CN108683173A - Dc distribution network fault condition population reconstructing method is pressed in ship - Google Patents
Dc distribution network fault condition population reconstructing method is pressed in ship Download PDFInfo
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- CN108683173A CN108683173A CN201810510996.9A CN201810510996A CN108683173A CN 108683173 A CN108683173 A CN 108683173A CN 201810510996 A CN201810510996 A CN 201810510996A CN 108683173 A CN108683173 A CN 108683173A
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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
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Abstract
The present invention is to provide dc distribution network fault condition population reconstructing method is pressed in a kind of ship.Equivalent circuit and mathematical model are established to each change of current device in distribution network；The object function of Distribution Networks Reconfiguration optimization is established in a manner of weight distribution, and allows capacity and generator to allow peak power as constraints in circuit；Adaptive weighting method is blended with pond algorithm is hybridized；It is population sequence by each branch breaking series model of distribution network, calculates load restoration amount and each circuit and generator parameter, then calculate switch motion number, search out optimal particle；Some particles are put into hybridization pond, make the particle phase mutual cross of hybridization pond by the inertia weight for adjusting particle；Iterations are made by actual circuit situation, algorithm iteration finishes, and generates the failure reconfiguration sequence of optimization.The present invention can be substantially reduced distribution network fault search and the complicated program process in Load flow calculation, effectively raise the speed of searching optimization and accuracy of failure reconfiguration algorithm.
Description
Technical field
The present invention relates to a kind of failure reconfiguration methods of ship power distribution network.
Background technology
Ship medium voltage DC Force system has become the main direction of development of next-generation ship, can be high energy on ship
A variety of load supplyings such as equipment, daily power load, steerable system and propulsion system.Straightening stream (the Medium in
Voltage DC, MVDC) distribution ship integrated power system other than having the advantages of ship integrated power system itself, also
Have the advantages that it is more potential, wherein the most important decoupling for being that by prime mover control and load control, further includes energy
It enough realizes higher efficiency and lower noise, and makes the weight saving of system, the power density of lifting system entirety,
The developing direction of Marine Synthesize Electric Propulsion System of new generation is asserted by countries in the world.
The synthesis electrical method system that DC distribution is pressed in, to the reliability and vitality of Ship Electrical Power System power supply
More stringent requirements are proposed.The reconstruct of system for distribution network of power network refers to if circuit malfunctions, by breaker or other protections
Device isolated fault load or generator make the important load of no failure part ensure generator, power supply component and circuit section
Point voltage fast recovery of power supply to the maximum extent within allowable range, enhances the stable operation ability of system and continuously for electric energy
Power.Therefore, for this emerging technology of straightening stream synthesis electrical method system in ship, effective event is designed to its distribution network
It is very necessary to hinder reconfiguration technique method.
Invention content
The purpose of the present invention is to provide one kind can be quickly and accurately to straightening stream synthesis electrical method system in ship
Dc distribution network fault condition population reconstructing method is pressed in the ship of distribution network progress failure reconfiguration optimization.
The object of the present invention is achieved like this：
1. straightening stream synthesis electrical method system power network establishes opening up for " branch-node " matrix and mends knot in pair ship
Structure mathematical model carries out fault trace search to entire distribution network using the scope searching principle, abort situation is reflected in " branch
In the corresponding position of road-node " matrix；
2. each change of current device establishes equivalent circuit and mathematical model in pair distribution network, " the branch-of distribution network is used
Node " matrix generates the branch admittance matrix of distribution network, further generates the node admittance matrix of distribution network；
3. establishing the object function of Distribution Networks Reconfiguration optimization in a manner of weight distribution, and circuit is allowed into capacity and hair
Motor allows peak power as constraints, establishes straightening stream synthesis electrical method system power network failure in ship and reconstructs
The mathematical model of optimization；
4. adaptive weighting method is blended with pond algorithm is hybridized, it is applied to Particles Moving and the variation of particle cluster algorithm
In the process；
5. being population sequence by each branch breaking series model of distribution network, and particle is initialized, the foundation is matched
The object function of electric network reconstruction and optimization is screened as the fitness function for the particle for improving particle cluster algorithm with constraints
Corresponding particle brings particle sequence in the steady-state analysis model of distribution network into, calculates corresponding load restoration amount, and each
Circuit and generator parameter, then the switch motion number of the sequence is calculated, optimal particle is searched out in an iterative process；
6. adjusting the inertia weight of particle according to the opposite optimum particle position of population, then some particles are put into miscellaneous
Pond is handed over, makes the particle phase mutual cross of hybridization pond according to certain probability；
7. making iterations by actual circuit situation, algorithm iteration finishes, and generates the failure reconfiguration sequence of optimization, is
A series of branch switch sequences.
For quick, accurate and what is be easy to implement carries out failure to straightening stream synthesis electrical method system power network in ship
Reconstruction and optimization, the present invention provides a kind of ship AC/DC network fault traces realized convenient for programming and steady-state analysis calculating side
Method-improvement junction point voltage method, and a kind of improvement particle cluster algorithm with excellent optimizing ability is devised, and then it is directed to distribution
The branch trouble of network different location provides corresponding best network reconfiguration scheme.
The characteristics of straightening stream (MVDC) distribution network fault condition population reconstructing method, mainly wraps in the ship of the present invention
It includes：
1, entire algorithm is opened up by distribution network mends structural modeling, fault search, distribution system steady-state analysis, reconstruct mathematical modulo
The compositions such as type structure, the improvement of population optimizing algorithm.
2, ship MVDC combined power distribution networks are established can reflect branch-node connection relation open up benefit structure
Model：" branch-node matrix equation ".Fault trace is carried out to distribution network using the scope searching principle, and network failure position is reflected
It penetrates in branch-node matrix equation.
3, improvement is adjusted to a kind of electric power system tide computational methods-junction point voltage method, establishes ship MVDC distribution
The node admittance matrix of network, and gaussian iteration method is used, make that it is suitable for the stable states of ship MVDC combined power distribution networks
Analysis can traverse network power distribution when calculating each branch trouble.
4, by mutually merging adaptive weighting method and hybridization pond algorithm, particle cluster algorithm is improved, it is improved
Optimizing ability；Consider system charge recovery and switch motion cost, reconstruction and optimization pattern function is established, by it as particle
The fitness function of group's algorithm, optimization reconfiguration scheme is searched out using above-mentioned improvement particle cluster algorithm.
The present invention carries out fault trace by the scope searching principle to ship MVDC distribution networks, and it is extensive to consider load power
Multiple and switch motion cost establishes the optimization objective function of Distribution Networks Reconfiguration.Present invention introduces adaptive weighting methods and miscellaneous
Pond algorithm is handed over, modified particle swarm optiziation is devised and is applied to the searching process of ship MVDC distribution network failure reconfigurations, and lead to
It crosses the method-junction point voltage method for calculating a kind of electric power system tide to be improved, makes that it is suitable for ship MVDC alternating current-direct currents to match
Steady-state analysis during electric network failure reconfiguration, this method can be substantially reduced in distribution network fault search and Load flow calculation
Complicated program process effectively raises the speed of searching optimization and accuracy of failure reconfiguration algorithm.
Description of the drawings
Fig. 1 is transverter equivalent circuit diagram；
Fig. 2 is Buck type DC-DC converter equivalent circuit diagrams；
Fig. 3 is the middle pressure dc distribution network Load flow calculation flow chart based on junction point voltage method；
Fig. 4 is straightening stream synthesis electrical method system power network failure reconstruction and optimization in the ship based on particle cluster algorithm
The flow chart of technology；
Fig. 5 is straightening stream synthesis electrical method system power lattice network schematic diagram in ship.
Specific implementation mode
Pressure dc distribution network fault condition population reconstructing method includes the following steps in the ship of the present invention：
1. straightening stream synthesis electrical method system power network establishes opening up for " branch-node " matrix and mends knot in pair ship
Structure mathematical model carries out fault trace search to entire distribution network using the scope searching principle, abort situation is reflected in " branch
In the corresponding position of road-node " matrix.
2. each change of current device (such as DC-DC converter, inverter etc.) establishes equivalent circuit and mathematical modulo in pair distribution network
Type is generated the branch admittance matrix of distribution network using " branch-node " matrix of distribution network, further generates power distribution network
Network failure can be clearly reflected in the admittance matrix of network by the node admittance matrix of network in this way, convenient for being directed to different positions
The distribution network failure set carries out corresponding steady-state analysis calculating.
3. considering load restoration electricity size and switch motion cost, distribution network weight is established in a manner of weight distribution
The object function of structure optimization, and allow capacity and generator to allow peak power as constraints in circuit, it establishes in ship
The mathematical model of straightening stream synthesis electrical method system power network failure reconstruction and optimization.
4. adaptive weighting method is blended with pond algorithm is hybridized, it is applied to Particles Moving and the variation of particle cluster algorithm
In the process, on the basis of basic particle group algorithm, improve its optimizing performance, make the complexity meter it is suitable for Distribution Networks Reconfiguration
Calculate environment.
5. each branch breaking sequence of distribution network (can be regarded as branch switch sequence) is modeled as population sequence, and just
Beginningization particle will establish fitness of the object function as the particle for improving particle cluster algorithm of Distribution Networks Reconfiguration optimization above
Function screens corresponding particle with constraints, and particle sequence is brought into the steady-state analysis model of distribution network, can calculate
Corresponding load restoration amount and each circuit and generator parameter, then the switch motion number of the sequence is calculated, to repeatedly
Optimal particle can be searched out during generation.
6. adjusting the inertia weight of particle according to the opposite optimum particle position of population, its ability of searching optimum is balanced,
Then some particles are put into hybridization pond, make the particle phase mutual cross of hybridization pond according to certain probability, enhances the diversity of population.
7. making iterations by actual circuit situation, algorithm iteration finishes, then produces the failure reconfiguration sequence optimized
Row are a series of branch switch sequences.
It illustrates below and the present invention is described in more detail.
In conjunction with Fig. 1, Fig. 2, the equivalent circuit of the changes of current device such as DC-DC converter, voltage source inverter is established, and builds it
Mathematical model, DC-DC converter mathematical models are as follows：
Voltage source inverter mathematical model is as follows：
Pd=UdId
P1=A+BIc+CIc 2
PC=Pd+P1
Wherein PdFor exchange side power, P1For change of current device loss power, Pc is exchange side power, SN、VN dFor transverter volume
Constant volume, DC side rated voltage, SB、VB dFor system reference capacity, DC reference voltage.
Each load, generator and main distribution board, inverter and its load motors at different levels of connection are considered as current source,
DC-DC converter finds out internal equivalent admittance by its equivalent circuit.
In conjunction with Fig. 3, it is numbered to each circuit node and branch by breadth-first search sequence, L branch of N number of node is matched
Electric network can be established the matrix M of N × L by node branch number, when electric current is saved through branch j from number p from the inflow of number i-node
Point flows out, then Mij=-1, Mij=+1；If node i is not connected directly with branch j, Mij=0.It is established by branch coded sequence
The vector X of L × 1, if number i branches work normally, Xi=1, if the branch disconnects, Xi=0.Then it is by the vector extensions
Diagonal matrix.Node admittance matrix is generated by following formula：
M1=M × diag (X)
Wherein Y is branch admittance matrix, in this way, in node admittance battle array YyIn, connect the member of fault branch between two nodes
Element will be cleared, and the failure of network is just extracted and has been embodied in node admittance battle array.
Give each node potential to assign forming initial fields node potential vector U by number, after can determine pressure drop-row of each branch to
Measure [Ub]l×1With electric current-column vector [Ib]l×1, i.e.,
Thus, it can obtain seeking the iterative formula of node potential：
Wherein：[Yy]-1For the inverse matrix of [Yy]；[U](k+1)For the node potential column vector in+1 step iterative process of kth；
ui(k)The conjugate of the potential vector of iterative process interior joint i is walked for kth.
Can iteration precision condition be set according to KCL, when a certain node resultant current is less than preset value, terminate iteration.So
After each load current, the parameters such as power can be found out according to data with existing.
In actual operation, it is possible that node admittance battle array is unusual or difficult situation of inverting, therefore Gauss can be used
Iterative method calculate node potential U：
The reconstruction and optimization mathematical model of distribution network is established, considers that the recovery of level-one load, object function are：
Consider that the recovery of I and II load, object function are：
I=1,2 in formula ..., k；P=1,2 ... .., m；Lj1 is level-one load, and Lj2 is secondary loads, Xi,
Xp=1 or 0, indicates the power supply loaded and does not power.
If considering the recovery of insignificant load, object function is：
L in formulaj3It is loaded for three-level, Xh=1 or 0, indicate three-level load power supply with do not power, each load restoration electricity can
It is obtained by calculation of tidal current.
Since switching manipulation needs to put into regular hour and manpower, the fewer switching manipulation the better, and switch
As possible using automatic change-over, object function is for conversion：
Min (f2)=Δ X
△ X are switch motion number.
Consider load capacity recovery and switching manipulation, final goal function are：
F=w1max(Lj1+Lj2+Lj3)+w2min(f2)
w1、w2For the weight number of distribution.
According to generator and each capacity of trunk limitation setting constraints：
α∑P≤Pmax
Ib≤Ibmax
Wherein, α is that generator goes out work(accounting, and P is that network parts consume power, and Pmax is that generator maximum limits work(
Rate, IbmaxIt is limited for the line current maximum capacity of branch road.
In conjunction with Fig. 4, initialization operation is carried out to particle cluster algorithm, predecessor and its speed is generated, does not have a particle sequence
A series of on off sequences are represented, wherein particle element value is 0,1,2, indicates that branch is disconnected, connection normal route, connected respectively
Backup path.
By the object function of Distribution Networks Reconfiguration optimization as particle fitness function, and screens out and be not inconsistent according to constraints
It is required that particle, calculate the fitness of each particle, the position of current each particle and fitness function value be stored in each micro-
In the pbest of grain, the optimal individual of adaptive value in all pbest and adaptive value are stored in gbest.
The position and speed of more new particle as the following formula：
vi,j(t+1)=wvi,j(t)+c1r1[pi,j-xi,j(t)]+c2r2[pg,j-xi,j(t)]
xi,j(t+1)=xi,j(t)+vi,j(t+1), j=1,2 ..., d
Then according to xi, the value range of j (t+1) determine its final value at this time：
(-∞,0.5)→0,[0.5,1.5)→1,[1.5,+∞)→2
Weight w is updated using nonlinear dynamic inertia weight coefficient formula, concrete operations see below formula：
Wherein, wmin、wmaxIndicate that the maximum value and minimum value of w, f indicate the current functional value of particle, f respectivelyavgAnd fmin
The fine-grained average target value of current institute and minimum target value are indicated respectively.
To each particle, its current adaptive value is compared with pbest, it is such as more excellent, then the value is updated into pbest, so
After update gbest；
Thereafter, the hybridization concept in genetic algorithm is used for reference, in each iteration, specified quantity is chosen according to probability of crossover
Particle is put into hybridization pond, and the random particles in pond can hybridize two-by-two, generate same number of filial generation particle, uses filial generation particle afterwards
Instead of parent particle.Filial generation particle position is obtained by the parent particle position intersection that count：
Child (x)=pparent1(x)+(1-p)·parent2(x) child (x)=pparent1(x)+(1-
p)·parent2(x)
Wherein, p is the random number between 0 to 1.
Filial generation speed is calculated by following formula：
If meeting iteration stopping condition, final result is exported；Continue to update particle position and speed if not satisfied, then returning
Degree, circular flow algorithm.
Claims (4)
1. dc distribution network fault condition population reconstructing method is pressed in a kind of ship, it is characterized in that：
1) " branch-node " matrix is established to straightening stream synthesis electrical method system power network in ship and opens up benefit structure number
Model is learned, fault trace search is carried out to entire distribution network using the scope searching principle, abort situation is reflected in " branch-section
In the corresponding position of point " matrix；
2) equivalent circuit and mathematical model are established to each change of current device in distribution network, uses " branch-node " of distribution network
Matrix generates the branch admittance matrix of distribution network, further generates the node admittance matrix of distribution network；
3) object function of Distribution Networks Reconfiguration optimization is established in a manner of weight distribution, and circuit is allowed into capacity and generator
Allow peak power as constraints, establishes straightening stream synthesis electrical method system power network failure reconstruction and optimization in ship
Mathematical model；
4) adaptive weighting method is blended with pond algorithm is hybridized, is applied to the process of the Particles Moving and variation of particle cluster algorithm
In；
5) it is population sequence by each branch breaking series model of distribution network, and initializes particle, power distribution network is established by described
The object function of network reconstruction and optimization is screened corresponding as the fitness function for the particle for improving particle cluster algorithm with constraints
Particle brings particle sequence in the steady-state analysis model of distribution network into, calculates corresponding load restoration amount and each circuit
And generator parameter, then the switch motion number of the sequence is calculated, optimal particle is searched out in an iterative process；
6) then some particles are put into hybridization by the inertia weight that particle is adjusted according to the opposite optimum particle position of population
Pond makes the particle phase mutual cross of hybridization pond according to certain probability；
7) iterations are made by actual circuit situation, algorithm iteration finishes, and generates the failure reconfiguration sequence of optimization, is a system
It is disbursed from the cost and expenses way switch sequence.
2. dc distribution network fault condition population reconstructing method is pressed in ship according to claim 1, it is characterized in that
The object function that Distribution Networks Reconfiguration optimization is established in a manner of weight distribution specifically includes：
The reconstruction and optimization mathematical model of distribution network is established, considers that the recovery of level-one load, object function are：
Consider that the recovery of I and II load, object function are：
I=1,2 in formula ..., k；P=1,2 ... .., m；Lj1It is loaded for level-one, Lj2For secondary loads, xi, Xp=1
Or 0, it indicates the power supply loaded and does not power；
If considering the recovery of insignificant load, object function is：
L in formulaj3It is loaded for three-level, Xh=1 or 0, indicate three-level load power supply with do not power, each load restoration electricity is by trend
Result of calculation obtains；
The conversion of switch uses automatic change-over, and object function is：
Min (f2)=Δ X
△ X are switch motion number；
Consider load capacity recovery and switching manipulation, final goal function are：
F=w1max(Lj1+Lj2+Lj3)+w2min(f2)
w1、w2For the weight number of distribution.
3. dc distribution network fault condition population reconstructing method is pressed in ship according to claim 2, it is characterized in that
Constraints is：
α∑P≤Pmax
Wherein, α is that generator goes out work(accounting, and P is that network parts consume power, PmaxPower, I are limited for generator maximumbmax
It is limited for the line current maximum capacity of branch road.
4. dc distribution network fault condition population reconstructing method is pressed in ship according to claim 1,2 or 3, it is special
Sign be it is described adaptive weighting method is blended with pond algorithm is hybridized, be applied to particle cluster algorithm Particles Moving and variation mistake
It is specifically included in journey：
Initialization operation is carried out to particle cluster algorithm, predecessor is generated and its speed, each particle sequence represents a series of open
Sequence is closed, wherein particle element value is 0,1,2, indicates that branch disconnects, connects normal route, connection backup path respectively,
By the object function of Distribution Networks Reconfiguration optimization as particle fitness function, and is screened out according to constraints and be not inconsistent requirement
Particle, calculate the fitness of each particle, the position of current each particle and fitness function value be stored in each particle
In pbest, the optimal individual of adaptive value in all pbest and adaptive value are stored in gbest,
The position and speed of more new particle as the following formula：
vi,j(t+1)=wvi,j(t)+c1r1[pi,j-xi,j(t)]+c2r2[pg,j-xi,j(t)]
xi,j(t+1)=xi,j(t)+vi,j(t+1), j=1,2 ..., d
Then according to xi, the value range of j (t+1) determine its final value at this time：
(-∞,0.5)→0,[0.5,1.5)→1,[1.5,+∞)→2
Weight w is updated using nonlinear dynamic inertia weight coefficient formula, concrete operations are as the following formula：
Wherein, wmin、wmaxIndicate that the maximum value and minimum value of w, f indicate the current functional value of particle, f respectivelyavgAnd fminRespectively
Indicate the fine-grained average target value of current institute and minimum target value,
To each particle, its current adaptive value is compared with pbest, it is such as more excellent, then the value is updated into pbest, then more
New gbest；
Thereafter, the hybridization in genetic algorithm is used for reference, in each iteration, the particle that specified quantity is chosen according to probability of crossover is put into
Hybridize in pond, the random particles in pond hybridize two-by-two, generate same number of filial generation particle, replace parent grain with filial generation particle afterwards
Son, filial generation particle position are obtained by the parent particle position intersection that count：
Child (x)=pparent1(x)+(1-p)·parent2(x) child (x)=pparent1(x)+(1-p)·
parent2(x)
Wherein, p is the random number between 0 to 1,
Filial generation speed is calculated by following formula：
If meeting iteration stopping condition, final result is exported；Continue to update particle position and speed if not satisfied, then returning,
Circular flow algorithm.
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| CN112173041A (en) * | 2020-05-09 | 2021-01-05 | 哈尔滨工程大学 | Ship comprehensive monitoring, control and risk assessment prediction method and system |
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| CN112909982A (en) * | 2021-01-15 | 2021-06-04 | 太原理工大学 | DC power distribution network optimization reconstruction method considering power transmission margin of converter |
| CN113538973A (en) * | 2021-07-20 | 2021-10-22 | 大连海事大学 | Automatic ship collision avoidance method based on improved particle swarm optimization |
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| CN112909982A (en) * | 2021-01-15 | 2021-06-04 | 太原理工大学 | DC power distribution network optimization reconstruction method considering power transmission margin of converter |
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