Household change relation matching method, system and equipment based on Hausdorff distance and medium

Abstract

The invention relates to a Hausdorff distance-based user-transformer relation matching method, equipment and a medium. The method comprises the following steps of S1, acquiring an electricity consumption measurement sampling sequence of M transformers and corresponding Y users within a set length time period in a set area range; s2, clustering the Y users into N user groups by adopting a clustering algorithm according to the electricity consumption measurement sampling sequences of the Y users, and determining an electricity consumption data sequence corresponding to each user group; and S3, for each user group, calculating the Hausdorff distance between the power consumption data sequence of the user group and the power consumption measurement sampling sequence of each transformer, and selecting the transformer corresponding to the minimum value of the Hausdorff distance as a matching transformer of all users in the current user group. Compared with the prior art, the method has the advantage of high user change relation matching accuracy.

Description

Technical Field

[0001] The present invention relates to the field of power data analysis, and in particular to a method, system, device and medium for matching household-to-transformer relationships based on Hausdorff distance. Background Art

[0002] The low-voltage distribution substation includes the substation transformer, power supply lines and users under the substation. The matching relationship between the substation (distribution transformer) and the users under the substation is the household-transformer relationship. Correctly determining the relationship between the user's meter and the transformer (substation) and obtaining accurate power consumption characteristic data are crucial for the management of heavy overload and low voltage problems in the substation and the stability of the power system. In particular, accurate analysis of power consumption characteristic data is also a prerequisite for accurate calculation and analysis of substation line losses. Therefore, the addition or re-planning of transformers, as well as user migration or cancellation, lead to problems in the corresponding relationship between transformers and users, and also put forward higher requirements for the analysis of power consumption characteristic data of user groups in low-voltage distribution substations.

[0003] In order to deal with the problem of frequently changing data feature analysis of low-voltage distribution networks, there are currently three key methods, but all of them have certain defects, including:

[0004] 1) A method for identifying the relationship between households and transformers in low-voltage distribution networks based on smart meter voltage data. The adaptive sharding aggregation approximation method is used to reduce the voltage data dimension and extract the voltage curve features. Then, the voltage feature data is clustered using the hierarchical balanced iterative reduction and clustering algorithm to achieve the identification of households and their transformers, but it is only suitable for processing time series of equal length.

[0005] 2) Based on the calculation of Euclidean distance (ED), the electricity consumption characteristics are measured and clustered. When the Euclidean distance is used to compare two time series, a one-to-one correspondence is established between each point in the sequence, but the changes on the time axis cannot be considered when aligning the time series, which sometimes makes the alignment unnatural.

[0006] 3) Dynamic Time Warping (DTW), which abandons the limitation of Euclidean distance and finds a continuous matching relationship that contains all the points in the two time series. Although DTW has been successfully applied to time series processing in many fields, sometimes DTW will produce unnatural distortions during alignment, affecting the matching accuracy. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a household change relationship matching method, system, device and medium based on Hausdorff distance with higher matching accuracy.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to a first aspect of the present invention, a household change relationship matching method based on Hausdorff distance is provided, comprising the following steps:

[0010] Step S1, obtaining a sampling sequence of power consumption measurement of M transformers and corresponding Y users within a set length time period within a set area;

[0011] Step S2: clustering the Y users into N user groups using a clustering algorithm according to the power consumption measurement sampling sequences of the Y users, and determining the power consumption data sequence corresponding to each user group;

[0012] Step S3: for each user group, respectively calculate the Hausdorff distance between its power consumption data sequence and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

[0013] Preferably, the power consumption measurement sampling sequence is a voltage measurement sampling sequence or a power measurement sampling sequence.

[0014] Preferably, the method of obtaining the electricity consumption measurement sampling sequence of M transformers and corresponding Y users for a set time period of a set length within a set area is specifically as follows: within the set area, a sampling interval is set, and user measurement data for a set time period is continuously collected to obtain the electricity consumption measurement sampling sequence of M transformers and corresponding Y users.

[0015] Preferably, the clustering of the Y users into N user groups is performed using a clustering algorithm according to the power consumption measurement sampling sequence of the Y users. Specifically, the clustering centers are obtained using a clustering algorithm according to the power consumption measurement sampling sequence of the Y users, and the Y users are clustered into N user groups.

[0016] Preferably, the clustering algorithm is a K-means clustering algorithm.

[0017] Preferably, the Hausdorff distance between the power consumption data sequence A of each user group and the power consumption measurement sampling sequence B of each transformer is the distance between the nearest points in the set A and the set B.

[0018] Preferably, the Hausdorff distance d between the power consumption data sequence A of each user group and the power consumption measurement sampling sequence B of each transformer isH (A,B), the calculation expression is:

[0019] d H (A,B)=max{h(A,B),h(B,A)}

[0020]

[0021] Where: || || is the Euclidean norm.

[0022] According to a second aspect of the present invention, a household change relationship matching system based on Hausdorff distance is provided, the system comprising:

[0023] The data acquisition module is used to obtain the power consumption measurement sampling sequence of M transformers and corresponding Y users within a set length time period within a set area;

[0024] A user group clustering module is used to cluster Y users into N user groups using a clustering algorithm based on the power consumption measurement sampling sequence of Y users, and determine the power consumption data sequence corresponding to each user group;

[0025] The matching module is used to calculate the Hausdorff distance between the power consumption data sequence of each user group and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

[0026] According to a third aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, any one of the methods described above is implemented.

[0027] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, any one of the methods described above is implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) The present invention evaluates the volatility of transformers and user groups through Hausdorff distance, calculates the Hausdorff distance between the power consumption measurement sampling sequence of the user group and each transformer, and selects the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group, so as to achieve more accurate user-transformer relationship identification.

[0030] (2) The Hausdorff distance is used to evaluate the volatility of transformers in the area, which can realize the matching calculation of unequal time series. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] Example 1

[0034] like Figure 1 As shown, this embodiment provides a household change relationship matching method based on Hausdorff distance, and the method includes the following steps:

[0035] Step S1, obtaining a sampling sequence of power consumption measurement of M transformers and corresponding Y users within a set length time period within a set area;

[0036] Step S2: clustering the Y users into N user groups using a clustering algorithm according to the power consumption measurement sampling sequences of the Y users, and determining the power consumption data sequence corresponding to each user group;

[0037] Step S3: for each user group, respectively calculate the Hausdorff distance between its power consumption data sequence and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

[0038] This embodiment also provides a household change relationship matching system based on Hausdorff distance, including:

[0039] The data acquisition module is used to obtain the power consumption measurement sampling sequence of M transformers and corresponding Y users within a set length time period within a set area;

[0040] A user group clustering module is used to cluster Y users into N user groups using a clustering algorithm based on the power consumption measurement sampling sequence of Y users, and determine the power consumption data sequence corresponding to each user group;

[0041] The matching module is used to calculate the Hausdorff distance between the power consumption data sequence of each user group and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

[0042] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0043] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0044] The processing unit performs the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 in any other appropriate manner (e.g., by means of firmware).

[0045] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0046] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0047] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0048] Example 2

[0049] like Figure 1 As shown, this embodiment provides a household change relationship matching method based on Hausdorff distance, and the method includes the following steps:

[0050] Step S1, obtaining a sampling sequence of power consumption measurement of M transformers and corresponding Y users within a set length time period within a set area.

[0051] The measurement data set includes three-phase active power and voltage measurement data of two transformers and 110 end-user meters in the power distribution network, as well as basic power supply facility records of power distribution transformers from January 1 to February 28, 2022. Among them, there are 59 households with distribution transformer K1 and 51 households with distribution transformer K2. The sampling interval of user measurement data and transformer operation monitoring data is 15 minutes.

[0052] After alignment and offset correction of the measurement data, each meter has 60 days of historical data. In order to increase the representativeness of the calculation results while increasing the data set and reduce the negative impact of interference on the classification results, the 2-day samples aligned with the time axis are used as the unit for the household-transformer similarity calculation. The historical measurements of each meter are divided into 58 2-day measurement segments, resulting in 58 samples.

[0053] In this embodiment, 85% of the samples are selected for training, and 15% of the samples are selected for testing.

[0054] Step S2: according to the power consumption measurement sampling sequences of the Y users, a clustering algorithm is used to cluster the Y users into N user groups, and a power consumption data sequence corresponding to each user group is determined.

[0055] In this embodiment, a K-means algorithm is used to obtain cluster centers, which are a measure of average similarity between users and transformers, and users with high voltage profile similarity are grouped into distribution transformers.

[0056] The power consumption data sequence corresponding to the cluster center is obtained as the power consumption data sequence of the current user group.

[0057] Step S3: for each user group, respectively calculate the Hausdorff distance between its power consumption data sequence and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

[0058] In this embodiment, the Hausdorff distance is used to evaluate the fluctuation measurement similarity between the historical measurement values ​​of users and transformers in a given area.

[0059] The Hausdorff distance d between the power consumption data sequence A of each user group and the power consumption measurement sampling sequence B of each transformer H (A,B) is the distance between the nearest point in set A and set B, and the calculation expression is:

[0060] d H (A,B)=max{h(A,B),h(B,A)}

[0061]

[0062] Where: || || is the Euclidean norm.

[0063] Table 1 below shows the Hausdorff distances between user group 1 and transformers K1 and K2.

[0064] Table 1

[0065] serial number User Group 1 Match Transformer K1 0.23 yes Transformer K2 0.51 no

[0066] It can be seen from Table 1 above that the Hausdorff distance between user group 1 and transformer K1 is the smallest, so the transformer matched by user group 1 is transformer K1.

[0067] The other settings in this embodiment are the same as those in Embodiment 1.

[0068] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A household change relationship matching method based on Hausdorff distance, characterized in that: The following steps are involved: Step S1, obtaining a sampling sequence of power consumption measurement of M transformers and corresponding Y users within a set length time period within a set area; Step S2: clustering the Y users into N user groups using a clustering algorithm according to the power consumption measurement sampling sequences of the Y users, and determining the power consumption data sequence corresponding to each user group; Step S3: for each user group, respectively calculate the Hausdorff distance between its power consumption data sequence and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

2. The household change relationship matching method based on Hausdorff distance according to claim 1 is characterized in that: The power consumption measurement sampling sequence is a voltage measurement sampling sequence or a power measurement sampling sequence.

3. The household change relationship matching method based on Hausdorff distance according to claim 1 is characterized in that: The method of obtaining the power consumption measurement sampling sequence of M transformers and corresponding Y users within a set length time period within a set area is specifically: within the set area, a sampling interval is set, and user measurement data of the set length time period is continuously collected to obtain the power consumption measurement sampling sequence of M transformers and corresponding Y users.

4. The household change relationship matching method based on Hausdorff distance according to claim 1, characterized in that: The method of clustering the Y users into N user groups by using a clustering algorithm according to the power consumption measurement sampling sequence of the Y users is specifically as follows: according to the power consumption measurement sampling sequence of the Y users, a clustering algorithm is used to obtain cluster centers, and the Y users are clustered into N user groups.

5. The household change relationship matching method based on Hausdorff distance according to claim 1 is characterized in that: The clustering algorithm is the K-means clustering algorithm.

6. The household change relationship matching method based on Hausdorff distance according to claim 1, characterized in that: The Hausdorff distance between the power consumption data sequence A of each user group and the power consumption measurement sampling sequence B of each transformer is the distance between the nearest point in set A and set B.

7. The household change relationship matching method based on Hausdorff distance according to claim 6, characterized in that: The Hausdorff distance d between the power consumption data sequence A of each user group and the power consumption measurement sampling sequence B of each transformer H (A,B), the calculation expression is: d H (A,B)=max{h(A,B),h(B,A)} Where: || || is the Euclidean norm.

8. A system based on the household change relationship matching method based on Hausdorff distance as claimed in claim 1, characterized in that: The system comprises: The data acquisition module is used to obtain the power consumption measurement sampling sequence of M transformers and corresponding Y users within a set length time period within a set area; A user group clustering module is used to cluster Y users into N user groups using a clustering algorithm based on the power consumption measurement sampling sequence of Y users, and determine the power consumption data sequence corresponding to each user group; The matching module is used to calculate the Hausdorff distance between the power consumption data sequence of each user group and the power consumption measurement sampling sequence of each transformer, and select the transformer corresponding to the minimum Hausdorff distance as the matching transformer for all users in the current user group.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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