CN108197709A - Photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm - Google Patents

Photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm Download PDF

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CN108197709A
CN108197709A CN201810092417.3A CN201810092417A CN108197709A CN 108197709 A CN108197709 A CN 108197709A CN 201810092417 A CN201810092417 A CN 201810092417A CN 108197709 A CN108197709 A CN 108197709A
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voltage
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power loss
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叶志伟
孙爽
苏军
王春枝
金灿
陈凤
郑逍
孙恒
孙一恒
常鹏阳
蔡文成
曹倩倩
张旭
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Hubei University of Technology
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Abstract

The present invention discloses the photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm, including:Obtain the voltage value of photovoltaic plant;Voltage difference, active power loss are worth to according to voltage;The minimal eigenvalue of the stability margin of expression system is obtained according to Jacobian matrix;Voltage difference, active power loss, minimal eigenvalue are normalized respectively, object function is worth to according to the voltage deviation after normalization, the active power loss after normalization, the minimal characteristic after normalization;Optimal value solution is carried out to object function according to hybrid rice algorithm, obtains the optimal value of object function;Corresponding optimal voltage deviation, optimal active power loss, optimal minimal eigenvalue are determined according to optimal value.Multi-objective reactive optimization based on hybrid rice algorithm is carried out to photovoltaic plant inside using the method for the present invention or system, reaches balanced photovoltaic plant station interior nodes voltage, improves its stability margin, and reduce the purpose of active power loss.

Description

Photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm
Technical field
It is more more particularly to the photovoltaic plant based on hybrid rice algorithm the present invention relates to photovoltaic plant idle work optimization field Target idle work optimization method and system.
Background technology
Photovoltaic plant is different from common power station, has the characteristics of its own is exclusive, i.e., only expires in weather conditions such as illumination When sufficient, just in state of generating electricity by way of merging two or more grid systems, belong to changeable, impact load, the variation of grid-connected power necessarily causes grid entry point electric The frequent fluctuation of pressure, especially in electric network fault, there are low voltage crossing processes for gird-connected inverter, it is impossible to provide enough nothings Work(may lead to system voltage long-time, substantially fall, induce power station and extensive off-grid accident occur.According to GB/T 19964—2012《Photo-voltaic power generation station access power system technology regulation》、GB/T 29321—2012《Benefit that photo-voltaic power generation station is idle Repay technical specification》Clear stipulaties photo-voltaic power generation station reactive power source include photovoltaic combining inverter and the idle benefit of photo-voltaic power generation station Device is repaid, photo-voltaic power generation station will make full use of the reactive capability and its regulating power of gird-connected inverter;When inverter is without power capacity When amount cannot meet system voltage adjusting needs, the reactive power compensator of installation suitable capacity should be concentrated in photo-voltaic power generation station, it must Install dynamic reactive compensation device when wanting additional.At present to the research of the idle control in large-sized photovoltaic power station only just for grid entry point voltage It is controlled, there is no possibility, stability margin and the economy operations that consideration photovoltaic plant builtin voltage gets over line.
Reactive Power Optimazation Problem is the nonlinear combinatorial optimization problem of a hybrid variable, multiple constraint and multiple target, at this There are many methods in one research field.Newton method is a kind of method of direct solution Kuhn-Tucker equation optimizing, the method with Lagrange multiplier methods handle equality constraint, and promise breaking variable inequality constraints is handled with penalty.Sun D are proposed with Newton method Based on optimal load flow to realize the optimization of System Reactive Power, it is practical which is acknowledged as newton optimal load flow algorithm Great leap, it for the first time combines the openness of electric system with Newton method so that calculation amount greatly reduces.The method it is excellent Point is that second dervative information is utilized, and convergence is fast, memory is saved using Sparse technology, available for large scale network;The drawback is that It is difficult to effectively determine constraint set, generally with trial iteration method, programming realization is difficult, and the Hessian battle arrays of corresponding control variable are diagonal Easily there is small value or zero in member, causes Singular Value, and the initial values of the Lagrange multipliers of introducing is to the stability shadow of iterative calculation It rings big.
Although traditional idle work optimization method calculates, rapid, convergence is reliable, some assumed conditions is needed, with artificial Extensive use of the intelligent algorithm in idle work optimization, has received good effect, simultaneously because genetic algorithm, particle cluster algorithm and The convergence bottleneck of traditional quanta particle swarm optimization causes algorithm to be easily trapped into locally optimal solution in the later stage and generate " dimension disaster " And globally optimal solution can not be sought obtaining, and then influence the effect that algorithm carries out large scale system optimization.
Invention content
The object of the present invention is to provide the photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm, Multi-objective reactive optimization based on hybrid rice algorithm is carried out to photovoltaic plant inside, reaches balanced photovoltaic plant station interior nodes electricity Pressure, improves its stability margin, and reduce the purpose of active power loss.
To achieve the above object, the present invention provides following schemes:
A kind of photovoltaic plant multi-objective reactive optimization method based on hybrid rice algorithm, including:
Obtain the voltage value of photovoltaic plant;
Voltage difference, active power loss are worth to according to the voltage;
The minimal eigenvalue of the stability margin of expression system is obtained according to Jacobian matrix;
The voltage difference, the active power loss, the minimal eigenvalue are normalized respectively, normalized The minimal eigenvalue after active power loss, normalization after rear voltage deviation, normalization;
According to the voltage deviation after the normalization, the active power loss after the normalization, the minimum after the normalization Feature is worth to object function;
Optimal value solution is carried out to the object function according to hybrid rice algorithm, obtains the optimal of the object function Value;
Corresponding optimal voltage deviation, optimal active power loss, optimal minimal eigenvalue are determined according to the optimal value.
Optionally, it is described voltage difference is worth to according to the voltage, active power loss specifically includes:
According to formulaVoltage difference is obtained, wherein, dv represents voltage difference, vijRepresent photovoltaic plant knot Point voltage,Represent the desired voltage of node ij, Δ vmaxRepresent the maximum deviation of node voltage, i=1,2 ..., m;J=1, 2 ..., n, m, n are positive integer;
According to formulaObtain active power loss, Wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent node ij With the phase difference of voltage between i (j+1).
Optionally, it is described that place is normalized to the voltage difference, the active power loss, the minimal eigenvalue respectively Reason, the active loss after voltage deviation, normalization after being normalized, the minimal eigenvalue after normalization specifically include:
According to formulaVoltage deviation after being normalized, wherein, dv*Represent the electricity after the normalization Deviation is pressed, dv represents the voltage difference, dvminRepresent minimum voltage difference;
According to formulaActive power loss after being normalized, wherein,After representing the normalization Active power loss, PlossRepresent the active power loss, Ploss_minRepresent minimum active power loss;
According to formulaMinimal eigenvalue after being normalized, whereinRepresent the normalization Minimal eigenvalue afterwards, λminRepresent the minimal eigenvalue, λmin_maxRepresent maximum minimal eigenvalue.
Optionally, the active power loss after the voltage deviation according to after the normalization, the normalization, the normalizing Minimal characteristic after change is worth to object function, specifically includes:
Object function minf (x) is determined according to the following formula:
Wherein c1Represent weighted value, the c of the voltage deviation2Described in expression Weighted value, the c of active power loss3Represent the weighted value of the minimal eigenvalue after the normalization, dv*After representing the normalization Voltage deviation,Represent the active power loss after the normalization,Represent the minimal eigenvalue after the normalization.
Optionally, it is described that optimal value solution is carried out to the object function according to hybrid rice algorithm, obtain the target The optimal value of function specifically includes:
Rice population number, maximum breeding number, maximum selfing number are initialized, each rice individual represents the target letter A kind of several values;
The fitness value of each individual in the rice population is calculated respectively;The fitness value represents the rice population The quality of middle individual;
Rice is ranked up according to the fitness value to obtain ideal adaptation degree series;
The individual adaptation degree sequence is divided into maintainer, sterile line, restorer;
The maintainer with the sterile line hybridize and generates new sterile line individual;
The restorer is subjected to selfing and generates new restorer individual;
Optimum individual is obtained according to the new sterile line individual and the new restorer individual, the optimum individual is Optimal value for the object function.
Optionally, it is described the individual adaptation degree sequence is divided into maintainer, sterile line, restorer to specifically include:
The ideal adaptation degree series include individual, and the quantity of the maintainer is A, and the quantity of the sterile line is A, the restorer quantity are N-2A.
A kind of photovoltaic plant multi-objective reactive optimization system based on hybrid rice algorithm, including:
Voltage value acquisition module, for obtaining the voltage value of photovoltaic plant;
Voltage value processing module, for being worth to voltage difference, active power loss according to the voltage;
Minimal eigenvalue acquisition module, for obtaining the minimal characteristic of the stability margin of expression system according to Jacobian matrix Value;
Normalized module, for returning respectively to the voltage difference, the active power loss, the minimal eigenvalue One change is handled, the active power loss after voltage deviation, normalization, the minimal eigenvalue after normalization after being normalized;
Object function determining module, for according to the voltage deviation after the normalization, the active net after the normalization Minimal characteristic after damage, the normalization is worth to object function;
Optimal value computing module for carrying out optimal value solution to the object function according to hybrid rice algorithm, obtains The optimal value of the object function;
Optimal value analysis module, for according to the optimal value determine corresponding optimal voltage deviation, optimal active power loss, Optimal minimal eigenvalue.
Optionally, the voltage value processing module specifically includes:
Voltage difference computing unit, for according to formulaVoltage difference is obtained, wherein, dv represents voltage Difference, vijRepresent photovoltaic plant node voltage,Represent the desired voltage of node ij, Δ vmaxRepresent the maximum of node voltage partially Difference, m, n are positive integer;
Active power loss computing unit, for according to formula Active power loss is obtained, wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent the phase difference of voltage between node ij and i (j+1).
Optionally, the normalized module specifically includes:
Voltage deviation computing unit, for according to formulaVoltage deviation after being normalized, wherein, dv*Represent the voltage deviation after the normalization, dv represents the voltage difference, dvminRepresent minimum voltage difference;
Active power loss computing unit, for according to formulaActive power loss after being normalized, In,Represent the active power loss after the normalization, PlossRepresent the active power loss, Ploss_minRepresent minimum active net Damage;
Minimal eigenvalue computing unit is normalized, for according to formulaMinimum after being normalized Characteristic value, whereinRepresent the minimal eigenvalue after the normalization, λminRepresent the minimal eigenvalue, λmin_maxIt represents Maximum minimal eigenvalue.
Optionally, the object function is specially:Wherein c1It represents Weighted value, the c of the voltage deviation2Represent weighted value, the c of the active power loss3Represent the minimal eigenvalue after the normalization Weighted value, dv*Represent the voltage deviation after the normalization,Represent the active power loss after the normalization,It represents Minimal eigenvalue after the normalization.
According to specific embodiment provided by the invention, the invention discloses following technique effects:
By establishing photovoltaic plant steady-state operation model and voltage is analyzed in the present invention, voltage difference, active is obtained Network loss, minimal eigenvalue;Object function is obtained after voltage difference, active power loss, minimal eigenvalue are normalized, is used Hybrid rice algorithm is solved to obtain optimal value, and corresponding optimal voltage deviation, optimal active is determined according to the optimal value Network loss, optimal minimal eigenvalue.Multi-objective reactive optimization based on hybrid rice algorithm is carried out to photovoltaic plant inside, reaches equal Weigh photovoltaic plant station interior nodes voltage, improves its stability margin, and reduce the purpose of active power loss.The algorithm optimizing of the invention Ability is strong, and computation complexity is low, and calculating speed is fast, there is the ability for jumping out locally optimal solution, and fast convergence rate can carry out the overall situation Search, is not easy to be absorbed in local optimum.
Description of the drawings
It in order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention Example, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these attached drawings Obtain other attached drawings.
Fig. 1 is photovoltaic plant multi-objective reactive optimization method flow diagram of the embodiment of the present invention;
Fig. 2 is photovoltaic plant multi-objective reactive optimization system structure diagram of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other without making creative work Embodiment shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, it is below in conjunction with the accompanying drawings and specific real Applying mode, the present invention is described in further detail.
Fig. 1 is photovoltaic plant multi-objective reactive optimization method flow diagram of the embodiment of the present invention.Referring to Fig. 1, one kind is based on miscellaneous The photovoltaic plant multi-objective reactive optimization method of rice algorithm is handed over, including:
Step 101:Obtain the voltage value of photovoltaic plant;
Step 102:Voltage difference, active power loss are worth to according to the voltage;
Step 103:The minimal eigenvalue of the stability margin of expression system is obtained according to Jacobian matrix;
Step 104:The voltage difference, the active power loss, the minimal eigenvalue are normalized respectively, obtained The active power loss after voltage deviation, normalization, the minimal eigenvalue after normalization after to normalization;
Step 105:According to the voltage deviation after the normalization, the active power loss after the normalization, the normalization Minimal characteristic afterwards is worth to object function;
Step 106:Optimal value solution is carried out to the object function according to hybrid rice algorithm, obtains the object function Optimal value;
Step 107:Corresponding optimal voltage deviation, optimal active power loss, optimal minimum spy are determined according to the optimal value Value indicative.
Multi-objective reactive optimization based on hybrid rice algorithm is carried out to photovoltaic plant inside using the above method of the present invention, Reach balanced photovoltaic plant station interior nodes voltage, improve its stability margin, and reduce the purpose of active power loss.
Wherein, it is described voltage difference is worth to according to the voltage, active power loss specifically includes:
According to formulaVoltage difference is obtained, wherein, dv represents voltage difference, vijRepresent photovoltaic plant knot Point voltage,Represent the desired voltage of node ij, Δ vmaxRepresent the maximum deviation of node voltage, i=1,2 ..., m;J=1, 2 ..., n, m, n are positive integer;
According to formulaObtain active power loss, Wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent node ij With the phase difference of voltage between i (j+1).
It is described that the voltage difference, the active power loss, the minimal eigenvalue are normalized respectively, returned The active loss after voltage deviation, normalization after one change, the minimal eigenvalue after normalization specifically include:
According to formulaVoltage deviation after being normalized, wherein, dv*After representing the normalization Voltage deviation, dv represent the voltage difference, dvminRepresent minimum voltage difference;
According to formulaActive power loss after being normalized, wherein,After representing the normalization Active power loss, PlossRepresent the active power loss, Ploss_minRepresent minimum active power loss;
According to formulaMinimal eigenvalue after being normalized, whereinRepresent the normalization Minimal eigenvalue afterwards, λminRepresent the minimal eigenvalue, λmin_maxRepresent maximum minimal eigenvalue.
After active power loss, the normalization after the voltage deviation according to after the normalization, the normalization Minimal characteristic is worth to object function, specifically includes:
Object function min f (x) are determined according to the following formula:
Wherein c1Represent weighted value, the c of the voltage deviation2Described in expression Weighted value, the c of active power loss3Represent the weighted value of the minimal eigenvalue after the normalization, dv*After representing the normalization Voltage deviation,Represent the active power loss after the normalization,Represent the minimal eigenvalue after the normalization.
It is described that optimal value solution is carried out to the object function according to hybrid rice algorithm, obtain the object function most The figure of merit specifically includes:
Rice population number, maximum breeding number, maximum selfing number are initialized, each rice individual represents the target letter A kind of several values;
The fitness value of each individual in the rice population is calculated respectively;The fitness value represents the rice population The quality of middle individual;
Rice is ranked up according to the fitness value to obtain ideal adaptation degree series;
The individual adaptation degree sequence is divided into maintainer, sterile line, restorer;The ideal adaptation degree series include Individual, the quantity of the maintainer is A, and the quantity of the sterile line is A, and the restorer quantity is N-2A.
The maintainer with the sterile line hybridize and generates new sterile line individual;
The restorer is subjected to selfing and generates new restorer individual;
Optimum individual is obtained according to the new sterile line individual and the new restorer individual, the optimum individual is Optimal value for the object function.
Fig. 2 is photovoltaic plant multi-objective reactive optimization system structure diagram of the embodiment of the present invention.Referring to Fig. 2, one kind is based on The photovoltaic plant multi-objective reactive optimization system of hybrid rice algorithm, including:
Voltage value acquisition module 201, for obtaining the voltage value of photovoltaic plant;
Voltage value processing module 202, for being worth to voltage difference, active power loss according to the voltage;
Minimal eigenvalue acquisition module 203, for obtaining the minimum of the stability margin of expression system according to Jacobian matrix Characteristic value;
Normalized module 204, for respectively to the voltage difference, the active power loss, the minimal eigenvalue into Row normalized, the active power loss after voltage deviation, normalization, the minimal eigenvalue after normalization after being normalized;
Object function determining module 205, for according to the voltage deviation, active after the normalization after the normalization Minimal characteristic after network loss, the normalization is worth to object function;
Optimal value computing module 206 for carrying out optimal value solution to the object function according to hybrid rice algorithm, obtains To the optimal value of the object function;
Optimal value analysis module 207, for determining corresponding optimal voltage deviation, optimal active net according to the optimal value Damage, optimal minimal eigenvalue.
It is idle excellent that the multiple target based on hybrid rice algorithm is carried out to photovoltaic plant inside using above system in the present invention Change, reach balanced photovoltaic plant station interior nodes voltage, improve its stability margin, and reduce the purpose of active power loss.The present invention The algorithm optimizing ability is strong, and computation complexity is low, and calculating speed is fast, there is the ability for jumping out locally optimal solution.Fast convergence rate.Energy Global search is enough carried out, is not easy to be absorbed in local optimum.
Wherein, the voltage value processing module specifically includes:
Voltage difference computing unit, for according to formulaVoltage difference is obtained, wherein, dv represents voltage Difference, vijRepresent photovoltaic plant node voltage,Represent the desired voltage of node ij, Δ vmaxRepresent the maximum deviation of node voltage, M, n are positive integer;
Active power loss computing unit, for according to formula Active power loss is obtained, wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent the phase difference of voltage between node ij and i (j+1).
The normalized module specifically includes:
Voltage deviation computing unit, for according to formulaVoltage deviation after being normalized, wherein, dv*Represent the voltage deviation after the normalization, dv represents the voltage difference, dvminRepresent minimum voltage difference;
Active power loss computing unit, for according to formulaActive power loss after being normalized, In,Represent the active power loss after the normalization, PlossRepresent the active power loss, Ploss_minRepresent minimum active net Damage;
Minimal eigenvalue computing unit is normalized, for according to formulaMinimum after being normalized Characteristic value, whereinRepresent the minimal eigenvalue after the normalization, λminRepresent the minimal eigenvalue, λmin_maxIt represents most Big minimal eigenvalue.
The object function is specially:Wherein c1Represent the voltage Weighted value, the c of deviation2Represent weighted value, the c of the active power loss3Represent the weight of the minimal eigenvalue after the normalization Value, dv*Represent the voltage deviation after the normalization,Represent the active power loss after the normalization,Return described in expression Minimal eigenvalue after one change.
It is solved in the present invention using hybrid rice algorithm, specific hybrid rice algorithm is specific as follows:
Step1 is initialized:The sum for setting rice population is N, and the ratio that wherein maintainer, sterile line account for group is A%, quantity are A=N × a/100, then the ratio that restorer accounts for group is (100-2a) %, and the dimension of the gene of each individual is D。Represent the gene of i-th of individual in group during the t times breeding,As t=0, i.e., Initial time generates N number of solution at random in solution spaceSpecifically generation formula is for it
Wherein j ∈ { 1,2 ..., D-1, D }, min xj, max xjRespectively represent search space jth dimension component maximum value with Minimum value.
It can determine whether following parameter during initialization:
1. rice population number N;
2. maximum breeding number maxIteration;
3. maximum selfing number maxTime.
Each rice individual represents each inverter and reactive power compensator allocation plan in a kind of station
Step2 fitness value calculations:The fitness value of each individual in population is calculated respectively according to the good and bad by water of rice Rice is ranked up, maintainer, sterile line, restorer quantity be respectively A, A, N-2A.
Step3 hybrid processes:Maintainer with sterile line hybridize and generates new sterile line individual.
For breeding each time, the number that hybrid process carries out is identical with the individual amount of sterile line.Hybridize each time, it will An individual is respectively chosen from sterile line and maintainer as male parent female parent, selection mode can randomly select can also be by one by one The mode of mapping is chosen.The mode of hybridization is that male parent and the gene of maternal corresponding position are added capable recombination according to random weight heavy phase And obtain an individual for possessing new gene.It calculates the fitness of new individual, and is criterion by itself and his father using greedy algorithm Sterile line individual comparison in this female parent retains fitness preferably individual to the next generation.
1. randomer hybridization
WhereinRepresent the jth Wiki of the new individual of kth time hybridization generation in the wheel breeding process because of r1,r2For Random number between [- 1,1], and r1+r2≠0.A, b are derived from { 1,2 ..., A }, X at randomAaRepresent a-th in sterile line Body, XBbRepresent b-th of individual in maintainer.The gene of the new individual of generation per one-dimensional all by sterile line and maintainer Random individual hybridizes to obtain with random ratio.
2. mapping hybridizes
A=b=k in formula, XAaRepresent a-th of individual in sterile line, XBbRepresent b-th of individual in maintainer.It generates New individual gene per one-dimensional miscellaneous with random ratio all by k-th in k-th sport maintainer of sterile line individual Friendship obtains.
Greedy algorithm selection is carried out to newly generated individual after hybridization.
If f (new_Xk) > f (XBk) by new_XkReplace XBkRetain to the next generation, if f (new_Xk)≤f(XBk) then by XBk Retain to the next generation.
Step4 selfing processes:Restorer selfing generates new restorer individual.
In breeding process, the number for being selfed progress is identical with the individual amount of restorer.It is selfed each time, participates in selfing Gene on each position of restorer individual all can be towards current optimal solution close to a random quantity.Calculate the adaptation of new individual It spends and according to greedy algorithm compared with the restorer individual before selfing, selection is preferably saved in the next generation.If being saved in down The individual of a generation is that the selfing number of individual so individual before being selfed will add 1.It is if being saved in follow-on individual The new individual generated is selfed, if new individual is better than current optimum individual, number is selfed and is set as 0, otherwise keeps it certainly Hand over number constant.If the selfing number of some restorer individual has reached limited number of times maxTime, then in next round breeding It will not participate in selfing process instead reset process in journey.
new_Xk=XSk+rand(0,1)(Xbest-XSr) (5)
New_X in formulakRepresent the new individual that kth time selfing generates in the wheel breeding process, XsRepresent the s in restorer Individual, XbestRepresent the optimum individual currently found, XSrFor the sr in restorer individual, wherein sr random values in { 1,2 ..., N-2A }.
Greedy algorithm selection is carried out to newly generated individual after similary selfing.
If f (new_Xk) > f (XSk) by new_XkReplace XSrRetain to its next-generation selfing number and remain unchanged, if f (new_Xk)≤f(XSk) then by XSkRetain to the next generation, selfing number adds 1, i.e. timeSk=timeSk+1。
If f (new_Xk) > f (Xbest) then by new_XkReplace the record of current optimum individual and be selfed number and set It is 0, timeSk=0.If timeSk>=maxTime, then in next-generation breeding, the individual without selfing process, but into Row reset process.
Step5 reset process:
Reset process is actually a subprocess of selfing process, for handling the restorer for reaching the selfing number upper limit Individual.Reset process will generate one group of gene at random in solution space, and this group of gene is added to the base for the individual for participating in resetting Because upper, while its selfing number will be arranged to 0.
The gene of the current obtained optimal individual of Step6 records:
Step (2) is jumped to if not up to maximum breeding algebraically maxIteration or less than optimization error, otherwise will The gene of current optimum individual exports as a result.The final result that the result of output is just.
Wherein, variable bound problem has been also related to during idle work optimization.Idle work optimization constraint is divided into control variable about Beam is constrained with state variable.
Control variables constraint includes:Inverter is without work output, and reactive power compensator is without work output, transformer tap Head.
State variable constraint includes:Photovoltaic plant node voltage.
Control variables constraint equation:
Qij_min≤Qij≤Qij_max
Qsvc_min≤Qsvc≤Qsvc_max
kt_min≤kt≤kt_max
State variable constraint equation:
vij_min≤vij≤vij_max
Each embodiment is described by the way of progressive in this specification, the highlights of each of the examples are with other The difference of embodiment, just to refer each other for identical similar portion between each embodiment.For system disclosed in embodiment For, since it is corresponded to the methods disclosed in the examples, so description is fairly simple, related part is said referring to method part It is bright.
Specific case used herein is expounded the principle of the present invention and embodiment, and above example is said The bright method and its core concept for being merely used to help understand the present invention;Meanwhile for those of ordinary skill in the art, foundation The thought of the present invention, in specific embodiments and applications there will be changes.In conclusion the content of the present specification is not It is interpreted as limitation of the present invention.

Claims (10)

  1. A kind of 1. photovoltaic plant multi-objective reactive optimization method based on hybrid rice algorithm, which is characterized in that including:
    Obtain the voltage value of photovoltaic plant;
    Voltage difference, active power loss are worth to according to the voltage;
    The minimal eigenvalue of the stability margin of expression system is obtained according to Jacobian matrix;
    The voltage difference, the active power loss, the minimal eigenvalue are normalized respectively, after being normalized The minimal eigenvalue after active power loss, normalization after voltage deviation, normalization;
    According to the voltage deviation after the normalization, the active power loss after the normalization, the minimal characteristic after the normalization It is worth to object function;
    Optimal value solution is carried out to the object function according to hybrid rice algorithm, obtains the optimal value of the object function;
    Corresponding optimal voltage deviation, optimal active power loss, optimal minimal eigenvalue are determined according to the optimal value.
  2. 2. multi-objective reactive optimization method according to claim 1, which is characterized in that described to be worth to according to the voltage Voltage difference, active power loss specifically include:
    According to formulaVoltage difference is obtained, wherein, dv represents voltage difference, vijRepresent photovoltaic plant node electricity Pressure,Represent the desired voltage of node ij, Δ vmaxRepresent the maximum deviation of node voltage, i=1,2 ..., m;J=1, 2 ..., n, m, n are positive integer;
    According to formulaActive power loss is obtained, wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent node ij and i (j + 1) phase difference of voltage between.
  3. 3. multi-objective reactive optimization method according to claim 1, which is characterized in that it is described respectively to the voltage difference, The active power loss, the minimal eigenvalue are normalized, having after the voltage deviation, normalization after being normalized Minimal eigenvalue after work(loss, normalization specifically includes:
    According to formulaVoltage deviation after being normalized, wherein, dv*Represent that the voltage after the normalization is inclined Difference, dv represent the voltage difference, dvminRepresent minimum voltage difference;
    According to formulaActive power loss after being normalized, wherein,Represent having after the normalization Work(network loss, PlossRepresent the active power loss, Ploss_minRepresent minimum active power loss;
    According to formulaMinimal eigenvalue after being normalized, whereinAfter representing the normalization Minimal eigenvalue, λminRepresent the minimal eigenvalue, λmin_maxRepresent maximum minimal eigenvalue.
  4. 4. multi-objective reactive optimization method according to claim 1, which is characterized in that it is described according to the normalization after The minimal characteristic after active power loss, the normalization after voltage deviation, the normalization is worth to object function, specific to wrap It includes:
    Object function min f (x) are determined according to the following formula:
    Wherein c1Represent weighted value, the c of the voltage deviation2Represent described active Weighted value, the c of network loss3Represent the weighted value of the minimal eigenvalue after the normalization, dv*Represent the voltage after the normalization Deviation,Represent the active power loss after the normalization,Represent the minimal eigenvalue after the normalization.
  5. 5. multi-objective reactive optimization method according to claim 1, which is characterized in that described according to hybrid rice algorithm pair The object function carries out optimal value solution, and the optimal value for obtaining the object function specifically includes:
    Rice population number, maximum breeding number, maximum selfing number are initialized, each rice individual represents the object function A kind of value;
    The fitness value of each individual in the rice population is calculated respectively;The fitness value represents a in the rice population The quality of body;
    Rice is ranked up according to the fitness value to obtain ideal adaptation degree series;
    The individual adaptation degree sequence is divided into maintainer, sterile line, restorer;
    The maintainer with the sterile line hybridize and generates new sterile line individual;
    The restorer is subjected to selfing and generates new restorer individual;
    Optimum individual is obtained according to the new sterile line individual and the new restorer individual, the optimum individual is institute State the optimal value of object function.
  6. 6. multi-objective reactive optimization method according to claim 5, which is characterized in that described by the individual adaptation degree sequence Row are divided into maintainer, sterile line, restorer and are specifically included:
    The ideal adaptation degree series include individual, and the quantity of the maintainer is A, and the quantity of the sterile line is A, institute Restorer quantity is stated as N-2A.
  7. 7. a kind of photovoltaic plant multi-objective reactive optimization system based on hybrid rice algorithm, which is characterized in that including:
    Voltage value acquisition module, for obtaining the voltage value of photovoltaic plant;
    Voltage value processing module, for being worth to voltage difference, active power loss according to the voltage;
    Minimal eigenvalue acquisition module, for obtaining the minimal eigenvalue of the stability margin of expression system according to Jacobian matrix;
    Normalized module, for the voltage difference, the active power loss, the minimal eigenvalue to be normalized respectively Processing, the active power loss after voltage deviation, normalization, the minimal eigenvalue after normalization after being normalized;
    Object function determining module, for according to the voltage deviation after the normalization, the active power loss after the normalization, institute It states the minimal characteristic after normalization and is worth to object function;
    Optimal value computing module for carrying out optimal value solution to the object function according to hybrid rice algorithm, obtains described The optimal value of object function;
    Optimal value analysis module, for determining corresponding optimal voltage deviation, optimal active power loss, optimal according to the optimal value Minimal eigenvalue.
  8. 8. multi-objective reactive optimization system according to claim 7, which is characterized in that the voltage value processing module is specific Including:
    Voltage difference computing unit, for according to formulaVoltage difference is obtained, wherein, dv represents voltage difference, vijRepresent photovoltaic plant node voltage,Represent the desired voltage of node ij, Δ vmaxThe maximum deviation of expression node voltage, m, N is positive integer;
    Active power loss computing unit, for according to formula Active power loss is obtained, wherein, PlossRepresent active power loss, Gi(j+1)ijRepresent the conductance between ij nodes and i (j+1) node, θi(j+1)ijRepresent the phase difference of voltage between node ij and i (j+1).
  9. 9. multi-objective reactive optimization system according to claim 7, which is characterized in that the normalized module is specific Including:
    Voltage deviation computing unit, for according to formulaVoltage deviation after being normalized, wherein, dv*Table Show the voltage deviation after the normalization, dv represents the voltage difference, dvminRepresent minimum voltage difference;
    Active power loss computing unit, for according to formulaActive power loss after being normalized, wherein,Represent the active power loss after the normalization, PlossRepresent the active power loss, Ploss_minRepresent minimum active power loss;
    Minimal eigenvalue computing unit is normalized, for according to formulaMinimal characteristic after being normalized Value, whereinRepresent the minimal eigenvalue after the normalization, λminRepresent the minimal eigenvalue, λmin_maxRepresent maximum Minimal eigenvalue.
  10. 10. multi-objective reactive optimization system according to claim 7, which is characterized in that the object function is specially:Wherein c1Represent weighted value, the c of the voltage deviation2Represent described active Weighted value, the c of network loss3Represent the weighted value of the minimal eigenvalue after the normalization, dv*Represent the voltage after the normalization Deviation,Represent the active power loss after the normalization,Represent the minimal eigenvalue after the normalization.
CN201810092417.3A 2018-01-31 2018-01-31 Photovoltaic plant multi-objective reactive optimization method and system based on hybrid rice algorithm Pending CN108197709A (en)

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