is described in detail the present invention with reference to above-described embodiment, those of ordinary skills in the art should understand that：Still The specific embodiment of the present invention can be modified or replaced equivalently, and without departing from any of spirit and scope of the invention Modification or equivalent replacement, are intended to be within the scope of the claims of the invention.

Claims (14)

  1. A kind of 1. method for being used to assess transformer DC magnetic bias influence, which is characterized in that including step：
    Calculate metro stray current influences probability to the magnetic bias of substation in the power grid divided in advance；
    Magnetic bias based on each substation influences the degree of association between probability, the importance of each website and each website, Calculate the risk of each substation's influenced by DC magnetic biasing；
    The risk of monitoring data and each substation's influenced by DC magnetic biasing based on monitoring station, calculating transformer are neutral The DC current impact factor of point；
    The DC current impact factor of risk and the transformer neutral point is influenced according to the magnetic bias of non-monitored website, is calculated The DC current of non-monitored website neutral point；
    Correlation rule based on monitoring data assesses transformer neutral point DC current level in power grid；
    Based on the assessment result to transformer neutral point DC current level in power grid, the magnetic bias warning grade of each website delimited, And draw a circle to approve the involved site-bound of magnetic bias influence.
  2. 2. according to the method described in claim 1, it is characterized in that, the metro stray current that calculates is to the power grid that divides in advance The magnetic bias of middle substation influences probability, including：
    Calculate arbitrary power transformation website tiTo the probability set distance P_ of subway station stray electrical current in the Grid of division distance：
    According to the probability set distance P_distance of stray electrical current, define probability and locally peel off factor Plof(ti)：M is subway station Number, S=[s1,s2,…sj…,sm] be subway station coordinate set；T=[t1,t2,…ti…,tn] it is substation site Coordinate set, n be substation site number；
    Locally peeled off factor P using probabilitylof(ti), it calculates probability and locally peels off the standard deviation e of the factorp_lof：
    Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
    The standard deviation e for the factor that locally peeled off based on the probabilityp_lof, using Gauss error function, calculate peeling off for power transformation website Rate loop (ti)：
    Rate loop (the t that peel off of the power transformation websitei) value range be [0,1].
  3. 3. according to the method described in claim 1, it is characterized in that, the magnetic bias based on each substation influence probability, The degree of association between the importance of each website and each website calculates the risk of each substation's influenced by DC magnetic biasing, Including：
    The importance D (i) of each power transformation website in electric power networks is calculated as follows：
    In formula, d (ti,tj) represent with node tiFor starting point, node tjShortest path length for terminal；S (i) represents arbitrary node Pass through node t betweeniShortest path number；Without node t between B (i) expression arbitrary nodes pairiShortest path number；
    Press the magnetic bias influence risk R for calculating each power transformation website：
    Wherein, loop is the rate that peels off of power transformation website, by falling for the path length between two stations；Two are connected in number calculating electric power networks Degree of association C (t between websitei,tj)。
  4. 4. according to the method described in claim 1, it is characterized in that, the correlation rule based on monitoring data, in power grid Transformer neutral point DC current level is assessed, including：
    In fjkHorizontal factor j is in i to transformer neutral point DC currentkSupport Ai,j→BiIt is calculated as follows：
    Sup(Ai,j→Bi)=P (Ai,j∪Bi)
    =(σ (Ai,j∪Bi)/|δi|) × 100%
    =(σ (Ai,j∪δi)/|δi|) × 100%；
    Factor j is in fjkLevel is to the confidence level A of transformer neutral point DC current leveli,j→BiIt is as follows：
    C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
    =(σ (Ai,j∪δi)/σ(Ai,j)) × 100%；
    Wherein, item collection Ai,j={ when transformer neutral point DC analogue quantity is in ikShi Yinsu j are in fjkIt is horizontal }；
    Item collection Bi={ transformer is in magnetic bias state }=δi；ikBelong to and divide transformer neutral point direct current by magnetic bias influence degree Section I={ the i of analog value1,i2,..,im}；δiNeutral point when (i=1,2 ..., N) is in magnetic bias state for transformer is straight Flow section and
  5. 5. according to the method described in claim 4, it is characterized in that,
    The support of transformer under the conditions of magnetic bias is chosen at higher than 70%, influence factor of the confidence level higher than 90% is as strong correlation , the transformer neutral point DC current level is assessed, obtains the transformer neutral point DC current set I1.
  6. 6. according to the method described in claim 1, it is characterized in that, the monitoring data based on monitoring station and each change The risk of power station influenced by DC magnetic biasing, the DC current impact factor of calculating transformer neutral point, including：
    The impact factor σ of transformer neutral point DC current in the electric power networks is calculated as follows：
    Wherein, R (j) is that the magnetic bias of each power transformation website influences risk, and I1 is the transformer neutral point DC current of monitoring station Set, s pass through node t between arbitrary node pairiShortest path number.
  7. 7. according to the method described in claim 1, it is characterized in that, described influence risk according to the magnetic bias of the monitoring station And the DC current impact factor of the transformer neutral point, the DC current of calculating non-monitored website neutral point, including：
    Impact factor σ based on DC current, non-monitored website neutral point direct current ikIt is calculated as follows：
    ik=σ R (k) ik∈ I2 k=1,2 ..., r；
    I2 is the transformer neutral point DC current set of non-monitored website.
  8. 8. a kind of system for being used to assess transformer DC magnetic bias influence, which is characterized in that the system includes：
    Magnetic bias influences probability evaluation entity, for calculating magnetic bias shadow of the metro stray current to substation in the power grid that divides in advance Ring probability；
    Risk computing module, for influencing probability, the importance of each website and described based on the magnetic bias of each substation The degree of association between each website calculates the risk of each substation's influenced by DC magnetic biasing；
    Impact factor computing module, for the monitoring data based on monitoring station and each substation's influenced by DC magnetic biasing Risk, the DC current impact factor of calculating transformer neutral point；
    DC current calculates module, for influencing risk and the transformer neutral point according to the magnetic bias of non-monitored website DC current impact factor calculates the DC current of non-monitored website neutral point；
    Neutral point direct current proficiency assessment module, it is neutral to transformer in power grid for the correlation rule based on monitoring data Point DC current levels are assessed；
    Magnetic bias warning grade delimit module, for based on the assessment result to transformer neutral point DC current level in power grid, The magnetic bias warning grade of each website delimited, and draw a circle to approve magnetic bias to influence involved site-bound.
  9. 9. system according to claim 8, which is characterized in that the magnetic bias influences probability evaluation entity, including：
    Probability set is apart from computational submodule, for calculating arbitrary power transformation website tiThe probability of subway station stray electrical current in region Collect distance P_distance：
    Probability locally peels off because of sub-definite submodule, and for the probability set distance P_distance according to stray electrical current, definition is general Rate locally peels off factor Plof(ti)：Numbers of the m for subway station, S=[s1,s2,…sj…,sm] be subway station coordinate collection It closes；T=[t1,t2,…ti…,tn] be substation site coordinate set, n be substation site number；
    Standard deviation computational submodule, for the factor P that locally peeled off using probabilitylof(ti), it calculates probability and locally peels off the mark of the factor Quasi- difference ep_lof：
    Wherein：E[(Plof(ti))2] locally peel off factor P for probabilitylof(ti) mathematic expectaion；
    The rate computational submodule that peels off of power transformation website, for the standard deviation e for the factor that locally peeled off based on the probabilityp_lof, utilize Gauss error function calculates the rate loop (t that peel off of power transformation websitei)：
    Rate loop (the t that peel off of the power transformation websitei) value range be [0,1].
  10. 10. system according to claim 8, which is characterized in that the risk computing module, including：
    Importance computational submodule, for the importance D (i) of each power transformation website in electric power networks to be calculated as follows：
    In formula, d (ti,tj) represent with node tiFor starting point, node tjShortest path length for terminal；S (i) represents arbitrary node Pass through node t betweeniShortest path number；Without node t between B (i) expression arbitrary nodes pairiShortest path number；
    Magnetic bias influences risk computational submodule, influences risk R for the magnetic bias of each power transformation website to be calculated as follows：
    Wherein, loop be power transformation website the rate that peels off, by between two stations path length fall；Two are connected in number calculating electric power networks Degree of association C (t between websitei,tj)。
  11. 11. system according to claim 8, which is characterized in that the monitoring station evaluation module, including：
    Support submodule is in f for calculating factor jjkLevel is in i to transformer neutral point DC currentkSupport：
    Sup(Ai,j→Bi)=P (Ai,j∪Bi)
    =(σ (Ai,j∪Bi)/|δi|) × 100%
    =(σ (Ai,j∪δi)/|δi|) × 100%；
    Confidence level submodule is in f for calculating factor jjkLevel is to the confidence level of transformer neutral point DC current level：
    C(Ai,j→Bi)=P (Ai,j∪Bi)/P(Ai,j)
    =(σ (Ai,j∪δi)/σ(Ai,j)) × 100%；
    Wherein, item collection Ai,j={ when transformer neutral point DC analogue quantity is in ikWhen j-th of factor be in fjkIt is horizontal }；Item collection Bi={ transformer is in magnetic bias state }=δi；ikBelong to and divide transformer neutral point DC simulation magnitude by magnetic bias influence degree Section I={ i1,i2,..,im}；δiNeutral point direct current section when (i=1,2 ..., N) is in magnetic bias state for transformer and
  12. 12. system according to claim 11, which is characterized in that the monitoring station evaluation module further includes transformer Neutral point direct current set chooses submodule, and for choosing, support is higher than 70% under the conditions of transformer is in magnetic bias, confidence Influence factor of the degree higher than 90% carries out forecast assessment to transformer neutral point DC current level, obtains as strong correlation item Transformer neutral point DC current set I1.
  13. 13. system according to claim 8, which is characterized in that transformer neutral point in the impact factor computing module The impact factor of DC current
    R (j) is that the magnetic bias of each power transformation website influences risk, and I1 is transformer neutral point DC current set, and s is arbitrary node Pass through node t betweeniShortest path number.
  14. 14. system according to claim 8, which is characterized in that the DC current is calculated in module in non-monitored website Property point DC current ik=σ R (k) ik∈ I2 k=1,2 ..., r；
    Wherein, I2 is the transformer neutral point DC current set of non-monitored website.
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CN109387680B (en) * 2018-10-15 2020-12-08 国网四川省电力公司电力科学研究院 Urban rail traction power supply system and urban electric power main transformer direct-current magnetic bias synchronous monitoring system
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CN111562423A (en) * 2020-04-17 2020-08-21 北京市燃气集团有限责任公司 Method and device for calculating influence range of subway stray current on buried pipeline
CN111562423B (en) * 2020-04-17 2022-08-05 北京市燃气集团有限责任公司 Method and device for calculating influence range of subway stray current on buried pipeline
CN112165179B (en) * 2020-09-24 2022-04-12 南方电网科学研究院有限责任公司 Method and device for analyzing electromagnetic coupling of subway overhead line system and power grid
CN112165179A (en) * 2020-09-24 2021-01-01 南方电网科学研究院有限责任公司 Method and device for analyzing electromagnetic coupling of subway overhead line system and power grid
CN112183004A (en) * 2020-09-30 2021-01-05 南方电网科学研究院有限责任公司 CDEGS-based power grid induction voltage analysis method
CN112183004B (en) * 2020-09-30 2024-02-13 南方电网科学研究院有限责任公司 CDEGS-based power grid induced voltage analysis method
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CN112415300A (en) * 2020-10-25 2021-02-26 国网湖北省电力有限公司电力科学研究院 Rail transit and transformer direct-current magnetic bias correlation analysis method
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CN113452007A (en) * 2021-06-29 2021-09-28 西南交通大学 Neutral point direct current calculation method for transformer of urban power grid
CN113665635A (en) * 2021-08-21 2021-11-19 国网湖北省电力有限公司电力科学研究院 Track traffic train operation position inversion method
CN114117832A (en) * 2022-01-27 2022-03-01 国网江西省电力有限公司电力科学研究院 Analysis method for dynamic stray current distribution of rail transit
CN114707351A (en) * 2022-04-25 2022-07-05 西南交通大学 Subway transfer station grounding grid equivalent method suitable for CDEGS simulation
CN114707351B (en) * 2022-04-25 2023-03-14 西南交通大学 Subway transfer station grounding grid equivalent method suitable for CDEGS simulation

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