Schneider Electric Usa, Inc.

Square D Company

Other

Fmc Corporation

Ammco Tools Technology Corporation

Service Station Products Company

Wheel Service Equipment Corporation

Ronald W Carter

Jon Andrew Bickel

Amjad Hasan

Larry E Curtis

Avery Dallas Long

Jerry M Hill

Kurt H Copley

Charles L Cunningham

Robert E Williams

Avery D Long

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. If the solutions disagree, a likely candidate is determined from a fuzzy logic module.

Robust automated hierarchical determination for power monitoring systems

A system and methods for determining points for an algorithm to calculate the frequency of a waveform measured from a monitoring device. Data points are measured from the waveform. A last point of interest from the previous cycle of the waveform is referenced. Four points of interest at equally spaced phase angles from the current cycle of the waveform are determined by calculating the average of the data points around each of the respective equally spaced phase angles of the current cycle of the waveform. The average value of the points of interest in the current cycle is subtracted from the four points of interest and the last point of interest from the previous cycle to compensate for DC bias. A frequency of the waveform is determined based on the determined four points of interest of the current cycle and the last point of interest from the previous cycle. The correction factor for determining sample frequency may be reduced for subsequent cycles in order to increase stability.

System and method for frequency measurement based on quadratic forms

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.

Hierarchy determination for power monitoring systems

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.

Methods and systems for automatic conversion of a utility monitoring system layout into a storage format

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 frequency variation data in a current/voltage signal monitored by the IED and to send the data to the controller and an associated cycle count. 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.

Automatic identification of multiple power grids using data synchronization

A system and methods for synchronizing quantized sampled data in a monitoring device. A variable frequency output signal is coupled to an analog to digital converter. A fixed frequency clock is coupled to the analog to digital converter. The analog to digital converter samples the output signal at a fixed frequency to produce high speed samples. A group of initial high speed samples is stored from the analog to digital converter over a fixed window of time. The group of initial high speed samples is interpolated to produce a group of fewer low speed samples from the initial group of high speed samples over the fixed window of time. The group of low speed samples is stored as a representation of the variable frequency output signal.

Quantizing sampled inputs using fixed frequency analog to digital conversions through interpolation

A method of monitoring the electrical power in multiple branch circuits of an AC electrical power distribution system comprises monitoring at least one voltage common to said multiple branch circuits using a main meter unit, monitoring currents of the multiple branch circuits using multiple current cards that receive a plurality of current inputs from current transducers in the multiple branch circuits, sampling the monitored voltage in the main meter unit and the monitored currents in the current cards multiple times in each cycle of the AC power signal, determining the magnitudes and angles of spectral components of the sampled current in the current cards, sending data representing the magnitudes and angles of at least selected spectral components from the current cards to the main meter unit, and storing the voltage samples and the magnitudes and angles of at least the selected spectral components in the main meter unit.

High density metering system

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.

Automated hierarchical classification for utility systems with multiple sources

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.

Virtual metering

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.

Automated precision alignment of data in a utility monitoring system

Methods of automatically configuring a power monitoring system based upon the locations of the monitoring devices in a hierarchy representing the spatial interrelationships of the monitoring devices. The power monitoring system includes a host computer communicatively coupled to a plurality of monitoring devices arranged in a hierarchy in an electrical system. Software on the host computer may execute any or all of an alarm aggregation algorithm for aggregating multiple alarms based on device location; a feature distribution algorithm for enabling/disabling selected device features; an evaluation algorithm for evaluating device applications; a device check algorithm for detecting flawed data; a custom configuration algorithm for customized configuration of thresholds on device-by-device basis; a host computer configuration algorithm for configuring the host computer; a redundancy algorithm for verifying an electrical event; an alarm configuration algorithm for configuring device thresholds; and a configuration error checking algorithm for detecting nomenclature issues.

Automated configuration of a power monitoring system using hierarchical context

A digital power metering system reduces spectral leakage when determining the frequencies present in a power signal by a technique that includes sampling the signal and performing a transformation from the time domain to the frequency domain. The system initially measures the fundamental frequency of the power signal, and samples the power signal at a known sampling rate to produce digital sample values representing the power signal. The system then determines whether the known sampling rate is within a preselected range of a rate that is coherent with the measured fundamental frequency. If the answer is negative, then the system resamples the signal at a rate that is coherent with the measured fundamental frequency to produce resample values representing the signal, so that the frequencies present in the power signal can be determined from the resample values.

Digital power metering system for reducing spectral leakage when determining the frequencies present in a power signal

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.

Automated integration of data in utility monitoring systems

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.

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Murfreesboro, TN, United States of America

Ronald W Carter