Abstract: In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements.

Abstract: In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements.

Abstract: In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements.

Abstract: In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements.

Abstract: A noisy data alignment algorithm for determining cycle count offsets for noisy pairs of n monitoring devices. A direct cycle count offset matrix is determined based upon the highest correlation coefficients produced by correlating frequency variation data from each device pair Dij. For each direct cycle count offset Mij, indirect cycle count offsets are calculated as a function of at least Mk, where k?i?j, to produce indirect cycle count offsets. The statistical mode of these indirect offsets is compared with the corresponding Mij in the matrix. When they differ, Mij in the direct matrix is adjusted to be equal to the statistical mode. All indirect cycle count offsets for all other unique device pairs, Mij, are calculated to iterate to a single solution in which all indirect cycle count offsets are equal to the corresponding direct cycle count offset. An optional verification algorithm is also provided.

Abstract: A method and system to detect and evaluate harmonic distortion in an electrical power system is disclosed. A plurality of monitoring devices is installed in the electrical power system. The hierarchy of the electrical power system is received. Data indicative of harmonic distortion on the electrical power system is received from at least two of said plurality of monitoring devices. The data indicative of harmonic distortion is automatically aligned in a temporal or pseudo-temporal context. An electrical characteristic of the harmonic distortion from the data indicative of harmonic distortion is determined.

Abstract: A data alignment algorithm that automatically aligns data from multiple monitoring devices to the same zero-crossings at the same point in time. Cycle-by-cycle frequency data is received from each monitoring device and a cross-correlation algorithm is performed to determine a correlation coefficient between a reference monitoring device and another monitoring device. The data of the other monitoring device is shifted by one cycle and another correlation coefficient is calculated by the cross-correlation algorithm. The data of the two monitoring devices is aligned at the point at which the maximum correlation coefficient is calculated or the point at which the correlation coefficient exceeds a threshold value. The clocks of the monitoring devices can also be synchronized at the same point of alignment.

Abstract: An auto-learned hierarchy algorithm that learns the hierarchical layout of a power monitoring system. Historical power data from each meter is received and placed into a data table. The main is assumed to be at the top of the hierarchy and is designated as the reference. A check matrix is developed indicating whether a possible connection exists between each meter pair combination. A correlation coefficient matrix (CCM) is calculated based on the data table, and entries in which no connection is possible are zeroed. The column for the reference meter from the CCM is copied to a correlation reference array (CRA), and the meter having the highest correlation with the reference meter in the CRA is marked as connected to the reference meter in a connection table. That meter's power is subtracted from the data table and the procedure is repeated until all meters have been analyzed.

Abstract: An automated integrated monitoring (IM) algorithm that automatically puts data from a utility monitoring system into context by temporally aligning the data to a common reference point and by identifying the location of each monitoring device in a hierarchy relative to other devices. Frequency variation data is received from all meters. The data is automatically aligned to a common reference point, such as a precise zero crossing, using a cross-correlation algorithm to determine the time delay at which the data is most correlated. Once the data is aligned, power data is received from all meters in a hierarchy, and the monitoring system layout is auto-learned using a correlation algorithm to determine which two meters are most likely correlated with one another based upon their historical power readings. Once the layout is complete, additional decisions regarding hardware and software configuration can automatically be made by the IM algorithm.

Abstract: A system for accumulating and evaluating electromagnetic phenomena of at least one power quality category of a power distribution system, using a circuit monitor or processing system that summarizes and trends electromagnetic phenomena. The system provides for expressing the power quality index as a single number for each power quality category present and for combining multiple power quality indices into a single overall power quality index.