CN107528312A - A kind of power system state estimation method - Google Patents
A kind of power system state estimation method Download PDFInfo
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- CN107528312A CN107528312A CN201710574224.7A CN201710574224A CN107528312A CN 107528312 A CN107528312 A CN 107528312A CN 201710574224 A CN201710574224 A CN 201710574224A CN 107528312 A CN107528312 A CN 107528312A
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- power system
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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
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of power system state estimation method, this method comprises the following steps：1) Power system state estimation model is established；2) estimation objective function is established based on the Power system state estimation model；3) derivative information of the estimation objective function is introduced, the estimation objective function is solved using evolution algorithm, obtains optimal POWER SYSTEM STATE.Compared with prior art, evolution algorithm is combined by the present invention with derivative information, in the structural framing of evolution algorithm heuristic search, introduces the derivative information of object function to guidance search, so as to obtain efficiently accurate Power system state estimation result, and the dominance of its solution can be ensured.
Description
Technical field
The present invention relates to operation and control of electric power system technical field, more particularly, to a kind of Power system state estimation side
Method.
Background technology
Power system state estimation is to estimate the state of each node in power network, for instructing the scheduling and control of power system
System.
Existing power system state estimation method can be divided into, the gauss-newton method based on least square, positive semidefinite relaxation
Method and intelligent algorithm.Certain defect be present in each method：
Gauss-newton method based on least square, because of its sensitiveness to initial point, it is difficult to try to achieve globe optimum；
Positive semidefinite method of relaxation, it tries to achieve solution without dominance (first derivative is not 0)；
Power system estimation based on intelligent algorithm, is only applicable to minor scale power net, as power network scale becomes big, its precision and asks
The solution time does not reach requirement.
Therefore, it is necessary to research and develop a kind of new power system state estimation method.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of POWER SYSTEM STATE
Method of estimation.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of power system state estimation method, this method comprise the following steps：
1) Power system state estimation model is established；
2) estimation objective function is established based on the Power system state estimation model；
3) derivative information of the estimation objective function is introduced, the estimation objective function is asked using evolution algorithm
Solution, obtains optimal POWER SYSTEM STATE.
In the step 1), Power system state estimation model is：
Z=h (v)+e
Wherein, z is metric data, and v is POWER SYSTEM STATE, and e is error in measurement, and h () is measurement equation.
In the step 2), estimation objective function is expressed as：
Wherein, m ∈ M, M gather to measure, wmData weighting, Z are measured to be correspondingmFor corresponding metric data, hm() is pair
Answer measurement equation.
The step 3) is specially：
301) population number N is setp, stopping criterion for iteration and select probability PG、PM；
302) iterations k=1 is made；
303) generation step：Successively to each individual i, judge whether random generating probability is less than PG, if so, then performing
Nidk(i)=idk(i)+u1·Dxk(i), wherein idk(i) the individual i in kth time iteration is represented, represents POWER SYSTEM STATE,
Dxk(i) kth time iteration individual i derivative, u are represented1For weight factor, if it is not, then performing Nidk(i)=idk(i)+u2·pdgk
(i), wherein pdgk(i)=pgbestk-idk(i), pgbestkRepresent individual best in kth generation individual, u2For weight factor；
304) make a variation step：Successively to each individual i, judge whether random generating probability is less than PM, if so, then performing
Uidk(i)=Nidk(i)+u3Pdr (i), wherein pdr (i) are random vector, u3For weight factor, if it is not, then performing step
305)；
305) more individual NidkAnd Uid (i)k(i) value of object function, the less individual of value is as follow-on
Body；
306) judge whether to meet stopping criterion for iteration, if so, then performing step 307), if it is not, then making k=k+1, return
Step 303)；
307) optimum individual POWER SYSTEM STATE corresponding with its is obtained.
The weight factor u1、u2、u3Obey the non-uniform probability distribution of [0,1].
The stopping criterion for iteration includes reaching maximum iteration or solution convergence.
Compared with prior art, the present invention has advantages below：
1) evolution algorithm is combined by this method with derivative information, in the structural framing of evolution algorithm heuristic search,
The derivative information of object function is introduced to guidance search, overcomes and initial point sensitivity is led based on least square gauss-newton method
The defects of being difficult to try to achieve globe optimum of cause, so as to obtain efficiently accurate Power system state estimation result.
2) it is different from positive semidefinite relaxation and solves the problem of POWER SYSTEM STATE cannot be guaranteed the dominance of solution, this method ensures solution
Dominance.
3) this method can also carry out fast and accurately state estimation under large power system.
4) in this method evolution algorithm, the purpose of generation step is that selection is generated by parsing derivative information or heuristic information
Individual of future generation, the purpose for the step that makes a variation are to expand hunting zone, precocity are avoided, so as to improve the accuracy of evolution algorithm.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the present invention.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
The present invention provides a kind of power system state estimation method, and this method comprises the following steps：
1) Power system state estimation model is established：Z=h (v)+e, wherein, z is metric data, and v is power system shape
State, e are error in measurement, and h () is measurement equation；
2) estimation objective function is established based on the Power system state estimation model：
Wherein, m ∈ M, M gather to measure, wmData weighting, Z are measured to be correspondingmFor corresponding metric data, hm() is corresponding measurement side
Journey；
3) derivative information of the estimation objective function is introduced, the estimation objective function is asked using evolution algorithm
Solution, obtains optimal POWER SYSTEM STATE.
As shown in figure 1, the step 3) is specially：
301) population number N is setp, stopping criterion for iteration (such as reach maximum iteration or solution convergence) and selection
Probability PG、PM；
302) iterations k=1 is made；
303) generation step：Successively to each individual i, judge whether random generating probability is less than PG, if so, then performing
Nidk(i)=idk(i)+u1·Dxk(i), wherein idk(i) the individual i in kth time iteration is represented, represents POWER SYSTEM STATE,
Dxk(i) kth time iteration individual i derivative, u are represented1For weight factor, the non-uniform probability distribution of [0,1] is obeyed, if it is not, then holding
Row Nidk(i)=idk(i)+u2·pdgk(i), wherein pdgk(i)=pgbestk-idk(i), pgbestkRepresent in kth generation individual
Best individual, u2For weight factor, u2Same u1；
304) make a variation step：Successively to each individual i, judge whether random generating probability is less than PM, if so, then performing
Uidk(i)=Nidk(i)+u3Pdr (i), wherein pdr (i) are random vector, u3For weight factor, u3Same u2And u1, if it is not,
Then perform step 305)；
305) more individual NidkAnd Uid (i)k(i) value of object function, the less individual of value is as follow-on
Body；
306) judge whether to meet stopping criterion for iteration, if so, then performing step 307), if it is not, then making k=k+1, return
Step 303)；
307) optimum individual POWER SYSTEM STATE corresponding with its is obtained.
On the node standard power systems test cases of IEEE 30,57,118, by the above method with based on least square
Gauss-newton method is compared with intelligent algorithm (such as genetic algorithm (GA), particle cluster algorithm (PSO), differential evolution algorithm (DE)), than
Compared with its degree of accuracy, as shown in table 1.
The degree of accuracy of table 1 is compared
In table 1,1.5999E+005 is scientific notation, and equal to 159990, it represents solved POWER SYSTEM STATE institute
Corresponding target function value.Target function value is smaller, represents that estimated POWER SYSTEM STATE is more accurate.
At present, in power system state estimation method, with positive semidefinite method of relaxation and intelligent algorithm (such as genetic algorithm (GA),
Particle cluster algorithm (PSO), differential evolution algorithm (DE)) time length is calculated, the gauss-newton method based on least square calculates the time
It is most short.As can be seen from Table 1, under big system, intelligent algorithm estimation effect can not meet required precision, therefore when ignoring its calculating
Between, table 2 shows the calculating time comparative result of each method.
The calculating time of table 2 compares (unit：Second)
| Test system | This method | Positive semidefinite method of relaxation | Gauss-newton method |
| IEEE 30 | 0.1958 | 1.62 | 0.0974 |
| IEEE 57 | 0.5956 | 4.32 | 0.1460 |
| IEEE 118 | 4.4398 | 21.6 | 0.5284 |
By this method it can be seen from Tables 1 and 2 within the acceptable time, power system the most accurate can obtain
State estimation result.
Preferred embodiment of the invention described in detail above.It should be appreciated that one of ordinary skill in the art without
Creative work can is needed to make many modifications and variations according to the design of the present invention.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical scheme, all should be in the protection domain being defined in the patent claims.
Claims (6)
1. a kind of power system state estimation method, it is characterised in that this method comprises the following steps：
1) Power system state estimation model is established；
2) estimation objective function is established based on the Power system state estimation model；
3) derivative information of the estimation objective function is introduced, the estimation objective function is solved using evolution algorithm,
Obtain optimal POWER SYSTEM STATE.
2. power system state estimation method according to claim 1, it is characterised in that in the step 1), power train
System state estimation model be：
Z=h (v)+e
Wherein, z is metric data, and v is POWER SYSTEM STATE, and e is error in measurement, and h () is measurement equation.
3. power system state estimation method according to claim 1, it is characterised in that in the step 2), estimate mesh
Scalar functions are expressed as：
<mrow>
<mover>
<mi>v</mi>
<mo>^</mo>
</mover>
<mo>=</mo>
<mi>arg</mi>
<mi> </mi>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<munder>
<mo>&Sigma;</mo>
<mi>m</mi>
</munder>
<msub>
<mi>w</mi>
<mi>m</mi>
</msub>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>z</mi>
<mi>m</mi>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>v</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
Wherein, m ∈ M, M gather to measure, wmData weighting, Z are measured to be correspondingmFor corresponding metric data, hm() is corresponding amount
Survey equation.
4. power system state estimation method according to claim 1, it is characterised in that the step 3) is specially：
301) population number N is setp, stopping criterion for iteration and select probability PG、PM；
302) iterations k=1 is made；
303) generation step：Successively to each individual i, judge whether random generating probability is less than PG, if so, then performing Nidk(i)
=idk(i)+u1·Dxk(i), wherein idk(i) the individual i in kth time iteration is represented, represents POWER SYSTEM STATE, Dxk(i) table
Show kth time iteration individual i derivative, u1For weight factor, if it is not, then performing Nidk(i)=idk(i)+u2·pdgk(i), wherein
pdgk(i)=pgbestk-idk(i), pgbestkRepresent individual best in kth generation individual, u2For weight factor；
304) make a variation step：Successively to each individual i, judge whether random generating probability is less than PM, if so, then performing Uidk(i)
=Nidk(i)+u3Pdr (i), wherein pdr (i) are random vector, u3For weight factor, if it is not, then performing step 305)；
305) more individual NidkAnd Uid (i)k(i) value of object function, the less individual of value are used as follow-on individual；
306) judge whether to meet stopping criterion for iteration, if so, step 307) is then performed, if it is not, k=k+1 is then made, return to step
303)；
307) optimum individual POWER SYSTEM STATE corresponding with its is obtained.
5. power system state estimation method according to claim 4, it is characterised in that the weight factor u1、u2、u3
Obey the non-uniform probability distribution of [0,1].
6. power system state estimation method according to claim 4, it is characterised in that the stopping criterion for iteration includes
Reach maximum iteration or solution convergence.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113315118A (en) * | 2021-04-26 | 2021-08-27 | 中国南方电网有限责任公司 | Power system state estimation method based on parallel computing and particle swarm optimization |
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| CN102831315A (en) * | 2012-08-23 | 2012-12-19 | 清华大学 | Accurate linearization method of measurement equation for electric power system state estimation |
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