CN115983428A - Cluster division method and device based on improved particle swarm optimization algorithm - Google Patents
Cluster division method and device based on improved particle swarm optimization algorithm Download PDFInfo
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Abstract
The invention provides a cluster partitioning method and a device based on an improved particle swarm optimization algorithm, wherein the method comprises the following steps: establishing a distributed photovoltaic power grid cluster division comprehensive performance index based on system information of the distributed photovoltaic power grid; according to the established cluster division comprehensive performance index, an improved BPSO algorithm based on an inertia weight linear decreasing strategy is adopted for iterative optimization, and a cluster division result is output; wherein the cluster divides the overall performance index
，
Is an index of modularity,
Is an index of the reactive power balance degree of the source load,
is the index of the matching degree of the source load power,
are weight coefficients.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a cluster division method and device based on an improved particle swarm optimization algorithm.
Background
Under the background of diversified, clean and low-carbonization new-form energy transformation, china pushes forward new and old energy transformation and develops distributed clean energy represented by photovoltaic rapidly, however, due to high-permeability distributed photovoltaic access, the power supply reliability of a power distribution network is reduced, the quality of electric energy is deteriorated, and the operating economy and safety of the power distribution network are reduced, so that the full absorption and optimized operation of high-proportion distributed photovoltaic are urgently needed to be realized through an effective regulation and control means so as to ensure the safe, economic and efficient operation of a power grid. Due to the dispersity of distributed photovoltaic distribution and the randomness of output, the traditional centralized regulation and control mode is used for controlling distributed photovoltaic, the requirement on system communication and calculation capacity is high, the control is complex, and the gradually developed cluster regulation and control mode indicates the direction for economically and efficiently accessing the distributed photovoltaic to a power grid.
The cluster concept is originated from the field of computers, and is applied to an electric power system by German scholars, a plurality of wind power stations are divided into a cluster, and the problem of voltage quality influence of large-scale grid connection of a plurality of wind generation sets is solved by utilizing the coordination relationship in the cluster. Later, domestic scholars perform cluster division on the distributed power supply based on physical distance, but the electrical coupling among nodes is not considered, and the cluster division effect is poor.
Schumann et al propose in a large-scale intermittent energy power generation grid-connected cluster coordination control framework to incorporate a distributed power supply with a close physical distance and strong node similarity into a cluster, and the electrical similarity between nodes is considered, so that the capacity of a system for receiving new energy is improved. And then more and more scholars use the electrical similarity among the nodes as a cluster division criterion and obtain the optimal solution of the cluster division by combining a community discovery algorithm, a cluster analysis algorithm and an improved intelligent algorithm. In the text of improving the particle swarm optimization algorithm and applying the particle swarm optimization algorithm to the reactive power partition of the power grid, lin et al adopt the particle swarm optimization algorithm (PS 0) to perform cluster division on the nodes of the power grid, evaluate the cluster division result by taking the modularity as a standard, and because the inertia constant of the cluster division result is a fixed value during iterative optimization, premature convergence of the optimization process is easy to fall into local optimization.
In order to solve the above problems, people are always seeking an ideal technical solution.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a cluster partitioning method and device based on an improved particle swarm optimization algorithm.
In order to achieve the purpose, the invention adopts the technical scheme that: a cluster partitioning method based on an improved particle swarm optimization algorithm comprises the following steps:
establishing a distributed photovoltaic power grid cluster division comprehensive performance index based on system information of the distributed photovoltaic power grid
Wherein the content of the first and second substances, ρ is a modularity index and->
Is a source charge reactive power balance degree index>
The active power matching degree index is source charge, and alpha, beta and gamma are weight coefficients;
and according to the established cluster division comprehensive performance index, performing iterative optimization by adopting an improved BPSO algorithm based on an inertia weight linear decreasing strategy, and outputting a cluster division result.
Preferably, the improved BPSO algorithm based on the inertial weight linear decreasing strategy performs iterative optimization, and the specific steps of outputting the cluster division result are as follows:
setting parameter initial values to generate an initial population;
calculating a fitness function value of each particle according to the cluster division comprehensive performance index, comparing the fitness value of the current position of each particle with the local optimal particle fitness value and the global optimal particle fitness value respectively, and taking the particle with a large fitness value as a local optimal particle and a global optimal particle;
according to w = w begin -(w begin -w final )g/g max Updating the inertia weight value of the particle, wherein w is belonged to [ w ∈ [ [ w ] final ，w begin ]，W begin Is an initial inertial weight coefficient, W final As final inertial weight coefficient, g max For a total number of iterationsCounting, wherein g is the current iteration number;
according to
Updating the particle velocity, wherein W is the updated inertia weight constant; />
The variable quantity of the position of the node d of the particle i in n +1 iterations is obtained; />
The local optimal position which is optimized by the node d of the particle i in the nth iteration is obtained; />
Searching a global optimal position optimized by the node d of the particle i in the nth iteration; e is the same or operation, when the numbers of the particle clusters are the same, 1 is taken, and when the numbers of the clusters are different, 0 is taken;
according to
Updating the particle position, wherein>
Numbering clusters of the d nodes of the particle i in n +1 iterations; />
Numbering clusters of m nodes of the particle i in n +1 iterations, wherein the m nodes are any nodes connected with the d nodes;
judging whether the set iteration times are reached, if not, continuing to iterate; and if so, outputting a dividing result.
Compared with the prior art, the method has outstanding substantive characteristics and remarkable progress, and particularly, on the basis of modularity dividing standard, two indexes of the active matching degree of the source load in the cluster and the reactive matching degree of the source load in the cluster are introduced, so that the method provides a cluster dividing comprehensive performance index which can reflect the strength of the electrical coupling between all nodes and can characterize the active and reactive matching degrees of the source load in the cluster, and further constructs a cluster dividing optimization model based on the cluster dividing comprehensive performance index, so that the dividing result has better active matching degree of the source load in the cluster and reactive matching degree of the source load in the cluster; and then, an improved binary particle swarm optimization algorithm with dynamically changed inertia weight is provided based on an inertia weight decreasing strategy so as to optimize the updating process of the position and the speed of the particles, promote the convergence of the optimization process to a better global optimal solution and further improve the cluster partitioning effect.
Drawings
FIG. 1 is a schematic flow diagram of a cluster partitioning method based on an improved particle swarm optimization algorithm.
Fig. 2 is an IEEE33 node photovoltaic access topology.
Fig. 3 is a schematic diagram of cluster division results of IEEE33 nodes based on a cluster division comprehensive performance index.
Fig. 4 is a schematic diagram of a cluster division result of IEEE33 nodes based on a modularity index.
FIG. 5 is a graph of the effect of inertial weights on fitness values.
Fig. 6 is a photovoltaic access topological diagram of a 10kV110 node in a certain region.
FIG. 7 is a graph of cluster division results of 10kV110 nodes based on a cluster division comprehensive performance index.
Detailed Description
The technical solution of the present invention is further described in detail by the following embodiments.
Example 1
The embodiment provides a cluster partitioning method based on an improved particle swarm optimization algorithm, as shown in fig. 1, including the following steps:
establishing a distributed photovoltaic power grid cluster division comprehensive performance index based on system information of the distributed photovoltaic power grid
Wherein rho is a modularity index,
Is a source charge reactive power balance degree index>
The active power matching degree index is source charge, and alpha, beta and gamma are weight coefficients;
and according to the established cluster division comprehensive performance index, performing iterative optimization by adopting an improved BPSO algorithm based on an inertia weight linear decreasing strategy, and outputting a cluster division result.
In specific implementation, the establishing step of the cluster division comprehensive performance index is as follows:
inputting system information of a distributed photovoltaic power grid needing cluster division, wherein the system information comprises a topological structure, impedance values of all branches, load node power and photovoltaic node output;
carrying out load flow calculation on the distributed photovoltaic power grid by adopting a Newton-Raphson method, extracting a reactive voltage sensitivity matrix in a Jacobian matrix, calculating an electrical distance matrix and a weight matrix, and establishing a modularity index rho based on an electrical distance based on the obtained weight matrix;
the modularity index is calculated by the formula
A vw ＝D vw ＝1-d vw /max(d vw ) The value is the weight of the connecting edge between two nodes in the distribution network, d vw ＝d vw ＝-lg(α vw ·α vw ) To be the electrical distance connecting node v and node w,
is the sum of all the network edge weights;
represents the sum of all the edge weights connected to the node v; δ (v, w) =1 when node v and node w are within the same cluster, otherwise δ (v, w) =10；
Calculating the active balance degree of each cluster according to the active power supply value and the active power demand value which can be provided in each cluster at the time t
Wherein, P i Is the active balance of cluster i, c is the number of clusters, P sup For an active power supply value, P, within the cluster need The required value of the active power in the cluster is obtained;
based on the obtained active balance degree of each cluster, calculating the average active balance degree of the clusters as the source load active balance degree index of the system
Calculating the reactive power balance degree of each cluster according to the reactive power supply value and the reactive power demand value which can be provided in each cluster at the time t
Wherein Q is i Reactive balance of cluster i, c number of clusters, Q sup For the internal reactive power supply value, Q, of the cluster need The demand value of the reactive power in the cluster is obtained;
based on the obtained reactive power balance degree of each cluster, calculating the average reactive power balance degree of the cluster to be used as the source load reactive power balance degree index of the system
Carrying out weighted combination on the modularity index, the source load active balance index and the source load reactive balance index to obtain a comprehensive performance index
It can be understood that, in this embodiment, on the basis of the modularity degree division standard, two indexes of the in-group charge active matching degree and the in-group charge reactive matching degree are introduced, the cluster division comprehensive performance index which can reflect the strength of the electrical coupling between the nodes, and can characterize the in-group charge active and reactive matching degrees is provided, and a cluster division optimization model based on the cluster division comprehensive performance index is further constructed, so that the division result has better in-group charge active matching degree and in-group charge reactive matching degree.
Further, the specific steps of iterative optimization by adopting the improved BPSO algorithm based on the inertial weight linear decreasing strategy are as follows:
setting parameter initial values to generate an initial population;
calculating a fitness function value of each particle according to the cluster division comprehensive performance index, comparing the fitness value of the current position of each particle with the local optimal particle fitness value and the global optimal particle fitness value respectively, and taking the particle with a large fitness value as a local optimal particle and a global optimal particle;
according to w = w begin -(w begin -w final )g/g max Updating the inertia weight value of the particle, wherein w is epsilon [ w ∈ [ [ w ] fnal ，w begin ]，W begin Is an initial inertial weight coefficient, W final For the final inertial weight coefficient, g max G is the current iteration number;
according to
Updating the particle velocity, wherein W is the updated inertia weight constant; />
The variable quantity of the position of the node d of the particle i in n +1 iterations; />
The local optimal position which is optimized by the node d of the particle i in the nth iteration is obtained; />
Searching a global optimal position optimized by the node d of the particle i in the nth iteration; e is an exclusive nor operation, and when the numbers of the particle clusters are the same, 1 is taken, and when the numbers of the clusters are different, 0 is taken;
according to
Updating the particle position, wherein>
Numbering clusters of the d nodes of the particle i in n +1 iterations; />
Cluster numbers of m nodes of the particle i in n +1 iterations are obtained, and the m nodes are any nodes connected with the d nodes;
judging whether the set iteration times are reached, if not, continuing to perform iteration; and if so, outputting a dividing result.
It can be understood that the change of the inertial weight may affect the convergence speed and the cluster partitioning effect of the particle swarm algorithm, and the cluster partitioning effect and the inertial weight have no obvious rule, so that in order to balance the optimization capability of the particles, the embodiment dynamically adjusts the inertial weight of the algorithm by using a weight linear decreasing strategy, so as to optimize the update process of the position and the speed of the particles, promote the optimization process to converge to a better global optimal solution, and further improve the cluster partitioning effect.
Example 2
In this embodiment, the improved BPSO algorithm based on the dynamic change of the inertial weight, which is provided in embodiment 1, is used for performing cluster division on an IEEE33 node system connected to a distributed power supply and an actual feeder system of 10kV in a certain county, so as to analyze the influence of factors such as a comprehensive performance index of cluster division and dynamic change of the inertial weight on a cluster division result.
(1) IEEE33 node example analysis
The structure of an IEEE33 node power distribution system is shown in FIG. 2, wherein 32 load nodes are arranged in the system, 15 photovoltaic power supply nodes are arranged in the system, the total photovoltaic installation capacity is 5.3684MW, and the total load required by the network is 3715+ j2300kVA.
Setting initial parameters of BPSO algorithm, wherein the particle number is 50, and learning factor c 1 ＝c 2 ＝1.49，W begin ＝0.9，W fina1 =0.4, the number of iterations is set to 100.
And carrying out cluster division on the system according to the parameter setting.
(1.1) Effect of index weights on Cluster partitioning
The fitness function of the particle group algorithm in embodiment 1 is an obtained cluster division comprehensive performance index, which comprehensively considers three indexes of modularity, group internal charge active matching degree and group internal charge reactive matching degree, and gives a certain weight to each index.
The weight assigned to each index may be influenced by subjective factors, in order to research the cluster division effect under different index weights, one index is set to be more important than the other two indexes for weighting, and the result obtained after division is shown in the following table, wherein the proportion occupied by the modularity in the weighting mode 1 is the largest, the proportion occupied by the source load active matching degree in the weighting mode 2 is the largest, and the source load reactive matching degree in the weighting mode 3 is the largest.
TABLE 1 influence table of index weight on cluster division
From the perspective of 3 indexes, it can be seen that, in the right assigning mode No. 2, the index value of the active matching degree is 1, which indicates that the active capacity available in the cluster can sufficiently meet the active demand of the intra-cluster load, although the modes 1 and 2 are used
The index values are the same, but the mode 2 has higher modularity and reactive matching degree, namely the electrical coupling between the division nodes in the division result is the best, which is convenient for the subsequent power grid cluster control, and the photovoltaic power supply in the cluster can provideThe reactive capacity can be fully matched with the reactive demand of the load; and mode 3->
The index value is the highest, although the reactive capacity provided inside the cluster can well meet the reactive demand of the load in the cluster, the other two index values are lower than those of the mode 1 and the mode 2, and the cluster division effect is poor.
When the weight occupied by the modularity is larger, the adaptability value of the cluster division comprehensive performance index is 0.803; when the active matching degree in the cluster occupies a large weight, the cluster division comprehensive performance index adaptability value is 0.875; when the reactive matching degree in the group occupies a larger weight, the fitness value is 0.805. Compared with the prior art, the cluster division fitness value of the weighting mode No. 2 is the highest, the three weighting modes are comprehensively considered, and the cluster division effect of the weighting mode No. 2 is the best, so that the weighting mode No. 2 is adopted for cluster division in the embodiment.
The cluster division result (node 0 is a balance node and does not participate in cluster division) is shown in fig. 3.
(1.2) Effect of index type on Cluster partitioning
At present, most cluster division researches are divided by taking a modularity index as a standard, in order to research the effectiveness of a cluster division method based on a cluster division comprehensive performance index, the section carries out cluster division by respectively using two methods (the cluster division index of the method 1 only considers the modularity index, and the cluster division index of the method 2 comprehensively considers the modularity index, a cluster internal load power matching index and a cluster internal load reactive power matching index), and carries out comparative analysis on division results.
The division results of method 1 are shown in fig. 4, and the division results of method 2 are shown in fig. 3.
The results obtained are shown in the following table.
Table 2 table of influence of index type on cluster division
From the above-mentioned division resultsIt can be seen that the number of clusters obtained by cluster division in the method 1 is 7, and after cluster division is performed by using the cluster division comprehensive performance index, the number of cluster division is reduced, although the modularity obtained by the method 2 is reduced by 1.5% compared with that obtained by the method 1, but the cluster division is performed by the method 2
The indexes are greatly improved, wherein the active matching degree in the group is increased by 26.6%, and the reactive matching degree in the group is increased by 27.2%.
The method 2 comprehensively considers the active and reactive demand of the load in the group and the capacity of the photovoltaic to provide active and reactive power while carrying out cluster division according to the modularity, so that the supply in the group can meet the demand as much as possible, and the corresponding index value is improved.
(1.3) influence of dynamic change of inertial weight on cluster partitioning
Two methods (method 1, inertia weight is constant 0.4, and method 2, inertia weight dynamic change) are used for carrying out cluster division on the nodes of the power distribution network based on the cluster division comprehensive performance indexes, the influence of the inertia weight dynamic change on the division result is researched, and the obtained result is shown in a figure 5 and a table 3.
As can be seen from fig. 5, when the inertial weight is kept to be 0.4, the fitness value obtained by performing cluster partitioning based on the cluster partitioning comprehensive performance index is 0.8651; when the inertial weight changes dynamically, the obtained fitness value is 0.8757, the fitness value is improved by 1.2%, namely cluster division is carried out based on 3 indexes of the modularity, the active matching degree of the source charge in the cluster and the reactive matching degree of the source charge in the cluster, and the cluster division effect can be improved by changing the dynamic inertia weight.
As can be seen from table 3, the method 2 has the greatest effect on the improvement of the modularity, the modularity of the method 2 is improved by 4.3% compared with the method 1, that is, the method 2 is used for dividing the nodes in the cluster, so that the coupling performance is better, and the dividing effect is better than the cluster dividing result based on a single modularity index.
TABLE 3 influence table of dynamic change of inertia weight on adaptability value
(2) Arithmetic example analysis of 10kV feeder system in certain county
A linear weight dynamic change improved BPSO algorithm is adopted to carry out cluster division on a 10kV feeder line 110 node system in a county area, the system comprises 109 load nodes, 16 photovoltaic access nodes, the total photovoltaic installation capacity is 1.7323MW, and the total load required by the network is 1642+ j918kVA. The feeder system structure and the photovoltaic access situation are shown in fig. 6.
Set the particle number to 50, learning factor c 1 ＝c 2 =1.49, wbegin =0.9, wfinal =0.4, the number of iterations is set to 100, and the index weight is set in accordance with the weighting method 2.
(2.1) Effect of index type on Cluster partitioning
The distribution network node is divided into clusters based on two methods (method 1 is divided based on a single modularity index, method 2 is divided based on a cluster division comprehensive performance index), and the obtained results are shown in a table 4.
Table 4 table of influence of index type on cluster division
From the above dividing results, the number of clusters obtained by the cluster division in the method 1 is 10, and after the cluster division is performed by using the cluster division comprehensive performance index, the number of the cluster division is reduced, although the modularity obtained in the method 2 is reduced by 6.5% compared with that obtained in the method 1, but the cluster division is performed by the method 2
The indexes are greatly improved, wherein the active matching degree in the group is increased by 36.4%, and the reactive matching degree in the group is increased by 30.8%.
It can be seen that the cluster division result obtained by the method 2 greatly improves the matching degree of active and reactive loads in the cluster, and can reduce the flow of active and reactive loads among the clusters, thereby reducing the network loss and verifying the effectiveness and superiority of the cluster division method.
Specifically, the cluster division result obtained by the method 2 is shown in fig. 7.
(2.2) influence of dynamic variation of inertial weights on cluster partitioning
The method uses two methods (method 1, inertia weight is constant 0.4, and method 2, inertia weight dynamic change) to perform cluster division on the 110 nodes of the actual power distribution network based on the cluster division comprehensive performance indexes, researches the influence of the inertia weight dynamic change on the cluster division result of the actual power distribution network, and obtains the result as shown in table 5.
TABLE 5 influence of dynamic change of inertial weight on fitness value
As can be seen from table 5, when the inertial weight is kept to be 0.4, the fitness value obtained by performing cluster partitioning based on the cluster partitioning comprehensive performance index is 0.6997; when the inertia weight is dynamically changed, the obtained fitness value is 0.7191, and the fitness value is improved by 2.8%. In an actual distribution network, the dynamic change of the inertial weight improves the three index values to different degrees, the modularity value, the active matching value of the group source load and the reactive matching value of the group source load of the method 2 are respectively improved by 1.4%, 4.4% and 1.1% compared with the method 1, namely the method 2 is used for dividing the nodes in the clusters, the coupling performance is better, and the power balance effect in the clusters is better.
Obviously, compared with the cluster division only depending on the modularity, the cluster division method provided by the invention is applied to the IEEE33 node system, and the obtained result is phi p And phi q Respectively improved by 26.6 percent and 27.2 percent; the method is applied to the actual distribution network system of 110 nodes in a county area, and phi in the obtained result p And phi q Respectively increased by 36.4%30.8 percent. The cluster division is carried out by utilizing the improved BPSO algorithm based on the comprehensive performance index, the power matching degree between the source loads in the cluster obtained by the division is fully considered while the electrical coupling of each node of the system is considered, the flow of power among the clusters is reduced through the mutual cooperation among the nodes in the cluster, the power in-situ balance is realized to the maximum extent, and therefore the cluster division result is improved.
Compared with the case that the inertia weight is constant, the BPSO algorithm with the inertia weight dynamically changing is applied to the IEEE33 node system based on the cluster division comprehensive performance index, and rho and phi in the obtained result q Respectively improved by 4.3 percent and 1.1 percent; the method is applied to a 110-node actual distribution network system in a county area, and rho and phi in the obtained result p 、φ q Respectively increased by 1.4%, 4.4% and 1.1%. The improved BPSO algorithm provided by the invention can optimize the particle optimizing process, so that the particles can be optimized to a better cluster division result in the iterative updating process.
Example 3
The embodiment provides a cluster partitioning device based on an improved particle swarm optimization algorithm, which includes:
the performance index establishing module is used for establishing a distributed photovoltaic power grid cluster division comprehensive performance index based on the system information of the distributed photovoltaic power grid;
and the particle swarm partitioning module is used for performing iterative optimization by adopting an improved BPS0 algorithm based on an inertial weight linear decreasing strategy according to the established cluster partitioning comprehensive performance index and outputting a cluster partitioning result.
Example 4
The present embodiment provides a computing device, characterized in that: the method comprises the following steps:
one or more processing units;
a storage unit for storing one or more programs,
wherein, when executed by the one or more processing units, the one or more programs cause the one or more processing units to perform the steps of the method for cluster partitioning based on improved particle swarm optimization as described in embodiment 1.
Example 5
The present embodiment provides a computer-readable storage medium having non-volatile program code executable by a processor, wherein the computer program, when executed by the processor, implements the steps of the cluster partitioning method based on the improved particle swarm optimization algorithm according to embodiment 1.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (10)
1. A cluster partitioning method based on an improved particle swarm optimization algorithm is characterized by comprising the following steps:
establishing a distributed photovoltaic power grid cluster division comprehensive performance index based on system information of the distributed photovoltaic power grid
Wherein the content of the first and second substances, ρ is a modularity index and->
Is a source charge reactive power balance degree index>
The active power matching degree index is source charge, and alpha, beta and gamma are weight coefficients;
and according to the established cluster division comprehensive performance index, performing iterative optimization by adopting an improved BPSO algorithm based on an inertia weight linear decreasing strategy, and outputting a cluster division result.
2. The improved particle swarm optimization algorithm-based cluster partitioning method according to claim 1, wherein the establishing of the cluster partitioning comprehensive performance index comprises the following steps:
inputting system information of a distributed photovoltaic power grid needing cluster division, wherein the system information comprises a topological structure, impedance values of all branches, load node power and photovoltaic node output;
carrying out load flow calculation on the distributed photovoltaic power grid by adopting a Newton-Raphson method, extracting a reactive voltage sensitivity matrix in a Jacobian matrix, calculating an electrical distance matrix and a weight matrix, and establishing a modularity index rho based on an electrical distance based on the obtained weight matrix;
calculating the active power balance degree of each cluster according to the active power supply value and the active power demand value which can be provided in each cluster at the time t; and based on the obtained active power balance degree of each cluster, calculating the average active power balance degree of the cluster to be used as the source load active power balance degree index of the system
Calculating the reactive power balance degree of each cluster according to the reactive power supply value and the reactive power demand value which can be provided in each cluster at the time t; and calculating the average reactive power balance degree of the clusters based on the obtained reactive power balance degree of each cluster, and using the average reactive power balance degree as the source load reactive power balance degree index of the system
Carrying out weighted combination on the modularity index, the source load active balance index and the source load reactive balance index to obtain a comprehensive performance index
3. The cluster division method based on the improved particle swarm optimization algorithm according to claim 2, wherein the calculation formula of the active power balance degree is as follows:
wherein, P i Is the active balance of cluster i, c is the number of clusters, P sup For an active power supply value, P, within the cluster need Is the value of the active power requirement inside the cluster.
4. The improved particle swarm optimization algorithm-based cluster partitioning method according to claim 2, wherein the corresponding weight β of the source-to-load reactive power balance index is the largest.
5. The improved particle swarm optimization algorithm-based cluster partitioning method according to claim 2, wherein the reactive power balance is calculated according to the following formula:
wherein Q is i Reactive balance of cluster i, c number of clusters, Q sup For the reactive power supply value, Q, inside the cluster need Is the value of the reactive power requirement inside the cluster.
6. The cluster partitioning method based on the improved particle swarm optimization algorithm according to claim 1, wherein the specific steps of iterative optimization by adopting the improved BPSO algorithm based on the inertial weight linear decrement strategy are as follows:
setting parameter initial values to generate an initial population;
calculating a fitness function value of each particle according to the cluster division comprehensive performance index, comparing the fitness value of the current position of each particle with the local optimal particle fitness value and the global optimal particle fitness value respectively, and taking the particle with a large fitness value as a local optimal particle and a global optimal particle;
according to w = w begin -(w begin -w final )g/g max Updating the inertia weight value of the particle, wherein w is belonged to [ w ∈ [ [ w ] final ，w begin ]，W begin Is an initial inertial weight coefficient, W final As final inertial weight coefficient, g max For a total of iterationsThe number of times, g, is the current iteration number;
according to
Updating the particle velocity, wherein W is the updated inertia weight constant; />
The variable quantity of the position of the node d of the particle i in n +1 iterations; />
The local optimal position which is optimized by the node d of the particle i in the nth iteration is obtained; />
Searching a global optimal position optimized by the node d of the particle i in the nth iteration; e is an exclusive nor operation, and when the numbers of the particle clusters are the same, 1 is taken, and when the numbers of the clusters are different, 0 is taken;
according to
Updating the particle position, wherein>
Numbering clusters of the d nodes of the particle i in n +1 iterations; />
Numbering clusters of m nodes of the particle i in n +1 iterations, wherein the m nodes are any nodes connected with the d nodes;
judging whether the set iteration times are reached, if not, continuing to iterate; and if so, outputting a dividing result.
7. A cluster partitioning device based on an improved particle swarm optimization algorithm is characterized by comprising:
a performance index establishing module for establishing distribution based on system information of the distributed photovoltaic power gridFormula photovoltaic electric wire netting cluster divides comprehensive properties index
Wherein the content of the first and second substances, ρ is a modularity index>
Is a source charge reactive power balance degree index>
The active power matching degree index is source charge, and alpha, beta and gamma are weight coefficients;
and the particle swarm partitioning module is used for performing iterative optimization by adopting an improved BPSO algorithm based on an inertial weight linear decreasing strategy according to the established cluster partitioning comprehensive performance index and outputting a cluster partitioning result.
8. The cluster partitioning device based on the improved particle swarm optimization algorithm according to claim 7, wherein the iterative optimization by the improved BPSO algorithm based on the inertial weight linear decreasing strategy comprises the following specific steps:
setting parameter initial values to generate an initial population;
calculating a fitness function value of each particle according to the cluster division comprehensive performance index, comparing the fitness value of the current position of each particle with the local optimal particle fitness value and the global optimal particle fitness value respectively, and taking the particle with a large fitness value as a local optimal particle and a global optimal particle;
according to w = w begin -(w begin -w final )g/g max Updating the inertia weight value of the particle, wherein w is belonged to [ w ∈ [ [ w ] final ，w begin ]，W begin Is an initial inertial weight coefficient, W final As final inertial weight coefficient, g max G is the current iteration number;
according to
Updating the particle velocity, wherein W is the updateA latter inertial weight constant; />
The variable quantity of the position of the node d of the particle i in n +1 iterations is obtained; />
The local optimal position which is optimized by the node d of the particle i in the nth iteration is obtained; />
Searching a global optimal position optimized by the node d of the particle i in the nth iteration; e is the same or operation, when the numbers of the particle clusters are the same, 1 is taken, and when the numbers of the clusters are different, 0 is taken;
according to
Updating the particle position, wherein>
Numbering clusters of the d nodes of the particle i in n +1 iterations; />
Cluster numbers of m nodes of the particle i in n +1 iterations are obtained, and the m nodes are any nodes connected with the d nodes;
judging whether the set iteration times are reached, if not, continuing to iterate; and if so, outputting a dividing result.
9. A computing device, characterized in that: the method comprises the following steps:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs when executed by the one or more processing units cause the one or more processing units to perform the steps of the improved particle swarm optimization algorithm based cluster partitioning method of any one of claims 1 to 6.
10. A computer-readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of the improved particle swarm optimization algorithm based cluster partitioning method according to any one of claims 1 to 6.
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