Topology and Phase Detection for Power Distribution Networks

Abstract

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

Description

[Technical field]

[0001] The present disclosure relates generally to power grids, and more particularly to discovering and maintaining topology and phase information for power grids. [Background technology]

[0002] Utility companies typically manually track the location of field installed power meters and their connectivity to distribution transformers. Summary of the Invention [Problem to be solved by the invention]

[0003] For large utility companies, the number of electric meters exceeds millions and the number of distribution transformers approaches one million, making this manual process time-consuming and susceptible to errors. Also, due to technical complexity, effort, time constraints, cost, equipment availability, etc., phase information is rarely recorded. Furthermore, the actual phase designation of electric meters and associated upstream assets may 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, etc. Similar problems may exist with three-phase distribution transformers and the multiple electric meters connected to them. The phase of individual electric meters is typically not recorded, but needs to be determined for purposes of phase load balancing in the power grid. [Means for solving the problem]

[0004] Aspects and embodiments are disclosed for an apparatus and process for discovering topology and phase information of an electrical power distribution system. In one embodiment, a system for discovering relationships between a plurality of meters in an electrical power distribution system includes a plurality of meters connected to the electrical power distribution system and located at different geographic locations, a correlator communicatively connected to the plurality of meters via a communication network, and a mapper communicatively connected to the plurality of meters via the communication network. The plurality of meters are communicatively connected via the communication network. Each meter of the plurality of meters is configured to generate and transmit data samples acquired at the meter. The correlator is configured to receive data samples from the plurality of meters, calculate correlations between the plurality of meters based on the data samples received from the plurality of meters, and transmit the calculated correlations via the communication network. The mapper is configured to receive the calculated correlations from the correlator via the communication network and determine relationships between at least the plurality of meters based on the calculated correlations.

[0005] In another embodiment, a method is provided that is executed by a meter for discovering relationships between a plurality of meters of an electrical distribution system that includes the meter. The method includes transmitting a message over a local wireless network communicatively connecting the plurality of meters that identifies the meter as a correlator, determining that a condition for generating and transmitting a data sample is met, and transmitting a request for the data sample to other meters of the plurality of meters over the local wireless network. The method includes receiving data samples from other meters of the plurality of meters over the local wireless network, calculating sample correlations for each meter pair among the plurality of meters based on the data samples received from the plurality of meters, and transmitting the calculated sample correlations to a mapper over the local wireless network to determine relationships between the plurality of meters.

[0006] In yet another embodiment, a method is provided that is performed by a mapper node for discovering relationships between a plurality of meters connected to an electric grid. The method includes receiving data samples from the plurality of meters via a communication network communicatively connecting the plurality of meters and the mapper node, and receiving via the communication network 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. The first set of correlations and the second set of correlations are calculated based on the data samples generated by the plurality of meters. The method further includes calculating a third set of correlations between the first group of meters of the plurality of meters and a 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.

[0007] These exemplary aspects and features are mentioned not to limit or define the subject matter described herein, but to provide examples to aid in understanding the concepts described herein. Other aspects, advantages, and features of the subject matter described herein will become apparent upon reading the entirety of the application. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating a power distribution system according to certain aspects of the present disclosure. [Diagram 2] FIG. 1 is a block diagram illustrating an example operating environment for discovering topology and phase information in an electrical distribution system, in accordance with certain aspects of the present disclosure. [Diagram 3] 1 illustrates an example of a set of meters electrically connected to the same substation and locally connected to be able to communicate with each other, voltage signals detected by the meters, and correlations between the set of meters in accordance with certain aspects of the present disclosure. [Figure 4]4 illustrates examples of two groups of meters (including the group of meters illustrated in FIG. 3) and correlations between meters in each group of meters according to certain aspects of the disclosure. [Figure 5A] According to certain aspects of the present disclosure, a mesh network hierarchy for a group of meters shown in FIG. 4 is illustrated. [Figure 5B] 1 illustrates an example of a mesh network hierarchy using multi-layer correlators in accordance with certain aspects of the present disclosure. [Figure 6] 1 illustrates an example process for identifying the topology or phases of a power distribution system according to certain aspects of the present disclosure. [Figure 7] FIG. 1 is a block diagram illustrating an example of a meter suitable for implementing aspects of the technology presented herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read in conjunction with the accompanying drawings.

[0010] Systems and methods are provided for detecting topology and phase information of assets in an electric power distribution system. The asset topology and phase information or relationship may include electrical connection relationships indicating the connection of meters to electric power distribution components such as transformers. The asset topology and phase information or relationship may further include phase relationships indicating the connection of meters to three phases of the electric power distribution system. For example, an asset (e.g., meter) in an electric power distribution system with a sensor may be configured to measure characteristic parameters (e.g., voltage, current, load impedance) of the electric power distribution network. Assets (e.g., transformers) connected to the same electric power distribution component may observe the same variation in the detected data, whereas assets connected to different components generally do not observe the same variation. Similarly, assets connected to the same phase may observe the same variation in the detected data, whereas assets connected to different phases will observe the variation in the detected data with distinct phase offsets, such as ±120 degree offset or 180 degree offset.

[0011] In this manner, an asset may be configured to generate samples of detected data and share the data samples with nearby assets for analysis via a local network connection, such as a wireless mesh network, that connects the assets. One of the nearby assets, typically an asset with greater computational resources, such as greater memory or processing power, may act as a correlator that collects the data samples generated by the nearby assets and performs higher level calculations and analysis, such as statistical correlation, on the shared data samples. Based on the analysis, it may be determined which assets are connected to the same distribution component or the same phase. To determine the relationship of the nearby assets to other assets in the distribution system, another asset with additional computational power may act as a mapper that combines multiple correlations from multiple correlators and determines which assets are connected to the same distribution component or the same phase.

[0012] 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 head-end system. By configuring assets to generate and share data samples with nearby assets over a local network connection instead of sending data samples to a head-end system, communication bandwidth usage in the network is optimized by local transactions and communication bandwidth requirements for the head-end system are significantly reduced. Furthermore, distributing computation among assets allows for the utilization of assets that have excess computation capacity or are otherwise idle most of the time. In this way, processing may be decentralized without sending all data to a single location (such as a head-end system) for large-scale processing operations, thereby reducing the distances that data must travel.

[0013] Furthermore, the analysis of data samples can be distributed to various correlators and mappers in the system, thereby reducing the computational requirements of individual assets. Compared to traditional topology and phase identification approaches, the identification process proposed in this application can be performed automatically and periodically without human intervention. Therefore, a more accurate and up-to-date detection of the topology and phase information of the power distribution system can be achieved.

[0014] In the following description, for simplicity, a meter is used as an example of an asset, a transformer is used as an example of an electric distribution component, and voltage data is used as an example of data sensed by an asset. However, it should be understood that the techniques presented herein are not limited to these examples and are applicable to other types of assets, electric distribution components, and sensed data.

[0015] Example Operating Environment FIG. 1 is a block diagram illustrating a power distribution system 100 according to various aspects of the disclosure. In FIG. 1, a power generation facility 110 may generate power. The generated power may be, for example, three-phase alternating current (AC) power. In a three-phase power system, three conductors each transmit AC of the same frequency and voltage amplitude relative to a common reference, but with a phase difference of 1 / 3 of a cycle between each. The power may be transmitted at high voltage (e.g., about 140-750 kV) via transmission line 115 to a substation 120.

[0016] In the substation 120, a step-down transformer 130 may step down the high voltage power to a voltage level more suitable for customer use, called distribution or "medium voltage", typically around 13 kV. The stepped-down three-phase power may be transmitted via feeders 140a, 140b, and 140c to a distribution transformer 150, which may further step down the voltage (e.g., 120-240V for residential customers). Each distribution transformer 150 and 155 may transmit single-phase and / or three-phase power to residential and / or commercial customers. From the distribution transformers 150, 155, the power is transmitted to the customer via a power meter 160. The power meter 160 may be supplied by the utility company and may be connected between the load (i.e., customer premises) and the distribution transformers 150, 155. Three-phase transformer 155 may transmit three-phase power to customer premises, for example, by feeding three lines at a street front. In some areas, customer premises are randomly connected to one of these lines to obtain single-phase power. This random or tapped connection results in the utility being unable to track which premises are on which phase. In addition to three-phase power, single-phase power may be transmitted from distribution transformer 150 to various customers from different phases of the three-phase power generated by the utility company, resulting in uneven loading on the multiple phases.

[0017] Sensors 180 may be distributed throughout the network in various assets, including but not limited to, feeder circuits, distribution transformers, etc. Sensors 180 may detect various circuit parameters, such as frequency, voltage, current magnitude, phase angle, to monitor the operation of power distribution system 100. It should be appreciated that the locations of the sensors shown in FIG. 1 are merely exemplary and that sensors may be located in other locations and additional or fewer sensors may be used.

[0018] As can be seen from FIG. 1, assets are each 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 can update such information when the electrical connections (i.e., how multiple components are electrically wired together) and phases of the power distribution system 100 change over time. The following description utilizes a meter as an example of an asset. It should be understood that the described techniques also apply to other types of assets configured with sensors, such as transformers, generators, contactors, re-closers, fuses, switches, street lights, ripple receivers, ripple generators, capacitor banks, batteries, synchronous condensers, etc.

[0019] 2 illustrates an exemplary operating environment 200 for discovery of topology and phase information in an electrical distribution system, such as the electrical distribution system 100 illustrated in FIG. 1. The environment 200 includes a mesh network 140 associated with the electrical distribution system for transmitting measurement data acquired by meters in the electrical distribution system. The mesh network 140 includes a plurality of electrical power meters 160 or meters 160 disposed at various geographic locations of customer premises through the electrical distribution system 100. The meters 160 may be implemented to measure various operational characteristics of the electrical distribution system 100, such as resource consumption characteristics or other characteristics related to electrical usage in the system. Exemplary characteristics include, but are not limited to, average or total electrical consumption, peak voltages of electrical signals, power surges, and load changes. In some embodiments, the meters 160 include commercial & industrial (C&I) meters, residential meters, and the like.

[0020] The meter 160 may transmit the collected or generated data as meter measurement data 122 to the root node 114 via the mesh network 140. The root node 114 of the mesh network 140 may be configured to communicate with the meter 160 to perform certain operations, such as managing the meter 112, collecting the measurement data 122 from the meter 112, and forwarding the data to the head-end system 104. The root node 114 may also be configured to function as a data measuring and processing node in its own right. The root node 114 may be a personal area network (PAN) coordinator, gateway, or any other device capable of communicating with the head-end system 104.

[0021] The root node 114 transmits the generated and collected meter measurement data 122 over another network 170, such as the Internet, an intranet, or any other data communication network, and ultimately to the head-end system 104. The head-end system 104 may act as a central processing system that receives data streams or messages from the root node 114. The head-end system 104, or other systems associated with the utility company, may process or analyze the collected data for various purposes, such as billing, performance analysis, or troubleshooting.

[0022] 2 illustrates a particular network topology (e.g., a DODAG tree), it should be understood that other network topologies are also possible (e.g., ring topology, mesh topology, star topology, etc.). Additionally, while the following description focuses on aspects of a group of meters 112, the techniques described herein may be applied by any meter in a mesh network, including meters 160 and root node 114.

[0023] It should be understood that the mesh network 140 communicatively connecting the meters 160 is separate from, and may overlap with, the network for distributing power in the power distribution system 100. Thus, two meters 160 that are near each other in the mesh network 140 may not be near each other in the power distribution network, and vice versa. Thus, two meters 160 connected to the same transformer may belong to different PANs, and meters 160 that belong to the same PAN may be connected to different transformers.

[0024] FIG. 3 illustrates an example of detecting topology or phase information of a power distribution system without involving a head-end 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 connection. The local network may be a mesh network 140, a part of a mesh network 140, or any network that allows communication between meters without involving a network for communicating with the head-end system 104. Each of these meters is connected to a distribution transformer 150A or 150B in one of the three phases. As discussed above, in some areas, a customer premises may be randomly connected to one of three power lines of three-phase power to obtain single-phase power. This results in the utility company being unable to track the phase information of the meters in the premises. Similarly, there may be multiple transformers near a customer premises, and the meters for that customer premises may be randomly connected to one of these transformers, resulting in the utility company being unable to track which premises are connected to which transformer. By utilizing the techniques disclosed herein, the electrical connection relationships between meters A, B, C, and D (indicating the connection of the meters to the transformers) can be determined using local network connections between these meters without transmitting data to a head-end system. Similarly, the phase relationships between these meters (indicating the connection of the meters to the three phases of the distribution system) can also be identified via local network communication.

[0025] Data collected at the individual meters 160, such as voltage, current, load impedance, or other, can be used to obtain topology and / or phase information for each of the meters 160. FIG. 3 shows an example of using voltage data collected by the 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, FIG. 3 shows voltage values ​​measured at different times. These voltage values ​​are illustrated using curves 302A, 302B, 302C, and 302D for meters A, B, C, and D, respectively.

[0026] As can be seen from the curves 302A-302D shown in FIG. 3, for example, fluctuations (e.g., peaks or dips) in voltage values ​​may occur due to large loads on or off in one or more of the power lines. If a fluctuation is detected at a particular meter, nearby meters connected to the same transformer as the particular meter may also detect the fluctuation at approximately the same time. Note that due to resistance of the line connecting the particular meter to its nearby meters, the fluctuation may be attenuated when detected by the nearby meters. On the other hand, for meters connected to a different transformer than the particular meter, the detected fluctuation may be different from that of the particular meter. Based on these characteristics of the power distribution system, a statistical analysis may be performed to determine whether multiple meters are connected to the same transformer. In some embodiments, 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.

[0027] For example, detected voltage values ​​at different meters may be transmitted to one of the meters in the group, which is referred to herein as a correlator, such as correlator meter B. The correlator may be selected by self-designation, where a meter capable of performing correlation may send a communication indicating that it is a correlator to other meters over a local network connection. Meters that can communicate with the correlator over a location network connection may form a group of meters 306 for which the correlator may perform correlations to determine how the group of meters is related. Meters in a group may communicate with each other using the same protocol used to transmit meter measurement data 122, or using a different type of protocol.

[0028] In some embodiments, the voltage data for transmission to the correlator may be generated by sampling the voltage values. Sampling may be performed, for example, by averaging the voltage values ​​within each time interval ΔT. The time interval may be configured to be the same as the time interval used to collect and generate the power consumption data, or other time intervals such as 15 minutes, 1 minute, or 30 seconds. Note that some applications or embodiments of the techniques presented herein for topology or phase identification may require sub-cycle level samples. The averaged voltage values ​​or voltage data samples, shown in FIG. 3 with a cross marker "x", may be transmitted to the correlator as the voltage data. In some embodiments, the voltage data may include a sequence of N averaged voltage values, where N is an integer value such as 50, 100, or 200.

[0029] Sending the voltage data to the correlator may be based on a predetermined schedule or may be triggered by an event. For example, the correlator may be configured to request voltage data from the meters in the group to send the voltage data at 12:00 AM every day. In another embodiment, the correlator may be configured to detect a triggering event and, upon detecting the triggering event, request voltage data from the meters in the group. The triggering event may include, for example, an event in which the difference between two consecutive voltage data samples is higher than a threshold voltage change. An example of detecting such a triggering event is shown in FIG. 3. In this embodiment, the correlator meter B detects that the voltage change ΔV between two consecutive voltage data samples is higher than a threshold. Upon detecting such an event, the correlator meter B detects the time T at which the event occurred. * The correlator sends a request for voltage data collected near meters A, C, and D. The correlator detects event times T * In another embodiment, meters A, C, and D are each configured to detect a triggering event. After detecting the event, each meter begins collecting samples of voltage data for a specified duration (before, after, or near the triggering event) for a given duration (or for a given number of samples). After collection is complete, the meters automatically send the collected samples to correlator B.

[0030] After receiving the voltage data, the correlator may perform cross-correlation on the voltage data to generate a correlation between each meter pair in the group of meters, referred to herein as correlation data. In the embodiment shown in FIG. 3, the correlation data is shown in a correlation matrix 312. Entry (i,j) of the correlation matrix represents the correlation between meter i and meter j. Entry (i,j) is the same as entry (j,i) because the correlation between meter i and meter j is the same as the correlation between meter j and meter i. Thus, the correlation matrix is ​​symmetric and FIG. 3 shows only the bottom half of the matrix. Furthermore, the diagonal elements of the correlation matrix are always all ones since a meter is perfectly correlated with itself. Thus, only half of the correlation matrix (excluding the diagonal elements) contains useful information and is included in the correlation data.

[0031] In some embodiments, the correlation between a pair of meters can be represented using a correlation coefficient between two sequences of voltage data samples from the pair of meters. The correlation coefficient ranges between -1 and 1, where 1 indicates the strongest positive correlation between the two sequences of voltage data samples, 0 indicates that the two sequences of voltage data samples are independent of each other, and -1 indicates the strongest negative correlation between the two sequences of voltage data samples. It should be understood that various other types of statistical correlations can be utilized herein to measure the similarity between the voltage data of the meters.

[0032] 3 indicates that there is 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 the other meters is small. This means that meters A, B, and D may be connected to the same transformer 150A, while meter C is connected to a different transformer 150B. In this manner, the topological relationships between groups of meters 306 in the power distribution network can be determined.

[0033] Correlations for meters in other groups can be generated similarly. FIG. 4 illustrates an example of two groups of meters, including the group of meters 306 illustrated in FIG. 3. FIG. 4 illustrates correlations between meters in each group of meters, according to certain aspects of the disclosure. In this example, a second group of meters 406, including meters E, F, G, H, and I, is connected to the same substation 120 as group 306, but through a different transformer than transformers 150A and 150B. In this group of meters 406, meter G is a correlator and can communicate with the other meters in the group via a local network connection. In some examples, the meters in group 306 may not be communicatively connected to the meters in group 406 via a local network connection. For example, the meters in group 306 and the meters in group 406 may be too far apart to communicate directly with each other.

[0034] Correlator meter G may perform correlations for the meters in group 406 in a manner similar to that performed by correlator meter B to generate correlation data 404. As shown in correlation data 404, because meters E and F are connected to the same transformer 150C, the correlation between the voltage data of meters E and F is as high as 0.9. Similarly, because meters G and H are connected to the same transformer 150D, the correlation between the voltage data of meters G and H is also high (0.92). Meter I, on the other hand, does not have a high correlation with the other meters in group 406 because it is not connected to a different transformer 105E than the transformers of the other meters in the group.

[0035] Correlator B and Correlator G may transmit their respective correlation data 312 and 404 to a node at a higher level in the correlation hierarchy that is communicatively connected to Correlator B and Correlator G. This node is referred to herein as the "mapper." The mapper may process the received correlation data to generate correlation data for the meters in both groups. FIG. 4 also shows aggregated correlation data 402 for the two groups 306 and 406. In FIG. 4, sub-matrix 312 is generated by and transmitted from Correlator B, and sub-matrix 404 is generated by and transmitted from Correlator G.

[0036] To determine the correlation across the two groups (i.e., correlation between meters A, B, C, D and meters E, F, G, H, I), the mapper may perform correlation between the meters in group 306 and the meters in group 406 based on the voltage data received from these two meters. In this way, the mapper may compile aggregated correlation data 402 for all meters in groups 306 and 406. Based on the completed correlation data 402, the mapper may perform a mapping of the meters to corresponding transformers. For example, the mapper may determine that two meters are connected to the same transformer if the correlation between these two meters is higher than a correlation threshold value such as 0.85. In the example shown in FIG. 4, the mapper may determine that meters A, B, and D are connected to one transformer, meters E and F are connected to another transformer, meters G and H are connected to a third transformer, and meters C and I are further connected to two separate transformers.

[0037] FIG. 5A illustrates an example correlation hierarchy for the groups of meters shown in FIG. 4 according to certain aspects of the disclosure. In some embodiments, a correlator 510 (such as correlator B or correlator G) may communicate with mapper 504 via mesh network 140 and is in a layer of the mesh network closer to the head-end system than the meters in the group. Correlator 510 receives voltage data 512 from the meters in the group and generates correlation data (e.g., correlation data 312 or correlation data 404) as described with respect to FIGS. 3 and 4. Correlator 510 further transmits the correlation data to mapper 504 to determine aggregate correlation data for the two groups of meters and to determine topology information for the meters. It should be understood that meters in a group, such as meters A, C, B, and D, may belong to multiple PANs that are different from one another. Mapper, correlator, and meters 160 may be in the same layer of mesh network 140 or in different layers.

[0038] While the above examples show the correlators to be meters, it should be further understood that other types of network devices, such as routers, collectors, etc., may also function as correlators. Similarly, the mapper 504 may be a meter, a router, a collector, or other types of network devices. As previously mentioned, the mapper may be a node of a mesh network that is one or more layers above the correlators, but not higher than the root node 114. While the above description focuses on meter groups where the correlators are in direct communication with other meters in each group, it should be further understood that a set of meter groups may include meters that are not in direct communication with the correlators. For example, a group (such as group 306) may further include meters (such as meter X in FIG. 5A) that can communicate with the correlators via other meters (such as meter A in FIG. 5A).

[0039] Although FIG. 5A shows the mapper 504 being used to determine topology information for only two groups of meters, the mapper may cover multiple groups of meters located at various tiers below the mapper in the correlation hierarchy. These meters may be collectively referred to as the collection of meters associated with the mapper. Correlators for individual groups in the collection of meters may provide correlation data for their respective groups to the mapper to determine topology information for those meters. In alternative or additional embodiments, instead of a correlator that sends correlation data directly to the mapper, a higher level correlator may be utilized that performs a portion of the correlation before sending the correlation data to the mapper.

[0040] 5B illustrates an example of a multi-layer correlator configuration according to certain aspects of the disclosure. In FIG. 5B, there are six groups of meters in the collection of meters associated with the mapper 504, and each correlator 510 represents its corresponding group of meters. In this example, the correlators 510 may send their respective correlation data 502 to a corresponding upper correlator 514. The upper correlator 514 aggregates the received correlation data 502 and calculates correlation values ​​between meters from different groups to generate aggregated correlation data 522 similar to the aggregated correlation data 402 illustrated in FIG. 4. The upper correlator 514 may then send the aggregated correlation data 522 to the mapper 504 to complete the remaining correlations and determine the topology information of the meters. Because part of the correlation calculations is shifted to the upper correlator 514, the workload of the mapper 504 may be reduced, and thus the computational power requirements of the mapper 504 may be reduced. Similar to the correlator 510, the top correlator 514 may be a meter, router, collector, or other type of network device capable of aggregating the correlated data 502. The top correlator 514 may be topologically located in a layer of the mesh network above or below the mapper 504 or corresponding correlator. Additional layers of top correlators may be added to further reduce the workload of the mapper 504.

[0041] The above description with respect to FIGS. 3-5B describes a method for detecting topology information associated with meters in a power distribution network. A similar mechanism may be utilized to detect phase information of meters. Since the voltages in different phases are offset, for example, by 120 degrees, a correlation of voltage values ​​of two meters may be used to determine whether two meters are in the same phase. Unlike topology detection, phase information detection requires that voltage data be generated at a smaller granularity due to the high frequency of voltages in the power line. For example, the meters may be configured to generate voltage data having a higher frequency, e.g., N voltage data samples per cycle, where N is an integer value such as 50, 100, etc. Similar to topology detection described above, the generated voltage data is transmitted from the meters to their respective correlators, and each correlator performs a correlation based on the received voltage data to generate correlation data. The correlation data may be further transmitted 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 may determine the meters connected to the same phase based on the complete correlation data. The mapper may further determine the exact phase of each individual meter by obtaining reference phase information, for example from a substation.

[0042] In some embodiments, mapper 504 may be a network device at a level corresponding to substation 120. In other words, the meters connected to substation 120 may be included in a collection of meters associated with mapper 504. In this manner, mapper 504 may identify topology and phase information for meters connected to substation 120. Typically, the topology and phase information above the substation level is known to the utility company, so by determining the topology and phase information for the meters below each substation, the utility company can obtain complete information about the topology and phase of the distribution system.

[0043] Reference is now made to FIG. 6, which includes several flow diagrams illustrating several processes 600A, 600B, and 600C for identifying a topology or phase of a power distribution system, according to certain aspects of the present disclosure. In particular, process 600A illustrates aspects of a decorrelator meter 160, process 600B illustrates aspects of a correlator 510, and process 600C illustrates aspects of a mapper 504. Meter 160, correlator 510, and mapper 504 may perform the operations of processes 600A, 600B, and 600C, respectively, by executing appropriate program code. Processes 600A, 600B, and 600C are collectively described below. For illustrative purposes, processes 600A, 600B, and 600C are described with reference to certain implementations shown in the drawings. However, other implementations are possible.

[0044] At block 612, the process 600B includes the correlator 510 advertising itself to the meters 160 in its group. As explained above, the meters 160 that can communicate directly with the correlator 510 over a local network connection form a group of meters associated with the correlator 510. The correlator 510 may send a message over the local network advertising itself as a correlator.

[0045] At block 602, process 600A includes a meter 160 in the group associated with correlator 510 receiving a message indicating that correlator 510 is the correlator for this group. Meter 160 may further store information associated with correlator 510, such as an address, so that voltage data generated by meter 160 can be sent to the correct correlator. In some scenarios, if meter 160 is in direct communication with multiple correlators, the voltage data may be received by each of these multiple correlators.

[0046] Similar to block 612, in block 632, process 600C includes the mapper 504 sending a message advertising itself as a mapper for meters 160 located in a layer of the mesh network 140 below the mapper. In block 614, process 600B includes the correlator 510 receiving and storing mapper information, such as the address of the mapper 504. The above operations can be viewed as initialization operations that prepare the meters 160, the correlator 510, and the mapper 504 for topology or phase detection.

[0047] In block 616, process 600B includes correlator 510 (which is also a meter) obtaining a voltage data sample based on the raw voltage values ​​detected in correlator 510. For example, correlator 510 may generate the voltage data sample by averaging the raw voltage values ​​detected within each time interval. Depending on the information to be detected, the time interval may be set to different values. For example, if topology information is to be detected, the time interval may be set to 15 minutes, 1 minute, or 1 second. For phase information detection, the time interval may be set to a smaller value than that for topology information detection, such as sub-cycle or sub-millisecond.

[0048] At block 618, process 600B includes determining whether correlation should be performed for topology or phase detection and therefore whether voltage data should be collected from meters 160 in the group so that correlation can be performed. Correlator 510 may make such a determination by detecting whether a triggering event has occurred. In some embodiments, a triggering event includes an event in which a voltage data sample detected in correlator 510 changes from a previous voltage data sample by more than a threshold amount. Other events may be used as triggering events for topology and phase detection. Correlator 510 may determine that correlation should be performed based on a predetermined schedule, such as performing correlation at a particular time of day (e.g., 12:00 a.m. every day). If correlator 510 determines that correlation should not be performed, process 600B includes continuing to obtain voltage data samples in correlator 510 at block 616. If the correlator 510 determines that a correlation should be performed, the process 600B includes, at block 620, requesting voltage data from the meters 160 in the group by sending a request for voltage data over the local network.

[0049] The process 600A includes, at block 604, the meter 160 obtaining raw voltage values ​​and generating voltage data samples based on the raw voltage values. Similar to the correlator 510 described above, the meter 160 may generate the voltage data samples by averaging raw voltage values ​​obtained at the meter 160 within each time interval. The time interval used by the meter 160 is the same as the time interval used by other meters in the group and by other meters in other parts of the power distribution system. At block 606, the process 600A includes receiving a voltage data request from the correlator 510. At block 608, the process 600A includes transmitting the voltage data 512 to the correlator 510 and to other nodes that can receive the voltage data. In some embodiments, the voltage data 512 includes a sequence of N voltage data samples, where N is a positive integer, such as 100.

[0050] At block 622, process 600B includes correlator 510 receiving voltage data 512 from the meter 160 and other meters in the group. At block 624, process 600B includes generating correlation data 502. Correlation data 502 includes pairwise correlations between meters 160 in the group associated with correlator 510. The correlations may be included in correlation data 502 in any format that is understandable by correlator 510 and mapper 504. For example, the correlations may be organized into a correlation matrix, as shown in FIG. 3, a one-dimensional vector, or any other type of data structure. Correlator 510 further transmits generated correlation data 502 to mapper 504.

[0051] The process 600C includes receiving voltage data from the meter 160 and other meters in the collection of meters associated with the mapper 504, at block 634. The mapper 504 receives these voltage data from these meters so that it can calculate correlations between meters from different groups, such as the correlations in the portion 408 of the aggregated correlation data 402. At block 636, the process 600C includes receiving correlation data 502 from the correlator 510 and other correlators associated with the mapper 504. Because the correlation data 502 generated by the correlator 510 only includes correlations between meters within a group, at block 638, the process 600C includes generating correlations for meter pairs from different groups. That is, the mapper 504 may generate correlations for each meter pair if the meters are from different groups. By combining these generated correlations with the correlation data 502 sent by the correlator 510, the mapper 504 may obtain complete correlation information for the collection of meters associated with the mapper 504. At block 640, the process 600C includes determining relationships of the set of meters associated with the mapper 504. The relationships may include topology information (e.g., electrical connection relationships of the meters indicating the connection of the meters to transformers), phase information (e.g., phase relationships indicating the connection of the meters to phases of the power distribution system), or both. At block 642, the mapper 504 outputs the determined relationships, for example to the head-end system 104.

[0052] 6 shows only correlator 510 and mapper 504, an upper correlator 514 may be used as described above with respect to FIG. 5B to offload some of the correlation calculations from mapper 504. Additionally, although FIG. 6 shows meter 160 and other meters transmitting voltage data 512 to correlator 510 in response to a request from correlator 510, other implementations are available. For example, if the decision whether to perform correlation is based on a predetermined schedule, meter 160 may be configured to automatically transmit voltage data 512 when the time for correlation expires according to the schedule.

[0053] In some embodiments, sending and receiving data between the meters 160, the correlators 510, the top correlators 514, and the mappers 504 is accomplished via a subscribe-publish mechanism. In this mechanism, a first node that wants to receive data on a particular topic from a second node may subscribe to the topic to the second node. The second node then publishes data on the particular topic. The first node and other nodes that have subscribed to the topic receive the data. In the topology and phase detection system presented herein, the correlators 510 may subscribe to voltage data to each of the meters in the group. When the voltage data 512 is ready, the meters 160 may publish their voltage data 512 once over the local network, and the correlators 510 and other subscribers can receive the published data. Similarly, the mappers 504 may subscribe to the correlation data 502 to each of the correlators 510 associated with the mappers 504 to receive the correlation data 502. The mapper 504 submits the voltage data 512 for each of the meters associated with it so that it may receive voltage data and calculate correlations for meters from different groups.

[0054] It should be noted that the topology or phase information may change over time. For example, new homes may be built and connected to the power line, and demolished buildings may be disconnected from the power line. Workers may change premises' connections to transformers while resolving problems associated with the power distribution system. To keep the topology or phase information up to date, the above process may be repeated periodically, such as once a week, or may be performed whenever needed.

[0055] The identified topology or phase information may be utilized in multiple applications. For example, the topology information may be utilized at the transformer level in enterprise load management, where multiple premises connected to the same transformer may coordinate with each other to offset their power consumption to keep the total power consumption at the transformer level below a given threshold to avoid power loss in the premises. In another embodiment, the phase information may be utilized by a utility company to balance the loads on different phases and to determine, for example, which of the three phases requires the installation of a new transformer.

[0056] Example Meter 7 illustrates an example meter 700, such as meter 160, correlator 510, or mapper 504, that can be used to implement the topology and phase detection described herein. Meter 700 includes a communications module 716 and a measurement module 718 connected via a serial connection 730. These two modules may be housed in the same device on separate boards, and thus local connection 730 may be an on-board socket. Alternatively, the modules may be housed separately, and thus local connection 730 may be a communications cable, such as a USB cable, or other conductor.

[0057] The functionality of the communication module 716 includes transmitting voltage data 512 (for the meter 160 and meter correlator 510), correlation data 502 (for the correlator 510), and other data to other nodes in the mesh network 140, and receiving data from other meters or nodes in the mesh network 140. The functionality of the metering module 718 includes functionality necessary to manage resources, particularly to enable access to resources and measure resource usage. The communication module 716 may include a communication device 712, such as an antenna and a radio device. Alternatively, the communication device 712 may be any device that enables 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 utilized to store data used by the processor 713 to perform its functions. The memory 714 may also store other data for the meter 700, such as the voltage data 512 and / or the correlation data 502.

[0058] The metrology module 718 may include a processor 721, a memory 722, and a measurement circuit 723. The processor 721 in the metrology module 718 controls the functions performed by the metrology module 718. The memory 722 stores data required by the processor 721 to perform its functions, such as the voltage data 512, and other data generated. The communication module 716 and the metrology module 718 communicate with each other through a local connection 730 to provide data required by the other modules. The measurement circuit 723 may handle measurements of resources and may be used as a sensor to collect sensor data. Both the communication module 716 and the metrology module 718 may include computer-executable instructions stored in a memory or another type of computer-readable medium, and one or more processors within the modules may execute the instructions to provide the functionality described herein.

[0059] As described above, the correlator or mapper may be a non-meter device, such as a router, collector, or other type of network device. In those scenarios, the non-meter device may include at least a processor for performing the functions of the device, including calculating the correlation between the voltage data. The non-meter device may further include a non-transitory computer-readable storage medium for storing data required by the processor to perform its functions and other data generated by the processor, such as the correlation data, the determined topology and phase information, etc. The non-meter device may further include a communication device to enable wireless or wired communication of at least the correlation data, the determined topology and phase information, and other data.

[0060] Overall consideration Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatus, or systems that would be known by one of ordinary skill in the art have not been described in detail so as not to obscure the claimed subject matter.

[0061] The features described herein are not limited to any particular hardware architecture or configuration. A computing device may include any suitable device of components that provide a result conditioned on one or more inputs. Suitable computing devices include general-purpose microprocessor-based computer systems that access stored software (i.e., computer-readable instructions stored on the computer system's memory) that programs or configures the computing system from a general-purpose computing device to a specialized computing device that implements one or more aspects of the subject matter of this application. The software used to program or configure the computing device may use any suitable programming, scripting, or other type of language, or combination of languages, to implement the disclosure contained herein.

[0062] Aspects of the methods disclosed herein may be implemented in the operation of such a computing device. The order of the blocks presented in the above examples may be changed, e.g., the blocks may be rearranged, combined, and / or divided into sub-blocks. Certain blocks or operations may be performed in parallel.

[0063] Use of "adapted to" or "configured to" herein is intended to be open and inclusive, not excluding apparatus adapted or configured to perform additional tasks or steps. Furthermore, use of "based on" is intended to be open and inclusive, in that a process, step, calculation, or other action "based on" one or more recited conditions or values ​​may in fact be based on additional conditions or values ​​beyond those recited. Headings, lists, and numbers contained in this application are for ease of description only and are not intended to be limiting.

[0064] While the subject matter of the present application has been described in detail with respect to certain embodiments thereof, it will be recognized that those skilled in the art, upon understanding the foregoing, may readily make modifications, variations, and equivalents of such embodiments. It is therefore to be understood that the present disclosure is presented for purposes of illustration and not limitation, and is not intended to exclude inclusion of such modifications, variations, and / or additions to the subject matter of the present application as would be readily apparent to one of ordinary skill in the art.

Claims

1. 1. A system for discovering relationships between a plurality of meters in an electrical distribution system, the system comprising: a plurality of meters connected to the power distribution system and located at different geographic locations, the plurality of meters communicatively connected to each other via a communications network; a correlator communicatively connected to the plurality of meters via the communication network; a mapper communicatively connected to the plurality of meters via the communication network; Each meter of the plurality of meters is receiving a request for a data sample; processing raw sensor values ​​sensed by said meter to generate data samples for said meter; configured to transmit the data samples over the communications network; The correlator is determining that a condition has occurred for collecting data samples from the plurality of meters; in response to determining that the condition is satisfied, transmitting a request for a data sample to the plurality of meters; receiving said data samples from said plurality of meters; calculating correlations between said plurality of meters based on data samples received from said plurality of meters; configured to transmit the calculated correlation over the communications network; The mapper above is receiving the calculated correlation from the correlator over the communications network; configured to determine a relationship between at least the plurality of meters based on the calculated correlation; a condition for collecting the data samples from the plurality of meters includes a difference between a first data sample acquired by the correlator and a second data sample previously acquired by the correlator exceeding a sample change threshold; system.

2. processing the raw sensor values ​​to generate data samples for the meter includes averaging the raw sensor values ​​sensed by the meter within each predetermined time interval; the data sample includes a predetermined number of averaged sensor values; The system of claim 1 .

3. the condition for collecting the data samples from the plurality of meters includes reaching a predetermined time point; The system of claim 1 .

4. the correlator comprises a meter; the correlator is further configured to calculate a correlation between the correlator and the plurality of meters. The system of claim 1 .

5. the mapper comprises at least one of a meter, a router, or a collector; The system of claim 1 .

6. The mapper above is receiving data samples of the plurality of meters and data samples of a second plurality of meters; calculating a correlation between the plurality of meters and the second plurality of meters based on the data samples of the plurality of meters and the data samples of the second plurality of meters; and further configured to determine a relationship between the plurality of meters and a plurality of meters in the second plurality of meters based on the calculated correlation. The system of claim 1 .

7. the relationship between two meters includes one or more of an electrical connection relationship between the two meters indicating a connection of the two meters to a transformer, and a phase relationship between the two meters indicating a connection of the two meters to a phase of the electrical distribution system. The system of claim 1 .

8. the correlator is further configured to transmit, over the communications network, a message identifying the correlator as a correlator; The mapper is further configured to transmit, over the communications network, a message identifying the mapper as a mapper. The system of claim 1 .

9. the data samples include voltage data samples; The system of claim 1 .

10. 1. A method executed by a meter for discovering relationships between meters of an electrical distribution system including the meter, the method comprising: transmitting a message over a local wireless network communicatively connecting the plurality of meters, the message identifying the meter as a correlator; determining that a condition for generating and transmitting a data sample has been met; transmitting a request for a data sample to other meters of the plurality of meters via the local wireless network; receiving the data samples from the other meters of the plurality of meters via the local wireless network; calculating a sample correlation for each meter pair among the plurality of meters based on data samples received from the plurality of meters; and transmitting the calculated sample correlations to a mapper over the local wireless network to determine relationships between the meters; a condition for collecting the data samples from the plurality of meters includes a difference between a first data sample acquired by the correlator and a second data sample previously acquired by the correlator exceeding a sample change threshold; method.

11. In response to determining that the condition is satisfied, processing raw sensor data sensed by the meter to generate a data sample for the meter. Further comprising: The method of claim 10.

12. the data samples are generated by averaging the raw sensor data within each time interval to generate averaged samples; the data sample includes a predetermined number of averaged sensor values; The method of claim 11.

13. the data samples include voltage data samples; The method of claim 10.

14. The relationship between the plurality of meters includes one or more of an electrical connection relationship between the plurality of meters and a phase relationship between the plurality of meters. The method of claim 10.

15. 1. A method performed by a mapper node for discovering relationships between a plurality of meters connected to an electricity grid, the method comprising: receiving data samples from the plurality of meters via a communications network communicatively connecting the plurality of meters and the mapper node; receiving, via the communications network, a first set of correlations between a first group of meters and a second set of correlations between a second group of meters of the plurality of meters; the first set of correlations and the second set of correlations are calculated based on data samples generated by the plurality of meters; The above method is calculating 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; determining relationships among the plurality of meters based on the first set of correlations, the second set of correlations, and the third set of correlations. method.

16. the mapper node comprises at least one of a meter, a router, or a collector; 16. The method of claim 15.

17. transmitting a message over the communications network advertising the mapper node to the plurality of meters; Further comprising:

16. The method of claim 15.

18. The relationship between the plurality of meters includes one or more of an electrical connection relationship between the plurality of meters and a phase relationship between the plurality of meters.

16. The method of claim 15.

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