Method and System for Topology Identification of Low-Voltage Distribution Substations Including Distributed Photovoltaics

Abstract

This application relates to the field of low-voltage distribution transformer area topology classification technology, specifically to a method and system for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic (PV) power. The method includes: acquiring electrical data of each topology node in a low-voltage distribution transformer area containing PV power; analyzing the correlation between electrical data of users in fully unconnected PV meter boxes, partially connected PV meter boxes, and fully connected PV meter boxes and the electrical data of branch boxes; calculating the phase topology connectivity between each meter box and each branch box based on the correlation; determining the phase topology relationship between the meter box, users, and branch boxes based on the topology connectivity; and constructing a five-layer topology relationship of transformer-branch box-meter box-user-phase by combining the voltage correlation between the branch box and the transformer; and verifying the five-layer topology relationship using daily electricity consumption data of each topology node. This improves the accuracy of low-voltage distribution transformer area topology identification.

Description

Technical Field

[0001] This application relates to the field of low-voltage distribution transformer area topology classification technology, specifically to a method and system for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power in low-voltage distribution substations continues to increase in new power systems, the operating environment of distribution networks is becoming increasingly complex. Due to the significant randomness and intermittency of distributed PV power output, the electrical coupling interference generated by its connection weakens the correlation of traditional single power characteristic signals (such as voltage similarity), causing the accuracy of existing automatic identification methods to drop significantly in scenarios with a high proportion of PV power, making it difficult to accurately clarify the hierarchical affiliation between "transformer-branch box-meter box-user".

[0003] Furthermore, low-voltage distribution network topology is characterized by dynamic changes. Existing identification methods suffer from low automation and poor timeliness, making it difficult to detect topology changes caused by user additions, removals, or wiring modifications in real time. They also struggle to update the topological relationships between users and phases promptly, increasing the difficulty of restoring power consumption data in areas with line losses. Therefore, overcoming electrical signal coupling interference in complex environments including distributed photovoltaic systems and utilizing multi-dimensional electrical data to achieve accurate identification and dynamic maintenance of topological relationships across all levels is a critical issue that urgently needs to be addressed for the transparent operation of current low-voltage distribution networks. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power, the method comprising the following steps:

[0006] Acquire electrical data for each topology node of a low-voltage distribution transformer area containing distributed photovoltaic power, wherein the topology node includes transformers, branch boxes, meter boxes, and user terminals;

[0007] Based on the distributed photovoltaic (PV) connection status of each user in the meter box, the meter boxes are divided into three categories: meter boxes with no PV connection, meter boxes with partial PV connection, and meter boxes with full PV connection.

[0008] The correlation between electrical data of users in fully unconnected photovoltaic meter boxes, partially connected photovoltaic meter boxes, and fully connected photovoltaic meter boxes and the electrical data of branch boxes were analyzed respectively.

[0009] Based on the aforementioned correlation, the topological connectivity of each phase between each meter box and each branch box is calculated;

[0010] Based on the topological connectivity, the phase topological relationship between the meter box, users and branch boxes is determined, and combined with the voltage correlation between the branch box and the transformer, a five-layer topological relationship of transformer-branch box-meter box-user-phase is constructed.

[0011] The five-layer topological relationship is verified by using the daily electricity consumption data of each topological node.

[0012] In one embodiment, the correlation specifically refers to:

[0013] Determine the first correlation of the same phase voltage signal between all users in the unconnected photovoltaic meter box and the branch box, and the second correlation of the same phase active power signal between all users in the fully connected photovoltaic meter box and the branch box.

[0014] In one embodiment, determining the correlation between the electrical data of the users in the partially connected photovoltaic meter box and the electrical data of the branch box includes:

[0015] For each user in a partially connected photovoltaic meter box who is not connected to distributed photovoltaics, the first correlation of the voltage signal of the same phase between the user and the branch box is calculated, and the second correlation of the active power signal of the same phase between all users in the partially connected photovoltaic meter box and the branch box is calculated.

[0016] In one embodiment, calculating the phase topological connectivity between each meter box and each branch box includes:

[0017] For a photovoltaic meter box that is not connected to any grid, the topological connectivity of each phase between the meter box and the branch box is the first correlation.

[0018] For a fully connected photovoltaic meter box, the topological connectivity of each phase between the meter box and the branch box is the second correlation.

[0019] In one embodiment, the topological connectivity of each phase between the partially connected photovoltaic meter box and the branch box is the weighted sum of the corresponding first correlation and second correlation.

[0020] In one embodiment, determining the phase topology relationship between the meter box, the user, and the branch box includes:

[0021] For each phase topology connectivity between the meter box and the branch box, determine the topology relationship between the phase lines corresponding to the maximum topology connectivity between the meter box and all its users and the branch box.

[0022] In one embodiment, the construction of the five-layer topology includes:

[0023] Calculate the mean of the correlation of all identical phase voltage signals between the branch box and the transformer, and denote it as the third correlation. Select the transformer corresponding to the maximum value of the third correlation as the transformer that has a topological relationship with the branch box, and obtain the five-layer topological relationship.

[0024] In one embodiment, verifying the five-layer topological relationship includes:

[0025] Obtain daily power consumption data for each topology node, calculate the power difference rate between upstream and downstream nodes based on the law of conservation of energy, and confirm the accuracy of the five-layer topology relationship if the power difference rate is within a preset threshold range; otherwise, automatically update the five-layer topology relationship.

[0026] In one embodiment, if the updated power difference rate still exceeds the preset threshold range, the low-voltage distribution area is determined to have abnormal line loss, and the abnormal topology nodes therein are identified.

[0027] Secondly, embodiments of this application also provide a low-voltage distribution area topology identification system containing distributed photovoltaics, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0028] This application has at least the following beneficial effects:

[0029] This application collects electrical data from multiple levels of nodes, including transformers, branch boxes, meter boxes, and users. It combines bottom-up phase topology connectivity screening (meter box-branch box) with top-down voltage correlation matching (transformer-branch box), which can accurately clarify the network affiliation of the entire "transformer-branch box-meter box-user-phase" hierarchy, achieving transparency of the entire distribution network architecture. Addressing voltage fluctuations and reverse power flow issues caused by distributed photovoltaic (PV) access, it abandons traditional identification methods relying on single voltage characteristics. Instead, it classifies meter boxes based on user access to distributed PV and analyzes the electrical data correlation between users and branch boxes differently for different PV penetration levels, calculating multi-dimensional topology connectivity. This effectively eliminates electrical signal interference caused by the randomness of PV output, significantly improving the accuracy of topology identification in complex distribution areas with high PV penetration. Furthermore, addressing the topology distortion caused by frequent changes in user wiring in low-voltage distribution networks, this application introduces daily electricity consumption data of nodes and uses the principle of energy conservation to routinely verify the generated five-layer topology relationship. This mechanism constructs a closed-loop verification system from "transient waveform feature matching" to "steady-state energy balance verification". When the underlying wiring is secretly changed, it can quickly detect power abnormalities and trigger automatic updates, which greatly improves the timeliness of transformer area topology maintenance and the reliability of operation. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the steps of a method for topology identification of a low-voltage distribution transformer area containing distributed photovoltaic power, provided in one embodiment of this application. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the low-voltage distribution transformer area topology identification method and system containing distributed photovoltaic power generation proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] The following description, in conjunction with the accompanying drawings, details the specific scheme of the low-voltage distribution transformer area topology identification method and system containing distributed photovoltaic power provided in this application.

[0035] Please see Figure 1 The diagram illustrates a flowchart of a method for topology identification of a low-voltage distribution substation containing distributed photovoltaic power, according to an embodiment of this application. The method includes the following steps:

[0036] S1, acquire electrical data of each topology node in a low-voltage distribution transformer area containing distributed photovoltaic power, wherein the topology node includes transformers, branch boxes, meter boxes, and user terminals.

[0037] This embodiment takes any low-voltage distribution transformer area as an example. It obtains the electrical data of each topology node in the low-voltage distribution transformer area from the power grid database of the low-voltage distribution transformer area. The topology nodes include transformers, branch boxes, meter boxes, and users. The electrical data includes the three-phase voltage timing signal and the three-phase active power timing signal of the transformers and branch boxes, the voltage timing signal and the active power timing signal of each user in the meter box, the daily electricity consumption of each user in the meter box, and the daily power transmission of the transformers and branch boxes.

[0038] To avoid data loss due to unstable data acquisition equipment or communication link blockage, each type of time-series electrical data acquired is preprocessed, and a segmented joint imputation method based on the missing duration is used for data completion. Specifically:

[0039] First, the number of consecutively missing sampling points in various types of time-series electrical data is counted and compared with a preset missing length threshold. In this embodiment, the missing length threshold is 3 sampling points, which can be set by the implementer.

[0040] For short-term missing data, where the number of consecutive missing sampling points is less than or equal to the missing length threshold, cubic spline interpolation is used to fill the missing segment using neighboring valid data before and after it. Specifically, a predetermined number of consecutive valid sampling points directly in front of the missing segment and the same number of consecutive valid sampling points directly behind it are selected as control points. A cubic spline interpolation polynomial is constructed to calculate and generate electrical data corresponding to the time position of the missing sampling points, which is then filled into the corresponding missing position. This ensures the smoothness and continuity of the timing electrical data after filling the short-term missing segment. In this embodiment, the predetermined number is set to 3, but the implementer can set it as needed.

[0041] For long-term missing data, i.e., the number of consecutive missing sampling points exceeds the missing length threshold: Since mathematical interpolation would cause severe distortion due to long-term missing data, and the photovoltaic output of the distribution area is greatly affected by weather conditions, a historical similar day feature filling method is adopted. Specifically: Meteorological tags within a specified historical period are obtained, and historical similar days with the same meteorological tag as the previous day are selected; electrical data within the same time period as the current missing segment are extracted from all historical similar days, and the arithmetic mean of the data at the same time point is calculated and filled into the corresponding time position of the current missing data. If no date with the same meteorological tag exists within the specified historical period, the data of all dates within the specified historical period at the same time period are directly extracted, and the arithmetic mean is calculated and used for filling. In this embodiment, the specified historical period is the past 15 days of the previous day, and the meteorological tags include sunny, cloudy, rainy, and snowy days; implementers can set these according to actual conditions.

[0042] After filling in the missing data for all sampling points using the segmented joint filling method described above, Z-score standardization is used to standardize the various types of time-series electrical data to eliminate the dimensional influence between different types of electrical data. If the value of a certain type of time-series electrical data has no fluctuation, resulting in a standard deviation of 0, then the standardized time-series electrical data sequence is directly recorded as a sequence of all zeros.

[0043] S2. Based on the distributed photovoltaic access status of each user in the meter box, the meter boxes are divided into fully unconnected photovoltaic meter boxes, partially connected photovoltaic meter boxes, and fully connected photovoltaic meter boxes. The correlation between the electrical data of users in the fully unconnected photovoltaic meter boxes, partially connected photovoltaic meter boxes, and fully connected photovoltaic meter boxes and the electrical data of the branch boxes are analyzed respectively.

[0044] In a typical low-voltage power distribution architecture, a single meter box typically houses the meters of multiple users and connects them all to the same phase line of a branch box. With the large-scale integration of distributed photovoltaic (PV) systems, the power flow distribution within the distribution area has changed significantly. Due to the highly random and intermittent nature of distributed PV output, the voltage at user nodes connected to PV systems experiences frequent disturbances due to PV fluctuations. This results in significant differences in the voltage timing characteristics between "PV-connected users" and "non-PV-connected users" within the meter box.

[0045] This difference directly leads to a significant reduction in the correlation between the overall voltage characteristics of the meter box and the voltage of the corresponding phase in the branch box, rendering traditional identification methods based on single voltage similarity ineffective and making it difficult to accurately determine the phase topology of the meter box and its connected users. However, despite the severe impact of disturbances on the voltage signal, according to the power balance principle, when a photovoltaic user generates reverse power flow, its active power signal and the power fluctuation of the branch box bus still maintain a high degree of physical consistency.

[0046] Therefore, based on the proportion of distributed photovoltaic (PV) access for users within the meter box, multi-dimensional data on "voltage correlation" and "active power correlation" are dynamically and collaboratively utilized. By focusing on voltage similarity for users without PV access and power coupling for users with PV access, and by performing weighted fusion, the electrical interference caused by PV access can be effectively overcome, and the hierarchical relationship between meter boxes, users, and phases can be accurately identified.

[0047] Since users typically register their connection to distributed photovoltaic systems with the power grid, the status of each user's connection to distributed photovoltaic systems can be obtained by retrieving the user files and ledgers for the distribution area.

[0048] Based on the user's connection to distributed photovoltaic in the meter box, the analysis is divided into the following three scenarios:

[0049] (1) No users in the meter box are connected to distributed photovoltaic systems: At this time, the voltage signals of all users in the meter box are not affected by distributed photovoltaic systems and have a high similarity to the voltage signal of a certain phase in the branch box. Therefore, this embodiment identifies the topological relationship between the meter box and the user and a certain phase line in the branch box based on the similarity between the voltage signals. Specifically:

[0050] Taking the A-phase voltage signal of the i-th meter box and the j-th branch box in a low-voltage distribution substation as an example, the voltage sequences of all users in the i-th meter box are obtained. The mean of the absolute values ​​of the Pearson correlation coefficients between the voltage sequences of all users in the i-th meter box and the A-phase voltage sequence of the j-th branch box is calculated and denoted as the first correlation. This first correlation reflects the degree of correlation between the voltage signals of the meter box and all its users and the A-phase line of the branch box. The Pearson correlation coefficient is a well-known technique, and implementers can choose other feasible correlation calculation methods, such as cosine similarity. It should be noted that if the denominator is 0 during the calculation of the Pearson correlation coefficient, the Pearson correlation coefficient is set to 0.

[0051] (2) Some users in the meter box are connected to distributed photovoltaic (PV): In this case, calculate the first correlation between the voltage sequence of all users in the i-th meter box who are not connected to PV and the A-phase voltage sequence of the j-th branch box. If the output of the distributed PV connected to the user is large and exceeds the user's required load, reverse power flow will generally occur, and there will be reverse active power in the line. That is, the active power signal of the three-phase line of the branch box is highly correlated with the active power signal of the user connected to PV. Therefore, calculate the mean of the absolute values ​​of the Pearson correlation coefficient between the active power sequence of all users in the i-th meter box and the A-phase active power sequence of the j-th branch box, and record it as the second correlation, which reflects the degree of correlation between the active power signals of the meter box and all users in it and the A-phase line of the branch box.

[0052] (3) All users in the meter box are connected to distributed photovoltaic: At this time, the active power signal of the A phase line of the branch box is highly correlated with the active power signal of the user. Therefore, the second correlation is calculated between the active power sequence of all users in the i-th meter box and the active power sequence of the A phase of the j-th branch box.

[0053] The greater the first correlation and the greater the second correlation, the higher the correlation between the voltage signal and active power signal of the user in the i-th meter box and the A-phase line of the j-th branch box, and the stronger the topological connectivity.

[0054] S3. Based on the correlation, calculate the topological connectivity of each phase between each table box and each branch box.

[0055] Based on the above analysis, the A-phase topological connectivity between all users in the i-th meter box and the j-th branch box is calculated to characterize the degree of topological connectivity between the meter box and all its users and the A-phase lines of the branch box. Specifically:

[0056] If none of the users in the i-th meter box are connected to the distributed photovoltaic system, then the A-phase topological connectivity between all users in the i-th meter box and the j-th branch box is... The expression is: ;in, The first correlation is between the user in the i-th meter box and the A-phase line in the j-th branch box.

[0057] If some users in the i-th meter box are connected to distributed photovoltaic systems, then the A-phase topological connectivity between all users in the i-th meter box and the j-th branch box is... The expression is: In the formula, The first correlation is between the user in the i-th meter box and the A-phase line in the j-th branch box. The second correlation is between the user in the i-th meter box and the A-phase line in the j-th branch box. This represents the weighting coefficient, with a value range of 0 to 1. In this embodiment... The weighting coefficient represents the percentage of users in the i-th meter box who are not connected to distributed photovoltaic power generation out of all users in the i-th meter box. The implementer can set the weighting coefficient according to the actual situation.

[0058] If all users in the i-th meter box are connected to distributed photovoltaic systems, then the A-phase topological connectivity between all users in the i-th meter box and the j-th branch box is... The expression is: In the formula, This represents the second correlation between the user in the i-th meter box and the A-phase line in the j-th branch box.

[0059] In low-voltage distribution substations containing distributed photovoltaic (PV) systems, the PV connection status of users within the meter boxes directly determines the coupling characteristics of their electrical systems. Based on the PV penetration of users within the meter boxes, three typical scenarios can be identified: completely unconnected, partially connected, and fully connected. For users not connected to PV, their single-phase voltage sequence exhibits extremely high waveform similarity to the voltage sequence of the corresponding branch box phase. However, for users connected to PV, the voltage signal experiences nonlinear fluctuations due to the randomness of PV output, resulting in a significant reduction in voltage correlation; yet, based on energy flow direction, their active power signal still maintains a high degree of physical coupling with the active power fluctuations of the phase line.

[0060] Therefore, by calculating the absolute value of the Pearson correlation coefficient between the single-phase electrical data sequence of each user within the meter box and the corresponding phase electrical data sequence of the branch box, and extracting the mean voltage correlation and the mean active power correlation, the topological connectivity of each phase can be constructed for different photovoltaic access scenarios of the meter box through a segmented fusion strategy. This effectively overcomes the limitations of single-dimensional data representation under photovoltaic interference and ensures the accuracy of meter box phase identification under different operating conditions.

[0061] Using the same calculation method as for calculating the topological connectivity of phase A, calculate the topological connectivity of phase B between all users in the i-th meter box and the j-th branch box. And the C-phase topological connectivity between all users in the i-th table box and the j-th branch box. These are used to characterize the topological connectivity between the meter box and all users within it, and the B-phase and C-phase lines of the branch box, respectively.

[0062] S4. Based on the topological connectivity, determine the phase topological relationship between the meter box and the user and the branch box, and combine the voltage correlation between the branch box and the transformer to construct a five-layer topological relationship of transformer-branch box-meter box-user-phase.

[0063] Based on the calculated topological connectivity of phase A B-phase topological connectivity and C-phase topological connectivity Identify the topological relationships between nodes in a low-voltage distribution transformer area containing distributed photovoltaic power. Specifically, use... The method filters for the maximum topological connectivity, assuming phase A has the highest topological connectivity. The maximum value indicates that there is a topological relationship between the i-th meter box and all its users and the A-phase line of the j-th branch box, thus identifying the topological relationship between "branch box-meter box-user-phase".

[0064] Furthermore, to establish the topological relationship between the "transformer-branch box," for the low-voltage distribution substation, the absolute values ​​of the Pearson correlation coefficients between the A-phase voltage sequence of the j-th branch box and the A-phase voltage sequence of the k-th transformer, the B-phase voltage sequence of the j-th branch box and the B-phase voltage sequence of the k-th transformer, and the C-phase voltage sequence of the j-th branch box and the C-phase voltage sequence of the k-th transformer are calculated. Then, the mean of these three Pearson correlation coefficients is calculated and denoted as the third correlation, reflecting the degree of topological correlation between the j-th branch box and the k-th transformer. The larger the third correlation, the higher the degree of topological correlation between the A / B / C three-phase voltage signals of the j-th branch box and the A / B / C three-phase voltage signals of the k-th transformer, and the stronger the topological connectivity. Therefore, in the low-voltage distribution substation, the transformer with the largest third correlation with the j-th branch box is selected as the transformer with a topological relationship with the j-th branch box, thereby accurately identifying the five-layer topological relationship of "transformer-branch box-meter box-user-phase" in the low-voltage distribution substation containing distributed photovoltaic power.

[0065] S5. The five-layer topological relationship is verified by using the daily electricity consumption data of each topological node.

[0066] To verify the accuracy of the identified five-layer topology relationship of "transformer-branch box-meter box-user-phase", a power flow balance check was performed on each level of the distribution area based on the principle of energy conservation. The specific logic is as follows:

[0067] Obtain the previous day's daily electricity statistics for each node at each level. Based on energy balance, the daily power transmission of each topology node should maintain a dynamic balance with the sum of the daily power transmissions of all its identified downstream branch nodes. That is, under ideal, lossless conditions, the daily power transmission of the transformer equals the sum of the daily power transmissions of all its downstream branch boxes; the daily power transmission of the corresponding phase of a branch box equals the sum of the daily electricity consumption of all meter boxes connected to that phase; and the daily electricity consumption of a meter box equals the sum of the daily electricity consumption of all users within that box.

[0068] Considering the inherent line losses and metering errors in low-voltage distribution substations during actual operation, this embodiment introduces a topology tolerance threshold. Implementers can flexibly set this threshold within the range of 5%-12% based on the substation's line loss level; in this embodiment, it is set to 10%. By calculating the difference rate M between the daily power transmission of each upstream node and the sum of the daily power transmission of all identified downstream nodes, if the difference rate M is within the topology tolerance threshold (i.e., less than or equal to the threshold), the currently identified five-layer topology relationship is deemed accurate. This verification mechanism, through closed-loop verification at the power transmission dimension, effectively compensates for the potential misjudgment risk of single waveform correlation analysis, ensuring the physical reliability of the topology identification results.

[0069] The difference rate M is obtained based on the principle of rate of change, that is... ,in, This represents the daily power transmission of the upstream topology node. This represents the daily power transmission / consumption of all topology nodes that are topologically connected to the upstream topology node.

[0070] If the difference rate If the difference rate M is greater than the topology tolerance threshold, it is determined that the transmission lines in the low-voltage distribution transformer area may have experienced changes in the topology due to users replacing their meter boxes, but these changes have not yet been updated in the low-voltage distribution transformer area's topology. In this case, the five-layer topology is automatically updated in a timely manner according to the topology identification method mentioned above. If the difference rate M of the automatically updated five-layer topology is still greater than the topology tolerance threshold, it is determined that there is an abnormal topology node in the low-voltage distribution transformer area's topology. The transmission or consumption data of the abnormal topology node may be inaccurate due to damage to the data acquisition device (such as a smart meter). Furthermore, when calculating the difference rate M, if the denominator is 0, and the numerator is also 0, the identified five-layer topology is determined to be accurate. If the numerator is not 0, the five-layer topology is automatically updated. If the automatically updated five-layer topology still shows a situation where the denominator is 0 and the numerator is not 0 during the calculation of the difference rate M, the low-voltage distribution transformer area's line loss is determined to be abnormal.

[0071] To identify abnormal topology nodes in a topology line, data verification of the topology nodes at both ends of the topology line is required. Specifically, taking the transformer-branch box topology line anomaly as an example, the difference rate M of electricity consumption between each downstream branch box and all meter boxes connected to it is calculated. If the difference rate M of each downstream branch box is less than or equal to the topology tolerance threshold, it indicates that the downstream branch box topology nodes are all normal, meaning the upstream transformer topology node is abnormal. If there is a branch box downstream of the topology line with a difference rate M greater than the topology tolerance threshold, then this downstream branch box is determined to be an abnormal topology node.

[0072] It is important to note that, since there are no downstream topology nodes for end users in the five-layer topology, when an anomaly is detected in the meter box-user topology, in order to identify the abnormal topology nodes, the daily electricity consumption of the end users in that topology for the past three months (excluding the day before the current day) is obtained and used as input to the ARIMA prediction algorithm. This predicts the daily electricity consumption of the user for the day before the current day, calculates the difference rate M between the predicted daily electricity consumption for the previous day and the actual value stored in the power grid database, and identifies the abnormal topology nodes based on the topology tolerance threshold. The specific process is not detailed here. The ARIMA prediction algorithm is a well-known technology, and its implementation is not described in detail.

[0073] To address the issue of inaccurate data collection from abnormal topology nodes, data restoration is necessary. Since electricity billing in the power grid system is primarily based on grid transmission / consumption, this embodiment mainly focuses on restoring the transmission / consumption data for abnormal topology nodes. Specifically, the aforementioned ARIMA prediction algorithm is used to predict the transmission / consumption of each topology node for the previous day. Then, a weighted sum is calculated based on the daily transmission / consumption recorded by the abnormal topology node in the power grid system database for the previous day and the predicted daily transmission / consumption. This yields the actual transmission / consumption after data restoration for the abnormal topology node. The weighting coefficient is 0.5 to minimize power grid losses while ensuring the accuracy of billing for each user.

[0074] Based on the same inventive concept as the above methods, this application also provides a low-voltage distribution transformer area topology identification system with distributed photovoltaics, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described low-voltage distribution transformer area topology identification methods with distributed photovoltaics.

[0075] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0076] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0077] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power, characterized in that, The method includes the following steps: Acquire electrical data for each topology node of a low-voltage distribution transformer area containing distributed photovoltaic power, wherein the topology node includes transformers, branch boxes, meter boxes, and user terminals; Based on the distributed photovoltaic (PV) connection status of each user in the meter box, the meter boxes are divided into three categories: meter boxes with no PV connection, meter boxes with partial PV connection, and meter boxes with full PV connection. The correlation between electrical data of users in fully unconnected photovoltaic meter boxes, partially connected photovoltaic meter boxes, and fully connected photovoltaic meter boxes and the electrical data of branch boxes were analyzed respectively. Based on the aforementioned correlation, the topological connectivity of each phase between each meter box and each branch box is calculated; Based on the topological connectivity, the phase topological relationship between the meter box, users and branch boxes is determined, and combined with the voltage correlation between the branch box and the transformer, a five-layer topological relationship of transformer-branch box-meter box-user-phase is constructed. The five-layer topological relationship is verified by using the daily electricity consumption data of each topological node.

2. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 1, characterized in that, The correlation specifically refers to: Determine the first correlation of the same phase voltage signal between all users in the unconnected photovoltaic meter box and the branch box, and the second correlation of the same phase active power signal between all users in the fully connected photovoltaic meter box and the branch box.

3. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 2, characterized in that, The determination of the correlation between the electrical data of users connected to the photovoltaic meter box and the electrical data of the branch box includes: For each user in a partially connected photovoltaic meter box who is not connected to distributed photovoltaics, the first correlation of the voltage signal of the same phase between the user and the branch box is calculated, and the second correlation of the active power signal of the same phase between all users in the partially connected photovoltaic meter box and the branch box is calculated.

4. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 3, characterized in that, The calculation of the phase topological connectivity between each meter box and each branch box includes: For a photovoltaic meter box that is not connected to any grid, the topological connectivity of each phase between the meter box and the branch box is the first correlation. For a fully connected photovoltaic meter box, the topological connectivity of each phase between the meter box and the branch box is the second correlation.

5. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 4, characterized in that, The topological connectivity of each phase between the partially connected photovoltaic meter box and the branch box is the weighted sum of the corresponding first correlation and second correlation.

6. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 1, characterized in that, The determination of the phase topology relationship between the meter box, the user, and the branch box includes: For each phase topology connectivity between the meter box and the branch box, determine the topology relationship between the phase lines corresponding to the maximum topology connectivity between the meter box and all its users and the branch box.

7. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 1, characterized in that, The construction of the five-layer topology includes: Calculate the mean of the correlation of all identical phase voltage signals between the branch box and the transformer, and denote it as the third correlation. Select the transformer corresponding to the maximum value of the third correlation as the transformer that has a topological relationship with the branch box, and obtain the five-layer topological relationship.

8. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 1, characterized in that, The verification of the five-layer topological relationship includes: The daily power consumption data of each topology node is obtained, and the power difference rate between upstream and downstream nodes is calculated based on the law of conservation of energy. If the power difference rate is within a preset threshold range, the accuracy of the five-layer topology relationship is confirmed; otherwise, the five-layer topology relationship is automatically updated.

9. The method for topology identification of low-voltage distribution transformer areas containing distributed photovoltaic power as described in claim 8, characterized in that, If the updated power difference rate still exceeds the preset threshold range, the low-voltage distribution area is determined to have abnormal line loss, and the abnormal topology nodes are identified.

10. A low-voltage distribution transformer area topology identification system including distributed photovoltaic power, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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