Abstract: A time synchronization device (TSD) that produces a synchronization signal and couples it onto energized power conductors in a power monitoring system. Monitoring devices coupled to the TSD include frequency detection algorithms, such as a Goertzel filter, for detecting the synchronization signal and interpreting the information encoded in the signal. The frequency of the synchronization signal may correspond to the fourth or tenth harmonic component of the fundamental frequency of the voltage on the power conductors. The magnitude of the signal is selected to be above the expected or established noise floor of the power monitoring system plus a predetermined threshold. The duration of the signal can be varied, such as lasting a full cycle of the fundamental frequency. Multiple TSD signals received in a predetermined sequence may be converted into digital words that convey time, configuration, reset, control, or other information to the monitoring device.

Abstract: Automated power factor correction analysis methods based on an automatically determined hierarchy representing how IEDs and transformers are linked together in an electrical system for reducing a utility bill, releasing capacity to the electrical system, reducing losses, and/or improving voltages. The automatically determined hierarchy places the system elements in spatial context and is exploited by the power factor correction analysis methods to identify power factor correction opportunities. Recommendations are made as to sizing and location of capacitors within the hierarchy where power factor improvements can be achieved. Harmonic distortion levels can be checked first to determine whether safe levels exist for capacitor banks. Recommendations are also checked to avoid leading power factors anywhere in the system due to the addition of capacitor banks. Capacitor bank location is tailored to the end-user's goal for power factor correction.

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 for automatically determining how monitoring devices in an electrical system having a main source of energy and at least one alternative source of energy (e.g., another utility source, a generator, or UPS system) are connected together to form a hierarchy. The end-user inputs identification information about the monitoring device(s) monitoring the alternative source of energy. The method receives time-series data from the monitoring devices and determines a model type of the electrical system by analyzing the monitoring device's time-series data. Once the model type is known, the method builds the complete monitoring system hierarchy in which the monitoring devices that are monitoring the main and alternative sources are placed properly. The method can also validate polarity nomenclature of the time-series data to account for end-user's varying polarity configurations.

Abstract: A voltage analysis algorithm for automatically determining anomalous voltage conditions in an electrical system monitored by a plurality of intelligent electronic devices (IEDs) and automatically making recommendations for ameliorating or eliminating the anomalous voltage conditions. The electrical system hierarchy is determined automatically or manually, and the algorithm receives voltage data from all capable IEDs. The voltage data is temporally aligned or pseudo-aligned to place the voltage data in both spatial and temporal context. The algorithm determines anomalous voltage conditions systemically by comparing measured voltage values against nominal or expected ones across the system. Based on the spatial and temporal context of the IEDs, the algorithm automatically identifies a source of the voltage deviation in the hierarchy, and recommends a modification associated with the source for mitigating the anomalous voltage condition.

Abstract: An automated hierarchy classification algorithm that searches for a child monitoring device's parent in a utility monitoring system by segmenting the device data measured by a given device pair and calculating a segment correlation coefficient for each data segment. Devices to be placed in the hierarchy are filtered by calculating the variance of their device data and eliminating devices with a low variance. Devices are ranked by computing the sum of squares of their device data and ordering the devices accordingly from highest to lowest. The device data is segmented and segment correlation coefficients are averaged to produce an overall correlation coefficient. Criteria are evaluated to determine whether a device pair is linked. A correlation coefficient is calculated using the complete data series of a device pair, and the solution produced by this approach is compared with the solution produced by segmenting the device data.

Abstract: Methods for improving the accuracy of characterizing unmonitored paths or virtual meters in a utility system. The hierarchical arrangement of IEDs in the utility system is determined. Measured quantities of a characteristic of the utility being monitored are received and error-adjusted using statistical or absolute methods. The statistical method accounts for the mean and standard deviation associated with error measurements of the subject IED, and the absolute method uses the absolute value of the error measurement, expressed as a percentage, to produce ranges of measured quantities within an error tolerance. The differences between the error-adjusted quantities are analyzed to determine whether an unmonitored path exists, and if so, whether the virtual meter is consuming or supplying the utility. The order in which IEDs are read is determined so that a parent and its children are read in sequence to increase synchronicity of the received data and the virtual meter evaluation.

Abstract: A method of automatically identifying whether intelligent electronic devices (IEDs) in a power monitoring system are in multiple electrical grids. A controller sends an instruction to each IED in a predetermined time sequence such that each IED receives the instruction at a different time, commanding each IED to begin logging variation data indicative of frequency variations in a current/voltage signal monitored by the IED and to send the variation data to the controller and an associated cycle count of a point in the current/voltage signal. The controller receives the variation data and associated cycle count and determines a peak correlation using a data alignment algorithm on IED pair combinations. If the IEDs are on the same electrical grid, the peak correlations should occur at cycle count offsets that match the order that the IEDs received the instruction. Any discrepancies in the expected order of peak correlations are flagged, and the corresponding IEDs are determined to be on different grids.

Abstract: A method of automatically learning how multiple devices are directly or indirectly linked in a monitoring system, comprises determining configuration parameters for the multiple devices in said system, receiving data measured by the devices, and grouping the devices into multiple segments according to at least one type of information selected from the group consisting of configuration parameters and data measured by said devices. Potential relationships of the devices in each segment are determined according to at least one type of information selected from the group consisting of configuration parameters and data measured by the devices, the hierarchies of the devices within individual segments are determined, and the hierarchies of the top-most device or devices in the segments are determined.

Abstract: A process monitoring method that aggregates monitoring devices and optionally sensors into one or more groups that are each related to a process of a utility system. The monitoring devices are organized into a monitoring system hierarchy manually or automatically. A process algorithm determines from the hierarchy which monitoring devices are connected to a load. Monitored data from load-connected monitoring device pairs are correlated to produce a correlation coefficient that is compared against a correlation threshold selected between 0 and 1. When the correlation coefficient exceeds the threshold, the device pair is grouped into a process group. Other device pairs exceeding the threshold are likewise grouped into the process group. Multiple processes may be determined with the process algorithm. Sensors may also be grouped manually with the process group containing monitoring devices, which may include virtual monitoring devices.

Abstract: A power monitoring system for monitoring characteristics of power transmitted through one or more power lines comprises a meter base and multiple option modules. The meter base includes a processor and associated circuitry for processing signals derived from sensors coupled to said power lines and producing output signals representing selected characteristics of the power transmitted through the power lines, and a housing containing the processor and the associated circuitry and having a first surface adapted to be mounted on a DIN rail, and a second surface containing multiple connectors for receiving multiple modules and electrically connecting the modules to the processor and the associated circuitry.

Abstract: A method and system of automatically correlating data measured by monitoring devices that monitor first and second electrical grids. The second electrical grid producing alternating current signals that are electrically isolated from alternating current signals produced by the first electrical grid. An example power monitoring device includes a controller, a first monitoring device interface and a second monitoring device interface. The first monitoring device interface is coupled to a first monitoring device in the first electrical grid and the second monitoring device interface is coupled to a second monitoring device in the second electrical grid. A first counter stores data counts of occurrences from the first electrical grid. A second counter stores data counts of occurrences from the second electrical grid.

Abstract: A method and system to detect and evaluate improper grounding/grounded bonds in an electrical power system is disclosed. An example method is detecting improper grounding in an electrical power system having a plurality of monitoring devices coupled to a grounded conductor and a grounding conductor. Data of the voltage between the grounded conductor and the grounding conductor is received from the plurality of monitoring devices. The spatial orientation of the data from the plurality of monitoring devices is determined within a hierarchy of the electrical power system. The voltage data received from the plurality of monitoring devices is compared to determine a bond (or lack of a bond) between the grounded conductor and the grounding conductor.

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: Methods for improving the accuracy of characterizing unmonitored paths or virtual meters in a utility system. The hierarchical arrangement of IEDs in the utility system is determined. Measured quantities of a characteristic of the utility being monitored are received and error-adjusted using statistical or absolute methods. The statistical method accounts for the mean and standard deviation associated with error measurements of the subject IED, and the absolute method uses the absolute value of the error measurement, expressed as a percentage, to produce ranges of measured quantities within an error tolerance. The differences between the error-adjusted quantities are analyzed to determine whether an unmonitored path exists, and if so, whether the virtual meter is consuming or supplying the utility. The order in which IEDs are read is determined so that a parent and its children are read in sequence to increase synchronicity of the received data and the virtual meter evaluation.

Abstract: Automated power factor correction analysis methods based on an automatically determined hierarchy representing how IEDs and transformers are linked together in an electrical system for reducing a utility bill, releasing capacity to the electrical system, reducing losses, and/or improving voltages. The automatically determined hierarchy places the system elements in spatial context and is exploited by the power factor correction analysis methods to identify power factor correction opportunities. Recommendations are made as to sizing and location of capacitors within the hierarchy where power factor improvements can be achieved. Harmonic distortion levels can be checked first to determine whether safe levels exist for capacitor banks. Recommendations are also checked to avoid leading power factors anywhere in the system due to the addition of capacitor banks. Capacitor bank location is tailored to the end-user's goal for power factor correction.

Abstract: A system and method to evaluate characteristics of notches in sine wave type electrical signals. An example method includes measuring a voltage waveform. A reference waveform is derived from the measured voltage waveform. A series of threshold voltage values is determined based on corresponding voltages of the reference waveform. The voltages of the voltage waveform are compared with the corresponding threshold voltages. The presence of a notch is indicated when a voltage of the voltage waveform is lesser in magnitude than the corresponding threshold voltage.

Abstract: A method and system of converting numerical infrastructure data relating to a utility monitoring system having elements arranged in a hierarchy to a graphic compatible storage data format. Numerical infrastructure data relating to the elements of the utility monitoring system is obtained. The numerical infrastructure data relating to the elements of the utility monitoring system is converted to the graphic compatible storage data format. The graphic compatible storage data format may be System Specification Description (SSD). The converted data is stored in a storage file. A user interface may access the storage file to generate a graphic display showing the elements of the utility monitoring system arranged in the hierarchy.

Abstract: A system and method of synchronizing measurements between a master monitor (130 and/or 132) and a slave monitor (154 and/or 164). A system controller (110) receives master samples representing a signal characteristic from the master monitor (130 and/or 132) and slave samples representing the signal characteristic from the slave monitor (154 and/or 164). The master samples and slave samples are aligned using correlation analysis to obtain the identification of a slave sample that aligns with a predetermined master sample. This identification is transmitted to the slave monitor and used to synchronize a master measurement with a slave measurement.

Abstract: A method of graphically representing values of at least one selected parameter at multiple monitoring points in a utility system, comprises receiving data measured at the multiple monitoring points; determining the values of the selected parameter at the multiple monitoring points, based on the received data, and using the values to generate a graphical representation of the values of the selected parameter at the multiple monitoring points, the graphical representation including shapes having (1) sizes representative of the magnitudes of the values and (2) locations representative of the hierarchy of the monitoring points. In one application, the selected parameter is at least one of electrical power and energy consumed in portions of an electrical power distribution system that correspond to the multiple monitoring points.