Topology and phase detection for power supply networks

Abstract

A system for detecting topology and phase information of an electrical power distribution system is provided. For example, the system includes a set of meters connected to the electrical power distribution system and can communicate with each other through a local network connection such as a mesh network. Each of the meters is configured to generate voltage data and transmit the voltage data to a correlator of the set of meters. The correlator calculates a correlation between each pair of meters in the set based on the received voltage data and further transmits the calculated correlations to a mapper through the mesh network. The mapper determines a topology or phase relationship between at least the set of meters based on the received correlations.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to power distribution networks, and more particularly to discovery and maintenance of topology and phase information of power distribution networks. BACKGROUND

[0002] Utility companies typically manually track the location of electric meters installed in the field and the connectivity of the electric meters to distribution transformers. For large utility companies, where distribution transformers are in the order of one million, the number of electric meters can be in the order of several million, making this manual process time consuming and error prone. Also, phase information is rarely recorded due to technical complexities, labor, time constraints, cost, equipment availability, and the like. In addition, the actual phase assignment of electric meters and associated upstream assets can change from time to time due to ongoing residential and commercial construction, demolition or renovation, utility equipment upgrades, repairs or maintenance, disaster response, fallen trees, storms, and the like. Similar problems can also exist in 3-phase distribution transformers with multiple electric meters connected to them. The phase of individual electric meters is typically not recorded but needs to be determined for the purpose of phase load balancing on the power grid. SUMMARY

[0003] Aspects and examples are disclosed for apparatuses and processes for detecting topology and phase information of a power distribution system. In one example, a system for discovering relationships among meters in a power distribution system includes a plurality of meters connected to the power distribution system and deployed at different geographic locations, a correlator communicatively connected to the plurality of meters over a communication network, and a mapper communicatively connected to the plurality of meters over the communication network. The plurality of meters are communicatively connected over a communication network. Each meter of the plurality of meters is configured to generate and transmit a data sample obtained at the meter. The correlator is configured to receive the data samples from the plurality of meters, compute correlations among the plurality of meters based on the data samples received from the plurality of meters, and transmit the computed correlations over the communication network. The mapper is configured to receive the computed correlations from the correlator over the communication network and determine relationships among at least the plurality of meters based on the computed correlations.

[0004] In another example, a method performed by a meter for discovering relationships between a plurality of meters of an electric power distribution system including the meter is provided. The method includes transmitting a message identifying the meter as a correlator over a local wireless network that communicatively connects the plurality of meters, determining that a condition for generating and transmitting a data sample has been satisfied, and transmitting a request for a data sample to other meters of the plurality of meters over the local wireless network. The method further includes receiving the data sample from the other meters of the plurality of meters over the local wireless network, computing a sample correlation for each pair of meters among the plurality of meters based on the data samples received from the plurality of meters, and causing the relationships between the plurality of meters to be determined by transmitting the computed sample correlations to a mapper via the local wireless network.

[0005] In yet another example, a method performed by a mapper node for discovering relationships between a plurality of meters connected to an electric power distribution network is provided. The method includes receiving data samples from the plurality of meters over a communication network that communicatively connects the plurality of meters and the mapper node, and receiving a first set of correlations between a first group of meters of the plurality of meters and a second set of correlations between a second group of meters of the plurality of meters over the communication network. The first set of correlations and the second set of correlations are computed based on data samples generated by the plurality of meters. The method further includes computing a third set of correlations between the first group of meters of the plurality of meters and the second group of meters of the plurality of meters based on the data samples of the plurality of meters, and determining relationships between the plurality of meters based on the first set of correlations, the second set of correlations, and the third set of correlations.

[0006] Reference to these descriptive aspects and features is not intended to limit or restrict the presently described subject matter to such example(s), but is intended to provide examples to aid understanding of the concepts described in this application. Further aspects, advantages and features of the presently described subject matter will become apparent after review of the entire application. BRIEF DESCRIPTION OF DRAWINGS

[0007] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, when considered in conjunction with the accompanying drawings.

[0008] Figure 1 is a block diagram illustrating an electric power distribution system in accordance with certain aspects of the present disclosure.

[0009] Figure 2 is a block diagram illustrating an illustrative operating environment for discovering topology and phase information in an electric power distribution system in accordance with certain aspects of the present disclosure.

[0010] Figure 3An example of a set of meters electrically connected to the same substation and locally communicatively connected to each other, voltage signals sensed by the meters, and correlations between the set of meters is shown in accordance with certain aspects of the present disclosure.

[0011] Figure 4 An example of two meter groups (including Figure 3 the meter groups shown in FIG. 1 1) and correlations between the meters in the respective meter groups is shown in accordance with certain aspects of the present disclosure.

[0012] Figure 5A An example of a mesh network hierarchy of meter groups shown in Figure 4 FIG. 12 in accordance with certain aspects of the present disclosure.

[0013] Figure 5B An example of a mesh network hierarchy using multi-layer correlators is shown in accordance with certain aspects of the present disclosure.

[0014] Figure 6 An example of a process for identifying topology or phase of a power distribution system is shown in accordance with certain aspects of the present disclosure.

[0015] Figure 7 is a block diagram depicting an example of a meter suitable for implementing aspects of the techniques and technologies presented herein. DETAILED DESCRIPTION

[0016] Systems and methods for detecting topology and phase information of assets in a power distribution system are provided. The topology and phase information or relationships of assets can include electrical connectivity relationships that indicate connections of meters to power distribution elements, such as transformers. The topology and phase information or relationships of assets can further include phase relationships that indicate connections of meters to three phases of a power distribution system. For example, assets (e.g., meters) equipped with sensors in a power distribution system can be configured to measure characteristic parameters (e.g., voltage, current, load impedance) of the power distribution network. Assets connected to the same power distribution component (e.g., transformer) can observe the same fluctuations in the sensed data, while assets connected to different components generally do not observe the same fluctuations. Likewise, assets connected to the same phase can observe the same fluctuations in the sensed data, while assets connected to different phases will observe fluctuations in the sensed data with unique phase offsets, such as a ±120 degree offset or a 180 degree offset.

[0017] From this, assets can be configured to generate samples of sensed data and share the data samples with neighboring assets for analysis over a local network connection, such as a wireless mesh network connecting the assets. One of the neighboring assets, typically an asset with greater computational resources such as memory or processing power, can act as a correlator to collect data samples generated by neighboring assets and perform higher-level computations and analysis, such as statistical correlations, on the shared data samples. Based on this analysis, it can be determined which assets are connected to the same power distribution element or to the same phase. To determine the relationship of the neighboring assets to other assets in the power distribution system, another asset with additional computational capacity can act as a mapper to combine multiple correlations from multiple correlators and determine assets connected to the same power distribution element or the same phase.

[0018] The techniques described in this disclosure improve the efficiency and accuracy of topology and phase detection for assets in a power distribution system and communication between assets and a headend system. By configuring assets to generate data samples and share the data samples with neighboring assets over a local network connection instead of sending the data samples to a headend system, the communication bandwidth usage in the network is optimized for local transactions and the communication bandwidth requirements of the headend system are significantly reduced. Additionally, by distributing computations among assets, assets with spare computational capacity and otherwise idle most of the time are utilized. In this way, processing can be decentralized for large-scale processing operations without sending all data to a single place, such as a headend system, which also reduces the distance data needs to be moved.

[0019] Additionally, by allowing analysis of data samples to be distributed to various correlators and mappers in the system, the computational requirements of each individual asset can be reduced. The identification processes presented herein can be implemented automatically and periodically without human intervention compared to traditional topology and phase identification approaches. As a result, more accurate and up-to-date detection of topology and phase information for a power distribution system can be achieved.

[0020] In the following description, meters are used as an example of assets and transformers are used as an example of power distribution components for simplicity. Voltage data is used as an example of data sensed by assets. However, it should be understood that the techniques presented herein are not limited to these examples, but are applicable to other types of assets, power distribution components, and sensed data.

[0021] Exemplary Operating Environment

[0022] Figure 1 is a block diagram illustrating a power distribution system 100 in accordance with various aspects of the present disclosure. In Figure 1In overview, power generation facility 110 can generate power. The generated power can be, for example, 3-phase alternating current (AC) power. In a 3-phase power system, three conductors each carry alternating current of the same frequency and voltage amplitude with respect to a common reference but with a phase difference of 1 / 3 of a cycle between each. The power can be transmitted at high voltage (e.g., around 140-750 kV) via transmission line 115 to power substation 120.

[0023] At power substation 120, step-down transformer 130 can step down the high voltage power to a voltage level more suitable for customer use, referred to as distribution or "medium" voltage, typically around 13 kV. The stepped down 3-phase power can be transmitted via feeders 140a, 140b, 140c to distribution transformers 150, which can further step down the voltage (e.g., 120-240 V for residential customers). Each distribution transformer 150, 155 can deliver single and / or 3-phase power to residential and / or commercial customers. From distribution transformers 150, 155, power is delivered to customers through electric meters 160. Electric meters 160 can be supplied by the electric utility and can be connected between the load (i.e., customer premises) and distribution transformers 150, 155. Three-phase transformers 155 can deliver 3-phase power to customer premises, for example, by feeding three lines on a street front. In some areas, to get single phase power, customer premises will be randomly connected to one of the three lines. This random connection or tapping causes the utility to lose track of what premises are on what phase. In addition to 3-phase power, single phase power can also be delivered from distribution transformers 150 to various customers from different phases of the 3-phase power generated by the utility, resulting in uneven loading on the phases.

[0024] Sensors 180 can be distributed throughout the network at various assets, such as, but not limited to, feeder circuits, distribution transformers, etc. Sensors 180 can sense various circuit parameters (e.g., frequency, voltage, current magnitude, and phase angle) to monitor the operation of power distribution system 100. It should be appreciated that, Figure 1 The illustrated locations of sensors in

[0025] As will be appreciated from the foregoing, Figure 1As can be seen, each asset is connected to one or more phases and one or more segments of the power distribution system 100. The disclosure presented herein can automatically identify the segments and phases of assets in the power distribution system 100, and this information is updated as the electrical connectivity of the power distribution system 100 (i.e., how the components are connected together with wires) and phases change over time. The following describes an example of using meters as assets. It should be understood that the described techniques are also applicable to other types of assets configured with sensors, such as transformers, generators, contactors, reclosers, fuses, switches, street lighting, ripple receivers, ripple generators, capacitor banks, batteries, synchronous condensers, etc.

[0026] Figure 2 This illustrates methods for discovering power distribution systems (such as, Figure 1 The illustration shows an operating environment 200 for topology and phase information in the power distribution system 100 shown. Environment 200 includes a mesh network 140 associated with the power distribution system for delivering measurement data obtained by meters within the power distribution system. Mesh network 140 includes multiple meters 160 deployed across various geographical locations at customer sites via the power distribution system 100. Meters 160 can be implemented to measure various operating characteristics of the power distribution system 100, such as resource consumption characteristics or other characteristics related to power usage in the system. Example characteristics include, but are not limited to, average or total power consumption, peak voltage of electrical signals, power surges, and load changes. In some examples, meters 160 include commercial and industrial (C&I) meters, residential meters, and so on.

[0027] Meter 160 can transmit collected or generated data as meter measurement data 122 to root node 114 via mesh network 140. Root node 114 of mesh network 140 can be configured to communicate with meter 160 to perform operations such as managing meter 112, collecting measurement data 122 from meter 112, and forwarding data to headend system 104. Root node 114 can also be configured to act as a node for its own measurement and data processing. Root node 114 can be a PAN coordinator, gateway, or any other device capable of communicating with headend system 104.

[0028] Root node 114 ultimately transmits the generated and collected instrument measurement data 122 to headend system 104 via another network 170 (such as the Internet, intranet, or any other data communication network). Headend system 104 can act as a central processing system receiving messages or data streams from root node 114. Headend system 104, or another system associated with the utility company, can process or analyze the collected data for various purposes, such as accounting, performance analysis, or troubleshooting.

[0029] It should be understood that, despite Figure 2 A specific network topology (e.g., DODAG tree) is described, but other network topologies are also possible (e.g., ring topology, mesh topology, star topology, etc.). Furthermore, although the following description will focus on aspects of a set of meters 160, the techniques described herein can be applied to any meter in a mesh network, including meters 160 and root node 114.

[0030] It should be understood that the mesh network 140 communicating with the electricity meters 160 is separate from the power distribution network in the power distribution system 100 and can cover that network. Therefore, two meters 160 that are neighbors in the mesh network 140 may not be neighbors in the power distribution network, and vice versa. Thus, it is quite possible that two meters 160 connected to the same transformer may belong to different PANs, and meters 160 belonging to the same PAN may be connected to different transformers.

[0031] Figure 3 This diagram illustrates an example of detecting topology or phase information of a power distribution system without involving the headend system. In this example, a group of meters 160 (A, B, C, and D) are electrically connected to the same substation 120 and communicatively connected to each other via a local network. The local network can be a mesh network 140, a portion of mesh network 140, or any network that enables communication between meters without involving the network used to communicate with the headend system 104. Each of these meters is connected to either distribution transformer 150A or 150B on one of the three phases. As discussed above, in some areas, a customer site may be randomly connected to one of the three power lines for three-phase power to obtain single-phase power. This causes the utility company to lose track of the phase information of the meters at the site. Similarly, there may be multiple transformers near a customer site, and the meters for that customer site may have been randomly connected to one of these transformers, causing the utility company to lose track of which site is on which transformer. By utilizing the techniques disclosed herein, the electrical connectivity relationships (indicating the connection from the instrument to the transformer) between these instruments can be determined through local network connections without sending data to the headend system. Similarly, the phase relationships (indicating the connection from the instrument to the three phases of the power distribution system) between these instruments can also be identified via local network communication.

[0032] To obtain topology and / or phase information for each of the meters 160, data collected at the individual meter 160, such as voltage, current, load impedance, or others, can be used. Figure 3 An example is shown of using voltage data collected by meters 160 to determine topology information for these meters 160. Phase information can be detected in a similar manner. For each of the four meters,Figure 3 The voltage values ​​measured at different time points are shown. These voltage values ​​are plotted using curves 302A, 302B, 302C, and 302D for instruments A, B, C, and D, respectively.

[0033] As from Figure 3 As can be seen in curves 302A-302D, fluctuations (e.g., peaks or troughs) may occur in voltage values ​​due to the switching on or off of large loads, for example, on one or more power lines. When a fluctuation is detected at a particular meter, an adjacent meter connected to the same transformer as that particular meter may also detect a fluctuation at approximately the same time. Note that the fluctuation may be attenuated by the time it is detected by the adjacent meter due to the resistance of the line connecting that particular meter to its adjacent meter. On the other hand, for meters connected to different transformers than the particular meter, the detected fluctuation may be different from that of the particular meter. Based on these properties of the power distribution system, statistical analysis can be performed to determine whether meters are connected to the same transformer. In some examples, correlation is used to measure the similarity between voltage values ​​detected by different meters at approximately the same time, thereby determining whether they are connected to the same transformer.

[0034] For example, voltage values ​​detected at different instruments can be transmitted to one of the instruments in the group, referred to herein as a correlator, such as correlator instrument B. The correlator can be selected through self-nomination, where an instrument capable of performing correlation can send communications indicating it is a correlator to other instruments via a local network connection. Instruments that can communicate with the correlator via a local network connection form a group of instruments 306, for which the correlator can perform correlation to determine how the group of instruments correlates. Instruments in the group can communicate with each other using the same protocol or different protocols for transmitting instrument measurement data 122.

[0035] In some examples, voltage values ​​can be sampled to generate voltage data for transmission to the correlator. Sampling can be performed, for example, by averaging the voltage values ​​over each time interval ΔT. The time interval can be configured to be the same as the time interval used to collect and generate power consumption data, or other time intervals (such as 15 minutes, 1 minute, or 30 seconds). Note that some applications or implementations of the techniques presented here for topology or phase identification may require sampling at the sub-cycle level. Figure 3 The average voltage value or voltage data sample illustrated using the cross marker "x" can be transmitted to the correlator as voltage data. In some implementations, the voltage data may include a sequence of N average voltage values, where N is an integer value such as 50, 100, or 200.

[0036] The transmission of voltage data to the correlator can be based on a predetermined schedule or triggered by an event. For example, the correlator can be configured to request voltage data from meters in a group to send the data at midnight each day. In another example, the correlator can be configured to detect a trigger event and, upon detection, request voltage data from meters in the group. A trigger event can include, for example, an event where the difference between two consecutive voltage data samples exceeds a voltage change threshold. Figure 3 The diagram illustrates an example of detecting such a triggering event. In this example, correlator instrument B detects a voltage change ΔV between two consecutive voltage data samples that exceeds a threshold. Upon detecting this event, correlator instrument B will adjust the time T at which the event occurs. * Requests for nearby collected voltage data are sent to instruments A, C, and D. The correlator can request that the instruments receive the data at the corresponding time T. * The sequence of N voltage data samples collected before, after, or around the trigger event is included in the voltage data transmitted to the correlator. In another example, each of instruments A, C, and D is configured to detect a trigger event. After the event is detected, each instrument begins collecting samples of voltage data for a given duration (or for a given number of samples) at a specified time interval (before, after, or around the trigger event). After collection is complete, the instrument automatically sends the collected samples to correlator B.

[0037] After receiving the voltage data, the correlator can perform cross-correlation on the voltage data to generate the correlation between each pair of instruments in the instrument group, referred to in this paper as the correlation data. Figure 3 In the example shown, correlation data is illustrated in correlation matrix 312. The entry (i,j) of the correlation matrix represents the correlation between instrument i and instrument j. Because the correlation between instrument i and instrument j is the same as the correlation between instrument j and instrument i, the entry (i,j) is identical to the entry (j,i). Therefore, the correlation matrix is ​​a symmetric matrix, and Figure 3 Only the lower half of the matrix is ​​shown. Furthermore, the diagonal of the correlation matrix always has all 1s because the instruments are perfectly correlated with themselves. Therefore, only half of the correlation matrix (excluding the diagonal) contains useful information and will be included in the correlation data.

[0038] In some examples, the correlation coefficient between two voltage data sample sequences from a pair of instruments can be used to represent the correlation between the instruments. The correlation coefficient ranges from -1 to 1, where 1 indicates the strongest positive correlation between the two voltage data sample sequences, 0 indicates that the two voltage data sample sequences are unrelated, and -1 indicates the strongest negative correlation between the two voltage data sample sequences. It should be understood that various other types of statistical correlation can be used to measure the similarity between the voltage data of the instruments.

[0039] Figure 3 The correlation matrix 312 shown indicates a strong correlation between the voltage data of meters A and B, meters A and D, and meters B and D. The correlation between the voltage data of meter C and other meters is low. This means that meters A, B, and D are likely connected to the same transformer 150A, while meter C is connected to a different transformer 150B. In this way, the topology among a group of meters 306 on the distribution network can be determined.

[0040] Correlation can be generated similarly for instruments in other groups. Figure 4 It shows including Figure 3 An example of two instrument groups of a set of instruments 306 shown. Figure 4 The correlation between instruments in corresponding instrument groups according to certain aspects of this disclosure is also illustrated. In this example, a second group of instruments 406, including instruments E, F, G, H, and I, is connected to the same substation 120 as a group of instruments 306, but via transformers different from transformers 150A and 150B. In this second group of instruments 406, instrument G is a correlator and can communicate with other instruments in the group via a local network connection. In some examples, instruments in a group of instruments 306 may communicate with instruments in the second group of instruments 406 without a local network connection. For example, instruments in a group of instruments 306 and instruments in a second group of instruments 406 may be too far apart to communicate directly with each other.

[0041] Correlator instrument G can perform correlation for the instruments in the second group of instruments 406 in a similar manner to that performed by correlator instrument B, and generate correlation data 404. As indicated in the correlation data 404, the correlation between the voltage data of instruments E and F is as high as 0.9 because instruments E and F are connected to the same transformer 150C. Similarly, the correlation between the voltage data of instruments G and H is also high (0.92) because instruments G and H are connected to the same transformer 150D. On the other hand, instrument I does not have a high correlation with the other instruments in the second group of instruments 406 because it is not connected to transformer 105E, which is a different transformer than the other instruments in the group.

[0042] Correlators B and G can send the corresponding correlation matrix 312 and correlation data 404 to a higher-level node in the correlation hierarchy that is communicatively connected to correlators B and G, referred to herein as a "mapper". The mapper can process the received correlation data to generate correlation data for all instruments in both groups. Figure 4 Aggregated correlation data 402 for the two sets of instruments 306 and 406 is also shown. Figure 4 In this context, correlation matrix 312 is generated by correlator B and transmitted from correlator B, and correlation data 404 is generated by correlator G and transmitted from correlator G.

[0043] To determine the correlation across two groups (i.e., between meters A, B, C, D and meters E, F, G, H, I), the mapper can perform correlation between meters in one group 306 and meters in the second group 406 based on voltage data received from these two meters. In this way, the mapper can complete aggregated correlation data 402 for all meters in the first group 306 and the second group 406. Based on the completed correlation data 402, the mapper can perform mapping from the meters to their corresponding transformers. For example, if the correlation between two meters is higher than a correlation threshold (e.g., 0.85), the mapper can determine that the two meters are connected to the same transformer. Figure 4 In the example shown, the mapper can determine that instruments A, B, and D are connected to one transformer, instruments E and F are connected to another transformer, instruments G and H are connected to a third transformer, and instruments C and I are connected to two other separate transformers.

[0044] Figure 5A Some aspects of this disclosure are shown. Figure 4 The example correlation hierarchy for the instrument group is shown. In some examples, correlators 510 (such as correlator B or correlator G) can communicate with mapper 504 via mesh network 140 and are located in a layer of the mesh network closer to the head-end system than the instruments in their group. (See above regarding...) Figure 3 and 4 As described, correlator 510 receives voltage data 512 from meters in its group and generates correlation data (e.g., correlation matrix 312 or correlation data 404). Correlator 510 further sends the correlation data to mapper 504 to determine aggregated correlation data for the two groups of meters and to determine the topology information of these meters. It should be understood that meters in a group (such as meters A, C, B, and D) may belong to different PANs. Mapper, correlator, and meter 160 may be in the same layer or in different layers of mesh network 140.

[0045] It should be further understood that although the above example shows a correlator as a meter, other types of network devices (such as routers, collectors) can also act as correlators. Similarly, mapper 504 can be a meter, router, collector, or another type of network device. As described above, a mapper can be a node in a mesh network that is one or more layers above the correlator but not above the root node 114. It should be further understood that although the above description focuses on meter groups in which the correlator communicates directly with other meters in each group, meter groups can also include meters that do not communicate directly with the correlator. For example, a group (such as a group of meters 306) can further include meters that can communicate via another meter (such as... Figure 5A Instrument A in the middle communicates with the correlator (such as, Figure 5A Instrument X in the middle).

[0046] although Figure 5A Mapper 504 is shown for determining topology information for only two instrument groups, but the mapper can cover a large number of instrument groups located at various layers below the mapper in the correlation hierarchy. These instruments can be collectively referred to as the instrument set associated with the mapper. Correlators for individual groups within the instrument set can provide their respective group's correlation data to the mapper to determine topology information for those instruments. In alternative or additional implementations, instead of correlators that send correlation data directly to the mapper, higher-level correlators can be used to perform the correlation part before sending the correlation data to the mapper.

[0047] Figure 5B An example of a multilayer correlator configuration according to certain aspects of this disclosure is shown. Figure 5B In this example, there are six groups of meters in the meter set associated with mapper 504, and each correlator 510 represents its corresponding meter group. Correlators 510 can send their corresponding correlation data 502 to the corresponding high-level correlator 514. The high-level correlator 514 aggregates the received correlation data 502 and calculates the correlation values ​​between meters from different groups to generate a correlation between the data and the corresponding high-level correlator. Figure 4The aggregated correlation data 402 shown is similar to the aggregated correlation data 522. The advanced correlator 514 can then send the aggregated correlation data 522 to the mapper 504 to complete the remaining correlations and determine the instrument's topology information. Because some of the correlation calculations are shifted to the advanced correlator 514, the workload of the mapper 504 can be reduced, and therefore, the computational power requirements of the mapper 504 can be lowered. Similar to correlator 510, the advanced correlator 514 can be an instrument, router, collector, or other type of network device capable of aggregating the correlation data 502. The advanced correlator 514 can be located topologically in a layer of a mesh network above or below the mapper 504 or a corresponding correlator. Additional layers of advanced correlators can be added to further reduce the workload of the mapper 504.

[0048] about Figure 3-5B The above description describes how topology information associated with meters in a power distribution network is detected. A similar mechanism can be used to detect the phase information of meters. Because voltages on different phases are offset by, for example, 120 degrees, the correlation of the voltage values ​​of two meters can also be used to determine whether the two meters are on the same phase. Unlike topology detection, phase information detection requires generating voltage data with finer granularity due to the high frequency of voltages on power lines. For example, meters can be configured to generate voltage data with a higher frequency, such as N voltage data samples for each cycle, where N is an integer value such as 50, 100, etc. Similar to topology detection discussed above, the generated voltage data is sent from the meters to their respective correlators, which perform correlation based on the received voltage data to generate correlation data. The correlation data can be further sent to a higher-level correlator or directly to a mapper, which aggregates the correlation data and generates a complete correlation between the set of meters associated with the mapper. The mapper can then determine meters connected to the same phase based on the complete correlation data. The mapper can further obtain reference phase information (e.g., from substations) to determine the precise phase for individual meters.

[0049] In some implementations, mapper 504 can be a network device at the level corresponding to substation 120. In other words, meters connected to substation 120 can be included in the meter set associated with mapper 504. Thus, mapper 504 can identify the topology and phase information of the meters connected to substation 120. Because the topology and phase information above the substation level is typically known to the utility company, by determining the topology and phase information for the meters below each substation, the utility company can obtain complete information related to the topology and phase of the power distribution system.

[0050] Now for reference Figure 6, Figure 6 This includes several flowcharts illustrating processes 600A, 600B, and 600C for identifying the topology or phase of a power distribution system according to certain aspects of this disclosure. Specifically, process 600A illustrates an aspect of the non-correlator meter 160, process 600B illustrates an aspect of the correlator 510, and process 600C illustrates an aspect of the mapper 504. The meter 160, correlator 510, and mapper 504 can respectively implement the operations in processes 600A, 600B, and 600C by executing appropriate program code. Processes 600A, 600B, and 600C will be described together below. For illustrative purposes, processes 600A, 600B, and 600C are described with reference to certain examples depicted in the figures. However, other implementations are possible.

[0051] At box 612, process 600B involves correlator 510 announcing itself to meters 160 in its group. As discussed above, meters 160 that communicate directly with correlator 510 via a local network connection form a meter group associated with correlator 510. Correlator 510 can send a message announcing itself as a correlator via the local network.

[0052] At block 602, process 600A involves: a meter 160 in a group associated with correlator 510 receiving a message indicating that correlator 510 is a correlator for that group. Meter 160 may further store information associated with correlator 510 (such as an address) such that voltage data generated by meter 160 can be sent to the correct correlator. In some scenarios, if meter 160 communicates directly with multiple correlators, the voltage data can be received by each of these multiple correlators.

[0053] Similar to box 612, at box 632, process 600C involves: mapper 504 sending a message advertising itself as a mapper for meter 160 located in the layer below the mapper in mesh network 140. At box 614, process 600B involves: correlator 510 receiving and storing mapper information, such as the address of mapper 504. These operations can be viewed as initialization operations for preparing meter 160, correlator 510, and mapper 504 for topology or phase detection.

[0054] At box 616, process 600B involves a correlator 510 (which is also an instrument) obtaining a voltage data sample based on the raw voltage value sensed at the correlator 510. For example, the correlator 510 can generate the voltage data sample by averaging the raw voltage values ​​sensed over individual time intervals. Depending on the information to be detected, the time interval can be set to different values. For example, if topology information is to be detected, the time interval can be set to 15 minutes, 1 minute, or 1 second. For phase information detection, the time interval can be set to a smaller value than that for topology information detection, such as a sub-cycle or even a sub-millisecond.

[0055] At block 618, process 600B involves determining whether a correlation should be performed for topology or phase detection, and thus determining whether voltage data should be collected from meters 160 in the group to enable the correlation to be performed. Correlator 510 can make this determination by detecting whether a triggering event has occurred. In some examples, triggering events include events where a voltage data sample detected at correlator 510 changes more than a threshold amount from a previous voltage data sample. Other events can be used as triggering events for topology and phase detection. Correlator 510 can also determine whether a correlation should be performed based on a predetermined schedule, such as performing the correlation at a specific time of day (e.g., midnight every day). If correlator 510 determines that a correlation should not be performed, process 600B involves, at block 616, continuing to obtain voltage data samples at correlator 510. If correlator 510 determines that a correlation should be performed, process 600B involves, at block 620, requesting voltage data from meters 160 in the group by transmitting a voltage data request via the local network.

[0056] Process 600A involves, at block 604, meter 160 obtaining a raw voltage value and generating voltage data samples based on that raw voltage value. Similar to correlator 510 described above, meter 160 can generate voltage data samples by averaging the raw voltage values ​​obtained at meter 160 over individual time intervals. The time intervals used by meter 160 are the same as those used by other meters in the group and other parts of the power distribution system. At block 606, process 600A involves receiving a voltage data request from correlator 510. At block 608, process 600A involves transmitting voltage data 512 to correlator 510 and other nodes that can receive voltage data. In some examples, voltage data 512 comprises a sequence of N voltage data samples, where N is a positive integer, such as 100.

[0057] At block 622, process 600B relates to: correlator 510 receiving voltage data 512 from meter 160 and other meters in its group. At block 624, process 600B relates to: generating correlation data 502. Correlation data 502 includes pairwise correlations between meters 160 in the group associated with correlator 510. Correlations can be included in correlation data 502 in any form that can be understood by correlator 510 and mapper 504. For example, it can be in the form of... Figure 3 The correlations are organized in the correlation matrix shown, in a one-dimensional vector, or in any other type of data structure. Correlator 510 further transmits the generated correlation data 502 to mapper 504.

[0058] Processor 600C involves, at block 634, receiving voltage data from meter 160 and other meters in the meter set associated with mapper 504. Mapper 504 receives this voltage data from these meters, allowing it to calculate correlations between meters from different groups, such as the correlations in portion 408 of aggregated correlation data 402. At block 636, process 600C involves receiving correlation data 502 from correlator 510 and other correlators associated with mapper 504. Because the correlation data 502 generated by correlator 510 only includes correlations between meters within a group, at block 638, process 600C involves generating correlations for pairs of meters from different groups. That is, mapper 504 can generate correlations for each pair of meters where the meters come from different groups. By combining these generated correlations with the correlation data 502 sent by correlator 510, mapper 504 can obtain complete correlation information for the meter set associated with mapper 504. At block 640, process 600C involves determining relationships between sets of meters associated with mapper 504. These relationships may include topology information (e.g., electrical connectivity relationships between meters indicating connections to transformers), phase information (e.g., phase relationships indicating connections between meters to phases in a power distribution system), or both. At block 642, mapper 504 outputs the determined relationships, for example, to headend system 104.

[0059] although Figure 6 Only correlator 510 and mapper 504 are shown, but can be understood as described above regarding Figure 5B As discussed, a high-level correlator 514 is used to shift some of the correlation calculations from mapper 504. Furthermore, although... Figure 6The illustration shows meter 160 and other meters sending voltage data 512 to correlator 510 in response to a request from correlator 510, but other implementations are also possible. For example, if it is determined whether to perform correlation based on a predetermined schedule, meter 160 can be configured to automatically send voltage data 512 according to the schedule when the time for correlation has arrived.

[0060] In some implementations, the transmission and reception of data between meter 160, correlator 510, advanced correlator 514, and mapper 504 is achieved through a publish-subscribe mechanism. In this mechanism, a first node wishing to receive data related to a specific topic from a second node can subscribe to that topic on the second node. The second node then publishes the data related to the specific topic. The first node and other nodes that have subscribed to the topic will receive the data. In the topology and phase detection system presented herein, correlator 510 can subscribe to voltage data for each meter in its group. When voltage data 512 is ready, meter 160 can publish that voltage data 512 once via the local network, and correlator 510 and other subscribers will be able to receive the published data. Similarly, mapper 504 can subscribe to correlation data 502 for each correlator 510 associated with mapper 504 in order to receive correlation data 502. Mapper 504 can also subscribe to voltage data 512 for each meter associated with it, allowing it to receive voltage data to calculate correlations for meters from different groups.

[0061] It should be noted that topology or phase information can change from time to time. For example, a new house may be built and connected to the power line, and a demolished building may be disconnected from the power line. The connection from the site to the transformer may be changed while the entire team is fixing problems associated with the power distribution system. To keep the topology or phase information up-to-date, the above process can be repeated periodically (e.g., once a week and / or whenever needed).

[0062] Identified topology or phase information can be utilized in multiple applications. For example, topology information can be used in corporate load management at the transformer level. In corporate load management, multiple locations connected to the same transformer can coordinate with each other to offset their power consumption, keeping the total power consumption at the transformer level below a given threshold to avoid power loss at the locations. In another example, phase information can be used by utility companies to balance loads on different phases and determine, for example, which of the three phases requires the installation of a new transformer.

[0063] Exemplary Instrument

[0064] Figure 7An exemplary instrument 700, such as meter 160, correlator 510, or mapper 504, that can be employed to implement the topology and phase detection described herein is illustrated. Instrument 700 includes a communication module 716 and a metering module 718 connected via a local or serial connection 730. These two modules can be housed in the same unit on a separate board; therefore, the local connection 730 can be a vehicle-mounted socket. Alternatively, the modules can be housed separately, and therefore, the local connection 730 can be a communication cable (such as a USB cable) or another conductor.

[0065] The communication module 716 functions to transmit voltage data 512 (for meter 160 and meter correlator 510), correlation data 502 (for correlator 510), and other data to other nodes in the mesh network 140; and to receive data from other meters or nodes in the mesh network 140. The metering module 718 functions to manage resources, specifically allowing access to resources and functions necessary for measuring the resources used. The communication module 716 may include a communication device 712, such as an antenna and a radio. Alternatively, the communication device 712 may be any device that allows wireless or wired communication. The communication module 716 may also include a processor 713 and a memory 714. The processor 713 controls the functions performed by the communication module 716. The memory 714 may be used to store data used by the processor 713 to perform its functions. The memory 714 may also store other data for meter 700, such as voltage data 512 and / or correlation data 502.

[0066] Metering module 718 may include processor 721, memory 722, and measuring circuitry 723. Processor 721 in metering module 718 controls the functions performed by metering module 718. Memory 722 stores data required by processor 721 to perform its functions and other data generated, such as voltage data 512. Communication module 716 and metering module 718 communicate with each other via local connection 730 to provide data required by the other module. Measuring circuitry 723 handles the measurement of resources and can be used as a sensor to collect sensor data. Both communication module 716 and metering module 718 may include computer-executable instructions stored in memory or another type of computer-readable medium, and one or more processors within the module can execute the instructions to provide the functions described herein.

[0067] As discussed above, correlators or mappers can be non-instrumented devices, such as routers, collectors, or other types of network devices. In those scenarios, the non-instrumented device may at least include a processor for performing the functions of the corresponding device, including calculating the correlation between voltage data. The non-instrumented device may further include a non-transient computer-readable storage medium for storing data required by the processor to perform its functions and other data generated by the processor (such as correlation data, determined topology and phase information, etc.). The non-instrumented device may further include a communication device that allows wireless or wired communication of at least the correlation data, determined topology and phase information, and other data.

[0068] General Considerations

[0069] This document sets forth numerous specific details to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter can be practiced without these specific details. In other instances, methods, apparatus, or systems known to those skilled in the art have not been described in detail so as not to obscure the claimed subject matter.

[0070] The features discussed herein are not limited to any particular hardware architecture or configuration. A computing device may include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include: a multi-purpose microprocessor-based computer system that accesses stored software (i.e., computer-readable instructions stored in the memory of a computer system) that programs or configures the computing system from a general-purpose computing device to a dedicated computing device that implements one or more aspects of this subject matter. The teachings contained herein in the software to be used in programming or configuring the computing device can be implemented using any suitable programming, scripting, or other type of language or combination of languages.

[0071] Aspects of the methods disclosed herein can be executed during the operation of such a computing device. The order of the boxes presented in the examples above can be varied; for example, the boxes can be reordered, combined, and / or decomposed into sub-boxes. Certain boxes or procedures can be executed in parallel.

[0072] The use of "adapted to" or "configured to" in this document is intended as an open and inclusive language that does not preclude the possibility of a device being adapted to or configured to perform additional tasks or steps. Similarly, the use of "based on" is intended to be open and inclusive, because a process, step, calculation, or other action "based on" one or more of the described conditions or values ​​may actually be based on additional conditions or values ​​beyond those described. The headings, lists, and numbering included in this document are for ease of interpretation only and are not intended to be limiting.

[0073] Although this subject matter has been described in detail with respect to its specific aspects, it should be understood that modifications, variations, and equivalents of such aspects can be readily made by those skilled in the art upon gaining an understanding of the foregoing. Accordingly, it should be understood that this disclosure has been presented for purposes of illustration rather than limitation, and that this disclosure does not exclude such modifications, variations, and / or additions to the subject matter as will be apparent to those skilled in the art.

Claims

1. A system for discovering relationships between meters in a power distribution system, comprising: Multiple meters, which are connected to the power distribution system through the same substation and are deployed in different geographical locations, wherein the multiple meters are communicatively connected through a communication network, and each of the multiple meters is configured to generate and transmit data samples obtained at the meter; A correlator, one of the plurality of instruments, is configured to: Receive the data sample from other instruments among the plurality of instruments; The correlation between the multiple instruments is calculated based on data samples received from the other instruments among the multiple instruments; as well as The calculated correlation is transmitted through the communication network; as well as The mapper, communicatively connected to the plurality of meters via the communication network, is configured to: The calculated correlation is received from the correlator via the communication network; as well as The relationship between at least the plurality of instruments is determined based on the calculated correlation; The relationship between the two instruments includes one or more of the following: the electrical connection between the two instruments indicating the connection of the two instruments to the transformer, or the phase relationship between the two instruments indicating the connection of the two instruments to the phase of the power distribution system; The aforementioned relationship is used to balance the load on different phases; The mapper mentioned above is a meter.

2. The system of claim 1, wherein The correlator is further configured to: It has been determined that the conditions for collecting data samples from the plurality of instruments have been met; and In response to determining that the condition has been met, a request for the data sample is sent to the plurality of instruments; and Each of the plurality of instruments is further configured to: Receive requests for data samples; Process the raw sensor values ​​sensed by the instrument to generate data samples for the instrument; and The data sample is transmitted via the communication network.

3. The system of claim 2, wherein processing raw sensor values ​​to generate a data sample of the instrument includes averaging the raw sensor values ​​sensed by the instrument at each predetermined time interval, and wherein the data sample includes a predetermined number of averaged sensor values.

4. The system of claim 2, wherein the conditions for collecting data samples from the plurality of instruments include: The scheduled time has been reached.

5. The system of claim 2, wherein the conditions for collecting data samples from the plurality of instruments include: The difference between the first data sample obtained by the correlator and the second data sample previously obtained by the correlator is greater than the threshold of sample change.

6. The system of claim 1, wherein the correlator is further configured to calculate the correlation between the correlator and the plurality of instruments.

7. The system of claim 1, wherein the mapper is further configured to: Receive data samples from the plurality of instruments and data samples from a second plurality of instruments; The correlation between the multiple instruments and the second set of multiple instruments is calculated based on data samples from the multiple instruments and data samples from the second set of multiple instruments; and The relationship between the plurality of instruments and the instruments in the second plurality of instruments is determined based on the calculated correlation.

8. The system of claim 1, wherein the correlator is further configured to transmit a message identifying the correlator as a correlator via the communication network, and wherein the mapper is further configured to transmit a message indicating that the mapper is a mapper via the communication network.

9. The system of claim 1, wherein the data sample includes a voltage data sample.

10. A method performed by an instrument for discovering relationships between multiple instruments in a power distribution system including the instrument, the method comprising: The message identifying the instrument as a correlator is transmitted via a local wireless network that connects the plurality of instruments. The plurality of meters are connected to the power distribution system via the same substation; the correlator is one of the plurality of meters. The conditions for generating and transmitting data samples have been met; The request for the data sample is sent to other instruments among the plurality of instruments via the local wireless network; The data sample is received from the other instruments among the plurality of instruments via the local wireless network; The sample correlation for each pair of meters among the plurality of meters is calculated based on data samples received from the plurality of meters. as well as The relationship between the multiple instruments is determined by transmitting the calculated sample correlation to the mapper via the local wireless network; The relationship between the two instruments includes one or more of the following: the electrical connection between the two instruments indicating the connection of the two instruments to the transformer, or the phase relationship between the two instruments indicating the connection of the two instruments to the phase of the power distribution system; The aforementioned relationship is used to balance the load on different phases; The correlator mentioned above is an instrument.

11. The method of claim 10, further comprising: In response to determining that the condition has been met, the raw sensor data sensed by the instrument is processed to generate a data sample of the instrument.

12. The method of claim 11, wherein the data sample is generated by averaging the raw sensor data over individual time intervals to generate an average sample, and wherein the data sample includes a predetermined number of average samples.

13. The method of claim 10, wherein the data sample includes a voltage data sample.

14. A method, executed by a mapper node, for discovering relationships between multiple meters connected to a power distribution system, the method comprising: Data samples are received from the multiple instruments through a communication network that connects the multiple instruments and the mapper node. The multiple meters mentioned above are connected to the power distribution system via the same substation; The communication network receives a first correlation set between a first group of meters and a second correlation set between two groups of meters, wherein the first correlation set is calculated by one of the meters in the first group as a correlator based on data samples generated by the other meters in the first group; and the second correlation set is calculated by one of the meters in the second group as a correlator based on data samples generated by the other meters in the second group. A third correlation set is calculated between the first group of meters and the second group of meters based on data samples from the multiple meters; as well as The relationships between the multiple instruments are determined based on the first correlation set, the second correlation set, and the third correlation set. The relationship between the two instruments includes one or more of the following: the electrical connection between the two instruments indicating the connection of the two instruments to the transformer, or the phase relationship between the two instruments indicating the connection of the two instruments to the phase of the power distribution system; The aforementioned relationship is used to balance the load on different phases; The mapper node mentioned above is an instrument.

15. The method of claim 14, further comprising: The message announcing the mapper node is transmitted to the plurality of instruments via the communication network.

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