CN111160404A - Method and device for analyzing reasonable value of line loss marking pole of power distribution network - Google Patents
Method and device for analyzing reasonable value of line loss marking pole of power distribution network Download PDFInfo
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Abstract
The invention discloses a method and a device for analyzing a reasonable value of a power distribution network line loss marking pole. Wherein, the method comprises the following steps: determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network; performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale; analyzing line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on a reference clustering value scale so as to select a target clustering value; determining a plurality of line loss reference domains based on the target cluster value, wherein each line loss reference domain comprises a plurality of sub-analysis factors; and determining a reasonable value of a line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
Description
Technical Field
The invention relates to the technical field of power distribution network line loss evaluation, in particular to a method and a device for analyzing a reasonable value of a power distribution network line loss marker post.
Background
In the correlation technique, along with the development of economy, the reliance to the electric power energy constantly improves, in the distribution network field, it is a very complicated problem to the line loss management of distribution network, because the factor that influences the line loss rate is many, consequently when calculating the line loss of distribution network, need the integrated consideration different service environment, the circuit trend, the condition such as voltage height, but do not have a reasonable line loss sighting rod analysis mode in the aspect of selecting to the distribution network line loss at present, can lead to the distribution network line loss problem to provide reasonable calculation mode like this, can't realize the quantization management.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for analyzing a reasonable value of a power distribution network line loss marking post, and at least solves the technical problems that power distribution network line loss marking post parameters cannot be reasonably provided in the related technology, so that power distribution network line loss cannot be quantitatively managed, and the management efficiency of the power distribution network line loss is influenced.
According to an aspect of the embodiment of the invention, a method for analyzing a reasonable value of a line loss marker post of a power distribution network is provided, which comprises the following steps: determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network; performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale; analyzing line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value; determining a plurality of line loss reference domains based on the target cluster values, wherein each line loss reference domain comprises a plurality of sub-analysis factors; and determining a reasonable value of the line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
Optionally, the step of determining a line loss data sample set for a plurality of analysis dimensions includes: acquiring equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network, wherein the equipment parameter information comprises at least one of the following: basic wiring parameter information and wiring-attached distribution transformation parameter information, wherein the theoretical line loss result data comprises at least one of the following data: historical theoretical line loss reported values, line loss planning reference values, line loss experience estimated values and contemporaneous line loss daily loss data; screening multi-dimensional sample data by using a data analysis tool, and outputting standardized sample data; performing initial inspection on the standardized sample data to obtain an initial line loss data sample set; and screening the initial line loss data sample set by adopting a preset data screening formula to obtain a plurality of analysis dimensionalities of line loss data sample sets.
Optionally, the step of performing systematic clustering analysis on the line loss data sample sets in different analysis dimensions to form a reference clustering value scale includes: determining a plurality of analysis dimensions, wherein the plurality of analysis dimensions includes at least one of: the method comprises the following steps of (1) belonging area of a power distribution network, power supply amount and line length, wherein the line length comprises total line length and trunk line length; determining the total number of clustering variables based on the analysis dimensions, and clustering variables close to each other by using a preset variable distance formula; performing cyclic clustering operation to enable the clustering number to reach the target clustering number, and finishing clustering work; and after finishing clustering work, analyzing the line loss data sample sets under different analysis dimensions by using a data analysis tool to form a reference clustering value scale.
Optionally, based on the reference clustering value scale, a preset clustering algorithm is adopted to analyze line loss data sample sets under different analysis dimensions to select a target clustering value, including: based on the reference clustering value scale, randomly selecting k data objects from n data objects as initial clustering centers; calculating the distance value between each line loss data object and the initial clustering center to obtain a plurality of clustering distance values; selecting a clustering distance value with the minimum numerical value as a clustering division reference, and dividing all line loss data objects to obtain a divided line loss clustering set; and calculating the clustering convergence of the line loss clustering set by using a standard measure function to determine a target clustering value.
Optionally, the plurality of line loss reference fields comprises at least one of: the method comprises a reference domain based on a line loss planning benchmark value, a reference domain based on a historical theoretical line loss value, a reference domain based on an empirical line loss estimation value and a reference domain based on a contemporaneous line loss daily loss value.
Optionally, the plurality of sub-analysis factors include at least one of: line loss benchmarking value, sample number and proportion.
Optionally, after determining a reasonable line loss metric corresponding to each of the analysis dimensions, the analysis method further includes: constructing a line loss scatter plot using a data analysis tool; performing multiple linear regression curve fitting based on the line loss scatter diagram to obtain a linear regression model; and displaying the line loss benchmark reasonable value and the multi-dimensional line loss benchmark reasonable value reference interval corresponding to each analysis dimension by using the linear regression model.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for analyzing a reasonable value of a power distribution network line loss benchmark, including: the first determining unit is used for determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network; the cluster analysis unit is used for carrying out system cluster analysis on the line loss data sample sets under different analysis dimensions to form a reference cluster value scale; the selecting unit is used for analyzing the line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value; a second determining unit, configured to determine a plurality of line loss reference domains based on the target cluster value, where each of the line loss reference domains includes a plurality of sub-analysis factors; and the third determining unit is used for determining a reasonable value of the line loss marker post corresponding to each analysis dimension by utilizing the data analysis decision tree and the plurality of line loss reference domains.
Optionally, the first determining unit includes: the first obtaining module is configured to obtain device parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network, where the device parameter information includes at least one of the following: basic wiring parameter information and wiring-attached distribution transformation parameter information, wherein the theoretical line loss result data comprises at least one of the following data: historical theoretical line loss reported values, line loss planning reference values, line loss experience estimated values and contemporaneous line loss daily loss data; the first screening module is used for screening multi-dimensional sample data by using a data analysis tool and outputting standardized sample data; the initialization checking module is used for carrying out initial checking on the standardized sample data to obtain an initial line loss data sample set; and the second screening module is used for screening the initial line loss data sample set by adopting a preset data screening formula to obtain a plurality of analysis dimensionality line loss data sample sets.
Optionally, the cluster analysis unit includes: a first determination module to determine a plurality of analysis dimensions, wherein the plurality of analysis dimensions includes at least one of: the method comprises the following steps of (1) belonging area of a power distribution network, power supply amount and line length, wherein the line length comprises total line length and trunk line length; the clustering module is used for determining the total number of clustering variables based on the analysis dimensions and clustering the variables close to each other by using a preset variable distance formula; the cyclic clustering module is used for performing cyclic clustering operation to enable the clustering number to reach the target clustering number and finish clustering work; and the first analysis module is used for analyzing the line loss data sample sets under different analysis dimensions by using a data analysis tool after finishing clustering work to form a reference clustering value scale.
Optionally, the selecting unit includes: the first selection module is used for randomly selecting k data objects from the n data objects as initial clustering centers on the basis of the reference clustering value scale; the first calculation module is used for calculating the distance value between each line loss data object and the initial clustering center to obtain a plurality of clustering distance values; the second selection module is used for selecting the clustering distance value with the minimum numerical value as a clustering division reference, and dividing all the line loss data objects to obtain a divided line loss clustering set; and the first calculation module is used for calculating the clustering convergence of the line loss clustering set by using a standard measure function so as to determine a target clustering value.
Optionally, the plurality of line loss reference fields comprises at least one of: the method comprises a reference domain based on a line loss planning benchmark value, a reference domain based on a historical theoretical line loss value, a reference domain based on an empirical line loss estimation value and a reference domain based on a contemporaneous line loss daily loss value.
Optionally, the plurality of sub-analysis factors include at least one of: line loss benchmarking value, sample number and proportion.
Optionally, the device for analyzing the reasonable value of the power distribution network line loss marker post further includes: the construction unit is used for constructing a line loss scatter diagram by using a data analysis tool after determining a line loss benchmark reasonable value corresponding to each analysis dimension; the fitting unit is used for performing multivariate linear regression curve fitting on the basis of the line loss scatter diagram to obtain a linear regression model; and the display unit is used for displaying the line loss benchmark reasonable value and the multi-dimensional line loss benchmark reasonable value reference interval corresponding to each analysis dimension by using the linear regression model.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above methods for analyzing the reasonable value of the line loss benchmarking of the power distribution network via executing the executable instructions.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute any one of the above methods for analyzing the reasonable value of the line loss benchmarks of the power distribution network.
In the embodiment of the invention, equipment parameter information and theoretical line loss result data required by the theoretical line loss of a power distribution network are adopted to determine line loss data sample sets of multiple analysis dimensions, then, the line loss data sample sets under different analysis dimensions are subjected to systematic clustering analysis to form a reference clustering value scale, then, the line loss data sample sets under different analysis dimensions are analyzed by adopting a preset clustering algorithm based on the reference clustering value scale to select a target clustering value, and multiple line loss reference domains are determined based on the target clustering value, wherein each line loss reference domain comprises multiple sub-analysis factors, and finally, a data analysis decision tree and multiple line loss reference domains are utilized to determine a reasonable value of a line loss scale rod corresponding to each analysis dimension. In this embodiment, can obtain the reference interval value of multidimension line loss sighting rod rational value through analysis line loss data sample, realize that line loss data is visual to solve unable reasonable distribution network line loss sighting rod parameter that provides among the correlation technique, lead to the unable quantization management of distribution network line loss, influence the technical problem of distribution network line loss managerial efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of an alternative method for analyzing a reasonable value of a loss marker post of a power distribution network according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an alternative analysis device for reasonable values of a power distribution network line loss benchmarking according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
To facilitate understanding of the present invention by those skilled in the art, some terms or nouns referred to in the embodiments of the present invention are explained below:
SPSS data mining tool: IBM SPSS Statistics utilize a set of powerful statistical functions that can make full use of the valuable information provided by the data. In addition to the generally common summary Statistics and rank calculations, SPSS Statistics also provides a wide range of basic statistical analysis functions, such as data summarization, counting, cross-analysis, classification, descriptive statistical analysis, factor analysis, regression, and cluster analysis.
Clustering analysis: the multivariate statistical analysis method for establishing classification can automatically classify a batch of sample (or variable) data according to a plurality of characteristics of the sample (or variable) data and the degree of affinity and sparseness of the properties without prior knowledge to generate a plurality of classification results, and as a result, the individual characteristics in the same class have similarity and the individual characteristics in different classes have larger difference. The embodiment of the invention adopts two clustering analysis methods: systematic clustering and K-Means clustering methods.
And (3) decision tree algorithm: the method is an important method in data mining, and the algorithm identifies a plurality of factors influencing object classification by learning existing data, constructs a decision tree classification model and classifies the objects. The decision tree architecture is composed of three parts: leaf nodes, decision nodes, and branches. The basic principle of the decision tree is: the whole data is classified according to the classification conditions specified in the algorithm to generate a decision node, and the classification is continued according to the algorithm rules until the data cannot be reclassified.
z-score normalization: this method performs normalization of data based on the mean and standard deviation (standard deviation) of the raw data. The original value x of A is normalized to x' using z-score. The z-score normalization method is applicable to cases where the maximum and minimum values of attribute A are unknown, or where there is outlier data that is out of range. Wherein:
new data is (original data-mean)/standard deviation.
In the embodiment of the invention, a data mining tool (for example, SPSS) can be used for carrying out sample selection, cluster analysis, decision tree analysis and the like on theoretical line loss and synchronous line loss data, so that an analysis method of reasonable values of line loss rate benchmarks based on the SPSS is realized, multi-dimensional analysis is carried out on the reasonable values of the line loss benchmarks by using multi-source theoretical line loss big data, a multi-dimensional line loss reference domain list is provided, and line loss visualization is realized.
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for analyzing reasonable values of loss metrics of a power distribution network, where the steps illustrated in the flowchart of the drawings may be implemented in a computer system, such as a set of computer-executable instructions, and where a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that illustrated or described herein.
Fig. 1 is a flowchart of an alternative method for analyzing a reasonable value of a loss marker post of a power distribution network according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S102, determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network;
step S104, performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale;
step S106, analyzing line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on a reference clustering value scale so as to select a target clustering value;
step S108, determining a plurality of line loss reference domains based on the target clustering value, wherein each line loss reference domain comprises a plurality of sub-analysis factors;
step S110, determining a reasonable value of a line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
Through the steps, the line loss data sample sets of multiple analysis dimensions can be determined by adopting equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network, then the line loss data sample sets under different analysis dimensions are subjected to system clustering analysis to form a reference clustering value scale, then the line loss data sample sets under different analysis dimensions are analyzed by adopting a preset clustering algorithm based on the reference clustering value scale to select a target clustering value, and multiple line loss reference domains are determined based on the target clustering value, wherein each line loss reference domain comprises multiple sub-analysis factors, and finally, a data analysis decision tree and multiple line loss reference domains are utilized to determine the reasonable value of a line loss benchmark corresponding to each analysis dimension. In this embodiment, can obtain the reference interval value of multidimension line loss sighting rod rational value through analysis line loss data sample, realize that line loss data is visual to solve unable reasonable distribution network line loss sighting rod parameter that provides among the correlation technique, lead to the unable quantization management of distribution network line loss, influence the technical problem of distribution network line loss managerial efficiency.
The embodiment of the invention can be applied to the use environments of the line loss of a power distribution network, the loss of a power grid line and the like.
The following describes embodiments of the present invention in detail with reference to the respective steps.
And S102, determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network.
Optionally, the step of determining a line loss data sample set of a plurality of analysis dimensions includes: acquiring equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network, wherein the equipment parameter information comprises at least one of the following: basic wiring parameter information and wiring-connected distribution transformation parameter information, and theoretical line loss result data comprises at least one of the following data: historical theoretical line loss reported values, line loss planning reference values, line loss experience estimated values and contemporaneous line loss daily loss data; screening multi-dimensional sample data by using a data analysis tool, and outputting standardized sample data; carrying out initial inspection on the standardized sample data to obtain an initial line loss data sample set; and screening the initial line loss data sample set by adopting a preset data screening formula to obtain a plurality of analysis dimensionality line loss data sample sets.
Wherein, basic distribution parameter information can utilize distribution basic equipment management, inserts and selects equipment platform account data, mainly includes: wiring harness information, distribution substation account information, and the like. And the theoretical line loss result data comprises: the method comprises the steps that historical theoretical line loss data and contemporaneous line loss daily loss data are obtained, the historical theoretical line loss data are accessed and screened for unqualified data, abnormal data are screened and standardized data are output based on a data mining tool, and effective data samples are formed. And accessing and screening unqualified data according to the data information with large line loss daily loss in the same period, screening abnormal data based on a data mining tool, outputting standardized data, and forming a line loss data sample set with a plurality of analysis dimensions.
And step S104, performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale.
As an optional embodiment of the present invention, the step of performing systematic clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale includes: determining a plurality of analysis dimensions, wherein the plurality of analysis dimensions includes at least one of: the method comprises the following steps of (1) belonging area of a power distribution network, power supply amount and line length, wherein the line length comprises the total line length and the length of a main line; determining the total number of clustering variables based on a plurality of analysis dimensions, and clustering variables close to each other by using a preset variable distance formula; performing cyclic clustering operation to enable the clustering number to reach the target clustering number, and finishing clustering work; and after finishing clustering work, analyzing the line loss data sample sets under different analysis dimensions by using a data analysis tool to form a reference clustering value scale.
Wherein the distance proximity means that the distance between the variables is the shortest.
The system clustering analysis is a traditional statistical clustering analysis method, and the determination of clustering influence mainly comprises the following steps: the power distribution network belongs to the area, namely the line loss rate, the power supply quantity, the line loss rate, the line length, the line loss rate and the like. The line length considers the influence change distribution of the total line length and the trunk line length on the line loss rate.
The system clustering process is as follows:
① assume there are a total of n variables, each of which is individually grouped into a class, with a total of n classes.
② according to the formula of the distance between the variables, two variables at a short distance are grouped into one class, and the other variables are still grouped into one class and copolymerized into n-1 class.
③ the two classes closest to the "distance" are further grouped together into one class, copolymerized into n-2 classes.
The above steps are continued until the number of polymerizations reaches a predetermined number.
According to the embodiment of the invention, the automatic analysis of the effective loss data sample sets in different dimensions can be realized based on the cluster analysis of the SPSS system, so that a reasonable reference cluster value scale is formed.
And step S106, analyzing the line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value.
In the embodiment of the invention, based on a reference clustering value scale, a preset clustering algorithm is adopted to analyze line loss data sample sets under different analysis dimensions so as to select a target clustering value, and the method comprises the following steps: based on a reference clustering value scale, randomly selecting k data objects from n data objects as initial clustering centers; calculating the distance value between each line loss data object and the initial clustering center to obtain a plurality of clustering distance values; selecting a clustering distance value with the minimum numerical value as a clustering division reference, and dividing all line loss data objects to obtain a divided line loss clustering set; and calculating the clustering convergence of the line loss clustering set by using a standard measure function to determine a target clustering value.
Optionally, the preset clustering algorithm may select a K-Means clustering algorithm, and a correlation coefficient method is used to analyze the correlation influence of the user on the distribution area and the wiring. Aiming at high-loss lines and transformer areas, solving a correlation coefficient k: the goal of k-means is to divide the data points into k object groups, find the center of each object group, and utilize a minimization function
Which is the center of the ith object group.
The above equation requires that each data point be as close as possible to the center of the group of objects to which they belong.
The method comprises the following specific steps:
① randomly selecting k objects from n data objects as initial cluster centers;
② calculating the distance between each object and the central objects according to the mean value of each clustering object (central object), and re-dividing the corresponding objects according to the clustering distance value with the minimum value;
③ recalculating the mean (center object) of each (changed) cluster;
④, the algorithm terminates when certain conditions are met, such as convergence of the function, and returns to step ② if the conditions are not met.
And analyzing the line loss data sample sets under different analysis dimensions according to a reference clustering value scale provided by the system clustering, analyzing the line loss sample sets of different dimensions respectively by adopting a preset clustering algorithm, and selecting the optimal clustering value to further obtain reasonable reference values of line loss under different dimensions.
Step S108, determining a plurality of line loss reference domains based on the target clustering value, wherein each line loss reference domain comprises a plurality of sub-analysis factors.
As an alternative embodiment of the present invention, the plurality of line loss reference fields includes at least one of: the method comprises a reference domain based on a line loss planning benchmark value, a reference domain based on a historical theoretical line loss value, a reference domain based on an empirical line loss estimation value and a reference domain based on a contemporaneous line loss daily loss value.
In an embodiment of the present invention, the plurality of sub-analysis factors includes at least one of: line loss benchmarking value, sample number and proportion.
Step S110, determining a reasonable value of a line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
In this embodiment of the present invention, after determining the reasonable value of the line loss benchmarking corresponding to each analysis dimension, the analysis method further includes: constructing a line loss scatter plot using a data analysis tool; performing multiple linear regression curve fitting based on the line loss scatter diagram to obtain a linear regression model; and displaying the line loss benchmark reasonable value corresponding to each analysis dimension and the multi-dimensional line loss benchmark reasonable value reference interval by using a linear regression model.
Optionally, each reasonable value reference interval of the line loss marking rod in each dimension has a corresponding reference boundary value, and a reasonable selection mode of the line loss of the power distribution network is realized through the reference boundary values.
In the embodiment of the invention, a data mining tool can be used for forming a multi-dimensional line loss reference domain, which comprises a line loss planning benchmark value reference domain, a calendar year theoretical line loss value reference domain, an empirical line loss estimation value reference domain and a contemporaneous line loss daily loss value reference domain, wherein each reference domain comprises auxiliary analysis factors such as a line loss benchmark value, a sample number, a ratio and the like. And analyzing reasonable values of the line loss benchmarks in dimensions of different areas, different power supply amounts, different wiring lengths and the like based on decision tree analysis. And performing multivariate linear regression curve fitting based on the line loss scatter diagram to obtain a linear regression model, realizing line loss visual reference, and providing a new big data analysis reference method for service personnel to perform loss reduction analysis.
The invention is illustrated below by means of a further alternative embodiment.
Fig. 2 is a schematic diagram of an alternative analysis apparatus for a reasonable value of a loss marker post of a power distribution network according to an embodiment of the present invention, as shown in fig. 2, the analysis apparatus may include:
the first determining unit 21 is configured to determine line loss data sample sets of multiple analysis dimensions based on device parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network;
the cluster analysis unit 22 is used for performing system cluster analysis on the line loss data sample sets under different analysis dimensions to form a reference cluster value scale;
the selecting unit 23 is configured to analyze the line loss data sample sets in different analysis dimensions by using a preset clustering algorithm based on the reference clustering value scale to select a target clustering value;
a second determining unit 24, configured to determine a plurality of line loss reference domains based on the target cluster value, where each line loss reference domain includes a plurality of sub-analysis factors;
and a third determining unit 25, configured to determine, by using the data analysis decision tree and the multiple line loss reference domains, a line loss benchmarking reasonable value corresponding to each analysis dimension.
The analysis device for the reasonable value of the power distribution network line loss benchmarks can determine line loss data sample sets of a plurality of analysis dimensions through the first determination unit 21 based on equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network, then the cluster analysis unit 22 carries out system cluster analysis on the line loss data sample sets under different analysis dimensions to form a reference cluster value scale, then the line loss data sample sets under different analysis dimensions can be analyzed by adopting a preset clustering algorithm based on the reference cluster value scale through the selection unit 23, to select a target cluster value, and determine a plurality of line loss reference domains based on the target cluster value by the second determining unit 24, each line loss reference domain comprises a plurality of sub-analysis factors, and finally, a third determining unit 25 can determine a line loss benchmarking reasonable value corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains. In this embodiment, can obtain the reference interval value of multidimension line loss sighting rod rational value through analysis line loss data sample, realize that line loss data is visual to solve unable reasonable distribution network line loss sighting rod parameter that provides among the correlation technique, lead to the unable quantization management of distribution network line loss, influence the technical problem of distribution network line loss managerial efficiency.
Optionally, the first determining unit includes: the first obtaining module is used for obtaining equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network, wherein the equipment parameter information comprises at least one of the following: basic wiring parameter information and wiring-connected distribution transformation parameter information, and theoretical line loss result data comprises at least one of the following data: historical theoretical line loss reported values, line loss planning reference values, line loss experience estimated values and contemporaneous line loss daily loss data; the first screening module is used for screening multi-dimensional sample data by using a data analysis tool and outputting standardized sample data; the initialization checking module is used for carrying out initial checking on the standardized sample data to obtain an initial line loss data sample set; and the second screening module is used for screening the initial line loss data sample set by adopting a preset data screening formula to obtain a plurality of analysis dimensionality line loss data sample sets.
In an embodiment of the present invention, the cluster analysis unit includes: a first determination module to determine a plurality of analysis dimensions, wherein the plurality of analysis dimensions includes at least one of: the method comprises the following steps of (1) belonging area of a power distribution network, power supply amount and line length, wherein the line length comprises the total line length and the length of a main line; the clustering module is used for determining the total number of clustering variables based on a plurality of analysis dimensions and clustering the variables close to each other by using a preset variable distance formula; the cyclic clustering module is used for performing cyclic clustering operation to enable the clustering number to reach the target clustering number and finish clustering work; and the first analysis module is used for analyzing the line loss data sample sets under different analysis dimensions by using a data analysis tool after finishing clustering work to form a reference clustering value scale.
As an alternative embodiment of the present invention, the selecting unit includes: the first selection module is used for randomly selecting k data objects from the n data objects as initial clustering centers on the basis of a reference clustering value scale; the first calculation module is used for calculating the distance value between each line loss data object and the initial clustering center to obtain a plurality of clustering distance values; the second selection module is used for selecting the clustering distance value with the minimum numerical value as a clustering division reference, and dividing all the line loss data objects to obtain a divided line loss clustering set; and the first calculation module is used for calculating the clustering convergence of the line loss clustering set by using a standard measure function so as to determine a target clustering value.
In an embodiment of the present invention, the plurality of line loss reference fields includes at least one of: the method comprises a reference domain based on a line loss planning benchmark value, a reference domain based on a historical theoretical line loss value, a reference domain based on an empirical line loss estimation value and a reference domain based on a contemporaneous line loss daily loss value.
Optionally, the plurality of sub-analysis factors include at least one of: line loss benchmarking value, sample number and proportion.
Another optional, the analysis device of distribution network line loss benchmarking reasonable value still includes: the construction unit is used for constructing a line loss scatter diagram by using a data analysis tool after determining a line loss benchmark reasonable value corresponding to each analysis dimension; the fitting unit is used for performing multiple linear regression curve fitting based on the line loss scatter diagram to obtain a linear regression model; and the display unit is used for displaying the line loss benchmark reasonable value corresponding to each analysis dimension and the multi-dimensional line loss benchmark reasonable value reference interval by using the linear regression model.
The device for analyzing the reasonable value of the distribution network line loss benchmarking may further include a processor and a memory, where the first determining unit 21, the cluster analyzing unit 22, the selecting unit 23, the second determining unit 24, the third determining unit 25, and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more, and the reasonable value of the line loss benchmarks corresponding to each analysis dimension is determined by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to execute any one of the above analysis methods for reasonable values of the line loss benchmarks of the power distribution network via executing the executable instructions.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute any one of the above methods for analyzing the reasonable value of the line loss benchmarks of the power distribution network.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network; performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale; analyzing line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on a reference clustering value scale so as to select a target clustering value; determining a plurality of line loss reference domains based on the target cluster value, wherein each line loss reference domain comprises a plurality of sub-analysis factors; and determining a reasonable value of a line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple 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, units or modules, and may be in an electrical 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 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for analyzing a reasonable value of a line loss marking pole of a power distribution network is characterized by comprising the following steps:
determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network;
performing system clustering analysis on the line loss data sample sets under different analysis dimensions to form a reference clustering value scale;
analyzing line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value;
determining a plurality of line loss reference domains based on the target cluster values, wherein each line loss reference domain comprises a plurality of sub-analysis factors;
and determining a reasonable value of the line loss marker post corresponding to each analysis dimension by using the data analysis decision tree and the plurality of line loss reference domains.
2. The analysis method of claim 1, wherein the step of determining a line loss data sample set for a plurality of analysis dimensions comprises:
acquiring equipment parameter information and theoretical line loss result data required by theoretical line loss of the power distribution network, wherein the equipment parameter information comprises at least one of the following: basic wiring parameter information and wiring-attached distribution transformation parameter information, wherein the theoretical line loss result data comprises at least one of the following data: historical theoretical line loss reported values, line loss planning reference values, line loss experience estimated values and contemporaneous line loss daily loss data;
screening multi-dimensional sample data by using a data analysis tool, and outputting standardized sample data;
performing initial inspection on the standardized sample data to obtain an initial line loss data sample set;
and screening the initial line loss data sample set by adopting a preset data screening formula to obtain a plurality of analysis dimensionalities of line loss data sample sets.
3. The analysis method according to claim 1, wherein the step of performing systematic clustering analysis on the line loss data sample sets in different analysis dimensions to form a reference cluster value scale comprises:
determining a plurality of analysis dimensions, wherein the plurality of analysis dimensions includes at least one of: the method comprises the following steps of (1) belonging area of a power distribution network, power supply amount and line length, wherein the line length comprises total line length and trunk line length;
determining the total number of clustering variables based on the analysis dimensions, and clustering variables close to each other by using a preset variable distance formula;
performing cyclic clustering operation to enable the clustering number to reach the target clustering number, and finishing clustering work;
and after finishing clustering work, analyzing the line loss data sample sets under different analysis dimensions by using a data analysis tool to form a reference clustering value scale.
4. The analysis method according to claim 1, wherein the step of analyzing the line loss data sample sets in different analysis dimensions by using a preset clustering algorithm based on the reference clustering value scale to select a target clustering value comprises:
based on the reference clustering value scale, randomly selecting k data objects from n data objects as initial clustering centers;
calculating the distance value between each line loss data object and the initial clustering center to obtain a plurality of clustering distance values;
selecting a clustering distance value with the minimum numerical value as a clustering division reference, and dividing all line loss data objects to obtain a divided line loss clustering set;
and calculating the clustering convergence of the line loss clustering set by using a standard measure function to determine a target clustering value.
5. The analysis method according to any one of claims 1 to 4, wherein the plurality of line loss reference fields comprises at least one of: the method comprises a reference domain based on a line loss planning benchmark value, a reference domain based on a historical theoretical line loss value, a reference domain based on an empirical line loss estimation value and a reference domain based on a contemporaneous line loss daily loss value.
6. The analysis method according to any one of claims 1 to 4, wherein the plurality of sub-analysis factors comprise at least one of: line loss benchmarking value, sample number and proportion.
7. The analysis method of claim 1, wherein after determining the line loss benchmarking equity value corresponding to each of the analysis dimensions, the analysis method further comprises:
constructing a line loss scatter plot using a data analysis tool;
performing multiple linear regression curve fitting based on the line loss scatter diagram to obtain a linear regression model;
and displaying the line loss benchmark reasonable value and the multi-dimensional line loss benchmark reasonable value reference interval corresponding to each analysis dimension by using the linear regression model.
8. The utility model provides an analytical equipment of distribution network line loss sighting rod reasonable value which characterized in that includes:
the first determining unit is used for determining line loss data sample sets of a plurality of analysis dimensions based on equipment parameter information and theoretical line loss result data required by the theoretical line loss of the power distribution network;
the cluster analysis unit is used for carrying out system cluster analysis on the line loss data sample sets under different analysis dimensions to form a reference cluster value scale;
the selecting unit is used for analyzing the line loss data sample sets under different analysis dimensions by adopting a preset clustering algorithm based on the reference clustering value scale so as to select a target clustering value;
a second determining unit, configured to determine a plurality of line loss reference domains based on the target cluster value, where each of the line loss reference domains includes a plurality of sub-analysis factors;
and the third determining unit is used for determining a reasonable value of the line loss marker post corresponding to each analysis dimension by utilizing the data analysis decision tree and the plurality of line loss reference domains.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the method for analyzing the reasonable value of the line loss benchmarking of the power distribution network according to any one of claims 1 to 7 through executing the executable instructions.
10. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the method for analyzing the reasonable value of the distribution network line loss benchmarking according to any one of claims 1 to 7.
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