CN109617083A - Distribution network failure restorative procedure based on particle swarm algorithm - Google Patents
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
The invention discloses a kind of distribution network failure restorative procedure based on particle swarm algorithm, the described method comprises the following steps: step 1: input power distribution network initial information；Step 2: according to wind-force output power probability density, going out wind power output power at random using Monte Carlo method；Step 3: setting population advises N, inertia weight w, Studying factors c1, c2, primary iteration speedAnd the number of iterations；Step 4: coded treatment being carried out for power distribution network, the number from 1 to n is carried out for distribution network, searches out all paths from 1 to n；Step 5: set objectives function；Step 6: being optimized using particle swarm algorithm.The problem of present invention is by formulating distribution network failure repairing and service restoration strategy, can efficiently solve the unreliability of distributed generation resource power supply and routinely can not provide electric energy for user, realizes more perfect, flexible, sustainable power supply.
Description
Technical field
The present invention relates to distribution network transform field, in particular to a kind of distribution network failure reparation side based on particle swarm algorithm
Method.
Background technique
The fast development of renewable energy, especially more and more power electronic devices are linked into power grid, to power grid
The links such as planning, operation, control all bring completely new challenge.In order to cope with this phenomenon, found using graph-theory techniques
Distribution network failure point, and it is crucial for enhancing distribution network failure to be automatically repaired ability by control switching load.It is therefore desirable to set
Count out it is a kind of power distribution network is reconstructed according to Graph-theoretical Approach and particle swarm algorithm, realize distribution network failure repairing and service restoration
Policy development, to solve the above problems.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of distribution network failure restorative procedure based on particle swarm algorithm, base
Power distribution network is reconstructed in Graph-theoretical Approach and particle swarm algorithm, to complete distribution network failure repairing and service restoration strategy
It formulates, improves the operation stability of power grid.
The purpose of the present invention is what is be achieved through the following technical solutions:
Distribution network failure restorative procedure based on particle swarm algorithm of the invention, comprising the following steps:
Step 1: input power distribution network initial information.
Step 2: according to wind-force output power probability density, going out wind power output power, wind-force at random using Monte Carlo method
Output power probability density is as shown in formula 1:
In formula, v is wind speed, and K is the form parameter of Weibull distribution, and C is the scale parameter of Weibull distribution.
In formula, PrIt is Wind turbines rated capacity, vci、vr、vc0Respectively cut wind speed, rated wind speed and cut-out wind speed；
B=-vci (4)
Then the reactive power of Wind turbines can be found out:
Step 3: setting population advises N, inertia weight w, Studying factors c1, c2, primary iteration speedAnd iteration time
Number；
Step 4: coded treatment is carried out for power distribution network, number from 1 to n is carried out for distribution network, search out from 1 to
All paths of n；
Step 5: set objectives function: with the minimum target of system cutting load amount:
In formula, N1 is load bus number when power distribution network operates normally；PaiNode i is negative when operating normally for power distribution network
Lotus；N2 is that power distribution network distribution network failure state restores afterload number of nodes；PbiRestore afterload node for distribution network failure state
Number；
With the minimum target of the via net loss of electric system:
In formula: n is branch number；R is the resistance of i-th of node；PiIt is the active power of node i, QiIt is the nothing of node i
Function power, UiIt is the voltage magnitude of branch i headend node.
Step 6: being optimized using particle swarm algorithm.
Particularly, in the step 1, initial information includes power distribution network network parameter, system node load, power supply output work
Rate, new energy output power probability density.
Particularly, in the step 4, coded treatment includes the coded treatment of individual paths and/or the coding of distribution network
Processing.
Particularly, in the step 5, multiple objective function is:
Min (x)=[k1f1(x)+k2f2(x)] (8)
In formula:Wherein,It is weight coefficient, PmaxIt is cutting load
The maximum value of amount, PminIt is the minimum value of cutting load amount, PmaxIt is the maximum value of cutting load amount, LminIt is the minimum value of system losses,
LmaxIt is the maximum value of system losses；
Wherein, PGiIt is the generator active power of i-th of node, PLiIt is the load active power of i-th of node, PDiIt is
The active power of the distributed generation resource of the injection of i-th of node, QGiIt is the generator reactive power of i-th of node, QDiIt is i-th
The reactive power of the distributed generation resource of a node injection, QLiIt is the reactive load power of i-th of node, ViIt is i-th of node electricity
Pressure, VJIt is j-th of node voltage, GijIt is i-th of node and j-th of node conductance, BijIt is i-th of node and j-th of node electricity
It receives, θijIt is i-th of node and j-th of node phase angle, ViminIt is i-th of node voltage minimum value, VimaxIt is i-th of node voltage
Maximum value, SijmaxIt is i-th, the maximum capacity of j node, SiJIt is i-th, the capacity of j node.
H is on-off times, HmaxIt is switch maximum times.
Particularly, in the step 6, the specific steps of optimization include:
S1: quantum rotation angle guidance value is calculated according to formula 10 and 11:
Wherein, f () is the target function value acquired in step 5),Be i-th of particle at the kth iteration
Quantum rotation angle Guiding factor.It is the globally optimal solution in optimization process,It is the local optimum in optimization process
Solution.
S2: quantum particle swarm rotation angle is updated according to formula 12 and 13:
Wherein, θkThe amplitude of rotation angle, k when iteration secondary for kthmaxFor the maximum value of the number of iterations.
S3: the bit update method of quantum particle swarm is as shown in formula 14:
Indicate the bit of i-th of particle at the kth iteration.
S4: according to the more new position value of formula 15:
Wherein, riIt is random number matrix, is made of 0-1；
S5: the local optimum vector sum global optimum vector of control is updated；
S6: examine and calculate allow whether meet convergence criterion | | x '-x | | < ε enters step S7 if inequality meets, otherwise
Return to S4；
S7: output optimum particle position value x obtains corresponding fail-over policy.
The beneficial effects of the present invention are:
By formulating distribution network failure repairing and service restoration strategy, distributed generation resource power supply can be efficiently solved not
Reliability and the problem of routinely can not providing electric energy for user, realize more perfect, flexible, sustainable power supply.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target and other advantages of the invention can be wanted by following specification and right
Book is sought to be achieved and obtained.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
The detailed description of one step, in which:
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the embodiment tree graph in the code processing method of individual paths；
Fig. 3 is 33 network node figure of IEEE；
Fig. 4 is the simplification figure being made of special joint and branch chain.
Specific embodiment
Hereinafter reference will be made to the drawings, and a preferred embodiment of the present invention will be described in detail.It should be appreciated that preferred embodiment
Only for illustrating the present invention, rather than limiting the scope of protection of the present invention.
A kind of distribution network failure restorative procedure based on particle swarm algorithm of the invention, comprising the following steps:
Step 1: input power distribution network initial information.In the present invention, initial information includes but is not limited to power distribution network network parameter,
System node load, output power of power supply, new energy output power probability density；
Step 2: according to wind-force output power probability density, go out wind power output power at random using Monte Carlo method, wherein
Wind-force output power probability density is shown below:
In formula, v is wind speed, and K is the form parameter of Weibull distribution, and C is the scale parameter of Weibull distribution.
In formula, PrIt is Wind turbines rated capacity, vci、vr、vc0Respectively cut wind speed, rated wind speed and cut-out wind speed.
B=-vci (4)
Then the reactive power of Wind turbines can be found out:
Step 3: setting population advises N, inertia weight w, Studying factors c1, c2, primary iteration speedAnd iteration time
Number.
Step 4: coded treatment is carried out for power distribution network, number from 1 to n is carried out for distribution network, search out from 1 to
All paths of n.Coded treatment includes the coded treatment of individual paths and the coded treatment of distribution network, is introduced respectively such as
Under:
(1) code processing method of individual paths:
S1: as shown in the tree graph of Fig. 1, it is assumed that encoded for the figure, the main path for finding the figure first is exactly 1-6-9-
11-5-2-4-12.The label for removing head and the tail generates sequence: [6,9,11,5,2,4]；
S2: sequence is begun look for from right to left, searches out the number stopping for violating descending arrangement, the number on the right is picked
It removes, is individually for one group, then proceed to search former sequence, until traversing entire sequence.Such as:: [6,9,15,2,4] will
It is split as: [2,4], [5], [6,9,11]；
S3: matrix is written into the mapping relations of each group: assuming that node number is q, then the first row is exactly from 2 to q
Equal difference arrangement, the second row is filled according to the corresponding relationship in S2.
S4: it for other vacant nodes, is then filled according to corresponding relationship in figure.For example, the last coding in Fig. 1
It is exactly [4,10,2,5,9,1,12,11,2,6].
(2) code processing method of distribution network, is exemplified below: as shown in Fig. 2, by taking IEEE33 meshed network as an example, point
Duan Kaiguan 32, interconnection switch 5, coding step is as follows:
S1: will all close the switch in network, find all main paths of node 1 to 33.This sentences [2-19-20-21-
8-9-15-16-17-18-33] for, according to S2 above) and S3) in method to the path carry out relationship map:
S2: distribution network residue node is also cut into different even blocks by this paths, as shown in Figure 3:
According to following three constraint condition:
The node of all mappings of a has to be the adjacent node for sharing a line in network therewith；
The difference of b mapped node institute's tie point number and the frequency of occurrence in mapping relations matrix is more than or equal to 1；
For c in addition to the main path, remaining mapping relations cannot mutually form new closed loop；
Corresponding relationship below can be obtained in the distribution network:
Step 5: set objectives function: with the minimum target of system cutting load amount:
In formula, N1 is load bus number when power distribution network operates normally；PaiNode i is negative when operating normally for power distribution network
Lotus；N2 is that power distribution network distribution network failure state restores afterload number of nodes；PbiRestore afterload node for distribution network failure state
Number；
If with the minimum target of the via net loss of electric system:
In formula: n is branch number；R is the resistance of i-th of node；PiIt is the active power of node i, QiIt is the nothing of node i
Function power, UiIt is the voltage magnitude of branch i headend node.
In the present embodiment, the multiple objective function of formulation is:
Minf (x)=[k1f1(x)+k2f2(x)] (8)
In formula:Wherein,It is weight coefficient, PmaxIt is cutting load
The maximum value of amount, PminIt is the minimum value of cutting load amount, PmaxIt is the maximum value of cutting load amount, LminIt is the minimum value of system losses,
LmaxIt is the maximum value of system losses.
Wherein, PGiIt is the generator active power of i-th of node, PLiIt is the load active power of i-th of node, PDiIt is
The active power of the distributed generation resource of the injection of i-th of node, QGiIt is the generator reactive power of i-th of node, QDiIt is i-th
The reactive power of the distributed generation resource of a node injection, QLiIt is the reactive load power of i-th of node, ViIt is i-th of node electricity
Pressure, VJIt is j-th of node voltage, GijIt is i-th of node and j-th of node conductance, BijIt is i-th of node and j-th of node electricity
It receives, θijIt is i-th of node and j-th of node phase angle, ViminIt is i-th of node voltage minimum value, VimaxIt is i-th of node voltage
Maximum value, SijmaxIt is i-th, the maximum capacity of j node, SiJIt is i-th, the capacity of j node.
H is on-off times, HmaxIt is switch maximum times.
Step 6: it is optimized using particle swarm algorithm, specifically includes the following steps:
S1: quantum rotation angle guidance value is calculated according to formula 10 and 11:
Wherein, f () is the target function value acquired in step 5),Be i-th of particle at the kth iteration
Quantum rotation angle Guiding factor.It is the globally optimal solution in optimization process,It is the local optimum in optimization process
Solution.
S2: quantum particle swarm rotation angle is updated according to formula 12 and 13:
Wherein, θkThe amplitude of rotation angle, k when iteration secondary for kthmaxFor the maximum value of the number of iterations.
S3: the bit update method of quantum particle swarm is as shown in formula 14:
Indicate the bit of i-th of particle at the kth iteration；
S4: according to the more new position value of formula 15:
Wherein, riIt is random number matrix, is made of 0-1；
S5: the local optimum vector sum global optimum vector of control is updated；
S6: examine and calculate allow whether meet convergence criterion | | x '-x | | < ε enters step S7 if inequality meets, otherwise
Return to S4；
S7: output optimum particle position value x obtains corresponding fail-over policy.
The present invention can efficiently solve distributed generation resource confession by formulating distribution network failure repairing and service restoration strategy
The unreliability of electricity and the problem of routinely can not providing electric energy for user, realize more perfect, flexible, sustainable power supply.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (5)
1. the distribution network failure restorative procedure based on particle swarm algorithm, it is characterised in that: the described method comprises the following steps:
Step 1: input power distribution network initial information；
Step 2: according to wind-force output power probability density, going out wind power output power, wind-force output at random using Monte Carlo method
Power probability density is as shown in formula 1:
In formula, v is wind speed, and K is the form parameter of Weibull distribution, and C is the scale parameter of Weibull distribution；
In formula, PrIt is Wind turbines rated capacity, vci、vr、vc0Respectively cut wind speed, rated wind speed and cut-out wind speed；
B=-vci (4)
Then the reactive power of Wind turbines can be found out:
Step 3: setting population advises N, inertia weight w, Studying factors c1, c2, primary iteration speedAnd the number of iterations；
Step 4: coded treatment being carried out for power distribution network, the number from 1 to n is carried out for distribution network, is searched out from 1 to n's
All paths；
Step 5: set objectives function: with the minimum target of system cutting load amount:
In formula, N1 is load bus number when power distribution network operates normally；PaiThe load of node i when being operated normally for power distribution network；N2
Restore afterload number of nodes for power distribution network distribution network failure state；PbiRestore afterload number of nodes for distribution network failure state；
With the minimum target of the via net loss of electric system:
In formula: n is branch number；R is the resistance of i-th of node；PiIt is the active power of node i, QiIt is the idle function of node i
Rate, UiIt is the voltage magnitude of branch i headend node；
Step 6: being optimized using particle swarm algorithm.
2. the distribution network failure restorative procedure according to claim 1 based on particle swarm algorithm, it is characterised in that: the step
In rapid 1, initial information includes power distribution network network parameter, system node load, output power of power supply, new energy output power probability
Density.
3. the distribution network failure restorative procedure according to claim 1 or 2 based on particle swarm algorithm, it is characterised in that: institute
It states in step 4, coded treatment includes the coded treatment of individual paths and/or the coded treatment of distribution network.
4. the distribution network failure restorative procedure according to claim 1 based on particle swarm algorithm, it is characterised in that: the step
In rapid 5, multiple objective function is:
Minf (x)=[k1f1(x)+k2f2(x)] (8)
In formula:Wherein,It is weight coefficient, PmaxIt is cutting load
The maximum value of amount, PminIt is the minimum value of cutting load amount, PmaxIt is the maximum value of cutting load amount, LminIt is the minimum value of system losses,
LmaxIt is the maximum value of system losses；
Wherein, PGiIt is the generator active power of i-th of node, PLiIt is the load active power of i-th of node, PDiIt is i-th
The active power of the distributed generation resource of the injection of node, QGiIt is the generator reactive power of i-th of node, QDiIt is i-th of node
The reactive power of the distributed generation resource of injection, QLiIt is the reactive load power of i-th of node, ViIt is i-th of node voltage, VjIt is
J-th of node voltage, GijIt is i-th of node and j-th of node conductance, BijIt is i-th of node and j-th of node susceptance, θijIt is
I-th of node and j-th of node phase angle, ViminIt is i-th of node voltage minimum value, VimaxIt is i-th of node voltage maximum value,
SijmaxIt is i-th, the maximum capacity of j node, SijIt is i-th, the capacity of j node.H is on-off times, HmaxIt is that switch is maximum
Number.
5. the distribution network failure restorative procedure according to claim 1 or 2 or 3 based on particle swarm algorithm, it is characterised in that:
In the step 6, the specific steps of optimization include:
S1: quantum rotation angle guidance value is calculated according to formula 10 and 11:
Wherein, f () is the target function value acquired in step 5),It is the amount of i-th of particle at the kth iteration
Sub- rotation angle Guiding factor.It is the globally optimal solution in optimization process,It is the locally optimal solution in optimization process.
S2: quantum particle swarm rotation angle is updated according to formula 12 and 13:
Wherein, θkThe amplitude of rotation angle, k when iteration secondary for kthmaxFor the maximum value of the number of iterations；
S3: the bit update method of quantum particle swarm is as shown in formula 14:
Indicate the bit of i-th of particle at the kth iteration；
S4: according to the more new position value of formula 15:
Wherein, riIt is random number matrix, is made of 0-1；
S5: the local optimum vector sum global optimum vector of control is updated；
S6: examine and calculate allow whether meet convergence criterion | | x '-x | | < ε enters step S7 if inequality meets, otherwise returns to
S4；
S7: output optimum particle position value x obtains corresponding fail-over policy.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109873409A (en) * | 2019-04-09 | 2019-06-11 | 中国计量大学 | A kind of restorative reconstructing method of distribution network failure |
| CN112580256A (en) * | 2020-12-02 | 2021-03-30 | 燕山大学 | Distributed power supply location and volume fixing method considering fault rate influence on electric automobile |
| CN117977578A (en) * | 2024-03-28 | 2024-05-03 | 广东电网有限责任公司广州供电局 | Distribution network fault self-healing method based on intelligent distributed feeder automation |
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| CN112580256A (en) * | 2020-12-02 | 2021-03-30 | 燕山大学 | Distributed power supply location and volume fixing method considering fault rate influence on electric automobile |
| CN112580256B (en) * | 2020-12-02 | 2022-04-01 | 燕山大学 | Distributed power supply location and volume fixing method considering fault rate influence on electric automobile |
| CN117977578A (en) * | 2024-03-28 | 2024-05-03 | 广东电网有限责任公司广州供电局 | Distribution network fault self-healing method based on intelligent distributed feeder automation |
| CN117977578B (en) * | 2024-03-28 | 2024-05-31 | 广东电网有限责任公司广州供电局 | Distribution network fault self-healing method based on intelligent distributed feeder automation |
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