DISTRIBUTED SMART GRID PROCESSING

• US 20150256387A1
• Filed: 03/10/2015
• Published: 09/10/2015
• Est. Priority Date: 03/10/2014
• Status: Active Grant

• Current Assignee: Itron Networked Solutions, Inc. (Itron, Inc.)
• Original Assignee: Silver Spring Networks, Inc. (Itron, Inc.)
• Inventors: SUM, Charles P., FLAMMER, George H. III
• Granted Patent: US 10,151,782 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01R 21/00 Arrangements for measuring electric power or power factor G01R7/12 takes precedence
• CPC Class Codes: G01R 25/00 Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
• CPC Class Codes: H02J 13/00017 using optical fiber
• CPC Class Codes: H02J 13/00022 using wireless data transmission
• CPC Class Codes: H02J 13/00024 by means of mobile telephony
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/007 Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing uninterruptable power supply H02J9/00
• CPC Class Codes: H02J 3/06 Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
• CPC Class Codes: H04L 41/0803 Configuration setting
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/0888 Throughput
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network
• CPC Class Codes: H04L 67/51 Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 69/28 Timers or timing mechanisms used in protocols
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 40/00 : Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
• CPC Class Codes: Y04S 40/124 : using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 40/126 : using wireless data transmission
• CPC Class Codes: Y04S 40/18 : Network protocols supporting networked applications, e.g. including control of end-device applications over a network
• CPC Class Codes: Y04S 40/20 : Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network (or any other device network). One or more servers coupled to the wireless mesh network configures a data ingestion cloud to receive and process the time series data from the nodes to generate data streams. The server(s) also configure a distributed processing cloud to perform historical analysis on data streams, and a real-time processing cloud to perform real-time analysis on data streams. The distributed processing cloud and the real-time processing cloud may interoperate with one another in response to processing the data streams. Specifically, the real-time processing cloud may trigger a historical analysis on the distributed processing cloud, and the distributed processing cloud may trigger real-time processing on the real-time processing cloud. Any of the processing clouds may encompass edge nodes configured to perform real-time processing and generate data streams.

Claims

1. A non-transitory computer-readable medium storing program instructions that, when executed by a processor, causes the processor to identify events associated with a network environment, by performing the steps of:

obtaining a first time series of data values from a first node in the network;

obtaining a second time series of data values from a second node in the network;

processing the first time series of data values and the second time series of data values to identify a first data trend; and

based on the first data trend, identifying a first network event associated with a first region of the network environment, wherein the first node and the second node reside within the first region.

2. The non-transitory computer-readable medium of claim 1, further comprising the steps of:

configuring the first node to;

collect first raw sensor data to generate the first time series of data values, or

process a third time series of data values received from a third node in the network environment to generate the first time series of data values; and

configuring the second node to;

collect second raw sensor data to generate the second time series of data values, or

process a fourth time series of data values received from a fourth node in the network environment to generate the second time series of data values.

3. The non-transitory computer-readable medium of claim 1, further comprising the steps of:

configuring the first node to process first textual data acquired from a first application programming interface (API) associated with a social media outlet to generate the first time series of data values; and

configuring the second node to process second textual data acquired from the first API to generate the second time series of data values.

4. The non-transitory computer-readable medium of claim 3, further comprising the steps of:

extracting a first portion of text from the first textual data that references the first network event; and

extracting a second portion of text from the second textual data that references the first network event.

5. The non-transitory computer-readable medium of claim 4, wherein the step of processing the first time series of data values and the second time series of data values comprises determining that both the first portion of text and the second portion of text reference the first network event.

6. The non-transitory computer-readable medium of claim 1, further comprising the steps of:

configuring the first node to measure a first consumption level associated with a first utility network component to generate the first time series of data values; and

configuring the second node to measure an aggregated consumption level associated with a plurality of second utility network components coupled to the first utility network component to generate the second time series of data values.

7. The non-transitory computer-readable medium of claim 6, wherein the step of processing the first time series of data values and the second time series of data values comprises determining that the first consumption level is substantially different than the aggregate consumption level, and wherein the step of identifying the first network event comprises determining that a non-technical loss has occurred within the first region.

8. The non-transitory computer-readable medium of claim 1, further comprising the steps of:

configuring the first node to;

measure a first voltage level associated with a first utility network component, and

compare the first voltage level to a running average of the first voltage level to generate the first time series of data values; and

configuring the second node to;

measure a second voltage level associated with a second utility network component, and

compare the second voltage level to a running average of the second voltage level to generate the second time series of data values.

9. The non-transitory computer-readable medium of claim 8, wherein processing the first time series of data values and the second time series of data values comprises:

determining, based on the first time series of data values, that the first voltage level substantially diverges from the running average of the first voltage level; and

determining, based on the second time series of data values, that the second voltage level substantially diverges from the running average of the second voltage level.

10. The non-transitory computer-readable medium of claim 9, wherein the step of identifying the first network event comprises determining that a sag or swell has occurred within a portion of a utility network associated with the first region.

13. The system of claim 10,

wherein the first upstream node is further configured to:

measure a first demand level associated with upstream component within an utility distribution infrastructure to generate the first time series of data values, and

wherein the second upstream node is configured to;

measure an aggregated demand level across a plurality of downstream components in the utility distribution infrastructure coupled downstream of the upstream component to generate the second time series of data values.

14. The system of claim 13, wherein the node is configured to process the first time series of data values and the second time series of data values by determining that the first demand level is substantially different than the aggregated demand level, and wherein the step of identifying the first network event comprises determining that a non-technical loss of electricity or water has occurred within the first region.

15. The non-transitory computer-readable medium of claim 1, further comprising the steps of:

configuring the first node to;

measure a first voltage level associated with a first utility network component, and

compare the first voltage level to a running average of the first voltage level to generate the first time series of data values; and

configuring the second node to;

measure a second voltage level associated with a second utility network component, and

compare the second voltage level to a running average of the second voltage level to generate the second time series of data values,

wherein the node processes the first time series of data values and the second time series of data values by;

determining, based on the first time series of data values, that the first voltage level substantially diverges from the running average of the first voltage level; and

determining, based on the second time series of data values, that the second voltage level substantially diverges from the running average of the second voltage level.

17. The computer-implemented method of claim 1, further comprising:

configuring the first node to process first textual data acquired from a first application programming interface (API) associated with a social media outlet to generate the first time series of data values; and

configuring the second node to process second textual data acquired from the first API to generate the second time series of data values.

18. The computer-implemented method of claim 17, further comprising:

extracting a first portion of text from the first textual data that references the first network event; and

extracting a second portion of text from the second textual data that references the first network event.

19. The computer-implemented method of claim 18, wherein processing the first time series of data values and the second time series of data values comprises determining that both the first portion of text and the second portion of text reference the first network event.

20. The computer-implemented method of claim 1, wherein each of the first portion of the text and the second portion of the text comprises a description of damage that has occurred to the network or to an underlying utility network that the first node and the second node are configured to monitor within the network environment.

11. A system for identifying events associated with a network environment, comprising:

a node that resides within a network, including;

a memory configured to store program code,

a processor configured to;

obtain a first time series of data values from a first upstream node in the network;

obtain a second time series of data values from a second upstream node in the network;

process the first time series of data values and the second time series of data values to identify a first data trend; and

based on the first data trend, identify a first network event associated with a first region of the network environment, wherein the node, the first upstream node, and the second upstream node reside within the first region.

12. The system of claim 11, further comprising:

the first upstream node, configured to;

collect first raw sensor data derived from an underlying network to generate the first time series of data values, or

process a third time series of data values received from a third node in an overarching network to generate the first time series of data values; and

the second upstream node, configured to;

collect second raw sensor data derived from an underlying network to generate the second time series of data values, or

process a fourth time series of data values received from a fourth node in an overarching network to generate the second time series of data values.

16. A computer-implemented method for identifying events associated with a network environment, the method comprising:

obtaining a first time series of data values from a first node in the network;

obtaining a second time series of data values from a second node in the network;

processing the first time series of data values and the second time series of data values to identify a first data trend; and

based on the first data trend, identifying a first network event associated with a first region of the network environment, wherein the first node and the second node reside within the first region.