CN104933478B - A kind of relay protection multiple-objection optimization setting method - Google Patents
A kind of relay protection multiple-objection optimization setting method Download PDFInfo
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Abstract
The invention discloses a kind of relay protection multiple-objection optimization setting method, including step (1)：Establish the object function that relay protection multiple-objection optimization is adjusted；According to the performance requirement of relay protection, the optimizing index of relay protection is selected, establishes the Model for Multi-Objective Optimization of relay protection setting；Step (2)：Select relay protection multiple-objection optimization tuning variable；Step (3)：The constraints of the tuning variable in model is adjusted in setting relay protection multiple-objection optimization；Step (4)：The object function adjusted using Chaos Genetic Algorithm solution relay protection multiple-objection optimization, obtains one group of Pareto optimal solution of the object function that relay protection multiple-objection optimization is adjusted；Step (5)：An optimal solution for meeting relay protection target is selected in the one group of Pareto optimal solution obtained using fuzzy membership method from step (4).This method can reflect that the synthesis of the reliability of relay protection, selectivity and quick-action is optimal.
Description
Technical field
The present invention relates to relay protection field, and in particular to a kind of relay protection multiple-objection optimization setting method.
Background technology
Relay protection is one of most important secondary device of power system, the safe and stable operation of power system is played to
Close important effect.Can protective relaying device meet " four property " that power network is proposed, you can by property, selectivity, quick-action and
Sensitivity requirements, whether reasonable it is heavily dependent on relay protection constant value.Evaluating the quality of relay protection setting scheme is
Action effect based on the overall protecting effect of all protections of the whole network rather than some protection.Modern distributive Operation of Electric Systems
Mode complex, there is multiple loops, the incorrect operation of relay protection can not only make the failure propagation of power system, very
The collapse of power system is caused to bad chain reaction may occur, causes large-area power-cuts and heavy economic losses, gives people
Production of living cause to have a strong impact on.Therefore, optimal relay protection is integrated based on all protection overall performance multiple targets of the whole network
Setting method has great researching value and realistic meaning.
The optimizing research of many relay protection constant values is all concentrated in the global optimization of definite time protection.Excessively stream is definited time-lag to protect
The back-up protection as current quick is protected, there is the characteristics of simple in construction, debugging is convenient and reliability is high.However, one
As in the case of, specified time overcurrent relay due to being influenceed by system operation mode, tend not to meet simultaneously sensitivity and
The requirement of actuating range.In order to give full play to the benefit of protected element, and do not cause to damage caused by long-time overheats, it is necessary to
Overcurrent protection of the installation with the more superior anti-time limit characteristic of performance.Document [1] is solved extensive using optimum theory first
Global optimum's setting program of power network overcurrent protection, each protection most short for optimization aim with all protection molar behavior times
Treat that setting valve is optimized variable, protection sensitivity, selectivity be constraints.It is excessively electric that document [2] has initially set up the inverse time lag
The mathematical modeling of protection is flowed, is protected fixed value adjusting problem to be converted into nature of nonlinear integral programming problem to handle, it is proposed that one
Kind optimizes processing using plant growth simulation algorithm to relay protection setting problem.But the above method is simply by table
The object function of sign relay protection performance is converted into single-goal function solution by way of weighted sum, using simple monocular
Mark optimized algorithm, can only once try to achieve a solution, and optimum results influenceed by weight it is very big.
In fact, due to the difference of electric network composition and load character, to relay protection adjust may relate to reliability,
The requirement of multiple optimization aims such as selectivity, sensitivity and quick-action, and each target stress it is also different.Therefore, instead
The adaptive setting problem of time-lag over-current protection can be abstracted as a multiple target, multivariable, the Global Optimal Problem of multiple constraint.
Pareto optimal solution sets are solved with advanced Multiobjective Intelligent optimized algorithm, can be right during relay protection setting calculation
The conflicting requirement such as its reliability, selectivity, sensitivity and quick-action is balanced and coordinated, then can easily,
Quantitatively consider different requirements, the experience and preference of relay protection engineer of electric network composition and load character, choose difference and stress mesh
Target inverse time over-current protection global optimum setting program.At present, there has been no patent and document to carry out correlative study and discussion.
The bibliography of this patent is：
Document [1] Urdaneta A J, Nadira R, Perez L G.Optimal Coordination of
Directional Overcurrent Relays in Interconnected Power Systems[J].IEEE Trans
on Power Delivery,1988,(3):703-911；
Document [2] Hu Hanmei, Zheng Hong, Li Jing, wait power distribution network relay protection settings of the based on plant growth simulation algorithm excellent
Research [J] electric power system protection and controls of change, 2012,40 (7):19-24.
The content of the invention
To solve the shortcomings of the prior art, the invention discloses a kind of relay protection multiple-objection optimization setting method,
This method can effectively realize the multiple optimization aims for adjusting relay protection, and cause the optimization aim tool for adjusting rear relay protection
There is the characteristics of relay protection combination property is optimal in power network.
To achieve the above object, concrete scheme of the invention is as follows：
A kind of relay protection multiple-objection optimization setting method, comprises the following steps：
Step (1)：Establish the object function that relay protection multiple-objection optimization is adjusted；
According to the performance of default relay protection, the corresponding optimizing index of relay protection is selected, establishes relay protection multiple target
Optimize the object function adjusted, obtain relay protection multiple-objection optimization and adjust model；
Step (2)：Determine that the tuning variable in model is adjusted in relay protection multiple-objection optimization：Time setting coefficient and startup
Electric current；
Step (3)：According to the mutual cooperation relation between relay, setting relay protection multiple-objection optimization is adjusted in model
Tuning variable constraints：
TDSimin≤TDSi≤TDSimax (1)
IPimin≤IPi≤IPimax (2)
Wherein, TDSiRepresent time setting coefficient in failure i, TDSiminMinimum time tuning coefficient in failure i is represented,
TDSimaxRepresent maximum time tuning coefficient in failure i, IPiThe electric current of relay, I are flowed through in expression failure iPiminRepresent failure i
In flow through the minimum current of relay, IPimaxThe maximum current of relay is flowed through in expression failure i；
Step (4)：The constraints set according to step (3), relay protection multiple target is solved using Chaos Genetic Algorithm
Optimize the object function adjusted, obtain one group of Pareto optimal solution of the object function that relay protection multiple-objection optimization is adjusted；
Step (5)：One is selected in the one group of Pareto optimal solution obtained using fuzzy membership method from step (4) completely
The optimal solution of sufficient relay protection target.
The optimizing index of relay protection in the step (1), including the reliability of relay protection, selectivity and quick-action
Property.
Model is adjusted in relay protection multiple-objection optimization in the step (1), includes the reliability mesh of relay protection setting
Scalar functions, selective object function and quick-action object function.
Relay protection multiple-objection optimization adjusts model and is in the step (1)：
Wherein, frel、fselAnd fquickRespectively represent the reliability objectives function of relay protection setting, relay protection setting
The quick-action object function of selective object function and relay protection setting；WithRespectively represent optimization after after
Relay after the selective object function of relay protection setting after reliability objectives function that electric protection is adjusted, optimization and optimization
The quick-action object function of protection seting.
The reliability objectives function f of the relay protection settingrelFor
Wherein, τiBe failure i from failure occur to failure removal time, PiThe hazard ratio occurred for failure.
The selective object function f of the relay protection settingselFor：
ΔtI, pq=tI, p-tI, q (6)
Wherein, i representing faults are numbered；ti,pFor the actuation time of the upstream protection device p of cooperation pair in failure i；ti,qFor
The downstream protection device q of cooperation pair actuation time in failure i；Δti,pqFor failure i protection cooperation to p, during q action
Between difference；σ is that the given time is differential；fselIt is smaller, represent that the selectivity of relay protection system is better.
The quick-action object function f of the relay protection settingquickFor：
Wherein, i representing faults are numbered；Δti,pqFor failure i protection cooperation to p, the difference of q actuation time；σ is given
Time it is differential.
The tool for the object function that relay protection multiple-objection optimization is adjusted is solved in the step (4) using Chaos Genetic Algorithm
Body process includes：
Step (4.1)：Selected population scale NP, maximum evolutionary generation GmaxWith chaos controlling parameter；
Step (4.2)：Initialize evolutionary generation G and parent population PG, produced using Logistic mapping chaotic models mixed
Ignorant vector, and then obtain j-th of component x of i-th of individual of initial populationi,j；
Step (4.3)：To parent population PGNon-dominated ranking, double branch league matches selection are carried out successively, are intersected and are made a variation, generation
Progeny population QG, wherein, parent population PGThe fitness each solved is exactly its non-dominant level；
Step (4.4)：By parent population PGWith progeny population QGIt is combined into population RG, to RGCarry out non-dominated ranking and determine RG
Whole non-domination solution leading surface F=(F1,F2...), wherein, RG=PG∪QG；
Step (4.5)：Calculate RGWhole non-domination solution leading surface F=(F1,F2...) and in FiCrowding distance, it is and right
FiCarry out crowding distance sequence；
Step (4.6)：Select FiThe minimum progeny population of crowding distance, judge non-of inferior quality level in progeny population be 1 it is individual
Body number NFirstWhether with population invariable number NPIt is equal, work as NFirst=NpWhen, then carry out adaptive chaos search refinement and variable
The region of search reduces, and produces new decision variable x "i,j：
x″I, j=(1- η) x 'I, j+ηxI, j (14)
In formula, η is Adaptive Control Coefficient；x′i,jTo be produced after adaptive chaos search refinement j-th of i-th of individual
Chaos Variable；xi,jFor j-th of component of i-th of individual of initial population；
Step (4.7)：Judge whether to reach maximum evolutionary generation, if evolutionary generation G is less than maximum evolutionary generation Gmax, then
Evolutionary generation G is made to be incremented by, and return to step (4.3)；Otherwise, all individuals for being 1 using non-of inferior quality level in population are as Pareto
Optimal solution set, export and terminate to run.
The determination method of Adaptive Control Coefficient is in the step (4.6)：
In formula, ξ is the parameter depending on object function, and K is iterations.
The detailed process of the step (5) includes：
Step (5.1)：Following membership function is established to each object function of relay protection：
Wherein, XkFor k-th of individual in Pareto optimal solution sets；λfmFunction after being normalized for m-th of object function
Value； fm(Xk) it is XkM-th of target function value；For XkM target function value minimum value；For XkM
The maximum of target function value；
Step (5.2)：Calculate total satisfaction N corresponding to each individual in Pareto optimal solution setsλ(k)：
Wherein, ηmFor the weight coefficient of m-th of object function；
According to total satisfaction corresponding to each individual in Pareto optimal solution sets, one total satisfaction is high expires for selection
The optimal solution of sufficient relay protection target.
Beneficial effects of the present invention are：
(1) this method obtains the setting program for considering that all protection overall performances are optimal in power network, and the program can
Reflect that the synthesis of the reliability of relay protection, selectivity and quick-action is optimal.
(2) this method establish it is multiple sign relay protection performances object functions, and description coordination relationship of protection and
The constraints of other hardware factors, employ integrated non-dominated ranking, elite retains, the multi-objective genetic algorithm of genetic evolution
Solve to obtain multiple-objection optimization disaggregation, then can easily, quantitatively consider electric network composition and load character it is different require, after
The experience and preference of engineer is protected, chooses global optimum's setting program that difference stresses target.
(3) this method input cost is low, and intelligent level is high, and engineering has a extensive future, can effectively be extended to and be based on
The optimal relay protection setting problem of all protection overall performances of the whole network.
Brief description of the drawings
Fig. 1 is the method flow diagram for adjusting multiple target suitable for relay protection of the present invention；
Fig. 2 is IEEE30 node power distribution web frame schematic diagrames.
Embodiment
The present invention will be further described with embodiment below in conjunction with the accompanying drawings：
As shown in figure 1, a kind of method for adjusting multiple target suitable for relay protection, comprises the following steps：
Step (1)：Establish the object function that relay protection multiple-objection optimization is adjusted；
According to the performance requirement of relay protection, the optimizing index of relay protection is selected, establishes more mesh of relay protection setting
Optimized model is marked, relay protection multiple-objection optimization is obtained and adjusts model
Step (2)：The tuning variable in model is adjusted in selection relay protection multiple-objection optimization：Time setting coefficient T DS and
Starting current IP；
Wherein, the acting characteristic equation of the conventional inverse time over-current protection of protection device is as follows
Wherein, t is operating time of protection；I is the electric current for flowing through relay.For protection device, adjusting for it is excellent
Change primarily directed to time setting coefficient T DS and starting current IPThe two parameters are carried out.
Therefore, the decision variable that relay protection optimization is adjusted is encoded to
[x1, x2..., xi..., xn]=[TDS1, IP1, TDS2, IP2..., TDSi, IPi..., TDSn, IPn] (10)；
Step (3)：The constraints of the tuning variable in model is adjusted in setting relay protection multiple-objection optimization：
TDSimin≤TDSi≤TDSimax (1)
IPimin≤IPi≤IPimax (2)
Wherein, TDSiRepresent time setting coefficient in failure i, TDSiminMinimum time tuning coefficient in failure i is represented,
TDSimaxRepresent maximum time tuning coefficient in failure i, IPiThe electric current of relay, I are flowed through in expression failure iPiminRepresent failure i
In flow through the minimum current of relay, IPimaxThe maximum current of relay is flowed through in expression failure i；
Formula (1) represents that time setting coefficient can only be in relay element value within institute's allowed band in itself；Formula (2) represents
Value within the scope of the starting current of relay should also allow at one；
Step (4)：According to the constraints in step (3), it is excellent that relay protection multiple target is solved using Chaos Genetic Algorithm
Change the object function adjusted, obtain one group of Pareto optimal solution of the object function that relay protection multiple-objection optimization is adjusted；
Step (5)：One is selected in the one group of Pareto optimal solution obtained using fuzzy membership method from step (4) completely
The optimal solution of sufficient relay protection target.
The optimizing index of the relay protection, including the reliability of relay protection, selectivity and quick-action.
Model, including the reliability objectives function of relay protection setting, relay are adjusted in the relay protection multiple-objection optimization
The quick-action object function of the selective object function and relay protection setting of protection seting.
1) reliability objectives function：The effect of relay protection seeks to reliably cut off failure after system jam,
The duration of short circuit current is reduced, reduces infringement of the failure to equipment.Therefore, the generation of various failures is considered to system
The infringement size brought, to occur to carry out the setting valve of relay protection to the time of failure removal as reliability index from failure
Optimization.The reliability objectives function f of the relay protection settingrelFor
Wherein, τiBe failure i from failure occur to failure removal time, PiThe hazard ratio occurred for failure.
2) selective object function
In order that power failure range reduces as far as possible, the selectivity of relay protection seeks to allow protection device that composition coordinates pair
The difference of actuation time is more than given time differential σ.Therefore, the cooperation of above-mentioned requirements is unsatisfactory for fewer, then relay protection system
The selection harmony of system is better.To be unsatisfactory for the cooperation of time differential requirement, to quantity, alternatively property index carries out relay protection
Setting valve optimization.The selective object function f of the relay protection settingselFor：
ΔtI, pq=tI, p-tI, q (6)
Wherein, σ is that the given time is differential；ti,pFor the actuation time of the upstream protection device p of cooperation pair in failure i；
ti,qFor the actuation time of the downstream protection device q of cooperation pair in failure i；Δti,pqFor failure i protection cooperation to p, q's is dynamic
Make the difference of time；fselIt is smaller, represent that the selectivity of relay protection system is better.
3) quick-action object function
In order to ensure the quick-action of protective relaying device, the actuation time difference of protection device cooperation pair should be as small as possible.Cause
This, the difference differential with the time using the actuation time difference of protection device cooperation pair carries out adjusting for relay protection as quick-action index
Value optimization.The quick-action object function f of the relay protection settingquickFor：
According to the target requirement of relay protection, it is whole that the relay protection Model for Multi-Objective Optimization adjusted can include relay protection
Fixed reliability objectives function, the quick-action object function of the selective object function and relay protection setting of relay protection setting
Among three combination of function or any two combination of function.
The Model for Multi-Objective Optimization of relay protection setting in the present embodiment is with the reliability mesh comprising relay protection setting
Exemplified by three scalar functions, selective object function and quick-action object function functions：
The Model for Multi-Objective Optimization of the relay protection setting is：
Wherein, frel、fselAnd fquickRespectively represent the reliability objectives function of relay protection setting, relay protection setting
The quick-action object function of selective object function and relay protection setting；WithRespectively represent optimization after after
Relay after the selective object function of relay protection setting after reliability objectives function that electric protection is adjusted, optimization and optimization
The quick-action object function of protection seting.
The expression formula that model is adjusted in relay protection multiple-objection optimization is：
Wherein, the object function of relay protection setting optimization is conflicting, it is impossible to reaches while optimal, it is necessary to use more
Objective optimization algorithm is solved.
The characteristics of optimizing for relay protection setting, solved using multi-Objective Chaotic genetic algorithm.This method combines non-
Dominated Sorting and elite retention strategy, using the search space of optimized variable in chaos ergodic refinement genetic algorithm, and according to
Evolution of Population state self-adaption adjusts search precision, improves search efficiency and convergence rate；Retained using the sequence of non-branch, elite
Keep ensure that evolution is carried out to Pareto global optimums disaggregation direction while population diversity Deng multiple-objection optimization strategy.
The tool for the object function that relay protection multiple-objection optimization is adjusted is solved in the step (4) using Chaos Genetic Algorithm
Body process is：
Step (4.1)：Selected population scale NP, maximum evolutionary generation GmaxWith chaos controlling parameter alpha, φ；
Step (4.2)：Make evolutionary generation G=0, chaos intialization population PG, chaotic model is mapped according to Logistic：
Produce chaos vector βi,j.Wherein,For the initial value with minute differences, i=1,2 ..., NP-1；J=1,2 ...,
m；NPFor population scale, m is decision vector dimension.Caused Chaos Variable is mapped to the span (x of decision variablejmin,
xjmax), obtain j-th of component x of i-th of individual of initial populationi,j, i.e.,
xI, j=xjmin+(xjmax-xjmin).βI, j(12)；
Step (4.3)：To parent population PGCarry out non-dominated ranking, parent population PGThe fitness of each solution be exactly it
Non-dominant level, then carry out double branch league matches selections, intersect and variation, generation progeny population QG, scale NP；
Step (4.4)：By parent population PGWith progeny population QGIt is combined into population RG, wherein, RG=PG∪QG, and to RGEnter
Row non-dominated ranking determines RGWhole non-domination solution leading surface F=(F1,F2,…)；
Step (4.5)：Calculate FiCrowding distance, and by parent population PGCaused progeny population PG+1With FiSeek union,
And to FiCrowding distance sequence is carried out, selects FiMinimum (the N of middle sequence crowding distanceP-|PG+1|) individual solution；
Step (4.6)：Judge parent population PGNon- of inferior quality level is 1 individual amount N in caused progeny populationFirstWhether
With population invariable number NPIt is equal, judge whether Advanced group species are optimal：
Work as NFirst=NPWhen, i.e., then select parent population PGCaused progeny population PG+1Preceding 10% adaptively mixed
Ignorant search refinement：If more excellent individual isThe region of search of variable is reduced into
In formula, φ is contraction factor, φ ∈ (0,0.5).To ensure that search space is not crossed the border, it is handled as follows：If x 'jmin
＜ xjminThen by xjminValue be assigned to x 'jminIf x 'jmax＞ xjmaxThen by xjmaxValue be assigned to x 'jmax；
Chaos vector β is produced according to formula (11)i,j, and caused Chaos Variable is mapped to the new scope of decision variable
(x′jmin,x′jmax), obtain j-th of Chaos Variable x ' of i-th of individuali,j；
By Chaos Variable x 'I, j withxi,jLinear combination as new decision variable x "i,j：
In formula, η is Adaptive Control Coefficient, carries out adaptive should determine that with the following method：
In formula, ξ is the parameter depending on object function, and K is iterations.
If not up to the maximum iteration of chaos optimization, iterations are incremented by；Otherwise following operate is continued；
Step (4.7)：If evolutionary generation G is less than maximum evolutionary generation Gmax, then make evolutionary generation be incremented by, and return to step
Step (4.3)；Otherwise, all individuals for being 1 using non-of inferior quality level in population export as Pareto optimal solution sets and terminate to transport
OK.
The detailed process of the step (5) includes：
Step (5.1)：Following membership function is established to each object function of relay protection：
Wherein, XkFor k-th of individual in Pareto optimal solution sets；Function after being normalized for m-th of object function
Value； fm(Xk) it is XkM-th of target function value；For XkM target function value minimum value；For XkM
The maximum of target function value；
Step (5.2)：Calculate the total satisfaction N of each individual in Pareto front endsλ(k)：
Wherein, ηmFor the weight coefficient of m-th of object function；
According to total satisfaction corresponding to each individual in Pareto optimal solution sets, one total satisfaction is high expires for selection
The optimal solution of sufficient relay protection target.
To verify the correctness of the Multipurpose Optimal Method suitable for relay protection setting proposed by the invention and effective
Property, the multiple-objection optimization of relay protection is carried out to the IEEE30 node power distributions net shown in Fig. 2 using method proposed by the invention
Adjust, network parameter is as shown in table 1, adjusts result, as shown in table 2.
The node coefficient branch parameters of table 1
Table 2 adjusts result
It can be seen that from Tables 1 and 2, this method of the invention can easily, quantitatively consider electric network composition and load
The different requirements of matter, the experience and preference of relay protection engineer, choose difference and stresses the inverse time over-current protection overall situation of target most
Excellent setting program.This method input cost is low, and intelligent level is high, and engineering has a extensive future, can effectively be extended to and be based on
The optimal relay protection setting problem of all protection overall performances of the whole network.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.
Claims (6)
1. a kind of relay protection multiple-objection optimization setting method, it is characterised in that comprise the following steps：
Step (1)：Establish the object function that relay protection multiple-objection optimization is adjusted；
According to the performance of default relay protection, the corresponding optimizing index of relay protection is selected, establishes relay protection multiple-objection optimization
The object function adjusted, obtain relay protection multiple-objection optimization and adjust model；
Step (2)：Determine that the tuning variable in model is adjusted in relay protection multiple-objection optimization：Time setting coefficient and startup electricity
Stream；
Step (3)：According to the mutual cooperation relation between relay, setting relay protection multiple-objection optimization is adjusted whole in model
Determine the constraints of variable：
TDSimin≤TDSi≤TDSimax
IPimin≤IPi≤IPimax
Wherein, TDSiRepresent time setting coefficient in failure i, TDSiminRepresent minimum time tuning coefficient in failure i, TDSimax
Represent maximum time tuning coefficient in failure i, IPiThe electric current of relay, I are flowed through in expression failure iPiminRepresent to flow in failure i
Cross the minimum current of relay, IPimaxThe maximum current of relay is flowed through in expression failure i；
Step (4)：The constraints set according to step (3), relay protection multiple-objection optimization is solved using Chaos Genetic Algorithm
The object function adjusted, obtain one group of Pareto optimal solution of the object function that relay protection multiple-objection optimization is adjusted；
Step (5)：In the one group of Pareto optimal solution obtained using fuzzy membership method from step (4) select one meet after
The optimal solution of electric protection target；
Wherein, model is adjusted in the relay protection multiple-objection optimization in the step (1), includes the reliability mesh of relay protection setting
Scalar functions, selective object function and quick-action object function；
The reliability objectives function f of the relay protection settingrelFor
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Wherein, τiBe failure i from failure occur to failure removal time, PiThe hazard ratio occurred for failure；
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<mo>,</mo>
<mi>p</mi>
<mi>q</mi>
</mrow>
</msub>
<mo>></mo>
<mi>&sigma;</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
ΔtI, pq=tI, p-tI, q
The quick-action object function f of the relay protection settingquickFor：
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>q</mi>
<mi>u</mi>
<mi>i</mi>
<mi>c</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<munder>
<mo>&Sigma;</mo>
<mi>i</mi>
</munder>
<mo>|</mo>
<msub>
<mi>&Delta;t</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>p</mi>
<mi>q</mi>
</mrow>
</msub>
<mo>-</mo>
<mi>&sigma;</mi>
<mo>|</mo>
</mrow>
Wherein, i representing faults are numbered；ti,pFor the actuation time of the upstream protection device p of cooperation pair in failure i；ti,qFor failure i
The downstream protection device q of middle cooperation pair actuation time；Δti,pqFor failure i protection cooperation to p, the difference of q actuation time；
σ is that the given time is differential；P is upstream protection device, and q is downstream protection device；fselIt is smaller, represent relay protection system
Selectivity is better.
A kind of 2. relay protection multiple-objection optimization setting method as claimed in claim 1, it is characterised in that the step (1)
In relay protection optimizing index, including the reliability of relay protection, selectivity and quick-action.
A kind of 3. relay protection multiple-objection optimization setting method as claimed in claim 1, it is characterised in that the step (1)
Middle relay protection multiple-objection optimization adjusts model and is：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>f</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>l</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>=</mo>
<mi>min</mi>
<mi> </mi>
<msub>
<mi>f</mi>
<mrow>
<mi>r</mi>
<mi>e</mi>
<mi>l</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>f</mi>
<mrow>
<mi>s</mi>
<mi>e</mi>
<mi>l</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>=</mo>
<mi>min</mi>
<mi> </mi>
<msub>
<mi>f</mi>
<mrow>
<mi>s</mi>
<mi>e</mi>
<mi>l</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>f</mi>
<mrow>
<mi>q</mi>
<mi>u</mi>
<mi>i</mi>
<mi>c</mi>
<mi>k</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mi> </mi>
<msub>
<mi>f</mi>
<mrow>
<mi>q</mi>
<mi>u</mi>
<mi>i</mi>
<mi>c</mi>
<mi>k</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Wherein, frel、fselAnd fquickThe selection of the reliability objectives function, relay protection setting of relay protection setting is represented respectively
The quick-action object function of property object function and relay protection setting；WithRepresent that the relay after optimization is protected respectively
Protect adjust reliability objectives function, optimization after relay protection setting selective object function and optimization after relay protection
The quick-action object function adjusted.
A kind of 4. relay protection multiple-objection optimization setting method as claimed in claim 1, it is characterised in that the step (4)
The middle detailed process that the object function that relay protection multiple-objection optimization is adjusted is solved using Chaos Genetic Algorithm is included：
Step (4.1)：Selected population scale NP, maximum evolutionary generation GmaxWith chaos controlling parameter；
Step (4.2)：Initialize evolutionary generation G and parent population PG, using Logistic mapping chaotic model produce chaos to
Amount, and then obtain j-th of component x of n-th of individual of initial populationn,j；
Step (4.3)：To parent population PGNon-dominated ranking, double branch league matches selection are carried out successively, intersects and makes a variation, and generate filial generation
Population QG, wherein, parent population PGThe fitness each solved is exactly its non-dominant level；
Step (4.4)：By parent population PGWith progeny population QGIt is combined into population RG, to RGCarry out non-dominated ranking and determine RGAll
Non-domination solution leading surface F=(F1,F2...), wherein, RG=PG∪QG；
Step (4.5)：Calculate RGWhole non-domination solution leading surface F=(F1,F2...) and in FnCrowding distance, and to FnEnter
Row crowding distance sorts；
Step (4.6)：Select FnThe minimum progeny population of crowding distance, judge non-of inferior quality level in progeny population for 1 individual amount
NFirstWhether with population invariable number NPIt is equal, work as NFirst=NPWhen, then carry out the field of search of adaptive chaos search refinement and variable
Between reduce, and produce new decision variable x "n,j：
x″N, j=(1- η) x 'N, j+ηxN, j
In formula, η is Adaptive Control Coefficient；x′n,jTo produce j-th of chaos of n-th of individual after adaptive chaos search refinement
Variable；xn,jFor j-th of component of n-th of individual of initial population；
Step (4.7)：Judge whether to reach maximum evolutionary generation, if evolutionary generation G is less than maximum evolutionary generation Gmax, then make into
Change algebraically G to be incremented by, and return to step (4.3)；Otherwise, it is all individuals that non-of inferior quality level in population is 1 are optimal as Pareto
Disaggregation, export and terminate to run.
A kind of 5. relay protection multiple-objection optimization setting method as claimed in claim 4, it is characterised in that the step
(4.6) the determination method of Adaptive Control Coefficient is in：
<mrow>
<mi>&eta;</mi>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<mi>K</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>K</mi>
</mfrac>
<mo>)</mo>
</mrow>
<mi>&xi;</mi>
</msup>
</mrow>
In formula, ξ is the parameter depending on object function, and K is iterations.
A kind of 6. relay protection multiple-objection optimization setting method as claimed in claim 1, it is characterised in that the step (5)
Detailed process include：
Step (5.1)：Following membership function is established to each object function of relay protection：
<mrow>
<msub>
<mi>&lambda;</mi>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>min</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>min</mi>
</msubsup>
</mrow>
</mfrac>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>max</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>min</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&GreaterEqual;</mo>
<msubsup>
<mi>f</mi>
<mi>m</mi>
<mi>max</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, XkFor k-th of individual in Pareto optimal solution sets；Functional value after being normalized for m-th of object function；fm
(Xk) it is XkM-th of target function value；For XkM target function value minimum value；For XkM target letter
The maximum of numerical value；
Step (5.2)：Calculate total satisfaction N corresponding to each individual in Pareto optimal solution setsλ(k)：
<mrow>
<msub>
<mi>N</mi>
<mi>&lambda;</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>3</mn>
</munderover>
<msub>
<mi>&eta;</mi>
<mi>m</mi>
</msub>
<msub>
<mi>&lambda;</mi>
<msub>
<mi>f</mi>
<mi>m</mi>
</msub>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein, ηmFor the weight coefficient of m-th of object function；
According to total satisfaction corresponding to each individual in Pareto optimal solution sets, select the high satisfaction of a total satisfaction after
The optimal solution of electric protection target.
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| CN109767034B (en) * | 2018-12-26 | 2021-06-18 | 广东电网有限责任公司广州供电局 | Relay protection constant value optimization method and device, computer equipment and storage medium |
| CN110323722B (en) * | 2019-06-12 | 2021-07-27 | 国网河北省电力有限公司 | Cross iteration method for relay protection information identification |
| CN110265981B (en) * | 2019-06-12 | 2021-07-27 | 国网河北省电力有限公司 | Incremental cross iteration method for relay protection information identification |
| CN110826776B (en) * | 2019-10-23 | 2024-01-05 | 国网四川省电力公司成都供电公司 | Initial solution optimization method based on dynamic programming in distribution network line transformation relation identification |
| CN111144623B (en) * | 2019-12-09 | 2023-03-31 | 云南电网有限责任公司临沧供电局 | Fixed value tuning method based on self-adaptive learning factor particle swarm |
| CN111834977B (en) * | 2020-07-31 | 2023-02-10 | 广东电网有限责任公司 | Parameter setting method, device, system and medium for inverse time limit overcurrent protection |
| CN112541299A (en) * | 2020-11-28 | 2021-03-23 | 安徽信息工程学院 | Relay protection fixed value optimization method based on genetic algorithm |
| CN112785037B (en) * | 2020-12-30 | 2024-02-23 | 苏州电力设计研究院有限公司 | Power distribution network inverse time limit overcurrent protection fixed value optimization method |
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