CN110287449A - It is a kind of meter and topologies change Distribution Network Harmonics measuring point Optimal Configuration Method - Google Patents
It is a kind of meter and topologies change Distribution Network Harmonics measuring point Optimal Configuration Method Download PDFInfo
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- CN110287449A CN110287449A CN201910534419.8A CN201910534419A CN110287449A CN 110287449 A CN110287449 A CN 110287449A CN 201910534419 A CN201910534419 A CN 201910534419A CN 110287449 A CN110287449 A CN 110287449A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/01—Arrangements for reducing harmonics or ripples
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/40—Arrangements for reducing harmonics
Abstract
The invention patent disclose it is a kind of meter and topologies change Distribution Network Harmonics measuring point Optimal Configuration Method.Distribution net work structure aiming at the problem that complicated and frequently adjustment, the Observability Analysis method of meter and interconnection switch movement probability coefficent is proposed, the node observability degree and system observability degree under network topology structure situation of change are calculated based on the incidence matrix between node；Using harmonic state estimation measurement matrix conditional number as the index for measuring harmonic state estimation precision, the harmonic wave measuring point Optimal Allocation Model for comprehensively considering system observability degree and measurement matrix conditional number is established.This method can effectively improve Observable range when system topology variation, and ensure that harmonic state estimation precision.
Description
Technical field
The present invention relates to distribution network electric energy quality fields, in particular to Distribution Network Harmonics measuring point Optimal Configuration Method.
Background technique
In recent years, with the continuous improvement of energy-saving and emission-reduction attention degree, the non-linear power electronic equipment such as frequency converter is answered
With increasingly extensive, thus caused by Distribution Network Harmonics pollution problem become increasingly conspicuous.The power quality of user has not only been influenced, and
And phenomena such as resonance, protection misoperation of harmonic wave initiation, also brings certain challenge to power grid operation, it has also become power supply enterprise
Industry and user's question of common concern.Building Distribution Network Harmonics on-line monitoring system is analysis harmonic wave occurrence features, statistics harmonic wave
The regularity of distribution proposes the basis of effective braking measure in turn.
In view of power distribution network network topology structure is complicated and easily variability is high, each node installation harmonic wave is supervised online in power grid
It is too big to survey terminal projects cost.Therefore, by the way that a certain number of harmonic waves on-line monitoring terminal is optimized configuration, reach power grid
To the greatest extent it is considerable, thus using harmonic state estimation technology carry out Distribution Network Harmonics on-line monitoring have important economy
And Practical significance.
Existing harmonic wave measures terminal optimized configuration method and is only applicable to single network topological structure at present, does not account for
In the case where system network topology variation, original measure configuration may make certain buses lose ornamental or harmonic wave
The problem of precision of state estimation is affected by error in measurement.Since power distribution network topological structure is complicated and flexible and changeable, harmonic wave
The installation site of on-line monitoring terminal directly determines its Observable region and harmonic state estimation precision, it is therefore necessary to grind
Study carefully the terminal optimized allocation problem of harmonic wave on-line monitoring for being suitable for network topology structure variation.
Summary of the invention
Technical problem solved by the present invention is frequently adjusting problem for distribution net work structure, meter and interconnection switch are proposed
The Observability Analysis method for acting probability coefficent is calculated under network topology structure situation of change based on the incidence matrix between node
Node observability degree and system observability degree；Using harmonic state estimation measurement matrix conditional number as measurement harmonic state estimation
The index of precision establishes and comprehensively considers the harmonic wave measuring point that system observability degree harmony wave state estimator surveys Matrix condition number
Optimal Allocation Model.
1. direct considerable node and indirectly considerable node judgement
Observability degree can be used to measure the quantity of the directly considerable node of system and indirect considerable node, i.e. system Observable
Range.Observability degree is divided into two kinds, respectively node observability degree and system observability degree in the present invention.It provides as follows: node
Observability degree refers in the case where system network topology variation, probability that node is observed.If node observability degree is 1,
Then claiming must considerable node；If node observability degree is 0, title must not considerable node；If node observability degree is 0~1, claim
The considerable node of probability.System observability degree refers to that ratio of the sum of each node observability degree with total node number, observability degree are 1 table
Show that system must be completely considerable；Observability degree is that 0 expression system must be completely inconsiderable；Observability degree is 0~1 expression Account Dept
Divide considerable.
When system network topology complexity, when node, number of branches are more, the judgement of system Observable range is more multiple
It is miscellaneous.The incidence relation in complex electric network network between each node is described using the incidence matrix between node in the present invention.
Incidence matrix A between node is n × n rank symmetrical matrix, and wherein n is number of nodes, and matrix element is by 0 or 1 group
At；Definition claims two nodes associated with each other when two nodes are coupled by a branch.If Aij=1, then it represents that i, j node are related
Connection；If Aij=0, then it represents that i, j node are not associated with.
System Observability Analysis first has to carry out directly considerable node and indirectly considerable node judgement.
1) assume that distribution number of nodes is n, share k kind measuring point allocation plan, establish directly considerable node judgment matrix:
Fi=AXi
In formula, F is k × n matrix, FiFor the i-th row element of F, indicate through directly considerable node judgement, i-th kind of measuring point
Each node ornamental under allocation plan.If Fij>=1, then i-th kind of measuring point allocation plan lower node j is directly considerable；If Fij=0,
Then i-th kind of measuring point allocation plan lower node j be not considerable.X is k × n rank matrix, XiFor the i-th row element of X, i-th kind of measurement is indicated
Point allocation plan.If Xij=1, then it represents that under i-th kind of measuring point allocation plan, node j is equipped with harmonic wave measuring equipment；If Xij=
0, then it represents that under i-th kind of measuring point allocation plan, node j does not have harmonic wave measuring equipment.
By direct considerable node judgment matrix, directly may be used in addition to measuring point under available every kind of measure configuration scheme
The node of sight, i.e., newly-increased directly considerable node.
2) for the newly-increased direct considerable node m under i-th kind of measuring point allocation plan, if it is anharmonic wave source injection section
Point can then establish indirect considerable node judgment matrix:
fi,m=Am-Xi
In formula, f is l × n rank matrix, and l is the newly-increased directly considerable of anharmonic wave source injection under i-th kind of measuring point allocation plan
The number of node；fi,mFor the corresponding row element of m node in the corresponding f matrix of i-th kind of measuring point allocation plan；AmTo be associated with square
Battle array m row；XiFor i-th kind of measure configuration scheme.
If fi,mIn, in addition to m column, an only column element is 1, then the corresponding node of the column is indirect considerable node, FiIn
The column add 1.
2. the node observability degree and system observability degree of meter and network topology structure variation calculate
Assuming that node i is measuring point, and 3 branches are shared at node i, wherein one is interconnection branch, distribution is typical
Interconnection switch disconnects under operational mode；If node i is measuring point, 2 branches can be made to side gusset under typical mode of operation
It is considerable；When power distribution network needs to be reconstructed, interconnection switch closure, 3 branches are considerable to side gusset at this time；Existed according to scene
Normally, interconnection switch, which acts probability, at node i is shown to the adjustment experience of distribution network structure under maintenance or emergency operating mode
Coefficient is Pi, then node i observability degree is 1+P at this timei。
It constructs interconnection switch and acts probability coefficent matrix P:
P=(P1 P2 … Pn)
P is 1 × n rank matrix, PiProbability coefficent is acted for interconnection switch at node i.
There is node observability degree matrix expression at this time:
Oi,j=Fi,j+Xi,j×Pj
In formula, O is k × n matrix, Oi,jIndicate the observability degree of i-th kind of measure configuration scheme lower node j.
System observability degree matrix are as follows:
In formula, K is the rank matrix of k × 1, KiFor the system observability degree matrix under i-th kind of measurement scheme.
3. comprehensively considering system observability degree and the harmonic wave measuring point optimization of harmonic state estimation measurement matrix conditional number being matched
Set model
Measurement matrix conditional number reflects the Degree of Ill Condition of measurement matrix, and value is bigger, and matrix is closer to morbid state, harmonic wave shape
State estimated accuracy is influenced also larger by error in measurement, is caused even if error in measurement very little, harmonic state estimation result also has
The case where large error.
The above analysis, the present invention propose that a kind of meter and the Distribution Network Harmonics measuring point of topologies change are distributed rationally
Method, in the case where guaranteeing the maximum situation of system observability degree, measurement matrix conditional number Cond (H) is minimum.Accordingly, harmonic content is established
Measuring point distributes objective function rationally:
In formula, σ1、σ2For the weight coefficient reciprocal of system observability degree and measurement matrix conditional number；HiIndicate i-th kind of amount
Measurement matrix in the case of measuring point arrangement.
Compared with prior art the beneficial effects of the present invention are:
Distribution Network Harmonics measuring point Optimal Configuration Method disclosed in the invention patent realizes and becomes in network topology structure
Under the premise of changing with a certain number of harmonic waves on-line monitoring terminal, system Observable range is maximum；And make system Observable range
While reaching maximum, it ensure that harmonic state estimation precision is influenced minimum by error in measurement.Solve existing at present match
Mains by harmonics measuring point Optimal Configuration Method can not be suitable for network topology structure and change, and cause reason measure configuration that may make
Certain buses, which lose ornamental or harmonic state estimation precision, is influenced the problem of becoming larger by error in measurement.
Detailed description of the invention
Attached drawing 1 is IEEE-33 node standard test system.
Attached drawing 2 is fitness with the number of iterations change curve.
Attached drawing 3 is when measuring point is 3,6,12,15,29, and whether there is or not the harmonic state estimation amplitudes in the case of error in measurement
Error comparison diagram.
Attached drawing 4 is when measuring point is 2,6,9,12,29, and whether there is or not the harmonic state estimation amplitude mistakes in the case of error in measurement
Poor comparison diagram.
Specific embodiment
Fig. 1 is IEEE-33 node standard test system, and harmonic wave measuring point is carried out by taking 5 subharmonic as an example and distributes emulation rationally
Analysis.Assuming that interconnection switch is closed between node 12 and node 22, objective function is solved using genetic algorithm.Target letter
σ in number1It is set as 0.9, σ2It is set as 0.1；Interconnection switch acts P in probability coefficent matrix P25=P29=0.6, P33=P18=0.5, P9
=P15=0.7, P21=P8=0.4, P12=P22Remaining element is 0 in=0.9, P；Choosing measuring point number is 5, initial population
5000,100 generation of the number of iterations.
Fitness is as shown in Figure 2 with the number of iterations change curve.It can be seen from the figure that optimal adaptation degree substantially 20~
In 30 generations, reached highest and tended towards stability, and optimal amount measuring point arrangement is shown in Table 1.
1 optimal amount measuring point arrangement situation of table
If taking no account of the influence of network topology structure variation, i.e. P is null matrix, and other conditions and parameter setting are constant, this
When optimal amount measuring point arrangement be shown in Table 2.
Optimal amount measuring point arrangement situation when 2 P of table is null matrix
By Tables 1 and 2 comparison it is found that if taking no account of the influence of network topology structure variation, system observability degree will
It reduces；I.e. in the biggish node installation measuring point of interconnection switch movement probability coefficent, due to the closure number phase of interconnection switch
To more, system is easier to be in the state of high observability degree.
If only considering system observability degree, measurement matrix conditional number, i.e. σ are not considered1It is set as 1, σ2Be set as 0, other conditions and
Parameter setting is constant, and optimal amount measuring point arrangement is shown in Table 3 at this time.
The optimal amount measuring point arrangement of 3, table consideration system observability degrees
As can be seen from Table 3, when only considering system observability degree, the measurement matrix condition of two kinds of optimal amount measuring point arrangements
Number is different.When measuring point is 3,6,12,15,29, to current measurement at measuring point 3,15, there are 10% errors and errorless respectively
Poor two kinds of situations are emulated, and for state estimation result as shown in table 4,5, the comparison of state estimation error is as shown in Figure 3.
Current measurement is without measurement error state estimated result at 4 measuring point 3,15 of table
Current measurement has 10% error in measurement state estimation result at 5 measuring point 3,15 of table
When measuring point is 2,6,9,12,29, to current measurement at measuring point 2,9, there are 10% errors and error free respectively
Two kinds of situations are emulated, and for state estimation result as shown in table 6,7, the comparison of state estimation error is as shown in Figure 4.
Current measurement is without measurement error state estimated result at 6 measuring point 2,9 of table
Current measurement has 10% error in measurement state estimation result at 7 measuring point 2,9 of table
Comparison diagram 3, Fig. 4, it is apparent that when measuring point be 3,6,12,15,29 when, due to measurement matrix conditional number compared with
Small, the state estimation error as caused by error in measurement is smaller；Compared to measuring point be 2,6,9,12,29 when, harmonic state estimation
System observability degree is identical, but precision is higher.
Claims (3)
1. the Distribution Network Harmonics measuring point Optimal Configuration Method of a kind of meter and topologies change, which is characterized in that centainly supervising
Under the premise of surveying terminal quantity, it is wavy to establish the system observability degree harmony comprehensively considered under network topology structure situation of change
State estimation measures the Distribution Network Harmonics measuring point Optimal Allocation Model of Matrix condition number, can effectively improve system topology variation
When Observable range, and ensure that harmonic state estimation precision.
2. the Distribution Network Harmonics measuring point Optimal Configuration Method of a kind of meter according to claim 1 and topologies change,
It is characterized in that, the system observability degree harmony wave state estimator comprehensively considered under network topology structure variation surveys square
The Distribution Network Harmonics measuring point Optimal Allocation Model of battle array conditional number specifically:
In formula, K is the rank matrix of k × 1, KiFor the system observability degree matrix under i-th kind of measurement scheme；H is harmonic state estimation amount
Survey matrix, Cond (Hi) be i-th kind of measure configuration scheme measurement matrix conditional number；σ1、σ2For system observability degree and measure square
The weight coefficient reciprocal of battle array conditional number.
3. the Distribution Network Harmonics measuring point Optimal Configuration Method of a kind of meter according to claim 1 and topologies change,
It is characterized in that, the system observability degree that the consideration network topology structure changes acts probability coefficent by meter and interconnection switch
Observability Analysis method obtain, the specific method is as follows:
Observability degree can be used to measure the quantity of the directly considerable node of system and indirect considerable node, i.e. system Observable model
It encloses.Observability degree is divided into two kinds, respectively node observability degree and system observability degree in the present invention.Provide as follows: node can
Observation degree refers in the case where system network topology variation, probability that node is observed.If node observability degree is 1,
Title must considerable node；If node observability degree is 0, title must not considerable node；If node observability degree is 0~1, claim general
The considerable node of rate.System observability degree refers to that ratio of the sum of each node observability degree with total node number, observability degree are 1 expression
System must be completely considerable；Observability degree is that 0 expression system must be completely inconsiderable；Observability degree is 0~1 expression components of system as directed
It is considerable.
When system network topology complexity, when node, number of branches are more, the judgement of system Observable range is complex.
The incidence relation in complex electric network network between each node is described using the incidence matrix between node in the present invention.Between node
Incidence matrix A is n × n rank symmetrical matrix, and wherein n is number of nodes, and matrix element is made of 0 or 1；Definition when two nodes by
When one branch connection, claim two nodes associated with each other.If Aij=1, then it represents that i, j node are associated；If Aij=0, then it represents that i,
J node is not associated with.
A) assume that distribution number of nodes is n, share k kind measuring point allocation plan, establish directly considerable node judgment matrix:
Fi=AXi
In formula, F is k × n matrix, FiFor the i-th row element of F, indicate through directly considerable node judgement, i-th kind of measuring point configuration side
Each node ornamental under case.If Fij>=1, then i-th kind of measuring point allocation plan lower node j is directly considerable；If Fij=0, then i-th
Kind measuring point allocation plan lower node j is not considerable.X is k × n rank matrix, XiFor the i-th row element of X, indicate that i-th kind of measuring point is matched
Set scheme.If Xij=1, then it represents that under i-th kind of measuring point allocation plan, node j is equipped with harmonic wave measuring equipment；If Xij=0, then
It indicates under i-th kind of measuring point allocation plan, node j does not have harmonic wave measuring equipment.
B) for the newly-increased direct considerable node m under i-th kind of measuring point allocation plan, if it injects node for anharmonic wave source,
Indirect considerable node judgment matrix can then be established:
fi,m=Am-Xi
In formula, f is l × n rank matrix, and l is the newly-increased directly considerable node that anharmonic wave source injects under i-th kind of measuring point allocation plan
Number；fi,mFor the corresponding row element of m node in the corresponding f matrix of i-th kind of measuring point allocation plan；AmFor incidence matrix m
Row；XiFor i-th kind of measure configuration scheme.If fi,mIn, in addition to m column, an only column element is 1, then the corresponding node of the column is
Indirect considerable node, FiIn the column add 1.
C) assume that node i is measuring point, and share 3 branches at node i, wherein one is interconnection branch, distribution typical case fortune
Interconnection switch disconnects under row mode；If node i is measuring point, it can make 2 branches can to side gusset under typical mode of operation
It sees；When power distribution network needs to be reconstructed, interconnection switch closure, 3 branches are considerable to side gusset at this time；According to scene just
Often, interconnection switch, which acts probability system, at node i is shown to the adjustment experience of distribution network structure under maintenance or emergency operating mode
Number is Pi, then node i observability degree is 1+P at this timei。
It constructs interconnection switch and acts probability coefficent matrix P:
P=(P1 P2 … Pn)
P is 1 × n rank matrix, PiProbability coefficent is acted for interconnection switch at node i.
There is node observability degree matrix expression at this time:
Oi,j=Fi,j+Xi,j×Pj
In formula, O is k × n matrix, Oi,jIndicate the observability degree of i-th kind of measure configuration scheme lower node j.
System observability degree matrix are as follows:
In formula, K is the rank matrix of k × 1, KiFor the system observability degree matrix under i-th kind of measurement scheme.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612225A (en) * | 2020-05-08 | 2020-09-01 | 国网山西省电力公司电力科学研究院 | Optimal configuration method of harmonic wave measuring device |
| CN112018760A (en) * | 2020-08-27 | 2020-12-01 | 四川大学 | Harmonic state estimation method based on asynchronous data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103904653A (en) * | 2014-04-23 | 2014-07-02 | 济南大学 | Observability analyzing method in power grid harmonic state estimation |
| CN105184508A (en) * | 2015-09-29 | 2015-12-23 | 东南大学 | Harmonic measuring point optimal allocation method for distribution network |
| CN105259472A (en) * | 2015-10-15 | 2016-01-20 | 广州供电局有限公司 | Harmonic source positioning method of electric energy quality monitoring device, and electric energy quality monitoring device thereof |
| CN105958487A (en) * | 2016-06-15 | 2016-09-21 | 山东大学 | Power distribution network measurement configuration method considering various network structures |
-
2019
- 2019-06-20 CN CN201910534419.8A patent/CN110287449B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103904653A (en) * | 2014-04-23 | 2014-07-02 | 济南大学 | Observability analyzing method in power grid harmonic state estimation |
| CN105184508A (en) * | 2015-09-29 | 2015-12-23 | 东南大学 | Harmonic measuring point optimal allocation method for distribution network |
| CN105259472A (en) * | 2015-10-15 | 2016-01-20 | 广州供电局有限公司 | Harmonic source positioning method of electric energy quality monitoring device, and electric energy quality monitoring device thereof |
| CN105958487A (en) * | 2016-06-15 | 2016-09-21 | 山东大学 | Power distribution network measurement configuration method considering various network structures |
Non-Patent Citations (2)
| Title |
|---|
| 陆文钦等: "基于改进人工蜂群算法的配电网谐波量测点优化配置", 《电工技术》 * |
| 韩美玉等: "考虑谐波状态估计精度的PMU优化配置研究", 《自动化与仪器仪表》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612225A (en) * | 2020-05-08 | 2020-09-01 | 国网山西省电力公司电力科学研究院 | Optimal configuration method of harmonic wave measuring device |
| CN112018760A (en) * | 2020-08-27 | 2020-12-01 | 四川大学 | Harmonic state estimation method based on asynchronous data |
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