Topology and phase automated discovery of electrical supply networks

Abstract

To provide a method and system for discovering a topology and phase of a power distribution system.SOLUTION: A group of a meter 160 connected to a power distribution system 100 processes sensor data obtained by the meter, generates a descriptor based on processed data, and transmits the descriptor to a headend system. After receiving the descriptor from the various meters in the power distribution system, the headend system applies a clustering algorithm to the descriptor of these meters to generate a grouping, and compares the current grouping with a historical grouping to determine a confidence level of the current grouping, and assigns a segment identifier and / or a phase identifier to one or more of the meters based on the confidence level.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] Technical Field The present disclosure relates generally to electrical distribution networks, and more particularly to discovering topology and phase information for electrical distribution networks. [Background technology]

[0002] background Electric utilities typically manually track the location of electric meters installed in the field and their connections to distribution transformers. For large utility companies, the number of electric meters can exceed millions and the number of distribution transformers can approach one million, making this manual process time-consuming and error-prone. Additionally, phase information is not recorded due to technical complexity, effort, time constraints, cost, and availability of equipment. Furthermore, the actual phase designation of electric meters and associated upstream assets can sometimes change due to work performed by line crews on system assets. Similar issues can exist with three-phase distribution transformers that have multiple electric meters connected to them. The phase of individual electric meters is typically not recorded but must be determined for the purpose of managing grid loads. Summary of the Invention

[0003] summary Aspects and examples are disclosed relating to apparatus and processes for discovering or identifying topology and phase information of assets in an electrical distribution system. For example, a method for discovering the topology and phase of one or more utility devices in a resource distribution system includes receiving, by a head-end system, descriptors from a plurality of utility devices connected to the electrical distribution system. The descriptors are generated at each utility device by processing sensor data acquired at each utility device. The method further includes, in response to determining that at least a threshold number of descriptors for a normal mode have been received, grouping the plurality of utility devices to generate a current grouping by applying a clustering algorithm to the descriptors of the plurality of utility devices, by the head-end system; comparing, by the head-end system, the current grouping with past groupings to determine a confidence level of the grouping; determining, by the head-end system, whether the confidence level exceeds a threshold confidence level; and, in response to determining that the confidence level exceeds the threshold confidence level, assigning, by the head-end system, at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices.

[0004] In another example, a method performed by a utility device to generate descriptors for discovering the topology and phase of an electrical power distribution system includes processing sensor data acquired by the utility device to generate processed data and determining whether the utility device is operating in a high speed mode or a normal mode. When the utility device is operating in the high speed mode, the utility device generates descriptors at a higher rate than when operating in the normal mode. The method further includes generating high speed descriptors based on the processed data in response to determining that the utility device is operating in the high speed mode, and transmitting the high speed descriptors to a head-end system communicatively connected to the utility device in response to determining that at least a first threshold number of high speed descriptors have been generated. The method also includes generating normal descriptors based on the processed data in response to determining that the utility device is operating in the normal mode, and transmitting the normal descriptors to the head-end system in response to determining that at least a second threshold number of normal descriptors have been generated, the second threshold number being higher than the first threshold number, and the high speed descriptors and the normal descriptors being different.

[0005] In a further example, a system includes a plurality of utility devices and a head-end system. The plurality of utility devices are connected to an electrical distribution system and communicatively connected to the head-end system. Each of the plurality of utility devices is configured to process sensor data acquired at the plurality of utility devices to generate processed data, and generate and transmit a fast descriptor or a normal descriptor based on the processed data. The head-end system is configured to receive the fast descriptor or the normal descriptor from the plurality of utility devices and determine whether the plurality of utility devices are operating in either a fast mode or a normal mode. In the fast mode, a segment identifier or a phase identifier is assigned to one of the plurality of utility devices for a shorter period of time than in the normal mode. In response to determining that the plurality of utility devices are operating in the normal mode, the head-end system is further configured to group the plurality of utility devices by applying a clustering algorithm to the normal descriptors of the plurality of utility devices to generate a current grouping, compare the current grouping with past groupings to determine a confidence level of the current grouping, and assign at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices based on the confidence level.

[0006] These exemplary aspects and features are mentioned not to limit or define the presently described subject matter, but to provide examples to aid in understanding the concepts described herein. Other aspects, advantages, and features of the presently described subject matter will become apparent after reviewing the entire application. [Brief explanation of the drawings]

[0007] These and other features, aspects, and advantages of the present disclosure will be better understood from the following detailed description when read in conjunction with the accompanying drawings. [Figure 1]FIG. 1 is a block diagram illustrating a power distribution system according to an aspect of the present disclosure. [Figure 2] FIG. 2 illustrates a utility management system in which meters in a distribution system can communicate with a head-end system to facilitate discovery of the topology and phases of the distribution system, according to an aspect of the disclosure. [Figure 3] FIG. 3 is an example of a process for generating descriptors in a meter of an electrical distribution system to facilitate topology and phase discovery by a head-end system, according to an aspect of the present disclosure. [Figure 4] FIG. 4 is an example of a process for processing sensor data in a meter of an electrical distribution system according to an aspect of the disclosure. [Figure 5] FIG. 5 is an example of a process for identifying the topology and phases of an electrical distribution system according to an aspect of the disclosure. [Figure 6] FIG. 6 is an example of a fast mode process for identifying the topology and phases of an electrical distribution system according to an aspect of the disclosure. [Figure 7] FIG. 7 is a block diagram illustrating an example of a meter suitable for implementing aspects of the techniques and technologies presented herein. [Figure 8] FIG. 8 is a block diagram illustrating an example of a suitable computing system for implementing aspects of the techniques and technologies presented herein. DETAILED DESCRIPTION OF THE INVENTION

[0008] Detailed Description Systems and methods are provided for discovering or identifying topology and phase information of assets in an electrical distribution system. For example, assets (e.g., meters, transformers, generators) in an electrical distribution system equipped with sensors may be configured to collect sensor data (e.g., voltage, current, load impedance, temperature). Each asset may preprocess the sensor data before transmitting it to a head-end system for topology and phase discovery. Preprocessing may include, for example, filtering the sensor data to remove or reduce noise or offset, converting the sensor data to the frequency domain, or normalizing the sensor data to generate processed sensor data. In some cases, preprocessing may include enhancing noise that represents unique characteristics or signatures localized to a small population of meters. Preprocessing may further include detecting events, such as disturbances or anomalies, in the sensor data and determining characteristics of the events, such as the duration of the event, the number of events, the frequency of the event, the time and severity of the event.

[0009] Based on the processed sensor data and the detected events, the asset can further generate descriptors, which may include, for example, a value change descriptor describing a value change of the sensor data, a ranking descriptor identifying a maximum or minimum value in the sensor data, a filtering descriptor including a filtered value of the sensor data, an event-based descriptor including the timing, frequency and magnitude of the event, the frequency ratio for different types of events, etc. The generated descriptors are transmitted to a head-end system for topology and phase discovery.

[0010] After receiving descriptors from various assets in the distribution system, the head-end system can perform clustering to group assets into different segments and different phases. Reference assets with known phase IDs and segment IDs can be utilized to assign specific phase IDs and segment IDs to unknown assets. The determined assignment information can be sent to individual assets and utilized for display, etc. Additionally, event timing information included in the descriptors can be utilized to synchronize the clocks of various assets in the distribution system. In some implementations, assets in the network with spare computing and communication resources (e.g., edge processors) can act as agents of the head-end system to perform these operations to reduce communication traffic to the head-end system and / or reduce the computing load on the head-end system.

[0011] The above-described operations may be performed when the asset operates in normal mode. In some scenarios, the asset may be configured to operate in a fast mode, in which segment and phase identification may be performed in a shorter period of time than in normal mode. In fast mode, an asset having an unknown segment or phase and its neighboring assets may be configured to generate fast descriptors based on processed sensor data without detecting an event or based on minor events that occur continuously. The asset may transmit the fast descriptors to a head-end system or edge processor at a higher rate than normal mode descriptors. Similarly, the head-end system may perform correlations on the fast descriptors to determine segment and phase assignments more quickly than in normal mode. The segment and phase assignments may be determined by utilizing neighboring assets as reference assets.

[0012] The techniques described in this disclosure improve the efficiency and accuracy of segment and phase detection for assets in a power distribution system and for communication between assets and a head-end system. By configuring assets to generate and transmit descriptors rather than raw sensor data, bandwidth consumption can be significantly reduced because descriptors typically have a much smaller size than raw sensor data. Furthermore, processing the sensor data locally at the asset allows each asset to identify the timing of events and the relative timing of different events. Such information allows for more accurate grouping of assets than raw sensor data. Furthermore, the head-end system can use the timing information to determine the offset of an asset's clock relative to a high-precision asset clock to perform high-precision time synchronization or time alignment between assets.

[0013] Example Operating Environment

[0014] FIG. 1 is a block diagram illustrating an exemplary power distribution system 100 according to various aspects of the present disclosure. In FIG. 1, a power generation facility 110 may generate electrical power. The generated electrical power may be, for example, three-phase alternating current (AC) power. In a three-phase power supply system, three conductors each carry an AC current of the same frequency and voltage amplitude relative to a common reference, but with a one-third-cycle phase difference between them. The electrical power may be transmitted at high voltage (e.g., on the order of 140-750 kV) via a transmission line 115 to an electrical power substation 120.

[0015] At the power substation 120, a step-down transformer 130 can step down the high-voltage power to a voltage level more suitable for customer use. The stepped-down three-phase power may be transmitted via feeders 140a, 140b, 140c to distribution transformers 150, which further step down the voltage (e.g., 120-240V for residential customers). Each distribution transformer 150, 155 may provide single-phase and / or three-phase power to residential and / or commercial customers. From the distribution transformers 150, 155, power is delivered to the customer via an electric meter 160. The electric meter 160 may be supplied by a utility company and may be connected between the load (i.e., the customer premises) and the distribution transformers 150, 155. Three-phase transformer 155 may, for example, feed three wires on a street frontage to provide three-phase power to customer premises. In some areas, customer premises are randomly connected to one of these wires to obtain single-phase power. Such random connections or taps prevent the power company from knowing which buildings are on which phase. In addition to three-phase power, single-phase power may be supplied to various customers from different phases of three-phase power generated by the utility company, resulting in uneven loading on the phases, from distribution transformer 150.

[0016] Sensors 180 may be distributed throughout the network in various assets, such as, but not limited to, feeder circuits, distribution transformers, etc. Sensors 180 may sense various circuit parameters, such as, for example, frequency, voltage, current magnitude, and phase angle, to monitor the operation of power distribution system 100. It should be understood that the illustrated locations of the sensors in FIG. 1 are merely exemplary, and that sensors may be located in other locations and additional or fewer sensors may be used.

[0017] As can be seen in FIG. 1 , 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 within the power distribution system 100 and update such information as the topology and phases of the power distribution system 100 change over time. The following description uses 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, reclosers, fuses, switches, streetlights, ripple receivers, ripple generators, capacitor banks, batteries, synchronous condensers, and the like. These assets, including meters, are collectively referred to herein as "utility devices."

[0018] 2 is a diagram illustrating a utility management system 200 according to various aspects of the present disclosure. The utility management system 200 may include an electric meter 160 (or simply, meter 160), a head-end system 210, and a repository 220. While FIG. 2 illustrates one electric meter 160 for ease of explanation, it should be understood that multiple electric meters 160 may be included in the utility management system 200.

[0019] Electric meter 160 may monitor and / or record various characteristics associated with the power distribution system, such as voltage, current, energy usage at customer premises 230, source impedance, load impedance, etc., and communicate that information to head-end system 210. For example, electric meter 160 may continuously monitor and record voltage fluctuations at customer premises 230 and the days of the week and times of day associated with voltage fluctuations at customer premises 230, and communicate that information to head-end system 210. Additionally, electric meter 160 may act as a sensor to detect and / or record anomalous readings and / or events. It should be understood that other information may be monitored and communicated by electric meter 160, such as, but not limited to, average power consumption, peak power, etc.

[0020] The electric meter 160 may communicate with the head-end system 210 via a wired or wireless communication interface using a communication protocol appropriate for the particular communication interface. Different wired or wireless communication interfaces and associated communication protocols may be implemented in the electric meter 160 for communication with the head-end system 210. For example, in some instances, a wired communication interface may be implemented, while in other instances, a wireless communication interface may be implemented for communication between the electric meter 160 and the head-end system 210. In some instances, a wireless mesh network may connect the electric meter 160. The electric meter 160 may transmit data to a collector (not shown) that communicates with another network for transmission to the head-end system 210. The collector may also function as an edge processor that processes data (e.g., descriptors) transmitted from the meter 160 to shift some processing load from the head-end system, particularly in high-speed modes described below. The electric meter 160 may communicate using radio frequency (RF), cellular, or power line communication. It should be understood that other communication schemes may also be used.

[0021] The communications network connecting the electric meter 160 and the head-end system 210, as described above, may be used to transmit data for topology and phase identification (e.g., descriptors, as described below). The communications network may also be used to provide other types of communications from the electric meter 160 to the head-end system 210, such as those used to report power consumption. In some examples, the communications network connecting the electric meter 160 and the head-end system 210, as described above, may overlay the power distribution network shown in FIG. 1. In the case of wireless communications, adjacent meters in the communications network may be different from adjacent meters in the power distribution network.

[0022] Head-end system 210 may also communicate with repository 220 via a wired or wireless communication interface. Repository 220 may be implemented using, for example, but not limited to, one or more hard disk drives, solid-state memory devices, or other computer-readable storage media. Other storage configurations may also be used. Repository 220 may be configured to store various data related to meters 160 and other assets in power distribution system 100. For example, repository 220 may include descriptors 226 transmitted by each of the assets, determined segments and phases 228 for each asset, etc.

[0023] The repository 220 may further include reference asset data 222 describing information related to reference assets having known segment information, phase information, or both. In some examples, segment and phase information is represented using segment and phase identifiers (IDs) that uniquely represent individual segments and phases. Any asset that shares the same phase or segment ID but has an unknown phase or network segment can have its phase or network segment inferred by the head-end system 210. The known phases and segments of a reference asset may be “out-of-band information” established by a process other than the automatic network topology discovery described herein. In some examples, a large amount of out-of-band information may be loaded, such that most assets have known phases and / or segments. The process presented herein can be used to validate that data and flag any inconsistencies.

[0024] Various other information related to assets in power distribution system 100 may be included in repository 220, such as parameters generated for a segment or asset. For example, a segment object may be created when head-end system 210 determines that a unique segment may exist. Segment parameters, such as a phase ID, a segment ID, a segment type ID, etc., may be created and associated with the segment.

[0025] Referring now to Figure 3, Figure 3 illustrates an example process for generating descriptors 226 by meters 160 of power distribution system 100 to facilitate topology and phase discovery by head-end system 210, according to certain aspects of the present disclosure. Meters 160 may perform the operations depicted in Figure 3 by executing appropriate program code. For illustrative purposes, process 300 will be described with reference to the example depicted in the figure. However, other implementations are possible.

[0026] In block 302, process 300 includes acquiring and preprocessing sensor data from a meter 160 or other asset. Depending on the type of asset and the sensors installed on the asset, the sensor data may include, but is not limited to, supply voltage, current, power, source impedance, load impedance, temperature, light level, humidity, pressure, sound, vibration, and other distribution network or environmental quantities. Preprocessing the sensor data may include filtering and normalizing the sensor data to remove or reduce noise, offset, etc. to generate processed sensor data. Preprocessing may further include detecting events, such as disturbances or anomalies, in the sensor data and determining characteristics of the events, such as event duration, number of events, time and severity of the event. In the case of a meter 160, the sensor data may include power voltage, current, power usage, etc. Additional details regarding acquiring and preprocessing sensor data are provided below with respect to FIG. 4.

[0027] At block 304, process 300 includes determining whether meter 160 is in high-speed mode. If meter 160 is in high-speed mode, meter 160 may generate and transmit descriptors (also referred to as "high-speed descriptors") to head-end system 210 at a higher rate than in normal mode so that segments or phases of meter 160 can be identified by head-end system 210 within a short time window. If meter 160 is not in high-speed mode, i.e., if meter 160 is operating in normal mode, meter 160 may generate descriptors 226 (also referred to as "normal descriptors") at a normal rate.

[0028] If the meter 160 is determined to be in normal mode, the process 300 includes generating a normal descriptor 226 based on the processed sensor data. The meter 160 may be configured to generate a series of normal descriptors 226 over a descriptor interval, such as 30 minutes, 1 hour, or 2 hours. The normal descriptor 226 may be generated to include filtered or normalized sensor data, detected events, and characteristics associated with the events. In some examples, the descriptor 226 may include vectors or scalar values ​​that describe the sensor data and detected events.

[0029] The meter 160 can be configured to generate and transmit different types of descriptors, such as, but not limited to, raw descriptors, raw statistical descriptors, normalized variation descriptors, ranking descriptors, filtering descriptors, interference characteristics descriptors, distribution characterization descriptors, signal frequency analysis descriptors, event timing analysis descriptors, event frequency analysis descriptors, and event magnitude analysis descriptors.

[0030] For example, raw descriptors can include descriptors with little or no preprocessing requirements. Raw descriptors are not normally transmitted to the head-end system 210 due to their high bandwidth consumption and limited contribution to the grouping process performed at the head-end system 210. In some examples, the sensor data in raw descriptors is not normalized. Rather, the sensor data values ​​can be raw values ​​and can include average values ​​over the descriptor interval. Raw descriptors can include root mean square (RMS) voltage during the interval, average voltage unbalance during the interval, average supply impedance, average temperature, average light level, average load impedance, average export power, average vibration, instantaneous communication channel address or name, etc.

[0031] The raw statistical descriptors may provide standard statistics of the sensor data, such as maximum, minimum, median, range, standard deviation, variance, etc. The raw statistical descriptors may be determined based on raw values ​​of the sensor data rather than normalized values. The raw statistical descriptors are often not transmitted to the head-end system 210 during normal mode.

[0032] A normalized change descriptor can represent a change in an underlying quantity. In this type of descriptor, the value of the sensor data is normalized or scaled using the average of the quantity of sensor data from the previous descriptor interval. Normalized change descriptors can include the normalized maximum RMS positive voltage change of a measurement window associated with the meter 160, the normalized maximum RMS positive voltage change of a half-cycle of the meter 160, the normalized maximum positive voltage change of a single sample, the normalized minimum RMS negative voltage change of the measurement window, and the change in supply frequency. Often, the normalized maximum value is transmitted to the head-end system 210 in normal mode.

[0033] The ranked descriptor can include values ​​such as the Nth highest maximum or Nth lowest minimum for a quantity during an interval. A single Nth maximum or minimum value or a series of Nth maximum or minimum values ​​can be included in the descriptor 226 and transmitted to the head-end system 210. Examples of ranked descriptors include, but are not limited to, the Nth maximum normalized RMS voltage change over a measurement window, the Nth maximum normalized RMS voltage change over a half cycle, and the Nth maximum normalized voltage change over a single sample.

[0034] Filtered descriptors can include values ​​generated by applying a digital filter to sensor data. Examples of filtered descriptors include, but are not limited to, the normalized maximum RMS positive voltage change of a single sample filtered to exclude impulses, the normalized maximum RMS positive voltage change of a single sample filtered to exclude fundamental mains voltages, the normalized Nth maximum RMS voltage change of a measurement window filtered over a period of time (e.g., 1 minute), and the normalized Nth maximum RMS voltage change of a measurement window filtered over a period of time (e.g., 5 minutes). Filters can also include finite impulse response (FIR) and infinite impulse response (IIR) digital filters to form bandpass, lowpass, highpass, or notch filters, among others. These filters can operate on high-rate raw samples (up to 1000 Hz) or low-rate processed measurement output (up to 1 Hz).

[0035] Event characteristic descriptors include descriptors that characterize characteristics of events such as disturbances or anomalies. For example, the maximum disturbance in a descriptor interval can be analyzed and included as a value of the event characteristic descriptor. Examples of event characteristic descriptors may include, but are not limited to, the duration of a ringing disturbance, the frequency of a ringing disturbance, the normalized peak magnitude of a ringing disturbance, the normalized magnitude of an impulse, the normalized magnitude of a voltage step change, the phase of a voltage step change, the duration of a short-term voltage excursion, the normalized voltage change of a short-term voltage excursion, etc.

[0036] A distribution descriptor can characterize a signal in the sensor data in the form of a distribution of samples into N distinct categories. Normalization can be performed so that the number of distinct values ​​is within a range, such as 5 to 10. A distribution descriptor can be the number of samples in a particular category, or a statistical property of the distribution, such as the median or mean. Examples of distribution descriptors include the total number of samples that fall within one of N distinct categories of normalized voltage magnitude, the total number of samples that fall within one of M distinct categories for a change in normalized voltage magnitude, or the average duration for a voltage magnitude to fall within one of N distinct categories of voltage magnitude.

[0037] Signal frequency analysis descriptors can be used to describe frequency content within the sensor data. For example, a Fast Fourier Transform (FFT) can be employed to determine the frequency content of the sensor data. Examples of signal frequency analysis descriptors include, but are not limited to, the normalized magnitude and frequency of the second-largest frequency detected within the measurement window over the interval period, the frequency of the largest frequency detected in the longest running ringing disturbance, the frequency of the largest frequency detected in the RMS voltage samples over the interval period, the frequency and magnitude of the longest running disturbance with a magnitude equal to or greater than p% of the nominal voltage (e.g., p=2), the total harmonic distortion (THD) of the sensor data, the change in THD, the change in the magnitude of the most prominent harmonic, etc.

[0038] Event timing analysis descriptors, event frequency analysis descriptors, and event magnitude analysis descriptors characterize different classes of events. Examples of these types of descriptors include the time of an event, the time when one filter's output is higher than or equal to another filter's output (e.g., for RMS voltage, when a 1-minute moving average crosses a 5-minute moving average), the duration between two events, the average duration between multiple events of the same type, the minimum / maximum / median / variance of the magnitude / duration / time / frequency of multiple events of the same type, the number of times a threshold is violated, the number of times a value falls within a valid range, and the magnitude of a filtered quantity when another quantity crosses a threshold. Other examples might include the duration between adjacent occurrences of N largest differences in RMS values, the normalized magnitude of the N largest difference RMS values, and the cumulative sum of the voltage magnitudes for all violation events. Note that the above times may be relative to an absolute time scale or to the occurrence time of some other event. Absolute timing may require precise timekeeping, while timing relative to some other event does not.

[0039] These types of descriptors are not mutually exclusive, and descriptors may be categorized into multiple types. Furthermore, meter 160 may be configured to generate and transmit a range of descriptors for each interval according to a descriptor configuration. For example, for an asset with a multiphase supply and current measurement capability, the descriptor configuration may be: average RMS voltage during the interval; average voltage unbalance during the interval; average supply impedance; the duration between the M longest intervals during which no crossing of a 1-minute moving average and a 5-minute moving average occurred; a count of the number of 1-minute and 5-minute crossings; a count of the number of voltage changes greater than 10% of the average RMS voltage of the previous interval; the time between a zero crossing and the start of the largest synchronization event; the number of ripple telegraph pulses; and the average duration for the voltage magnitude to be in one of 10 different categories of voltage magnitude.

[0040] At block 308, the process 300 includes appending the generated normal descriptor to a normal descriptor block. In some examples, normal descriptors may be organized in terms of normal descriptor blocks having a fixed size. Newly generated descriptors may be added to a current descriptor block until the descriptor block is full. A descriptor block filled with regular descriptors may then be added to a descriptor profile associated with the meter 160. The descriptor profile may be configured to maintain normal descriptor blocks to be transmitted to the head-end system 210 and normal descriptor blocks that have recently been transmitted to the head-end system 210. In other embodiments, normal descriptors are maintained in the descriptor profile without involving descriptor blocks.

[0041] At block 310, process 300 includes determining whether the generated normal descriptor is ready for transmission to head-end system 210. In some examples, a normal descriptor is ready for transmission to head-end system 210 when there are K or more descriptor blocks in the descriptor profile, where K is a natural number. In other examples, a normal descriptor is ready for transmission to head-end system 210 when normal descriptors for L descriptor intervals have been generated. For example, meter 160 may be configured to transmit normal descriptors for every four descriptor intervals at a time to head-end system 210. Other criteria may be utilized to determine whether a normal descriptor can be transmitted to head-end system 210. At block 312, process 300 includes transmitting normal descriptors, such as the oldest N descriptor blocks in the descriptor profile or descriptors for the past L descriptor intervals, to head-end system 210. At block 314, process 300 includes deleting the oldest descriptor in the descriptor profile, such as the descriptor sent at block 312, to free up storage space for the new descriptor. Process 300 then returns to block 302 to collect more sensor data for generating the new descriptor.

[0042] If it is determined at block 304 that the meter 160 is in fast mode, the process 300 includes generating a fast descriptor at block 320. In some cases, the meter 160 may be manually configured to operate in fast mode. For example, a technician may install a new meter or repair an existing meter at the customer premises 230 and need to quickly determine the segment and phase identifiers of the new or existing meter. To obtain such information, the technician may configure a meter with unknown or unverified segment and phase identifiers (also referred to as an “unknown meter”) through user input to operate in fast mode. Additionally, the technician may further configure neighboring meters of the unknown meter with known segment or phase information to operate in fast mode. These neighboring meters can be used as reference meters during the phase and segment identification process. In another example, the head-end system 210 may send instructions to the unknown meter and its neighboring meters to operate in fast mode in response to a technician request or other action initiated at the head-end, in order to quickly determine the unknown meter's segment and phase.

[0043] To speed up the process, fast descriptors can be generated and sent to the head-end system 210 at a higher rate than regular descriptors, such as twice per minute. In this way, the head-end system 210 can determine the phase and segment of an unknown meter and provide that information back to the meter and / or field technician in near real time. Fast descriptors can include data or descriptors that require fewer computational resources, such as raw descriptors or raw statistical descriptors that do not involve time-consuming processing like FFTs. Also, when generating fast descriptors, the meter does not need to wait for a chance service anomaly. However, due to a possible lack of time synchronization between meters, the head-end system may need to perform additional processing in the form of time correlation of fast descriptors from multiple meters.

[0044] At block 322, process 300 includes appending the high-speed descriptor to a high-speed descriptor block or other type of data structure. At block 324, process 300 includes determining whether enough high-speed descriptors have been generated, for example, whether a predetermined number of high-speed descriptors have been generated, whether the high-speed descriptor block is full, or whether high-speed descriptors for a high-speed mode interval have been generated. If not, process 300 includes obtaining more sensor data at block 302 to generate additional high-speed descriptors. If enough high-speed descriptors have been generated, process 300 includes transmitting the generated high-speed descriptors to head-end system 210 at block 326. In some examples, the high-speed mode interval is much shorter than the descriptor interval in normal mode. At block 328, process 300 includes deleting high-speed descriptors to make room for new descriptors. The process further includes obtaining additional sensor data at block 302.

[0045] Referring now to Figure 4, Figure 4 illustrates an example process for processing sensor data in a meter 160 of an electrical distribution system 100, according to certain aspects of the present disclosure. Meter 160 may perform the operations depicted in Figure 4 by executing appropriate program code. For illustrative purposes, process 400 will be described with reference to the example depicted in the figure. However, other implementations are possible.

[0046] At block 402, process 400 includes obtaining reading samples from sensors associated with meter 160 and configured to measure and generate sensor data. At block 404, process 400 includes performing data processing on the current and past samples. Examples of preprocessing include, but are not limited to, statistical processing, mathematical operations, and digital signal processing (DSP). Statistical processing may include, for example, determining instantaneous values, maximum values, minimum values, mean values, median values, value ranges, standard deviations, ranked list N max / min values, and sample counts. Mathematical operations may include RMS operations, differentiation, and / or integration. DSP operations may include FFT, finite impulse response filters (FIR), infinite impulse response filters (IIR), wavelet transforms, normalization, and / or resampling.

[0047] At block 406, process 400 includes normalizing the processed values ​​to identify events, such as disturbances or anomalies. Normalization can be performed, for example, by dividing the processed values ​​by the average voltage magnitude from the preceding descriptor interval. Identifying events can be performed by comparing the processed values ​​to one or more thresholds or by matching the values ​​to reference values. The thresholds can be fixed, static values, configurable thresholds controlled by the head-end system 210, or dynamic variables that are a function of the sensor data itself (e.g., instantaneous sensor values ​​can be compared to short-term averages of sensor values). An event can be identified if the processed value is higher than the corresponding threshold (or lower, depending on the type of threshold), outside of a valid range, or matches a reference value. For example, a voltage anomaly event can be identified if the average voltage value is higher than a high voltage threshold, lower than a low voltage threshold, or outside of a valid range. Multiple events can be similarly identified.

[0048] At block 408, process 400 includes determining characteristics or properties of the identified events. For example, characteristics of the events may include the time of occurrence of the event, the severity of the disturbance in the event (e.g., measured by the amount by which a processed value exceeds a threshold or deviates from a valid range), the number of events in a class of events, the time interval between various events, etc. These characteristics of the events may be associated with each event and used by meter 160 to generate a descriptor, as described above with respect to FIG. 3.

[0049] At block 410, process 400 includes adjusting a normalization factor and a reference time. For example, normalization may be performed based on the mean and standard deviation of the sensor data. The mean may be updated to include new sensor data as more sensor data is collected. The standard deviation may be updated as well. Additionally, if meter 160 is instructed by head-end system 210 to adjust the reference time, meter 160 may further perform a reference time adjustment during pre-processing.

[0050] In some examples, the meter 160 may further perform timing analysis related to the events during preprocessing. The timing analysis may include recording the time of the event and / or the time elapsed since the previous event. Determining and reporting the time period between events, rather than the time of the event, may be beneficial because the time period between events does not require precise time synchronization between the meters 160. In other words, the relative time between events is insensitive to timing inaccuracies in the meters themselves and the head-end system 210.

[0051] Preprocessing the sensor data can reduce the size of the data sent to the head-end system 210. This descriptor data can have a relatively low data rate of approximately 50 bytes / hour, compared to raw sensor data rates of approximately 13 Kbytes / hour or 32 Mbytes / hour in some systems. Preprocessing can therefore provide the advantage of allowing high-bandwidth data to be processed without being sent over a communication channel to the head-end system 210, thereby reducing network bandwidth consumption.

[0052] Referring now to FIG. 5, FIG. 5 illustrates an example of a process 500 for identifying the topology and phase of the power distribution system 100, according to certain aspects of the present disclosure. The head-end system 210 can perform the operations depicted in FIG. 5 by executing appropriate program code in the meter 160. For illustrative purposes, the process 500 will be described with reference to the example depicted in the figure. However, other implementations are possible.

[0053] At block 502, the process 500 includes receiving a descriptor from a meter 160 in the power distribution system 100. Depending on the operating mode of the meters 160 in the power distribution system 100, the descriptor may be received from a small set of meters 160 configured to operate in high-speed mode or from meters 160 in the power distribution system 100 configured to operate in normal mode. At block 504, the process 500 includes determining whether the received descriptor is a high-speed descriptor. In some examples, the high-speed descriptor may include a flag indicating that the descriptor was generated by the meter 160 while operating in high-speed mode. The head-end system 210 can determine whether the received descriptor is a high-speed descriptor by detecting the flag. In other examples, the head-end system 210 can determine a descriptor as a high-speed descriptor based on the size and frequency of receipt of the descriptor. For example, a high-speed descriptor may be transmitted using a smaller packet size and transmitted at a higher frequency.

[0054] If the head-end system 210 determines that the descriptor is a high-speed descriptor, the process 500 includes performing high-speed mode processing. Details regarding high-speed mode processing are provided below with respect to FIG. 6. If the head-end system 210 determines that the descriptor is not a high-speed descriptor, the process 500 includes performing normal mode processing beginning at block 506. In block 506, the head-end system 210 may store the received descriptors for the associated meter 160. In block 508, the head-end system 210 may determine whether a sufficient number of descriptors have been received (e.g., whether the number of received descriptors exceeds a threshold). In some implementations, the head-end system 210 is configured to identify meter segments and phases for each interval. In other words, the head-end system 210 identifies segments and phases based on descriptors collected throughout the detection interval. The detection interval can be set to an integer number of descriptor intervals (e.g., 4 hours or 8 hours for a 1-hour descriptor interval).

[0055] If an insufficient number of descriptors are received, the process 500 includes receiving more descriptors at block 502. If a sufficient number of descriptors are received, the process 500 includes grouping the meters 160 based on the received descriptors, such as the received descriptors for the current detection interval, at block 510. The grouping may be performed using, for example, K-means clustering, K-medoids clustering (PAM), hierarchical clustering, fuzzy clustering, model-based clustering, density-based clustering, hybrid clustering, etc. The head-end system 210 can evaluate the clustering tendency, determine the number of clusters, and evaluate the cluster quality. The grouping or clustering results can be used to automatically adjust the clustering method used to group the descriptors.

[0056] At block 512, process 500 includes comparing the current grouping with past groupings to determine a confidence level for the grouping. For example, if the current grouping matches the past groupings (e.g., the same meters are always grouped into the same group), the quality of the grouping can be determined to be high and a high confidence level can be assigned to the grouping; otherwise, a low confidence level is assigned.

[0057] In some examples, the confidence level may be determined based on groupings using different types of descriptors. As described above, the meter 160 may generate and transmit multiple descriptors of different types based on the descriptor configuration. For each descriptor type, the head-end system 210 may perform clustering to assign meters to several groups. Groups for one type of descriptor may overlap with groups of another type to form a union. This union may include a collection of meters connected to the same segment. However, this union may include meters that do not belong to the same segment or may omit meters that should be included. This problem can be mitigated by determining and comparing unions across multiple detection intervals. If a meter is in the same union for a majority of cases across multiple detection intervals, it may be declared to belong to the segment associated with that union. Such a grouping may be assigned a high confidence level; otherwise, a low confidence level is assigned.

[0058] At block 514, process 500 includes comparing the confidence level to a confidence threshold. If the confidence exceeds the threshold, the meter may be determined to belong to the determined group, and the segment ID and phase ID of a reference meter in that group may be assigned to the meter in that group, as shown at block 518. If the confidence does not exceed the threshold, head-end system 210 may select and send a new descriptor configuration to meter 160 at block 516 so that meter 160 can generate a different set of descriptors to increase the confidence of the grouping. For example, if an existing descriptor configuration that does not include a signal frequency descriptor does not provide a confidence level that exceeds the threshold, head-end system 210 may select a new descriptor configuration to include one or more signal frequency descriptors. Head-end system 210 may select this new descriptor configuration if disturbances having a particular frequency are observed in power distribution system 100. Descriptor configurations may be selected differently for meters 160 at different locations in power distribution system 100. In this way, head-end system 210 can tailor the descriptors transmitted by meter 160 to suit the type of power network disturbances and parameters prevalent at a given time and / or at a given point in power distribution system 100. If the descriptors are selected correctly, there will be a high signal-to-noise ratio of the descriptor values, and clustering quality may be improved. Process 500 may then proceed to block 502 to receive additional descriptors. In some examples, block 516 may be executed after a determination that the confidence level of multiple rounds of operation and grouping is below a threshold. At that point, head-end system 210 may be configured to generate and transmit a new descriptor configuration to meter 160. In some examples, if, after multiple iterations, none of the neighboring meters have a phase ID and segment ID, new phase IDs and segment IDs may be created and assigned to the meter. Similarly, if no neighboring meters are found, new phase IDs and segment IDs may be created and assigned to the meter.

[0059] In some implementations, after a meter is identified and classified, head-end system 210 can instruct meters 160 to disable or reduce descriptor transmission activity to reduce communication network bandwidth consumption. Head-end system 210 may also choose to do so for unclassified meters so that head-end system 210 can focus computing efforts on specific areas within power distribution system 100. In further implementations, head-end system 210 may temporarily disable descriptor generation and transmission operations on all or some of the meters so that other functions of the meter can take priority or to avoid collecting data at times known to be erroneous.

[0060] Referring now to Figure 6, Figure 6 illustrates an example of a fast mode process 600 for identifying the topology and phases of the power distribution system 100, in accordance with certain aspects of the present disclosure. The head-end system 210 can perform the operations illustrated in Figure 6 by executing appropriate program code. For illustrative purposes, the process 600 will be described with reference to the specific example depicted in the figure. However, other implementations are possible.

[0061] At block 602, process 600 includes storing the received high-speed descriptors for the associated meter. At block 604, process 600 includes determining whether enough high-speed descriptors have been received. For example, head-end system 210 may be configured to identify the meter's segment and phase every Y high-speed intervals, where Y is a positive integer. The high-speed interval is much shorter than the normal mode detection interval described above with respect to block 508 of FIG. 5 . For example, the high-speed interval may be set to 1 minute, 30 seconds, or even shorter. If high-speed descriptors for the past Y high-speed intervals have not been received, process 600 proceeds to block 614 to collect more high-speed descriptors (this may be block 502 of FIG. 5 ). If high-speed descriptors for the past Y high-speed intervals have been received, process 600 at block 606 includes grouping meters by performing correlation between high-speed descriptors of meters operating in high-speed mode. Due to the short interval for collecting sensor data from the meters, the high-speed descriptors may not include events such as disturbances and anomalies like normal descriptors. Rather, fast descriptors mostly involve small variations in sensor data values, such as voltage changes, variations in total harmonic distortion (THD), temperature, source impedance, current, etc. For this type of data, correlation-based grouping can provide more accurate grouping results.

[0062] At block 608, process 600 includes assigning a segment ID and / or phase ID to the unknown meter. For example, the segment ID and / or phase ID of a neighboring meter in the same group as the unknown meter may be assigned to the unknown meter. The assigned segment ID and / or phase ID may be stored in asset data 224 of repository 220. At block 610, process 600 includes sending the assigned information to the unknown meter. The unknown meter may display the assigned information on a display device. In examples where the meter is configured in express mode by a field technician, the technician can read the assigned information and proceed with the installation or repair procedure accordingly. Alternatively, or additionally, the meter's assigned information can be obtained from head-end system 210 when needed. At block 612, process 600 includes sending an instruction to the meter to exit express mode. The meter can then generate and transmit descriptors in normal mode, as described above with respect to FIGS. 3 and 4. In some examples, the meter can exit express mode by receiving an instruction via user input at the respective meter.

[0063] While the above disclosure focuses on meters, it should be understood that the operations performed by meters to generate descriptors can be performed by other types of assets to facilitate asset phase and segment operations. Additionally, in the absence of reference asset information to assign phase and segment IDs, the head-end system 210 may generate or create different phase and / or segment IDs for different groups of assets such that the different groups of assets can be distinguished from one another.

[0064] While the above disclosure focuses on a single head-end system 210 receiving all descriptors, it should be further understood that other arrangements are possible. For example, the power distribution system 100 may be divided into individual zones, with each zone having its own dedicated head-end system. A zone head-end system may have no relationship with other head-end systems or may coordinate its activities with other head-end systems. In a multi-head-end environment, there may be a hierarchical arrangement, with a single top-level head-end system coordinating the activities of all other zone head-end systems. Each zone head-end system may be responsible for processing the majority of the descriptor data and forward limited descriptor data to the top-level head-end system. In this way, the data processing requirements of any single head-end system can be limited and bandwidth requirements reduced. Zone head-end systems can be selectively activated to process high-speed descriptor data from nearby assets. In certain examples, edge processors may be employed to process descriptors in individual zones and forward the processed descriptor data to the top-level head-end system.

[0065] Additionally, as noted above, the descriptors sent to the head-end system 210 are not permanently stored in the meter 160. In some examples, only the data used to make the current determination of which segments are associated with which assets is stored. This data is continually refreshed, and older data is continually deleted. For example, descriptors from the last N intervals are stored (e.g., N ranges from 10 to 200). Additionally, descriptors from assets during multiple intervals for different classes of critical events may be stored. Critical events may include rare faults in the power distribution system detected by multiple assets. For each critical event, the intervals before, during, and after the event may be stored, and for each class, the intervals of the past M critical events are stored (e.g., M=10).

[0066] As described above, meters generate descriptors over several time intervals, and the head-end system 210 performs identification based on the descriptors over these time intervals. This requires time synchronization across all assets that send descriptors to the head-end system 210. In some power distribution systems 100, time errors can be as large as ±2 minutes. Time inaccuracies adversely affect grouping activities performed by the head-end system 210 due to descriptors having different values ​​that would otherwise be identical. To mitigate this problem, the time interval can be selected to be large relative to the amount of time error. For example, a one-hour interval can be selected to overcome a two-minute timing error.

[0067] However, this mechanism may not work in high-speed mode, where a small number of assets in close physical proximity transmit data over short intervals (e.g., about 10 seconds) for a short period of time. In these cases, the head-end system 210 can perform correlation operations over multiple time intervals for a limited number of descriptors to determine the time offset for each asset. Because asset sensors will have experienced similar electrical noise, their patterns can be used as synchronization events that can be detected using correlation. Once the time offsets are known for each asset, the descriptor grouping process described above can be performed.

[0068] The disclosure presented herein can also be used to synchronize the clocks of assets within the power distribution system 100. Depending on the asset's hardware capabilities, sub-cycle (e.g., 20 ms @ 50 Hz) preprocessing may be possible in assets with advanced capabilities. Therefore, short-duration disturbances such as high-frequency ringing of a few milliseconds or large voltage fluctuations can be detected by these assets. Although there may be propagation delays due to power lines, transformers, and capacitor banks, assets in a small area can be assumed to have the same detection time for this type of event. Such events can be used to synchronize the clocks of multiple assets that detect the event with an accuracy close to the sensor sampling period (e.g., ±2 ms). For example, these synchronization events can be included in a descriptor so that the head-end system 210 can identify them and use the occurrence time of the synchronization event as the reference time for all other events in the descriptor received by the head-end system 210 to adjust the timing of those events so that all events are synchronized. The head-end system 210 can further calculate the time offset of each asset from the reference clock and send time correction instructions to these assets to correct their respective clocks. Alternatively or additionally, the head-end system 210 can transmit a reference time of the synchronization event to the asset. The asset can determine the difference between the reference time and each time it observes the synchronization event. The asset can then adjust its local clock based on the difference.

[0069] Longer duration synchronization events can exist in the form of high-frequency tones or pulses, which are conducted disturbances superimposed on the mains voltage. Tones have a longer disturbance duration (e.g., 50 ms–5000 ms) than pulses (e.g., shorter than 50 ms). Pulses may be more attenuated than tones due to impedance and filtering delays. These can be unintentional events from sources such as grid-tied inverters, adjustable-speed drives, and switch-mode power supplies, or intentional events such as ripple telegrams injected for load control. A series of pulses or tones can last for a few milliseconds, tens of seconds (in the case of ripple telegrams), or even continuously (in the case of faulty equipment). These signals can even persist for several mains cycles. The time to detect these events may be long due to the filtering delay of the meter itself, but synchronization is still possible because the delay is a known constant. Long duration disturbances can occur at different frequencies in different areas of the network. Assets may need to be configured to search specific frequencies if searching all frequencies is not an option due to processing limitations. The head-end system 210 can automatically configure assets to focus on specific frequencies depending on the interference present in the area.

[0070] Assets that synchronize time using grid events with greater precision, such as better than + / - 3.3 ms, can determine their phase. Many types of grid events occur simultaneously in all phases. Positive-going zero crossings in the voltage signal occur every 20 ms, with each phase having a 6.666 ms delay. By calculating the period from the zero crossing to the grid synchronization event, the phase can be determined directly because the periods of each phase A, B, and C differ by a multiple of 6.666 ms. The period is represented by a descriptor and sent to the head-end system 210, where a determination can be made by comparing the period of an asset with an unknown phase with an asset with a known phase.

[0071] Synchronization events in the power distribution system 100 are useful for identifying segments as well as phases. Some classes of synchronization events may propagate across multiple assets and segments. The reception time can serve as a reference point for the occurrence time of the local event. Assets on the same segment will have a similar period between the occurrence time of the synchronization event and the occurrence time of the local disturbance. This period is transmitted to the head-end system 210 in the form of a descriptor, where it can be grouped with other similar descriptors to identify the asset's segment and phase. Furthermore, a synchronization event can trigger the transmission of a time adjustment message from the head-end system 210 to the asset. This can occur if some assets contain high-precision clocks. When a high-precision asset reports a synchronization event to the head-end system 210, the report message can also include when the event occurred relative to its high-precision clock. In this way, the head-end system 210 knows the exact time the event occurred. Using this information, the head-end system 210 can notify other assets that also detected the event of the exact time the event occurred, allowing the other assets to adjust their clocks accordingly.

[0072] Exemplary Meter

[0073] FIG. 7 illustrates an example meter 700, such as meter 160, that can be employed to implement the sensor data collection and descriptor generation described herein. Meter 700 includes a communications module 716 and a metering module 718 connected via a local or serial connection 730. These two modules may be housed in the same unit on separate boards, and thus local connection 730 may be an on-board socket. Alternatively, these two modules may be housed in the same unit with a single processor and memory block that performs multiple tasks, including sensing / metering and communication coordination. Alternatively, the modules may be housed separately, and thus local connection 730 may be a communications cable, such as a USB cable, or another conductor.

[0074] The functionality of the communications module 716 includes transmitting the descriptors 226 and other data to other meters or the head-end system 210 and receiving data from the head-end system 210 or other meters. The functionality of the metering module 718 includes functions necessary for managing resources, particularly for granting access to resources and metering resource usage. The communications module 716 may include a communications device 712, such as an antenna and radio. Alternatively, the communications device 712 may be any device that enables wireless or wired communications. The communications module 716 may also include a processor 713 and a memory 714. The processor 713 controls the functions performed by the communications 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 temporarily store other data for the meter 700, such as the descriptors 226.

[0075] The instrumentation module 718 may include a processor 721, a memory 722, and a measurement circuit 723. The measurement circuit 723 may process measurements of resources and may be used as a sensor to collect sensor data. The processor 721 of the instrumentation module 718 controls the functions performed by the instrumentation module 718. For example, the processor 721 is configured to calculate the descriptors 226, as described above, based on the sensor data obtained by the measurement circuit 723. The memory 722 stores data necessary for the processor 721 to perform its functions. The memory 722 also stores the descriptors 226 calculated by the processor 721. The communications module 716 and the instrumentation module 718 communicate with each other via a local connection 730 to provide data needed by the other module. Both the communications module 716 and the instrumentation module 718 may include computer-executable instructions stored in memory or another type of computer-readable medium, and one or more processors within the modules can execute the instructions to provide the functionality described herein.

[0076] Example of a Head-End System for Implementing Certain Embodiments

[0077] Any suitable computing system or group of computing systems may be used to perform the operations described herein. For example, Figure 8 illustrates an example computing system 800. An implementation of computing system 800 may be used for head-end system 210.

[0078] The depicted example of computing system 800 includes a processor 802 communicatively coupled to one or more memory devices 804. The processor 802 executes computer-executable program code stored in the memory devices 804, accesses information stored in the memory devices 804, or both. Examples of processor 802 include a microprocessor, an application specific integrated circuit ("ASIC"), a field programmable gate array ("FPGA"), or any other suitable processing device. The processor 802 may include any number of processing devices, including a single processing device.

[0079] The memory device 804 includes any suitable non-transitory computer-readable medium for storing program code 805, program data 807, or both. The computer-readable medium may include any electronic, optical, magnetic, or other storage device capable of providing computer-readable instructions or other program code to a processor. Non-limiting examples of computer-readable media include magnetic disks, memory chips, ROM, RAM, ASICs, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include, for example, processor-specific instructions generated by a compiler or interpreter from code written in any suitable computer programming language, including C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript.

[0080] Computing system 800 configures processor 802 to execute program code 805 to perform one or more of the operations described herein. Examples of program code 805 include, in various embodiments, program code used to identify segments and phases of an asset based on normal and fast descriptors, or other suitable applications that perform one or more of the operations described herein. The program code may reside in memory device 804 or any suitable computer-readable medium and may be executed by processor 802 or any other suitable processor.

[0081] In some embodiments, one or more memory devices 804 store program data 807, including one or more datasets described herein. Examples of these datasets include historical secure tokens, a global token table, etc. In some embodiments, one or more of the datasets, models, and functions are stored in the same memory device (e.g., one of memory devices 804). In additional or alternative embodiments, one or more of the programs, datasets, models, and functions described herein are stored in different memory devices 804 accessible via a data network. Also included in computing system 800 are one or more buses 806. Bus 806 communicatively couples each of one or more components of computing system 800.

[0082] In some embodiments, computing system 800 also includes a network interface device 810. Network interface device 810 includes any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks. Non-limiting examples of network interface device 810 include an Ethernet network adapter, a modem, and / or the like. Computing system 800 can use network interface device 810 to communicate with one or more other computing devices over a data network.

[0083] Computing system 800 may also include a number of external or internal devices, input devices 820, presentation devices 818, or other input or output devices. For example, computing system 800 is shown with one or more input / output ("I / O") interfaces 808. I / O interface 808 may receive input from input devices or provide output to output devices. Input device 820 may include any device or group of devices suitable for receiving visual, auditory, or other suitable input that controls or affects the operation of processor 802. Non-limiting examples of input device 820 include a touchscreen, a mouse, a keyboard, a microphone, another mobile computing device, etc. Presentation device 818 may include any device or group of devices suitable for providing visual, auditory, or other suitable sensory output. Non-limiting examples of presentation device 818 include a touchscreen, a monitor, a speaker, a separate mobile computing device, etc.

[0084] 8 depicts input device 820 and presentation device 818 as being local to the computing device executing head-end system 210, other implementations are possible. For example, in some embodiments, one or more of input device 820 and presentation device 818 may comprise remote client computing devices that communicate with computing system 800 via network interface device 810 using one or more data networks described herein.

[0085] General Considerations

[0086] Numerous specific details are described 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, apparatuses, or systems that would be known by those skilled in the art have not been described in detail so as not to obscure the claimed subject matter.

[0087] 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 provides a result conditional 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 present subject matter. Any suitable programming, scripting, or other type of language or combination of languages ​​may be used to implement the teachings contained herein in the software used in programming or configuring a computing device.

[0088] Aspects of the methods disclosed herein may be performed in operation of such a computing device. The order of the blocks presented in the above examples may be changed; for example, the blocks may be reordered, combined, and / or divided into sub-blocks. Certain blocks or processes may be performed in parallel.

[0089] The use of "adapted to" or "configured to" herein is intended as open and inclusive language that does not preclude devices adapted or configured to perform additional tasks or steps. Furthermore, the use of "based on" is intended to be open and inclusive in the sense that when a process, step, calculation, or other operation is "based on" one or more recited conditions or values, it may in fact be based on additional conditions or values ​​beyond those recited. Headings, lists, and numbering contained herein are for ease of description only and are not intended to be limiting.

[0090] While the present subject matter has been described in detail with respect to particular aspects thereof, it will be understood that those skilled in the art, once armed with the foregoing understanding, will be able to readily make modifications, variations, and equivalents to such aspects. Accordingly, it will be understood that the present disclosure has been presented for purposes of illustration and not limitation, and is not intended to exclude the inclusion of modifications, variations, and / or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.

[0091] The present invention may be more readily understood by reference to the following numbered items: C1 - A method for discovering the location and phase of a topology of one or more utility devices in a resource distribution system, comprising: receiving, by a head-end system, descriptors from a plurality of utility devices connected to the electrical distribution system, the descriptors being generated at each utility device by processing sensor data acquired at each utility device; In response to determining that at least a threshold number of descriptors for the normal mode have been received, applying, by the head-end system, a clustering algorithm to the descriptors of the plurality of utility devices to group the plurality of utility devices to generate a current grouping; comparing, by the head-end system, the current groupings with past groupings to determine a confidence level for the groupings; determining, by the head-end system, whether the confidence exceeds a threshold confidence value; assigning, by the head-end system, at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices in response to determining that the reliability exceeds the threshold reliability value; and A method comprising: C2-Furthermore, determining whether the plurality of utility devices are operating in a high-speed mode, wherein in the high-speed mode, the segment identifiers or the phase identifiers are assigned to the utility devices for a shorter period than in the normal mode; responsive to determining that the plurality of utility devices operate in a high speed mode and receiving sufficient descriptors for the high speed mode, grouping the plurality of utility devices by correlating the received descriptors for the high speed mode; generating assignment information by assigning at least one of a segment identifier or a phase identifier to an unknown utility device of the plurality of utility devices; transmitting the allocation information to the unknown utility device; sending instructions to the plurality of utility devices to exit high speed mode; The method according to C1, comprising: C3-Furthermore, The method of claim C1, comprising generating and transmitting, by the head-end system, a new descriptor configuration to one or more of the plurality of utility devices in response to determining that the reliability does not exceed the threshold reliability value. C4—Comparing the current grouping with past groupings to determine the reliability of the groupings; assigning a high confidence level in response to determining that the set of utility devices are grouped together in the current grouping and the previous grouping; and assigning a low confidence level in response to determining that the set of utility devices are grouped differently in the current grouping than in the previous grouping. The method according to C1, comprising: C5-Furthermore, determining that the descriptor includes a synchronization event; adjusting the timing of other events in the descriptor using the time of occurrence of the synchronization event as a reference time; The method according to C1, comprising: C6-Furthermore, determining a time offset of one or more of the plurality of utility devices based on the synchronization event; sending instructions to said one or more of said plurality of utility devices to correct their clocks according to said respective time offsets; The method according to C5, comprising: C7-Headend System; a plurality of utility devices connected to a power distribution system and communicatively connected to a head-end system; In a system comprising: Each utility device of multiple utility devices is processing the sensor data obtained at each utility device to generate processed data; generating and transmitting a high-speed or normal descriptor based on the processed data; configured to: The headend system comprises: receiving high speed or normal descriptors from the plurality of utility devices; determining whether the plurality of utility devices are operating in either a high speed mode or a normal mode, wherein in the high speed mode, a segment identifier or a phase identifier is assigned to one of the plurality of utility devices for a shorter period of time than in the normal mode; and In response to determining that the plurality of utility devices operate in the normal mode, applying a clustering algorithm to the general descriptors of the plurality of utility devices to group the plurality of utility devices to generate a current grouping; comparing the current grouping with past groupings to determine the confidence of the current grouping; and assigning at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices based on the reliability; configured to: system. C8—each utility device of said plurality of utility devices is further configured to determine whether each utility device operates in said high speed mode or said normal mode; The system of C7, wherein the normal descriptor is generated and transmitted in response to determining that the utility device operates in the normal mode. C9—each utility device of the plurality of utility devices further comprises: generating high-speed descriptors based on the processed data in response to determining that each utility device operates in the high-speed mode; determining that at least a first threshold number of high-speed descriptors have been generated; and transmitting the high-speed descriptors to the head-end system. The system of claim 8, configured to: C10—each utility device of the plurality of utility devices further comprises: In response to determining that each utility device operates in the normal mode, determining that at least a second threshold number of normal descriptors have been generated; in response to determining that at least the second threshold number of normal descriptors have been generated, the normal descriptors are transmitted to the head-end system. The system described in C9. C11 - determining that at least a first threshold number of high speed descriptors have been generated includes determining that at least a first threshold number of high speed descriptors have been generated for the high speed mode interval; Determining that at least a second threshold number of normal descriptors have been generated includes determining that at least a second threshold number of normal descriptors have been generated for a predetermined number of descriptor intervals; the high-speed mode interval is shorter than the descriptor interval; The system described in C10. C12—The headend system further comprises: determining whether the confidence exceeds a threshold confidence value; generating and transmitting a new descriptor configuration to one or more of the plurality of utility devices in response to determining that the confidence does not exceed the threshold confidence value; configured to: assigning at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices is performed in response to determining that the reliability exceeds a threshold reliability value. The system described in C7. C13—In response to determining that the plurality of utility devices operate in high speed mode, the head-end system further grouping the plurality of utility devices by correlating the high-speed descriptors; generating assignment information by assigning at least one of a segment identifier or a phase identifier to the unknown utility device of the plurality of utility devices using a neighboring utility device of the unknown utility device of the plurality of utility devices as a reference; transmitting the allocation information to the unknown utility device; sending an instruction to the plurality of utility devices to terminate high speed mode; configured to: The system described in C7.

Claims

1. 1. A method for discovering the location and phase of a topology of one or more utility devices in a resource distribution system, comprising: receiving, by a head-end system, descriptors from a plurality of utility devices connected to the electrical distribution system, the descriptors being generated at each utility device by processing sensor data acquired at each utility device; In response to determining that at least a threshold number of descriptors for the normal mode have been received, applying, by the head-end system, a clustering algorithm to the descriptors of the plurality of utility devices to group the plurality of utility devices to generate a current grouping; comparing, by the head-end system, the current groupings with past groupings to determine a confidence level for the groupings; determining, by the head-end system, whether the confidence exceeds a threshold confidence value; assigning, by the head-end system, at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices in response to determining that the reliability exceeds the threshold reliability value; and A method comprising:

2. Furthermore, determining whether the plurality of utility devices are operating in a high-speed mode, wherein in the high-speed mode, the segment identifiers or the phase identifiers are assigned to the utility devices for a shorter period than in the normal mode; responsive to determining that the plurality of utility devices operate in a high speed mode and receiving sufficient descriptors for the high speed mode, grouping the plurality of utility devices by correlating the received descriptors for the high speed mode; generating assignment information by assigning at least one of a segment identifier or a phase identifier to an unknown utility device of the plurality of utility devices; transmitting the allocation information to the unknown utility device; sending instructions to the plurality of utility devices to exit high speed mode; The method of claim 1 , comprising:

3. Furthermore, 2. The method of claim 1, further comprising generating and transmitting, by the head-end system, a new descriptor configuration to one or more of the plurality of utility devices in response to determining that the reliability does not exceed the threshold reliability value.

4. Comparing the current grouping to past groupings to determine the reliability of the groupings assigning a high confidence level in response to determining that the set of utility devices are grouped together in the current grouping and the previous grouping; and assigning a low confidence level in response to determining that the set of utility devices are grouped differently in the current grouping than in the previous grouping. The method of claim 1 , comprising:

5. Furthermore, determining that the descriptor includes a synchronization event; adjusting the timing of other events in the descriptor using the time of occurrence of the synchronization event as a reference time; The method of claim 1 , comprising:

6. Furthermore, determining a time offset of one or more of the plurality of utility devices based on the synchronization event; sending instructions to said one or more of said plurality of utility devices to correct their clocks according to said respective time offsets; The method of claim 5 , comprising:

7. 1. A method performed by a utility device to generate descriptors for discovering topology and phases of an electrical power distribution system, the method comprising: processing the sensor data acquired by the utility device to generate processed data; determining whether the utility device operates in a high speed mode or a normal mode, wherein when the utility device operates in the high speed mode, it generates descriptors at a higher rate than when it operates in the normal mode; In response to determining that the utility device operates in the high speed mode, generating a fast descriptor based on the processed data; and In response to determining that at least a first threshold number of high speed descriptors have been generated, transmitting the high speed descriptors to a head-end system communicatively coupled to the utility device. and In response to determining that the utility device operates in the normal mode, generating a general descriptor based on the processed data; and transmitting the normal descriptors to the head-end system in response to determining that at least a second threshold number of normal descriptors have been generated. and Including, the second threshold number is higher than the first threshold number, and the fast descriptor and the normal descriptor are different. method.

8. Processing the sensor data to generate the processed data includes: including one or more of filtering the sensor data, transforming the sensor data to the frequency domain, or normalizing the sensor data. The method of claim 7.

9. The step of processing the sensor data further comprises: identifying a disturbance by determining that a value of the processed data is outside a predetermined range; determining a characteristic of the disturbance; and The method of claim 8, comprising:

10. The method of claim 9 , wherein the general descriptor is generated to include one or more characteristics of the disturbance.

11. Furthermore, 8. The method of claim 7, comprising adjusting a reference time of the utility device in response to receiving an instruction to correct the reference time of the utility device from the head-end system.

12. Furthermore, operating the utility device in a high speed mode in response to receiving a first user input at the utility device or a first command from the head-end system; exiting the high speed mode in response to receiving a second user input at the utility device or a second command from the head-end system; The method of claim 7, comprising:

13. Furthermore, receiving allocation information for the utility device from the headend system; displaying the allocation information on a display device associated with the utility device; The method of claim 7, comprising:

14. a head-end system; a plurality of utility devices connected to a power distribution system and communicatively connected to a head-end system; In a system comprising: Each utility device of multiple utility devices is processing the sensor data obtained at each utility device to generate processed data; generating and transmitting a high-speed or normal descriptor based on the processed data; configured to: The headend system comprises: receiving high speed or normal descriptors from the plurality of utility devices; determining whether the plurality of utility devices are operating in either a high speed mode or a normal mode, wherein in the high speed mode, a segment identifier or a phase identifier is assigned to one of the plurality of utility devices for a shorter period of time than in the normal mode; and In response to determining that the plurality of utility devices operate in the normal mode, applying a clustering algorithm to the general descriptors of the plurality of utility devices to group the plurality of utility devices to generate a current grouping; comparing the current grouping with past groupings to determine the confidence of the current grouping; and assigning at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices based on the reliability; configured to: system.

15. each utility device of the plurality of utility devices is further configured to determine whether the utility device operates in the high-speed mode or the normal mode; The system of claim 14 , wherein the normal descriptor is generated and transmitted in response to determining that the utility device operates in the normal mode.

16. Each utility device of the plurality of utility devices further comprises: generating high-speed descriptors based on the processed data in response to determining that each utility device operates in the high-speed mode; determining that at least a first threshold number of high-speed descriptors have been generated; and transmitting the high-speed descriptors to the head-end system. The system of claim 15 configured to:

17. Each utility device of the plurality of utility devices further comprises: In response to determining that each utility device operates in the normal mode, determining that at least a second threshold number of normal descriptors have been generated; in response to determining that at least the second threshold number of normal descriptors have been generated, the normal descriptors are transmitted to the head-end system.

17. The system of claim 16.

18. Determining that at least a first threshold number of high speed descriptors have been generated includes determining that at least a first threshold number of high speed descriptors have been generated for the high speed mode interval; Determining that at least a second threshold number of normal descriptors have been generated includes determining that at least a second threshold number of normal descriptors have been generated for a predetermined number of descriptor intervals; the high-speed mode interval is shorter than the descriptor interval; 20. The system of claim 17.

19. The headend system further comprises: determining whether the confidence exceeds a threshold confidence value; generating and transmitting a new descriptor configuration to one or more of the plurality of utility devices in response to determining that the confidence does not exceed the threshold confidence value; configured to: assigning at least one of a segment identifier or a phase identifier to one or more of the plurality of utility devices is performed in response to determining that the reliability exceeds a threshold reliability value. The system of claim 14.

20. In response to determining that the plurality of utility devices operate in a high speed mode, the head-end system further grouping the plurality of utility devices by correlating the high-speed descriptors; generating assignment information by assigning at least one of a segment identifier or a phase identifier to the unknown utility device of the plurality of utility devices using a neighboring utility device of the unknown utility device of the plurality of utility devices as a reference; transmitting the allocation information to the unknown utility device; sending an instruction to the plurality of utility devices to terminate high speed mode; configured to: The system of claim 14.

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