CN111507510A - Optimization method, device, equipment and storage medium for self-healing scheme of power distribution network - Google Patents

Optimization method, device, equipment and storage medium for self-healing scheme of power distribution network Download PDF

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CN111507510A
CN111507510A CN202010268406.3A CN202010268406A CN111507510A CN 111507510 A CN111507510 A CN 111507510A CN 202010268406 A CN202010268406 A CN 202010268406A CN 111507510 A CN111507510 A CN 111507510A
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赵瑞锋
李波
郭文鑫
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Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a method, a device, equipment and a storage medium for optimizing a self-healing scheme of a power distribution network, wherein the method comprises the following steps: s1, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network; s2, constructing an initial particle group by N particles, wherein the position X of the particle iiComprises the following steps:
Figure DDA0002442218620000011
i＝1,2,…,N，
Figure DDA0002442218620000012
the switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network; s3, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration; s4, updating the current position of the corresponding particle i according to the t-th position based on the Metropolis criterion; s5, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration, outputting the current position, if not, taking t +1 as a new t, and returning to the step S3; and S6, when t is the preset iteration times, outputting the t-th position of the particle i with the optimal fitness from all the particles.

Description

Optimization method, device, equipment and storage medium for self-healing scheme of power distribution network
Technical Field
The application relates to the technical field of power distribution networks, in particular to an optimization method, device, equipment and storage medium of a self-healing scheme of a power distribution network.
Background
With the development and application of distributed power sources, a power distribution network gradually becomes a comprehensive energy system integrating a source, a network and a load. The power distribution network fault self-healing means that after a fault occurs in a power distribution network, the topological structure of the power distribution network is changed by adjusting the positions of a section switch and a contact switch, and a non-fault power failure area is quickly self-healed. Because the factors such as switch position, load self-healing quantity, network constraint and the like need to be comprehensively considered, the power supply self-healing of the power distribution network is a nonlinear combination optimization problem with multiple targets, multiple combinations and multiple constraints.
In recent years, heuristic search algorithms are more and more widely applied to power distribution network fault self-healing, and typical heuristic search algorithms include genetic algorithms, particle swarm algorithms, artificial bee colony algorithms and the like. Although the above methods can determine the fault self-healing scheme, the accuracy is low.
Disclosure of Invention
The application provides an optimization method, device, equipment and storage medium of a self-healing scheme of a power distribution network, and solves the technical problem of low accuracy of the existing fault location method of the power distribution network.
In view of this, the first aspect of the present application provides an optimization method for a self-healing scheme of a power distribution network, including:
s1, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network;
s2, constructing an initial particle group by N particles, wherein the position X of the particle iiComprises the following steps:
Figure BDA0002442218600000011
i＝1,2,…,N，
Figure BDA0002442218600000012
the switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network;
s3, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration;
s4, updating the current position of the corresponding particle i according to the t-th position based on the Metropolis criterion;
s5, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration, outputting the current position, if not, taking t +1 as a new t, and returning to the step S3;
and S6, when t is the preset iteration times, outputting the t-th position of the particle i with the optimal fitness from all the particles.
Optionally, the self-healing model to be optimized includes:
Figure BDA0002442218600000021
in the formula, min f is a self-healing model to be optimized, k (k) is whether the kth switch is operated, if so, k (k) is 1, otherwise, k (k) is 0, M is a non-fault power-loss area node set, and liIs a switch xiLoad of corresponding node, λiIs a switch xiThe importance coefficient of the corresponding node load, L, is the total amount of the power-off load, IiIs a switch xiCorresponding branch current, RiIs the impedance of the corresponding branch, PlossThe active loss at the non-fault position is shown as a, b and c as weight coefficients.
Optionally, step S3 specifically includes:
s31, carrying out the t iteration on the initial particle swarm, and calculating the t stress and the t inertial mass of the particle i after the iteration;
s32, determining a corresponding tth acceleration according to the tth stress and the tth inertial mass, and determining a tth position based on the tth acceleration and the t-1 speed of the t-1 th iteration of the corresponding particle i.
Optionally, step S4 specifically includes:
when the t-th adaptability value of the particle i is larger than the t-1-th adaptability value, taking the t-th position as the current position of the corresponding particle i, otherwise, according to the Metropolis criterion, when the Metropolis criterion is met, taking the t-th position as the current position of the corresponding particle i, otherwise, keeping the current position of the particle i unchanged;
wherein the Metropolis criteria specifically include:
Figure BDA0002442218600000022
in the formula, TtTo simulate the temperature of the anneal at the t-th iteration, fiti(t) is the fitness value, rand, of the particle i at the t-th iterationiIs [0,1 ]]Random variable within, fiti(t-1) is the fitness value of particle i at the t-1 th iteration.
Optionally, the tth inertial mass is:
Figure BDA0002442218600000031
in the formula, Mi(t) is the t inertial mass of particle i at the t iteration, mi(t)、mj(t) is a variable in the middle of the equation,
Figure BDA0002442218600000032
fiti(t) is the tth fitness value of the particle i at the tth iteration, best (t) and worst (t) are respectively the optimal fitness value and the worst fitness value of all the particles at the tth iteration,
Figure BDA0002442218600000033
optionally, the tth force is:
Figure BDA0002442218600000034
in the formula, Fi k(t) is the tth force of particle i at the tth iteration,
Figure BDA0002442218600000035
is the attraction of particle i to particle j in the kth dimension, randjIs [0,1 ]]A random variable within.
Optionally, the t-th position is:
Figure BDA0002442218600000036
wherein S (t) is the t-th position,
Figure BDA0002442218600000037
for the switch state of switch k in particle i in the t-th iteration,
Figure BDA0002442218600000038
for the switch state of switch k in particle i in the t-1 th iteration, randiIs [0,1 ]]A random variable within the set of random variables,
Figure BDA0002442218600000039
the velocity of particle i in the t-th iteration.
The second aspect of the present application provides an optimization device of distribution network self-healing scheme, include:
the model establishing unit is used for establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network;
a particle swarm construction unit for constructing an initial particle swarm from the N particles, wherein the position X of the particle iiComprises the following steps:
Figure BDA00024422186000000310
i＝1,2,…,N，
Figure BDA00024422186000000311
the switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network;
the iteration unit is used for carrying out the t-th iteration on the initial particle swarm and calculating the t-th position of the particle i after the iteration;
the updating unit is used for updating the current position of the corresponding particle i according to the t-th position based on a Metropolis criterion;
the judging unit is used for judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized or not, if so, stopping iteration and outputting the current position, and if not, triggering the iteration unit after t +1 is used as a new t;
and the output unit is used for outputting the t-th position of the particle i with the optimal fitness from all the particles when t is the preset iteration times.
The third aspect of the present application provides an optimization device for a self-healing scheme of a power distribution network, including a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the optimization method of the power distribution network self-healing scheme according to the first aspect according to instructions in the program code.
A fourth aspect of the present application provides a storage medium, where the storage medium is configured to store program codes, and the program codes are configured to execute the optimization method of the self-healing scheme of the power distribution network according to the first aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides an optimization method of a self-healing scheme of a power distribution network, which comprises the following steps: s1, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network; s2, constructing an initial particle group by N particles, wherein the position X of the particle iiComprises the following steps:
Figure BDA0002442218600000041
i＝1,2,…,N，
Figure BDA0002442218600000042
is split in particle iThe switch state of the switch k is closed, and d is the switch number of the fault power distribution network; s3, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration; s4, updating the current position of the corresponding particle i according to the t-th position based on the Metropolis criterion; s5, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration, outputting the current position, if not, taking t +1 as a new t, and returning to the step S3; s6, when t is the preset iteration number, the t-th position of the particle i with the optimal fitness is output from all the particles, in the application, the position of the particle i output by the method is the state of a group of switches, the self-healing scheme of the switches in the power distribution network corresponds to the position, and aiming at the problem that power supply in a non-fault area needs to be dynamically recovered after the power distribution network has a fault, the performance of the universal gravitation search algorithm is improved by introducing a simulated annealing thought and updating the positions of the particles according to probability by using a Metropolis criterion, so that the integral convergence and accuracy are improved, and the fault self-healing scheme of the power distribution network is effectively provided.
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Fig. 1 is a schematic flow chart of a first embodiment of a method for optimizing a self-healing scheme of a power distribution network according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a second embodiment of a method for optimizing a self-healing scheme of a power distribution network according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an application example of an optimization method of a self-healing scheme of a power distribution network in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an optimization device of a self-healing scheme of a power distribution network in an embodiment of the present application.
Detailed Description
The embodiment of the application provides an optimization method, device, equipment and storage medium for a self-healing scheme of a power distribution network, and solves the technical problem of low accuracy of the existing fault positioning method of the power distribution network.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a schematic flow chart of a first embodiment of a method for optimizing a self-healing scheme of a power distribution network in an embodiment of the present application is shown.
Step 101, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network.
The method comprises the steps that factors such as a switch position, a load self-healing amount and network constraint are comprehensively considered for power distribution network fault self-healing, and therefore after a power distribution network fault occurs, a to-be-optimized self-healing model is established according to the switch position, the load self-healing amount and the network constraint in the fault power distribution network.
It is understood that the network constraints in this embodiment include: the constraint conditions include: topology constraints, voltage amplitude constraints, and branch capacity constraints.
The topological constraints include: the power distribution network after fault reconstruction must be of a radial structure.
Voltage amplitude constraint: voltage V of each nodeiCan not exceed the upper limit V allowed by normal operationi.maxAnd a lower limit Vi.min。
Branch capacity constraint: apparent power S of branchlCannot exceed its maximum capacity Sl,max。
And 102, constructing an initial particle swarm by the N particles.
In addition, the position X of the particle iiComprises the following steps:
Figure BDA0002442218600000061
i＝1,2,…,N，
Figure BDA0002442218600000062
the switching state of the switch k in the particle i, and d is the number of the switches of the fault distribution network.
The application considers the switch position of the switch, becauseThis xkAll are variables of 0 or 1, wherein
Figure BDA0002442218600000063
A value of 0 indicates that the switch is not closed, otherwise, the switch is closed.
Position X of particle iiComprises the following steps:
Figure BDA0002442218600000064
i＝1,2,…,N，xkthe switching state of the switch k in the particle i and the switching number of the fault distribution network d represent a group of solutions of the switch position in the fault distribution network represented by the particle i.
And 103, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration.
And after the initial particle swarm is created, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration. It should be noted that the initial value of t is 1, which may vary according to the content of the steps during the execution of the method, and is not fixedly equal to 1.
And step 104, updating the current position of the corresponding particle i according to the t-th position based on the Metropolis criterion.
And after the t-th position is obtained, updating the current position of the particle i according to the Metropolis criterion and the t-th position.
And 105, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration, outputting the current position, and if not, taking t +1 as new t, and returning to the step 103.
The convergence conditions in this embodiment are: the optimal fitness of the particles is continuously kept for a certain number of iterations.
And 106, outputting the t-th position of the particle i with the optimal fitness from all the particles when t is the preset iteration times.
In this embodiment, when t is the preset iteration number, the t-th position of the particle i with the optimal fitness is output from all the particles.
In the optimization method of the self-healing scheme of the power distribution network in the embodiment, the positions of the particles i output by the method are states of a group of switches, the self-healing scheme of the switches in the power distribution network corresponds to the self-healing scheme of the switches in the power distribution network, and aiming at the problem that power supply in a non-fault area needs to be dynamically recovered after a power distribution network fault occurs, the positions of the particles are updated according to probability by introducing a simulated annealing thought, so that the performance of a universal gravitation search algorithm is improved, the overall convergence and accuracy are improved, and the self-healing scheme of the power distribution network fault is effectively provided.
The foregoing is a first embodiment of the method for optimizing the self-healing scheme of the power distribution network provided by the embodiment of the present application, and the following is a second embodiment of the method for optimizing the self-healing scheme of the power distribution network provided by the embodiment of the present application.
Referring to fig. 2, a flowchart of a second embodiment of a method for optimizing a self-healing scheme of a power distribution network in an embodiment of the present application includes:
step 201, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network.
It should be noted that the self-healing model to be optimized includes:
Figure BDA0002442218600000071
in the formula, min f is a self-healing model to be optimized, k (k) is whether the kth switch is operated, if so, k (k) is 1, otherwise, k (k) is 0, M is a non-fault power-loss area node set, and liIs a switch xiLoad of corresponding node, λiIs a switch xiThe importance coefficient of the corresponding node load, L, is the total amount of the power-off load, IiIs a switch xiCorresponding branch current, RiIs the impedance of the corresponding branch, PlossThe active loss at the non-fault position is shown as a, b and c as weight coefficients.
Step 202, constructing an initial particle group by the N particles.
In addition, the position X of the particle iiComprises the following steps:
Figure BDA0002442218600000072
i＝1,2,…,N，
Figure BDA0002442218600000073
the switching state of a switch k in a particle i, and d is the number of switches of a faulted distribution network
And 203, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th stress and the t-th inertial mass of the particle i after the iteration.
Note that, the tth inertial mass is:
Figure BDA0002442218600000074
in the formula, Mi(t) is the t inertial mass of particle i at the t iteration, mi(t)、mj(t) is a variable in the middle of the equation,
Figure BDA0002442218600000075
fiti(t) is the tth fitness value of the particle i at the tth iteration, best (t) and worst (t) are respectively the optimal fitness value and the worst fitness value of all the particles at the tth iteration,
Figure BDA0002442218600000081
the t-th stress is:
Figure BDA0002442218600000082
in the formula, Fi k(t) is the tth force of particle i at the tth iteration,
Figure BDA0002442218600000083
is the attraction of particle i to particle j in the kth dimension, randjIs [0,1 ]]A random variable within.
And 204, determining a corresponding tth acceleration according to the tth stress and the tth inertial mass, and determining a tth position based on the tth acceleration and the t-1 speed of the t-1 iteration of the corresponding particle i.
Note that the tth acceleration is:
Figure BDA0002442218600000084
the t-th position is:
Figure BDA0002442218600000085
wherein S (t) is the t-th position,
Figure BDA0002442218600000086
for the switch state of switch k in particle i in the t-th iteration,
Figure BDA0002442218600000087
for the switch state of switch k in particle i in the t-1 th iteration, randiIs [0,1 ]]A random variable within the set of random variables,
Figure BDA0002442218600000088
the velocity of particle i in the t-th iteration. According to this formula, the optimization problem for solving the binary variable, the greater the absolute value of the velocity, the greater the possibility that the position of the particle is changed; conversely, the smaller the absolute value of the velocity, the smaller the possibility that the position of the particle is changed.
And step 205, when the tth fitness value of the particle i is greater than the tth-1 fitness value, taking the tth position as the current position of the corresponding particle i, otherwise, according to the Metropolis criterion, when the Metropolis criterion is met, taking the tth position as the current position of the corresponding particle i, and otherwise, keeping the current position of the particle i unchanged.
It should be noted that the Metropolis criteria specifically include:
Figure BDA0002442218600000089
in the formula, TtTo simulate the temperature of the anneal at the t-th iteration, fiti(t) is the fitness value, rand, of the particle i at the t-th iterationiIs [0,1 ]]Inside ofRandom variable, fiti(t-1) is the fitness value of particle i at the t-1 th iteration.
And step 206, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration and outputting the current position, otherwise, taking t +1 as a new t, and returning to the step 203.
It should be noted that step 206 is similar to step 105 of the first embodiment, and reference may be specifically made to the above description, which is not repeated herein.
And step 207, outputting the t-th position of the particle i with the optimal fitness from all the particles when t is the preset iteration times.
In the optimization method of the self-healing scheme of the power distribution network in the embodiment, the positions of the particles i output by the method are states of a group of switches, the self-healing scheme of the switches in the power distribution network corresponds to the self-healing scheme of the switches in the power distribution network, and aiming at the problem that power supply in a non-fault area needs to be dynamically recovered after a power distribution network fault occurs, the positions of the particles are updated according to probability by introducing a simulated annealing thought, so that the performance of a universal gravitation search algorithm is improved, the overall convergence and accuracy are improved, and the self-healing scheme of the power distribution network fault is effectively provided.
The foregoing is a second embodiment of the method for optimizing the self-healing scheme of the power distribution network provided in the embodiment of the present application, and the following is an application example of the method for optimizing the self-healing scheme of the power distribution network provided in the embodiment of the present application.
In this application example, the adopted test system is a relatively complex power distribution network structure with a voltage level of 10kV in a city and a county as shown in fig. 3. The system comprises 43 feeder line sections, 15 feeder lines, 5 substation buses, 29 section switches and 16 interconnection switches. The electrical limit of each power supply point and line switch (section switch and interconnection switch) is 400A, and the total load of the system is 3.072 kA. In the figure, a solid circle represents closing, a hollow circle represents opening, and the load supplied by the corresponding feeder segment is arranged in brackets.
Assuming that the bus E fails, the breakers S9, S10, S11, S12, S13, S14, S15 open to isolate the bus E, restoring as much of the lost load as possible from the remaining 8 feeders. As can be seen from fig. 3, there are 16 tie switches connecting the non-fault power loss region and the normal region, so the total length of the particles is 16, and each dimension of the particles corresponds to 16 tie switch branches. The population size (i.e. the total number of particles) was taken to be 20 and the preset number of iterations was 60. The fault recovery scheme obtained by the method provided by the application is to close the tie switches 3, 8, 15, 28, 34, 42, 43, 44, 45, open the section switches 5, 16, 20, 35, 36 and recover the outage load 906A.
In order to verify the effectiveness of the fault recovery algorithm, the following three schemes are respectively operated: (1) performing a particle swarm algorithm; (2) a basic universal gravitation algorithm; (3) the simulated annealing-universal gravitation algorithm provided by the application compares the operation results of three conditions and is shown in table 1.
TABLE 1
Figure BDA0002442218600000101
As can be seen from table 1, although convergence is achieved in case 2, the algorithm falls into "precocity" as seen by comparing the results of case 1 and case 3; the optimal recovery schemes are obtained in the scheme 1 and the scheme 3, but the simulated annealing-universal gravitation algorithm provided by the application is greatly improved in calculation efficiency compared with a particle swarm algorithm, and the high efficiency of the fault recovery algorithm provided by the application is verified.
The above is an application example of the method for optimizing the self-healing scheme of the power distribution network according to the embodiment of the present application, and the following is an embodiment of the apparatus for optimizing the self-healing scheme of the power distribution network according to the embodiment of the present application, and please refer to fig. 4 specifically.
In this embodiment, an optimization device of distribution network self-healing scheme includes:
the model establishing unit 401 is configured to establish a self-healing model to be optimized based on a switching position, a load self-healing amount and network constraints of the fault power distribution network;
a particle swarm constructing unit 402 for constructing an initial particle swarm from the N particles, wherein the position X of the particle iiComprises the following steps:
Figure BDA0002442218600000102
i＝1,2,…,N，xkthe switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network;
an iteration unit 403, configured to perform a tth iteration on the initial particle swarm and calculate a tth position of the particle i after the iteration;
an updating unit 404, configured to update the current location of the corresponding particle i according to the tth location based on the Metropolis criterion;
a determining unit 405, configured to determine whether the current position of the particle i satisfies a convergence condition of the self-healing model to be optimized, if yes, stop iteration, output the current position, and if not, trigger the iteration unit 403 after t +1 is used as a new t;
and the output unit 406 is configured to output the t-th position of the particle i with the optimal fitness from all the particles when t is the preset iteration number.
In the embodiment, aiming at the problem that power supply in a non-fault area needs to be dynamically recovered after a power distribution network fault, the performance of the universal gravitation search algorithm is improved by introducing a simulated annealing thought and updating the particle position according to the Metropolis criterion and probability, so that the overall convergence and accuracy are improved, and a power distribution network fault self-recovery scheme is effectively provided.
The embodiment of the application also provides optimization equipment of the self-healing scheme of the power distribution network, which comprises a processor and a memory; the memory is used for storing the program codes and transmitting the program codes to the processor; the processor is configured to perform the optimization method of the first embodiment or the second embodiment according to instructions in the program code.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiment of the present application further provides a storage medium, wherein the storage medium is used for storing a program code, and the program code is used for executing the optimization method of the first embodiment or the second embodiment.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A method for optimizing a self-healing scheme of a power distribution network is characterized by comprising the following steps:
s1, establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network;
s2, constructing an initial particle group by N particles, wherein the position X of the particle iiComprises the following steps:
Figure FDA0002442218590000011
Figure FDA0002442218590000012
Figure FDA0002442218590000013
the switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network;
s3, carrying out the t-th iteration on the initial particle swarm, and calculating the t-th position of the particle i after the iteration;
s4, updating the current position of the corresponding particle i according to the t-th position based on the Metropolis criterion;
s5, judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized, if so, stopping iteration, outputting the current position, if not, taking t +1 as a new t, and returning to the step S3;
and S6, when t is the preset iteration times, outputting the t-th position of the particle i with the optimal fitness from all the particles.
2. The method for optimizing a self-healing scheme of a power distribution network according to claim 1, wherein the self-healing model to be optimized comprises:
Figure FDA0002442218590000014
in the formula, min f is a self-healing model to be optimized, k (k) is whether the kth switch is operated, if so, k (k) is 1, otherwise, k (k) is 0, M is a non-fault power-loss area node set, and liIs a switch xiLoad of corresponding node, λiIs a switch xiThe importance coefficient of the corresponding node load, L, is the total amount of the power-off load, IiIs a switch xiCorresponding branch current, RiIs the impedance of the corresponding branch, PlossThe active loss at the non-fault position is shown as a, b and c as weight coefficients.
3. The method for optimizing a self-healing scheme of a power distribution network according to claim 1, wherein the step S3 specifically includes:
s31, carrying out the t iteration on the initial particle swarm, and calculating the t stress and the t inertial mass of the particle i after the iteration;
s32, determining a corresponding tth acceleration according to the tth stress and the tth inertial mass, and determining a tth position based on the tth acceleration and the t-1 speed of the t-1 th iteration of the corresponding particle i.
4. The method for optimizing a self-healing scheme of a power distribution network according to claim 3, wherein the step S4 specifically includes:
when the t-th adaptability value of the particle i is larger than the t-1-th adaptability value, taking the t-th position as the current position of the corresponding particle i, otherwise, according to the Metropolis criterion, when the Metropolis criterion is met, taking the t-th position as the current position of the corresponding particle i, otherwise, keeping the current position of the particle i unchanged;
wherein the Metropolis criteria specifically include:
Figure FDA0002442218590000021
in the formula, TtTo simulate the temperature of the anneal at the t-th iteration, fiti(t) is the fitness value, rand, of the particle i at the t-th iterationiIs [0,1 ]]Random variable within, fiti(t-1) is the fitness value of particle i at the t-1 th iteration.
5. The method for optimizing a self-healing scheme of a power distribution network according to claim 3, wherein the tth inertial mass is:
Figure FDA0002442218590000022
in the formula, Mi(t) is the t inertial mass of particle i at the t iteration, mi(t)、mj(t) is a variable in the middle of the equation,
Figure FDA0002442218590000023
fiti(t) is the tth fitness value of the particle i at the tth iteration, best (t) and worst (t) are respectively the optimal fitness value and the worst fitness value of all the particles at the tth iteration,
Figure FDA0002442218590000024
6. the method for optimizing a self-healing scheme of a power distribution network according to claim 3, wherein the tth stress is:
Figure FDA0002442218590000025
in the formula, Fi k(t) is the tth force of particle i at the tth iteration,
Figure FDA0002442218590000026
is the attraction of particle i to particle j in the kth dimension, randjIs [0,1 ]]A random variable within.
7. The method for optimizing a self-healing scheme of a power distribution network according to claim 3, wherein the tth location is:
Figure FDA0002442218590000031
wherein S (t) is the t-th position,
Figure FDA0002442218590000032
for the switch state of switch k in particle i in the t-th iteration,
Figure FDA0002442218590000033
for the switch state of switch k in particle i in the t-1 th iteration, randiIs [0,1 ]]A random variable within the set of random variables,
Figure FDA0002442218590000034
the velocity of particle i in the t-th iteration.
8. The utility model provides an optimization device of distribution network self-healing scheme which characterized in that includes:
the model establishing unit is used for establishing a self-healing model to be optimized based on the switching position, the load self-healing quantity and the network constraint of the fault power distribution network;
a particle swarm construction unit for constructing an initial particle swarm from the N particles, wherein the position X of the particle iiComprises the following steps:
Figure FDA0002442218590000035
Figure FDA0002442218590000036
the switching state of a switch k in the particle i is shown, and d is the switching number of a fault power distribution network;
the iteration unit is used for carrying out the t-th iteration on the initial particle swarm and calculating the t-th position of the particle i after the iteration;
the updating unit is used for updating the current position of the corresponding particle i according to the t-th position based on a Metropolis criterion;
the judging unit is used for judging whether the current position of the particle i meets the convergence condition of the self-healing model to be optimized or not, if so, stopping iteration and outputting the current position, and if not, triggering the iteration unit after t +1 is used as a new t;
and the output unit is used for outputting the t-th position of the particle i with the optimal fitness from all the particles when t is the preset iteration times.
9. The optimization equipment for the self-healing scheme of the power distribution network is characterized by comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the optimization method of the power distribution network self-healing scheme according to any one of claims 1 to 7 according to instructions in the program code.
10. A storage medium for storing a program code, wherein the program code is configured to execute the optimization method for the self-healing scheme of the power distribution network according to any one of claims 1 to 7.
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