A household variable relationship identification method and system based on current event probability

Abstract

The application provides a kind of household transformer relationship identification method and system based on current event probability, comprising, respectively extracting the current transient signal characteristics of transformer and the user to be identified in area, the current transient signal characteristics at least include transient starting time, transient signal type and power value after transient signal stabilization;According to the preset household transformer relationship matching probability, determine the corresponding household transformer relationship matching event between the transformer and the user to be identified in area;Statistical household transformer relationship matching event in the number of correct matches, when the number of correct matches in household transformer relationship matching event is greater than the preset judgment threshold, determine that the user to be identified is associated with transformer, obtain the final household transformer relationship identification result.The application combines the transient signal probability distribution of deterministic event high probability and accidental event low probability in low-voltage area, realizes the household transformer relationship in area.

Description

Technical Field

[0001] This invention relates to the field of household transformer relationship identification technology, and in particular to a method and system for household transformer relationship identification based on the probability of current events. Background Technology

[0002] Automatic topology identification (AMI) technology for low-voltage distribution areas mainly falls into two categories: signal matching and data-driven methods. The first is the signal matching method, including matching methods based on HPLC communication signals and injected power signals. This type of method has high matching accuracy, but it fails in complex hybrid power grid scenarios and requires the installation of dedicated signal generators, resulting in high construction and maintenance costs. Furthermore, it affects the success rate of AMI data acquisition and poses certain operational safety hazards. The second is the data-driven method, including analysis methods based on voltage spatiotemporal similarity and power energy conservation. This type of method utilizes existing AMI data and does not require additional hardware installation, thus reducing engineering construction costs. However, this method has high requirements for data quality. Currently, data noise interference in the AMI system significantly affects the computability and accuracy of topology identification. In addition, the accuracy is further reduced for low-power distribution areas, photovoltaic distribution areas, and adjacent distribution areas on the same feeder. Summary of the Invention

[0003] The purpose of this invention is to propose a method and system for identifying the relationship between households and transformers based on the probability of current events, and to solve the technical problem of how to combine the probability distribution of transient signals of high probability deterministic events and low probability random events in low-voltage transformer areas to identify the relationship between households and transformers within the transformer area.

[0004] On the one hand, a method for identifying household-transformer relationships based on the probability of current events is provided, including:

[0005] The transient current signal features of the transformer in the distribution area and the user to be identified are extracted respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes.

[0006] The event of matching the household-transformer relationship between the transformer substation and the user to be identified is determined according to the preset household-transformer relationship matching probability. The household-transformer relationship matching probability includes at least the probability of correct household-transformer relationship matching and the probability of incorrect household-transformer relationship matching. The event of matching the household-transformer relationship includes at least the probability of correct household-transformer relationship matching and the probability of incorrect household-transformer relationship matching.

[0007] The number of correct matches in the events of matching household-transformer relationships is counted. When the number of correct matches in the events of matching household-transformer relationships is greater than the preset judgment threshold, it is determined that the user to be identified is connected to the transformer in the distribution area, and the final household-transformer relationship identification result is obtained.

[0008] Preferably, the event of determining the matching of the corresponding user-transformer relationship between the transformer substation and the user to be identified includes:

[0009] The events of matching the household-transformer relationship follow a binomial distribution of the number of times the matching current transient signal is matched and the probability of matching the household-transformer relationship.

[0010] Preferably, the judgment threshold is determined according to the following process:

[0011] The critical number of correct and incorrect matches in the event of matching household-change relationships is determined according to a preset probability density function, and this critical number of matches is used as the threshold for judging household-change relationships; the critical number of correct and incorrect matches is the number of times when the number of correct and incorrect matches is equal.

[0012] Preferably, the probability density function includes:

[0013]

[0014]

[0015] Among them, P co To correctly match the probability density function, P er Let be the probability density function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

[0016] Preferably, the probability of a correct match between the household and the change relationship is 0.3.

[0017] Preferably, the probability of a mismatch in the household-change relationship is 0.05.

[0018] On the other hand, a household-transformer relationship identification system based on current event probability is also provided to implement the household-transformer relationship identification method based on current event probability, including:

[0019] The feature extraction module is used to extract the transient current signal features of the transformer and the user to be identified, respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes.

[0020] The event matching module is used to determine the corresponding household-transformer relationship matching event between the transformer substation and the user to be identified based on a preset household-transformer relationship matching probability. The household-transformer relationship matching probability includes at least a correct household-transformer relationship matching probability and a wrong household-transformer relationship matching probability. The event of household-transformer relationship matching includes at least a correct household-transformer relationship matching and a wrong household-transformer relationship matching.

[0021] The relationship identification module is used to count the number of correct matches in the events of household-transformer relationship matching. When the number of correct matches in the events of household-transformer relationship matching is greater than the preset judgment threshold, it is determined that the user to be identified is connected to the transformer in the distribution area, and the final household-transformer relationship identification result is obtained.

[0022] Preferably, the event matching module is further configured to make the events of matching household-transformer relationships follow a binomial distribution of the number of times the matching current transient signal is matched and the probability of matching household-transformer relationships.

[0023] Preferably, the relationship recognition module is further configured to determine the critical number of correct and incorrect matches in the event of household change relationship matching based on a preset probability density function, and use the critical number of matches as the threshold for judging household change relationship matching.

[0024] Preferably, the probability density function includes:

[0025]

[0026]

[0027] Among them, P co To correctly match the probability density function, P er Let be the probability density function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

[0028] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0029] The present invention provides a method and system for identifying the relationship between a user and a transformer based on the probability of current events. It utilizes the characteristics of transient current signals when a user experiences a load event to perform probability matching. Based on the characteristic that the events that can be matched in the transient signal sequence satisfy the binomial distribution, and combined with the transient signal probability distribution of high probability of deterministic events and low probability of random events in the low-voltage distribution area, the method and system can identify the relationship between a user and a transformer within the distribution area. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0031] Figure 1 This is a schematic diagram of the main process of a household-transformer relationship identification method based on current event probability in an embodiment of the present invention.

[0032] Figure 2 This is a logical schematic diagram of a household-transformer relationship identification method based on current event probability in an embodiment of the present invention.

[0033] Figure 3 This is a schematic diagram of a probability density function in an embodiment of the present invention.

[0034] Figure 4 This is a schematic diagram of a household-transformer relationship identification system based on the probability of current events in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0036] like Figure 1 and Figure 2 The diagram shown is a schematic representation of an embodiment of a household-transformer relationship identification method based on current event probability provided by the present invention. In this embodiment, the method includes the following steps:

[0037] Step S1: Extract the transient current signal features of the transformer in the distribution area and the user to be identified, respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes. It is understood that transient signals are rich in feature information and are the external manifestation of the operating mechanisms of different types of electrical appliances. The description formula is as follows:

[0038] L={X1,X2,...,X n}

[0039] X n Representing the nth dimension of transient feature information, based on the difficulty of extraction technology, typical transient feature information is as follows: Ts, transient start time; Type, transient signal type; Pstea, power value of the transient signal after stabilization; Pmean, average power of the transient process; Ipeak, peak value of transient current; Irms, effective value of transient current; Fcrest, crest factor; T, total duration of the transient process.

[0040] Feature matching involves two scenarios: matchable and unmatchable. Matchable matching is further divided into correct and incorrect matches. Different combinations of feature information will present different matching probabilities, and the more types of feature information there are, the more complex the matching situation becomes, and the higher the probability of unmatchable results. Therefore, it is necessary to comprehensively consider the selection of feature dimensions. In this embodiment, combining theoretical analysis and practical field factors, Ts, Type, and Pstea are selected as matching features for transient current signals.

[0041] Step S2: Determine the matching events of the household-transformer relationship between the transformer substation and the user to be identified based on the preset matching probabilities. The matching probabilities include at least the probability of a correct match and the probability of an incorrect match. The matching events also include at least two types of matching events. Understandably, the probabilities of correct and incorrect matching events are related to residents' lifestyles and are relatively stable, which can be considered a binomial distribution. Selected transient current signal matching features are used to collect and match the corresponding signals. Combining theoretical analysis and experimental calculations with sample data, the correct matching probability p1 is 0.3, and the incorrect matching probability p2 is 0.05.

[0042] In a specific embodiment, the event of determining the matching of the corresponding household-transformer relationship between the transformer substation and the user to be identified includes: the event of matching the household-transformer relationship follows a binomial distribution of the number of times the matching current transient signal is used and the probability of matching the household-transformer relationship. This is represented as follows:

[0043] L~B(m, p)

[0044] Where L represents the event of a correct or incorrect match in the relationship between the household and the transformer; m represents the number of times the matching current transient signal is used for matching; and p represents the probability of a correct or incorrect match.

[0045] Step S3 involves counting the number of correct matches in the user-transformer relationship matching events. When the number of correct matches exceeds a preset judgment threshold, the user to be identified is determined to be connected to the transformer in the distribution area, thus obtaining the final user-transformer relationship identification result. Understandably, the number of current transient signals that can be collected from a single user daily ranges from 0 to 50, with an average of approximately 20 per day. This allows for 100 matching current transient signals to be collected over 3 to 5 days. When a user completes at least 20 feature matches of current transient signals with a transformer during these 100 matches, based on the set judgment threshold, the user can be considered connected to that transformer, thus achieving user-transformer relationship identification.

[0046] In a specific embodiment, the judgment threshold is determined according to the following process: a critical number of correct and incorrect matches is determined based on a preset probability density function, and this critical number is used as the judgment threshold for household-transfer relationship matching; the critical number of correct and incorrect matches is the number of times when correct and incorrect matches are equal. The probability density function includes:

[0047]

[0048]

[0049] Among them, Pco To correctly match the probability density function, P er Let be the probability density function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

[0050] When m = 100, the corresponding probability density function is as follows: Figure 3 As shown in the figure, the x-coordinate k of the vertical dashed line on the left is P. er The critical k value is (L≤k)=0.95; the x-coordinate of the vertical dashed line on the right is P. co The critical value of k is (L≥k)=0.95. As can be seen from the graph, when k=20, exceeding k incorrect matches is a low-probability event, while exceeding k correct matches is a high-probability event. Therefore, 20 can be chosen as the threshold for judging household-change relationship matching.

[0051] In one example, six typical application scenarios were designed in a National Energy Internet Demonstration Zone (NEIDZ) in a certain location, including three typical network topologies. The relevant parameters are shown in the table below:

[0052]

[0053] Using the above scenarios, a comparative experiment was conducted between the method of this invention and two existing typical recognition methods. The comparison results are shown in the table below:

[0054]

[0055] As can be seen from the table, this method has significant advantages in both recognition speed and accuracy.

[0056] like Figure 4 As shown, the present invention also provides a household-transformer relationship identification system based on current event probability, for implementing the household-transformer relationship identification method based on current event probability, comprising:

[0057] The feature extraction module is used to extract the transient current signal features of the transformer and the user to be identified, respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes.

[0058] The event matching module is used to determine the corresponding household-transformer relationship matching event between the transformer substation and the user to be identified based on a preset household-transformer relationship matching probability. The household-transformer relationship matching probability includes at least a correct household-transformer relationship matching probability and a wrong household-transformer relationship matching probability. The event of household-transformer relationship matching includes at least a correct household-transformer relationship matching and a wrong household-transformer relationship matching.

[0059] The relationship identification module is used to count the number of correct matches in the events of household-transformer relationship matching. When the number of correct matches in the events of household-transformer relationship matching is greater than the preset judgment threshold, it is determined that the user to be identified is connected to the transformer in the distribution area, and the final household-transformer relationship identification result is obtained.

[0060] In a specific embodiment, the event matching module is further used to make the events of matching household-transformer relationships follow a binomial distribution of the number of times the matching current transient signal is matched and the probability of matching household-transformer relationships.

[0061] Specifically, the relationship recognition module is further configured to determine, based on a preset probability density function, the critical number of correct and incorrect matches in a household-transfer relationship matching event, and use this critical number of matches as the threshold for judging household-transfer relationship matching. The probability density function includes:

[0062]

[0063]

[0064] Among them, P co To correctly match the probability density function, P er Let be the probability density function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

[0065] It should be noted that the system described in the above embodiments corresponds to the method described in the above embodiments. Therefore, the parts of the system described in the above embodiments that are not described in detail can be obtained by referring to the content of the method described in the above embodiments, and will not be repeated here.

[0066] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0067] The present invention provides a method and system for identifying the relationship between a user and a transformer based on the probability of current events. It utilizes the characteristics of transient current signals when a user experiences a load event to perform probability matching. Based on the characteristic that the events that can be matched in the transient signal sequence satisfy the binomial distribution, and combined with the transient signal probability distribution of high probability of deterministic events and low probability of random events in the low-voltage distribution area, the method and system can identify the relationship between a user and a transformer within the distribution area.

[0068] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for identifying household-transformer relationships based on the probability of current events, characterized in that, include: The transient current signal features of the transformer in the distribution area and the user to be identified are extracted respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes. The event of matching the household-transformer relationship between the transformer substation and the user to be identified is determined according to the preset household-transformer relationship matching probability. The household-transformer relationship matching probability includes at least the probability of correct household-transformer relationship matching and the probability of incorrect household-transformer relationship matching. The event of matching the household-transformer relationship includes at least the probability of correct household-transformer relationship matching and the probability of incorrect household-transformer relationship matching. The number of correct matches in the events of matching household-transformer relationships is counted. When the number of correct matches in the events of matching household-transformer relationships is greater than the preset judgment threshold, it is determined that the user to be identified is connected to the transformer in the distribution area, and the final household-transformer relationship identification result is obtained. The judgment threshold is determined according to the following process: The critical number of correct and incorrect matches in the event of matching household-change relationships is determined based on the preset probability distribution function, and this critical number of matches is used as the threshold for judging household-change relationship matching. The critical number of correct matches and incorrect matches is the number of times when the number of correct matches and incorrect matches are equal; The probability distribution function includes: Among them, P co To correctly match the probability distribution function, P er Let be the probability distribution function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

2. The method as described in claim 1, characterized in that, The event that determines the matching of the corresponding user-transformer relationship between the transformer substation and the user to be identified includes: The events of matching the household-transformer relationship follow a binomial distribution of the number of times the matching current transient signal is matched and the probability of matching the household-transformer relationship.

3. The method as described in claim 2, characterized in that, The probability of a correct match between the household and the change relationship is 0.

3.

4. The method as described in claim 3, characterized in that, The probability of a mismatch in the household-change relationship is 0.

05.

5. A household-transformer relationship identification system based on the probability of current events, used to implement the method as described in any one of claims 1-4, characterized in that, include: The feature extraction module is used to extract the transient current signal features of the transformer and the user to be identified, respectively. The transient current signal features include at least the transient start time, the transient signal type, and the power value after the transient signal stabilizes. The event matching module is used to determine the corresponding household-transformer relationship matching event between the transformer substation and the user to be identified based on a preset household-transformer relationship matching probability. The household-transformer relationship matching probability includes at least a correct household-transformer relationship matching probability and a wrong household-transformer relationship matching probability. The event of household-transformer relationship matching includes at least a correct household-transformer relationship matching and a wrong household-transformer relationship matching. The relationship identification module is used to count the number of correct matches in the events of household-transformer relationship matching. When the number of correct matches in the events of household-transformer relationship matching is greater than the preset judgment threshold, it is determined that the user to be identified is connected to the transformer in the distribution area, and the final household-transformer relationship identification result is obtained. The relationship recognition module is also used to determine the critical number of correct and incorrect matches in the event of household change relationship matching according to a preset probability distribution function, and use the critical number of matches as the threshold for judging household change relationship matching. The critical number of correct matches and incorrect matches is the number of times when the number of correct matches and incorrect matches are equal; The probability distribution function includes: Among them, P co To correctly match the probability distribution function, P er Let be the probability distribution function for incorrect matching; L be the event of correct or incorrect matching of the household-transformer relationship; k be the number of times a correct or incorrect match is achieved; p1 be the probability of correct matching, p2 be the probability of incorrect matching; and m be the number of times matching is performed using the transient current signal.

6. The system as described in claim 5, characterized in that, The event matching module is also used to make the events of matching household-transformer relationships follow a binomial distribution of the number of times the matching current transient signal is matched and the probability of matching household-transformer relationships.

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