CN113890015A - Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm - Google Patents

Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm Download PDF

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CN113890015A
CN113890015A CN202111124728.1A CN202111124728A CN113890015A CN 113890015 A CN113890015 A CN 113890015A CN 202111124728 A CN202111124728 A CN 202111124728A CN 113890015 A CN113890015 A CN 113890015A
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魏业文
吴光源
李俊杰
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Abstract

The distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm carries out day-ahead power prediction on DG output power and load power through an EEMD-SVR combined prediction model and historical power data; inputting relevant initial parameters such as initial parameters of a power distribution network, load power prediction amount, DG prediction output value and the like; constructing a segment-loss function according to the power prediction data to determine the optimal number of segments; the dynamic reconstruction time period division in the day is realized by improving a fuzzy C optimal clustering analysis algorithm; determining a time interval division scheme and equivalent load centers of all time intervals according to a clustering algorithm; respectively performing static reconstruction optimization on each time interval of the power distribution network by improving a bacterial foraging algorithm; and reconstructing the optimized adjustment scheme of each time interval in a day, calculating and determining the daily operation network loss and voltage fluctuation conditions of the power distribution network, and outputting the solved relevant parameters. The method is simple and efficient, can be applied to medium and low voltage distribution networks with new energy access, and has certain popularization and practical values.

Description

Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm
Technical Field
The invention relates to the technical field of dynamic reconstruction of a power distribution network, in particular to a dynamic reconstruction method of the power distribution network based on an improved fuzzy C-means clustering algorithm.
Background
In the actual operation of the power distribution network, the reconfiguration of the power distribution network is used as an important optimization means of operation, and the goals of reducing the network loss, balancing the load and the like are achieved by controlling the switching states of a tie switch and a section switch in the network. Under the big background that new energy is gradually accessed into a distribution network in a large scale, the load of a power grid and the output of a Distributed Generation (DG) are always in a time-varying state, the running state of the distribution network is complex, and the static reconstruction is difficult to realize the running state optimization of the distribution network in multiple time periods. In order to ensure the safe and reliable operation of the power distribution network, the load and the time-varying characteristics of the DG need to be fully considered, and the dynamic reconstruction of the power distribution network under multiple time discontinuities needs to be researched. Considering the negative influence of the switching action loss and frequent actions on the operation of the power distribution network, the key for ensuring the dynamic reconfiguration effect is to realize reasonable switching actions and optimize the reliable solution of the reconfiguration scheme.
The dynamic reconstruction time interval division of the power distribution network is an important factor influencing the optimized operation of the power distribution network, and the segmentation idea mainly comprises the following steps: a pre-segmentation period reduction strategy and a similarity index aggregation segmentation strategy. The period reduction strategy can reduce unnecessary reconstruction operation, but is greatly influenced by the judgment threshold of period division; the aggregation segmentation strategy realizes time interval division in a day through specific indexes, can effectively avoid the influence of human factors on the segmentation mode, and ensures the objectivity of data segmentation.
The fuzzy C-means clustering algorithm (FCM) can realize the clustering division of data through the membership between the characteristics of the data and the similarity indexes, and each clustering segment meets the minimum criterion of the weighted error sum of squares in the sections. When the FCM algorithm carries out power data clustering, only size similarity is considered, and the time sequence of data is not considered; meanwhile, considering the influence of the number of segments on the reconstruction effect, the optimal time interval division number needs to be determined.
Disclosure of Invention
Aiming at the problems existing in the field of distribution network reconstruction under the background of new energy access, the invention provides a dynamic reconstruction method of a distribution network based on an improved fuzzy C-means clustering algorithm, which adopts the improved fuzzy C-means clustering algorithm (FCMC) to realize the division of reconstruction time periods and determines the optimal number of segments through a segment-loss function so as to ensure the reliability of algorithm segmentation; and optimizing the data clustering time sequence and the optimal segment number. The method is simple and efficient, can be applied to medium and low voltage distribution networks with new energy access, and has certain popularization and practical values.
The technical scheme adopted by the invention is as follows:
firstly, performing day-ahead power prediction on DG output power and load power based on historical power data; then, dynamically reconstructing the time interval division of the operation power data according to a reconstruction time interval division strategy; and finally, according to the load center of each time period, performing reconstruction optimization adjustment at different time periods respectively to realize day-to-day dynamic reconstruction optimization adjustment of the power distribution network.
The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm comprises the following steps:
step one, performing day-ahead power prediction on DG output power and load power through an EEMD-SVR combined prediction model and historical power data;
inputting relevant initial parameters such as initial parameters of the power distribution network, load power prediction amount, DG prediction output value and the like;
step three, constructing a segment-loss function according to the power prediction data to determine the optimal segment number; the dynamic reconstruction time period division in the day is realized by improving a fuzzy C optimal clustering analysis algorithm;
step four, determining a time interval division scheme and equivalent load centers of all time intervals according to a clustering algorithm;
step five, respectively performing static reconstruction optimization on each time interval of the power distribution network by improving a bacterial foraging algorithm;
and step six, reconstructing the optimized adjustment scheme of each time interval in a day, calculating and determining the daily operation network loss and voltage fluctuation conditions of the power distribution network, and outputting the solved relevant parameters.
Through the steps, the dynamic reconfiguration of the power distribution network is realized.
In the first step, the prediction model is combined according to the EEMD-SVR, as shown in FIG. 5. See the records in the document [1] Lijunjie, Shiqiang, Hu qun Yong, He Li Xin wind power prediction based on EEMD-SVR model [ J ] electric appliance and energy efficiency management technology, 2020(11):22-28.
Determining DG and load power prediction, superposing the DG and the load power prediction, determining an equivalent load power curve of a power distribution system, and further establishing a power data matrix in the day; to ensure the accuracy of the reconstruction segment, the whole day is divided into 48 time segments at time segment division intervals of 30 min. The equivalent load power of the node j in the time period t is set as follows: xt,j＝XL(t,j)+XDG(t, j), then the intra-day power data matrix is represented as:
Figure BDA0003278376620000021
in the formula (1), X is equivalent load power, namely active power and reactive power, in each time period of 48 time periods in a day, and n is the number of nodes;
assuming that the intra-day power data matrix can be divided into c classes, the fuzzy clustering center M can be expressed as:
Figure BDA0003278376620000031
in the formula (2), MCThe equivalent power value of a node j of the c-th class center is obtained, and n is the number of nodes;
and setting a fuzzy c-means clustering model according to the minimum criterion of weighted error square sum in the sections:
Figure BDA0003278376620000032
in the formula (3), ut,jRepresenting the membership degree of the sample j and the t-th clustering center; m represents a factor of membership. C is the second cluster center.
By updating the membership degree of the clustering center and each point, the objective function J gradually tends to be stable, when the deviation of the membership degree is judged to meet the convergence criterion, the deviation in the section reaches the minimum, and the power data completes the optimized clustering.
The specific clustering center updating rule, the membership updating rule and the algorithm convergence criterion are as follows:
Figure BDA0003278376620000033
Figure BDA0003278376620000034
Figure BDA0003278376620000035
in the formula, r represents the iteration times of the algorithm, and epsilon is an error threshold value; x is the number ofjExpressed as the equivalent load power of the node j in the period t; miRepresenting the equivalent load power at the ith cluster center node j; mkRepresenting the equivalent load power at the k-th clustering center node j;
Figure BDA0003278376620000036
representing the membership degree of the sample j and the t-th clustering center when the membership degree factor is m;
Figure BDA0003278376620000037
representing the membership degree of the sample j and the t-th clustering center when the iteration number is r + 1;
Figure BDA0003278376620000038
expressed as the degree of membership of the sample j to the t-th cluster center for the number of iterations r + 1.
In the third step, according to power prediction data, which refers to the 24-hour power data in the day ahead predicted by the EEMD-SVR combined prediction model, a segment loss function is constructed, the optimal segment number is determined, and the weighted error square sum in the segment is defined as a segment loss function S (c), namely:
d(t,j)＝||xj-Mt||2＝(xj-Mt)T(xj-Mt) (7)
Figure BDA0003278376620000041
in the formula (7), j is the jth node, and t is the tth period; t is the transpose of the first image,
in the formula (8), C is the C-th clustering center, and n is the number of nodes.
Considering that time interval division is not suitable to be too much, meanwhile, in order to reduce calculation amount, the segmentation range is set to be 1-24, and by setting different initial iteration segment numbers of the FCMC, loss function values under different segment numbers can be obtained respectively, and data fitting can be carried out on the loss functions. The piecewise loss function curve is shown in figure 1. As can be seen from fig. 1, the number of segments is inversely related to the loss function, and the rate of change of the loss function is smaller as the number of segments increases. In order to ensure the efficiency of time interval division, the number of segments corresponding to the extreme point of the change rate of the loss function is taken as the optimal number of segments, and the optimal time interval number solving problem is converted into the solution of the function slope extreme value, so that the solving formula of the optimal number of segments F is as follows:
Figure BDA0003278376620000042
Figure BDA0003278376620000043
Figure BDA0003278376620000044
in the formula, Sd(c) A slope representing the number of adjacent segments; sZ(c) A slope rate of change representing the number of adjacent segments; argmax represents the value of the number of segments at the maximum value of the slope rate of change of the number of adjacent segments. And finally, determining the division number of the optimal time period through a segmentation loss function and a slope change rate selection rule.
In the formula, Sd(c) A slope representing the number of adjacent segments; sZ(c) A slope rate of change representing the number of adjacent segments; argmax represents the value of the number of segments at the maximum value of the slope rate of change of the number of adjacent segments. And finally, determining the division number of the optimal time period through a segmentation loss function and a slope change rate selection rule.
In the formula (9), S (c +1) and S (c) are piecewise loss functions of the c +1 th clustering center respectively; a piecewise-loss function of the c-th cluster center;
in the formula (10), Sd(c)、Sd(c-1) an optimal number of segments for the c-th cluster center; c-1 optimal number of segments of cluster center;
in the formula (11), c belongs to [1,24] and is represented as that the value range of the c-th cluster center is 1-24.
In the fourth step, the determining a time interval division scheme and an equivalent load center of each time interval according to a clustering algorithm comprises the following steps:
equivalent power time sequence division and time period correction:
and determining an optimal fuzzy C-means clustering result of the equivalent load power according to the optimal number of the segments, performing class division and time sequence arrangement on the equivalent load power of the clustering result, dividing the class quantity of adjacent time periods into one time period, and finally obtaining t time sequence clustering time periods.
Considering that the time sequence division scheme may have isolated time periods, time period correction is carried out by adopting a loss function minimum principle, the isolated time periods are fused with adjacent time periods with better similarity, time sequence optimal time period correction is carried out on the isolated time periods, time period division continuity is guaranteed, and the slope of the loss function is taken to finally obtain the corrected F-segment equivalent power time sequence division scheme. In order to ensure the efficiency of time interval division, the number of the segments corresponding to the extreme point of the change rate of the loss function is taken as the optimal number of the segments, and the optimal time interval number solving problem is converted into the solving of the function slope extreme value.
And because the similarity of the clustered data of the same class is higher, and the clustering center is used as the data center, the clustering center is used as the equivalent load power characteristic quantity of different time periods. Considering the power distribution system with the equivalent load power close to each other, the power flow similarity degree of the system is higher, so that the intra-day optimal reconstruction optimization can be converted into the static reconstruction optimization in F time intervals, and the overall intra-day reconstruction adjustment effect is ensured through the static optimal adjustment in different time intervals. The time division flowchart is shown in fig. 2.
Step five, respectively carrying out static reconstruction optimization on each time interval of the power distribution network by improving a bacterial foraging algorithm,
step 1: setting operating parameters and initializing bacterial populations, including: population size N, number of swims NsNumber of chemotaxis NcNumber of breeding NreNumber of migrations NedFixed migration probability PedA walking step length c (i), a chemotaxis iteration parameter j, a propagation iteration parameter k and a migration iteration parameter l;
step 2: taking the bacteria as a solution of a solution space, and calculating an initial fitness value J (i, J, k, l) of each bacteria;
step 3: judging whether the migration times meet the cycle conditions, if so, continuing to operate, and executing a dispersion operation cycle; if the dispersing times are reached, the optimization is stopped;
step 4: judging whether the breeding times meet the circulation condition, if so, continuing running, sorting and selecting half of the bacteria with advantages according to the activity of the bacteria and finishing the breeding operation; if the reproduction times are reached, returning to Step 3;
step 5: judging whether the chemotaxis times meet a circulation condition, if so, executing the bacteria chemotaxis operation, judging whether the fitness value is improved after the chemotaxis operation, if so, continuing moving towards the direction, otherwise, executing the turning operation and moving for one step; if the number of breeding has been reached, the process returns to Step 4.
And step six, reconstructing the optimized adjustment scheme of each time interval in a day, calculating and determining the daily operation network loss and voltage fluctuation conditions of the power distribution network, and outputting the solved relevant parameters.
The invention discloses a power distribution network dynamic reconstruction method based on an improved fuzzy C-means clustering algorithm, which has the following technical effects:
1) the time interval division strategy is suitable for a medium-low voltage distribution network with a high-proportion new power supply access, and the influence of uncertain power supply output in the distribution network on dynamic reconstruction time interval division is solved.
2) The improved fuzzy mean value C clustering algorithm adopted by the invention considers the influence of the time sequence of the data and the number of segments on the reconstruction, and realizes the optimal time interval division of the dynamic reconstruction.
3) The time interval division strategy and the reconstruction method recorded by the invention are simple and efficient, can be applied to medium and low voltage distribution networks containing new energy access, and have certain popularization and practical values.
4) According to the invention, a loss function is introduced in the dynamic reconstruction period dividing process, and the optimal number of segments is determined by a loss function slope change rate selection rule. The segmentation result is more reasonable and is embodied by the minimum loss function.
5) The improved bacterial foraging algorithm has the advantages of wide search domain, high convergence speed and the like, and the improved artificial intelligence algorithm can be widely applied to the practical power grid engineering.
6) The time interval division strategy and the overall distribution network dynamic reconstruction method complete verification at 24H before the day, and can be applied to time interval division of low-voltage distribution networks in half a month or the whole month. The method has wide application prospect under the background of high-proportion access of new energy to the distribution network.
Drawings
Fig. 1 is a plot of the piecewise loss function.
Fig. 2 is an optimal time interval division flowchart of the fuzzy C optimal mean clustering.
Fig. 3 is a flow chart of a power distribution network dynamic reconfiguration optimization strategy.
Fig. 4 is a flow chart of an improved bacterial foraging algorithm.
FIG. 5 is a diagram of EEMD-SVR combined prediction model.
FIG. 6(a) is a flow chart of an improved bacterial foraging algorithm run;
FIG. 6(b) is a flowchart showing the step5 algorithm for determining the number of chemotaxis.
Fig. 7 is a graph of predicted values of load and distributed power supply power.
Fig. 8 is a sectional diagram of the equivalent power of a distribution system including DG.
FIG. 9(a) is a graph comparing the loss of the network at different times before and after reconstruction;
fig. 9(b) is a graph comparing daily loss curves before and after reconstruction.
FIG. 10(a) is a voltage probability density distribution diagram;
fig. 10(b) is a normal distribution graph.
Detailed Description
The method for optimizing the day-to-day dynamic reconfiguration of the power distribution network mainly comprises the following steps: power prediction of a power distribution system, day dynamic time interval division and static reconstruction optimization solution of each time interval. Firstly, carrying out day-ahead power prediction on multi-source DG output power and load power based on historical power data; then, dynamically reconstructing the time interval division of the operation power data according to a reconstruction time interval division strategy; and finally, according to the load center of each time interval, performing reconstruction optimization adjustment in different time intervals respectively to realize day-to-day dynamic reconstruction optimization adjustment of the power distribution network.
(1) And introducing a loss function in the dynamic reconstruction period dividing process, and determining the optimal number of the segments by a loss function slope rate selection rule.
(2) The invention discloses an optimal time interval division method based on fuzzy C optimal mean clustering, namely, optimal time interval division is determined by a loss function and an improved fuzzy mean clustering algorithm.
(3) According to the dynamic reconfiguration overall optimization strategy of the power distribution network, the output of a distributed power supply is determined through prediction, 24H time period division is completed by adopting the proposed dynamic time period division strategy, after the optimal number of sections is determined, each section is statically reconfigured by adopting an improved bacterial foraging algorithm, and a reconfiguration result is obtained; namely, the dynamic reconfiguration of the distribution network in the day is completed.
Example (b):
in order to verify the dynamic reconstruction effect of DGs, photovoltaic and wind power distributed generation is accessed to an IEEE33 node system, and the access nodes are 5 nodes and 31 nodes. Meanwhile, the original prediction data of the wind power and the photovoltaic DG are referred to the related data of the reconstruction research [ D ]. Beijing: Beijing university of transportation, 2016. of the active power distribution network containing the distributed power supply, and the power prediction is solved by combining a prediction model, wherein the predicted values of the related power are shown in figures 7 and 8.
According to the wind power, the photovoltaic DG output and the load power prediction data, a power distribution network equivalent power curve graph can be made, the reconstruction time interval division in the day is achieved through a time interval division strategy, and an equivalent power sectional graph is shown in FIG. 8. As can be seen from the segmented diagram in fig. 8, the reconstruction time period of the power distribution network is divided into 6 time periods, which are: 0-4, 4-6, 6-8.5, 8.5-16, 16-22 and 22-24. By performing dynamic optimization adjustment on each time interval, the following reconstruction optimization result can be obtained.
TABLE 1 DG-containing time-phased reconstitution results
Figure BDA0003278376620000071
The table shows that the dynamic reconfiguration strategy still has a good effect on the active network loss optimization of the reconfiguration of the distribution system with the DGs, the active network loss is reduced from 989.7kW to 600.8kW, the network loss reduction rate is 64.73%, and the loss reduction effect is obviously improved compared with the dynamic reconfiguration without the DGs.
As can be seen from the network loss comparison graphs at different times before and after the power distribution network is reconstructed and the network loss curve comparison graphs in days in fig. 9(a) and 9(b), the loss reduction optimization effect is improved in a plurality of reconstruction periods, and the active loss is obviously reduced; due to the influence of uncertain output of the distributed power supply, the network loss optimization effect of partial sections is not ideal, the loss reduction rate is poor, and the integral active loss of the power distribution system in the day still presents the optimization characteristic. The comparison graph of the network loss curves before and after dynamic reconstruction also shows that the dynamic reconstruction active loss is obviously optimized compared with that before reconstruction.
In order to evaluate the voltage stability optimization effect of dynamic reconstruction on the distribution system containing the DGs, a node voltage probability density distribution graph and a voltage normal distribution graph before and after reconstruction are constructed, as shown in fig. 10(a) and 10 (b).
As can be seen from the probability distribution diagram of fig. 10(a), the overall voltage distribution concentration and the voltage deviation after reconstruction are still effectively improved compared with those before reconstruction, the voltage after reconstruction is between 0.94 and 1.0, and the node voltages are more distributed on two sides of 0.96, and the concentration is obviously improved, that is: the dynamic reconfiguration strategy proves the voltage stability optimization effect of the DG-containing power distribution system.
From the analysis, the dynamic reconfiguration optimization method provided by the invention can realize the active network loss optimization and the voltage deviation optimization of the system containing the DG power distribution system, ensure the economic and reliable operation of the power distribution network and prove the effectiveness and feasibility of the reconfiguration strategy.

Claims (6)

1. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm is characterized by comprising the following steps: firstly, performing day-ahead power prediction on DG output power and load power based on historical power data; then, dynamically reconstructing the time interval division of the operation power data according to a reconstruction time interval division strategy; and finally, according to the load center of each time period, performing reconstruction optimization adjustment at different time periods respectively to realize day-to-day dynamic reconstruction optimization adjustment of the power distribution network.
2. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm is characterized by comprising the following steps of:
step one, performing day-ahead power prediction on DG output power and load power through an EEMD-SVR combined prediction model and historical power data;
inputting relevant initial parameters such as initial parameters of the power distribution network, load power prediction amount, DG prediction output value and the like;
step three, constructing a segment-loss function according to the power prediction data to determine the optimal segment number; the dynamic reconstruction time period division in the day is realized by improving a fuzzy C optimal clustering analysis algorithm;
step four, determining a time interval division scheme and equivalent load centers of all time intervals according to a clustering algorithm;
step five, respectively performing static reconstruction optimization on each time interval of the power distribution network by improving a bacterial foraging algorithm;
step six, reconstructing an optimized adjustment scheme of each time interval in a day, calculating and determining the daily running network loss and voltage fluctuation conditions of the power distribution network, and outputting solved relevant parameters;
through the steps, the dynamic reconfiguration of the power distribution network is realized.
3. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm according to claim 2, characterized in that: in the first step, DG and load power prediction is determined according to an EEMD-SVR combined prediction model, the DG and the load power prediction are superposed, an equivalent load power curve of a power distribution system can be determined, and a power data matrix in the day is further established; in order to ensure the accuracy of reconstruction segmentation, the whole day is divided into 48 time periods by taking 30min as a time period division interval; the equivalent load power of the node j in the time period t is set as follows: xt,j＝XL(t,j)+XDG(t, j), then the intra-day power data matrix is represented as:
Figure FDA0003278376610000011
in the formula (1), X is equivalent load power, namely active power and reactive power, in each time period of 48 time periods in a day, and n is the number of nodes;
assuming that the intra-day power data matrix can be divided into c classes, the fuzzy clustering center M can be expressed as:
Figure FDA0003278376610000021
in the formula (2), MCThe equivalent power value of a node j of the c-th class center is obtained, and n is the number of nodes;
and setting a fuzzy c-means clustering model according to the minimum criterion of weighted error square sum in the sections:
Figure FDA0003278376610000022
in the formula (3), ut,jRepresenting the membership degree of the sample j and the t-th clustering center; m represents a factor of membership; c is the first clustering center;
by updating the membership degree of the clustering center and each point, the target function J gradually tends to be stable, when the deviation of the membership degree is judged to meet the convergence criterion, the deviation in the section reaches the minimum, and the power data completes the optimized clustering;
the specific clustering center updating rule, the membership updating rule and the algorithm convergence criterion are as follows:
Figure FDA0003278376610000023
Figure FDA0003278376610000024
Figure FDA0003278376610000025
in the formula, r represents the iteration times of the algorithm, and epsilon is an error threshold value; x is the number ofjExpressed as the equivalent load power of the node j in the period t; miRepresenting the equivalent load power at the ith cluster center node j; mkRepresenting the equivalent load power at the k-th clustering center node j;
Figure FDA0003278376610000026
representing the membership degree of the sample j and the t-th clustering center when the membership degree factor is m;
Figure FDA0003278376610000027
representing the membership degree of the sample j and the t-th clustering center when the iteration number is r + 1;
Figure FDA0003278376610000028
expressed as the degree of membership of the sample j to the t-th cluster center for the number of iterations r + 1.
4. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm according to claim 2, characterized in that: in the third step, according to power prediction data, which refers to the 24-hour power data in the day ahead predicted by the EEMD-SVR combined prediction model, a segment loss function is constructed, the optimal segment number is determined, and the weighted error square sum in the segment is defined as a segment loss function S (c), namely:
d(t,j)＝||xj-Mt||2＝(xj-Mt)T(xj-Mt) (7)
Figure FDA0003278376610000031
in the formula (7), j is the jth node, and t is the tth period; t is the transpose of the first image,
in the formula (8), C is the C-th clustering center, and n is the number of nodes;
setting a segmentation range to be 1-24, and setting different initial iteration segment numbers of FCMC to respectively obtain loss function values under different segment numbers and perform data fitting on the loss functions;
the number of segments is in negative correlation with the loss function, and the change rate of the loss function is smaller as the number of segments is increased; in order to ensure the efficiency of time interval division, the number of segments corresponding to the extreme point of the change rate of the loss function is taken as the optimal number of segments, and the optimal time interval number solving problem is converted into the solution of the function slope extreme value, so that the solving formula of the optimal number of segments F is as follows:
Figure FDA0003278376610000032
Figure FDA0003278376610000033
Figure FDA0003278376610000034
in the formula, Sd(c) A slope representing the number of adjacent segments; sZ(c) A slope rate of change representing the number of adjacent segments; argmax represents the value of the segment number when the maximum value of the slope change rate of the adjacent segment number is obtained; finally, the optimal time interval division number can be determined through a segmentation loss function and a slope change rate selection rule;
in the formula (9), S (c +1) and S (c) are piecewise loss functions of the c +1 th clustering center respectively; a piecewise-loss function of the c-th cluster center;
in the formula (10), Sd(c)、Sd(c-1) an optimal number of segments for the c-th cluster center; c-1 optimal number of segments of cluster center;
in the formula (11), c belongs to [1,24] and is represented as that the value range of the c-th cluster center is 1-24.
5. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm according to claim 2, characterized in that: in the fourth step, the determining a time interval division scheme and an equivalent load center of each time interval according to a clustering algorithm comprises the following steps:
equivalent power time sequence division and time period correction:
determining an optimal fuzzy C-means clustering result of the equivalent load power according to the optimal number of segments, performing class division and time sequence arrangement on the equivalent load power of the clustering result, dividing the class quantity of adjacent time periods into one time period, and finally obtaining t time sequence clustering time periods;
considering that the time sequence division scheme may have isolated time periods, performing time period correction by adopting a loss function minimum principle, fusing the isolated time periods with adjacent time periods with better similarity, performing time sequence optimal time period correction on the isolated time periods, and ensuring time period division continuity, namely obtaining a loss function slope to finally obtain a corrected F-segment equivalent power time sequence division scheme;
because the similarity of the clustered data of the same type is higher, and the clustering center is used as the data center, the clustering center is used as the equivalent load power characteristic quantity of different time periods; considering the power distribution system with the equivalent load power close to each other, the power flow similarity degree of the system is higher, so that the intra-day optimal reconstruction optimization can be converted into the static reconstruction optimization in F time intervals, and the overall intra-day reconstruction adjustment effect is ensured through the static optimal adjustment in different time intervals.
6. The power distribution network dynamic reconstruction method based on the improved fuzzy C-means clustering algorithm according to claim 2, characterized in that: and step five, respectively performing static reconstruction optimization on the power distribution network in each time period by improving a bacterial foraging algorithm:
step 1: setting operating parameters and initializing bacterial populations, including: population size N, number of swims NsNumber of chemotaxis NcNumber of breeding NreNumber of migrations NedFixed migration probability PedA walking step length c (i), a chemotaxis iteration parameter j, a propagation iteration parameter k and a migration iteration parameter l;
step 2: taking the bacteria as a solution of a solution space, and calculating an initial fitness value J (i, J, k, l) of each bacteria;
step 3: judging whether the migration times meet the cycle conditions, if so, continuing to operate, and executing a dispersion operation cycle; if the dispersing times are reached, the optimization is stopped;
step 4: judging whether the breeding times meet the circulation condition, if so, continuing running, sorting and selecting half of the bacteria with advantages according to the activity of the bacteria and finishing the breeding operation; if the reproduction times are reached, returning to Step 3;
step 5: judging whether the chemotaxis times meet a circulation condition, if so, executing the bacteria chemotaxis operation, judging whether the fitness value is improved after the chemotaxis operation, if so, continuing moving towards the direction, otherwise, executing the turning operation and moving for one step; if the number of breeding has been reached, the process returns to Step 4.
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