CN109004643A - Based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm - Google Patents
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Abstract
The invention discloses a kind of based on the power distribution network network reconfiguration optimization method for improving particle swarm algorithm, comprising: (1) initialization population；(2) judge whether distribution at this time meets radial determination requirement by depth-priority-searching method, if meeting, continue step (3), otherwise return to step (1)；(3) the objective function f (x) of each particle is calculated；(4) speed of more new particle and position, and calculate the health degree of each particle；(5) judge whether particle is healthy, if particle is healthy, jump to step (7)；Otherwise continue step (6)；(6) ill particle is updated；(7) if fitness value is more optimal than current group big, continue step (8), otherwise jump to step (9)；(8) if reaching maximum number of iterations, result is exported；Conversely, being denoted as current optimal value；(9) judge whether to reach the non-update times of group's maximum, be, export reconstruction and optimization result；Otherwise, step (2) are jumped to.
Description
Technical field
It is the invention belongs to power distribution network reconfiguration optimisation technique field, in particular to a kind of based on the distribution for improving particle swarm algorithm
Net reconstruction and optimization method.
Background technique
Power distribution network reconfiguration is to be opened in power supply and demand balance and under the premise of meet capacity and voltage and constrain by changing segmentation
It closes, the assembled state of interconnection switch, that is, selects the supply path of user, so that reaching reduces distribution network loss, realizes that load is equal
Weighing apparatusization improves quality of voltage, eliminates the purpose of circuit overload.
There are many switches in power distribution network, wherein mainly including interconnection switch and block switch.In normal operation
Lower interconnection switch is generally opened, logical for providing optional power supply to guarantee the requirement of power distribution network open loop operation, loop design
Road；Block switch is generally closed, isolated fault, to guarantee that power grid operates normally.Therefore under normal operating conditions, can pass through
Change the folding condition of interconnection switch and block switch to change network topology, to change the power flow in network, to reach
Improve the safety of operation of power networks and the target of economy.Under failure operation state, by the switch shape for changing interconnection switch
Power failure load is transferred on the feeder line of normal operation by state, to realize fault recovery.
Distribution Networks Reconfiguration is a kind of important measures for optimizing distribution system, it is by determining whether switch opens or closes
To optimize distribution system.Power distribution network reconfiguration is that an index (such as: via net loss, quality of voltage etc.) is optimal in determining network, with
Ensure that distribution network is radial etc..
Power distribution network reconfiguration is a complicated large-scale non-designated polynomial combinatorial optimization problem.From last century 80 years
In generation, is widely studied power distribution network reconfiguration.Proposing can be roughly divided into two types: 1. traditional mathematics optimization algorithm,
Such as linear programming technique, branch exchange method and optimal flow pattern algorithm etc..Linear programming algorithm has serious " dimension disaster "
Problem.Therefore, it is difficult to meet actual requirement；Branch exchange method and optimal flow pattern algorithm with calculating linear programming algorithm speed
Degree is many compared to improving, but finally still depends on initial convergence network structure.But mathematically, also lack global excellent
Change.2. intelligent algorithm, such as simulated annealing, genetic algorithm and improved algorithm etc..
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of based on the distribution network for improving particle swarm algorithm
Reconstruction and optimization method can be improved the global optimizing ability of reconstruction and optimization, solve to occur in power distribution network reconfiguration a large amount of infeasible
The technical issues of solution.
The invention discloses based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, health degree is introduced, judges grain
Whether son does not update for a long time, if particle number reaches setting value, note particle is healthy particle, continues optimizing；Otherwise, grain
Son is denoted as ill particle, and is given treatment to, it is made to get well, and concrete operations are as follows:
The health degree for setting particle is as follows:
Hi=100-min (wpNp+wsNs,100) (7)
NpFor the non-update times of particle；NsFor the number of oscillation of particle；wpFor the weighting coefficient of the stagnation number of particle；
wsFor the weighting coefficient of the number of oscillation of particle；
It is as follows to determine whether particle shakes:
For the position of i-th of particle in the t times iteration, if formula (8) is set up, NsValue add 1；Otherwise NsValue it is constant；
When the health degree of particle is lower than threshold value initially set, ill particle is given treatment to: utilizing the grain in following formula
The original ill particle of son substitution:
Pi=0.5* (Gi+rand1(1,d)*(Gi-xi) (9)
xi=Gi+rand2(1,d)*(Gi-Pi) (10)
xiFor the position of the i-th particle；PiIt is optimal for the individual of particle i；GiFor the global optimum of population；rand1(1,d)
It is that d of the numerical value between [0,1] ties up row vector with rand2 (1, d).
Specifically includes the following steps:
S1: initialization population encodes population according to network structure；
S2: topological inspection is carried out to network corresponding to each particle using depth-first tree searching method, judgement is at this time
It is radial whether distribution meets, if meeting, arrives S3, otherwise returns to S1；
S3: the objective function f (x) of each particle is calculated；Each particle current location is set as the optimal P of individuali, GiIt is complete
Office is optimal；
The objective function f (x) of each particle may be expressed as:
Min y=f (x) (1)
It is indicated until particle i to the t times iteration in the optimal position of individual that d dimension is found are as follows:
Pi=(Pi1,Pi2,...,Pid)t (3)
It is indicated until particle group to the t times iteration in the global optimum position that d dimension is found are as follows:
Gi=(Gi1,Gi2,...,Gid)t (4)
S4: the speed of more new particle and position, and calculate the health degree of each particle；
S5: judge whether particle is healthy, if particle is healthy, jump to S7；Otherwise continue S6；
S6: ill particle is updated；
S7: if fitness value is more optimal than current group big, continues S8, otherwise jump to S9；The fitness value is target
Function calculated result:
Min y=f (x) (1)
S8: if reaching maximum number of iterations, result is exported；Conversely, being denoted as current optimal value；
S9: judging whether to reach the non-update times of group's maximum, be, exports reconstruction and optimization result；Otherwise, S2 is jumped to.
Wherein, S2 specific steps are as follows:
S2-1: generating node branch incidence matrix, and the line number of matrix represents number of nodes, and matrix column number indicates circuitry number,
Node branch incidence matrix is Sparse Array, the interior only 0 and 1 liang of number of matrix, the node and 1 place that 1 row for representing 1 place indicates
Column represent branch be connected；
S2-2: it calculates the closure circuitry number in distribution network: branch sum in power distribution network is acquired according to node incidence matrix
Amount, if branch total quantity=effective number of nodes -1 in power distribution network, carries out S2-3, conversely, being then judged as that distribution does not meet radiation
Shape:
S2-3: judging whether power distribution network is connection network, if power distribution network be connection network, power distribution network be it is radial, instead
It, there are isolated nodes in power distribution network, do not meet radial.Concrete operations are as follows:
A. the node at a branch both ends is obtained according to node incidence matrix；
B. assume the branch disconnected, using the node of branch one end as starting point, another end node is terminal, utilizes deep search
Method, checking whether can be from one end node searching to another end node, if can be with, and two nodes are connection；
C. examine whether node and remaining node are connected to, if each node can search remaining node, this
A node is all connected to all nodes, and power distribution network is radial；If one of node cannot find its corresponding terminal node,
Then there are isolated nodes in the power distribution network, do not meet radial.
The position and speed of i-th of particle respectively indicates in the t times iteration in S4 are as follows:
Lid, UidThe upper limit and lower limit of the respectively particle i in d dimension space flight position, vmax,id, vmin,idRespectively particle i
In the upper limit and lower limit of d dimension space flying speed.
The optimizing renewal process of particle swarm algorithm can be indicated with following formula:
W is inertia weight, c1、c2For Studying factors,For the random positive real number in section [0,1], xt id, vt idPoint
Not Wei in the t times iteration i-th of particle position and speed, Pt id, Gt idIt is tieed up until respectively particle i to the t times iteration in d
The optimal position of individual found and global optimum position.
The utility model has the advantages that the present invention using improve particle swarm algorithm carry out power distribution network reconfiguration optimization, without to particle carry out into
Change operation, algorithm is simple.Radial judgement is carried out to power distribution network using Depth Priority Searching simultaneously.For particle swarm algorithm
The problem of later period particle is single, is easy to precocious, easily falls into local optimum, present invention introduces the concepts of health degree.Health degree
Essence is to judge whether particle does not update for a long time, if particle update times reach requirement, note particle is healthy particle, according to original
The rule come continues optimizing；If particle does not update repeatedly, it is denoted as ill particle, needs to give treatment to ill particle, so that
It is got well.It is 201410649037.7 based on the power distribution network reconfiguration optimization side for improving particle swarm algorithm with number of patent application
The patent of invention of method is compared, and this patent global optimizing ability is strong, and algorithm is simple, can obtain optimal solution in a short time.
Detailed description of the invention
Fig. 1 is IEEE16 node power distribution web frame figure.
Fig. 2 is algorithm flow chart.
Specific embodiment
The present invention is further explained with reference to the accompanying drawings and examples.
Power distribution network reconfiguration optimization method based on improvement particle swarm algorithm of the invention, comprising the following steps:
S1: initialization population encodes population according to network structure；
S2: topological inspection is carried out to network corresponding to each particle using depth-first tree searching method, judgement is at this time
Whether distribution meets radial determination requirement, if meeting, continues S3, otherwise returns to S1；Concrete operations are as follows:
S2-1: it generates the initial data of distribution network: generating node branch incidence matrix, the line number of matrix represents node
Number, every a line represent a node, and matrix column number indicates that circuitry number, each column indicate that a branch, node branch are associated with square
Battle array is a Sparse Array, only 0 and 1 liang of number in matrix, 1 represent 1 where the row node and 1 that indicates where column representative
Branch is connected.So a column share 21, i.e. a branch is connected with two nodes.
S2-2: it calculates the closure circuitry number in distribution network: can be shared by node incidence matrix in the hope of in power distribution network one
How many branches, if all circuitry numbers=effective number of nodes -1 in power distribution network, carries out S2-3, conversely, not being then radial
Network returns to S1.
S2-3: judge whether distribution network is connection network:
A. the node at a branch both ends can be obtained according to node incidence matrix；
B. assume the branch disconnected, using the node of branch one end as starting point, another end node is terminal, utilizes deep search
Method, checking whether can be from one end node searching to another end node, if can be with, the two nodes are connections；
C. according to the method for b, examine whether node and remaining all node are connected to, if each node can search
Remaining node, then this node is all connected to all nodes, then power distribution network is radial networks；If one of node is simultaneously
Its corresponding terminal node cannot be found, then it is not radial networks that there are isolated nodes in the power distribution network.
S3: the objective function f (x) of each particle is calculated；Each particle current location is set as the optimal P of individuali, GiIt is complete
Office is optimal；
Wherein, the objective function f (x) of each particle may be expressed as:
Min y=f (x) (1)
In formula, l is the sum of power distribution network branch；For the active loss of branch b；
It may be expressed as: until particle i to the t times iteration in the optimal position of individual that d dimension is found
Pi=(Pi1,Pi2,...,Pid)t (3)
It can be indicated until particle group to the t times iteration in the global optimum position that d dimension is found are as follows:
Gi=(Gi1,Gi2,...,Gid)t (4)
S4: according to the speed and position of formula (5) and (6) more new particle；The strong of each particle is calculated according to formula (7)
Kang Du, concrete operations are as follows:
The position and speed of i-th of particle can respectively indicate in the t times iteration are as follows:
Lid, UidThe upper limit and lower limit of the respectively particle i in d dimension space flight position, vmax,id, vmin,idRespectively particle i
In the upper limit and lower limit of d dimension space flying speed.
The optimizing renewal process of particle swarm algorithm can be indicated with following formula:
In formula, w is inertia weight, is taken between 0.4-0.9；c1、c2It is nonnegative constant for Studying factors, general value is 2；For the random positive real number in section [0,1]；
The health degree that the present invention sets particle is as follows:
Hi=100-min (wpNp+wsNs,100) (7)
NpFor the non-update times of particle；NsFor the number of oscillation of particle；wpFor the weighting coefficient of the stagnation number of particle；
wsFor the weighting coefficient of the number of oscillation of particle.
Formula (8) is to determine whether particle shakes:
If formula (8) is set up, NsValue add 1；Otherwise NsValue it is constant.
S5: judge whether particle is healthy, if particle is healthy, jump to S6；Otherwise continue S5；
S6: updating ill particle, and concrete operations are as follows:
When the health degree of particle is lower than threshold value initially set, ill particle is given treatment to, is calculated to improve population
The global optimizing ability of method avoids particle precocious, and the present invention substitutes original ill particle using the particle in following formula:
Pi=0.5* (Gi+rand1(1,d)*(Gi-xi) (9)
xi=Gi+rand2(1,d)*(Gi-Pi) (10)
In above formula, xiFor the position of the i-th particle；PiIt is optimal for the individual of particle i；GiFor the global optimum of population；
Rand1 (1, d) and rand2 (1, d) are that d of the numerical value between [0,1] ties up row vector.
S7: if fitness value is more optimal than current group big, continues S8, otherwise jump to S9；
S8: if reaching maximum number of iterations, optimal result is exported；Conversely, being denoted as current optimal value；
S9: judging whether to reach the non-update times of group's maximum, if so, output reconstruction and optimization result；Otherwise, S2 is jumped to.
Embodiment:
By taking IEEE16 node power distribution web frame figure shown in FIG. 1 as an example, IEEE16 node reconstruct front and back parameter comparison table is such as
Shown in lower:
Table 1IEEE16 node reconstruct front and back parameter comparison table
| Interconnection switch | Network loss (kW) | Minimum node voltage (kV) |
| Before reconstruct (14,15,16) | 593.6 | 10.0691 |
| After reconstruct (9,10,15) | 546.9 | 10.0825 |
Particle swarm algorithm mean iterative number of time is 29.53 times before improving, and maximum number of iterations is 38 times, and optimal solution probability is
86.67%, average operating time is 4.89 seconds.The mean iterative number of time of particle swarm algorithm is 29.53 times after improvement, greatest iteration
Number is 40 times, and optimal solution probability is 93.33%, and average operating time is 6.65 seconds.Compared it is found that population is calculated after improving
The arithmetic speed of method does not have much difference compared with unmodified particle swarm algorithm, but the probability for obtaining optimal solution is obvious
Improved particle swarm algorithm is bigger.
Claims (6)
1. based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: introduce health degree, judge that particle is
No long-time does not update, if particle number reaches setting value, note particle is healthy particle, continues optimizing；
Otherwise, particle is denoted as ill particle, and is given treatment to, it is made to get well, and concrete operations are as follows:
The health degree for setting particle is as follows:
Hi=100-min (wpNp+wsNs,100) (7)
NpFor the non-update times of particle；NsFor the number of oscillation of particle；wpFor the weighting coefficient of the stagnation number of particle；wsFor grain
The weighting coefficient of the number of oscillation of son；
It is as follows to determine whether particle shakes:
xi tFor the position of i-th of particle in the t times iteration, if formula (8) is set up, NsValue add 1；Otherwise NsValue it is constant；
When the health degree of particle is lower than threshold value initially set, ill particle is given treatment to: being replaced using the particle in following formula
For original ill particle:
Pi=0.5* (Gi+rand1(1,d)*(Gi-xi) (9)
xi=Gi+rand2(1,d)*(Gi-Pi) (10)
xiFor the position of the i-th particle；PiIt is optimal for the individual of particle i；GiFor the global optimum of population；Rand1 (1, d) with
Rand2 (1, d) is that d of the numerical value between [0,1] ties up row vector.
2. according to claim 1 based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: tool
Body the following steps are included:
S1: initialization population encodes population according to network structure；
S2: topological inspection is carried out to network corresponding to each particle using depth-first tree searching method, judges distribution at this time
Whether meet it is radial, if meeting, arrive S3, otherwise return to S1；
S3: the objective function f (x) of each particle is calculated；Each particle current location is set as the optimal P of individuali, GiMost for the overall situation
It is excellent；
The objective function f (x) of each particle may be expressed as:
Min y=f (x) (1)
It is indicated until particle i to the t times iteration in the optimal position of individual that d dimension is found are as follows:
Pi=(Pi1,Pi2,...,Pid)t (3)
It is indicated until particle group to the t times iteration in the global optimum position that d dimension is found are as follows:
Gi=(Gi1,Gi2,...,Gid)t (4)
S4: the speed of more new particle and position, and calculate the health degree of each particle；
S5: judge whether particle is healthy, if particle is healthy, jump to S7；Otherwise continue S6；
S6: ill particle is updated；
S7: if fitness value is more optimal than current group big, continues S8, otherwise jump to S9；The fitness value is objective function
Calculated result:
Min y=f (x) (1)
S8: if reaching maximum number of iterations, result is exported；Conversely, being denoted as current optimal value；
S9: judging whether to reach the non-update times of group's maximum, be, exports reconstruction and optimization result；Otherwise, S4 is jumped to.
3. according to claim 2 based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: institute
State S2 specific steps are as follows:
S2-1: generating node branch incidence matrix, and the line number of matrix represents number of nodes, and matrix column number indicates circuitry number, node
Branch incidence matrix is Sparse Array, only 0 and 1 liang of number in matrix, the column where the node and 1 that 1 row for representing 1 place indicates
The branch of representative is connected；
S2-2: calculating the closure circuitry number in distribution network: acquiring branch total quantity in power distribution network according to node incidence matrix, if
Branch total quantity=effective number of nodes -1, then carry out S2-3 in power distribution network, conversely, it is radial to be then judged as that distribution is not met:
S2-3: judging whether power distribution network is connection network, if power distribution network be connection network, power distribution network be it is radial, conversely, matching
There are isolated nodes in power grid, do not meet radial.
4. according to claim 3 based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: institute
It is as follows to state S2-3 concrete operations:
A. the node at a branch both ends is obtained according to node incidence matrix；
B. assume the branch disconnected, using the node of branch one end as starting point, another end node is terminal, utilizes the side of deep search
Method, checking whether can be from one end node searching to another end node, if can be with two nodes are connection；
C. examine whether node and remaining node are connected to, if each node can search remaining node, this section
Point is all connected to all nodes, and power distribution network is radial；It, should if one of node cannot find its corresponding terminal node
There are isolated nodes in power distribution network, do not meet radial.
5. according to claim 2 based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: institute
The position and speed for stating i-th of particle in the t times iteration in S4 respectively indicates are as follows:
xid∈[Lid,Uid]
vid∈[vmin,id,vmax,id]
Lid, UidThe upper limit and lower limit of the respectively particle i in d dimension space flight position, vmax,id, vmin,idRespectively particle i is tieed up in d
The upper limit and lower limit of space flight speed.
6. according to claim 5 based on the power distribution network reconfiguration optimization method for improving particle swarm algorithm, it is characterised in that: grain
The optimizing renewal process of swarm optimization can be indicated with following formula:
W is inertia weight, c1、c2For Studying factors, r1 t、For the random positive real number in section [0,1], xt id, vt idRespectively
The position and speed of i-th of particle, P in t iterationt id, Gt idIt is found until respectively particle i to the t times iteration in d dimension
The optimal position of individual and global optimum position.
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| CN110474324A (en) * | 2019-08-01 | 2019-11-19 | 国网甘肃省电力公司电力科学研究院 | A kind of reconstruction method of power distribution network and system |
| CN111640043A (en) * | 2020-05-19 | 2020-09-08 | 福州大学 | Power distribution network reconstruction method and device, computer equipment and storage medium |
| CN112365195A (en) * | 2020-12-03 | 2021-02-12 | 国网河北省电力有限公司信息通信分公司 | Spark distributed improved particle swarm algorithm-based power distribution network fault post-reconstruction method |
| CN112736912A (en) * | 2020-12-28 | 2021-04-30 | 上海电力大学 | Distribution network reconstruction method based on annealing brownian motion and single ring optimization |
| CN112803404A (en) * | 2021-02-25 | 2021-05-14 | 国网河北省电力有限公司经济技术研究院 | Self-healing reconstruction planning method and device for power distribution network and terminal |
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