A User Phase Sequence Identification Method for Low-Voltage Distribution Radio Areas Based on a Hybrid Algorithm Framework

Abstract

This invention discloses a low-voltage distribution transformer area user phase sequence identification method based on a hybrid algorithm framework, comprising the following steps: 1) calculating the total active power variation sequence of each user cluster; 2) based on the variation sequence C i 1) Calculate the mean absolute variability (MAV) for each user cluster; 2) Sort the user clusters in descending order based on the MAV; 3) Sort the user clusters in descending order based on the user cluster G'. g 5) Calculate the active power change sequence of each phase of the transformer; g The correlation r between the total change in active power and the active power change sequence of each phase of the transformer; if the correlation is maximum and the active power of that phase of the transformer is greater than that of the user cluster G' g The total active power is then used as the current user cluster G'. g Phase sequence identification. This invention overcomes the dependence on the quality of a single data source through an algorithmic fusion framework, enabling more comprehensive data analysis, improving the accuracy and reliability of identification, and achieving higher precision user phase sequence identification.

Description

Technical Field

[0001] This invention relates to the field of phase sequence identification in low-voltage distribution networks, specifically a method for identifying the phase sequence of users in low-voltage distribution substations based on a hybrid algorithm framework. Background Technology

[0002] Phase sequence identification in low-voltage distribution networks is crucial for improving the operational efficiency, safety, and reliability of the power grid, as well as supporting its modernization and intelligent management. Currently, methods for obtaining clear user phase sequence relationships mainly include carrier communication-based methods, voltage sequence clustering analysis-based methods, correlation analysis-based methods, and linear integer programming-based methods. Through research, the inventors have discovered that existing technologies have at least the following drawbacks:

[0003] The carrier communication-based method uses electricity meters to send corresponding signals and identifies the phase sequence of users based on the received response signals. The advantage of this method is that it can directly obtain the user's phase sequence information, but it has high requirements for equipment communication and signal processing, and may require additional equipment installation and maintenance, resulting in relatively high costs. Voltage sequence clustering analysis mainly relies on the similarity of voltage fluctuations to cluster users and identify phase sequence. It typically uses unsupervised clustering algorithms, such as k-means or density-based clustering algorithms (such as DBSCAN). These methods need to consider the temporal characteristics and fluctuation patterns of the voltage data to improve the accuracy of clustering. However, voltage fluctuation curves are easily affected by changes in user load, which may affect the stability and accuracy of the clustering results. The phase sequence identification method based on voltage and active power correlation identifies the phase sequence by analyzing the correlation between electrical quantities such as voltage and active power. The advantage of this method is that the data is easy to obtain, but it may be affected by data quality, such as missing data or outliers, which may reduce the accuracy of identification. Furthermore, linear programming-based modeling methods describe the phase sequence relationships of users by establishing a mathematical model and then solving it using integer programming algorithms. This approach can provide a new perspective on the phase sequence identification problem; however, as the number of users to be identified increases, the model solution may become more complex and computationally intensive, placing high demands on computing resources. Summary of the Invention

[0004] The purpose of this invention is to provide a low-voltage distribution radio area user phase sequence identification method based on a hybrid algorithm framework, comprising the following steps:

[0005] 1) Divide the low-voltage distribution radio area into user clusters G = {G1, G2, ..., G...} j};

[0006] 2) Calculate the total active power change sequence C = {C1, C2, ..., C} for each user cluster. j};

[0007] 3) Based on the changing sequence C i Calculate the mean absolute variability (MAV) for each user cluster;

[0008] 4) Based on the mean absolute variability (MAV), sort the user clusters in descending order and update the user clusters of the low-voltage distribution area, denoted as G′={G′1,G′2,…,G′ j};

[0009] 5) Based on user cluster G′ g Calculate the active power change sequence C for each phase of the transformer. T,k ;

[0010] 6) Calculate user cluster G′ g The correlation r between the total change in active power and the active power change sequence of each phase of the transformer; if the correlation is maximum and the active power of that phase of the transformer is greater than that of the user cluster G′. g The total active power is then used as the current user cluster G′. g Phase sequence; initial value of g is 1;

[0011] 7) Determine whether g > j is true. If not, return to step 5). If yes, end the phase sequence recognition and output the recognition result.

[0012] Furthermore, in step 1), dividing the low-voltage distribution transformer area into user clusters refers to dividing the low-voltage distribution transformer area into user clusters based on the principle of voltage similarity.

[0013] Furthermore, the voltage similarity principle states that the voltage fluctuations of users belonging to the same phase sequence exhibit similarities.

[0014] Furthermore, in step 1), the steps of dividing the low-voltage distribution radio area into user clusters include:

[0015] 1.1) Cluster the n users in the low-voltage distribution transformer area into j user clusters, denoted as G = {G1, G2, ..., G...} j};

[0016] 1.2) Assign zero-power users to the user cluster of non-zero-power users whose voltage fluctuations are more similar to zero-power users by a certain value, and update user cluster G.

[0017] Furthermore, in step 1.1), the method for clustering n users in a low-voltage distribution area into j user clusters includes the PAM clustering algorithm based on the minimum Davis-Bourdin index.

[0018] Furthermore, in step 2), the sequence of changes in the total active power under each user cluster is denoted as...

[0019] The total active power of each user cluster changes as follows:

[0020]

[0021] In the formula, This represents the sum of active power of user i at different times.

[0022] Furthermore, in step 3), the mean absolute variability (MAV) is shown below:

[0023]

[0024] In the formula, M is the length of the active sequence.

[0025] Furthermore, in step 5), the active power change sequence of each phase of the transformer is denoted as...

[0026] Among them, the change in active power c of phase k of the transformer t (P Tk As shown below:

[0027]

[0028]

[0029] In the formula, t = 1, 2, ..., T M ;T M The number of sample sequences between two different time points; P represents the active power of phase k of the transformer. Loss,k h represents the line loss of phase k of the transformer. i Represents user cluster G′ g The decision variable is whether user i belongs to phase sequence k; k = {A, B, C} represents phase sequence; n is the number of users in the current low-voltage distribution radio area; m represents time.

[0030] Furthermore, in step 6), the correlation r is as follows:

[0031]

[0032] In the formula, For user cluster G′ g The sum of the active power change sequence and the mean of the active power change sequence of each phase of the transformer.

[0033] The technical effects of this invention are undeniable, and its beneficial effects are as follows:

[0034] 1) This invention requires no additional equipment installation, which can significantly reduce costs. At the same time, this invention reduces the need for equipment maintenance, thus lowering long-term operating costs.

[0035] 2) This invention eliminates the need to wait for the installation and configuration of new equipment, enabling rapid implementation and accelerating project startup.

[0036] 3) This invention does not rely on high signal processing and communication capabilities, and can adapt to environments with poor signal strength. This invention can be applied under various network conditions, and can even be used in areas with inadequate network infrastructure.

[0037] 4) This invention overcomes the dependence on the quality of a single data source through an algorithm fusion framework, which can analyze data more comprehensively, improve the accuracy and reliability of identification, and achieve higher precision user phase sequence identification.

[0038] 5) This invention can be integrated into existing power monitoring systems without requiring large-scale system reconstruction. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of voltage fluctuations between different phase sequences in a low-voltage distribution area.

[0040] Figure 2 A schematic diagram of voltage fluctuation curves for some users in a certain phase;

[0041] Figure 3 This is a schematic diagram of the phase sequence identification method of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0043] Example 1:

[0044] See Figures 1 to 3 A method for identifying the phase sequence of users in a low-voltage distribution radio area based on a hybrid algorithm framework includes the following steps:

[0045] 1) Divide the low-voltage distribution radio area into user clusters G = {G1, G2, ..., G...} j};

[0046] 2) Calculate the total active power change sequence C = {C1, C2, ..., C} for each user cluster. j};

[0047] 3) Based on the changing sequence C i Calculate the mean absolute variability (MAV) for each user cluster;

[0048] 4) Based on the mean absolute variability (MAV), sort the user clusters in descending order and update the user clusters of the low-voltage distribution area, denoted as G′={G′1,G′2,...,G′ j};

[0049] 5) Based on user cluster G′ g Calculate the active power change sequence C for each phase of the transformer. T,k ;

[0050] 6) Calculate user cluster G′ g The correlation r between the total change in active power and the active power change sequence of each phase of the transformer; if the correlation is maximum and the active power of that phase of the transformer is greater than that of the user cluster G′. g The total active power is then used as the current user cluster G′. g Phase sequence; initial value of g is 1;

[0051] 7) Determine whether g > j is true. If not, return to step 5). If yes, end the phase sequence recognition and output the recognition result.

[0052] In step 1), dividing the user clusters of the low-voltage distribution transformer area refers to dividing the user clusters of the low-voltage distribution transformer area based on the principle of voltage similarity.

[0053] The principle of voltage similarity states that the voltage fluctuations of users belonging to the same phase sequence exhibit similarities.

[0054] Step 1) involves dividing the low-voltage distribution transformer area into user clusters, including:

[0055] 1.1) Cluster the n users in the low-voltage distribution transformer area into j user clusters, denoted as G = {G1, G2, ..., G...} j};

[0056] 1.2) Assign zero-power users to the user clusters of non-zero-power users whose voltage fluctuations are more similar to zero-power users by a certain value (first, use the PAM clustering algorithm based on the minimum Davis-Bourdin exponent to divide users with non-zero active power into j user clusters, and then assign users with zero active power to these j user clusters), and update user cluster G.

[0057] In step 1.1), the method for clustering n users in a low-voltage distribution area into j user clusters includes the PAM clustering algorithm based on the minimum Davis-Bourdin index.

[0058] In step 2), the sequence of changes in the total active power under each user cluster is denoted as follows:

[0059] The total active power of each user cluster changes as follows:

[0060]

[0061] In the formula, This represents the sum of active power of user i at different times.

[0062] In step 3), the mean absolute variability (MAV) is shown below:

[0063]

[0064] In the formula, M is the length of the active sequence.

[0065] In step 5), the active power change sequence of each phase of the transformer is denoted as...

[0066] Among them, the change in active power c of phase k of the transformer t (P T,k As shown below:

[0067]

[0068] In the formula, t = 1, 2, ..., T M ;T M The number of sample sequences between two different time points; P represents the active power of phase k of the transformer. Loss,k h represents the line loss of phase k of the transformer. i Represents user cluster G′ g The decision variable for whether user i belongs to phase sequence k; k = {A, B, C} represents the phase sequence; n is the number of users in the current low-voltage distribution transformer area; for example, assuming m = 5 and t = 3 at this time, then formula 3 calculates p. 5 -p 2 The changes in active power at these two moments.

[0069] In step 6), the correlation r is shown below:

[0070]

[0071] In the formula, For user cluster G′ g The sum of the active power change sequence and the mean of the active power change sequence of each phase of the transformer.

[0072] Example 2:

[0073] A method for user phase sequence identification in low-voltage distribution radio areas based on a hybrid algorithm framework includes the following steps:

[0074] 1) Divide the low-voltage distribution radio area into user clusters G = {G1, G2, ..., G...} j};

[0075] 2) Calculate the total active power change sequence C = {C1, C2, ..., C} for each user cluster. j};

[0076] 3) Based on the changing sequence C i Calculate the mean absolute variability (MAV) for each user cluster;

[0077] 4) Based on the mean absolute variability (MAV), sort the user clusters in descending order and update the user clusters of the low-voltage distribution area, denoted as G′={G′1,G′2,...,G′ j};

[0078] 5) Based on user cluster G′ g Calculate the active power change sequence C for each phase of the transformer. T,k ;

[0079] 6) Calculate user cluster G′ g The correlation r between the total change in active power and the active power change sequence of each phase of the transformer; if the correlation is maximum and the active power of that phase of the transformer is greater than that of the user cluster G′. g The total active power is then used as the current user cluster G′. g Phase sequence; initial value of g is 1;

[0080] 7) Determine whether g > j is true. If not, return to step 5). If yes, end the phase sequence recognition and output the recognition result.

[0081] Example 3:

[0082] A method for identifying the phase sequence of users in a low-voltage distribution transformer area based on a hybrid algorithm framework, with the same technical content as in Embodiment 2, further, in step 1), dividing the user clusters in the low-voltage distribution transformer area refers to dividing the user clusters in the low-voltage distribution transformer area based on the principle of voltage similarity.

[0083] Example 4:

[0084] A low-voltage distribution area user phase sequence identification method based on a hybrid algorithm framework, with the same technical content as any one of embodiments 2-3. Furthermore, the voltage similarity principle refers to the similarity of voltage fluctuations of users belonging to the same phase sequence.

[0085] Example 5:

[0086] A method for identifying the phase sequence of users in a low-voltage distribution radio station based on a hybrid algorithm framework, with the same technical content as any one of embodiments 2-4, further comprising, in step 1), the step of dividing the low-voltage distribution radio station user clusters as follows:

[0087] 1.1) Cluster the n users in the low-voltage distribution transformer area into j user clusters, denoted as G = {G1, G2, ..., G...} j};

[0088] 1.2) Assign zero-power users to the user cluster of non-zero-power users whose voltage fluctuations are more similar to zero-power users by a certain value, and update user cluster G.

[0089] Example 6:

[0090] A method for identifying the phase sequence of users in a low-voltage distribution radio station based on a hybrid algorithm framework, with the same technical content as any one of embodiments 2-5, further wherein, in step 1.1), the method for clustering n users in the low-voltage distribution radio station into j user clusters includes a PAM clustering algorithm based on the minimum Davis-Bourdin index.

[0091] Example 7:

[0092] A method for user phase sequence identification in low-voltage distribution radio areas based on a hybrid algorithm framework, with technical content identical to any one of embodiments 2-6, further wherein, in step 2), the sequence of changes in the total active power under each user cluster is denoted as...

[0093] The total active power of each user cluster changes as follows:

[0094]

[0095] In the formula, Let m be the total active power of user i at time m.

[0096] Example 8:

[0097] A method for user phase sequence identification in low-voltage distribution substations based on a hybrid algorithm framework, with technical content identical to any one of embodiments 2-7, further wherein, in step 3), the mean absolute variability (MAV) is as follows:

[0098]

[0099] In the formula, M is the length of the active sequence.

[0100] Example 9:

[0101] A method for identifying the phase sequence of users in a low-voltage distribution transformer area based on a hybrid algorithm framework, with technical content identical to any one of embodiments 2-8, further wherein, in step 5), the active power change sequence of each phase of the transformer is denoted as...

[0102] Among them, the change in active power c of phase k of the transformer t (P Tk As shown below:

[0103]

[0104] In the formula, t = 1, 2, ..., T M ;T M The number of sample sequences between two different time points; P represents the active power of phase k of the transformer. Loss,k h represents the line loss of phase k of the transformer. i Represents user cluster G′ g The decision variable is whether user i belongs to phase sequence k; k = {A, B, C} represents phase sequence; n is the number of users in the current low-voltage distribution radio area; m represents time.

[0105] Example 10:

[0106] A method for user phase sequence identification in low-voltage distribution radio areas based on a hybrid algorithm framework, with technical content identical to any one of embodiments 2-9, further wherein, in step 6), the correlation r is as follows:

[0107]

[0108] In the formula, For user cluster G′ g The sum of the active power change sequence and the mean of the active power change sequence of each phase of the transformer.

[0109] Example 11:

[0110] A hybrid algorithm framework is proposed for low-voltage distribution area user phase sequence identification. This method is based on the principle of voltage fluctuation similarity. In a low-voltage distribution area, due to factors such as power flow, load distribution, and line impedance, users belonging to the same phase sequence exhibit high similarity in voltage fluctuations. The similarity in voltage fluctuations is even more significant between users who are electrically close.

[0111] Figure 1 This represents the voltage data of users belonging to different phase sequences under a real low-voltage distribution substation at 24:00 on a certain day. It can be seen that users belonging to the three phases A, B, and C have a significant similar trend in voltage fluctuation. Figure 2 The graph shows the voltage fluctuation curves of six users belonging to phase B at 24:00 on a given day. It can be seen that even among users in the same phase sequence, users 1-2 and users 3-6 exhibit higher similarity in voltage fluctuations. Considering the physical connections of the users on site, the topological locations of these two groups of users are closer, leading to the enhanced similarity in their voltage fluctuations.

[0112] Furthermore, according to the law of conservation of power, the active power generated by each phase of a low-voltage distribution transformer is equal to the sum of the active power consumed by all users under that phase and the line losses:

[0113]

[0114] Where k = {A, B, C} represents the phase sequence, P T,k P is the active power of a certain phase of the transformer, n is the number of users in that transformer substation, and P is the active power of a certain phase of the transformer. Loss,k For the line loss of phase k, h i The decision variable representing whether user i belongs to phase sequence k exists:

[0115]

[0116] Equation (1) shows that there is a direct, approximately linear correlation between the active power of a phase of a distribution transformer and the active power of the users under that phase. Although the active power loss under the corresponding phase sequence will weaken the correlation to a certain extent, it can still be used as a basis for judging the phase sequence of users.

[0117] Since the trend of power variation over time reveals the similarity of power consumption patterns between users and their respective phases better than the absolute value of power, it allows for the inference of their affiliation. Therefore, the changes in the active power sequence of each transformer phase and the user can be expressed as:

[0118]

[0119] Where m represents time, and t is the number of samples between two different times. Referring to the literature, in this patent, t = {1, 2, 3, 4}, resulting in the following sequence of active power changes:

[0120] C = [c1, c2, c3, c4](5)

[0121] Furthermore, equation (6) represents the Pearson correlation coefficient, which is used to determine the correlation between changes in active power of users and transformers in each phase. In the equation, C... i C T , k represent the active power change sequences of user i and transformer k phase, respectively.

[0122]

[0123] like Figure 2 The diagram shows a flowchart of the low-voltage distribution transformer area user phase sequence identification method based on a hybrid algorithm framework according to the present invention, which includes the following steps:

[0124] Step 201: Based on the principle of voltage similarity, user clustering is performed. The PAM (Partitioning Around Medoids) clustering algorithm based on the minimum Davis-Bouldin (DB) exponent is used to cluster the n users under the transformer area into j user clusters G, where G = {G1, G2, ..., G...} jMeanwhile, since some users in low-voltage distribution areas do not use electricity (commonly found in rural distribution areas), it is impossible to calculate the correlation sequence of active power changes. Therefore, while considering the minimum DB index, zero-power users should be classified into appropriate user clusters to ensure that the phase sequence of these users can also be effectively identified. Equations (3) and (5) are used to calculate the active power correlation sequence of each phase of the transformer, and equations (4) and (5) are used to calculate the correlation sequence C of the total active power under each user cluster. Subsequently, equation (7) is used to calculate the mean absolute variability (MAV) of each user cluster to sort the user cluster identification order. The larger the MAV, the more obvious the power change, which can reduce the difficulty of identification and also increase the accuracy of subsequent user identification. Where T M =4, where M is the length of the active sequence.

[0125]

[0126] Step 202: The larger the MAV of user cluster Gj, the earlier it is identified. Calculate r(C j C T (k), select the phase sequence with the highest correlation and where the sum of the power provided by the transformer of that phase and the active power of the user cluster is greater than 0, and this is the phase sequence to which the current user cluster belongs.

[0127] Step 203: Determine if all user clusters have been identified. If not, C needs to be updated. T,k Otherwise, terminate phase sequence recognition and output the recognition result.

Claims

1. A method for user phase sequence identification in low-voltage distribution radio areas based on a hybrid algorithm framework, characterized in that, Includes the following steps: 1) Divide the low-voltage distribution radio area into user clusters G = {G1, G2, ..., G...} j }; 2) Calculate the total active power change sequence C = {C1, C2, ..., C} for each user cluster. j }; 3) Based on the changing sequence C i Calculate the mean absolute variability (MAV) for each user cluster; 4) Based on the mean absolute variability (MAV), sort the user clusters in descending order and update the user clusters of the low-voltage distribution area, denoted as G′={G′1,G′2,...,G′ j }; 5) Based on user cluster G′ g Calculate the active power change sequence C for each phase of the transformer. T,k ; 6) Calculate user cluster G′ g The correlation r between the total change in active power and the active power change sequence of each phase of the transformer; if the correlation is maximum and the active power of that phase of the transformer is greater than that of the user cluster G′. g The total active power is then used as the current user cluster G′. g Phase sequence; initial value of g is 1; 7) Determine if g > j is true. If not, return to step 5). If yes, end phase sequence recognition and output the recognition result. In step 2), the sequence of changes in the total active power under each user cluster is denoted as follows: The total active power of each user cluster changes as follows: c t (P i )=P i m -P i m-t (1) In the formula, P i m P i m-t This represents the sum of active power of user i at different times; In step 3), the mean absolute variability (MAV) is shown below: In the formula, M is the length of the active sequence; In step 5), the active power change sequence of each phase of the transformer is denoted as... Among them, the change in active power c of the transformer k-phase t (P Tk As shown below: In the formula, t = 1, 2, ..., T M ;T M The number of sample sequences between two different time points; P represents the active power of phase k of the transformer. Loss,k h represents the line loss of phase k of the transformer. i Represents user cluster G′ g The decision variable is whether user i belongs to phase sequence k; k = {A, B, C} represents phase sequence; n is the number of users in the current low-voltage distribution radio area; m represents time.

2. The low-voltage distribution radio area user phase sequence identification method based on a hybrid algorithm framework according to claim 1, characterized in that, In step 1), dividing the user clusters of the low-voltage distribution transformer area refers to dividing the user clusters of the low-voltage distribution transformer area based on the principle of voltage similarity.

3. The low-voltage distribution radio station user phase sequence identification method based on a hybrid algorithm framework according to claim 2, characterized in that, The principle of voltage similarity states that the voltage fluctuations of users belonging to the same phase sequence exhibit similarities.

4. The low-voltage distribution radio station user phase sequence identification method based on a hybrid algorithm framework according to claim 1, characterized in that, Step 1) involves dividing the low-voltage distribution transformer area into user clusters, including: 1.1) Cluster the n users in the low-voltage distribution transformer area into j user clusters, denoted as G = {G1, G2, ..., G...} j }; 1.2) Assign zero-power users to the user cluster of non-zero-power users whose voltage fluctuations are more similar to zero-power users by a certain value, and update user cluster G.

5. The low-voltage distribution radio station user phase sequence identification method based on a hybrid algorithm framework according to claim 4, characterized in that, In step 1.1), the method for clustering n users in a low-voltage distribution area into j user clusters includes the PAM clustering algorithm based on the minimum Davis-Bourdin index.

6. The low-voltage distribution radio area user phase sequence identification method based on a hybrid algorithm framework according to claim 1, characterized in that, In step 6), the correlation r is shown below: In the formula, For user cluster G′ g The sum of the active power change sequence and the mean of the active power change sequence of each phase of the transformer.

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