Square D Company

Other

Schneider Electric Usa, Inc.

Larry E Curtis

Jon Andrew Bickel

Ronald W Carter

James F Clark

Marcelo Alejandro Perez

E Avery Ashby

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. Each of the multiple option modules includes a housing having a first surface containing a connector that is complementary with the multiple connectors in the second surface of the meter base so that any of the option modules can be plugged into any of the multiple connectors in the second surface of the meter base, and circuitry within the housing of the option module for interconnecting with the processor and the associated circuitry in the meter base. The first surface of the meter base housing may be adapted for optional mounting on the DIN rail or a flat panel, and contains a connector for mating with a complementary connector on the opposite side of the flat panel through a hole in the flat panel.

Modular power monitoring system

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

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

A system for determining the location of a disturbance event in a power distribution system. More specifically, the present invention provides a system for determining the direction of a disturbance event at the individual meter location and a system for analyzing this data from multiple meter locations to locate the cause of the event in the power system. The system includes a power feed bus for supplying electrical signals and a circuit monitor coupled to the feed bus. The circuit monitor is responsive to detect the disturbance event by comparing a disturbance event signal with a pre-event signal on a plurality of time scales.

Disturbance direction detection in a power monitoring system

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.

Method for power quality summary and trending

A circuit monitor monitors one or more metered values in an electrical circuit, and includes a system for selecting alarm setpoints based upon learned characteristics of normal operation of metered values. The circuit monitor includes a controller and a memory. The memory includes a test register of alarms selected to be part of a learning process. The controller includes summaries and characterizes the one or more metered values as histogram formatted data corresponding to metered values identified by a test register of alarms selected to be part of a learning process. The controller stores the histogram formatted data in the memory and organizes the stored data into a plurality of memory locations, each covering a fixed range of time duration. The controller selects setpoints which produce a minimum product of metered value multiplied by time duration for Over Alarms or a maximum product of metered value multiplied by time duration for Under Alarms.

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Murfreesboro, TN, United States of America

Larry E Curtis