DISTRIBUTED SMART GRID PROCESSING

• US 20150255983A1
• 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 10,809,288 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, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.

Claims

1. A computer-implemented method for performing a distributed processing operation across a network of nodes, the method comprising:

configuring a first node that resides at a first physical location within a first network to execute a first stream function on a first time series of data to generate a first data stream;

configuring a second node that resides at a second physical location within the first network to execute a second stream function on a second time series of data to generate a second data stream;

obtaining the first data stream from the first node via one or more network connections;

obtaining the second data stream from the second node via one or more network connections; and

processing the first data stream and the second data stream to generate a time series of processing results.

2. The computer-implemented method of claim 1, wherein configuring the first node comprises transmitting a specification of the first stream function to the first node, wherein the first stream function is specified by a user via a software development tool kit.

3. The computer-implemented method of claim 1, wherein the first time series of data comprises a series of data values recorded by a sensor array coupled to the first node and a series of timestamps corresponding to the series of data values, and the first node executes the first stream function to repair missing or corrupted data within the first time series of data.

4. The computer-implemented method of claim 1, wherein the first time series of data comprises a series of data values and a corresponding series of timestamps, and the first time series of data is received from another node that is coupled to the first node and configured to generate the first time series of data.

5. The computer-implemented method of claim 1, wherein processing the first data stream and the second data stream comprises generating a time series of correlation values between data values in the first data stream and data values in the second data stream, and further comprising identifying a network event within a second network underlying the first network based on the time series of correlation values.

6. The computer-implemented method of claim 5, wherein the first data stream includes a first voltage value associated with the first node, the second data stream includes a second voltage value associated with the second node, and the time series of correlation values indicates that the first voltage value and the second voltage value both differ from a nominal voltage value by a threshold amount.

7. The computer-implemented method of claim 6, wherein the first voltage value and the second voltage value exceed the nominal voltage value by the threshold amount, and the network event comprises a swell within the second network, or the first voltage value and the second voltage value fall below the nominal voltage value by the threshold amount, and the network event comprises a sag within the second network.

8. The computer-implemented method of claim 5, wherein the first data stream reflects usage patterns associated with a first consumer to which the first node is coupled, the second data stream reflects usage patterns associated with a second consumer to which the second node is coupled, and further comprising detecting fraud associated with the first consumer or the second consumer based on the time series of correlation values.

9. A non-transitory computer-readable medium storing program instructions that, when executed by a processing unit, cause the processing unit to perform a distributed processing operation across a network of nodes, by performing the steps of:

configuring a first node that resides at a first physical location within a first network to execute a first stream function on a first time series of data to generate a first data stream;

configuring a second node that resides at a second physical location within the first network to execute a second stream function on a second time series of data to generate a second data stream;

obtaining the first data stream from the first node via one or more network connections;

obtaining the second data stream from the second node via one or more network connections; and

processing the first data stream and the second data stream to generate a time series of processing results.

10. The non-transitory computer-readable medium of claim 9, wherein the step of configuring the first node comprises transmitting a specification of the first stream function to the first node, wherein the first stream function is specified by a user via a software development tool kit.

11. The non-transitory computer-readable medium of claim 9, wherein the first time series of data comprises a series of data values recorded by a sensor array coupled to the first node and a series of timestamps corresponding to the series of data values, and wherein the first node executes the first stream function to repair missing or corrupted data within the first time series of data.

12. The non-transitory computer-readable medium of claim 9, wherein the first time series of data comprises a series of data values and a corresponding series of timestamps, and the first time series of data is received from another node that is coupled to the first node and configured to generate the first time series of data.

13. The non-transitory computer-readable medium of claim 9, wherein the step of processing the first data stream and the second data stream comprises generating a time series of correlation values between data values in the first data stream and data values in the second data stream, and further comprising identifying a network event within a second network underlying the first network based on the time series of correlation values.

14. The non-transitory computer-readable medium of claim 13, wherein the first data stream includes a first voltage value associated with the first node, the second data stream includes a second voltage value associated with the second node, and the time series of correlation values indicates that the first voltage value and the second voltage value both differ from a nominal voltage value by a threshold amount.

15. The non-transitory computer-readable medium of claim 14, wherein the first voltage value and the second voltage value exceed the nominal voltage value by the threshold amount, and the network event comprises a swell within the second network, or the first voltage value and the second voltage value fall below the nominal voltage value by the threshold amount, and the network event comprises a sag within the second network.

16. The non-transitory computer-readable medium of claim 13, wherein the first data stream reflects usage patterns associated with a first consumer to which the first node is coupled, the second data stream reflects usage patterns associated with a second consumer to which the second node is coupled, and further comprising detecting fraud associated with the first consumer or the second consumer based on the time series of correlation values.

17. A system configured to perform distributed processing operations, the system comprising:

a mesh network that includes a plurality of nodes that are coupled to one another and that exchange data with one another, wherein each node in the mesh network is associated with one or more elements included in an underlying network;

a stream network that includes a plurality of data streams, wherein different nodes in the mesh network execute stream functions to generate the data streams included in the plurality of data streams, and each data stream in the plurality of data streams comprises a time series of data values; and

a server machine coupled to the mesh network that;

configures the different nodes in the mesh network to execute the stream functions,

collects the data streams generated by the different nodes, and

processes the data streams to identify events occurring within the underlying network.

18. The system of claim 18, wherein the server machine configures a node in the mesh network by transmitting a specification of the first stream function to the first node, wherein the first stream function is specified by a user via a software development tool kit.

19. The system of claim 18, wherein the server machine processes the data streams to identify an event occurring in the underlying network by:

performing a correlation operation between two or more data streams to generate a time series of correlation values;

determining that a subset of the time series of correlation values exceeds a threshold correlation value.

20. The system of claim 18, wherein the underlying network comprises an electricity distribution infrastructure, an element in the underlying network comprises an electricity distribution element, and a node associated with the element comprises a monitoring device configured to monitor one or more parameters associated with the distribution of electricity by the element.