CN116093984A - Parallel three-phase imbalance optimization method based on space and user load time sequence characteristics - Google Patents
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Abstract
The invention provides a parallel three-phase unbalanced optimization method based on space and user load time sequence characteristics, which divides nodes of a district user into a plurality of node sets M according to the network structure of a low-voltage distribution network and the degree of density of the access positions of the district user i . And classifying the users of each node set independently based on the FCM clustering algorithm to obtain typical user curves of various users of each node. Replacing the actual load of each type of user in each node by a typical user curve, creating an improved genetic algorithm considering parent gene alternate inheritance, and performing optimization solving by taking the minimum average three-phase unbalance of one day as an objective function for each node set to formAnd the parallel optimization structure can reduce the three-phase unbalance of the whole transformer area and the main feeder line after optimization. The invention can solve the problem of unbalanced three-phase load, and particularly can be used with a multi-stage power distribution network, thereby reducing line loss and improving the power supply reliability of the power distribution network.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a parallel three-phase imbalance optimization method based on space and user load time sequence characteristics.
Background
The current low-voltage distribution network has increasingly outstanding various electricity utilization problems, in particular to three-phase unbalance problems due to the characteristics of huge scale and complex structure. Due to randomness of power utilization of the users in the transformer areas in time, uncertainty of power utilization load and space variability of feeder lines of the users in the transformer areas in the whole low-voltage power distribution network, a large number of three-phase imbalance phenomena exist in the actual engineering of the low-voltage power distribution network, and adverse effects are generated on power loss reduction of the power distribution network, power utilization quality of the users in the transformer areas, normal temperature operation of a transformer and the like.
At present, most of measures for solving the problem of three-phase unbalance are to compensate three-phase unbalanced voltage or current at the low-voltage side of a transformer through a power electronic converter, a reactive compensation device and the like, and network structures of low-voltage distribution networks in actual engineering are not considered. In a real situation, the locations of the access feeder lines of the users in the area are not uniformly changed, but there is a dense division, and the previous treatment methods do not carefully consider these factors, so that the treatment effect deviates from the estimated treatment effect.
In addition to the compensation method, the automatic phase change switch is also applied to the problem of three-phase imbalance treatment, and when the three-phase imbalance of the users in the transformer area is monitored to a certain degree, a command is issued to the phase change switch to reduce the three-phase imbalance. However, the maximum commutation times of the commutation switches are limited and the price of the commutation switches is high, and only 5-8 commutation switches are installed in one area. Therefore, a more excellent three-phase unbalance optimization method of the low-voltage power distribution network is designed, and the method becomes a research direction.
Disclosure of Invention
The invention is based on the network structure of the low-voltage distribution network and the time sequence characteristics of the user load of the platform region, the whole low-voltage distribution network is subjected to node division, the user sets of the platform region of each node are subjected to independent cluster analysis to form a typical load database of each node set, an improved genetic algorithm considering the inheritance of parent genes is provided, optimization treatment is carried out by taking the average three-phase imbalance throughout the day as an optimization target, a parallel multi-thread treatment optimization structure is formed, long-acting three-phase balance can be realized only through one-time phase change operation, each node is independent, the three-phase balance degree is not influenced, and independent treatment of the region is easier to realize.
A parallel three-phase imbalance optimization method based on space and user load time sequence features comprises the following steps:
s1, dividing N area users of a low-voltage power distribution network into a plurality of node sets { M }, according to the situation of wiring density of the area users i I=1, 2, …, m, where N and m are positive integers;
wherein each node set comprises the same electric distance and compact wiring positions of the users of the area, and each node setM i Comprises n i Individual cell users, i.e. n 1 +n 2 +...+n i +...+n m =n, where N i Is a positive integer;
s2, acquiring an average daily load data matrix P in a calibration period of N transformer area users of the low-voltage power distribution network;
P＝[P 1 P 2 ... P k ... P N ] T (1)
wherein P represents an average daily load matrix formed by N pieces of user load power data; p (P) k An average daily load power matrix representing the kth user, k=1, 2, …, N;
s3, for each node set M i Is combined with the independent analysis of the cluster effectiveness function to determine the optimal classification number c i * And according to the optimal classification number c i * Clustering P by adopting a fuzzy C-means clustering algorithm, and taking a clustering center of each node user as a typical load curve of the user, wherein the clustering method specifically comprises the following steps:
s31, determining a cluster effectiveness function L (U, c), wherein U is a membership matrix, and c is a classification number;
s32, for each node set, respectively solving c by using an FCM clustering algorithm i From 2 to n i Membership matrix U of (C) i And a cluster center V i Wherein c i A classification number for each set of nodes;
s33, obtaining the optimal classification number c of each node set by using a cluster effectiveness function L (U, c) i * ；
S34, according to the optimal classification number c of each node i * For each node set M i Classifying the inner users, and taking the clustering center of each type of users as a typical load curve;
s4, creating an improved genetic algorithm considering parent gene alternate inheritance;
the improved genetic algorithm considering parent gene alternate inheritance is that after the crossover process of each iteration of the genetic algorithm, 4 individuals are selected for competition together from the parent and the offspring generated by the parent, two individuals with the highest fitness are selected to enter a new group, and then mutation operation is carried out, specifically:
s41, determining population quantity, taking fixed phase sequence combinations of given users as gene codes, and initializing a 0 th generation population;
s42, taking the average three-phase unbalance of the given user in the whole day as the fitness, calculating the fitness of each individual of the population, and storing the optimal individual;
s43, removing phase sequence combinations with higher three-phase imbalance;
s44, performing pairwise crossing operation on the population after the selection operation, competing 4 individuals of the parent and the offspring generated by the parent after the crossing operation is finished, and selecting two individuals with lower three-phase imbalance degree from the whole day to enter a new population;
s45, performing mutation operation on the new population, and expanding the gene search space; judging whether an algorithm ending condition is met, if not, returning to the step S42, and if so, outputting an optimal solution, wherein the optimal solution is a station user phase sequence combination corresponding to the lowest average three-phase imbalance degree in the whole day;
s5, applying an improved genetic algorithm considering parent gene alternate inheritance to carry out parallel independent optimization treatment on each node by taking the minimum average three-phase unbalance of the whole day as an objective function, and finally determining the optimal phase sequence of the users in the areas of all nodes so as to enable the phase sequence combination of the whole area to reach a three-phase balance state;
s51, according to the classification result of each node user in the step S3, taking the obtained various typical load curves of each node as actual load curves of the platform region users;
s52, calculating a three-phase imbalance power index according to the load power of each node user, wherein the calculation expression is as follows:
wherein alpha is A, B, C three phases, p αi An alpha-phase instantaneous power value for node i;
for instantaneous three-phase average power of node i, g αi Representing the alpha-phase imbalance of node i, p Ai A phase A instantaneous power value of the node i; p is p B B-phase instantaneous power value for node i; p is p Ci A C-phase instantaneous power value for node i; />
S53, according to the established three-phase imbalance power index, carrying out node optimization on each node by taking the minimum average three-phase imbalance of 24 hours as an objective function according to the established improved genetic algorithm considering parent gene alternate inheritance, wherein the node optimization comprises the following specific steps:
s531, aiming at the node 1, inputting a user average load matrix of a district after the typical load curve of the node 1 is replaced, carrying out optimization solution by taking the minimum three-phase imbalance of the whole day as an objective function based on the improved genetic algorithm which is created in the S4 and takes the parent gene alternate inheritance into consideration, and taking the optimal solution as the optimal access phase sequence of each user of the node 1;
s532, aiming at the node 2, only inputting a typical load curve of the node 2 to replace the average load matrix of the users in the platform area, and continuing to perform optimization solution to determine the optimal access phase sequence of the users in each platform area of the node 2; and repeatedly executing the optimization operation according to the nodes until the optimal phase sequence of all the station area users is determined, and at the moment, the three-phase unbalance degree of the whole station area and the main feeder line node is the lowest.
Further, the step S1 specifically includes the following steps:
s11, investigating the line laying connection condition of the whole area, ascertaining the user accessed by each telegraph pole distribution box, and establishing a power topological graph of the whole area;
s12, according to the established topological graph of the district network and the difference of district users in electrical distance, dividing nodes of N users in the district to obtain a plurality of node sets { M } i I=1, 2, …, m }, each of whichNode set M i The wiring positions of the users in the contained areas are compact, and the electrical distances are the same.
Further, the step S2 specifically includes:
s21, acquiring load data D in N user calibration periods of the low-voltage power distribution network by using a data acquisition and monitoring control system with calibration time limit as a sampling interval;
s22, averaging the daily load power data D in the calibration period to obtain an average daily load data matrix P of the users in the platform region:
P＝[P 1 P 2 ... P k ... P N ] T (1)
P k ＝[P k1 P k2 ... P kt ... P k24 ] (2)
wherein P represents an average daily load matrix formed by N pieces of user load power data; p (P) k Average daily load power matrix representing kth user, k=1, 2, …, N, P kt Represents the average load power of the kth user at the t-th time sampling point, t=1, 2, …, 24.
Further, the calibration period in the step S2 is 30 days.
Further, the definition of the cluster validity function L (U, c) in the step S31 is as follows:
wherein n is the total number of samples, m is the ambiguity, the value is 2, x k For the kth sample data, U represents a membership matrix, U jk Representing the membership degree, v, of the kth sample data relative to the jth class in the membership degree matrix U j Represents the cluster center of the j-th class, c represents the classification number, V 0 Is the center of all of the samples,
the cluster effectiveness function L (U, c) is a very large index.
Further, the step S33 specifically includes:
different c for each node i Membership matrix U with corresponding values i And a cluster center V i Respectively substituting into the cluster effectiveness function formula (3), when L (U i * ；c i * )＝max{L(U i ；c i ) When (at) c i * Is the optimal number of classifications for node i, U at this time i * And V i * For node i at the best class number c i * Membership matrix and cluster center.
Further, in the step S53, the node optimization is specifically:
the load matrix data of each node is used as the input of an improved genetic algorithm, the average three-phase imbalance of the whole day is minimum as an objective function, iterative solution is carried out, when node optimization is carried out, all nodes are independent of each other and do not affect each other, namely, when the treatment is optimized, only three-phase balance of a single node is concerned.
Further, the calibration time limit in the step S21 is 1 hour.
Further, the calibration time limit in the step S21 is 1 hour.
Compared with the prior art, the invention has the following beneficial effects:
1. when the three-phase unbalance optimization is carried out, the network structure of the low-voltage distribution network, the degree of density of the access position of the station user, the time sequence characteristics of the user load and the like are taken into consideration, and compared with the traditional treatment scheme, the three-phase unbalance optimization method is more fit and practical and has a better treatment effect.
2. The invention adopts the mode that each node user independently carries out optimization treatment to finally determine the optimal phase sequence of all the users in the platform area, and independently opens each node user to form a parallel multi-thread optimization structure. Because the typical load databases of the nodes are different, and the nodes are not associated in optimizing the treatment, the independent treatment of the areas is easier to realize.
3. Compared with the traditional genetic algorithm, the genetic algorithm taking the parent gene alternate inheritance into consideration has better performance.
Drawings
FIG. 1 is a flow chart of steps of a parallel three-phase imbalance remediation method based on spatial and user load timing characteristics of the present invention;
FIG. 2 is a node topology diagram of a low voltage power distribution network after node division according to one embodiment of the present invention;
FIG. 3 is a flow chart of an improved genetic algorithm that accounts for parent gene alternate inheritance in accordance with one embodiment of the present invention;
FIG. 4 is a graph showing three-phase imbalance of an exemplary node 4 as a function of the number of iterations of the improved genetic algorithm in accordance with one embodiment of the present invention;
FIG. 5 illustrates real-time three-phase imbalance of a pre-optimization low-voltage distribution network in accordance with one embodiment of the present invention;
fig. 6 is a graph showing real-time three-phase imbalance of an optimized low-voltage distribution network according to an embodiment of the present invention.
Detailed Description
Aiming at the defects of the prior art, the invention provides a parallel three-phase unbalanced optimization method based on space and user load time sequence characteristics, which is characterized in that node division is carried out on users in a transformer area from a network structure of a low-voltage distribution network, and then each node is independently optimized by adopting a genetic algorithm of parent gene alternate inheritance to form a parallel optimization structure. The three-phase unbalance degree is reduced, the relevance among all areas of the whole low-voltage power distribution network is weakened, the independent management of the areas is easier to realize, and the optimal phase sequence access scheme of all the users in the areas is finally determined. The problem of three-phase imbalance is present in various parts of the overall power system, while the effect of three-phase imbalance in low-voltage distribution networks is significant and prominent. The low-voltage distribution network is generally a 0.4kV power transmission line, and like most rural areas or communities in China at present, a transformer supplies power to a village or a community, and power users supplied by the same transformer are collectively called as a platform user.
As an embodiment, the parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load of the invention, as shown in fig. 1, comprises the following steps:
s1, dividing N area users of a low-voltage power distribution network into a plurality of node sets { M }, according to the situation of wiring density of the area users i I = 1,2, …, m }; for each node set M i The wiring positions of the users in the contained areas are compact, the electrical distances are the same, and n is contained in total i Individual cell users, i.e. n 1 +n 2 +...+n i +...+n m =n. The method specifically comprises the following steps:
s11, investigating the line laying connection condition of the whole area, ascertaining the user accessed by each telegraph pole distribution box, and establishing a power topological graph of the whole area;
s12, according to the established network topology diagram of the platform region, dividing nodes of N users of the platform region into m node sets according to the difference of the users of the platform region in the electrical distance;
s2, acquiring load data D of N users of the low-voltage power distribution network in the next month through the SCADA system.
Collecting daily load power of N users of the low-voltage power distribution network at sampling intervals of 1h, and averaging the daily load power of nearly thirty days to obtain an average daily load matrix P of users in a platform region:
P＝[P 1 P 2 ... P k ... P N ] T (1)
P k ＝[P k1 P k2 ... P kt ... P k24 ] (2)
wherein P represents an average daily load matrix formed by N pieces of user load power data; p (P) k Average daily load power matrix representing kth user, k=1, 2, …, N, P kt Represents the average load power of the kth user at the t-th time sampling point, t=1, 2, …, 24.
S3, carrying out parallel independent analysis on average daily load data of users in each node set platform area by adopting a clustering effectiveness function, and determining an optimal classification number c i * Clustering the load data based on fuzzy C-means clustering algorithm to form a typical load database of each node set, wherein the typical load database comprisesThe body comprises:
s31, determining a clustering effectiveness function L (U, c);
wherein n is the total number of samples, m is the ambiguity, the value is 2, x k For the kth sample data, U represents a membership matrix, U jk Representing the membership degree, v, of the kth sample data relative to the jth class in the membership degree matrix U j Represents the cluster center of the j-th class, c represents the classification number, V 0 Is the center of all of the samples,
the cluster effectiveness function L (U, c) is a very large index, and the larger the value is, the better the cluster result is.
S32, for each node set, respectively solving c by using an FCM clustering algorithm i From 2 to n i Membership matrix U of (C) i And a cluster center V i ，c i Wherein c i A classification number for each set of nodes;
s321, for node 1, from 2 to n 1 Traversal c 1 Each c is obtained separately 1 Corresponding membership matrix U 1 And a cluster center V 1 ；
S322, continuing to perform cluster validity analysis on the next node 2 until all nodes complete the cluster validity analysis, and finally obtaining different c of each node i Corresponding U i And V i So as to obtain the optimal classification number of each node according to the cluster effectiveness function.
S33, obtaining the optimal classification number of each node by using a cluster effectiveness function L (U, c);
different c for each node i Membership matrix U with corresponding values i And a cluster center V i Respectively substituting into the cluster effectiveness function formula (3), when L (U i * ；c i * )＝max{L(U i ；c i ) When (at) c i * Is the optimal number of classifications for node i, U at this time i * And V i * For node i at the best class number c i * Membership matrix and cluster center.
S34, according to the optimal classification number c of each node i * For each node set n i Classifying individual users, and taking the clustering center of each type of users as a typical load curve;
s4, creating an improved genetic algorithm considering parent gene alternate inheritance. The improved genetic algorithm considering parent gene alternate inheritance refers to that after the crossover process of each iteration of the genetic algorithm, 4 individuals are selected to compete together with the parent and the offspring generated by the parent, two individuals with the highest fitness are selected to enter a new group, and then mutation operation is carried out;
s41, determining population quantity, taking fixed phase sequence combinations of given determined users as gene codes, and initializing the 0 th generation population.
S42, taking the average three-phase unbalance of the given user in the whole day as the fitness, calculating the fitness of each individual of the population, and storing the optimal individual.
S43, selecting the population for roulette with excellent fitness, and eliminating phase sequence combinations with higher three-phase imbalance.
S44, performing pairwise crossover operation on the population after the selection operation, competing 4 individuals in total on the parent and the offspring generated by the parent after the crossover operation is finished, and selecting two individuals with higher fitness to enter a new population, so that the excellent parent genes can be inherited in a separated way, and the excellent gene segments cannot be lost due to the crossover operation.
S45, performing mutation operation on the new population, and expanding the gene search space. And judging whether the algorithm ending condition is met, if not, returning to the S42, and if so, ending the algorithm and outputting the optimal solution.
S5, applying an improved genetic algorithm considering parent gene alternate inheritance to carry out parallel independent optimization treatment on each node by taking the minimum average three-phase unbalance of the whole day as an objective function, and finally determining the optimal phase sequence of the users in the areas of all nodes, so that the phase sequence combination of the whole area reaches a three-phase balance state.
S51, according to the classification result of each node user, taking various typical load curves of each node as actual load curves of the platform region users;
s52, calculating a three-phase imbalance power index according to the load power of each node user, wherein the calculation expression is as follows:
wherein alpha is A, B, C three phases, p αi An alpha-phase instantaneous power value for node i;
for instantaneous three-phase average power of node i, g αi Representing the alpha-phase imbalance of node i, p Ai A phase A instantaneous power value of the node i; p is p B B-phase instantaneous power value for node i; p is p Ci A C-phase instantaneous power value for node i;
and S53, according to the established three-phase imbalance power index, carrying out parallel node optimization on each node by taking the minimum average three-phase imbalance of the whole day as an objective function according to the established improved genetic algorithm considering parent gene alternate inheritance. The specific node optimization is to take the load matrix data of each node as the input of an improved genetic algorithm, take the minimum average three-phase unbalance of the whole day as an objective function and carry out iterative solution. When node optimization is carried out, all nodes are independent of each other and do not affect each other, namely, only three-phase balance of a single node is concerned when optimizing treatment;
s531, aiming at the node 1, only inputting a region user average load matrix after the typical load curve of the node 1 is replaced, carrying out optimization solution by taking the minimum three-phase imbalance of all days as an objective function based on the improved genetic algorithm which is created in the S4 and takes the parent gene alternate inheritance into consideration, and taking the solution result of the algorithm as the optimal access phase sequence of each user of the node 1.
S532, aiming at the node 2, only inputting a typical load curve of the node 2 to replace the average load matrix of the users in the platform region, and continuing to perform optimization solution to determine the optimal access phase sequence of the users in each platform region of the node 2. And repeatedly executing the optimization operation according to the nodes until the optimal phase sequence of all the users in the transformer area is determined, and at the moment, the unbalance of the three phases of the whole transformer area and the main feeder line is the lowest.
The following is a specific implementation manner of the present invention in a low-voltage distribution network, and in this embodiment, the low-voltage distribution network has 543 users.
Step one, dividing the whole station area into 11 nodes according to the different access positions and the density degree of the users in each station area. The network topology of the power distribution network after node division is shown in fig. 2. Table 1 shows the number of users of each node after node division.
TABLE 1
And step two, acquiring an average daily load matrix D of the power distribution network 543 of users in the last month through the SCADA system.
And thirdly, performing cluster analysis on 11 nodes of the power distribution network by adopting a cluster effectiveness function, determining the optimal classification number of each node, classifying each node independently, and forming a typical load curve database of each node. Table 2 is the best classification number for each node.
TABLE 2
And step four, adopting an improved genetic algorithm considering ancestor gene alternate inheritance to carry out optimized treatment on each node independently, wherein the algorithm flow is shown in figure 3. Taking node 4 as an example, fig. 4 is the change of the objective function, i.e., the three-phase imbalance, with the number of algorithm iterations.
And fifthly, independently carrying out parallel optimization treatment on each node, and determining the optimal phase sequence of 543 users of the power distribution network without considering the influence of three-phase balance among the nodes.
As shown in fig. 5, in order to control the real-time three-phase unbalance degree of the low-voltage side and each node of the transformer of the low-voltage distribution network before treatment, the three-phase unbalance degree of the low-voltage side of the transformer is up to 16.0083%, the three-phase unbalance degree of the rest main feeder line nodes is also up to more than 15%, and particularly the three-phase unbalance degree of the node 6 is up to 41.6851%.
And solving by taking the minimum average three-phase unbalance of 24 hours a day as an objective function and adopting a genetic algorithm considering parent gene alternate inheritance according to a parallel optimization structure.
As shown in fig. 6, after the serial optimization of the network structure of the distribution network is considered, the three-phase unbalance of the low-voltage side of the transformer is reduced to 0.84895%, and the three-phase unbalance of the rest main feeder nodes is mostly lower than 3%. As can be seen from the comparison between the diagrams 5 and 6, the invention not only reduces the three-phase unbalance of the low-voltage side of the transformer, but also gives consideration to the three-phase unbalance of the main feeder node in the power distribution network, and has better effect on treating the three-phase unbalance.
According to the invention, node division is carried out on the users in the area into a plurality of node sets M according to the network structure of the low-voltage distribution network and the density degree of the access positions of the users in the area i Each set has n in total i And the individual users. And classifying users of each node set independently based on the FCM clustering algorithm to obtain a typical user curve of each node. The typical user curve is used for replacing the actual load of each type of user in each node, an improved genetic algorithm considering parent gene alternate inheritance is provided, and optimization solution is carried out on each node set by taking the minimum average three-phase imbalance of one day as an objective function to form a parallel optimization structure, so that the optimal phase sequence of all the users in the platform region is finally determined. The method is suitable for solving the three-phase load in the planning of the power distribution networkThe unbalanced problem, especially cooperate multistage distribution network to use, reduce the circuit loss, improve the power supply reliability of distribution network.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, for example, the sampling number of the load matrix may be in units of weeks or months, and thus, various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (8)
1. A parallel three-phase imbalance optimization method based on space and user load time sequence features is characterized by comprising the following steps:
s1, dividing N area users of a low-voltage power distribution network into a plurality of node sets { M }, according to the situation of wiring density of the area users i I=1, 2, …, m, where N and m are positive integers;
wherein each node set comprises the same electric distance and compact wiring positions of the users of the area, and each node set M i Comprises n i Individual cell users, i.e. n 1 +n 2 +...+n i +...+n m =n, where N i Is a positive integer;
s2, acquiring an average daily load data matrix P in a calibration period of N transformer area users of the low-voltage power distribution network;
P＝[P 1 P 2 ... P k ... P N ] T (1)
wherein P represents an average daily load matrix formed by N pieces of user load power data; p (P) k An average daily load power matrix representing the kth user, k=1, 2, …, N;
s3, for each node set M i Is combined with the independent analysis of the cluster effectiveness function to determine the optimal classification number c i * And according to the optimal classification number c i * Clustering P by adopting a fuzzy C-means clustering algorithm, and taking a clustering center of each node user as a clustering centerTypical load curves for such users include:
s31, determining a cluster effectiveness function L (U, c), wherein U is a membership matrix, and c is a classification number;
s32, for each node set, respectively solving c by using an FCM clustering algorithm i From 2 to n i Membership matrix U of (C) i And a cluster center V i Wherein c i A classification number for each set of nodes;
s33, obtaining the optimal classification number c of each node set by using a cluster effectiveness function L (U, c) i * ；
S34, according to the optimal classification number c of each node i * For each node set M i Classifying the inner users, and taking the clustering center of each type of users as a typical load curve;
s4, creating an improved genetic algorithm considering parent gene alternate inheritance;
the improved genetic algorithm considering parent gene alternate inheritance is that after the crossover process of each iteration of the genetic algorithm, 4 individuals are selected for competition together from the parent and the offspring generated by the parent, two individuals with the highest fitness are selected to enter a new group, and then mutation operation is carried out, specifically:
s41, determining population quantity, taking fixed phase sequence combinations of given users as gene codes, and initializing a 0 th generation population;
s42, taking the average three-phase unbalance of the given user in the whole day as the fitness, calculating the fitness of each individual of the population, and storing the optimal individual;
s43, removing phase sequence combinations with higher three-phase imbalance;
s44, performing pairwise crossing operation on the population after the selection operation, competing 4 individuals of the parent and the offspring generated by the parent after the crossing operation is finished, and selecting two individuals with lower three-phase imbalance degree from the whole day to enter a new population;
s45, performing mutation operation on the new population, and expanding the gene search space; judging whether an algorithm ending condition is met, if not, returning to the step S42, and if so, outputting an optimal solution, wherein the optimal solution is a station user phase sequence combination corresponding to the lowest average three-phase imbalance degree in the whole day;
s5, applying an improved genetic algorithm considering parent gene alternate inheritance to carry out parallel independent optimization treatment on each node by taking the minimum average three-phase unbalance of the whole day as an objective function, and finally determining the optimal phase sequence of the users in the areas of all nodes so as to enable the phase sequence combination of the whole area to reach a three-phase balance state;
s51, according to the classification result of each node user in the step S3, taking the obtained various typical load curves of each node as actual load curves of the platform region users;
s52, calculating a three-phase imbalance power index according to the load power of each node user, wherein the calculation expression is as follows:
wherein alpha is A, B, C three phases, p αi An alpha-phase instantaneous power value for node i;
for instantaneous three-phase average power of node i, g αi Representing the alpha-phase imbalance of node i, p Ai A phase A instantaneous power value of the node i; p is p B B-phase instantaneous power value for node i; p is p Ci A C-phase instantaneous power value for node i;
s53, according to the established three-phase imbalance power index, carrying out node optimization on each node by taking the minimum average three-phase imbalance of 24 hours as an objective function according to the established improved genetic algorithm considering parent gene alternate inheritance, wherein the node optimization comprises the following specific steps:
s531, aiming at the node 1, inputting a user average load matrix of a district after the typical load curve of the node 1 is replaced, carrying out optimization solution by taking the minimum three-phase imbalance of the whole day as an objective function based on the improved genetic algorithm which is created in the S4 and takes the parent gene alternate inheritance into consideration, and taking the optimal solution as the optimal access phase sequence of each user of the node 1;
s532, aiming at the node 2, only inputting a typical load curve of the node 2 to replace the average load matrix of the users in the platform area, and continuing to perform optimization solution to determine the optimal access phase sequence of the users in each platform area of the node 2; and repeatedly executing the optimization operation according to the nodes until the optimal phase sequence of all the station area users is determined, and at the moment, the three-phase unbalance degree of the whole station area and the main feeder line node is the lowest.
2. The parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load according to claim 1, wherein the step S1 is specifically as follows:
s11, investigating the line laying connection condition of the whole area, ascertaining the user accessed by each telegraph pole distribution box, and establishing a power topological graph of the whole area;
s12, according to the established topological graph of the district network and the difference of district users in electrical distance, dividing nodes of N users in the district to obtain a plurality of node sets { M } i I=1, 2, …, M }, where each node set M i The wiring positions of the users in the contained areas are compact, and the electrical distances are the same.
3. The parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load according to claim 1, wherein the step S2 is specifically:
s21, acquiring load data D in N user calibration periods of the low-voltage power distribution network by using a data acquisition and monitoring control system with calibration time limit as a sampling interval;
s22, averaging the daily load power data D in the calibration period to obtain an average daily load data matrix P of the users in the platform region:
P＝[P 1 P 2 ... P k ... P N ] T (1)
P k ＝[P k1 P k2 ... P kt ... P k24 ] (2)
wherein P represents an average daily load matrix formed by N pieces of user load power data; p (P) k Average daily load power matrix representing kth user, k=1, 2, …, N, P kt Represents the average load power of the kth user at the t-th time sampling point, t=1, 2, …, 24.
4. The parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load according to claim 1, wherein the calibration period in the step S2 is 30 days.
5. The parallel three-phase imbalance optimization method based on spatial and user load timing characteristics according to claim 1, wherein the definition of the cluster effectiveness function L (U, c) in step S31 is as follows:
wherein n is the total number of samples, m is the ambiguity, the value is 2, x k For the kth sample data, U represents a membership matrix, U jk Representing the membership degree, v, of the kth sample data relative to the jth class in the membership degree matrix U j Represents the cluster center of the j-th class, c represents the classification number, V 0 Is the center of all of the samples,
the cluster effectiveness function L (U, c) is a very large index.
6. The parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load according to claim 1, wherein the step S33 is specifically:
different c for each node i Clerical effect corresponding to valueAttribute matrix U i And a cluster center V i Respectively substituting into the cluster effectiveness function formula (3), when L (U i * ；c i * )＝max{L(U i ；c i ) When (at) c i * Is the optimal number of classifications for node i, U at this time i * And V i * For node i at the best class number c i * Membership matrix and cluster center.
7. The parallel three-phase imbalance optimization method based on the time sequence characteristics of the space and the user load according to claim 1, wherein the node optimization in the step S53 is specifically:
the load matrix data of each node is used as the input of an improved genetic algorithm, the average three-phase imbalance of the whole day is minimum as an objective function, iterative solution is carried out, when node optimization is carried out, all nodes are independent of each other and do not affect each other, namely, when the treatment is optimized, only three-phase balance of a single node is concerned.
8. A parallel three-phase imbalance optimization method based on spatial and user load timing characteristics according to claim 3, characterized in that the calibration time limit in step S21 is 1 hour.
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