CN102799950B - Network of ship reconstruction and optimization method based on particle cluster algorithm - Google Patents

Abstract

The invention discloses a kind of network of ship reconstruction and optimization method based on particle cluster algorithm, the method has merged particle cluster algorithm and Small Population algorithm, using the combination of circuit breaker switch states whole between network of ship median generatrix and each load as feasible solution, each feasible solution is as a particle, all set constituent particle groups of feasible solution, first population is carried out particle cluster algorithm optimizing, the optimal solution space that population is obtained by recycling Small Population algorithm carries out optimizing, utilizes the optimal feasible solution obtained to be reconstructed network of ship.The method, compared with existing method, takies during machine less, calculates speed faster, meets the requirement of network of ship reconstruct real-time.

Description

Network of ship reconstruction and optimization method based on particle cluster algorithm

Technical field

The invention belongs to Swarm Intelligent Computation field, be specifically related to network of ship reconstruction and optimization based on particle cluster algorithm Method.

Background technology

Power system network reconstruct refers to by periodic adjustment network structure, reaches balanced load, eliminates overload, improves Supply voltage quality, it is ensured that the purpose of Electrical Safety.The reconfiguration of electric networks of boats and ships is different from the reconfiguration of electric networks on land, major embodiment : when 1, the main target of network of ship reconstruct is to occur in fault or fight impaired, keep to greatest extent high priority The continued power of load, maintains the workload demand that boats and ships are most basic；2, owing to Ship Electrical Power System is compared with the power system of land, Power circuit is shorter, and impedance is the least, and its active power loss is negligible；3, Ship Electrical Power System fault occurs the most prominent So, for ensureing continued power, switch on-off must complete reconfiguration of electric networks in a short period of time, therefore it is required that reconstructed velocity is fast； 4, network of ship reconstruct is the reconstruct to the whole network, and the power of sub-load can change due to the difference of system conditions.

In order to seek the optimum reconstruct mode of network of ship, the method that current Chinese scholars proposes is mainly intelligent search Method, multiple agent method, specialist system and integer programming method etc..Although said method is capable of the weight of network of ship Structure, obtains certain effect of optimization, but feature for network of ship reconstruct does not carries out adaptive improvement, need artificially from Candidate solution is concentrated and is selected according to network of ship operating condition, considers the by-ends such as active power loss simultaneously, calculates time mistake Long, it is impossible to meet the requirement of real-time of network of ship reconstruction.

Summary of the invention

The problem existed for the optimum reconstructing method of above-mentioned network of ship, the present invention proposes one based on particle cluster algorithm Network of ship reconstruction and optimization method, the method has merged particle cluster algorithm and Small Population algorithm, improve network of ship reconstruct Real-time.The technological means that the present invention uses is as follows:

A kind of network of ship reconstruction and optimization method based on particle cluster algorithm, it is characterised in that: by network of ship median generatrix And between each load, the combination of whole circuit breaker switch states is as feasible solution, and each feasible solution is as a particle, the most feasible The set constituent particle group solved, comprises the following steps:

Step 1: the parameters optimization of population, random initializtion population are set；

Step 2: judge whether current iteration number is less than number of iterations N1 performing particle cluster algorithm, is then to perform step 3, no Then perform step 4；

Step 3: use object function 1 to calculate in population and perform step 6 after the fitness value of each particle；

Step 4: perform step 5 after choosing M optimal particle in current particle group；

Step 5: use object function 2 to perform step 6 after calculating particle fitness value；

Step 6: judge the position of the particle of each particle current iteration number k in populationFitness value whether be more than The optimal location p that corresponding particle search is crossed_ibestFitness value, be then execution step 7, the history otherwise retaining this particle is optimum Position, performs step 8；

Step 7: update the optimal location p of corresponding particle_ibest, orderPerform step 8 afterwards；

Step 8: judge the optimal location of all particles in current iteration number kFitness value whether more than record The history global optimum position g of all particles in population_bestFitness value, be then execution step 9, otherwise retain this particle History global optimum position g_best, perform step 10；

Step 9: the history global optimum position of more new particle, orderPerform step 10 afterwards；

Step 10: update the Position And Velocity vector of current particle group, perform step 11 afterwards；

Step 11: judge whether current iteration number reaches every N2 generation, is then to perform step 12, otherwise performs step 13；

Step 12: reinitialize all particles but retain global optimum's fitness value optimum with individuality and position arrow Amount；

Step 13: judge whether to meet end condition, be, terminate, obtains optimal feasible solution, according to optimal feasible solution pair Electrical network is reconstructed, and otherwise returns step 2.

The inventive method compared with prior art has the advantages that

1, the present invention introduces Dynamic Neighborhood method on the basis of existing particle cluster algorithm and processes multi-objective optimization question, it is not necessary to Solve concentration by network of ship operating condition from Pareto and select final result, directly by the operating condition (war of network of ship Bucket, motor-driven, navigation etc.) optimal solution output can be directly obtained as input parameter, it is not necessary to manual operation, with Pareto disaggregation Compare and be more suitable for network of ship reconstruction.

2, the present invention is by merging Small Population technology, the population of general particle swarm optimization algorithm in standard particle group's algorithm Scale is 20 ~ 50, and Small Population population scale is 5；And after the certain algebraically of every iteration, update all particles, retain simultaneously The individual position individual with local optimum of global optimum and fitness value.The particle swarm optimization algorithm using Small Population technology is being protected Drastically increase search efficiency while card search precision, more adapt to the reality of network of ship reconstruction compared with existing algorithm The requirement of time property.It addition, its population regular update technology can balance global search and local search ability, improve population excellent Change the search efficiency of algorithm.

3, the algorithm that the present invention proposes is compared with existing method, and it takies during machine less, calculates speed faster, can be the most defeated Go out reconfiguration of electric networks result.

Accompanying drawing explanation

Below in conjunction with drawings and Examples, the present invention is further elaborated:

Fig. 1 is the flow chart of present invention reconfiguration of electric networks based on particle cluster algorithm optimization method.

Fig. 2 is the network topological diagram of one 8 node Ship Electrical Power Systems.

Fig. 3 is the fitness value change curve applying the inventive method under three kinds of operating modes.

Detailed description of the invention

As it is shown in figure 1, the inventive method has merged particle cluster algorithm and Small Population algorithm, by network of ship median generatrix with each Between load, the combination of whole circuit breaker switch states is as feasible solution, each feasible solution as a particle, whole feasible solutions Set constituent particle group.The method of the present invention comprises the following steps:

Step 1: the parameters optimization of population, random initializtion population are set.This parameters optimization includes: inertia weight, Individual accelerator coefficient, society's accelerator coefficient, perform particle cluster algorithm number of iterations N1, perform Small Population algorithm number of iterations N2, Population scale pop_Size, dimension dim of feasible solution, position upper limit X_max of feasible solution, feasible solution position lower limit X _ Min, the maximal rate of feasible solution limit V_max, greatest iteration number iter_max of feasible solution.

Then the step of random initializtion population is: calculate position vector X of each random particles；To each random particles Position vector X round；Calculate the velocity vector V of each random particles.That is:

X=X_min+(X_max-X_min)*rand();

X=round(X);

V=V_min+(V_max-V_min)*rand();

In formula, X is pop_Size row dim column matrix, and in matrix, each element representation is X_ij∈ [0,1], i are row matrix Number, j is matrix columns；Rand () is the random number between [0,1], and round (X) function is by each element in matrix X Carry out round.

Step 2: judge whether current iteration number is less than number of iterations N1 performing particle cluster algorithm, is then to perform step 3, no Then perform step 4.

Step 3: use object function 1 to calculate in population and perform step 6, object function after the fitness value of each particle 1 is expressed as:

fitness=sum(X*load_pri)

In formula, fitnes s is fitness value, and load_pri is the load priority vector pre-set, sum (X* Load_pri) represent that in position vector, each element is sued for peace with each element multiplication in load priority vector again.

Step 4: perform step 5 after choosing M optimal particle in current particle group.

Step 5: using object function 2 to perform step 6 after calculating particle fitness value, object function 2 is expressed as:

fitness=sum(X*load_magnit)

In formula, fitness is fitness value, and load_magnit is load capacity vector, and sum (X*load_magnit) is In position vector, each element is sued for peace with each element multiplication in load capacity vector again.

Step 6: judge the position of the particle of each particle current iteration number k in populationFitness value whether be more than The optimal location p that corresponding particle search is crossed_ibestFitness value, be then execution step 7, the history otherwise retaining this particle is optimum Position, performs step 8.

Step 7: update the optimal location p of corresponding particle_ibest, orderPerform step 8 afterwards.

Step 8: judge the optimal location of all particles in current iteration number kFitness value whether more than record The history global optimum position g of all particles in population_bestFitness value, be then execution step 9, otherwise retain this particle History global optimum position g_best, perform step 10.

Step 9: the history global optimum position of more new particle, orderPerform step 10 afterwards.

Step 10: update the Position And Velocity vector of current particle group, perform step 11, the procedural representation of renewal afterwards For:

$v_{\mathrm{ij}}^{k+1}=\mathrm{\ω}\·v_{\mathrm{ij}}^k+c_1\·r_1\·({p_{\mathrm{best}}}_i^k-x_{\mathrm{ij}}^k)+c_2\·r_2(g_{\mathrm{best}}^k-x_{\mathrm{ij}}^k);$

if(rand()<S(vij))then xij=1;

else xij=0；

$S\left(v_{\mathrm{ij}}\right)=\frac{1}{1+e^{-v_{\mathrm{ij}}}};$

In formula,Representing particle i jth dimension is speed during k+1 in number of iterations, and ω is inertia weight, and c1 is individual acceleration Coefficient, c2 is society's accelerator coefficient, and r1, r2 are the random number between [0,1],When being k for i-th particle to number of iterations Optimal location,It is optimal location during k for population to number of iterations.

Step 11: judge whether current iteration number reaches every N2 generation, is then to perform step 12, otherwise performs step 13.

Step 12: reinitialize all particles but retain global optimum's fitness value optimum with individuality and position arrow Amount, updates: X=X_min+ (X_max-X_min) * rand () as follows;X=round(X).

Step 13: judge whether to meet end condition, be, terminate, obtain optimal feasible solution, electrical network is reconstructed, no Then return step 2.This end condition can be greatest iteration number iter_max set in advance or other end condition.

Aforesaid way is exemplified below: assume to use said method to the electrical network under 8 node Ship Electrical Power System faults The optimal feasible solution of reconstruct carries out optimizing, as in figure 2 it is shown, this 8 node Ship Electrical Power System includes main generator MTG1, main generating Machine MTG2, auxiliary generator ATG1, auxiliary generator ATG2, the generated output of two main generators is respectively 36MW, two auxiliary generatings It is 8 bus nodes that the generated output of machine is respectively 4MW, Bus1 ~ Bus8, and B1 ~ B20 is chopper, and L1 ~ L8 is 8 loads.Respectively The capacity met and priority weighting are as shown in following table one:

Table one

Load sequence number	L1	L2	L3	L4	L5	L6	L7	L8
									Load capacity (MW)	20	1	4	1	20	4	4	2
Priority weighting	1	10	5	10	3	5	5	6

According to above-mentioned table one, under power system is in normal operational condition, whole generator connecting in parallel with system run, whole loads Normal work.If breaking down at bus nodes Bus1, main generator MTG1 and load L8 is affected and exits electrical network, boats and ships Grid generation power loss 36MW, load loss 2MW, available generated output residue 44MW, load general power is 54MW, the most right After electrical network is reconstructed, the load guarantee power grid security of at least 10MW need to be excised.But need after excision load to meet boats and ships electricity Net the requirement to load also load priority under different operating mode, it is assumed that network of ship includes three kinds of operating conditions, under each operating mode Load and priority requirements are as shown in following table two:

Table two

Fault bus	Operating mode	Minimum priority	Minimum load (MW)	Generated output (MW)	Load power (MW)
						1	Fight	30	30	44	54

1	Motor-driven	20	40	44	54
						1	Navigation	6	44	44	54

In the case of breaking down at bus nodes Bus1, power system as shown in Figure 2 is entered with reference to above-mentioned steps Line reconstruction, now the load priority vector in above-mentioned steps is [1 10 5 10 355 6], and load capacity vector is [20 141 20 44 2], simulated environment is: Intel (R) Core (TM) 2 Duo [email protected] 2.93GHz CPU, 2.00 GB internal memories, Windows 7 operating system, Matlab 2010 development environment, repeat 100 times altogether.The application present invention The average calculation times of method is as follows with the contrast of existing discrete particle cluster algorithm, Small Population algorithm average calculation times respectively Shown in table three, final load condition is as shown in following table four:

Table three

Table four

In table three, DPSO represents existing discrete particle cluster algorithm, and SPPSO represents existing Small Population algorithm, DNSPPSO generation The algorithm that the particle cluster algorithm of the table present invention is combined with Small Population algorithm.As can be seen from the table, the present invention and prior art phase Ratio, it is shorter that it calculates the time, is more suitable for network of ship reconstruction..

In table four, 01111110 under fight operating mode represents L1, L8 and disconnects, and other load is connected；Under motor-driven operating mode 11011000 represent L1, L2, L4, L5 connects, and other load disconnects；There are three kinds of feasible solutions under running working condition, are respectively 10001100(L1, L5, L6 connect, other load disconnect), 10101000(L1, L3, L5 connect, other load disconnect), 10001010(L1, L5, L7 connect, and other load disconnects).If Fig. 3 is for using inventive algorithm once to imitate simulation example During Zhen, the change of fitness value under three kinds of operating modes.

Claims (5)

1. a network of ship reconstruction and optimization method based on particle cluster algorithm, it is characterised in that: by network of ship median generatrix with Between each load, the combination of whole circuit breaker switch states is as feasible solution, and each feasible solution is as a particle, whole feasible solutions Set constituent particle group, comprise the following steps:

Step 1: the parameters optimization of population, random initializtion population are set；

Step 2: judge whether current iteration number is less than number of iterations N1 performing particle cluster algorithm, is then to perform step 3, otherwise holds Row step 4；

Step 3: use object function 1 to calculate in population and perform step 6 after the fitness value of each particle；

Step 4: perform step 5 after choosing M optimal particle in current particle group；

Step 5: use object function 2 to perform step 6 after M the optimal particle chosen is calculated particle fitness value；

Step 6: judge the position of the particle of each particle current iteration number k in populationFitness value whether more than correspondence The optimal location p that particle search is crossed_ibestFitness value, be then execution step 7, otherwise retain the history optimum position of this particle Put, perform step 8；

Step 7: update the optimal location p of corresponding particle_ibest, orderPerform step 8 afterwards；

Step 8: judge the optimal location of all particles in current iteration number kFitness value whether more than the particle of record The history global optimum position g of all particles in Qun_bestFitness value, be then execution step 9, otherwise retain going through of this particle History global optimum position g_best, perform step 10；

Step 9: the history global optimum position of more new particle, orderPerform step 10 afterwards；

Step 10: update the Position And Velocity vector of current particle group, perform step 11 afterwards；

Step 11: judge whether current iteration number reaches N2, is then to perform step 12, otherwise performs step 13, and wherein N2 is for holding The number of iterations of row Small Population algorithm；

Step 12: reinitialize all particles but retain global optimum's fitness value optimum with individuality and position vector；

Step 13: judge whether to meet end condition, be, terminate, obtains optimal feasible solution, according to optimal feasible solution to electrical network It is reconstructed, otherwise returns step 2；

Wherein, parameters optimization includes: inertia weight, individual accelerator coefficient, society's accelerator coefficient, the iteration of execution particle cluster algorithm Number N1, execution number of iterations N2 of Small Population algorithm, population scale pop_Size, dimension dim of feasible solution, the position of feasible solution Upper limit X_max, the position lower limit X _ min of feasible solution, the maximal rate of feasible solution limit V_max, the friction of feasible solution V_min processed, greatest iteration number iter_max of feasible solution；

The step of random initializtion population is: calculate position vector X of each random particles；Position to each random particles Vector X rounds；Calculate the velocity vector V of each random particles, it may be assumed that

X=X_min+ (X_max-X_min) * rand ()；

X=round (X)；

V=V_min+ (V_max-V_min) * rand ()；

In formula, X is pop_Size row dim column matrix, and in matrix, each element representation is X_ij∈ [0,1], i are matrix line number, and j is Matrix columns；Rand () is the random number between [0,1], and each element in matrix X is carried out four by round (X) function House five enters to round.

Network of ship reconstruction and optimization method the most according to claim 1, it is characterised in that: object function 1 is expressed as:

Fitness=sum (X*load_pri)

In formula, fitness is fitness value, and load_pri is the load priority vector pre-set, sum (X*load_pri) Represent that in position vector, each element is sued for peace with each element multiplication in load priority vector again.

Network of ship reconstruction and optimization method the most according to claim 1, it is characterised in that: object function 2 is expressed as:

Fitness=sum (X*load_magnit)

In formula, fitness is fitness value, and load_magnit is load capacity vector, and sum (X*load_magnit) is position In vector, each element is sued for peace with each element multiplication in load capacity vector again.

Network of ship reconstruction and optimization method the most according to claim 1, it is characterised in that update the position of current particle group It is expressed as with the step of velocity:

$v_{ij}^{k+1}=\mathrm{\&omega;}\&CenterDot;v_{ij}^k+c_1\&CenterDot;r_1\&CenterDot;({p_{best}}_i^k-x_{ij}^k)+c_2\&CenterDot;r_2\&CenterDot;(g_{best}^k-x_{ij}^k)$

$if(rand\left(\right)<S(v_{ij}^k\left)\right)then{x}_{ij}^k=1;$

$else{x}_{ij}^k=0;$

$sig\left(v_{ij}^k\right)=\frac{1}{1+e^{-v_{ij}^k}};$

In formula,Representing particle i jth dimension is speed during k+1 in number of iterations,Represent particle i jth to tie up when number of iterations is k Speed,Representing particle i jth dimension is position during k in number of iterations, and ω is inertia weight, c₁For individual accelerator coefficient, c₂For Society's accelerator coefficient, r₁,r₂For the random number between [0,1],It is optimal location during k for i-th particle to number of iterations,It is optimal location during k for population to number of iterations.

Network of ship reconstruction and optimization method the most according to claim 1, it is characterised in that end condition is set in advance Greatest iteration number iter_max.