Schneider Electric Usa, Inc.

Square D Company

Amjad Hasan

Jon Andrew Bickel

Ronald W Carter

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 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 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.

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Murfreesboro, TN, United States of America

Amjad Hasan