CN111640043B

CN111640043B - Power distribution network reconstruction method and device, computer equipment and storage medium

Info

Publication number: CN111640043B
Application number: CN202010424504.1A
Authority: CN
Inventors: 陈日成; 林云志; 夏建勇; 罗金
Original assignee: Fuzhou University; Third Engineering Co Ltd of China Railway Electrification Engineering Group Co Ltd
Current assignee: Fuzhou University; Third Engineering Co Ltd of China Railway Electrification Engineering Group Co Ltd
Priority date: 2020-05-19
Filing date: 2020-05-19
Publication date: 2022-07-08
Anticipated expiration: 2040-05-19
Also published as: CN111640043A

Abstract

The application relates to a power distribution network reconstruction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a parameter population according to the power distribution network reconstruction operation parameters; and updating the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first updating strategy, and calculating the fitness value of each particle updated each time. Selecting particles meeting a preset fitness condition from the fitness values of the particles as first particles; migrating the first particles into a second sub-population as second particles, updating the particle speed and the particle position of the second particles according to a preset second updating strategy, calculating the fitness value corresponding to each second particle updated each time, and selecting the particles meeting the preset fitness condition from the second particles as third particles; and when the third particles meet the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data in the third particles. By adopting the method, the voltage of the power distribution network is stabilized and the line loss is reduced.

Description

Power distribution network reconstruction method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of power distribution network technologies, and in particular, to a power distribution network reconfiguration method and apparatus, a computer device, and a storage medium.

Background

The distribution network is a radial topological structure network composed of distribution electrical elements, loads and connecting lines, and is the most complex part in an electric power system. The reconfiguration of the distribution network can directly affect the power supply reliability and the power supply efficiency of the power system. When the load demand in the distribution network increases, the power system must guarantee to maintain higher voltage level on all buses through distribution network reconfiguration, namely, the voltage stability of the distribution network side is maintained, and then the power supply efficiency is guaranteed. In addition, a large number of distributed power sources are connected to the existing power distribution network, and due to the connection of the distributed power sources, the prediction difficulty of load consumption power in the power distribution network system is increased, and the uncertainty of the power distribution network system and the reconstruction difficulty of the power distribution network are increased.

In the traditional method, a meta-heuristic method (such as an Ant Colony Optimization (ACO), a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA) and the like) is adopted to realize the reconstruction of the power distribution network, however, when the traditional method determines a reconstruction scheme of the power distribution network, the convergence of the algorithm is poor, the calculation time is long, and the voltage stability of the power distribution network is poor and the active power loss is high.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a power distribution network reconfiguration method, apparatus, computer device and storage medium.

In a first aspect, the present application provides a power distribution network reconfiguration method, where the method includes:

acquiring a preset parameter population according to operation parameters required by power distribution network reconstruction, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of operation parameters for power distribution network reconstruction;

updating the particle speed and the particle position of each particle of a first sub-population in the parameter population according to a preset first updating strategy, calculating the fitness value corresponding to each particle in the first sub-population updated each time, and selecting the particle meeting a preset fitness condition from the fitness values of each particle as the first particle;

migrating the first particles into a second sub-population as second particles in the second sub-population, updating the particle speed and the particle position of the second particles according to a preset second updating strategy, calculating the fitness value corresponding to each second particle updated each time, and selecting the particles meeting a preset fitness condition from the fitness values of each second particle as third particles;

and when the third particles meet preset target particle evaluation indexes, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the third particles.

As an optional embodiment, the parameter population further includes a third sub-population, and the method further includes:

migrating the first particles and the third particles into a third sub-population to serve as fourth particles in the third sub-population, and performing cross variation among the fourth particles according to a preset cross variation algorithm;

calculating the fitness value corresponding to each of the fourth particles after cross variation, and selecting the particles meeting a preset fitness condition from the fitness values of the fourth particles as fifth particles;

and when the fifth particles meet preset target particle evaluation indexes, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the fifth particles.

As an optional implementation manner, the updating, according to a preset first update strategy, the particle velocity and the particle position of each particle of a first sub-population in the parameter population includes:

obtaining a first inertia weight factor at the previous moment according to a preset inertia weight attenuation coefficient, a first maximum inertia weight factor, a first minimum inertia weight factor, a maximum iteration number and a current iteration number;

fusing the particle position, the particle speed, the individual optimal fitness value, the global optimal fitness value, the preset learning factor, the first inertia weight factor and the random number of the particles in the first sub-population at the previous moment to obtain the particle speed of each particle in the first sub-population at the current moment;

and obtaining the current-time particle position of each particle in the first sub-population according to the current-time particle speed of the particle in the first sub-population and the previous-time particle position.

As an optional implementation manner, the updating the particle velocity and the particle position of the second particle according to the preset second update strategy includes:

obtaining a second inertia weight factor at the previous moment according to a preset second maximum inertia weight factor, a second minimum inertia weight factor, the maximum iteration times and the current iteration times;

fusing the particle speed and the particle fitness value of each particle at the current moment in the first sub-population with the particle position, the particle speed, the particle fitness value, the individual optimal fitness value, the global optimal fitness value, a preset learning factor, a second inertia weight factor and a random number of a particle at the previous moment in the second sub-population to obtain the particle speed of the second particle at the current moment;

and obtaining the particle position of the second particle at the current moment according to the particle speed of the particles in the second sub-population at the current moment, the particle position of the previous moment, the position influence factor, the individual optimal fitness value and the global optimal fitness value.

As an optional implementation manner, the obtaining a preset parameter population according to an operation parameter required by power distribution network reconstruction, where the parameter population includes a plurality of sub-populations, and each particle in the parameter population includes a group of operation parameters for power distribution network reconstruction, includes:

acquiring each operation parameter value of an actual operation state of the power distribution network, and determining a position range of a distributed power supply in the power distribution network and an output power range of the distributed power supply according to each operation parameter value of the actual operation state, wherein each operation parameter value comprises position data of each distributed power supply in the power distribution network and output power data of each distributed power supply;

and acquiring a preset parameter population according to the position range and the output power range, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of randomly initialized operation parameter data.

As an optional implementation, the method further comprises:

normalizing the output power data of the distributed power supply and the position data of the distributed power supply in the randomly initialized operation parameter data to obtain normalized output power data of the distributed power supply and normalized position data of the distributed power supply;

and acquiring a preset power distribution network reconstruction fitness function, and calculating the fitness value of each particle in the parameter population according to the power distribution network reconstruction fitness function.

As an optional implementation manner, after the obtaining a preset parameter population according to the operation parameters required by the power distribution network reconfiguration, the method further includes:

acquiring the switch state data of each branch meeting the radial structure of the power distribution network from a database;

when the distribution of the operation state data of the branch switches of the power distribution network, which is contained in any particle in the parameter population, does not accord with the operation state data of each branch switch meeting the radial structure of the power distribution network, the operation parameter data of the particle in the parameter population is initialized.

As an optional implementation manner, the preset target particle evaluation index is a power distribution network reconstruction fitness threshold, and when the third particle meets the preset target particle evaluation index, the control of the power distribution network to reconstruct the power distribution network according to the operation parameter data included in the third particle includes:

and when the target fitness value corresponding to the third particle is greater than or equal to the power distribution network reconstruction fitness threshold value, controlling the power distribution network to reconstruct according to the operation parameter data contained in the third particle.

As an optional implementation manner, the preset target particle evaluation index is a maximum iteration threshold, and when the third particle meets the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data included in the third particle includes:

and when the update iteration times of the third particles are larger than or equal to the maximum iteration time threshold, controlling the power distribution network to reconstruct according to the operation parameter data contained in the third particles.

In a second aspect, a power distribution network reconfiguration device is provided, the device comprising:

the acquisition module is used for acquiring a preset parameter population according to operation parameters required by power distribution network reconstruction, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of operation parameters for power distribution network reconstruction;

the first selection module is used for updating the particle speed and the particle position of each particle of a first sub-population in the parameter population according to a preset first updating strategy, calculating the fitness value corresponding to each particle in the first sub-population updated each time, and selecting the particle meeting a preset fitness condition from the fitness values of each particle as the first particle;

a second selection module, configured to migrate the first particle into a second sub-population as a second particle in the second sub-population, update a particle speed and a particle position of the second particle according to a preset second update policy, calculate a fitness value corresponding to each updated second particle, and select a particle that meets a preset fitness condition from the fitness values of each second particle as a third particle;

and the control module is used for controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the third particles when the third particles meet the preset target particle evaluation index.

In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

migrating the first particles into a second sub-population to serve as second particles in the second sub-population, updating the particle speed and the particle position of the second particles according to a preset second updating strategy, calculating the fitness value corresponding to each second particle updated each time, and selecting the particles meeting a preset fitness condition from the fitness values of each second particle to serve as third particles;

The application provides a power distribution network reconstruction method, a power distribution network reconstruction device, computer equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps that a computer device obtains a preset parameter population according to operation parameters required by power distribution network reconstruction, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of operation parameters for power distribution network reconstruction. Then, the computer device updates the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first updating strategy, calculates the fitness value corresponding to each particle in the first sub-population updated each time, and selects the particle meeting a preset fitness condition from the fitness values of each particle as the first particle. Secondly, the computer device migrates the first particles into a second sub-population as second particles in the second sub-population, updates the particle speed and the particle position of the second particles according to a preset second updating strategy, calculates a fitness value corresponding to each second particle updated each time, and selects particles meeting a preset fitness condition from the fitness values of each second particle as third particles; and finally, when the third particles meet preset target particle evaluation indexes, the computer equipment controls the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the third particles. By adopting the method, the power distribution network can be reconstructed according to a better reconstruction scheme, and the problems of power supply stability of the power distribution network and reduction of line loss are solved.

Drawings

Fig. 1 is a flowchart of a power distribution network reconfiguration method according to an embodiment of the present disclosure;

fig. 2 is a flowchart of a method for acquiring a preset parameter population according to an embodiment of the present disclosure;

fig. 3 is a flowchart for acquiring a power distribution network reconstruction fitness function according to an embodiment of the present disclosure;

fig. 4 is a flowchart of a method for selecting target particles from a third sub-population according to an embodiment of the present disclosure;

fig. 5 is a block diagram of a power distribution network reconfiguration device according to an embodiment of the present disclosure;

fig. 6 is an internal structural diagram of a computer device in the embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In an embodiment, as shown in fig. 1, a power distribution network reconfiguration method is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:

step 101, obtaining a preset parameter population according to operation parameters required by power distribution network reconstruction, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of operation parameters for power distribution network reconstruction.

In implementation, a computer device (i.e., a terminal device) acquires a preset parameter population according to an operation parameter required by power distribution network reconstruction, where the operation parameter includes a switching state of each branch in a power distribution network topology, a position of each distributed power source in a current power distribution network topology, and an output power, the parameter population may include a plurality of sub-populations, and each particle in the parameter population includes a set of operation parameters for power distribution network reconstruction. Optionally, the parameter population may include at least two sub-populations, and may also include three or more sub-populations, which is not limited in the embodiment of the present application. In the embodiment of the present application, the parameter population includes a first sub-population and a second sub-population.

And step 102, updating the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first updating strategy, calculating the fitness value corresponding to each particle in the first sub-population updated each time, and selecting the particle meeting a preset fitness condition from the fitness values of each particle as the first particle.

In implementation, the computer device updates the particle velocity and the particle position of each particle of a first sub-population (which may be referred to as a general population) in the parameter population according to a preset first update strategy, calculates a fitness value corresponding to each particle updated each time, and selects a particle satisfying a preset fitness condition as the first particle from the fitness values of each particle. The first update strategy is an update strategy in a first sub-population (common population), and the computer device determines the first update strategy by adopting a strategy of focusing on global search and then focusing on local search.

Specifically, the computer device may attenuate the coefficient k according to a preset inertia weight_pFirst maximum inertia weight factor

First minimum inertia weight factor

The maximum iteration time T and the current iteration time T are used for obtaining a first inertia weight factor at the previous moment

The specific first inertia weight factor updating formula is as follows:

the computer device then compares the particle position x of the particle of the previous moment in the first sub-population_i(t) particle velocity v_i(t) individual optimum fitness value pBest_iGlobal optimum fitness value gBest and preset learning factor c_1,2First inertia weight factor

And a random number r_1,2Fusing to obtain the particle velocity v of each particle in the first sub-population at the current moment_i(t + 1). Specific current momentThe sub-velocity update formula is:

wherein r is_1,2A random number of 0 to 1, a learning factor c₁＝c₂2. (A large number of experiments show that c₁＝c₂Updating the formula 2 may achieve better convergence).

Finally, the computer device determines the particle velocity v of the particles in the first sub-population at the current moment_i(t +1) and the particle position x at the previous time_i(t) obtaining the particle position x of each particle in the first sub-population at the current moment_i(t + 1). The particle position update formula at the current moment is as follows:

x_i(t+1)＝x_i(t)+v_i(t+1)

meanwhile, the computer equipment calculates the fitness value corresponding to each particle updated each time, and selects the particles meeting the preset fitness condition from the fitness values of the particles as first particles. Optionally, the preset fitness condition may be the largest and the second largest fitness value among the fitness values, and the largest fitness value particle and the second largest fitness value particle selected according to the fitness condition are used as the first particle. The preset fitness condition may also be a preset fitness threshold, and particles greater than or equal to the fitness threshold may all be used as the first particles, which is not limited in the embodiment of the present application.

And 103, migrating the first particles into the second sub-population as second particles in the second sub-population, updating the particle speed and the particle position of the second particles according to a preset second updating strategy, calculating the fitness value corresponding to each updated second particle, and selecting the particles meeting the preset fitness condition from the fitness values of the second particles as third particles.

In an implementation, the computer device migrates the determined first particle into a second sub-population (which may be referred to as an evolved population) as a second particle in the second sub-population (the evolved population). At the same timeAnd the computer equipment updates the particle speed and the particle position of the second particles according to a preset second updating strategy, calculates the fitness value corresponding to each second particle updated each time, and selects the particles meeting the preset fitness value condition from the fitness values of the second particles as third particles. Wherein the second update strategy is an update strategy in a second sub-population (evolved population) which contains the first particles from the first sub-population, and therefore, the inertia weight which refers to the current particle speed in the second sub-population is added to the current particle speed update formula in the second sub-population

And in addition, the particle speed and the particle fitness value of each particle in the first sub-population are introduced, and the individual optimal fitness value and the global optimal fitness value in the second sub-population are regulated and controlled through a preset position influence factor.

Specifically, the computer device may be configured to determine a second predetermined maximum inertia weight factor

Second minimum inertia weight factor

Obtaining a second inertia weight factor at the previous moment by the maximum iteration time T and the current iteration time T

The specific updating formula of the second inertia weight factor is as follows:

the computer device then compares the current time particle velocity v in the first sub-population_i(t +1) particle fitness value f_iAnd the particle position of the particle at the previous time in the second sub-population

Individual optimum fitness value

Global optimum fitness value gBest and preset learning factor c_1,2Second inertia weight factor

And a random number r_1,2Fusing to obtain the particle velocity of the second particle at the current moment

The specific formula for updating the particle velocity of the second particle at the current moment is as follows:

wherein alpha (superscript) represents the evolved population,

representing the sum of fitness values of all individuals in the first population. r is_1,2Random number of 0 to 1, learning factor c₁＝c₂2. (A large number of experiments show that c₁＝c₂Updating the formula 2 may achieve better convergence). Alpha is alpha_1,2,3Called position influence factor and is required to satisfy alpha₁+α₂+α₃＝1。

Meanwhile, the computer equipment calculates the fitness value corresponding to each second particle updated every time, and selects the particles meeting the preset fitness condition from the fitness values of the second particles as third particles. Optionally, the preset fitness condition may be a maximum fitness value among the fitness values, and the maximum fitness value particle selected according to the fitness condition is used as the third particle.

As an alternative implementation, after the computer device updates the particle position and the particle velocity of each particle in the first sub-population and the second sub-population, respectively, the updated operation parameters included in each particle need to be modified, so as to improve the accuracy of each operation parameter in the power distribution network. The specific modification positive process is as follows:

wherein, γ₀For the switch state off threshold, gamma₁In order to close the threshold for the switch state,

for the corrected switch state of each branch circuit of the power distribution network,

for each distributed power source location in the power distribution grid,

is the output power of each distributed power source. By correcting the updated operation parameters contained in the particles, the operation parameters of the particles are adjusted in real time, and the accuracy of the operation parameters in the power distribution network is improved.

And 104, when the third particles meet the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the third particles.

In implementation, the computer device evaluates the obtained fitness value of the third particle, and when the third particle meets a preset target particle evaluation index, the third particle is determined to be a target optimal particle (global maximum fitness value particle), and the computer device controls the power distribution network to reconstruct the power distribution network according to the operation parameters included in the third particle.

Optionally, the preset target particle evaluation index is a power distribution network reconstruction fitness threshold, and when the target fitness value corresponding to the third particle is greater than or equal to the power distribution network reconstruction fitness threshold, that is, it is indicated that the target fitness value of the third particle already meets the fitness value of the optimal reconstruction scheme of the power distribution network, the computer device controls the power distribution network to reconstruct according to the operation parameter data included in the third particle.

Optionally, if the preset target particle evaluation index is a maximum iteration threshold, when the update iteration times of the particle positions and the particle speeds in the third particles are greater than or equal to the maximum iteration threshold, controlling the power distribution network to reconstruct according to the operation parameter data included in the third particles.

The application provides a power distribution network reconstruction method, which comprises the following steps: the computer equipment obtains a preset parameter population according to operation parameters required by power distribution network reconstruction, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of operation parameters for power distribution network reconstruction. Then, the computer device updates the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first updating strategy, calculates the fitness value corresponding to each particle in the first sub-population updated each time, and selects the particle meeting a preset fitness condition from the fitness values of each particle as the first particle. Secondly, migrating the first particles into a second sub-population as second particles in the second sub-population, updating the particle speed and the particle position of the second particles according to a preset second updating strategy, calculating a fitness value corresponding to each second particle updated each time, and selecting particles meeting a preset fitness condition from the fitness values of each second particle as third particles by the computer equipment; and finally, when the third particles meet preset target particle evaluation indexes, the computer equipment controls the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the third particles. By adopting the method, the power distribution network can be reconstructed according to a better reconstruction scheme, and the problems of power supply stability of the power distribution network and reduction of line loss are solved.

In one embodiment, as shown in fig. 2, the specific processing steps of the computer device obtaining the preset parameter population according to the operation parameters required by the power distribution network reconstruction are as follows:

and step 1011, obtaining each operation parameter value of the actual operation state of the power distribution network, and determining the position range of the distributed power supply in the power distribution network and the output power range of the distributed power supply according to each operation parameter value of the actual operation state, wherein each operation parameter value comprises position data of each distributed power supply in the power distribution network and output power data of each distributed power supply.

In implementation, the computer device obtains each operation parameter value in the actual operation state of the current power distribution network, where each operation parameter value includes position data and output power data of each distributed power source in the current power distribution network, and optionally, each operation parameter value may further include switch state data of each branch of the power distribution network. Then, the computer equipment determines the position range of the distributed power supply in the power distribution network in each position data (namely, determines the maximum position upper limit and the minimum position lower limit); the computer device determines an output power range (i.e., determines a maximum output power and a minimum output power) for the distributed power supply in each output power data.

Step 1012, obtaining a preset parameter population according to the position range and the output power range, where the parameter population includes a plurality of sub-populations, and each particle in the parameter population includes a set of randomly initialized operation parameter data.

In implementation, the computer device correspondingly obtains a preset parameter population according to the position range and the output power range, wherein the parameter population comprises a plurality of sub-populations, and each particle in the parameter population comprises a group of randomly initialized operation parameter data.

Specifically, the computer device obtains a preset parameter population according to the operation parameters required by power distribution network reconstruction, and the operation parameter vector corresponding to each particle in the parameter population can be represented as follows:

x_k＝[SW₁,SW₂,...,SW_m,Loc_DG₁,Loc_DG₂,...,Loc_DG_n,P_DG₁,P_DG₂,...,P_DG_n]

wherein k is the population specification modulus and takes the value of 1-N_pop，N_popTo the upper limit of the number of particles in the population of the parameter, SW₁,SW₂...,SW_mThe switching state of each branch in the distribution network distribution topological graph is shown, m is the distribution network branch number, and Loc _ DG₁,Loc_DG₂,...,Loc_DG_nFor each distributed power source location in the distribution network, P _ DG₁,P_DG₂,...,P_DG_nIs the output power of each distributed power source.

The computer equipment randomly initializes the operation parameter vector corresponding to each particle within the determined position range and output power range of the distributed power supply, and the applied random initialization formula is as follows:

SW_i＝round[rand_i]

Loc_DG_i＝round[Loc_DG_iMAX+rand×(Loc_DG_iMAX-Loc_DG_iMIN)]

P_DG_i＝round[P_DG_iMAX+rand×(P_DG_iMAX-P_DG_iMIN)]

wherein round represents rounding integer operation, and rand represents random value within 0-1. Switch state SW of each branch of power distribution network_iThe value is 0 or 1, 0 represents that the switch is opened, and 1 represents that the switch is closed; loc _ DG_iMAXDenotes the maximum upper limit of position, Loc _ DG_iMINDenotes the minimum position ceiling, Loc _ DG_iIndicating the randomly initialized distributed power locations. P _ DG_iMAXDenotes the maximum output power, P _ DG_iMINDenotes the minimum output power, P _ DG_iIndicates the random initialThe initialized distributed power supplies output power.

By adopting the method for acquiring the preset parameter population by the computer equipment, the range threshold of each operation parameter is determined according to the current power distribution network operation parameter value, and the preset parameter population is acquired according to the range threshold, namely, the operation parameter data randomly initialized by each particle in the parameter population all meet the preset range threshold, so that the parameter population conforms to the current power distribution network operation state, and the current power distribution network is better reconstructed.

As an optional implementation manner, after a preset parameter population is obtained, on-off state data of each branch meeting the radial structure of the power distribution network is obtained in a database; when the distribution of the operation state data of the branch switches of the power distribution network contained in any particle in the parameter population does not accord with the operation state data of each branch switch meeting the radial structure of the power distribution network, the operation parameter data of the particle in the parameter population is reinitialized.

In implementation, the computer device first needs to determine whether the switching state parameters of the branch switches of the power distribution network among the operation parameters included in each particle after random initialization enable the power distribution network to meet a preset radial structure of the power distribution network. Specifically, the computer device queries pre-stored branch switch state data (reference data) meeting the radial structure of the power distribution network in a database, compares the branch switch state data of the power distribution network contained in the particle with the queried branch switch state data (reference data), and if the branch switch state data of the power distribution network contained in the particle is not in the branch switch data (reference data), the computer device determines that the operating parameters contained in the particle do not meet the requirement of the radial structure of the power distribution network. And when the distribution of the distribution network branch switch operation state data contained in any particle in the parameter population does not meet the radial structure of the distribution network, the computer equipment reinitializes the operation parameter data of the particle in the parameter population.

Optionally, if the power distribution network branch switch state data included in the particle is in each branch switch state data (reference data), the computer device determines that the operating parameter included in the particle meets the requirement of the radial structure of the power distribution network. And then, the computer equipment continuously judges whether the operation parameters of the particles in the parameter population meet the constraint conditions of the power distribution network in operation. The specific process is as follows:

the computer device also needs to determine whether the operating parameters included in each particle satisfy constraint conditions for the operation of the power distribution network, where the specific constraint conditions are as follows:

0≤I_j≤I_max j＝1,2,3,...,m

wherein, V_iIn order to obtain the node voltage of the distribution network,

is the lower limit threshold of the node voltage of the power distribution network,

the upper limit threshold value of the node voltage of the power distribution network is set; I.C. A_jIs a branch current, I_maxIs an upper threshold value of the branch current; p_subFor grid input power, P_{NL_k}Load power in the distribution network, N is the total number of distributed power sources, and N is₀The total number of loads in the distribution network. When any particle in the parameter population does not meet the constraint condition of the power distribution network during operation, the computer equipment reinitializes the operation parameter data of the particle in the parameter population.

As an optional implementation manner, when any particle in the parameter population satisfies the constraint condition of the power distribution network during operation, the computer device executes step 1013, that is, the computer device first needs to pre-process operation parameter data of each particle initialized at random, and then obtains a power distribution network reconstruction fitness function corresponding to the parameter population, so the application further provides a method for obtaining the power distribution network reconstruction fitness function, as shown in fig. 3, the specific processing procedure is as follows:

and 1013, normalizing the output power data of the distributed power supply and the position data of the distributed power supply in the randomly initialized operation parameter data to obtain normalized output power data of the distributed power supply and normalized position data of the distributed power supply.

In implementation, after the computer device randomly initializes the operation parameters included in each particle in the parameter population, the computer device may further perform normalization processing on the distributed power source location data and the distributed power source output power data included in each group of operation parameters, specifically, a normalization formula adopted by the computer device is as follows:

wherein, Loc _ DG_iIndicating the location of the randomly initialized distributed power supply,

representing the location of the normalized distributed power source. P _ DG_iRepresenting the distributed power supply output power after random initialization,

representing the output power of the normalized distributed power supply. And the values of the position data and the output power data after normalization are all between 0 and 1.

And 1014, acquiring a preset power distribution network reconstruction fitness function, and calculating the fitness value of each particle in the parameter population according to the power distribution network reconstruction fitness function.

In implementation, the computer device obtains a preset power distribution network reconstruction fitness function, and calculates the fitness value of each particle in the parameter population according to the power distribution network reconstruction fitness function. The obtained power distribution network reconstruction fitness function is an objective function established aiming at two objectives of reducing active power loss and maintaining voltage stability in the power distribution network. Specifically, the computer device first determines an active power loss in the power distribution network, where an expression of the active power loss is as follows:

wherein, P_lossIs the amount of active power loss, m is the number of branches, U_i,P_i,Q_iRespectively branch voltage, branch active power and branch reactive power r of the power distribution network_iIs a branch resistance.

Secondly, the computer device analyzes the voltage stability, and the formula of the voltage stability analysis method is as follows:

wherein, VSI_i+1The voltage stability index, VSI, of the (i +1) th node of the power distribution network_i+1The index is between 0 and 1. VSI_i+1The closer the value is to 0, the weaker the voltage stability of the node; VSI_i+1The closer the value is to 1, the stronger the voltage stability of the node. P_i+1,Q_i+1Respectively the total active power and reactive power of a node i +1 in the power distribution network, U_iThe node voltage at node i. r is_i,x_iRespectively branch resistance and reactance.

Finally, the computer equipment takes the reduction of active power loss and the maintenance of voltage stability as targets, and establishes a power distribution network reconstruction fitness function F_{multi_obj}As follows:

wherein, VSI_iIs an indicator of voltage stability, ε₁,ε₂Is a weight coefficient, and specifies epsilon₁,ε₂>0 and epsilon₁+ε₂1. The computer equipment can obtain the pre-established power distribution network reconstruction fitness function and calculate the fitness value corresponding to each particle in the parameter population according to the fitness function.

By adopting the method for acquiring the preset power distribution network reconstruction fitness function, the computer equipment needs to pre-process each operation parameter initialized randomly in the parameter population in advance, namely, each operation parameter is normalized by a normalization method. Then, the computer device obtains a preset power distribution network reconstruction fitness function according to the normalized running parameter data, calculates the fitness value of each particle in the parameter population according to the fitness function, and further selects excellent particles (particles with larger fitness values) in the parameter population so as to control the power distribution network to reconstruct the optimal running parameter.

In another embodiment of the present application, the computer device adds a third sub-population to perform a power distribution network reconstruction method on the basis that the parameter population includes the first sub-population and the second sub-population, and the specific processing procedure is as follows from step 105 to step 107.

And 105, migrating the first particles and the third particles into the third sub-population as fourth particles in the third sub-population, and performing cross variation among the fourth particles according to a preset cross variation algorithm.

In an implementation, the computer device migrates the first and third particles in the first sub-population into a third sub-population (which may be referred to as an elite population), i.e., each time the update process, the third sub-population (elite population) receives the superior particles (first and third particles) from the first and second sub-populations as a fourth particle in the third sub-population. Furthermore, optionally, in order to maintain the number of particles in the third sub-population, the computer device may also choose to discard the particles with the poorer fitness value among the fourth particles. Then, the computer device, according to a preset cross mutation algorithm,and performing cross mutation on each fourth particle. For example, the computer device first performs a cross-processing on each particle, wherein the operating parameter data in particle i is [ x [ ]₁,x₂x₃,x₄,x₅,x₆]The operating parameter data in particle j is [ y₁,y₂y₃,y₄,y₅,y₆]The computer equipment crosses the two particles, the cross point is randomly selected, and if the cross point is a third position point, the two particles are crossed and varied to be a particle i^*The operating parameter data in (1) is [ x ]₁,x₂x₃,y₄,y₅,y₆]The operating parameter data in the particles is [ y ]₁,y₂y₃,x₄,x₅,x₆]. And meanwhile, carrying out secondary inspection on the particles subjected to cross variation by the computer equipment, if the particles subjected to cross variation are checked to meet the operation constraint condition of the power distribution network of the radiation type network structure of the power distribution network, continuing carrying out variation processing on the particles by the computer equipment, wherein in the operation parameters contained in each particle, the value of the operation state parameter of a branch switch of the power distribution network is 0 or 1, so that variation is not needed, and only carrying out variation on the position data and the output voltage data of each distributed power supply in the operation parameters in the power distribution network according to a preset variation probability. The specific variation process is as follows:

wherein,

for the location data of each of the mutated distributed power sources in the power distribution network,

for the varied output power of each distributed power source,η_L、η_pfor a particular constant of the variation factor, N (0,1) is the normal distribution. Similarly, the computer device needs to check the operation parameters of each particle after variation, and when the operation parameters of the particles after variation meet the constraint condition of the operation of the power distribution network, the data of each operation parameter included in the particles are corrected.

And 106, calculating the fitness value corresponding to each fourth particle after cross variation, and selecting the particles meeting the preset fitness condition from the fitness values of the fourth particles as fifth particles.

In an implementation, the computer device calculates a fitness value corresponding to each of the fourth particles after the cross mutation, and selects a particle satisfying a preset fitness condition (e.g., selecting the highest fitness value) as the fifth particle from among the fitness values of the fourth particles.

Optionally, the computer device may further move a fifth particle selected from the third sub-population (elite population) back to the first sub-population (normal population) and the second sub-population (evolutionary population) to guide the first sub-population and the second sub-population to develop, even if the fitness values determined by the first sub-population and the second sub-population are better.

And 107, when the fifth particles meet the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the fifth particles.

In implementation, the computer device performs fitness evaluation on the fifth particles selected from the third sub-population (elite population), and when the fifth particles meet a preset target particle evaluation index, the fifth particles are indicated as target optimal particles (global maximum fitness value particles), and the computer device controls the power distribution network to reconstruct the power distribution network according to operation parameter data included in the fifth particles. The specific target particle evaluation index may be the same as the evaluation index in step 104, so the specific evaluation process is similar to step 104, and the details are not repeated in this embodiment.

In the power distribution network reconstruction method, the third sub-population (elite population) is added into the parameter population, and when the fifth particle is selected by the computer equipment, the situation that the selected fifth particle is not the global optimal particle (particle corresponding to the global maximum fitness value) is effectively avoided by performing jump search near the more excellent particles (the first particle and the third particle), so that the operation parameter data contained in the fifth particle selected by the computer is the optimal operation parameter data in the power distribution network reconstruction scheme, and the problems of power supply stability of the power distribution network and reduction of line loss are effectively solved.

It should be understood that although the various steps in the flow charts of fig. 1-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least some of the steps in fig. 1-4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.

In one embodiment, as shown in fig. 5, there is provided a power distribution network reconfiguration device, including: an obtaining module 510, a first selecting module 520, a second selecting module 530, and a control module 540, wherein:

the obtaining module 510 is configured to obtain a preset parameter population according to an operation parameter required by power distribution network reconstruction, where the parameter population includes a plurality of sub-populations, and each particle in the parameter population includes a group of operation parameters for power distribution network reconstruction.

The first selecting module 520 is configured to update the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first update policy, calculate a fitness value corresponding to each particle in the first sub-population updated each time, and select a particle meeting a preset fitness condition from the fitness values of each particle as the first particle.

A second selecting module 530, configured to migrate the first particle into the second sub-population as a second particle in the second sub-population, update the particle velocity and the particle position of the second particle according to a preset second update policy, calculate a fitness value corresponding to each updated second particle, and select a particle that meets a preset fitness condition from the fitness values of each second particle as a third particle.

And the control module 540 is configured to control the power distribution network to reconstruct the power distribution network according to the operation parameter data included in the third particles when the third particles meet the preset target particle evaluation index.

As an optional implementation, the apparatus further comprises:

and the processing module is used for transferring the first particles and the third particles into the third sub-population as fourth particles in the third sub-population and carrying out cross variation among the fourth particles according to a preset cross variation algorithm.

And the third selection module is used for calculating the fitness value corresponding to each fourth particle after cross variation, and selecting the particles meeting the preset fitness condition from the fitness values of the fourth particles as fifth particles.

And the second control module is used for controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data contained in the fifth particles when the fifth particles meet the preset target particle evaluation index.

As an optional implementation manner, the first selection module 520 is specifically configured to obtain a first inertia weight factor at a previous time according to a preset inertia weight attenuation coefficient, a first maximum inertia weight factor, a first minimum inertia weight factor, a maximum iteration number, and a current iteration number.

And fusing the particle position, the particle speed, the individual optimal fitness value, the global optimal fitness value, the preset learning factor, the first inertia weight factor and the random number of the particles in the first sub-population at the previous moment to obtain the particle speed of each particle in the first sub-population at the current moment.

And obtaining the particle position of each particle in the first sub-population at the current moment according to the particle speed of the particle in the first sub-population at the current moment and the particle position of the particle in the previous moment.

As an optional implementation manner, the second selecting module 530 is specifically configured to obtain a second inertia weight factor at a previous time according to a preset second maximum inertia weight factor, a second minimum inertia weight factor, a maximum iteration number, and a current iteration number.

And fusing the current-time particle speed and the particle fitness value of each particle in the first sub-population, and the particle position, the particle speed, the particle fitness value, the individual optimal fitness value, the global optimal fitness value, the preset learning factor, the second inertia weight factor and the random number of the particle in the second sub-population to obtain the current-time particle speed of the second particle.

As an optional implementation manner, the obtaining module 510 is specifically configured to obtain each operation parameter value of the actual operation state of the power distribution network, and determine a location range of the distributed power sources in the power distribution network and an output power range of the distributed power sources according to each operation parameter value of the actual operation state, where each operation parameter value includes location data of each distributed power source in the power distribution network and output power data of each distributed power source.

As an optional implementation manner, the obtaining module 510 is specifically configured to perform normalization processing on the output power data of the distributed power source and the position data of the distributed power source in the operation parameter data after random initialization, so as to obtain normalized output power data of the distributed power source and normalized position data of the distributed power source.

As an optional implementation manner, after acquiring the preset parameter population according to the operation parameters required by the power distribution network reconfiguration, the apparatus further includes:

the judging module is used for acquiring the switching state data of each branch meeting the radial structure of the power distribution network from the database;

when the distribution of the operation state data of the branch switches of the power distribution network contained in any particle in the parameter population does not accord with the operation state data of each branch switch meeting the radial structure of the power distribution network, the operation parameter data of the particle in the parameter population is reinitialized.

As an optional implementation manner, the preset target particle evaluation index is a power distribution network reconstruction fitness threshold, and the control device 540 is specifically configured to control the power distribution network to reconstruct according to the operation parameter data included in the third particles when the target fitness value corresponding to the third particles is greater than or equal to the power distribution network reconstruction fitness threshold.

As an optional implementation manner, the preset target particle evaluation index is a maximum iteration threshold, and the control device 540 is specifically configured to control the power distribution network to perform reconstruction according to the operation parameter data included in the third particle when the update iteration number of the third particle is greater than or equal to the maximum iteration number threshold.

For specific limitations of the power distribution network reconfiguration device, reference may be made to the above limitations on the power distribution network reconfiguration method, which is not described herein again. The modules in the power distribution network reconfiguration device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of power distribution network reconstruction. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method for reconfiguring a power distribution network, the method comprising:

when the third particles meet preset target particle evaluation indexes, controlling the power distribution network to reconstruct the power distribution network according to operation parameter data contained in the third particles;

the updating the particle speed and the particle position of each particle of the first sub-population in the parameter population according to a preset first updating strategy comprises:

obtaining a first inertia weight factor at the previous moment according to a preset inertia weight attenuation coefficient, a first maximum inertia weight factor, a first minimum inertia weight factor, a maximum iteration number and a current iteration number; the first inertia weight factor updating formula is as follows:

wherein,

is the first largest inertia weight factor and is,

is a first minimum inertia weight factor, T is the maximum iteration number, T is the current iteration number,

a first inertia weight factor at a previous moment; e is an irrational number, k_pIs inertia weight attenuation coefficient;

fusing the particle position, the particle speed, the individual optimal fitness value, the global optimal fitness value, the preset learning factor, the first inertia weight factor and the random number of the particles in the first sub-population at the previous moment to obtain the particle speed of each particle in the first sub-population at the current moment; the updating formula of the particle speed at the current moment is as follows:

wherein v is_i(t +1) is the particle velocity of each particle in the first sub-population at the current time, x_i(t) is the particle position of the particle at the previous moment in said first sub-population, i represents the current particle, v_i(t) is the particle velocity of the particle at the previous time,

for the individual optimal fitness value, gBest is the global optimal fitness value, c₁，c₂Is a preset learning factor, r₁、r₂A random number of 0 to 1;

obtaining the particle position of each particle in the first sub-population at the current moment according to the particle speed of the particle in the first sub-population at the current moment and the particle position of the particle in the previous moment; the updating formula of the particle position at the current moment is as follows: x is the number of_i(t+1)＝x_i(t)+v_i(t+1)

Wherein x is_i(t +1) is the particle position of each particle in the first sub-population at the current time.

2. The method of claim 1, wherein the parameter population further comprises a third sub-population, the method further comprising:

3. The method according to claim 1, wherein the updating the particle velocity and the particle position of the second particle according to a preset second update strategy comprises:

and obtaining the particle position of the second particle at the current moment according to the particle speed of the particle in the second sub-population at the current moment, the particle position of the previous moment, the position influence factor, the individual optimal fitness value and the global optimal fitness value.

4. The method of claim 1, wherein the obtaining a preset parameter population according to the operational parameters required for power distribution network reconstruction, the parameter population including a plurality of sub-populations, each particle in the parameter population including a set of operational parameters for power distribution network reconstruction, comprises:

acquiring each operation parameter value of an actual operation state of the power distribution network, and determining a position range of the distributed power supply in the power distribution network and an output power range of the distributed power supply according to each operation parameter value of the actual operation state, wherein each operation parameter value comprises position data of each distributed power supply in the power distribution network and output power data of each distributed power supply;

5. The method of claim 4, further comprising:

6. The method of claim 4, wherein after obtaining the predetermined parameter population according to the operational parameters required for power distribution grid reconfiguration, the method further comprises:

when the distribution of the operation state data of the branch switches of the power distribution network, which is contained in any particle in the parameter population, does not accord with the operation state data of each branch switch meeting the radial structure of the power distribution network, the operation parameter data of the particle in the parameter population is reinitialized.

7. The method of claim 1, wherein the preset target particle evaluation index is a power distribution network reconstruction fitness threshold, and when the third particle meets the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data included in the third particle comprises:

8. The method of claim 1, wherein the preset target particle evaluation index is a maximum iteration threshold, and when the third particle satisfies the preset target particle evaluation index, controlling the power distribution network to reconstruct the power distribution network according to the operation parameter data included in the third particle comprises:

9. A power distribution network reconfiguration device, said device comprising:

the first selection module is used for updating the particle speed and the particle position of each particle of a first sub-population in the parameter population according to a preset first updating strategy, calculating the fitness value corresponding to each particle in the first sub-population updated each time, and selecting the particle meeting a preset fitness condition from the fitness values of each particle as the first particle; the first selecting module is configured to update the particle velocity and the particle position of each particle of the first sub-population in the parameter population according to a preset first update policy, and includes:

wherein,

is the first largest inertia weight factor and is,

is a first minimum inertia weight factor, T is a maximum number of iterations, T is a current number of iterations,

fusing the particle position, the particle speed, the individual optimal fitness value, the global optimal fitness value, the preset learning factor, the first inertia weight factor and the random number of the particles in the first sub-population at the previous moment to obtain the particle speed of each particle in the first sub-population at the current moment; the updating formula of the particle velocity at the current moment is as follows:

wherein v is_i(t +1) is the particle velocity of each particle in the first sub-population at the current time, x_i(t) is the position of the particle in the first sub-population at the previous moment in time, v_i(t) is the particle velocity of the particle at the previous time,

obtaining the particle position of each particle in the first sub-population at the current moment according to the particle speed of the particle in the first sub-population at the current moment and the particle position of the particle in the previous moment; the updating formula of the particle position at the current moment is as follows: x is a radical of a fluorine atom_i(t+1)＝x_i(t)+v_i(t+1)

Wherein x is_i(t +1) is the particle position of each particle in the first sub-population at the current moment;

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.

11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.