Method and system for on-line passive detection of phase connections of wattmeter

Abstract

The invention provides a passive phase connection detection method for feeder lines of a power distribution network. The method comprises the following steps: acquiring an ammeter list of each feeder line of the power distribution network; acquiring assumed phase connection data and historical voltage data of each electricity meter in the electricity meter list of each feeder line; assigning a weight value of assumed phase connection data to each electricity meter in the electricity meter list of the first feed line; when new voltage data is detected for a first electricity meter of the first feeder line, an analysis algorithm is started, and the analysis algorithm comprises the steps that for the first electricity meter, the voltage change between the new voltage data and historical voltage data is calculated; for the first electricity meter, identifying a phase connection of the electricity meter based on a voltage difference of adjacent electricity meters within a predetermined threshold number of nearest neighbor electricity meters in the electricity meter list of the first power supply line; if the identified phase connection is most common in the nearest adjacent electricity meters, increasing an allocation weight value of the phase connection of the first electricity meter; distributing electricity meters for the phase connection with the maximum distribution weight; and repeating the analysis algorithm for the next electricity meter of the first feed line.

Description

Technical Field

[0001] The field of the present disclosure relates to methods and systems for detecting phase connections of electricity meters. Specifically, it relates to methods and systems for online passive detection of phase connections of electricity meters on the feeder lines of a distribution network. Background Art

[0002] A distribution network consists of a substation connected to multiple feeder lines that distribute electricity to end consumers. In most countries, the network is mainly three-phase, while the phases of end consumers can consist of single-phase (residential), two-phase, and multi-phase (commercial and industrial) loads. The network is designed to operate at maximum efficiency when the three phases are balanced. This requires planning and distributing single-phase and two-phase loads across the three-phase feeder lines of the network to balance the overall demand.

[0003] The planning software used by utilities utilizes network configuration data. The data can include network settings, policies, controls, and the phase connections of each electricity meter on the network. Therefore, inaccurate phase information can lead to poor planning and a suboptimal network. The planning software uses this information to predict the extent of power outages, enabling faster outage management. Therefore, inaccurate information results in insufficient outage management, misidentified phase connections lead to longer outage times, and require engineers to work overtime to resolve, resulting in customer dissatisfaction.

[0004] Other problems that arise are errors in the phase models of the network planning software. Although these errors are rare, they tend to accumulate over time, leading to significant network problems later. In addition, wires may be incorrectly installed on unexpected phases, or after a wire breaks due to, for example, a storm, it may be reconnected to a different phase.

[0005] In addition, some utilities use hardware to identify the phase connections of electricity meters. This can be slow and requires engineers to manually evaluate the phase connections of individual electricity meters. The hardware and labor costs may ultimately mean that this is a passive method that is only taken after a problem is discovered. In addition, some electricity meters require a signal to be injected into each meter and the time until the voltage reaches zero to be measured. Injecting the signal requires the electricity meter to have a specific phase detection function, so the function is limited.

[0006] Attempts have been made to alleviate these problems and provide better determination of phase connections, such as those disclosed by Therrien et al. in "Assessment of Measurement-Based Phase Identification Methods" (IEEE Open Access Journal of Power and Energy, Vol. 8, p. 128, March 2021). However, problems still exist in accurately detecting phase connections, and current methods require some extensive and complex algorithms. Therefore, there is a need to more accurately determine phase connections while providing a simple and efficient implementation for distribution networks. Summary of the Invention

[0007] In the present disclosure, a passive phase connection detection method for a feeder line of a distribution network is provided. The method includes: obtaining a list of meters for each feeder line of the distribution network; obtaining the assumed phase connection data and historical voltage data of each meter in the list of meters for each feeder line; assigning a weight value to the assumed phase connection data for each meter in the list of meters for the first feeder line; when new voltage data is detected for a first meter in the list of meters for the first feeder line, starting an analysis algorithm, where the analysis algorithm includes: for the first meter, calculating the voltage change between the new voltage data and the historical voltage data; for the first meter, identifying the phase connection of the meter based on the voltage differences of adjacent meters within a predetermined threshold number of nearest neighbor meters in the list of meters for the first feeder line; if the identified phase connection is the most common among the nearest adjacent meters, increasing the assigned weight value of the phase connection of the first meter; assigning the meter to the phase connection with the largest assigned weight; and repeating the analysis algorithm for the next meter in the first feeder line.

[0008] The analysis algorithm can be executed for all feeder lines of the distribution network. This allows for the determination of the phase connections of the entire or part of the distribution network and thus leads to better planning of the power distribution within the network.

[0009] The method can be implemented online such that the analysis algorithm processes data incrementally as new data arrives. Since the system can transmit information and instructions across the system architecture, this makes the method passive and reduces or alleviates the need for user intervention.

[0010] The voltage change can be positive, or the voltage change can be negative.

[0011] The voltage change of each meter can be related to a common time interval. This ensures that the voltage changes between the meter and adjacent meters are obtained at the same time interval to accurately compare the changes in voltage values.

[0012] The phase can be selected from the following ranges: single-phase; two-phase; three-phase; or any suitable combination. Thus, the method can be implemented on any distribution network.

[0013] The distribution network can be an Advanced Metering Infrastructure (AMI) network. AMI has the ability to remotely collect real-time electricity consumption and other information of customers via a two-way communication system, and provide the ability to manage services and power demand, thus forming a more efficient overall distribution network system.

[0014] The distribution network can consist of meters selected from the following ranges: radio frequency (RF) meters, Wi-Fi meters, cellular meters, etc., so as to be able to obtain power measurements and transmit them to the central gateway or server database of the distribution network.

[0015] The assumed phase connection data and historical voltage data of each meter can be obtained from the database on the distribution network and / or from the internal memory of the meter. Having the information already stored on the system allows the analysis algorithm to be triggered at selected time intervals, thus reducing computing resources. For example, the system can collate data once an hour, while the analysis algorithm can be triggered every 24 hours, every few days, or longer.

[0016] The method can also include: identifying the phase connections of a subset of the meters on each feeder line based on the meters within a predetermined threshold number of the nearest neighbor meters.

[0017] The subset of the meters on each feeder line can be substantially the square root of the total meters on the feeder.

[0018] Weight values can be selected such that meters with a high confidence in the assumed phase connection have a higher weight value compared to meters with a low confidence in the assumed phase connection.

[0019] The detection of new voltage data can be set to be triggered at fixed time intervals. Having fixed time intervals means that the analysis is performed at regular intervals, thus reducing computing resources again.

[0020] The time interval can be lower than the data acquisition frequency of the data stored in the database on the distribution network. This allows the time interval to be set at a longer interval than the data acquisition on the distribution network, thus reducing the frequency of analysis and thus reducing the computing processing requirements.

[0021] The method can also include: updating the phase association of each meter. This keeps the phase association information of the analyzed meters up-to-date, so that changes in phase connections can be identified and the power distribution within the network can be planned more accurately.

[0022] The updated phase association of each meter can be stored in the database on the distribution network.

[0023] In the present disclosure, a passive phase connection detection system for a feeder line of a distribution network is provided. The system includes: a distribution network; a communication module; a data acquisition and management module; a database; a plurality of electricity meters associated with a plurality of feeders; and a computing device, wherein the computing device includes one or more processors configured to execute a passive phase connection detection method.

[0024] The present invention proposes a method for passively identifying the connection phase of electricity meters almost in real time using voltage data recorded by utilities via Advanced Metering Infrastructure (AMI).

[0025] The present invention solves the problem of the uncertainty of the connection phase of electrical connections using a passive online algorithm that updates the electricity meter phase connection data almost in real time. The data required by this algorithm has already been measured and stored by utilities, and the algorithm is lightweight and thus has little impact on the processing workload of this data. Except for smart electricity meters capable of recording and reporting voltage data and the computing resources required to store and process this data, the proposed method does not require additional hardware.

[0026] Furthermore, this method is passive and "online" because the phase connection is periodically inferred using the latest data and does not require manual intervention by utilities. The proposed method is applicable to any smart electricity meter capable of measuring and reporting voltage data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present disclosure will be described by way of example only with reference to the accompanying drawings, in which:

[0028] Figure 1 A schematic diagram of the components of the system architecture is shown; and

[0029] Figure 2 A flowchart of the method steps for online passive detection of the phase connection of an electricity meter is shown. DETAILED DESCRIPTION

[0030] Figure 1 A schematic diagram of the system architecture 100 is shown. The system architecture 100 includes a network 102, preferably a smart network 102, which includes multiple feeder lines with a plurality of electricity meters. The smart network can consist of radio frequency (RF) electricity meters configured on an RF mesh network, or Wi-Fi electricity meters or cellular electricity meters, etc., such that data can be transmitted across the distribution network. The electricity meters on each feeder are associated with respective endpoints, such as in a home or commercial building, etc., and are capable of recording electrical activity measurements at the endpoints, such as the electricity consumed, voltage, current, etc. The electricity meters also have the function of remotely transmitting this information to a centralized gateway 104 via any known communication means, such as TCP / IP or User Datagram Protocol / IP, as Figure 1As shown. Then, the central gateway 104 transfers the data to the data acquisition and management software 106. The electricity meter can also communicate directly with the database on the server of the distribution network. The data acquisition and management software 106 communicates with the electricity meters on the smart network 102 such that the data is sent to the software at fixed regular time intervals (such as every 15 minutes, every 30 minutes, every hour, every 2 hours, etc.). The acquired data can be stored in the database on the distribution network. The utility can maintain its own cyber-physical configuration database outside the system architecture 100, including the phases to which each customer is connected. The update frequency of the utility data repository is typically much lower than that of the database of the distribution network.

[0031] The smart network 102 allows the system 100 to monitor, manage, and predict the performance and power requirements across the distribution network, enabling the network to operate in a balanced and efficient manner. To improve efficiency and reduce errors in the phase connections across the distribution network, a passive phase detection system is integrated with the distribution network. This is shown in Figure 1 as the phase detection algorithm 108 and the updated phase tag database 110. The phase detection is preferably performed online, i.e., when new data arrives, the data is processed using the phase detection algorithm 108 and the updated phase tag database 110 that communicate with the distribution network. The phase detection algorithm 108 obtains the voltage data from the data acquisition and management software 106. The phase detection algorithm 108 performs an analysis on the acquired data according to the phase connection detection method (combined with Figure 2 discussed). Then, the phase detection algorithm 108 stores the phase connections of each electricity meter on each of the feeder lines in the updated phase tag database 110. Once the analysis is completed at that particular time interval, the updated phase tag database 110 feeds the updated phase connection data into the data repository 112 such that the model can be adjusted based on whether any phase changes occur on any of the measured electricity meters. As Figure 1 shown, the system architecture 100 shows the data repository 112 where the utility can access and maintain information about the cyber-physical configuration.

[0032] Figure 2 A flowchart 200 of the method steps for passive detection of the phase connection of the electricity meters on the feeder lines of the distribution network is shown. This method automatically ensures the up-to-date status of the electricity meters assigned to the phases. As regarding Figure 1As discussed, the method is integrated into the existing data lines of the distribution network, adding a small lightweight analysis step to the current system architecture 100 of the distribution network. The method relies on the knowledge that meters connected to different phases will exhibit different voltage patterns due to being at different points in the voltage cycle (i.e., different phases). The first initialization step 202 involves: assigning a large weight value to the current assumed phase of each meter. This information can typically be obtained from the distribution network model, although there may be some errors. The idea behind these weights is to seed the algorithm with our current understanding of the network state. These weights can vary for each meter to reflect our confidence in the current phase of the meter, i.e., a meter with a high confidence assumed phase connection will be given a higher weight value compared to a meter with a low confidence assumed phase connection. As shown in step 202, the phase detection algorithm 108 obtains a list of feeder lines (i.e., feeders (F)), and the meters (M_F) associated with each feeder on the distribution network. Once the information about the feeder (F) and meters (M_F) is obtained, data regarding the assumed phase connection of each meter and the latest voltage readings can be obtained based on the current state of the network. This data can be obtained from the data acquisition and management software 106 and / or from data storage devices on the distribution network. For each meter on each feeder line, a weight value is assigned based on the accuracy of the assumed phase. For example, a meter that has been recently inspected will be assigned a high weight value for the assumed phase connection because, based on the recent inspection of the meter, the phase assignment is likely to be correct.

[0033] An example of the initialization step 202 applied to a simple scenario is provided. The scenario can be considered a utility that includes eight single-phase residential customers, each with a smart meter that streams usage information to a central repository. After these meters are installed, their phase connections are stored in a static repository. An initial table of the meters, their current assumed phase labels, and associated weights is created. A weight of 30 is assigned to phase A, which means the meter will need to be identified as belonging to a different phase at least 30 times before it can be reclassified as a different phase. The choice of the initial weight value can be adjusted to reflect knowledge of the network state. For example, a meter that has recently been verified to be in phase A can be assigned a higher weight than other meters. In this example, the utility currently believes it has three phase A meters, two phase B meters, and three phase C meters, with equal weights for each meter, as shown in the following table (Table 1).

[0034]

[0035] Table 1: Assigning weights to different phases of meters with assumed phase connections.

[0036] As Figure 2As shown in the flowchart of FIG. 200, the next step of method 200 is to trigger analysis algorithm 204. Analysis algorithm 204 waits until new voltage data arrives for the first meter of the first feeder line or for all meters of the first feeder line. Once new voltage data is detected, the analysis algorithm is started, as shown in step 206 of the flowchart 200 of FIG. Figure 2 . If desired, the triggering of the analysis algorithm can be performed manually (i.e., not passively). Analysis algorithm 206 first calculates the difference between the new voltage data and the previous or historical voltage readings, which are obtained from data acquisition and management software 106 and / or data storage devices on the distribution network for the first meter of the first feeder line. Similarly, the difference between the new voltage data and the historical voltage data can be calculated for each meter of the first feeder line. Analysis algorithm 206 calculates whether the voltage between the two readings has increased or decreased. Then, analysis algorithm 206 identifies the phase connection of the first meter based on adjacent meters in the list of meters on the feeder line. The phase connection of the first meter is identified when the voltage changes of the first meter and the adjacent meters are within a predetermined threshold, or for each meter of the first feeder line. The voltage difference for each meter must be related to a common time interval. Figure 2 As shown in step 206 of the flowchart 200 of FIG. . If desired, the triggering of the analysis algorithm can be performed manually (i.e., not passively). Analysis algorithm 206 first calculates the difference between the new voltage data and the previous or historical voltage readings, which are obtained from data acquisition and management software 106 and / or data storage devices on the distribution network for the first meter of the first feeder line. Similarly, the difference between the new voltage data and the historical voltage data can be calculated for each meter of the first feeder line. Analysis algorithm 206 calculates whether the voltage between the two readings has increased or decreased. Then, analysis algorithm 206 identifies the phase connection of the first meter based on adjacent meters in the list of meters on the feeder line. The phase connection of the first meter is identified when the voltage changes of the first meter and the adjacent meters are within a predetermined threshold, or for each meter of the first feeder line. The voltage difference for each meter must be related to a common time interval.

[0037] Using the same simple scenario of a utility including eight single-phase residential customers as described above, the voltage differences and adjacent phases are stored in a data storage device or repository on the distribution network. This data is used to create a second table for each meter on the feeder line. Table 2 below provides exemplary data.

[0038]

[0039] Table 2: Calculated voltage differences, identified nearest neighbors, and nearest neighbor phases for each meter.

[0040] The set of nearest neighbor meters can be selected as a subset of all the meters on the feed line (i.e., those meters with the most similar voltage differences). The size of the subset of meters depends on the total number (N) on the feed line, i.e., the number of customers on the same feed. The subset can be chosen as the square root of the total number of meters on the feed line, i.e., √N. In the example shown in Table 2, three nearest neighbor meters are selected as the subset for analysis, which is approximately √8. Other selection methods can be used to select other numbers of nearest neighbors (k) in the k-nearest neighbor algorithm, such as other known heuristics or fixed values depending on the data sample set. As shown in Table 2, the voltage difference of Meter 1 is calculated as +1.2V, and the nearest neighbor meters to Meter 1 within the selected subset of meters are Meter 2, Meter 3, and Meter 4, with phase connections of A, A, and B respectively. Thus, since A is the most common phase among the nearest neighbor meters, Meter 1 is assigned the phase label A. This is consistent with the assumed phase connection of Meter 1 as shown in Table 1. However, from the analysis in Table 2, Meter 4 has three nearest neighbor meters, all of which have phase A, while from Table 1, it can be seen that the assumed phase connection of Meter 4 is phase B. Therefore, there may be an error in the assumed phase connection of Meter 4.

[0041] To evaluate whether there is an error in the assumed phase connection of a meter, a weight value is added to the most common phase of the meter's nearest neighbors. Thus, if the identified phase connection is equivalent to that of the adjacent meters, the analysis algorithm 206 increases the assigned weight value of the phase connection of the first meter. For example, +1 is added to the weight value of the phase of each meter with the most common phase among the adjacent meters. For example, using the same simple scenario of a utility including eight single-phase households, the weight value of phase A of Meter 1 will be increased by +1. This is the case when the analysis algorithm 206 obtains the assumed phase connection of Meter 1 as phase A as shown in Table 1, and from further analysis, the data in Table 2 shows that the most common phase among the nearest neighbors of Meter 1 is also A. Thus, Meter 1 adds +1 to the weight of phase A, resulting in a weight value of 31. However, for Meter 4, the assumed phase connection is determined to be B (Table 1), but the analysis returns phase A (Table 2), so +1 is added to the weight value of phase A since all the nearest adjacent meters have a phase connection of phase A. Thus, Meter 4 has a weight value of 1 for phase A and a weight value of 30 for phase B. This is shown in Table 3 below.

[0042]

[0043] Table 3: Incremental weight values based on nearest neighbor phase analysis.

[0044] After analyzing the first electricity meter, the analysis algorithm 206 assigns the electricity meter to the phase connection with the maximum allocation weight, stores the result in the data storage device, and then repeats the analysis for the next electricity meter and subsequent electricity meters on the first feeder line. Once the analysis algorithm 206 has performed the analysis on each electricity meter on the first feeder line, the analysis algorithm 206 repeats the analysis for all feeders on the distribution network. The data of all electricity meters on all feeder lines is sorted, and the designated label of each electricity meter is updated to the phase with the maximum weight, as Figure 2 shown in step 208 of

[0045] Using the example of eight single-phase household customers again, the phase label assigned to electricity meter 4 will remain phase B until 30 iterations of phase A are determined, and then a new phase label can be assigned to the electricity meter, which becomes the phase with the maximum weight value. For example, after more than 30 iterations, electricity meter 4 will have phase weight values of A = 31, B = 30, and C = 0. At the same time, if electricity meter 1 is continuously determined to be phase A through more than 30 iterations, electricity meter 1 will have phase weight values of A = 61, B = 0, and C = 0. It will be appreciated that the initial weight values of the phase connections can vary depending on the frequency of detecting new voltage data. For example, if it is selected to trigger once every 1 hour, i.e., sample new voltage data, the initial weight value may be higher than the value selected if the trigger is set to once every 6 hours. Similarly, if the system is complex and more data would be beneficial, a high initial weight value and an increased trigger frequency can be selected. Once the trigger frequency and the weight values of the phase connections are selected, method 200 does not require further user input and runs passively online without interruption, i.e., continuously. When the designated phase connection label of an electricity meter changes, the method can also provide an alert or notification to the user on an electrical device connected to the communication link on the distribution network. If necessary, this alert allows the user to intervene and reconfigure the planning software or network model, or initiate a manual inspection of the electricity meter.

[0046] It will be understood that the above-described embodiments of the present invention are given by way of example only, and various modifications can be made to the embodiments without departing from the scope of the present invention defined in the appended claims.

Claims

1. A passive phase connection detection method for a feeder line in a distribution network, the method comprising: Obtain a list of electricity meters for each feeder line of the distribution network; Obtain the assumed phase connection data and historical voltage data for each electricity meter in the list of electricity meters for each feeder line; Assign a weight value to the assumed phase connection data for each electricity meter in the list of electricity meters for the first feeder line; When new voltage data is detected for the first electricity meter of the first feeder line, start an analysis algorithm, where the analysis algorithm includes: For the first electricity meter, calculate the voltage change between the new voltage data and the historical voltage data; For the first electricity meter, identify the phase connection of the electricity meter based on the voltage differences of adjacent electricity meters within a predetermined threshold number of nearest neighbor electricity meters in the list of electricity meters for the first feeder line; If the identified phase connection is the most common among the nearest adjacent electricity meters, increase the assigned weight value of the phase connection of the first electricity meter; Assign the electricity meter to the phase connection with the maximum assigned weight; and Repeat the analysis algorithm for the next electricity meter of the first feeder line.

2. The method according to claim 1, wherein, Execute the analysis algorithm for all feeder lines of the distribution network.

3. The method according to claim 1 or 2, wherein, The method is implemented online such that the analysis algorithm processes data incrementally when new data arrives.

4. The method according to claim 1 or 2 or 3, wherein, The voltage change is positive.

5. The method according to claim 1 or 2 or 3, wherein, The voltage change is negative.

6. The method according to any one of the preceding claims, wherein, The voltage change of each electricity meter is related to a common time interval.

7. The method according to any one of the preceding claims, wherein, The phase is selected from the following ranges: single-phase; two-phase; three-phase; or any suitable combination.

8. The method according to any one of the preceding claims, wherein, The distribution network is an Advanced Metering Infrastructure (AMI) network.

9. The method according to any one of the preceding claims, wherein, The distribution network consists of electricity meters selected from the following ranges: radio frequency (RF) meters, Wi-Fi meters, cellular meters, etc.

10. The method according to any one of the preceding claims, wherein, Obtain the assumed phase connection data and historical voltage data for each electricity meter from a database on the distribution network and / or from the internal memory of the electricity meter.

11. The method according to any one of the preceding claims, further comprising: Identify the phase connection of a subset of electricity meters on each feeder line based on electricity meters within a predetermined threshold number of nearest neighbor electricity meters.

12. The method according to claim 11, wherein, The subset of electricity meters on each feeder line is substantially the square root of the total number of electricity meters on the feeder.

13. The method according to any one of the preceding claims, wherein, Select the weight values such that electricity meters with a high confidence in the assumed phase connection have a higher weight value than electricity meters with a low confidence in the assumed phase connection.

14. The method according to any one of the preceding claims, wherein, The detection of the new voltage data is set to be triggered at a fixed time interval.

15. The method according to claim 14, wherein, The time interval is lower than the data acquisition frequency of the data stored in the database on the distribution network.

16. The method according to any one of the preceding claims, further comprising: Update the phase association of each electricity meter.

17. According to the method of claim 16, wherein, The updated phase association of each electricity meter is stored in the database on the distribution network.

18. A passive phase connection detection system for a feeder line of a distribution network, the system comprising: Distribution network; Communication module; Data acquisition and management module; Database; Multiple electricity meters associated with multiple feeders; And A computing device, where the computing device includes one or more processors configured to execute the passive phase connection detection method according to claim 1.

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