CN114676955A - Fuzzy C-means clustering feeder load property analysis method - Google Patents
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Abstract
The invention relates to a fuzzy C-means clustering feeder load property analysis method, which comprises the following steps: collecting historical load data of a feeder line, and clustering the historical load data to find a target load data sequence of a clustering center; calculating a target change trend sequence of the target load data sequence; presetting c typical load data sequences, and calculating reference change trend sequences of various typical load data sequences; calculating time bending Euclidean distances between the target change trend sequence and each reference change trend sequence; solving membership functions of the target change trend sequence relative to each reference change trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance; and determining a maximum membership function, and taking the type of the typical load data sequence corresponding to the maximum membership function as the type of the feeder line. According to the invention, the excellent characteristics of the change trend sequence and the time bending Euclidean distance are combined, and the accuracy of classification fitting of the load property of the feeder line is improved.
Description
Technical Field
The invention relates to the field of feeder load property classification, in particular to a feeder load property analysis method based on fuzzy C-means clustering.
Background
In the services of distribution network scheduling operation, fault handling, demand side management and the like, a scheduler and a power utilization manager need to master the load property of a feeder line power supply power user in a distribution network, so that mode adjustment, post-accident recovery and load side management are performed in a targeted manner.
The clustering method is an important tool for analyzing and classifying the loads of the feeders. The traditional clustering analysis is a hard partition, each object to be identified is strictly classified into a certain class, the class has the property of not being similar to each other, and feeder line power supply users are diverse, often do not have the strict division condition, and are not suitable for the traditional clustering method. The fuzzy clustering algorithm can realize the 'softening point' according to the change trend of the data, and provides a theoretical basis for analyzing the complex feeder load data. Chinese patent [201911199324.1] proposes a power load clustering method, which obtains load time sequences of each user, and inserts phase shifts into the load time sequences of each user to obtain embedded sequences; generating mapping function parameters by adopting a linear neural regression method for the embedded sequence; and clustering the users by utilizing the similarity of the mapping function parameters of each user to obtain a typical load curve. The method is based on aggregation of load data analysis of each user, is established on the basis of the characteristics of the known user load data, and is not suitable for assisting in regulating and controlling the feeder load data of the personnel to analyze the user load property. Chinese patent [201910859952.1] proposes a power consumer clustering electricity consumption behavior characteristic analysis method based on load decomposition. After the total electric load is decomposed into basic-level load and seasonal load, the two levels of load are respectively subjected to cluster analysis by using a fuzzy C-means algorithm, important characteristic indexes of each type of typical users are calculated to analyze the load regulation and control potential of each type of typical users, and the electric power users are further classified according to the regulation and control potential of each type of typical users. The method considers the seasonal characteristics of the load, is still limited to classifying the variation trend of the load data, and does not further analyze the load properties corresponding to various types of load data.
Disclosure of Invention
In order to solve the above technical problem or at least partially solve the above technical problem, the present invention provides a method for analyzing feeder load properties by fuzzy C-means clustering.
The invention provides a method for analyzing the load property of a feeder line by fuzzy C-means clustering, which comprises the following steps: collecting historical load data of the feeder line according to a time sequence, and clustering the historical load data to find a target load data sequence of a clustering center;
calculating a target change trend sequence of the target load data sequence;
presetting c typical load data sequences, and calculating reference change trend sequences of various typical load data sequences;
calculating time bending Euclidean distances between the target change trend sequence and each reference change trend sequence;
solving membership functions of the target change trend sequence relative to each reference change trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance;
and determining a maximum membership function, and taking the type of the typical load data sequence corresponding to the maximum membership function as the type of the feeder load.
Furthermore, when the historical load data of the feeder line is acquired according to the time sequence, the time intervals between adjacent sample data in the acquired historical load data are the same, and the sample size is larger than the set sample size threshold; the typical payload data sequence is consistent with the dimensions of the target payload data sequence, and the time intervals between the elements of the target payload data sequence and the typical payload data sequence are consistent.
Still further, the calculating the target trend sequence of the target load data sequence includes: acquiring all adjacent elements in the target load data sequence, acquiring the elements of the target change trend sequence according to the time interval between the adjacent elements of the target load data sequence on the difference ratio of the later element to the former element in the adjacent elements, and arranging the elements of the target change trend sequence according to the time sequence to form the target change trend sequence.
Further, c typical load data sequences are preset, and calculating the reference change trend sequence of each typical load data sequence comprises: acquiring all adjacent elements in each typical load data sequence, acquiring the elements of the reference change trend sequence of the typical load data sequence according to the time interval between the adjacent elements in the typical load data sequence on the difference ratio of the following elements to the preceding elements in the adjacent elements, and arranging the elements of the reference change trend sequences in time sequence to form the reference change trend sequence.
Further, the calculating a time-warping euclidean distance between the target trend series and each reference trend series includes:
calculating a target variation trend sequence [ x ] 1'，x2'，x3'……xn']And respective reference trend sequences [ yr1'，yr2'，yr3'……yrn']And r is 1,2,3 … … c, the euclidean distance between them, the formula is as follows:
euclidean distance matrix D constructed by using Euclidean distancern×n；
Constructing an accumulative cost matrix, wherein elements in the accumulative cost matrix are as follows:
the time-warping euclidean distance between the target trend series and each reference trend series is then:
Dr DTW(X'，Yr')＝Lr(n，n)。
furthermore, solving the membership function formula of the target change trend sequence relative to each reference change trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance is as follows:
further, an electricity price charging model based on the feeder type is created, and the feeder electricity price charging is carried out according to the determined feeder load type and the electricity price charging model.
Further, a feeder type based project management planning strategy is created, and projects are planned according to the feeder type.
Further, a fault loss metering model based on the feeder type is created, and fault loss is metered according to the feeder type with the fault.
Further, a feeder load management optimization strategy based on the feeder type is created, and the corresponding feeder load management optimization strategy is executed according to the feeder type.
Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
The invention relates to a feeder load property analysis method based on fuzzy C-means clustering, which utilizes historical load data of feeders to perform clustering to find a target load data sequence reflecting the feeder load property in a clustering center, arranges element time sequences in the target load data sequence, and calculates a target change trend sequence of the target load data sequence. And setting up typical load data sequences of the c types of feeders and calculating reference change trend sequences of various typical load data sequences. And calculating the time-bending Euclidean distance between the target change trend sequence and each reference change trend sequence to more accurately judge the matching between the target load data sequence and the typical load data sequence. Solving membership functions of the target change trend sequence relative to each reference change trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance; and determining a maximum membership function, and taking the type of the typical load data sequence corresponding to the maximum membership function as the type of the feeder line. Thereby more accurately determining the type of feeder load.
According to the invention, the excellent characteristics of the change trend sequence and the time bending Euclidean distance are combined, and the precision of the classification fitting of the load properties of the feeder line is improved. The result can provide effective reference for the selection of demand side management items, the formulation of electricity prices, the calculation of fault loss, the optimization of load management and the like.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a feeder load property analysis method based on fuzzy C-means clustering according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present invention provides a method for analyzing feeder load properties by using fuzzy C-means clustering, including:
s100, acquiring historical load data of a feeder line with a load type to be determined according to a time sequence, acquiring the historical load data with a sample size larger than a set sample size threshold from an automatic feeder line management system in a specific implementation process, and acquiring the historical load data of the feeder line, wherein the time intervals between adjacent sample data in the acquired historical load data are the same.
S200, clustering the historical load data to find a target load data sequence X of a clustering center [ X ═ X- 1，x2，……xn+1]；
S300, calculating a target change trend sequence of the target load data sequence; in a specific implementation process, the calculating a target change trend sequence of the target load data sequence includes:
acquiring all adjacent elements in the target load data sequence; such as xiAnd xi+1Wherein i is 1,2, … …, n.
Acquiring the elements of the target change trend sequence according to the time interval between the adjacent elements of the target load data sequence on the difference ratio of the later element to the former element in the adjacent elements; namely:
arranging the elements of the target variation trend sequence in a time sequence to form a target variation trend sequence X ═ X1'，x2'，……xn']。
S400, presetting typical load data sequences of c load type feeders, and calculating reference change trend sequences of various typical load data sequences by using the method in the step S300;
specifically, the typical load data sequence of the c-type feeder is as follows:
Yr＝[yr1，yr2，……yrn+1]wherein r is 1,2,3, … … c. The dimension of the target load data sequence is n +1, the time interval between adjacent elements is equal to the time interval between adjacent elements of the target load data sequence, and the dimension is delta t.
Acquiring all adjacent elements in each typical load data sequence; such as yjAnd yj+1Wherein j is 1,2, … …, n.
Obtaining the elements of the reference trend sequence according to the time interval between the adjacent elements in the typical load data sequence on the difference ratio of the following element to the previous element in the adjacent elements; namely:
arranging the elements of the target reference variation trend sequence in time sequence to form a reference variation trend sequence Yr'＝[yr1'，yr2'，……yrn']。
S500, calculating a time-warping Euclidean distance between the target change trend sequence and each reference change trend sequence;
in a specific implementation process, the calculating a time-warping euclidean distance between the target variation trend sequence and each reference variation trend sequence includes:
detecting whether the dimension of the target variation trend sequence is consistent with the dimension of the reference variation trend sequence or not, and if not, feeding back corresponding error information; if the target change trend sequence [ x ] is calculated by using a formula for calculating Euclidean distance1'，x2'，x3'……xn']And respective reference trend sequences [ yr1'，yr2'，yr3'……yrn']R is 1,2,3 … … c, the euclidean distance between them; the formula is as follows:
use the instituteThe solved Euclidean distance is used for constructing an n multiplied by n order Euclidean distance matrix Drn×n。
Constructing an accumulative cost matrix, wherein elements in the accumulative cost matrix are as follows:
if L isr(i，j)＝Dr(i，j)+Lr(i, j-1), then represents that X 'is left-shifted by a time interval matching sequence Y'; if L isr(i，j)＝Dr(i，j)+Lr(i-1, j), then the sequence X 'is right-shifted by one time interval matching sequence Y'.
The time-warping euclidean distance between the target trend series and each reference trend series is then:
Dr DTW(X'，Yr')＝Lr(n，n)。
s600, solving membership functions of the target change trend sequences relative to all reference change trend sequences by using a fuzzy C-means clustering method and a time-warping Euclidean distance; specifically, solving the membership function formula of the target change trend sequence relative to each reference change trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance is as follows:
s700, determining a maximum membership function, and taking the type of the typical load data sequence corresponding to the maximum membership function as the type of the feeder load.
Example 2
The embodiment of the application provides an electricity price charging mode based on a feeder type:
and establishing a feeder type-based electricity price charging model, determining the load type of the target feeder by using the fuzzy C-means clustering feeder load property analysis method, and calculating the electricity price charging of the target feeder according to the determined load type of the target feeder and the electricity price charging model.
Example 3
The embodiment of the application provides a project management planning mode based on feeder types:
and creating a project management planning strategy based on feeder types, determining the load type of each feeder by using the fuzzy C-means clustering feeder load property analysis method, and planning the region where the project is located and the connected feeders according to the project management planning strategy and the load types of each feeder.
Example 4
The embodiment of the application provides a fault estimation mode based on feeder types:
and establishing a fault loss metering model based on the feeder type, determining the load type of the faulty feeder by using the feeder load property analysis method of fuzzy C-means clustering, and metering the fault loss according to the type of the faulty feeder and the fault loss metering model.
Example 5
The embodiment of the application provides a fault estimation mode based on feeder types:
and establishing a feeder load management optimization strategy based on the feeder type, determining the load type of the target feeder by using the fuzzy C-means clustering feeder load property analysis method, and executing a corresponding feeder load management optimization strategy according to the feeder type.
Example 6
The embodiment of the invention provides a device for realizing a feeder load property analysis method of fuzzy C-means clustering, which comprises a processing unit, a bus unit, a storage unit, a display unit, a communication unit and an input unit, wherein the bus unit is electrically connected with the processing unit, the storage unit, the display unit, the communication unit and the input unit. The storage unit stores at least one instruction, and the processing unit reads and executes the instruction to realize the feeder load property analysis method of the fuzzy C-means clustering.
Example 7
The embodiment of the invention provides a storage medium of a feeder load property analysis method for realizing fuzzy C-means clustering, wherein the storage medium of the feeder load property analysis method for realizing fuzzy C-means clustering stores at least one instruction, and the feeder load property analysis method for realizing the fuzzy C-means clustering is realized by reading and executing the instruction.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A fuzzy C-means clustering feeder load property analysis method is characterized by comprising the following steps: collecting historical load data of the feeder line according to a time sequence, and clustering the historical load data to find a target load data sequence of a clustering center;
calculating a target change trend sequence of the target load data sequence;
presetting c typical load data sequences, and calculating reference change trend sequences of various typical load data sequences;
Calculating time bending Euclidean distances between the target change trend sequence and each reference change trend sequence;
solving a membership function of the target variation trend sequence relative to each reference variation trend sequence by using a fuzzy C-means clustering method and a time-warping Euclidean distance;
and determining a maximum membership function, and taking the type of the typical load data sequence corresponding to the maximum membership function as the type of the feeder load.
2. The method for analyzing the feeder load characteristics of the fuzzy C-means cluster as claimed in claim 1, wherein when the historical load data of the feeder is collected according to the time sequence, the time intervals between adjacent sample data in the collected historical load data are the same, and the sample size is larger than the set sample size threshold; the typical payload data sequence is consistent with the dimensions of the target payload data sequence, and the time intervals between the elements of the target payload data sequence and the typical payload data sequence are consistent.
3. A method for feed line load property analysis by fuzzy C-means clustering as claimed in claim 1 wherein said calculating a target trend sequence of target load data sequences comprises: acquiring all adjacent elements in the target load data sequence, acquiring the elements of the target change trend sequence according to the time interval between the adjacent elements of the target load data sequence on the difference ratio of the later element to the former element in the adjacent elements, and arranging the elements of the target change trend sequence according to the time sequence to form the target change trend sequence.
4. A feedline load property analysis method according to claim 1, wherein the step of presetting C typical load data sequences and the step of calculating the reference trend sequence of each typical load data sequence comprises: acquiring all adjacent elements in each typical load data sequence, acquiring the elements of the reference change trend sequence of the typical load data sequence according to the time interval between the adjacent elements in the typical load data sequence on the difference ratio of the following elements to the preceding elements in the adjacent elements, and arranging the elements of the reference change trend sequences in time sequence to form the reference change trend sequence.
5. A method for feed line load property analysis by fuzzy C-means clustering as claimed in claim 1, wherein said calculating a time-warping euclidean distance between a target trend sequence and each reference trend sequence comprises:
calculating a target variation trend sequence [ x ]1'，x2'，x3'……xn']And respective reference trend sequences [ yr1'，yr2'，yr3'……yrn']And r is 1,2,3 … … c, the euclidean distance between them, the formula is as follows:
euclidean distance matrix D constructed by using Euclidean distancern×n；
Constructing an accumulative cost matrix, wherein elements in the accumulative cost matrix are as follows:
the time-warping euclidean distance between the target trend series and each reference trend series is then:
Dr DTW(X'，Yr')＝Lr(n，n)。
6. A feed line load property analysis method based on fuzzy C-means clustering as claimed in claim 1, wherein the membership function formula of the target variation trend sequence to each reference variation trend sequence is solved by using a fuzzy C-means clustering method and a time-warping euclidean distance as follows:
7. the method for analyzing the feeder load properties based on the fuzzy C-means clustering of claim 1, wherein a feeder type-based electricity price billing model is created, and the feeder electricity price billing is performed according to the determined feeder load type and the electricity price billing model.
8. The method for analyzing the feeder load properties through the fuzzy C-means clustering as claimed in claim 1, wherein a feeder type-based project management planning strategy is created, and projects are planned according to feeder types.
9. The method for analyzing the feeder load properties based on the fuzzy C-means clustering is characterized in that a feeder type-based fault loss metering model is created, and fault loss is metered according to the type of the feeder with the fault.
10. The method for analyzing the feeder load properties based on the fuzzy C-means clustering of claim 1, wherein a feeder load management optimization strategy based on a feeder type is created, and a corresponding feeder load management optimization strategy is executed according to the feeder type.
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