CN105391049B - A kind of electrical network parameter method of estimation considering probability distribution - Google Patents
A kind of electrical network parameter method of estimation considering probability distribution Download PDFInfo
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Abstract
The present invention relates to a kind of electrical network parameter methods of estimation considering probability distribution, belong to dispatching automation of electric power systems field；This method includes：The newest model of power grid and real time data were obtained for the period with 15 minutes, power grid parameter identification software for calculation is called, recognizes and estimate grid equipment parameter；Device parameter estimated result under the section is updated in power grid calculating parameter, completion status estimation again calculates；Calculating parameter estimated value substitutes into the big error points difference of front and back measurement, and result is saved in database.The believable parameter estimation result of some equipment on the day of acquisition using time as index calculates device parameter estimation day availability；02 times of set device initial value are statistics section, and with the 5% of the original value of the equipment of setting for statistical interval range, statistic estimated value is distributed in each interval probability and average measurement does not conform to lattice point difference.The parameter value probability optimization target for calculating each statistics section, provides the estimates of parameters of equipment.The method increase grid equipment parameter Estimation accuracys.
Description
Technical field
The invention belongs to dispatching automation of electric power systems fields, more particularly to a kind of to consider that the electrical network parameter of probability distribution is estimated
Meter method.
Background technology
The on-line running of the middle-and-high-ranking application of intelligent grid supporting system technology puies forward the power grid underlying parameter quality of data
Increasingly higher demands are gone out.How from the electric network data of magnanimity, the equipment for effectively picking out parameter inaccuracy improves scheduling
The automation foundation quality of data and state estimation qualification rate, it has also become restrict dispatching of power netwoks technology support system application level into one
Walk the bottleneck problem improved.Traditional electrical network parameter method of estimation carries out parameter identification primarily directed to single operation of power networks section
And estimation.It, may be different with the parameter value that different section estimates since electric network swim and the method for operation constantly change；Cause
This needs to carry out probability analysis to estimation parameter to improve the accuracy of parameter Estimation in long time scale.
Invention content
The purpose of the present invention is to further increase the accuracy of grid equipment parameter Estimation, promote dispatching of power netwoks technology branch
System application level is held, proposes a kind of electrical network parameter method of estimation considering probability distribution, the method increase line parameter circuit values to estimate
The accuracy of meter.
The electrical network parameter method of estimation proposed by the present invention for considering probability distribution, this method include：With 15 minutes for the period
The newest model of power grid and real time data are obtained, power grid parameter identification software for calculation is called, recognizes and estimate grid equipment parameter；
Device parameter estimated result under the section is updated in power grid calculating parameter, completion status estimation again calculates；Calculate ginseng
Number estimated value substitutes into the big error points difference of front and back measurement, and result is saved in database.On the day of acquisition using time as index
The believable parameter estimation result of some equipment calculates device parameter estimation day availability；0-2 times of set device initial value be
Section is counted, with the 5% of the original value of the equipment of setting for statistical interval range, statistic estimated value is distributed peace in each interval probability
Equal measurement does not conform to lattice point difference.The parameter value probability optimization target for calculating each statistics section, provides the parameter Estimation of equipment
Value.
The electrical network parameter method of estimation proposed by the present invention for considering probability distribution, advantage are as follows：
1, in the electrical network parameter method of estimation proposed by the present invention for considering probability distribution, it is contemplated that join under different power grid sections
The variation of number estimated value has selected probability of occurrence maximum and has reduced to measure the unqualified most effective estimates of parameters of point, improve
The accuracy of parameter Estimation.
2, the method for the present invention take full advantage of various parameters algorithm for estimating as a result, can be total with many kinds of parameters algorithm for estimating
More accurate parameter estimation result is obtained with cooperation.
3, the method for the present invention is continuously increased estimates of parameters with calculation times and the traversal of a variety of sections and the method for operation
Number, statistical parameter estimated value probability distribution, can constantly correct and improve device parameter estimation accuracy.
Description of the drawings
Fig. 1 is the flow diagram of the method for the present invention.
Specific implementation mode
The electrical network parameter method of estimation combination accompanying drawings and embodiments proposed by the present invention for considering probability distribution are described in detail such as
Under：
The electrical network parameter method of estimation proposed by the present invention for considering probability distribution, flow is as shown in Figure 1, include following step
Suddenly：
1) the newest model of power grid and real time data were obtained for the period with 15 minutes, calls power grid parameter identification to calculate soft
Part recognizes and estimates grid equipment parameter；By the device parameter estimated result V under the sectionseIt is updated in power grid calculating parameter,
Again completion status estimation calculates；Calculating parameter estimated value substitutes into the big error points difference of front and back measurement, and result is saved in number
According in library；Specific implementation step is as follows：
11) measuring value of measuring point and the deviation Se of state estimation are definederrAs shown in formula (1)：
In formula (1), PmeasIndicate the measuring value of a measuring point of some circuit；PseIndicate the state estimation of the measuring point
Value；PbaseIndicate a reference value, PbaseIt is related to the voltage class where circuit；
12) whether it is big error dot according to the different threshold decision measuring points of metric data type set：
When metric data is active metric data, the threshold value that sets is 2%, as deviation Seerr>2%, then the measuring point
For big error dot；
When metric data is idle metric data, the threshold value that sets is 3%, as deviation Seerr>3%, then the measuring point
For big error dot；
13) statistical parameter estimated value substitutes into state estimation and calculates the front and back big error dot quantity of measurement, calculates and measures big error
Points difference：
Mbias=Mp-Mh (2)
In formula (2), MpBig error dot quantity, M are measured before being substituted into for estimation parameterhBig miss is measured after being substituted into for estimation parameter
Not good enough quantity；
14) work as MbiasWhen >=1, expression parameter estimated value can improve state estimation result, by parameter name, type, ginseng
Number estimated value Vse、MbiasAnd the measuring section time is saved in parameter estimation result database.The moment estimates of parameters is set
It is credible；Work as MbiasWhen ＜ 1, parameter estimation result is not preserved, which is set to insincere.
2) the believable parameter estimation result of some equipment of the acquisition same day using time as index, calculates device parameter estimation
Day availability；0-2 times of set device initial value is statistics section, setting interval Rset=0.05, statistic estimated value is each
Interval probability is distributed and average measurement does not conform to lattice point difference；The parameter value probability optimization target for calculating each statistics section, gives
Go out the estimates of parameters of equipment.Specific implementation step is as follows：
21) authentication parameters for obtaining some equipment of the same day in database estimate value list, count believable the results list number
Measure Tar；
Calculating parameter estimates day availability Par=Tar×100.0/96 (3)
In formula (3), parameter Estimation day availability ParFor range between 0%-100%, the numerical value the big, indicates probability statistics
Upper estimates of parameters confidence level is higher.
22) 0-2 times of set device initial value is statistics section, setting interval Rset=0.05, calculate each probability system
Count section；
I-th statistics interval limit be：Zl-i=Vbase×Rset×i (4)
I-th statistics the section upper limit be：Zh-i=Vbase×Rset×(i+1) (5)
In formula (4), (5), VbaseFor device parameter initial value, 0≤i ＜ 40；
23) believable the results list quantity T in each section is countedar-iWith the big error dot difference sum T of measurementm-i；
If Zl-i≤Vse-j≤Zh-i, then：
Tar-i=Tar-i+1 (6)
Tm-i=Tm-i+Mbias-j (7)
In formula (6), (7), Vse-jFor some estimates of parameters in parameter Estimation value list, Mbias-jFor the parameter Estimation
It is worth the corresponding big error dot difference of measurement；
24) believable the results list quantity T in each statistics section is shown with curve modear-iBig error dot is poor with measuring
It is worth sum Tm-i, intuitive display parameters estimated value probability distribution；
25) the parameter value probability optimization target T in each statistics section is calculatedop-i：
Top-i=Tar-i×Rar+Tm-i×(1-Rar) (8)
R in formula (8)arFor probability optimization target factor, RarIt can be set by the user, it is 0.8 to take initial value；26) institute is obtained
There is T in sectionop-iThe upper limit value Z in the corresponding section of maximum valuel-iWith lower limiting value Zh-i；27) V is calculatedse-i=(Zl-i+Zh-i)×
0.5, by Vse-iIt is set as the estimates of parameters of the device parameter.
Claims (3)
1. a kind of electrical network parameter method of estimation considering probability distribution, which is characterized in that
This method specifically includes following steps：
1) the newest model of power grid and real time data were obtained for the period with 15 minutes, calls power grid parameter identification software for calculation, distinguishes
Know and estimates grid equipment parameter；Device parameter estimated result under the section is updated in power grid calculating parameter, it is again complete
It is calculated at state estimation；Calculating parameter estimated value substitutes into the big error points difference of front and back measurement, and result is saved in database；
2) the believable parameter estimation result of some equipment of the acquisition same day using time as index, calculating device parameter estimation day can
With rate；0-2 times of set device initial value is statistics section, and with the 5% of the original value of the equipment of setting for statistical interval range, statistics is estimated
Evaluation is distributed in each interval probability and average measurement does not conform to lattice point difference；The parameter value probability for calculating each statistics section is excellent
Change target, provides the estimates of parameters of equipment.
2. method as described in claim 1, which is characterized in that the step 1) specifically includes following steps：
11) measuring value of measuring point and the deviation Se of state estimation are definederrAs shown in formula (1)：
In formula (1), PmeasIndicate the measuring value of a measuring point of some circuit；PseIndicate the state estimation of the measuring point；
PbaseIndicate a reference value, PbaseIt is related to the voltage class where circuit；
12) whether it is big error dot according to the different threshold decision measuring points of metric data type set：
When metric data is active metric data, the threshold value that sets is 2%, as deviation Seerr>2%, then the measuring point is big
Error dot；
When metric data is idle metric data, the threshold value that sets is 3%, as deviation Seerr>3%, then the measuring point is big
Error dot；
13) statistical parameter estimated value substitutes into state estimation and calculates the front and back big error dot quantity of measurement, calculates and measures big error points
Difference：
Mbias=Mp-Mh (2)
In formula (2), MpBig error dot quantity, M are measured before being substituted into for estimation parameterhBig error dot is measured after being substituted into for estimation parameter
Quantity；
14) work as MbiasWhen >=1, expression parameter estimated value can improve state estimation result, by parameter name, type, parameter Estimation
Value Vse、MbiasAnd the measuring section time is saved in parameter estimation result database, which is set to credible；
Work as MbiasWhen ＜ 1, parameter estimation result is not preserved, which is set to insincere.
3. method as described in claim 1, which is characterized in that the step 2) specifically includes following steps：
21) authentication parameters for obtaining some equipment of the same day in database estimate value list, count believable the results list quantity Tar；
Calculating parameter estimates day availability Par=Tar×100.0/96 (3)
In formula (3), parameter Estimation day availability ParFor range between 0%-100%, the numerical value the big, indicates to join in probability statistics
Number estimated value confidence level is higher；
22) 0-2 times of set device initial value is statistics section, setting statistical interval range Rset=0.05, calculate each probability system
Count section；
I-th statistics interval limit be：Zl-i=Vbase×Rset×i (4)
I-th statistics the section upper limit be：Zh-i=Vbase×Rset×(i+1) (5)
In formula (4), (5), VbaseFor device parameter initial value, 0≤i ＜ 40；
23) believable the results list quantity T in each section is countedar-iWith the big error dot difference sum T of measurementm-i；
If Zl-i≤Vse-j≤Zh-i, then：
Tar-i=Tar-i+1 (6)
Tm-i=Tm-i+Mbias-j (7)
In formula (6), (7), Vse-jFor some estimates of parameters in parameter Estimation value list, Mbias-jIt is corresponded to for the estimates of parameters
The big error dot difference of measurement；
24) believable the results list quantity T in each statistics section is shown with curve modear-iBig error dot difference is total with measuring
Number Tm-i, intuitive display parameters estimated value probability distribution；
25) the parameter value probability optimization target T in each statistics section is calculatedop-i：
Top-i=Tar-i×Rar+Tm-i×(1-Rar) (8)
R in formula (8)arFor probability optimization target factor, RarIt can be set by the user, it is 0.8 to take initial value；
26) T in all sections is obtainedop-iThe upper limit value Z in the corresponding section of maximum valuel-iWith lower limiting value Zh-i；
27) V is calculatedse-i=(Zl-i+Zh-i) × 0.5, by Vse-iIt is set as the estimates of parameters of the device parameter.
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| CN103066591A (en) * | 2012-12-13 | 2013-04-24 | 广东电网公司东莞供电局 | Power grid parameter deviation identification method based on real-time measurement |
| CN103164625A (en) * | 2013-03-21 | 2013-06-19 | 国家电网公司 | Method capable of estimating all parameters of personal access system (PAS) by measured data |
| CN104090166A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Power grid line parameter on-line identification method considering state estimation large error points |
| CN104123459A (en) * | 2014-07-22 | 2014-10-29 | 国家电网公司 | Circuit semi-accommodation parameter estimation method based on reactive loss deviation |
| CN104836223A (en) * | 2014-11-14 | 2015-08-12 | 浙江大学 | Power grid parameter error and bad data coordinated identification and estimation method |
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| US9286582B2 (en) * | 2009-06-22 | 2016-03-15 | Johnson Controls Technology Company | Systems and methods for detecting changes in energy usage in a building |
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| CN103066591A (en) * | 2012-12-13 | 2013-04-24 | 广东电网公司东莞供电局 | Power grid parameter deviation identification method based on real-time measurement |
| CN103164625A (en) * | 2013-03-21 | 2013-06-19 | 国家电网公司 | Method capable of estimating all parameters of personal access system (PAS) by measured data |
| CN104090166A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Power grid line parameter on-line identification method considering state estimation large error points |
| CN104123459A (en) * | 2014-07-22 | 2014-10-29 | 国家电网公司 | Circuit semi-accommodation parameter estimation method based on reactive loss deviation |
| CN104836223A (en) * | 2014-11-14 | 2015-08-12 | 浙江大学 | Power grid parameter error and bad data coordinated identification and estimation method |
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| 考虑参数不确定的电网可靠性概率分布特征;赵渊等;《电网技术》;20130831;第37卷(第8期);第2165-2172页 * |
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