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.

Bypass detection modules associated with a device are provided. The bypass detection module is configured to determine if one of more switches have been opened to remove service from a customer; monitor electrical characteristic associated with both a line side of the device and a load side of the device responsive to determining that one of more of the one or more switches have been opened to remove service from the customer; and determine a state of the device based on the monitored electrical characteristics.

Position sensing modules associated with a device are provided. The position sensing modules are configured to receive electrical characteristics associated with one or more switches of a device over a predetermined period of time, the one or more switches being configured to connect service to or disconnect service from a customer; calculate a match indicator for each phase of the device including the one or more switches, the match indicator indicating whether an electrical characteristic on a load-side of the device matches a same electrical characteristic on a line-side of the device for each phase of the device; and determine a position of the one or more switches based on the received electrical characteristics and the calculated match indicator for each phase of the device.

A system for detecting electricity theft with an integrity checks analysis includes a graphical user interface (GUI) configured to display information related to a flow of electricity within a power distribution system and a controller in communication with the GUI. The controller is configured to receive electrical readings taken by a plurality of electricity meters and examine the electrical readings of the plurality of electricity meters for electricity theft indicators. The controller is also configured to determine a probability that electricity is being stolen at each of the plurality of electricity meters according to any electricity theft indicators affiliated therewith and output each probability to the GUI for display.

Analysis of smart meter and/or similar data based on frequency content is disclosed. In various embodiments, for each of a plurality of resource consumption nodes a time series data including for each of a series of observation times a corresponding resource consumption data associated with that observation time is received. At least a portion of the time series data, for each of at least a subset of the plurality of resource consumption nodes, is transformed into a frequency domain. A feature set based at least in part on the resource consumption data as transformed into the frequency domain is used to detect that resource consumption data associated with a particular resource consumption node is anomalous.

A system for detecting theft of electricity from a utility includes a controller configured to receive electricity readings from an upstream metering device configured to sense electricity flowing therethrough and electricity readings from a first downstream metering device that is electrically downstream from the upstream metering device and configured to sense electricity flowing to a first load. The controller is further configured to compare the electricity readings from the first downstream metering device to the electricity readings from the upstream metering device. The controller is additionally configured to calculate a level of interference with an electrical path through the upstream metering device based on an extent that the electricity readings from the first downstream metering device deviate from the electricity readings from the upstream metering device and to output to the utility the level of interference with the electrical path.

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.

Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

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.

Embodiments of the invention provide an electric meter assembly including a meter support platform or base able to reversibly electrically couple to a transformer providing inputs including an AC power supply, phase voltage, and/or a phase current. Output of the meter support platform or base can be DC power, AC phase voltage signals and/or AC phase current. Further, a removable or portable meter can couple to the meter support platform or base and to an electric meter system including a meter core coupled to a wireless transmitter, an antenna, an integrated power sensor, and data meter front end. The system includes a data manager configured for electric service analysis including energy usage, and/or interval temperature, phase voltage, current and phase angle in real time, electric energy kWh and kVARh values in a user-specified period, wrong meter base installation detection, physical location of the removable or portable meter, and tamper detection.