A method for determining the phase of an electric meter includes: selecting a population of electric meters; selecting a historical time range based at least in part on the selected population of electric meters; selecting an interval period; creating profiles for each electric meter in the selected population of electric meters based on the voltage measurements over one or more interval periods; calculating a correlation coefficient matrix based on the profiles; clustering the selected population of electric meters into a plurality of clusters; and determining a phase of each cluster of the plurality of clusters.

A three-phase power meter can monitor power on both 3-wire and 4-wire power lines. The power meter measures at least two voltages between phase conductors of the power line, and at least one voltage between a phase conductor and a neutral conductor of the power line when the neutral conductor is available. Using at least some of the measured voltages, the power meter can then operate in a first mode when coupled to a 3-wire power line to determine power on the power line based on the measured voltages, or operate in a second mode when coupled to a 4-wire power line to determine power on the power line based on the measured voltages.

In an embodiment, an electronic device includes a package power zone controller. The device monitors the overall power consumption of multiple components of a package. The package power zone controller may detect workloads in which the package components (e.g. different types of processors, peripheral hardware, etc.) are each consuming relatively low levels of power, but the overall power consumption is greater than a desired target. The package power zone controller may implement various mechanisms to reduce power consumption in such cases.

Monitoring and assessing power performance changes of one or more wind turbines of a wind farm. For each wind turbine to be monitored, a group of reference wind turbines is defined. During a training period a transfer function is generated for each monitored wind turbine. The transfer function establishes a relationship between locally measured wind speeds at each of the reference wind turbines and the power performance data for the monitored wind turbine obtained during the training period. During one or more subsequent test periods, measured power performance data for the monitored wind turbines is compared to predicted power performance data. The predicted power performance data is obtained by means of the locally measured wind speeds at the corresponding reference wind turbines during the test period(s) and the previously generated transfer function for the monitored wind turbine. This allows even small and/or gradual power performance degradation to be detected.

Distributed grid network intelligence enables data aggregation at a local control node. In a consumer node, a meter is on a consumer side of a point of common coupling (PCC). The meter can receive one or more external grid inputs and one or more local sensor inputs. The grid inputs can come from sources outside the PCC, and the local sensor inputs monitor conditions at the PCC and/or within the PCC. The meter can identify power demand within the PCC and calculate an output power to generate with a local power converter. The calculation is not simply based on power demand, but on aggregation information, including the one or more external grid inputs, the one or more local sensor inputs, and the power demand for the local load. The local power converter can then output power in accordance with the calculated output power.

Detection and correction of technical and non-technical errors in smart grid power distribution are described. A system, method and non-transitory computer readable medium having instructions stored therein that, when executed by one or more processors, causes the one or more processors to perform a method for detecting and correcting technical and non-technical power losses in a smart grid that feature the following functions: remotely characterizing and updating the cables impedances, detecting and classifying the types of losses, estimating the technical and non-technical power losses when a check or smart meter is in error, estimating losses due to tapping a power cable by a registered or an unregistered user, and estimating losses due to a cyber attack. Technical errors corrected are impedance and reactance losses in the power distribution. Non-technical errors identified and corrected are no error, check meter in error, smart meter in error, tapping service cables, or cyber attacks.

An electronic device monitoring system includes a server, a first electronic device, and a first power feature identifying device. The first electronic device is electrically connected to a grid. A first voltage and a first current are variable. The first power feature identifying device communicates with the server by a first communication protocol. The first power feature identifying device is disposed between the first electronic device and the grid. The first power feature identifying device extracts a plurality of voltage-time features, a plurality of current-time features, and a plurality of voltage-current features of the first electronic device in a first predetermined time, and the first power feature identifying device transmits those features to the server to generate a first power profile based thereon. The server monitors whether the first electronic device is in normal operation based on the first power profile.

A three-phase power meter can monitor power on both 3-wire and 4-wire power lines. The power meter measures at least two voltages between phase conductors of the power line, and at least one voltage between a phase conductor and a neutral conductor of the power line when the neutral conductor is available. Using at least some of the measured voltages, the power meter can then operate in a first mode when coupled to a 3-wire power line to determine power on the power line based on the measured voltages, or operate in a second mode when coupled to a 4-wire power line to determine power on the power line based on the measured voltages.

The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.

A calibrated measurement of the angular misalignment x of a radiofrequency antenna pointing towards a slow mobile, radio transmitter along an antenna axis, the antenna being in radio communication with the mobile.