CN115378000A - Power distribution network operation state evaluation method based on interval two-type fuzzy clustering analysis - Google Patents
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Abstract
The invention relates to a power distribution network operation state evaluation method based on interval type two fuzzy clustering analysis, which comprises the following steps of: step 1, collecting unbalanced monitoring data of a power distribution station; step 2, applying an interval type two-type fuzzy c-means clustering algorithm, and randomly selecting two groups of data in unbalanced monitoring data as initial clustering centers in an interval type two-type fuzzy c-means clustering algorithm iteration process; step 3, calculating the imbalance degree among the clusters; step 4, calculating a clustering center, and updating a membership matrix of the unbalanced data; and 5, evaluating the running state of the power distribution network. According to the invention, data with states difficult to distinguish are listed in the boundary area, operation and maintenance personnel are reminded to pay attention to the boundary data, and the clustering precision and the state evaluation precision are improved.
Description
Technical Field
The invention belongs to the technical field of power distribution network operation planning, relates to a power distribution network operation state evaluation method, and particularly relates to a power distribution network operation state evaluation method based on interval two-type fuzzy clustering analysis.
Background
The running state of the power distribution network directly influences the power supply reliability of the power system, a small amount of abnormal data still exist in the high-stability normal running data of the power distribution network, and the two kinds of data jointly form the unbalanced data of the power distribution network. For the processing of unbalanced data sets, the existing research mainly focuses on the data preprocessing level, that is, the unbalanced data sets are converted into roughly balanced data sets through technologies such as "oversampling" and "undersampling" and then are analyzed by using the existing algorithm.
The oversampling technology generates new data samples by continuously interpolating the data samples of the minority class clusters, increases the scale of the minority class clusters and reduces the class cluster imbalance degree; the undersampling technology randomly selects the samples of the majority clusters, reduces the samples of the majority clusters and reduces the imbalance degree of the cluster scales. Although methods using data preprocessing such as sampling can solve the problem of cluster-like size imbalance to some extent, these methods inevitably result in data overfitting or information loss.
Through searching, the published patent documents which are the same as or similar to the invention are not found.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a distribution network operation state evaluation method based on interval two-type fuzzy clustering analysis, which can improve clustering precision and state evaluation precision.
The invention solves the practical problem by adopting the following technical scheme:
a power distribution network operation state evaluation method based on interval two-type fuzzy clustering analysis comprises the following steps:
step 1, collecting unbalance monitoring data of a power distribution station, and setting threshold parameters of common fault types in the unbalance monitoring data of the power distribution station;
step 2, applying an interval two-type fuzzy c-means clustering algorithm, randomly selecting two groups of data in unbalanced monitoring data as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, and setting clustering analysis parameters according to historical data characteristics;
step 3, calculating Euclidean distances between each sample in the unbalanced monitoring data and an initial clustering center, dividing the data samples in the unbalanced monitoring data into a lower approximate set or a boundary region of a normal cluster or an abnormal cluster according to the Euclidean distances, and calculating the unbalance degree between the clusters;
step 4, substituting the cluster unbalance degree obtained in the step 3 into an optimized clustering center updating formula based on interval two-type fuzzy clustering analysis to perform iterative calculation, calculating a clustering center, and updating a membership matrix of unbalanced data;
step 5, comparing the clustering center obtained by calculation in the step 4 with the clustering center of the last iteration, counting samples of the approximate set and the boundary area under each cluster if the clustering center is not updated, and evaluating the operation state of the power distribution network; otherwise, returning to the step 3;
moreover, the specific method for setting the threshold parameter of the common fault type in the imbalance monitoring data of the distribution substation in step 1 is as follows: and setting a threshold parameter of a common fault type in the unbalanced monitoring data of the power distribution station according to a common fault index system of the power system.
Moreover, the specific method of the step 2 is as follows:
two kinds of data in the unbalanced detection data are randomly selected to serve as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, one group of data serves as the initial clustering centers of normal clusters of the power distribution network, the other group of data serves as the initial clustering centers of abnormal clusters of the power distribution network, and a distance judgment threshold value and a fuzzy coefficient are set according to historical data characteristics of actual operation records of the power distribution network system.
Moreover, the specific step of calculating the cluster imbalance in step 3 includes:
(1) Calculating a first Euclidean distance between each data sample in the unbalanced monitoring data set and a normal cluster center and a second Euclidean distance between each data sample and an abnormal cluster center, judging the size of the first Euclidean distance and the second Euclidean distance, and acquiring the ratio of a larger numerical value to a smaller numerical value of the first Euclidean distance and the second Euclidean distance;
(2) Comparing the obtained ratio with a distance judgment threshold, and if the ratio is greater than the distance judgment threshold, dividing the data sample into a lower approximate set of the corresponding class cluster with a smaller Euclidean distance; otherwise, dividing the image into boundary areas;
(3) Respectively calculating the ratio of the sample number of the lower approximate set in the normal cluster and the abnormal cluster to the sample number of all the lower approximate sets in the imbalance monitoring data to obtain the imbalance degree between the normal cluster and the abnormal cluster:
the calculation formula of the imbalance f is as follows:
wherein the content of the first and second substances,
representing the number of approximate set samples on a few class clusters of the cross class cluster in the current loop iteration,
the number of samples of the approximation set over most class clusters that represent cross class clusters.
Further, the specific steps of step 4 include:
(1) Substituting the cluster unbalance degree calculated in the step 3 into the optimized clustering center v i The formula after updating the formula is as follows:
wherein v is i Cluster center for ith iteration, ω l And ω b Upper and lower approximate weighting coefficients, f is the degree of imbalance, m is the blur coefficient, X ij As data samples, a ij Is a fuzzy membership degree of two types, i Cand
respectively, a lower approximation area and a bounding area data set.
(2) According to degree of membership mu ij Updating a membership degree matrix of the unbalanced data by a calculation formula, wherein the membership degree mu ij The calculation formula of (2) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
andμ ij respectively represent mu ij Upper and lower degree of membership, distance d ij Representing cluster centers v of the ith iteration i And sample x j A distance d between them zj Representing cluster center v of the ith iteration i And sample data sample x z The distance between them; k is the number of the cluster types, i Cand
respectively, lower approximation area and border area data sets.
Further, the specific steps of step 5 include:
(1) Performing iterative update calculation on the clustering center according to the clustering result of the data samples in the step 3;
(2) If the clustering center is not updated any more, counting samples of the lower approximation set and the boundary area in the corresponding cluster, and evaluating the running state of the power distribution network;
(3) Counting approximate set samples under the normal cluster, determining that the samples belong to normal data, marking the data with '0', and indicating that the data correspond to the power distribution network operation system and no fault occurs in the type; counting approximate set samples under the fault cluster, determining that the samples belong to fault samples, and marking the samples with '1' to indicate that the type of fault occurs in the power distribution network operation system; counting boundary area samples, and marking the samples with 2' to indicate that the type of fault possibly occurs in the future in the power distribution network operation system;
(4) And if the cluster center is continuously updated, returning to the step 3.
The invention has the advantages and beneficial effects that:
1. the invention provides a method for evaluating the running state of a power distribution network based on interval two-type fuzzy clustering analysis. And data with states difficult to distinguish are listed in the boundary area, operation and maintenance personnel are reminded to pay attention to the boundary data, and clustering precision and state evaluation precision are improved.
2. The method for evaluating the running state of the power distribution network based on the interval type two fuzzy clustering analysis uses an interval type two c mean value fuzzy clustering method based on the local fuzzy measurement of the boundary region, and brings the data unbalance factor into the updating calculation function of the clustering center, so that the clustering center is not only related to the membership function of the unbalanced data set, but also related to the unbalance among clusters; the data samples in the boundary area are subjected to calculation analysis and clustering processing through an interval two-type fuzzy c-means clustering algorithm, and an optimized clustering center updating function considering the local fuzzy measurement of the boundary area is introduced, so that the clustering effect of the interval two-type fuzzy c-means clustering method on the unbalanced operation data of the power distribution network is improved. The improved aggregation clustering algorithm is used for analyzing unbalanced data of frequent alarms of the power distribution network and evaluating the running state of the power distribution network, so that the fault type of the alarm can be judged, and the possibility of the alarm of the power distribution network in the future can be predicted.
Drawings
FIG. 1 is a flow chart of a method for evaluating an operation state of a power distribution network according to the present invention;
FIG. 2 is a spatial distribution diagram of dimension reduction data according to the present invention;
fig. 3 is a diagram of conventional fuzzy c-means clustering results.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a method for evaluating the operation state of a power distribution network based on interval two-type fuzzy clustering analysis is shown in figure 1 and comprises the following steps:
step 1, collecting unbalance monitoring data of a power distribution station, and setting threshold parameters of common fault types in the unbalance monitoring data of the power distribution station;
the specific method for setting the threshold parameter of the common fault type in the unbalanced monitoring data of the distribution station in the step 1 is as follows: and setting a threshold parameter of a common fault type in the unbalance monitoring data of the power distribution station according to a common fault index system of the power system.
Step 2, applying an interval two-type fuzzy c-means clustering algorithm, randomly selecting two groups of data in unbalanced monitoring data as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, and setting clustering analysis parameters according to historical data characteristics;
the specific method of the step 2 comprises the following steps:
two kinds of data in the unbalanced detection data are randomly selected to serve as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, one group of data serves as the initial clustering centers of normal clusters of the power distribution network, the other group of data serves as the initial clustering centers of abnormal clusters of the power distribution network, and a distance judgment threshold value and a fuzzy coefficient are set according to historical data characteristics of actual operation records of the power distribution network system.
Step 3, calculating Euclidean distances between each sample in the unbalanced monitoring data and an initial clustering center, dividing the data samples in the unbalanced monitoring data into a lower approximate set or a boundary region of a normal cluster or an abnormal cluster according to the Euclidean distances, and calculating the unbalance degree between the clusters;
the specific step of calculating the cluster imbalance in step 3 includes:
(1) Calculating a first Euclidean distance between each data sample in the unbalanced monitoring data set and a normal cluster center and a second Euclidean distance between each data sample and an abnormal cluster center, judging the size of the first Euclidean distance and the second Euclidean distance, and acquiring the ratio of a larger numerical value to a smaller numerical value of the first Euclidean distance and the second Euclidean distance;
(2) Comparing the obtained ratio with a distance judgment threshold, and if the ratio is greater than the distance judgment threshold, dividing the data sample into a lower approximate set of the corresponding class cluster with a smaller Euclidean distance; otherwise, dividing the image into boundary areas;
(3) Respectively calculating the ratio of the sample number of the lower approximate set in the normal cluster and the abnormal cluster to the sample number of all the lower approximate sets in the imbalance monitoring data to obtain the imbalance degree between the normal cluster and the abnormal cluster:
the calculation formula of the imbalance f is as follows:
wherein the content of the first and second substances,
representing the number of approximate set samples on a few class clusters of the cross class cluster in the current loop iteration,
the number of samples of the approximation set over most class clusters that represent cross class clusters.
Step 4, substituting the cluster unbalance degree obtained in the step 3 into an optimized clustering center updating formula based on interval two-type fuzzy clustering analysis to perform iterative calculation, calculating a clustering center, and updating a membership matrix of unbalanced data;
the specific steps of the step 4 comprise:
(1) Substituting the cluster unbalance degree calculated in the step 3 into the optimized clustering center v i And the formula after updating the formula is as follows:
wherein v is i Cluster center for ith iteration, ω l And omega b Approximate weighting coefficients from top to bottom, f is the imbalance, m is the blur coefficient, X ij As data samples, a ij Is a fuzzy membership degree of two types, i Cand
respectively, a lower approximation area and a bounding area data set.
(2) According to degree of membership mu ij Updating a membership degree matrix of the unbalanced data by a calculation formula, wherein the membership degree mu ij The calculation formula of (2) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
andμ ij respectively represent mu ij Upper and lower degree of membership, distance d ij Representing the cluster center v of the ith iteration i And sample x j A distance d between them zj Representing the cluster center v of the ith iteration i And sample data sample x z The distance between them; k is the number of the cluster types, i Cand
respectively, lower approximation area and border area data sets.
Step 5, comparing the clustering center obtained by calculation in the step 4 with the clustering center of the last iteration, counting samples of the approximate set and the boundary area under each cluster if the clustering center is not updated any more, and evaluating the operation state of the power distribution network; otherwise, returning to the step 3;
the specific steps of the step 5 comprise:
(1) Performing iterative update calculation on the clustering center according to the clustering result of the data samples in the step 3;
(2) If the clustering center is not updated any more, counting samples of the lower approximate set and the boundary area in the corresponding clustering center, and evaluating the running state of the power distribution network;
(3) Counting approximate set samples under the normal cluster, determining that the samples belong to normal data, marking the data with '0', and indicating that the data correspond to the power distribution network operation system and no fault occurs in the type; counting approximate set samples under the fault cluster, wherein the samples are determined to belong to fault samples, and marking the samples with '1' to indicate that the type of fault occurs in the power distribution network operation system; counting boundary area samples, and marking the samples with 2' to indicate that the type of fault possibly occurs in the future in the power distribution network operation system;
(4) And if the cluster center is continuously updated, returning to the step 3.
In this embodiment, the process of the method for evaluating the operation state of the power distribution network is as shown in fig. 1, and an interval type two c mean value fuzzy clustering method based on the local fuzzy measure of the boundary area is used, so that the data imbalance factor is incorporated into the update calculation function of the clustering center, and the clustering center is not only related to the membership function of the unbalanced data set, but also related to the imbalance among clusters; the data samples of the boundary area are subjected to calculation analysis and clustering processing through an interval two-type fuzzy c-means clustering algorithm, and an optimized clustering center updating function considering the local fuzzy measurement of the boundary area is introduced, so that the clustering effect of the interval two-type fuzzy c-means clustering method on the unbalanced operation data of the power distribution network is improved. The improved clustering algorithm is used for analyzing unbalanced data of frequent alarms of the power distribution network and evaluating the running state of the power distribution network, so that the fault type of the alarm can be judged, and the possibility of future alarms of the power distribution network can be predicted.
The part optimizes a clustering center updating formula of the interval type two fuzzy c-means algorithm on the basis of considering the local fuzzy measurement of the boundary area, can reduce the adverse effect of the boundary area occupied by most clusters on the clustering effect of few clusters, so that the clustering center of small-scale clusters is always maintained at a more ideal position, and can also inhibit the phenomenon that data originally belonging to most clusters are mistakenly divided into at least a plurality of clusters, thereby better retaining the data characteristics of the few clusters, therefore, the clustering performance of the algorithm on unbalanced data can be improved, and the accuracy and rapidity of data aggregation of the power distribution network can be actually improved.
The working principle of the invention is as follows:
the invention relates to a method for evaluating the running state of a power distribution network based on interval two-type fuzzy clustering analysis, which improves clustering precision and state evaluation precision by listing data with states difficult to distinguish into boundary regions. The invention comprises the following steps: 1) Acquiring unbalanced monitoring data of a certain power distribution station, and setting threshold parameters of common fault types in the unbalanced monitoring data; 2) Randomly selecting two groups of data in the unbalanced monitoring data as an initial clustering center in the iterative process of the interval two-type fuzzy c-means clustering algorithm by using an interval two-type fuzzy c-means clustering algorithm, and setting clustering analysis parameters according to the characteristics of historical data; 3) Calculating Euclidean distances between each sample in the unbalanced monitoring data and an initial clustering center, dividing the data samples in the unbalanced monitoring data into a lower approximate set or a boundary region of a normal cluster or an abnormal cluster according to the Euclidean distances, and calculating the unbalance degree between the clusters; 4) Substituting the cluster unbalance degree obtained by the calculation in the step 3) into an optimized cluster center updating formula based on interval two-type fuzzy cluster analysis to perform iterative calculation, calculating a cluster center, and updating a membership matrix of unbalanced data; 5) Comparing the clustering center obtained by calculation in the step 4) with the clustering center of the last iteration, counting samples of the similarity set and the boundary area under each cluster if the clustering center is not updated any more, and evaluating the running state of the power distribution network, otherwise, iterating the step 3).
Compared with the existing method, the invention optimizes the cluster center updating formula of the interval type two-type fuzzy c-means algorithm on the basis of considering the local fuzzy measurement of the boundary area, can reduce the adverse effect of the boundary area occupied by most clusters on the clustering effect of few clusters, thereby enabling the cluster center of small-scale clusters to be always maintained at a more ideal position, and also inhibiting the phenomenon that the data originally belonging to most clusters are wrongly divided into at least a plurality of clusters, thereby better keeping the data characteristics of the few clusters, improving the clustering performance of the algorithm on unbalanced data and improving the accuracy and rapidity of data aggregation of the power distribution network.
The invention is further illustrated by the following specific examples:
1) Example parameters
The embodiment adopts the unbalanced monitoring data of a certain power distribution station, screens different types of abnormal fault data compared with the normal operation data of the power distribution network from the unbalanced monitoring data according to the common fault index system of the power system, and analyzes the threshold parameter required by each fault. The common alarm information of the power distribution system and the alarm threshold values of the monitoring variables are shown in tables 1 and 2.
Table 1 common alarm list for power distribution systems
TABLE 2 commonly used monitoring variables for distribution systems
Certain low voltage distribution equipment test data is shown in table 3.
TABLE 3 example of monitoring data for a low voltage distribution device
In order to more intuitively show the effect of the algorithm, main feature projections in the data are found out through Principal Component Analysis (PCA), noise and redundancy are eliminated, the 6-dimensional sample data selected in table 3 is reduced to a 3-dimensional space, and the result is shown in fig. 2-fig. 3, wherein a pattern "star" represents a few types of abnormal data, and a pattern "9633;" represents a majority types of safe operation data.
2) Results of the experiment
Fig. 2 and fig. 3 show the effect graphs of the conventional fuzzy c-means algorithm and the interval type two c-means algorithm, respectively. In the figure, patterns "\ 9633;" indicate correctly clustered samples; the pattern "+" represents a sample originally belonging to an approximate area under the majority class cluster which is wrongly divided into the minority class cluster; the pattern "o" represents samples divided into boundary regions.
According to the experimental test results of 20 groups of data under the state evaluation model provided by the invention, although the traditional fuzzy c-means clustering method introduces concepts of rough concentration on upper and lower approximation and divides some unpredictable samples into the boundary space, the algorithm does not consider that the cluster size imbalance can influence the clustering result, the clustering effect on the cluster size imbalance data aggregation clustering effect is not ideal, 2 groups of most cluster samples in the clustering result of the traditional fuzzy c-means clustering algorithm are clustered by mistake for at least several clusters, 5 groups of most cluster samples are clustered by mistake for the boundary space, and the result on the state evaluation of the power distribution network is also not ideal. The method for evaluating the running state of the power distribution network based on the interval type two fuzzy clustering algorithm optimizes the cluster center updating formula on the basis of considering the imbalance of the cluster scale, and compared with the traditional algorithm, the improved algorithm is more suitable for the cluster analysis of the unbalanced running data of the power distribution system. In the clustering result of the interval two-type fuzzy c-means clustering method, only one group of most cluster data is clustered by errors for at least several clusters, and one group of most cluster data is clustered by errors for at least several cluster boundary regions. Therefore, the evaluation method provided by the invention can effectively carry out aggregation analysis on the unbalanced monitoring data generated by the power distribution network equipment, and then carry out evaluation on the running state of the power distribution network, thereby effectively improving the running warning speed of the power distribution network system and the accuracy of predicting faults.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (6)
1. A power distribution network operation state evaluation method based on interval two-type fuzzy clustering analysis is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting unbalance monitoring data of a power distribution station, and setting threshold parameters of common fault types in the unbalance monitoring data of the power distribution station;
step 2, applying an interval two-type fuzzy c-means clustering algorithm, randomly selecting two groups of data in unbalanced monitoring data as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, and setting clustering analysis parameters according to historical data characteristics;
step 3, calculating Euclidean distances between each sample in the unbalanced monitoring data and an initial clustering center, dividing the data samples in the unbalanced monitoring data into a lower approximate set or a boundary region of a normal cluster or an abnormal cluster according to the Euclidean distances, and calculating the unbalance degree between the clusters;
step 4, substituting the cluster unbalance degree obtained in the step 3 into an optimized clustering center updating formula based on interval two-type fuzzy clustering analysis to perform iterative calculation, calculating a clustering center, and updating a membership matrix of unbalanced data;
step 5, comparing the clustering center obtained by calculation in the step 4 with the clustering center of the last iteration, counting samples of an approximation set and a boundary area under each cluster if the clustering center is not updated any more, and evaluating the running state of the power distribution network; otherwise, returning to the step 3.
2. The method for evaluating the operating state of the power distribution network based on the interval type two fuzzy clustering analysis according to claim 1, wherein: the specific method for setting the threshold parameter of the common fault type in the unbalanced monitoring data of the distribution station in the step 1 is as follows: and setting a threshold parameter of a common fault type in the unbalance monitoring data of the power distribution station according to a common fault index system of the power system.
3. The method for evaluating the operating state of the power distribution network based on the interval type two fuzzy clustering analysis according to claim 1, wherein: the specific method of the step 2 comprises the following steps:
two kinds of data in the unbalanced detection data are randomly selected to serve as initial clustering centers in an interval two-type fuzzy c-means clustering algorithm iteration process, one group of data serves as the initial clustering centers of normal clusters of the power distribution network, the other group of data serves as the initial clustering centers of abnormal clusters of the power distribution network, and a distance judgment threshold value and a fuzzy coefficient are set according to historical data characteristics of actual operation records of the power distribution network system.
4. The method for evaluating the operating state of the power distribution network based on the interval type two fuzzy clustering analysis according to claim 1, wherein: the specific step of calculating the cluster imbalance in step 3 includes:
(1) Calculating a first Euclidean distance between each data sample in the unbalanced monitoring data set and a normal cluster center and a second Euclidean distance between each data sample and an abnormal cluster center, judging the size of the first Euclidean distance and the second Euclidean distance, and acquiring the ratio of a larger numerical value to a smaller numerical value of the first Euclidean distance and the second Euclidean distance;
(2) Comparing the obtained ratio with a distance judgment threshold, and if the ratio is greater than the distance judgment threshold, dividing the data sample into a lower approximate set of the corresponding class cluster with a smaller Euclidean distance; otherwise, dividing the image into boundary areas;
(3) Respectively calculating the ratio of the sample number of the lower approximate set in the normal cluster and the abnormal cluster to the sample number of all the lower approximate sets in the imbalance monitoring data to obtain the imbalance degree between the normal cluster and the abnormal cluster:
the calculation formula of the imbalance degree f is as follows:
wherein the content of the first and second substances,
representing the number of approximate set samples on a few class clusters of the cross class cluster in the current loop iteration,
representing the number of samples of the approximation set over most of the cross class clusters.
5. The method for evaluating the operating state of the power distribution network based on the interval type two fuzzy clustering analysis according to claim 1, wherein: the specific steps of the step 4 comprise:
(1) Substituting the cluster unbalance degree calculated in the step 3 into the optimized clustering center v i The formula after updating the formula is as follows:
wherein v is i Cluster center for ith iteration, ω l And omega b Approximate weighting factor from top to bottom, f is the degree of imbalance, m is the blur factor, X ij As data samples, a ij Is a fuzzy membership degree of two types, i Cand
respectively, a lower approximation area and a bounding area data set.
(2) According to degree of membership mu ij Updating a membership degree matrix of the unbalanced data by a calculation formula, wherein the membership degree mu ij The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
andμ ij respectively represent mu ij Upper and lower degree of membership, distance d ij Representing cluster centers v of the ith iteration i And sample x j A distance d between them zj Representing cluster center v of the ith iteration i And sample data sample x z The distance therebetween; k is the number of the cluster types, i Cand
are respectively close to the lower partLike region and bounding region data sets.
6. The method for evaluating the operating state of the power distribution network based on the interval type two fuzzy clustering analysis according to claim 1, wherein: the specific steps of the step 5 comprise:
(1) Performing iterative update calculation on the clustering center according to the clustering result of the data samples in the step 3;
(2) If the clustering center is not updated any more, counting samples of the lower approximation set and the boundary area in the corresponding cluster, and evaluating the running state of the power distribution network;
(3) Counting approximate set samples under the normal cluster, determining that the samples belong to normal data, marking the data with '0', and indicating that the data does not have the type of fault corresponding to the power distribution network operation system; counting samples of an approximate set under a fault cluster, determining that the samples belong to fault samples, marking the samples with '1', and indicating that the type of fault occurs in the power distribution network operation system; counting boundary area samples, and marking the samples with 2' to indicate that the type of fault possibly occurs in the future in the power distribution network operation system;
(4) And if the clustering center is continuously updated, returning to the step 3.
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