A method of determining an energy load on a power distribution component, and a system for storing such method are presented. The method entails collecting meter data (in various formats) and weather data. The meter data and the weather data are correlated to generate tuning equations, each of which is associated with at least one of the meters and indicates a weather sensitivity of that meter. Any meter data that is in the hourly or daily format are normalized to generate normalized hourly loadshapes that are independent of weather conditions and weekly variations. These normalized hourly loadshapes are combined with the tuning equations to generate a set of model coefficients for each of the meters. The set of model coefficients reflects weather conditions and weekly variations for one of the meters, and is useful for determining an energy load on the power distribution component.
System And Method Of High Volume Import, Validation And Estimation Of Meter Data
A meter data management system includes one or more application servers configured to receive, validate and estimate received meter data. The one or more application servers may include various memory/media elements for storing raw and transformed meter data as well as software in the form of computer-executable instructions, which are executed by a processor to implement a variety of functions, including establishing a mapping between the plurality of external devices and a plurality of preconfigured processing workbins (e.g., collections of meter channels in the same time zones), transforming the received meter data into a plurality of data files identified by timing parameter and workbin, validating data provided in each data file, estimating missing or invalid data, and automatically writing validated (and optionally estimated) data as import files to a database server. Such clean data files are then imported to one or more dedicated databases installed on the database server.
Detection Of Electrical Theft From A Transformer Secondary
- Liberty Lake WA, US Robert Sonderegger - Oakland CA, US
International Classification:
G01R 11/24 G01R 22/06 G01R 19/165
Abstract:
Techniques for identifying electrical theft are described herein. In an example, a secondary voltage of a transformer may be inferred by repeated voltage and current measurement at each meter associated with the transformer. A difference in measured voltage values, divided by a difference in measured current values, estimates impedance at the meter. The calculated impedance, together with measured voltage and current values, determine a voltage at the transformer secondary. Such voltages calculated by each meter associated with a transformer may be averaged, to indicate the transformer secondary voltage. A transformer having lower-than-expected secondary voltage is identified, based in part on comparison to the secondary voltages of other transformers. Each meter associated with the identified transformer may be evaluated to determine if the unexpected voltage is due to a load on the transformer. If a load did not result in the unexpected secondary voltage, power diversion may be reported.
- Liberty Lake WA, US Hartman Van Wyk - Montloius sur Loire, FR Robert Sonderegger - Oakland CA, US Chris Higgins - Wake Forest NC, US
International Classification:
G01R 25/00 G01D 4/00
Abstract:
Determination of electrical network topology and connectivity are described herein. A zero-crossing is indicated at a time when the line voltage of a conducting wire in an electrical grid is zero. Such zero-crossings may be used to measure time within a smart grid, and to determine the connectivity of, and the electrical phase used by, particular network elements. A first meter may receive a phase angle determination (PAD) message, including zero-crossing information, sent from a second meter, hereafter called a reference meter. The first meter may compare the received zero-crossing information to its own zero-crossing information. A phase difference may be determined between the first meter and the reference meter from which the PAD message originated. The first meter may pass the PAD message to additional meters, which propagate the message through the network. Accordingly, an electrical phase used by meters within the network may be determined.
Detection Of Electrical Theft From A Transformer Secondary
- Liberty Lake WA, US Robert Sonderegger - Oakland CA, US
International Classification:
G01R 11/24 G01R 22/06 G01R 19/165
Abstract:
Techniques for identifying electrical theft are described herein. In an example, a secondary voltage of a transformer may be inferred by repeated voltage and current measurement at each meter associated with the transformer. A difference in measured voltage values, divided by a difference in measured current values, estimates impedance at the meter. The calculated impedance, together with measured voltage and current values, determine a voltage at the transformer secondary. Such voltages calculated by each meter associated with a transformer may be averaged, to indicate the transformer secondary voltage. A transformer having lower-than-expected secondary voltage is identified, based in part on comparison to the secondary voltages of other transformers. Each meter associated with the identified transformer may be evaluated to determine if the unexpected voltage is due to a load on the transformer. If a load did not result in the unexpected secondary voltage, power diversion may be reported.
Detection Of Electrical Theft From A Transformer Secondary
- Liberty Lake WA, US Robert Sonderegger - Oakland CA, US
International Classification:
G01R 11/24 G01R 19/165
Abstract:
Techniques for identifying electrical theft are described herein. In an example, a secondary voltage of a transformer may be inferred by repeated voltage and current measurement at each meter associated with the transformer. A difference in measured voltage values, divided by a difference in measured current values, estimates impedance at the meter. The calculated impedance, together with measured voltage and current values, determine a voltage at the transformer secondary. Such voltages calculated by each meter associated with a transformer may be averaged, to indicate the transformer secondary voltage. A transformer having lower-than-expected secondary voltage is identified, based in part on comparison to the secondary voltages of other transformers. Each meter associated with the identified transformer may be evaluated to determine if the unexpected voltage is due to a load on the transformer. If a load did not result in the unexpected secondary voltage, power diversion may be reported.
- Liberty Lake WA, US Hartman Van Wyk - Montloius sur Loire, US Robert C. Sonderegger - Oakland CA, US Christopher M. Higgins - Wake Forest NC, US
International Classification:
G01R 25/00
Abstract:
Determination of electrical network topology and connectivity are described herein. A zero-crossing is indicated at a time when the line voltage of a conducting wire in an electrical grid is zero. Such zero-crossings may be used to measure time within a smart grid, and to determine the connectivity of, and the electrical phase used by, particular network elements. A first meter may receive a phase angle determination (PAD) message, including zero-crossing information, sent from a second meter, hereafter called a reference meter. The first meter may compare the received zero-crossing information to its own zero-crossing information. A phase difference may be determined between the first meter and the reference meter from which the PAD message originated. The first meter may pass the PAD message to additional meters, which propagate the message through the network. Accordingly, an electrical phase used by meters within the network may be determined.
- Liberty Lake WA, US Robert Sonderegger - Oakland CA, US
Assignee:
Itron, Inc. - Liberty Lake WA
International Classification:
G01R 19/00
Abstract:
Techniques for determining aspects of a topology of a smart grid are described herein, and particularly for determining if one or more electrical meters are connected to the same transformer. In one example, time-stamped voltage data is collected from at least two meters. The voltage data may indicate a slight transient change in voltage resulting from a consumer turning on or off an electrical load. In particular, the slight voltage changes may be sensed by all meters attached to a same transformer based on electrical load changes by any one of the customers on the same transformer. Using the time-stamped voltage data, a time-series of voltage-changes may be generated for each electrical meter. A correlation between the time-series of voltage-changes of pairs of meters may be calculated, to thereby determine an affinity between the meters, and particularly if they are connected to a same transformer.
Name / Title
Company / Classification
Phones & Addresses
Robert Sonderegger President
MORGAN SYSTEMS CORPORATION
2560 Ninth St #211, Berkeley, CA 94710 2560 9 St, Berkeley, CA 94710
Itron since Apr 2003
Director, Engineering Advisor
Silicon Energy Jun 2000 - Mar 2003
Chief Application Scientist
SRC Systems May 1991 - May 2000
Vice President, R&D
Morgan Systems Jun 1984 - Apr 1991
President
Lawrence Berkeley National Laboratory Sep 1977 - Jun 1984
Staff Scientist
Education:
Princeton University 1973 - 1977
Ph.D., Mechanical Engineering
Eidgenössische Technische Hochschule Zürich 1968 - 1973
Final Diploma, Physics
Skills:
Smart Grid Energy Efficiency Software Development Renewable Energy Product Management Software Engineering Smart Metering Energy Management Energy
Itron Apr 1, 2003 - Dec 9, 2016
Director, Engineering Advisor
Silicon Energy Jun 2000 - Mar 2003
Chief Application Scientist
Src Systems May 1991 - May 2000
Vice President, R and D
Morgan Systems Jun 1984 - Apr 1991
President
Lawrence Berkeley National Laboratory Sep 1, 1977 - Jun 1, 1984
Staff Scientist
Education:
Princeton University 1973 - 1977
Doctorates, Doctor of Philosophy, Energy, Mechanical Engineering
Eth Zürich Jan 1, 1968 - Jan 1, 1973
Collegio Papio 1964 - 1968
Federal Institute of Technology Zurich
Skills:
Energy Efficiency Energy Management Smart Grid Energy Software Development Program Management Software Engineering Renewable Energy Smart Metering Integration Product Management Project Management Management Strategy Engineering R&D Leadership Enterprise Software Analysis Business Analysis Solar Energy Business Development Automation Simulations Database Design Data Analysis Big Data Physics
Interests:
Collecting Antiques Exercise Home Improvement Reading Sports The Arts Home Decoration Photograph Cooking Gardening Outdoors Electronics Music Movies Collecting Travel Boating Investing Traveling