CN115833116B - Power distribution network reconstruction optimization method based on multi-objective optimization - Google Patents
Power distribution network reconstruction optimization method based on multi-objective optimization Download PDFInfo
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Abstract
The invention discloses a power distribution network reconstruction optimization method based on multi-objective optimization, which comprises the following steps: s1, according to an existing DG power distribution network circuit multi-objective optimization static reconstruction model, acquiring active power, voltage offset and load balance of a power distribution network circuit under the static model; s2, dividing the existing DG power distribution network circuit multi-objective optimization static reconstruction model into equal power distribution network reconstruction time periods according to the active power, the voltage offset and the load balance degree of the power distribution network circuit, and acquiring a load clustering curve of the power distribution network circuit; s3, a load clustering curve of the power distribution network circuit adopts a power distribution network multi-objective optimization dynamic reconstruction model based on IPSO, and clusters with similar magnitudes of the load clustering curves of each power distribution network circuit are used as clustering centers, so that the reconstruction efficiency of the power distribution network is improved, the influence of each node change of a power distribution network branch on the power flow distribution of the system is reduced, and the power supply reliability and the running economy of the power distribution network are improved.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a power distribution network reconstruction optimization method based on multi-objective optimization.
Background
The distribution network is an important hub for connecting a transmission line and a user, the power supply reliability and the electric energy utilization efficiency of the distribution network are extremely required to be improved to cope with the access of a large-scale distributed power source DG and the development of electric power marketization, the distribution network reconstruction changes the tide distribution by changing the operation structure of a system, the most economical means for improving the power supply quality of the distribution network is provided, and the multi-objective optimization reconstruction research on the distribution network has a certain value.
The power distribution network reconstruction is a multi-constraint multi-objective large-scale nonlinear combination optimization problem, the power distribution network reconstruction problem mainly comprises static reconstruction and dynamic reconstruction, the static reconstruction is completed by taking a load value of a certain time section as a basis in a short period of power distribution network operation, the dynamic reconstruction considers the change of load in a long period, the optimization is performed according to a formulated switch operation schedule, and in the prior art, the optimization method for the power distribution network reconstruction further has the following defects:
firstly, the static reconstruction algorithm of the power distribution network has low optimizing precision and long optimizing time, directly influences the reconstruction efficiency of the power distribution network, has single optimizing target, and does not consider multi-target optimization;
secondly, the dynamic reconfiguration of the power distribution network is carried out by dividing reconfiguration time periods by taking the influence of the overall load change of the system into consideration, the influence of the change of each node in the system on the power flow distribution of the system is not considered, the reconfiguration target is single, and multi-index optimization under the condition of considering the switching operation times is not considered;
thirdly, only the single difference of the amplitude or the form of the power load curve is considered by a load clustering algorithm adopted in the prior art, the change of the amplitude and the form of the power load curve is not comprehensively considered, the clustering accuracy is directly affected, the reconstruction period is divided inaccurately, and the power supply reliability and the operation economy of the power distribution network are affected.
Disclosure of Invention
The invention aims to provide a power distribution network reconstruction optimization method based on multi-objective optimization, which aims to solve the technical problems that in the prior art, the static reconstruction optimization objective of a power distribution network is single, multi-objective optimization is not considered, multi-index optimization under the condition of not considering the switching operation times is not considered, and the clustering accuracy is poor.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a power distribution network reconstruction optimization method based on multi-objective optimization comprises the following steps:
step S1, according to an existing DG power distribution network circuit multi-objective optimization static reconstruction model, obtaining active power, voltage offset and load balance of the power distribution network circuit under the static model;
s2, dividing the existing DG power distribution network circuit multi-objective optimization static reconstruction model into equal power distribution network reconstruction time periods according to the active power, voltage offset and load balance degree of the power distribution network circuit, keeping the load of each node of the power distribution network circuit constant in a certain time period, and obtaining a load clustering curve of the power distribution network circuit;
and S3, adopting an IPSO-based power distribution network multi-objective optimization dynamic reconstruction model through load clustering curves of power distribution network circuits, taking clusters with similar magnitudes of the load clustering curves of each power distribution network circuit as a clustering center, and optimizing the dynamic reconstruction model according to the clustering center.
In the step S1, network information of the power distribution network is obtained according to the existing DG power distribution network circuit, the power distribution network circuit is encoded based on a ring network decimal encoding strategy, and active power, voltage offset and load balance of the power distribution network circuit in a static mode are obtained by using power flow calculation.
As a preferred scheme of the invention, the ring network decimal encoding strategy updates particle information mainly through an improved particle swarm algorithm to obtain the network information of the distribution network in the same period and solve the global optimal solution of active power, voltage offset and load balance of the distribution network circuit, wherein the improved particle swarm algorithm comprises:
acquiring network information of a power distribution network circuit according to the existing DG power distribution network circuit, setting self-adaptive inertia weight of each branch for the power distribution network circuit, and setting particle swarm population size
To restrict the branch number of the distribution network;
initializing particle swarm population size using ant colony random spanning tree
Obtaining the fitness value of the particles through tide calculation;
sorting according to the fitness value of the particles, dividing the population into N sub-populations according to the frog-leaping grouping thought, and obtaining the sub-population optimal value and the global optimal value from the N sub-populations to solve the active power, the voltage offset and the load balance of the optimal power distribution network circuit.
As a preferred scheme of the invention, active power, voltage offset and load balance of the optimal power distribution network circuit are taken as optimization targets, a dynamic reconstruction model of the power distribution network is constructed, each branch of the power distribution network circuit is similarly clustered by taking outer morphology similarity as load balance, the morphology similarity clustering is carried out on the load by adopting a pearson similarity measurement function, and the initialization of a clustering center is completed by the maximum and minimum distances, and the method comprises the following steps:
setting the number of common nodes of each branch of power distribution network circuit system
Taking +.A. according to the existing DG distribution network circuit multi-objective optimization static reconstruction model in the reconstruction time period>
Load values of the time points are obtained, and the same reconstruction period of the power distribution network is obtained;
power distribution network circuit load data set with same time period
For the corresponding load state of each time point in (a)
Representation of->
，
Representation->
Load state of nth node at moment, corresponding load sample matrix->
The expression is:
for the load sample matrix
Standardized processing is carried out to obtain an average value +.>
Standard deviation;
for the normalized load sample matrix
And carrying out outer morphology similarity clustering by using a Pearson similarity measurement function, and setting a minimum morphology similarity threshold value among classes and a value range of a clustering number as final optimization data.
As a preferred scheme of the invention, the method for carrying out outer morphology similarity clustering by adopting the Pearson similarity measurement function comprises the following steps:
based on a matrix of load samples
Setting the clustering number->
The value range->
Setting the minimum morphological similarity threshold value between classes as +.>
Screening the minimum pearson coefficient according to the pearson similarity matrix between loads of the same branch>
Corresponding two load states->
and
Will->
and
The two load states are set as a cluster, and the Pearson coefficient is acquired>
The expression is:
wherein ,
and
Belonging to the time point->
Is a different time of day;
the similarity of two samples selected from the pearson similarity matrix between loads of the same branch to the first cluster is greater than a minimum morphological similarity threshold
Taking two samples with the smallest similarity as a second cluster, and screening samples with similarity larger than a minimum morphological similarity threshold value with all samples of the first two clusters>
Selecting two samples with minimum similarity as the third cluster until +.>
And (5) clustering, and ending initializing similar clusters.
As a preferable scheme of the invention, the similarity of samples which are not clustered to each clustering center is calculated after the similar clustering is initialized
Non-clustered samples are treated with minimal similarity +.>
Clustering is carried out to finish->
A cluster center is determined according to the average value of the samples in each cluster, and the +.>
The cluster centers of the individual clusters are expressed as:
will be the first
First of clustering centers>
The dimensions are expressed as:
wherein ,
is->
The total number of samples for each cluster center;
according to the first
Cluster center of individual clusters->
Determining the clustering number as +.>
Clustering evaluation index->
Is expressed as: />
wherein ,
representing the number of clusters>
Indicate->
Total number of samples of each cluster center, +.>
Representing pearson coefficients of samples in the respective clusters to the respective cluster centers.
As a preferable scheme of the invention, the evaluation index of the outer morphology clustering is balanced according to the load
Performing close clustering on the inner layer amplitude values of the load balancing by taking Euclidean distance as a similarity measurement function, and restricting the minimum morphological similarity threshold value between the outer layer morphological clustering classes of the load balancing to be +.>
The method comprises the following steps:
setting a clustering number C of inner-layer amplitude similarity clustering of load balancing, and screening the minimum pearson coefficient through a pearson similarity matrix between loads of the same branch
Corresponding two load states->
and
Calculating two load states->
and
Euclidean distance between them, obtaining the minimum distance threshold +.>
The expression is:
iterating by taking the weighted square sum of each sample to all load cluster centers as an objective function, and calculating the weight by the objective functionThe derivative has a minimum value to constrain the minimum morphological similarity threshold between the best cluster classes
。
As a preferable scheme of the invention, the method is based on the minimum morphological similarity threshold value among the optimal cluster classes
Clustering evaluation index combined->
And dynamically dividing a reconstruction period, and carrying out dynamic reconstruction on the power distribution network on the clustered load information.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the multi-objective optimization dynamic reconstruction of the power distribution network is completed by utilizing the reconstruction strategy of the power distribution network based on IPSO, each branch of the power distribution network circuit is similarly clustered by taking the outer layer morphology similarity and the inner layer amplitude similarity as load balancing, the reconstruction period is divided, the clustering center of the corresponding cluster in each reconstruction period is taken as a load state, the multi-objective optimization dynamic reconstruction of the power distribution network is completed, the amplitude and the morphology of the load balancing curve are clustered, different scenes are adapted, the reconstruction efficiency of the power distribution network is improved, the influence of the change of each node of the branch of the power distribution network on the distribution flow distribution of the system is reduced, and the power supply reliability of the power distribution network and the running economy of the power distribution network are improved.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
Fig. 1 is a flowchart of a power distribution network reconstruction optimization method based on multi-objective optimization provided by an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the invention provides a power distribution network reconstruction optimization method based on multi-objective optimization, which comprises the following steps:
step S1, according to an existing DG power distribution network circuit multi-objective optimization static reconstruction model, obtaining active power, voltage offset and load balance of the power distribution network circuit under the static model;
in the embodiment, in consideration of the influence of the switching operation times on the service life of the switch in the dynamic reconstruction, the invention establishes a dynamic reconstruction mathematical model of the power distribution network by taking the minimum active power loss, the minimum voltage offset index, the optimal load balance and the minimum switching operation times in the whole reconstruction period as optimization targets.
S2, dividing the existing DG power distribution network circuit multi-objective optimization static reconstruction model into equal power distribution network reconstruction time periods according to the active power, voltage offset and load balance degree of the power distribution network circuit, keeping the load of each node of the power distribution network circuit constant in a certain time period, and obtaining a load clustering curve of the power distribution network circuit;
in this embodiment, according to the power injected into the end node of the distribution network or the branch current and the power supply voltage corresponding to the end node, the end performs load flow calculation from the end to the power supply end to obtain the operation parameters of the distribution network, so as to obtain the load clustering curve of the distribution network circuit.
And S3, adopting an IPSO-based power distribution network multi-objective optimization dynamic reconstruction model through load clustering curves of power distribution network circuits, taking clusters with similar magnitudes of the load clustering curves of each power distribution network circuit as a clustering center, and optimizing the dynamic reconstruction model according to the clustering center.
In the embodiment, a double-layer clustering method based on morphological similarity and amplitude similarity is applied to load clustering, a reconstruction period is divided into a plurality of reconstruction periods taking a clustering center as load state information, and multi-objective optimization dynamic reconstruction of the power distribution network is completed by combining with an IPSO-based power distribution network reconstruction strategy.
In step S1, network information of a power distribution network is obtained according to an existing DG power distribution network circuit, the power distribution network circuit is encoded based on a ring network decimal encoding strategy, and active power, voltage offset and load balance of the power distribution network circuit in a static mode are obtained through load flow calculation.
In this embodiment, the ring network decimal encoding strategy is based on the fact that the dimension of each group of solutions is equal to the number of basic ring networks in the power distribution system, equal to the number of tie switches, and the dimension of each group is equal to the number of basic ring networks when all nodes in the power distribution system are recoded according to the basic ring networks, and the value of each dimension is equal to the corresponding node code.
In this embodiment, the ring network decimal encoding rule is set as follows: and coding the switch to which the ring network belongs again by taking the ring network as a unit, closing the switches except all the ring networks, and keeping the public branch number between the two ring networks to be not more than 1.
The ring network decimal coding strategy updates particle information mainly through an improved particle swarm algorithm to obtain the network information of the power distribution network in the same period and solve the global optimal solution of active power, voltage offset and load balance of the power distribution network circuit, wherein the improved particle swarm algorithm comprises:
acquiring network information of a power distribution network circuit according to the existing DG power distribution network circuit, setting self-adaptive inertia weight of each branch for the power distribution network circuit, and setting particle swarm population size
To restrict the branch number of the distribution network;
initializing particle swarm population size using ant colony random spanning tree
Adaptation of particles by tidal current calculationA degree value;
sorting according to the fitness value of the particles, dividing the population into N sub-populations according to the frog-leaping grouping thought, and obtaining the sub-population optimal value and the global optimal value from the N sub-populations to solve the active power, the voltage offset and the load balance of the optimal power distribution network circuit.
In the embodiment, the accuracy of the obtained power distribution network voltage offset index, active power loss and load balance degree is improved through an improved particle swarm algorithm, and the accuracy of dynamic reconstruction of the power distribution network is improved.
The active power, the voltage offset and the load balance of the optimal power distribution network circuit are used as optimization targets, a dynamic reconstruction model of the power distribution network is constructed, each branch of the power distribution network circuit is subjected to similar clustering by taking outer morphology similarity as load balance, the pearson similarity measurement function is adopted to carry out morphology similarity clustering on the load, and the cluster center initialization is completed through the maximum and minimum distances, and the method comprises the following steps:
setting the number of common nodes of each branch of power distribution network circuit system
Taking +.A. according to the existing DG distribution network circuit multi-objective optimization static reconstruction model in the reconstruction time period>
Load values of the time points are obtained, and the same reconstruction period of the power distribution network is obtained;
power distribution network circuit load data set with same time period
For the corresponding load state of each time point in (a)
Representation of->
，
Representation->
Load state of nth node at moment, corresponding load sample matrix->
The expression is:
for the load sample matrix
Standardized processing is carried out to obtain an average value +.>
Standard deviation;
for the normalized load sample matrix
And carrying out outer morphology similarity clustering by using a Pearson similarity measurement function, and setting a minimum morphology similarity threshold value among classes and a value range of a clustering number as final optimization data.
In the embodiment, each branch of the power distribution network circuit is similarly clustered by taking the outer-layer morphology similarity as load balancing, the reconstruction period division is completed, the clustering center of the corresponding cluster in each reconstruction period is taken as a load state, the multi-objective optimization dynamic reconstruction of the power distribution network is completed, the reconstruction efficiency of the power distribution network is improved, the influence of the change of each node of the branch of the power distribution network on the power flow distribution of the system is reduced through the clustering of the amplitude and the morphology of the load balancing curve, and the power supply reliability of the power distribution network is improved.
The outer layer morphology similarity clustering method adopting the pearson similarity measurement function comprises the following steps:
based on a matrix of load samples
Setting the clustering number->
The value range->
Setting the minimum morphological similarity threshold value between classes as +.>
Screening the minimum pearson coefficient according to the pearson similarity matrix between loads of the same branch>
Corresponding two load states->
and
Will->
and
The two load states are set as a cluster, and the Pearson coefficient is acquired>
The expression is:
wherein ,
and
Belonging to the time point->
Is a different time of day;
selecting two samples to the first cluster from the pearson similarity matrix between loads of the same branchSimilarity is greater than a minimum morphological similarity threshold
Taking two samples with the smallest similarity as a second cluster, and screening samples with similarity larger than a minimum morphological similarity threshold value with all samples of the first two clusters>
Selecting two samples with minimum similarity as the third cluster until +.>
And (5) clustering, and ending initializing similar clusters.
In the present embodiment, the number of clusters is counted
The value range of (2) is set to +.>
Minimum morphological similarity threshold in outer clusters +.>
And setting the clustering result to be 0.23, acquiring the clustering result of the load balancing of the same branch of the power distribution network, sequencing the clustering result to divide the reconstruction period, setting the maximum reconstruction times to be not more than 4 times, and correcting the clustering which does not meet the condition according to the minimum Euclidean distance between the clustering results and the adjacent clustering to determine the final reconstruction period, thereby improving the clustering accuracy.
Calculating the similarity of samples which are not clustered to each clustering center after initializing similar clusters
Non-clustered samples are treated with minimal similarity +.>
Clustering is carried out to finish->
Clustering according to eachThe average value of the samples in the cluster determines the cluster center, and the +.>
The cluster centers of the individual clusters are expressed as:
will be the first
First of clustering centers>
The dimensions are expressed as:
wherein ,
is->
The total number of samples for each cluster center;
according to the first
Cluster center of individual clusters->
Determining the clustering number as +.>
Clustering evaluation index->
Is expressed as:
wherein ,
representing the number of clusters>
Indicate->
Total number of samples of each cluster center, +.>
Representing pearson coefficients of samples in the respective clusters to the respective cluster centers.
The evaluation index is clustered according to the load balancing outer layer morphology
Performing close clustering on the inner layer amplitude values of the load balancing by taking Euclidean distance as a similarity measurement function, and restricting the minimum morphological similarity threshold value between the outer layer morphological clustering classes of the load balancing to be +.>
The method comprises the following steps:
setting a clustering number C of inner-layer amplitude similarity clustering of load balancing, and screening the minimum pearson coefficient through a pearson similarity matrix between loads of the same branch
Corresponding two load states->
and
Calculating two load states->
and
Euclidean distance between them, obtaining the minimum distance threshold +.>
The expression is:
iteration is carried out by taking the weighted square sum of each sample to all load cluster centers as an objective function, and a minimum value exists on the weighted partial derivative through the objective function so as to restrict the minimum morphological similarity threshold value among the optimal cluster types
。
Based on a minimum morphological similarity threshold between the optimal cluster classes
Clustering evaluation index combined->
And dynamically dividing a reconstruction period, and carrying out dynamic reconstruction on the power distribution network on the clustered load information.
According to the invention, the multi-objective optimization dynamic reconstruction of the power distribution network is completed by utilizing the reconstruction strategy of the power distribution network based on IPSO, each branch of the power distribution network circuit is similarly clustered by taking the outer layer morphology similarity and the inner layer amplitude similarity as load balancing, the reconstruction period is divided, the clustering center of the corresponding cluster in each reconstruction period is taken as a load state, the multi-objective optimization dynamic reconstruction of the power distribution network is completed, the amplitude and the morphology of the load balancing curve are clustered, different scenes are adapted, the reconstruction efficiency of the power distribution network is improved, the influence of the change of each node of the branch of the power distribution network on the distribution flow distribution of the system is reduced, and the power supply reliability of the power distribution network and the running economy of the power distribution network are improved.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements may be made to the present application by those skilled in the art, which modifications and equivalents are also considered to be within the scope of the present application.
Claims (6)
1. The power distribution network reconstruction optimization method based on multi-objective optimization is characterized by comprising the following steps of:
step S1, according to an existing DG power distribution network circuit multi-objective optimization static reconstruction model, obtaining active power, voltage offset and load balance of the power distribution network circuit under the static model;
s2, dividing the existing DG power distribution network circuit multi-objective optimization static reconstruction model into equal power distribution network reconstruction time periods according to the active power, voltage offset and load balance degree of the power distribution network circuit, keeping the load of each node of the power distribution network circuit constant in a certain time period, and obtaining a load clustering curve of the power distribution network circuit;
s3, adopting an IPSO-based power distribution network multi-objective optimization dynamic reconstruction model through load clustering curves of power distribution network circuits, taking clusters with similar magnitudes of the load clustering curves of each power distribution network circuit as clustering centers, and optimizing the dynamic reconstruction model according to the clustering centers;
in the step S1, network information of a power distribution network is obtained according to an existing DG power distribution network circuit, the power distribution network circuit is encoded based on a ring network decimal encoding strategy, and active power, voltage offset and load balance of the power distribution network circuit in a static mode are obtained by using load flow calculation;
the ring network decimal coding strategy updates particle information mainly through an improved particle swarm algorithm to obtain the network information of the power distribution network in the same period and solve the global optimal solution of active power, voltage offset and load balance of the power distribution network circuit, wherein the improved particle swarm algorithm comprises:
acquiring network information of a power distribution network circuit according to the existing DG power distribution network circuit, setting self-adaptive inertia weight of each branch for the power distribution network circuit, and setting particle swarm population size
To restrict the branch number of the distribution network;
by using antsInitializing particle swarm population size for swarm random spanning tree
Obtaining the fitness value of the particles through tide calculation;
sorting according to the fitness value of the particles, dividing the population into N sub-populations according to the frog-leaping grouping thought, and obtaining the sub-population optimal value and the global optimal value from the N sub-populations to solve the active power, the voltage offset and the load balance of the optimal power distribution network circuit.
2. The power distribution network reconstruction optimization method based on multi-objective optimization according to claim 1, wherein active power, voltage offset and load balance of the optimal power distribution network circuit are taken as optimization targets, a dynamic reconstruction model of the power distribution network is constructed, each branch of the power distribution network circuit takes outer morphology similarity as load balance for similar clustering, pearson similarity measurement function is adopted for similar clustering of loads, and cluster center initialization is completed through maximum and minimum distances, and the method comprises the following steps:
setting the number of common nodes of each branch of power distribution network circuit system
Taking +.A. according to the existing DG distribution network circuit multi-objective optimization static reconstruction model in the reconstruction time period>
Load values of the time points are obtained, and the same reconstruction period of the power distribution network is obtained;
power distribution network circuit load data set with same time period
Corresponding load status of each time point of (a)>
Indicating (I)>
； wherein
，
Representation->
Load state of nth node at moment, corresponding load sample matrix->
The expression is: />
For the load sample matrix
Standardized processing is carried out to obtain an average value +.>
Standard deviation;
for the normalized load sample matrix
And carrying out outer morphology similarity clustering by using a Pearson similarity measurement function, and setting a minimum morphology similarity threshold value among classes and a value range of a clustering number as final optimization data.
3. The power distribution network reconstruction optimization method based on multi-objective optimization according to claim 2, wherein the outer layer morphological similarity clustering method adopting the pearson similarity metric function comprises the following steps:
based on a matrix of load samples
Setting the clustering number->
The value range->
Setting the minimum morphological similarity threshold value between classes as +.>
Screening the minimum pearson coefficient according to the pearson similarity matrix between loads of the same branch>
Corresponding two load states->
and
Will->
and
The two load states are set as a cluster, and the Pearson coefficient is acquired>
The expression is:
wherein ,
and
Belonging to the time point->
Is a different time of day;
the similarity of two samples selected from the pearson similarity matrix between loads of the same branch to the first cluster is greater than a minimum morphological similarity threshold
Taking two samples with the smallest similarity as a second cluster, and screening samples with similarity larger than a minimum morphological similarity threshold value with all samples of the first two clusters>
Selecting two samples with minimum similarity as the third cluster until +.>
And (5) clustering, and ending initializing similar clusters.
4. A multi-objective optimization-based power distribution network reconstruction optimization method according to claim 3, wherein the similarity between samples which are not clustered and each clustering center is calculated after similar clustering is initialized
Non-clustered samples are treated with minimal similarity +.>
Clustering is carried out to finish->
A cluster center is determined according to the average value of the samples in each cluster, and the +.>
The cluster centers of the individual clusters are expressed as:
will be the first
First of clustering centers>
The dimensions are expressed as:
wherein ,
is->
The total number of samples for each cluster center;
according to the first
Cluster center of individual clusters->
Determining the clustering number as +.>
Clustering evaluation index->
Is expressed as:
wherein ,
representing the number of clusters>
Indicate->
Total number of samples of each cluster center, +.>
Representing pearson coefficients of samples in the respective clusters to the respective cluster centers.
5. The multi-objective optimization-based power distribution network reconstruction optimization method according to claim 4, wherein the evaluation index is clustered according to load balancing outer layer morphology
Performing close clustering on the inner layer amplitude values of the load balancing by taking Euclidean distance as a similarity measurement function, and restricting the minimum morphological similarity threshold value between the outer layer morphological clustering classes of the load balancing to be +.>
The method comprises the following steps:
setting a clustering number C of inner-layer amplitude similarity clustering of load balancing, and screening the minimum pearson coefficient through a pearson similarity matrix between loads of the same branch
Corresponding two load states->
and
Calculating two load states
and
Euclidean distance between them, obtaining the minimum distance threshold +.>
The expression is:
iteration is carried out by taking the weighted square sum of each sample to all load cluster centers as an objective function, and a minimum value exists on the weighted partial derivative through the objective function so as to restrict the minimum morphological similarity threshold value among the optimal cluster types
。
6. The multi-objective optimization-based power distribution network reconstruction optimization method according to claim 5, wherein the optimization method is based on a minimum morphological similarity threshold value among the optimal cluster classes
Clustering evaluation index combined->
And dynamically dividing a reconstruction period, and carrying out dynamic reconstruction on the power distribution network on the clustered load information. />
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