CN106712050A - Improved leapfrogging algorithm-based power grid reactive power optimization method and device - Google Patents
Improved leapfrogging algorithm-based power grid reactive power optimization method and device Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
- H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
- H02J3/1871—Methods for planning installation of shunt reactive power compensators
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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Abstract
Embodiments of the present invention provide an improved leapfrogging algorithm-based power grid reactive power optimization method and a device thereof, and relate to the technical field of the power system planning technology. The method and the device are applied to the reactive power optimization of a power distribution network. In this way, the better convergence and the higher degree of optimization are realized. The method comprises the steps of obtaining the control variables of the power distribution network and the state variables of the power distribution network; obtaining a specified number of frog individuals and frog populations corresponding to each of all the specified number of frog individuals; according to the frog populations, obtaining an initial reactive voltage; according to the initial reactive voltage, obtaining the optimum value of a local reactive voltage; and obtaining an optimal solution of a global reactive voltage according to the optimum value of the local reactive voltage. The optimal solution of the global reactive voltage is applied to the reactive power optimization of the power distribution network. The method and the device are used for the reactive configuration of the power distribution network.
Description
Technical field
The present invention relates to Power System Planning technical field, more particularly to a kind of electric network reactive-load of the algorithm that leapfroged based on improvement
Optimization method and device.
Background technology
Network loss is reduced, the economy for improving power system power transmission efficiency and Operation of Electric Systems is Operation of Electric Systems department
The practical problem for facing, is also one of Main way of power system research.Line voltage control is largely idle
Optimal Control Problem, the whether reasonable safety and stablization for directly affecting power system of System Reactive Power distribution, and imitated with economy
Benefit is linked directly.Reactive power optimization of power system problem belongs to a part of optimal power flow problems.
The Reactive Power Optimazation Problem of power system is a multivariable, the mixing nonlinear programming problem of multiple constraint, its operation
The existing continuous variable of variable (such as power conserving voltage, the idle of generator are exerted oneself), have again discrete variable (such as load tap changer position
Put, the switching capacity of compensation reactor and capacitor) so that optimization process is sufficiently complex, therefore need to set up system model, proposes
With improvement respective algorithms, with the optimization of the means completion system of science, that is, the idle work optimization parameter for power distribution network is obtained.Tradition
Reactive Power Optimization Algorithm for Tower including interior point method, genetic, ant colony algorithm, particle cluster algorithm etc..
The general idle work optimization method for mentioning on the one hand is mainly that rule are optimized to reactive power compensator comprising two aspects
Draw, be on the other hand to voltage ＆ reactive power-control.The Mathematical Modeling of idle work optimization method includes that object function (presses certain standard
Then set, such as economic optimum or voltage-regulation coefficient is optimal) and constraints (such as setting of node voltage level, on-load voltage regulation
Adjustment of load tap changer etc.).Mathematically idle work optimization it is actual under constraints solve object function value
Problem.The Mathematical Modeling of idle work optimization method is divided into two classes again, mathematical optimization based on analytic solutions and based on the optimal iteration of intelligence
Optimal power flow problems optimization.But current idle work optimization method convergence is poor, degree of optimization is relatively low, part is easily trapped into most
It is excellent.
The content of the invention
The application provides a kind of based on the reactive power optimization method and device for improving the algorithm that leapfrogs, and can be used in distribution
The idle work optimization of net, makes it have more preferable convergence and degree of optimization.
In a first aspect, The embodiment provides a kind of based on the reactive power optimization method for improving the algorithm that leapfrogs,
Including：The control variables of power distribution network and the state variable of power distribution network are obtained, the control variables of power distribution network is used to indicate power distribution network
Adjustable generator node voltage amplitude, the adjustable transformer application of adjustable tap gear of power distribution network and the reactive-load compensation of power distribution network
Capacitor switching group number, the state variable of power distribution network is used for the voltage and the generator of power distribution network of the PQ nodes for indicating power distribution network
Idle exert oneself；Acquisition includes the frog of the frog individuality of specified quantity and each opposition individual with the frog of specified quantity
The control variables of the individual power distribution network in setting range of the frog of the frog population of body, wherein specified quantity；According to frog kind
Group obtains initial reactive voltage；The optimal value of local reactive voltage is obtained according to initial reactive voltage, and according to local idle electricity
The optimal value of pressure obtains the optimal solution of Global, and the optimal solution of Global is used for the idle work optimization of power distribution network.
Second aspect, The embodiment provides a kind of based on the reactive power optimization device for improving the algorithm that leapfrogs,
Including：Acquisition module, for obtaining the control variables of power distribution network and the state variable of power distribution network, the control variables of power distribution network is used
The adjustable transformer application of adjustable tap gear and distribution of adjustable generator node voltage amplitude, power distribution network in instruction power distribution network
The reactive-load compensation capacitor switching group number of net, the state variable of power distribution network is used for the voltage of the PQ nodes for indicating power distribution network and matches somebody with somebody
The idle of the generator of power network is exerted oneself；Processing module, the frog individuality of specified quantity and and specified quantity are included for obtaining
The individual each opposition of frog the individual frog population of frog, it is matching somebody with somebody in setting range that wherein the frog of specified quantity is individual
The control variables of power network；Processing module, is additionally operable to obtain initial reactive voltage according to frog population；Processing module, is additionally operable to root
The optimal value of local reactive voltage is obtained according to initial reactive voltage, and obtains global idle according to the optimal value of local reactive voltage
The optimal solution of voltage, the optimal solution of Global is used for the idle work optimization of power distribution network.
The embodiment provides a kind of based on the reactive power optimization method and device for improving the algorithm that leapfrogs, pass through
Obtain power distribution network control variables and power distribution network state variable, and obtain include specified quantity frog individuality and with finger
The frog population of the frog individuality of the individual each opposition of the frog of fixed number amount, initial reactive voltage, root are obtained according to frog population
The optimal value of local reactive voltage is obtained according to initial reactive voltage, and obtains global idle according to the optimal value of local reactive voltage
The optimal solution of voltage.It is updated based on the algorithm that leapfrogs when just starting, Chaos Search helps out, so as to accelerate frog
Individual to be moved at global optimum position, with the increase of local area deep-searching iterations, algorithm is progressed into chaos
Based on search, so as to help algorithm to jump out local extremum, more preferable convergence and optimization journey are made it have compared with prior art
Degree, additionally it is possible to improve the operational efficiency and the quality of power supply of power network, reduces system active power loss.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, embodiment will be described below
Needed for the accompanying drawing to be used be briefly described, it should be apparent that, drawings in the following description are only more of the invention
Embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, can also be attached according to these
Figure obtains other accompanying drawings.
A kind of showing based on the reactive power optimization method for improving the algorithm that leapfrogs that Fig. 1 is provided by embodiments of the invention
Meaning property flow chart；
A kind of reactive power optimization method of algorithm that leapfroged based on improvement that Fig. 2 is provided by another embodiment of the present invention
Indicative flowchart；
A kind of showing based on the reactive power optimization device for improving the algorithm that leapfrogs that Fig. 3 is provided by embodiments of the invention
Meaning property structure chart.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
For the ease of clearly describing the technical scheme of the embodiment of the present invention, in an embodiment of the present invention, employ " the
One ", the printed words such as " second " make a distinction to function and the essentially identical identical entry of effect or similar item, and those skilled in the art can
To understand that the printed words such as " first ", " second " are not to be defined to quantity and execution order.
As shown in Figure 1, The embodiment provides a kind of based on the reactive power optimization side for improving the algorithm that leapfrogs
Method, including：
101st, the control variables of power distribution network and the state variable of power distribution network are obtained.
Specifically, the control variables of power distribution network is used to indicate adjustable generator node voltage amplitude, the power distribution network of power distribution network
Adjustable transformer application of adjustable tap gear and power distribution network reactive-load compensation capacitor switching group number, the state variable of power distribution network
For indicating the voltage of the PQ nodes of power distribution network and the idle of generator of power distribution network to exert oneself.
102nd, the frog for including the frog individuality of specified quantity and each opposition individual with the frog of specified quantity is obtained
Individual frog population.
Specifically, the frog individuality of wherein specified quantity is the control variables of the power distribution network in setting range.
103rd, initial reactive voltage is obtained according to frog population.
104th, the optimal value of local reactive voltage is obtained according to initial reactive voltage, and according to the optimal of local reactive voltage
Value obtains the optimal solution of Global.
Wherein, the optimal solution of Global is used for the idle work optimization of power distribution network.
The embodiment provides a kind of based on the reactive power optimization method for improving the algorithm that leapfrogs, matched somebody with somebody by obtaining
The control variables of power network and the state variable of power distribution network, and acquisition includes the frog individuality of specified quantity and and specified quantity
The individual each opposition of frog the individual frog population of frog, initial reactive voltage is obtained according to frog population, according to initial
Reactive voltage obtains the optimal value of local reactive voltage, and obtains Global according to the optimal value of local reactive voltage
Optimal solution.Be updated based on the algorithm that leapfrogs when just starting, Chaos Search helps out, thus accelerate frog individual to
Moved at global optimum position, with the increase of local area deep-searching iterations, algorithm progresses into and is with Chaos Search
It is main, so as to help algorithm to jump out local extremum, more preferable convergence and degree of optimization are made it have compared with prior art, moreover it is possible to
The operational efficiency and the quality of power supply of power network are enough improved, system active power loss is reduced.
Further, as shown in Figure 2, The embodiment provides a kind of based on the power network for improving the algorithm that leapfrogs
Idle work optimization method, including：
201st, according to X=(VGK,Ti,Cj) obtain power distribution network control variables X, according to U=(Vi,QGj) obtain power distribution network
State variable U.
Wherein, wherein VGKIt is adjustable generator node voltage amplitude, the T of power distribution networkiFor the adjustable transformer of power distribution network can
Adjust tap gear, CjIt is reactive-load compensation capacitor switching group number, the V of power distribution networkiIt is the voltage of the PQ nodes of power distribution network, QGjIt is
The generator of power distribution network it is idle exert oneself, i be the candidate compensation buses number of power distribution network, j be adjustable transformer number, the k of power distribution network
It is the generator nodes of power distribution network.The coding of all control variables can be expressed as X=[C, T, VG]=[C1,C2,…,Ci,
T1,T2,…,Tj,VG1,VG2,…,VGK]。
202nd, setting range (a is obtainedi,bi) in the N number of frog of specified quantity it is individual, and according toObtain finger
The frog of the individual each opposition of N frog of fixed number amount is individualSo as to constitute the frog population that scale is 2N.
Specifically, can be with N frog individuality x of random initializtioni∈(ai,bi), and it is individual to obtain its opposition successively It is the frog population of 2N so as to constitute scale, and calculates all individual fitness values in the population, and according to
It is ranked up from excellent to bad according to fitness value, the individual composition initial population of N frog therein is randomly choosed with certain probability, generally
The probability that the foundation better individuality that is fitness value of rate selection is selected into initial population is higher, and probability selection formula is pi=
(2N-i)/2N, i=1,2 ..., 2N.
203rd, the fitness value of all frogs individualities in the frog population is calculated.
204th, all frog individualities in frog population are ranked up according to fitness value.
205th, the select probability p of each frog individuality in the frog individuality after sequence is obtainediAnd according to select probability piIn row
The individual composition initial population of N number of frog is selected in frog individuality after sequence.
Specifically, according to pi=(2N-i)/2N, obtains the select probability of each frog individuality in the frog individuality after sequence
pi, wherein i=1,2 ..., 2N, and according to select probability piThe individual composition of N number of frog is selected in frog individuality after sequence just
Beginning population.
206th, initial reactive voltage is obtained.
Specifically, according to equality constraint equationAnd inequality constraints equationObtain initial reactive voltage
207th, the optimum individual position X of frog population is updated according to fitness valuebWith global optimum body position XgAnd update
Determine worst body position newX after the renewal of sub-group in current iterationwi。
Specifically, updating the optimum individual position X of frog population according to fitness valuebWith global optimum body position Xg, and
According to newXwi(k)=Xwi(k)·exp·[(1-exp(-α·y(k)))·(μ·Xwi(k)·(1-Xwi(k)))]+e-2α·y(k)·ΩiK () updates worst body position newX after the renewal for determining sub-group in current iterationwi。
Wherein, Ω (k) is the step-length more new variables that leapfrogs, and y (k) is Chaos Variable, can be according to Ω (k)=a1·rand
()·(Xb(k)-Xw(k)) step-length renewal variable Ω (k) that leapfrogs is obtained, obtaining chaos according to y (k)=y (k-1) (1+ λ) becomes
Amount y (k), wherein in formula, Xb(k) and XwK () represents the optimum individual and worst individuality during current sub-population kth time iteration respectively,
Rand () is generally evenly distributed in the random number between [0,1], a1It is constant；K is iterations；λ is the chaos factor less than 1,
XwiK () represents worst individual X of the current sub-population in kth time iterationwThe i-th dimension of (k), ΩiK () is the i-th dimension of Ω (k), α
Normal number is with μ.
208th, newX is judgedwFitness value whether be better than XwFitness value.
Work as newXwFitness value be better than XwFitness value when, perform step 209, work as newXwFitness value not
Better than XwFitness value when, perform step 210.
209th, newX is usedwSubstitution Xw。
210th, update Ω (k) and y (k) and generate newXw。
Specifically, working as newXwFitness value be better than XwFitness value when, then use newXwSubstitution Xw, work as newXwIt is suitable
Answer angle value not better than XwFitness value when, then update Ω (k) and y (k), and according to newXwi(k)=Omin+(Omax-
Omin) rand () generations newXw, wherein OmaxAnd OminThe maximum and minimum value of algorithm search scope, rand are represented respectively
() is the random individual being evenly distributed between [0,1].
211st, the individual foundation fitness value of frog in frog population is ranked up from excellent to bad, and successively according to triangle
Select probability 2 (m+1-i)/[m (m+1)] picks out Q frog n sub-group of individual composition, and is solved according to n sub-group complete
The optimal solution of office's reactive voltage.
212nd, determine whether all sub-groups are completed local area deep-searching.
When it is determined that all sub-groups are completed local area deep-searching, step 213 is performed.
When it is determined that simultaneously not all sub-group is completed local area deep-searching, execution step 207.
213rd, determine whether to meet global mixed iteration number of times G.
When it is determined that meeting overall situation mixed iteration number of times G, step 214 is performed.
When it is determined that be unsatisfactory for global mixed iteration number of times G, then performing step 215.
214th, the optimal solution of Global is exported.
215th, all frog individualities are re-mixed, then all frog individualities in frog population are arranged according to fitness value
Sequence simultaneously performs step 205.
The embodiment provides a kind of based on the reactive power optimization method for improving the algorithm that leapfrogs, matched somebody with somebody by obtaining
The control variables of power network and the state variable of power distribution network, and acquisition includes the frog individuality of specified quantity and and specified quantity
The individual each opposition of frog the individual frog population of frog, initial reactive voltage is obtained according to frog population, according to initial
Reactive voltage obtains the optimal value of local reactive voltage, and obtains Global according to the optimal value of local reactive voltage
Optimal solution.Be updated based on the algorithm that leapfrogs when just starting, Chaos Search helps out, thus accelerate frog individual to
Moved at global optimum position, with the increase of local area deep-searching iterations, algorithm progresses into and is with Chaos Search
It is main, so as to help algorithm to jump out local extremum, more preferable convergence and degree of optimization are made it have compared with prior art, moreover it is possible to
The operational efficiency and the quality of power supply of power network are enough improved, system active power loss is reduced.
As shown in Figure 3, The embodiment provides a kind of configuration device 301 of power distribution network, including：
Acquisition module 302, for obtaining the control variables of power distribution network and the state variable of power distribution network.
Specifically, the control variables of power distribution network is used to indicate adjustable generator node voltage amplitude, the power distribution network of power distribution network
Adjustable transformer application of adjustable tap gear and power distribution network reactive-load compensation capacitor switching group number, the state variable of power distribution network
For indicating the voltage of the PQ nodes of power distribution network and the idle of generator of power distribution network to exert oneself.
Processing module 303, includes that the frog of specified quantity is individual and frog of with specified quantity is individual respectively for obtaining
From the individual frog population of the frog of opposition.
Specifically, the frog individuality of wherein specified quantity is the control variables of the power distribution network in setting range.
Processing module 303, is additionally operable to obtain initial reactive voltage according to frog population.
Processing module 303, is additionally operable to be obtained according to initial reactive voltage the optimal value of local reactive voltage, and according to part
The optimal value of reactive voltage obtains the optimal solution of Global.
Wherein, the optimal solution of Global is used for the idle work optimization of power distribution network.
The embodiment provides a kind of based on the reactive power optimization device for improving the algorithm that leapfrogs, matched somebody with somebody by obtaining
The control variables of power network and the state variable of power distribution network, and acquisition includes the frog individuality of specified quantity and and specified quantity
The individual each opposition of frog the individual frog population of frog, initial reactive voltage is obtained according to frog population, according to initial
Reactive voltage obtains the optimal value of local reactive voltage, and obtains Global according to the optimal value of local reactive voltage
Optimal solution.Be updated based on the algorithm that leapfrogs when just starting, Chaos Search helps out, thus accelerate frog individual to
Moved at global optimum position, with the increase of local area deep-searching iterations, algorithm progresses into and is with Chaos Search
It is main, so as to help algorithm to jump out local extremum, more preferable convergence and degree of optimization are made it have compared with prior art, moreover it is possible to
The operational efficiency and the quality of power supply of power network are enough improved, system active power loss is reduced.
Further, acquisition module 302, specifically for：According to X=(VGK,Ti,Cj) obtain power distribution network control variables
X, according to U=(Vi,QGj) obtain power distribution network state variable U.
Wherein, wherein VGKIt is adjustable generator node voltage amplitude, the T of power distribution networkiFor the adjustable transformer of power distribution network can
Adjust tap gear, CjIt is reactive-load compensation capacitor switching group number, the V of power distribution networkiIt is the voltage of the PQ nodes of power distribution network, QGjIt is
The generator of power distribution network it is idle exert oneself, i be the candidate compensation buses number of power distribution network, j be adjustable transformer number, the k of power distribution network
It is the generator nodes of power distribution network.The coding of all control variables can be expressed as X=[C, T, VG]=[C1,C2,…,Ci,
T1,T2,…,Tj,VG1,VG2,…,VGK]。
Processing module 303, specifically for obtaining setting range (ai,bi) in the N number of frog of specified quantity it is individual, and according toThe frog for obtaining the individual each opposition of N frog of specified quantity is individualSo as to constitute the frog kind that scale is 2N
Group.
Specifically, can be with N frog individuality x of random initializtioni∈(ai,bi), and it is individual to obtain its opposition successively It is the frog population of 2N so as to constitute scale, and calculates all individual fitness values in the population, and according to
It is ranked up from excellent to bad according to fitness value, the individual composition initial population of N frog therein is randomly choosed with certain probability, generally
The probability that the foundation better individuality that is fitness value of rate selection is selected into initial population is higher, and probability selection formula is pi=
(2N-i)/2N, i=1,2 ..., 2N.
Processing module 303, the fitness value specifically for calculating all frogs individualities in the frog population, according to fitness
Value is ranked up to all frog individualities in frog population.The selection for obtaining each frog individuality in the frog individuality after sequence is general
Rate piAnd according to select probability piThe individual composition initial population of N number of frog is selected in frog individuality after sequence.
Specifically, according to pi=(2N-i)/2N, obtains the select probability of each frog individuality in the frog individuality after sequence
pi, wherein i=1,2 ..., 2N, and according to select probability piThe individual composition of N number of frog is selected in frog individuality after sequence just
Beginning population；Obtain initial reactive voltage.
Specifically, according to equality constraint equationAnd inequality constraints equationObtain initial reactive voltage.
Processing module 303, the optimum individual position X specifically for updating frog population according to fitness valuebWith the overall situation most
Excellent body position XgAnd update worst body position newX after the renewal for determining sub-group in current iterationwi。
Specifically, updating the optimum individual position X of frog population according to fitness valuebWith global optimum body position Xg, and
According to newXwi(k)=Xwi(k)·exp·[(1-exp(-α·y(k)))·(μ·Xwi(k)·(1-Xwi(k)))]+e-2α·y(k)·ΩiK () updates worst body position newX after the renewal for determining sub-group in current iterationwi。
Wherein, Ω (k) is the step-length more new variables that leapfrogs, and y (k) is Chaos Variable, can be according to Ω (k)=a1·rand
()·(Xb(k)-Xw(k)) step-length renewal variable Ω (k) that leapfrogs is obtained, obtaining chaos according to y (k)=y (k-1) (1+ λ) becomes
Amount y (k), wherein in formula, Xb(k) and XwK () represents the optimum individual and worst individuality during current sub-population kth time iteration respectively,
Rand () is generally evenly distributed in the random number between [0,1], a1It is constant；K is iterations；λ is the chaos factor less than 1,
XwiK () represents worst individual X of the current sub-population in kth time iterationwThe i-th dimension of (k), ΩiK () is the i-th dimension of Ω (k), α
Normal number is with μ.
Processing module 303, specifically for judging newXwFitness value whether be better than XwFitness value.
Work as newXwFitness value be better than XwFitness value when, use newXwSubstitution Xw, work as newXwFitness value not
Better than XwFitness value when, update Ω (k) and y (k) and generate newXw。
Specifically, working as newXwFitness value be better than XwFitness value when, then use newXwSubstitution Xw, work as newXwIt is suitable
Answer angle value not better than XwFitness value when, then update Ω (k) and y (k), and according to newXwi(k)=Omin+(Omax-
Omin) rand () generations newXw, wherein OmaxAnd OminThe maximum and minimum value of algorithm search scope, rand are represented respectively
() is the random individual being evenly distributed between [0,1].
Processing module 303, specifically for the individual foundation fitness value of frog in frog population is arranged from excellent to bad
Sequence, and according to triangle select probability 2 (m+1-i)/[m (m+1)] pick out Q frog n sub-group of individual composition, and root successively
The optimal solution of Global is solved according to n sub-group.
Processing module 303, is additionally operable to determine whether all sub-groups are completed local area deep-searching.
When it is determined that all sub-groups are completed local area deep-searching, it is determined whether meet global mixed iteration number of times G,
When it is determined that meeting overall situation mixed iteration number of times G, the optimal solution of Global is exported.
The embodiment provides a kind of based on the reactive power optimization device for improving the algorithm that leapfrogs, matched somebody with somebody by obtaining
The control variables of power network and the state variable of power distribution network, and acquisition includes the frog individuality of specified quantity and and specified quantity
The individual each opposition of frog the individual frog population of frog, initial reactive voltage is obtained according to frog population, according to initial
Reactive voltage obtains the optimal value of local reactive voltage, and obtains Global according to the optimal value of local reactive voltage
Optimal solution.Be updated based on the algorithm that leapfrogs when just starting, Chaos Search helps out, thus accelerate frog individual to
Moved at global optimum position, with the increase of local area deep-searching iterations, algorithm progresses into and is with Chaos Search
It is main, so as to help algorithm to jump out local extremum, more preferable convergence and degree of optimization are made it have compared with prior art, moreover it is possible to
The operational efficiency and the quality of power supply of power network are enough improved, system active power loss is reduced.
Through the above description of the embodiments, it is apparent to those skilled in the art that the present invention can be with
Realized with hardware, or firmware is realized, or combinations thereof mode is realized.When implemented in software, can be by above-mentioned functions
Storage is transmitted in computer-readable medium or as one or more instructions on computer-readable medium or code.Meter
Calculation machine computer-readable recording medium includes computer-readable storage medium and communication media, and wherein communication media includes being easy to from a place to another
Any medium of individual place transmission computer program.Storage medium can be any usable medium that computer can be accessed.With
As a example by this but it is not limited to：Computer-readable medium can include random access memory (English full name：Random Access
Memory, English abbreviation：RAM), read-only storage (English full name：Read Only Memory, English abbreviation：ROM), electricity can
EPROM (English full name：Electrically Erasable Programmable Read Only
Memory, English abbreviation：EEPROM), read-only optical disc (English full name：Compact Disc Read Only Memory, English
Referred to as：CD-ROM) or other optical disc storages, magnetic disk storage medium or other magnetic storage apparatus or can be used in carry or
Desired program code of the storage with instruction or data structure form simultaneously can be by any other medium of computer access.This
Outward.Any connection can be appropriate as computer-readable medium.If for example, software be use coaxial cable, optical fiber cable,
Twisted-pair feeder, digital subscriber line (English full name：Digital Subscriber Line, English abbreviation：DSL it is) or such as red
The wireless technology of outside line, radio and microwave etc is transmitted from website, server or other remote sources, then coaxial electrical
The wireless technology of cable, optical fiber cable, twisted-pair feeder, DSL or such as infrared ray, wireless and microwave etc is included in computer-readable
In the definition of medium.
Through the above description of the embodiments, it is apparent to those skilled in the art that, when with software
When mode realizes the present invention, can will store in computer-readable medium or logical for the instruction or code that perform the above method
Computer-readable medium is crossed to be transmitted.Computer-readable medium includes computer-readable storage medium and communication media, wherein communicating
Medium includes being easy to being transmitted from a place to another place any medium of computer program.Storage medium can be calculated
Any usable medium that machine can be accessed.As example but it is not limited to：Computer-readable medium can include RAM, ROM, electricity can
EPROM (full name：Electrically erasable programmable read-only memory,
Referred to as：EEPROM), CD, disk or other magnetic storage apparatus or can be used in carrying or store with instruction or data
The desired program code of structure type simultaneously can be by any other medium of computer access.
The above, specific embodiment only of the invention, but protection scope of the present invention is not limited thereto, and it is any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all contain
Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.
Claims (14)
1. a kind of based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that including：
The control variables of power distribution network and the state variable of the power distribution network are obtained, the control variables of the power distribution network is used to indicate
The adjustable generator node voltage amplitude of the power distribution network, the adjustable transformer application of adjustable tap gear of the power distribution network and institute
The reactive-load compensation capacitor switching group number of power distribution network is stated, the state variable of the power distribution network is used to indicate the PQ of the power distribution network to save
The voltage of point and the idle of generator of the power distribution network are exerted oneself；
Acquisition includes that the frog individuality of specified quantity and the respective frog for opposing individual with the frog of the specified quantity are individual
Frog population, wherein the control variables of the individual power distribution network in setting range of the frog of the specified quantity；
Initial reactive voltage is obtained according to the frog population；
The optimal value of local reactive voltage is obtained according to the initial reactive voltage, and according to the optimal of the local reactive voltage
Value obtains the optimal solution of Global, and the optimal solution of the Global is used to configure the power distribution network.
2. it is according to claim 1 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that described to obtain
The control variables of power distribution network and the state variable of the power distribution network are taken, including：
According to X=(VGK,Ti,Cj) obtain power distribution network control variables X, according to U=(Vi,QGj) obtain the state of the power distribution network
Variable U, wherein VGKIt is adjustable generator node voltage amplitude, the T of the power distribution networkiFor the adjustable transformer of the power distribution network can
Adjust tap gear, CjIt is reactive-load compensation capacitor switching group number, the V of the power distribution networkiIt is the electricity of the PQ nodes of the power distribution network
Pressure, QGjBe the power distribution network generator it is idle exert oneself, the candidate compensation buses number that i is the power distribution network, j be the distribution
The adjustable transformer number of net, k are the generator nodes of the power distribution network.
3. it is according to claim 1 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that described to obtain
Take the frog of the frog individuality of frog individuality and each opposition individual with the frog of the specified quantity including specified quantity
Population, including：
Obtain setting range (ai,bi) in the N number of frog of specified quantity it is individual, and according toObtain the specified number
The frog of the individual each opposition of N frog of amount is individualSo as to constitute the frog population that scale is 2N.
4. it is according to claim 1 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that described
Initial reactive voltage is obtained according to the frog population, including：
The fitness value of all frogs individualities in the frog population is calculated, and according to fitness value in the frog population
All frog individualities are ranked up；
According to pi=(2N-i)/2N, obtains the select probability p of each frog individuality in the frog individuality after the sequencei, wherein i
=1,2 ..., 2N, and according to select probability piN number of frog initial kind of individual composition is selected in frog individuality after the sequence
Group；
According to equality constraint equationAnd inequality constraints equation
Obtain the initial reactive voltage.
5. it is according to claim 1 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that described
The optimal value of local reactive voltage is obtained according to the initial reactive voltage, including：
The optimum individual position X of the frog population is updated according to fitness valuebWith global optimum body position Xg, and according to
newXwi(k)=Xwi(k)·exp·[(1-exp(-α·y(k)))·(μ·Xwi(k)·(1-Xwi(k)))]+e-2α·y(k)·
ΩiK () updates worst body position newX after the renewal for determining sub-group in current iterationwi, wherein, Ω (k) is the step that leapfrogs
More new variables long, y (k) is Chaos Variable；
Work as newXwFitness value be better than XwFitness value when, then use newXwSubstitution Xw, work as newXwFitness value it is unexcellent
In XwFitness value when, then update Ω (k) and y (k), and according to newXwi(k)=Omin+(Omax-Omin) rand () lifes
Into newXw, wherein OmaxAnd OminThe maximum and minimum value of algorithm search scope are represented respectively, and rand () is to be evenly distributed on
[0,1] random individual between.
6. it is according to claim 1 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that described
The optimal solution of Global is obtained according to the optimal value of the local reactive voltage, including：
The individual foundation fitness value of frog in the frog population is ranked up from excellent to bad, and successively according to triangle selection
Probability 2 (m+1-i)/[m (m+1)] picks out Q frog n sub-group of individual composition, and is solved according to the n sub-group complete
The optimal solution of office's reactive voltage.
7. it is according to claim 6 based on the reactive power optimization method for improving the algorithm that leapfrogs, it is characterised in that the side
Method also includes：
After all sub-groups are completed local area deep-searching, if meeting global mixed iteration number of times G, export described complete
The optimal solution of office's reactive voltage.
8. a kind of based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that including：
Acquisition module, for obtaining the control variables of power distribution network and the state variable of the power distribution network, the control of the power distribution network
Variable processed is used to indicate adjustable generator node voltage amplitude, adjustable point of the adjustable transformer of the power distribution network of the power distribution network
The reactive-load compensation capacitor switching group number of joint gear and the power distribution network, the state variable of the power distribution network is used to indicate institute
The idle of generator of the voltage and the power distribution network of stating the PQ nodes of power distribution network is exerted oneself；
Processing module, includes that the frog of specified quantity is individual and frog of with the specified quantity is individual respective right for obtaining
The individual frog population of vertical frog, wherein the control of the individual power distribution network in setting range of the frog of the specified quantity
Variable processed；
The processing module, is additionally operable to obtain initial reactive voltage according to the frog population；
The processing module, is additionally operable to be obtained according to the initial reactive voltage optimal value of local reactive voltage, and according to institute
The optimal value for stating local reactive voltage obtains the optimal solution of Global, and the optimal solution of the Global is used to match somebody with somebody
Put the power distribution network.
9. it is according to claim 8 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described to obtain
Modulus block, specifically for：
According to X=(VGK,Ti,Cj) obtain power distribution network control variables X, according to U=(Vi,QGj) obtain the state of the power distribution network
Variable U, wherein VGKIt is adjustable generator node voltage amplitude, the T of the power distribution networkiFor the adjustable transformer of the power distribution network can
Adjust tap gear, CjIt is reactive-load compensation capacitor switching group number, the V of the power distribution networkiIt is the electricity of the PQ nodes of the power distribution network
Pressure, QGjBe the power distribution network generator it is idle exert oneself, the candidate compensation buses number that i is the power distribution network, j be the distribution
The adjustable transformer number of net, k are the generator nodes of the power distribution network.
10. it is according to claim 8 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described
Processing module, specifically for：
Obtain setting range (ai,bi) in the N number of frog of specified quantity it is individual, and according toObtain the specified number
The frog of the individual each opposition of N frog of amount is individualSo as to constitute the frog population that scale is 2N.
11. is according to claim 8 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described
Processing module, specifically for：
The fitness value of all frogs individualities in the frog population is calculated, and according to fitness value in the frog population
All frog individualities are ranked up；
According to pi=(2N-i)/2N, obtains the select probability p of each frog individuality in the frog individuality after the sequencei, wherein i
=1,2 ..., 2N, and according to select probability piN number of frog initial kind of individual composition is selected in frog individuality after the sequence
Group；
According to equality constraint equationAnd inequality constraints equation
Obtain the initial reactive voltage.
12. is according to claim 8 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described
Processing module, specifically for：
The optimum individual position X of the frog population is updated according to fitness valuebWith global optimum body position Xg, and according to
newXwi(k)=Xwi(k)·exp·[(1-exp(-α·y(k)))·(μ·Xwi(k)·(1-Xwi(k)))]+e-2α·y(k)·
ΩiK () updates worst body position newX after the renewal for determining sub-group in current iterationwi, wherein, Ω (k) is the step that leapfrogs
More new variables long, y (k) is Chaos Variable；
Work as newXwFitness value be better than XwFitness value when, then use newXwSubstitution Xw, work as newXwFitness value it is unexcellent
In XwFitness value when, then update Ω (k) and y (k), and according to newXwi(k)=Omin+(Omax-Omin) rand () lifes
Into newXw, wherein OmaxAnd OminThe maximum and minimum value of algorithm search scope are represented respectively, and rand () is to be evenly distributed on
[0,1] random individual between.
13. is according to claim 8 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described
Processing module, specifically for：
The individual foundation fitness value of frog in the frog population is ranked up from excellent to bad, and successively according to triangle selection
Probability 2 (m+1-i)/[m (m+1)] picks out Q frog n sub-group of individual composition, and is solved according to the n sub-group complete
The optimal solution of office's reactive voltage.
14. is according to claim 13 based on the reactive power optimization device for improving the algorithm that leapfrogs, it is characterised in that described
Processing module is additionally operable to：
After all sub-groups are completed local area deep-searching, if meeting global mixed iteration number of times G, export described complete
The optimal solution of office's reactive voltage.
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| CN107994582A (en) * | 2017-10-24 | 2018-05-04 | 中国南方电网有限责任公司电网技术研究中心 | Reconstruction method of power distribution network and system containing distributed generation resource |
| CN111987750A (en) * | 2020-09-03 | 2020-11-24 | 国网重庆市电力公司电力科学研究院 | Online monitoring method and system for safety phase-advancing capability margin of generator |
| CN113591378A (en) * | 2021-07-27 | 2021-11-02 | 上海电机学院 | Hybrid energy storage capacity configuration method and device based on improved hybrid frog-leaping algorithm |
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| CN107171339A (en) * | 2017-05-27 | 2017-09-15 | 国网河南省电力公司电力科学研究院 | A kind of distribution network voltage idle work optimization method containing microgrid |
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| CN111987750B (en) * | 2020-09-03 | 2022-04-19 | 国网重庆市电力公司电力科学研究院 | Online monitoring method and system for safety phase-advancing capability margin of generator |
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