Abstract: A method, apparatus, and system for disconnecting loads from the electrical grid based on a power line frequency are disclosed. An electricity meter may monitor a power line frequency of a source power line connected to the electricity meter, and in response to determining that the power line frequency is lower than a disconnect threshold frequency, may open an internal switch and disconnect a load side output of the electricity meter from the source power line.

Abstract: A method, apparatus, and system for disconnecting loads from the electrical grid based on a power line frequency are disclosed. An electricity meter may monitor a power line frequency of a source power line connected to the electricity meter, and in response to determining that the power line frequency is lower than a disconnect threshold frequency, may open an internal switch and disconnect a load side output of the electricity meter from the source power line.

Abstract: A method, apparatus, and system for disconnecting loads from the electrical grid based on a power line frequency are disclosed. An electricity meter may monitor a power line frequency of a source power line connected to the electricity meter, and in response to determining that the power line frequency is lower than a disconnect threshold frequency, may open an internal switch and disconnect a load side output of the electricity meter from the source power line.

Abstract: In an electrical grid, several electricity meters may be associated with the same transformer, and may measure electricity sold to respective customers. In an example, one electricity meter executes manager software, while the other electricity meters execute agent software. One or more applications operated on each electricity meter, and control metrology devices, data processing, operation of radio(s) and/or a powerline communications modem, aspects of electrical phase determination, etc. The agent software operating on each electricity meter may relay messages from applications operating on that electricity meter to other (possibly similar) applications operating on other electricity meters. Each message sent by each instantiation of agent software may include a networking score of that electricity meter. The manager software may additionally communicate with a data collector and/or main office server(s).

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 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: 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: 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: 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: In a distributed meter data management (MDM) system of a smart grid, a network computing device, such as a network router deployed in the field, may receive data from one or more utility meters, sensors, control devices, or other utility data collection devices that are communicatively coupled to the network computing device. The network computing device may process the data received from the utility data collection devices to generate processed data usable by a consumer. The network computing device may also send data to and/or receive data from a centralized control system, MDM system, and/or smart grid analytic applications at a utility central office. The network computing device may then provide the processed data directly to the consumer, with or without first sending the data to a centralized MDM system of the utility.

Abstract: In a distributed meter data management (MDM) system of a smart grid, a network computing device, such as a network router deployed in the field, may receive data from one or more utility meters, sensors, control devices, or other utility data collection devices that are communicatively coupled to the network computing device. The network computing device may process the data received from the utility data collection devices to generate processed data usable by a consumer. The network computing device may also send data to and/or receive data from a centralized control system, MDM system, and/or smart grid analytic applications at a utility central office. The network computing device may then provide the processed data directly to the consumer, with or without first sending the data to a centralized MDM system of the utility.