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.

Abstract: Techniques for detecting electrical meter bypass theft are described herein. In one example, a time-series of voltage-changes and current-changes associated with electrical consumption measured at a meter are obtained. The time series may track associated voltage and current changes at short intervals (e.g., 5-minutes). The voltage and current changes may indicate a slight voltage change when an appliance is turned on or off. An analysis (e.g., a regression analysis) may be performed on the voltage-changes against the current-changes. Using the correlation from the analysis, it may be determined if the meter was bypassed.

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.

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.

Abstract: Techniques for detecting high impedance conditions in an electrical grid are described herein. In one example, impedance is calculated for each of a plurality of locations within the electrical grid, such as at electrical meters. The impedances may be calculated as a change in voltage divided by a change in current, such as between sequential voltage/current measurements. Statistics may be maintained, including the calculated impedances. In three examples, statistics may be used to identify growth in impedance over multiple days, to identify growth in impedance over multiple hours, and to identify a meter for which impedance is higher than impedance for other meters attached to a single transformer. In a further example, instances of impedance over a threshold value may be identified, from among the maintained statistics. The instances of high impedance may be reported for reasons including cost and safety.

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.

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.

Abstract: Techniques for detecting high impedance conditions in an electrical grid are described herein. In one example, impedance is calculated for each of a plurality of locations within the electrical grid, such as at electrical meters. The impedances may be calculated as a change in voltage divided by a change in current, such as between sequential voltage/current measurements. Statistics may be maintained, including the calculated impedances. In three examples, statistics may be used to identify growth in impedance over multiple days, to identify growth in impedance over multiple hours, and to identify a meter for which impedance is higher than impedance for other meters attached to a single transformer. In a further example, instances of impedance over a threshold value may be identified, from among the maintained statistics. The instances of high impedance may be reported for reasons including cost and safety.

Abstract: Techniques for detecting high impedance conditions in an electrical grid are described herein. In one example, impedance is calculated for each of a plurality of locations within the electrical grid, such as at electrical meters. The impedances may be calculated as a change in voltage divided by a change in current, such as between sequential voltage/current measurements. Statistics may be maintained, including the calculated impedances. In three examples, statistics may be used to identify growth in impedance over multiple days, to identify growth in impedance over multiple hours, and to identify a meter for which impedance is higher than impedance for other meters attached to a single transformer. In a further example, instances of impedance over a threshold value may be identified, from among the maintained statistics. The instances of high impedance may be reported for reasons including cost and safety.

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.

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.

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.

Abstract: Techniques for detecting high impedance conditions in an electrical grid are described herein. In one example, impedance is calculated for each of a plurality of locations within the electrical grid, such as at electrical meters. The impedances may be calculated as a change in voltage divided by a change in current, such as between sequential voltage/current measurements. Statistics may be maintained, including the calculated impedances. In three examples, statistics may be used to identify growth in impedance over multiple days, to identify growth in impedance over multiple hours, and to identify a meter for which impedance is higher than impedance for other meters attached to a single transformer. In a further example, instances of impedance over a threshold value may be identified, from among the maintained statistics. The instances of high impedance may be reported for reasons including cost and safety.

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.

Abstract: Techniques for detecting electrical meter bypass theft are described herein. In one example, a time-series of voltage-changes and current-changes associated with electrical consumption measured at a meter are obtained. The time series may track associated voltage and current changes at short intervals (e.g., 5-minutes). The voltage and current changes may indicate a slight voltage change when an appliance is turned on or off. An analysis (e.g., a regression analysis) may be performed on the voltage-changes against the current-changes. Using the correlation from the analysis, it may be determined if the meter was bypassed.

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.

Abstract: Techniques for analyzing a utility infrastructure are described herein. In one example, data is obtained from a utility system. The data may include consumption measurement information, consumption measurement exceptions and/or system events. Exceptions may include data indicating a possible problem, such as significantly increased or decreased consumption, reduced voltage, etc. Events may include data on power down actions, meter removal, etc. Attributes may be considered, including demographic information, weather information, economic information, etc. The data may be filtered by comparison to known patterns of measurements, exceptions, events and/or attributers that indicate an analytic event. Accordingly, analytic events may include important system information that is inferred from large quantities of data. Analytic events may be reported to an operator through operation of a user interface.

Abstract: 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.

Abstract: Described herein is an improved torque analyzing apparatus for indicating the torque transmitted by power tools. In addition to the housing, bearing supports and torque indicator or pointer, the apparatus includes a torque input shaft that is frictionally connected with a torsion or spiral spring through a resilient sleeve on the shaft. The torsion spring is connected to and transmits torque to a spring housing which is connected to a torsion bar. The torque indicator or pointer is mounted on the spring housing. The torsion spring or spiral spring permits several rotations of the torque input shaft and provides a gradual increase in the torque that is applied to the torsion bar.